repo
stringclasses
679 values
path
stringlengths
6
122
func_name
stringlengths
2
76
original_string
stringlengths
87
70.9k
language
stringclasses
1 value
code
stringlengths
87
70.9k
code_tokens
sequencelengths
20
6.91k
docstring
stringlengths
1
21.7k
docstring_tokens
sequencelengths
1
1.6k
sha
stringclasses
679 values
url
stringlengths
92
213
partition
stringclasses
1 value
aleju/imgaug
imgaug/augmentables/batches.py
UnnormalizedBatch.fill_from_augmented_normalized_batch
def fill_from_augmented_normalized_batch(self, batch_aug_norm): """ Fill this batch with (normalized) augmentation results. This method receives a (normalized) Batch instance, takes all ``*_aug`` attributes out if it and assigns them to this batch *in unnormalized form*. Hence, the datatypes of all ``*_aug`` attributes will match the datatypes of the ``*_unaug`` attributes. Parameters ---------- batch_aug_norm: imgaug.augmentables.batches.Batch Batch after normalization and augmentation. Returns ------- imgaug.augmentables.batches.UnnormalizedBatch New UnnormalizedBatch instance. All ``*_unaug`` attributes are taken from the old UnnormalizedBatch (without deepcopying them) and all ``*_aug`` attributes are taken from `batch_normalized` converted to unnormalized form. """ # we take here the .data from the normalized batch instead of from # self for the rare case where one has decided to somehow change it # during augmentation batch = UnnormalizedBatch( images=self.images_unaug, heatmaps=self.heatmaps_unaug, segmentation_maps=self.segmentation_maps_unaug, keypoints=self.keypoints_unaug, bounding_boxes=self.bounding_boxes_unaug, polygons=self.polygons_unaug, line_strings=self.line_strings_unaug, data=batch_aug_norm.data ) batch.images_aug = nlib.invert_normalize_images( batch_aug_norm.images_aug, self.images_unaug) batch.heatmaps_aug = nlib.invert_normalize_heatmaps( batch_aug_norm.heatmaps_aug, self.heatmaps_unaug) batch.segmentation_maps_aug = nlib.invert_normalize_segmentation_maps( batch_aug_norm.segmentation_maps_aug, self.segmentation_maps_unaug) batch.keypoints_aug = nlib.invert_normalize_keypoints( batch_aug_norm.keypoints_aug, self.keypoints_unaug) batch.bounding_boxes_aug = nlib.invert_normalize_bounding_boxes( batch_aug_norm.bounding_boxes_aug, self.bounding_boxes_unaug) batch.polygons_aug = nlib.invert_normalize_polygons( batch_aug_norm.polygons_aug, self.polygons_unaug) batch.line_strings_aug = nlib.invert_normalize_line_strings( batch_aug_norm.line_strings_aug, self.line_strings_unaug) return batch
python
def fill_from_augmented_normalized_batch(self, batch_aug_norm): """ Fill this batch with (normalized) augmentation results. This method receives a (normalized) Batch instance, takes all ``*_aug`` attributes out if it and assigns them to this batch *in unnormalized form*. Hence, the datatypes of all ``*_aug`` attributes will match the datatypes of the ``*_unaug`` attributes. Parameters ---------- batch_aug_norm: imgaug.augmentables.batches.Batch Batch after normalization and augmentation. Returns ------- imgaug.augmentables.batches.UnnormalizedBatch New UnnormalizedBatch instance. All ``*_unaug`` attributes are taken from the old UnnormalizedBatch (without deepcopying them) and all ``*_aug`` attributes are taken from `batch_normalized` converted to unnormalized form. """ # we take here the .data from the normalized batch instead of from # self for the rare case where one has decided to somehow change it # during augmentation batch = UnnormalizedBatch( images=self.images_unaug, heatmaps=self.heatmaps_unaug, segmentation_maps=self.segmentation_maps_unaug, keypoints=self.keypoints_unaug, bounding_boxes=self.bounding_boxes_unaug, polygons=self.polygons_unaug, line_strings=self.line_strings_unaug, data=batch_aug_norm.data ) batch.images_aug = nlib.invert_normalize_images( batch_aug_norm.images_aug, self.images_unaug) batch.heatmaps_aug = nlib.invert_normalize_heatmaps( batch_aug_norm.heatmaps_aug, self.heatmaps_unaug) batch.segmentation_maps_aug = nlib.invert_normalize_segmentation_maps( batch_aug_norm.segmentation_maps_aug, self.segmentation_maps_unaug) batch.keypoints_aug = nlib.invert_normalize_keypoints( batch_aug_norm.keypoints_aug, self.keypoints_unaug) batch.bounding_boxes_aug = nlib.invert_normalize_bounding_boxes( batch_aug_norm.bounding_boxes_aug, self.bounding_boxes_unaug) batch.polygons_aug = nlib.invert_normalize_polygons( batch_aug_norm.polygons_aug, self.polygons_unaug) batch.line_strings_aug = nlib.invert_normalize_line_strings( batch_aug_norm.line_strings_aug, self.line_strings_unaug) return batch
[ "def", "fill_from_augmented_normalized_batch", "(", "self", ",", "batch_aug_norm", ")", ":", "# we take here the .data from the normalized batch instead of from", "# self for the rare case where one has decided to somehow change it", "# during augmentation", "batch", "=", "UnnormalizedBatch", "(", "images", "=", "self", ".", "images_unaug", ",", "heatmaps", "=", "self", ".", "heatmaps_unaug", ",", "segmentation_maps", "=", "self", ".", "segmentation_maps_unaug", ",", "keypoints", "=", "self", ".", "keypoints_unaug", ",", "bounding_boxes", "=", "self", ".", "bounding_boxes_unaug", ",", "polygons", "=", "self", ".", "polygons_unaug", ",", "line_strings", "=", "self", ".", "line_strings_unaug", ",", "data", "=", "batch_aug_norm", ".", "data", ")", "batch", ".", "images_aug", "=", "nlib", ".", "invert_normalize_images", "(", "batch_aug_norm", ".", "images_aug", ",", "self", ".", "images_unaug", ")", "batch", ".", "heatmaps_aug", "=", "nlib", ".", "invert_normalize_heatmaps", "(", "batch_aug_norm", ".", "heatmaps_aug", ",", "self", ".", "heatmaps_unaug", ")", "batch", ".", "segmentation_maps_aug", "=", "nlib", ".", "invert_normalize_segmentation_maps", "(", "batch_aug_norm", ".", "segmentation_maps_aug", ",", "self", ".", "segmentation_maps_unaug", ")", "batch", ".", "keypoints_aug", "=", "nlib", ".", "invert_normalize_keypoints", "(", "batch_aug_norm", ".", "keypoints_aug", ",", "self", ".", "keypoints_unaug", ")", "batch", ".", "bounding_boxes_aug", "=", "nlib", ".", "invert_normalize_bounding_boxes", "(", "batch_aug_norm", ".", "bounding_boxes_aug", ",", "self", ".", "bounding_boxes_unaug", ")", "batch", ".", "polygons_aug", "=", "nlib", ".", "invert_normalize_polygons", "(", "batch_aug_norm", ".", "polygons_aug", ",", "self", ".", "polygons_unaug", ")", "batch", ".", "line_strings_aug", "=", "nlib", ".", "invert_normalize_line_strings", "(", "batch_aug_norm", ".", "line_strings_aug", ",", "self", ".", "line_strings_unaug", ")", "return", "batch" ]
Fill this batch with (normalized) augmentation results. This method receives a (normalized) Batch instance, takes all ``*_aug`` attributes out if it and assigns them to this batch *in unnormalized form*. Hence, the datatypes of all ``*_aug`` attributes will match the datatypes of the ``*_unaug`` attributes. Parameters ---------- batch_aug_norm: imgaug.augmentables.batches.Batch Batch after normalization and augmentation. Returns ------- imgaug.augmentables.batches.UnnormalizedBatch New UnnormalizedBatch instance. All ``*_unaug`` attributes are taken from the old UnnormalizedBatch (without deepcopying them) and all ``*_aug`` attributes are taken from `batch_normalized` converted to unnormalized form.
[ "Fill", "this", "batch", "with", "(", "normalized", ")", "augmentation", "results", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/augmentables/batches.py#L225-L277
valid
aleju/imgaug
imgaug/parameters.py
Positive
def Positive(other_param, mode="invert", reroll_count_max=2): """ Converts another parameter's results to positive values. Parameters ---------- other_param : imgaug.parameters.StochasticParameter Other parameter which's sampled values are to be modified. mode : {'invert', 'reroll'}, optional How to change the signs. Valid values are ``invert`` and ``reroll``. ``invert`` means that wrong signs are simply flipped. ``reroll`` means that all samples with wrong signs are sampled again, optionally many times, until they randomly end up having the correct sign. reroll_count_max : int, optional If `mode` is set to ``reroll``, this determines how often values may be rerolled before giving up and simply flipping the sign (as in ``mode="invert"``). This shouldn't be set too high, as rerolling is expensive. Examples -------- >>> param = Positive(Normal(0, 1), mode="reroll") Generates a normal distribution that has only positive values. """ return ForceSign( other_param=other_param, positive=True, mode=mode, reroll_count_max=reroll_count_max )
python
def Positive(other_param, mode="invert", reroll_count_max=2): """ Converts another parameter's results to positive values. Parameters ---------- other_param : imgaug.parameters.StochasticParameter Other parameter which's sampled values are to be modified. mode : {'invert', 'reroll'}, optional How to change the signs. Valid values are ``invert`` and ``reroll``. ``invert`` means that wrong signs are simply flipped. ``reroll`` means that all samples with wrong signs are sampled again, optionally many times, until they randomly end up having the correct sign. reroll_count_max : int, optional If `mode` is set to ``reroll``, this determines how often values may be rerolled before giving up and simply flipping the sign (as in ``mode="invert"``). This shouldn't be set too high, as rerolling is expensive. Examples -------- >>> param = Positive(Normal(0, 1), mode="reroll") Generates a normal distribution that has only positive values. """ return ForceSign( other_param=other_param, positive=True, mode=mode, reroll_count_max=reroll_count_max )
[ "def", "Positive", "(", "other_param", ",", "mode", "=", "\"invert\"", ",", "reroll_count_max", "=", "2", ")", ":", "return", "ForceSign", "(", "other_param", "=", "other_param", ",", "positive", "=", "True", ",", "mode", "=", "mode", ",", "reroll_count_max", "=", "reroll_count_max", ")" ]
Converts another parameter's results to positive values. Parameters ---------- other_param : imgaug.parameters.StochasticParameter Other parameter which's sampled values are to be modified. mode : {'invert', 'reroll'}, optional How to change the signs. Valid values are ``invert`` and ``reroll``. ``invert`` means that wrong signs are simply flipped. ``reroll`` means that all samples with wrong signs are sampled again, optionally many times, until they randomly end up having the correct sign. reroll_count_max : int, optional If `mode` is set to ``reroll``, this determines how often values may be rerolled before giving up and simply flipping the sign (as in ``mode="invert"``). This shouldn't be set too high, as rerolling is expensive. Examples -------- >>> param = Positive(Normal(0, 1), mode="reroll") Generates a normal distribution that has only positive values.
[ "Converts", "another", "parameter", "s", "results", "to", "positive", "values", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/parameters.py#L1919-L1954
valid
aleju/imgaug
imgaug/parameters.py
Negative
def Negative(other_param, mode="invert", reroll_count_max=2): """ Converts another parameter's results to negative values. Parameters ---------- other_param : imgaug.parameters.StochasticParameter Other parameter which's sampled values are to be modified. mode : {'invert', 'reroll'}, optional How to change the signs. Valid values are ``invert`` and ``reroll``. ``invert`` means that wrong signs are simply flipped. ``reroll`` means that all samples with wrong signs are sampled again, optionally many times, until they randomly end up having the correct sign. reroll_count_max : int, optional If `mode` is set to ``reroll``, this determines how often values may be rerolled before giving up and simply flipping the sign (as in ``mode="invert"``). This shouldn't be set too high, as rerolling is expensive. Examples -------- >>> param = Negative(Normal(0, 1), mode="reroll") Generates a normal distribution that has only negative values. """ return ForceSign( other_param=other_param, positive=False, mode=mode, reroll_count_max=reroll_count_max )
python
def Negative(other_param, mode="invert", reroll_count_max=2): """ Converts another parameter's results to negative values. Parameters ---------- other_param : imgaug.parameters.StochasticParameter Other parameter which's sampled values are to be modified. mode : {'invert', 'reroll'}, optional How to change the signs. Valid values are ``invert`` and ``reroll``. ``invert`` means that wrong signs are simply flipped. ``reroll`` means that all samples with wrong signs are sampled again, optionally many times, until they randomly end up having the correct sign. reroll_count_max : int, optional If `mode` is set to ``reroll``, this determines how often values may be rerolled before giving up and simply flipping the sign (as in ``mode="invert"``). This shouldn't be set too high, as rerolling is expensive. Examples -------- >>> param = Negative(Normal(0, 1), mode="reroll") Generates a normal distribution that has only negative values. """ return ForceSign( other_param=other_param, positive=False, mode=mode, reroll_count_max=reroll_count_max )
[ "def", "Negative", "(", "other_param", ",", "mode", "=", "\"invert\"", ",", "reroll_count_max", "=", "2", ")", ":", "return", "ForceSign", "(", "other_param", "=", "other_param", ",", "positive", "=", "False", ",", "mode", "=", "mode", ",", "reroll_count_max", "=", "reroll_count_max", ")" ]
Converts another parameter's results to negative values. Parameters ---------- other_param : imgaug.parameters.StochasticParameter Other parameter which's sampled values are to be modified. mode : {'invert', 'reroll'}, optional How to change the signs. Valid values are ``invert`` and ``reroll``. ``invert`` means that wrong signs are simply flipped. ``reroll`` means that all samples with wrong signs are sampled again, optionally many times, until they randomly end up having the correct sign. reroll_count_max : int, optional If `mode` is set to ``reroll``, this determines how often values may be rerolled before giving up and simply flipping the sign (as in ``mode="invert"``). This shouldn't be set too high, as rerolling is expensive. Examples -------- >>> param = Negative(Normal(0, 1), mode="reroll") Generates a normal distribution that has only negative values.
[ "Converts", "another", "parameter", "s", "results", "to", "negative", "values", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/parameters.py#L1957-L1992
valid
aleju/imgaug
imgaug/parameters.py
Sigmoid.create_for_noise
def create_for_noise(other_param, threshold=(-10, 10), activated=True): """ Creates a Sigmoid that is adjusted to be used with noise parameters, i.e. with parameters which's output values are in the range [0.0, 1.0]. Parameters ---------- other_param : imgaug.parameters.StochasticParameter See :func:`imgaug.parameters.Sigmoid.__init__`. threshold : number or tuple of number or iterable of number or imgaug.parameters.StochasticParameter,\ optional See :func:`imgaug.parameters.Sigmoid.__init__`. activated : bool or number, optional See :func:`imgaug.parameters.Sigmoid.__init__`. Returns ------- Sigmoid A sigmoid adjusted to be used with noise. """ return Sigmoid(other_param, threshold, activated, mul=20, add=-10)
python
def create_for_noise(other_param, threshold=(-10, 10), activated=True): """ Creates a Sigmoid that is adjusted to be used with noise parameters, i.e. with parameters which's output values are in the range [0.0, 1.0]. Parameters ---------- other_param : imgaug.parameters.StochasticParameter See :func:`imgaug.parameters.Sigmoid.__init__`. threshold : number or tuple of number or iterable of number or imgaug.parameters.StochasticParameter,\ optional See :func:`imgaug.parameters.Sigmoid.__init__`. activated : bool or number, optional See :func:`imgaug.parameters.Sigmoid.__init__`. Returns ------- Sigmoid A sigmoid adjusted to be used with noise. """ return Sigmoid(other_param, threshold, activated, mul=20, add=-10)
[ "def", "create_for_noise", "(", "other_param", ",", "threshold", "=", "(", "-", "10", ",", "10", ")", ",", "activated", "=", "True", ")", ":", "return", "Sigmoid", "(", "other_param", ",", "threshold", ",", "activated", ",", "mul", "=", "20", ",", "add", "=", "-", "10", ")" ]
Creates a Sigmoid that is adjusted to be used with noise parameters, i.e. with parameters which's output values are in the range [0.0, 1.0]. Parameters ---------- other_param : imgaug.parameters.StochasticParameter See :func:`imgaug.parameters.Sigmoid.__init__`. threshold : number or tuple of number or iterable of number or imgaug.parameters.StochasticParameter,\ optional See :func:`imgaug.parameters.Sigmoid.__init__`. activated : bool or number, optional See :func:`imgaug.parameters.Sigmoid.__init__`. Returns ------- Sigmoid A sigmoid adjusted to be used with noise.
[ "Creates", "a", "Sigmoid", "that", "is", "adjusted", "to", "be", "used", "with", "noise", "parameters", "i", ".", "e", ".", "with", "parameters", "which", "s", "output", "values", "are", "in", "the", "range", "[", "0", ".", "0", "1", ".", "0", "]", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/parameters.py#L2175-L2198
valid
aleju/imgaug
imgaug/augmentables/polys.py
Polygon.area
def area(self): """ Estimate the area of the polygon. Returns ------- number Area of the polygon. """ if len(self.exterior) < 3: raise Exception("Cannot compute the polygon's area because it contains less than three points.") poly = self.to_shapely_polygon() return poly.area
python
def area(self): """ Estimate the area of the polygon. Returns ------- number Area of the polygon. """ if len(self.exterior) < 3: raise Exception("Cannot compute the polygon's area because it contains less than three points.") poly = self.to_shapely_polygon() return poly.area
[ "def", "area", "(", "self", ")", ":", "if", "len", "(", "self", ".", "exterior", ")", "<", "3", ":", "raise", "Exception", "(", "\"Cannot compute the polygon's area because it contains less than three points.\"", ")", "poly", "=", "self", ".", "to_shapely_polygon", "(", ")", "return", "poly", ".", "area" ]
Estimate the area of the polygon. Returns ------- number Area of the polygon.
[ "Estimate", "the", "area", "of", "the", "polygon", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/augmentables/polys.py#L148-L161
valid
aleju/imgaug
imgaug/augmentables/polys.py
Polygon.project
def project(self, from_shape, to_shape): """ Project the polygon onto an image with different shape. The relative coordinates of all points remain the same. E.g. a point at (x=20, y=20) on an image (width=100, height=200) will be projected on a new image (width=200, height=100) to (x=40, y=10). This is intended for cases where the original image is resized. It cannot be used for more complex changes (e.g. padding, cropping). Parameters ---------- from_shape : tuple of int Shape of the original image. (Before resize.) to_shape : tuple of int Shape of the new image. (After resize.) Returns ------- imgaug.Polygon Polygon object with new coordinates. """ if from_shape[0:2] == to_shape[0:2]: return self.copy() ls_proj = self.to_line_string(closed=False).project( from_shape, to_shape) return self.copy(exterior=ls_proj.coords)
python
def project(self, from_shape, to_shape): """ Project the polygon onto an image with different shape. The relative coordinates of all points remain the same. E.g. a point at (x=20, y=20) on an image (width=100, height=200) will be projected on a new image (width=200, height=100) to (x=40, y=10). This is intended for cases where the original image is resized. It cannot be used for more complex changes (e.g. padding, cropping). Parameters ---------- from_shape : tuple of int Shape of the original image. (Before resize.) to_shape : tuple of int Shape of the new image. (After resize.) Returns ------- imgaug.Polygon Polygon object with new coordinates. """ if from_shape[0:2] == to_shape[0:2]: return self.copy() ls_proj = self.to_line_string(closed=False).project( from_shape, to_shape) return self.copy(exterior=ls_proj.coords)
[ "def", "project", "(", "self", ",", "from_shape", ",", "to_shape", ")", ":", "if", "from_shape", "[", "0", ":", "2", "]", "==", "to_shape", "[", "0", ":", "2", "]", ":", "return", "self", ".", "copy", "(", ")", "ls_proj", "=", "self", ".", "to_line_string", "(", "closed", "=", "False", ")", ".", "project", "(", "from_shape", ",", "to_shape", ")", "return", "self", ".", "copy", "(", "exterior", "=", "ls_proj", ".", "coords", ")" ]
Project the polygon onto an image with different shape. The relative coordinates of all points remain the same. E.g. a point at (x=20, y=20) on an image (width=100, height=200) will be projected on a new image (width=200, height=100) to (x=40, y=10). This is intended for cases where the original image is resized. It cannot be used for more complex changes (e.g. padding, cropping). Parameters ---------- from_shape : tuple of int Shape of the original image. (Before resize.) to_shape : tuple of int Shape of the new image. (After resize.) Returns ------- imgaug.Polygon Polygon object with new coordinates.
[ "Project", "the", "polygon", "onto", "an", "image", "with", "different", "shape", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/augmentables/polys.py#L191-L220
valid
aleju/imgaug
imgaug/augmentables/polys.py
Polygon.find_closest_point_index
def find_closest_point_index(self, x, y, return_distance=False): """ Find the index of the point within the exterior that is closest to the given coordinates. "Closeness" is here defined based on euclidean distance. This method will raise an AssertionError if the exterior contains no points. Parameters ---------- x : number X-coordinate around which to search for close points. y : number Y-coordinate around which to search for close points. return_distance : bool, optional Whether to also return the distance of the closest point. Returns ------- int Index of the closest point. number Euclidean distance to the closest point. This value is only returned if `return_distance` was set to True. """ ia.do_assert(len(self.exterior) > 0) distances = [] for x2, y2 in self.exterior: d = (x2 - x) ** 2 + (y2 - y) ** 2 distances.append(d) distances = np.sqrt(distances) closest_idx = np.argmin(distances) if return_distance: return closest_idx, distances[closest_idx] return closest_idx
python
def find_closest_point_index(self, x, y, return_distance=False): """ Find the index of the point within the exterior that is closest to the given coordinates. "Closeness" is here defined based on euclidean distance. This method will raise an AssertionError if the exterior contains no points. Parameters ---------- x : number X-coordinate around which to search for close points. y : number Y-coordinate around which to search for close points. return_distance : bool, optional Whether to also return the distance of the closest point. Returns ------- int Index of the closest point. number Euclidean distance to the closest point. This value is only returned if `return_distance` was set to True. """ ia.do_assert(len(self.exterior) > 0) distances = [] for x2, y2 in self.exterior: d = (x2 - x) ** 2 + (y2 - y) ** 2 distances.append(d) distances = np.sqrt(distances) closest_idx = np.argmin(distances) if return_distance: return closest_idx, distances[closest_idx] return closest_idx
[ "def", "find_closest_point_index", "(", "self", ",", "x", ",", "y", ",", "return_distance", "=", "False", ")", ":", "ia", ".", "do_assert", "(", "len", "(", "self", ".", "exterior", ")", ">", "0", ")", "distances", "=", "[", "]", "for", "x2", ",", "y2", "in", "self", ".", "exterior", ":", "d", "=", "(", "x2", "-", "x", ")", "**", "2", "+", "(", "y2", "-", "y", ")", "**", "2", "distances", ".", "append", "(", "d", ")", "distances", "=", "np", ".", "sqrt", "(", "distances", ")", "closest_idx", "=", "np", ".", "argmin", "(", "distances", ")", "if", "return_distance", ":", "return", "closest_idx", ",", "distances", "[", "closest_idx", "]", "return", "closest_idx" ]
Find the index of the point within the exterior that is closest to the given coordinates. "Closeness" is here defined based on euclidean distance. This method will raise an AssertionError if the exterior contains no points. Parameters ---------- x : number X-coordinate around which to search for close points. y : number Y-coordinate around which to search for close points. return_distance : bool, optional Whether to also return the distance of the closest point. Returns ------- int Index of the closest point. number Euclidean distance to the closest point. This value is only returned if `return_distance` was set to True.
[ "Find", "the", "index", "of", "the", "point", "within", "the", "exterior", "that", "is", "closest", "to", "the", "given", "coordinates", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/augmentables/polys.py#L222-L259
valid
aleju/imgaug
imgaug/augmentables/polys.py
Polygon.is_fully_within_image
def is_fully_within_image(self, image): """ Estimate whether the polygon is fully inside the image area. Parameters ---------- image : (H,W,...) ndarray or tuple of int Image dimensions to use. If an ndarray, its shape will be used. If a tuple, it is assumed to represent the image shape and must contain at least two integers. Returns ------- bool True if the polygon is fully inside the image area. False otherwise. """ return not self.is_out_of_image(image, fully=True, partly=True)
python
def is_fully_within_image(self, image): """ Estimate whether the polygon is fully inside the image area. Parameters ---------- image : (H,W,...) ndarray or tuple of int Image dimensions to use. If an ndarray, its shape will be used. If a tuple, it is assumed to represent the image shape and must contain at least two integers. Returns ------- bool True if the polygon is fully inside the image area. False otherwise. """ return not self.is_out_of_image(image, fully=True, partly=True)
[ "def", "is_fully_within_image", "(", "self", ",", "image", ")", ":", "return", "not", "self", ".", "is_out_of_image", "(", "image", ",", "fully", "=", "True", ",", "partly", "=", "True", ")" ]
Estimate whether the polygon is fully inside the image area. Parameters ---------- image : (H,W,...) ndarray or tuple of int Image dimensions to use. If an ndarray, its shape will be used. If a tuple, it is assumed to represent the image shape and must contain at least two integers. Returns ------- bool True if the polygon is fully inside the image area. False otherwise.
[ "Estimate", "whether", "the", "polygon", "is", "fully", "inside", "the", "image", "area", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/augmentables/polys.py#L262-L280
valid
aleju/imgaug
imgaug/augmentables/polys.py
Polygon.is_partly_within_image
def is_partly_within_image(self, image): """ Estimate whether the polygon is at least partially inside the image area. Parameters ---------- image : (H,W,...) ndarray or tuple of int Image dimensions to use. If an ndarray, its shape will be used. If a tuple, it is assumed to represent the image shape and must contain at least two integers. Returns ------- bool True if the polygon is at least partially inside the image area. False otherwise. """ return not self.is_out_of_image(image, fully=True, partly=False)
python
def is_partly_within_image(self, image): """ Estimate whether the polygon is at least partially inside the image area. Parameters ---------- image : (H,W,...) ndarray or tuple of int Image dimensions to use. If an ndarray, its shape will be used. If a tuple, it is assumed to represent the image shape and must contain at least two integers. Returns ------- bool True if the polygon is at least partially inside the image area. False otherwise. """ return not self.is_out_of_image(image, fully=True, partly=False)
[ "def", "is_partly_within_image", "(", "self", ",", "image", ")", ":", "return", "not", "self", ".", "is_out_of_image", "(", "image", ",", "fully", "=", "True", ",", "partly", "=", "False", ")" ]
Estimate whether the polygon is at least partially inside the image area. Parameters ---------- image : (H,W,...) ndarray or tuple of int Image dimensions to use. If an ndarray, its shape will be used. If a tuple, it is assumed to represent the image shape and must contain at least two integers. Returns ------- bool True if the polygon is at least partially inside the image area. False otherwise.
[ "Estimate", "whether", "the", "polygon", "is", "at", "least", "partially", "inside", "the", "image", "area", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/augmentables/polys.py#L283-L301
valid
aleju/imgaug
imgaug/augmentables/polys.py
Polygon.is_out_of_image
def is_out_of_image(self, image, fully=True, partly=False): """ Estimate whether the polygon is partially or fully outside of the image area. Parameters ---------- image : (H,W,...) ndarray or tuple of int Image dimensions to use. If an ndarray, its shape will be used. If a tuple, it is assumed to represent the image shape and must contain at least two integers. fully : bool, optional Whether to return True if the polygon is fully outside of the image area. partly : bool, optional Whether to return True if the polygon is at least partially outside fo the image area. Returns ------- bool True if the polygon is partially/fully outside of the image area, depending on defined parameters. False otherwise. """ # TODO this is inconsistent with line strings, which return a default # value in these cases if len(self.exterior) == 0: raise Exception("Cannot determine whether the polygon is inside the image, because it contains no points.") ls = self.to_line_string() return ls.is_out_of_image(image, fully=fully, partly=partly)
python
def is_out_of_image(self, image, fully=True, partly=False): """ Estimate whether the polygon is partially or fully outside of the image area. Parameters ---------- image : (H,W,...) ndarray or tuple of int Image dimensions to use. If an ndarray, its shape will be used. If a tuple, it is assumed to represent the image shape and must contain at least two integers. fully : bool, optional Whether to return True if the polygon is fully outside of the image area. partly : bool, optional Whether to return True if the polygon is at least partially outside fo the image area. Returns ------- bool True if the polygon is partially/fully outside of the image area, depending on defined parameters. False otherwise. """ # TODO this is inconsistent with line strings, which return a default # value in these cases if len(self.exterior) == 0: raise Exception("Cannot determine whether the polygon is inside the image, because it contains no points.") ls = self.to_line_string() return ls.is_out_of_image(image, fully=fully, partly=partly)
[ "def", "is_out_of_image", "(", "self", ",", "image", ",", "fully", "=", "True", ",", "partly", "=", "False", ")", ":", "# TODO this is inconsistent with line strings, which return a default", "# value in these cases", "if", "len", "(", "self", ".", "exterior", ")", "==", "0", ":", "raise", "Exception", "(", "\"Cannot determine whether the polygon is inside the image, because it contains no points.\"", ")", "ls", "=", "self", ".", "to_line_string", "(", ")", "return", "ls", ".", "is_out_of_image", "(", "image", ",", "fully", "=", "fully", ",", "partly", "=", "partly", ")" ]
Estimate whether the polygon is partially or fully outside of the image area. Parameters ---------- image : (H,W,...) ndarray or tuple of int Image dimensions to use. If an ndarray, its shape will be used. If a tuple, it is assumed to represent the image shape and must contain at least two integers. fully : bool, optional Whether to return True if the polygon is fully outside of the image area. partly : bool, optional Whether to return True if the polygon is at least partially outside fo the image area. Returns ------- bool True if the polygon is partially/fully outside of the image area, depending on defined parameters. False otherwise.
[ "Estimate", "whether", "the", "polygon", "is", "partially", "or", "fully", "outside", "of", "the", "image", "area", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/augmentables/polys.py#L303-L332
valid
aleju/imgaug
imgaug/augmentables/polys.py
Polygon.clip_out_of_image
def clip_out_of_image(self, image): """ Cut off all parts of the polygon that are outside of the image. This operation may lead to new points being created. As a single polygon may be split into multiple new polygons, the result is always a list, which may contain more than one output polygon. This operation will return an empty list if the polygon is completely outside of the image plane. Parameters ---------- image : (H,W,...) ndarray or tuple of int Image dimensions to use for the clipping of the polygon. If an ndarray, its shape will be used. If a tuple, it is assumed to represent the image shape and must contain at least two integers. Returns ------- list of imgaug.Polygon Polygon, clipped to fall within the image dimensions. Returned as a list, because the clipping can split the polygon into multiple parts. The list may also be empty, if the polygon was fully outside of the image plane. """ # load shapely lazily, which makes the dependency more optional import shapely.geometry # if fully out of image, clip everything away, nothing remaining if self.is_out_of_image(image, fully=True, partly=False): return [] h, w = image.shape[0:2] if ia.is_np_array(image) else image[0:2] poly_shapely = self.to_shapely_polygon() poly_image = shapely.geometry.Polygon([(0, 0), (w, 0), (w, h), (0, h)]) multipoly_inter_shapely = poly_shapely.intersection(poly_image) if not isinstance(multipoly_inter_shapely, shapely.geometry.MultiPolygon): ia.do_assert(isinstance(multipoly_inter_shapely, shapely.geometry.Polygon)) multipoly_inter_shapely = shapely.geometry.MultiPolygon([multipoly_inter_shapely]) polygons = [] for poly_inter_shapely in multipoly_inter_shapely.geoms: polygons.append(Polygon.from_shapely(poly_inter_shapely, label=self.label)) # shapely changes the order of points, we try here to preserve it as # much as possible polygons_reordered = [] for polygon in polygons: found = False for x, y in self.exterior: closest_idx, dist = polygon.find_closest_point_index(x=x, y=y, return_distance=True) if dist < 1e-6: polygon_reordered = polygon.change_first_point_by_index(closest_idx) polygons_reordered.append(polygon_reordered) found = True break ia.do_assert(found) # could only not find closest points if new polys are empty return polygons_reordered
python
def clip_out_of_image(self, image): """ Cut off all parts of the polygon that are outside of the image. This operation may lead to new points being created. As a single polygon may be split into multiple new polygons, the result is always a list, which may contain more than one output polygon. This operation will return an empty list if the polygon is completely outside of the image plane. Parameters ---------- image : (H,W,...) ndarray or tuple of int Image dimensions to use for the clipping of the polygon. If an ndarray, its shape will be used. If a tuple, it is assumed to represent the image shape and must contain at least two integers. Returns ------- list of imgaug.Polygon Polygon, clipped to fall within the image dimensions. Returned as a list, because the clipping can split the polygon into multiple parts. The list may also be empty, if the polygon was fully outside of the image plane. """ # load shapely lazily, which makes the dependency more optional import shapely.geometry # if fully out of image, clip everything away, nothing remaining if self.is_out_of_image(image, fully=True, partly=False): return [] h, w = image.shape[0:2] if ia.is_np_array(image) else image[0:2] poly_shapely = self.to_shapely_polygon() poly_image = shapely.geometry.Polygon([(0, 0), (w, 0), (w, h), (0, h)]) multipoly_inter_shapely = poly_shapely.intersection(poly_image) if not isinstance(multipoly_inter_shapely, shapely.geometry.MultiPolygon): ia.do_assert(isinstance(multipoly_inter_shapely, shapely.geometry.Polygon)) multipoly_inter_shapely = shapely.geometry.MultiPolygon([multipoly_inter_shapely]) polygons = [] for poly_inter_shapely in multipoly_inter_shapely.geoms: polygons.append(Polygon.from_shapely(poly_inter_shapely, label=self.label)) # shapely changes the order of points, we try here to preserve it as # much as possible polygons_reordered = [] for polygon in polygons: found = False for x, y in self.exterior: closest_idx, dist = polygon.find_closest_point_index(x=x, y=y, return_distance=True) if dist < 1e-6: polygon_reordered = polygon.change_first_point_by_index(closest_idx) polygons_reordered.append(polygon_reordered) found = True break ia.do_assert(found) # could only not find closest points if new polys are empty return polygons_reordered
[ "def", "clip_out_of_image", "(", "self", ",", "image", ")", ":", "# load shapely lazily, which makes the dependency more optional", "import", "shapely", ".", "geometry", "# if fully out of image, clip everything away, nothing remaining", "if", "self", ".", "is_out_of_image", "(", "image", ",", "fully", "=", "True", ",", "partly", "=", "False", ")", ":", "return", "[", "]", "h", ",", "w", "=", "image", ".", "shape", "[", "0", ":", "2", "]", "if", "ia", ".", "is_np_array", "(", "image", ")", "else", "image", "[", "0", ":", "2", "]", "poly_shapely", "=", "self", ".", "to_shapely_polygon", "(", ")", "poly_image", "=", "shapely", ".", "geometry", ".", "Polygon", "(", "[", "(", "0", ",", "0", ")", ",", "(", "w", ",", "0", ")", ",", "(", "w", ",", "h", ")", ",", "(", "0", ",", "h", ")", "]", ")", "multipoly_inter_shapely", "=", "poly_shapely", ".", "intersection", "(", "poly_image", ")", "if", "not", "isinstance", "(", "multipoly_inter_shapely", ",", "shapely", ".", "geometry", ".", "MultiPolygon", ")", ":", "ia", ".", "do_assert", "(", "isinstance", "(", "multipoly_inter_shapely", ",", "shapely", ".", "geometry", ".", "Polygon", ")", ")", "multipoly_inter_shapely", "=", "shapely", ".", "geometry", ".", "MultiPolygon", "(", "[", "multipoly_inter_shapely", "]", ")", "polygons", "=", "[", "]", "for", "poly_inter_shapely", "in", "multipoly_inter_shapely", ".", "geoms", ":", "polygons", ".", "append", "(", "Polygon", ".", "from_shapely", "(", "poly_inter_shapely", ",", "label", "=", "self", ".", "label", ")", ")", "# shapely changes the order of points, we try here to preserve it as", "# much as possible", "polygons_reordered", "=", "[", "]", "for", "polygon", "in", "polygons", ":", "found", "=", "False", "for", "x", ",", "y", "in", "self", ".", "exterior", ":", "closest_idx", ",", "dist", "=", "polygon", ".", "find_closest_point_index", "(", "x", "=", "x", ",", "y", "=", "y", ",", "return_distance", "=", "True", ")", "if", "dist", "<", "1e-6", ":", "polygon_reordered", "=", "polygon", ".", "change_first_point_by_index", "(", "closest_idx", ")", "polygons_reordered", ".", "append", "(", "polygon_reordered", ")", "found", "=", "True", "break", "ia", ".", "do_assert", "(", "found", ")", "# could only not find closest points if new polys are empty", "return", "polygons_reordered" ]
Cut off all parts of the polygon that are outside of the image. This operation may lead to new points being created. As a single polygon may be split into multiple new polygons, the result is always a list, which may contain more than one output polygon. This operation will return an empty list if the polygon is completely outside of the image plane. Parameters ---------- image : (H,W,...) ndarray or tuple of int Image dimensions to use for the clipping of the polygon. If an ndarray, its shape will be used. If a tuple, it is assumed to represent the image shape and must contain at least two integers. Returns ------- list of imgaug.Polygon Polygon, clipped to fall within the image dimensions. Returned as a list, because the clipping can split the polygon into multiple parts. The list may also be empty, if the polygon was fully outside of the image plane.
[ "Cut", "off", "all", "parts", "of", "the", "polygon", "that", "are", "outside", "of", "the", "image", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/augmentables/polys.py#L342-L403
valid
aleju/imgaug
imgaug/augmentables/polys.py
Polygon.shift
def shift(self, top=None, right=None, bottom=None, left=None): """ Shift the polygon from one or more image sides, i.e. move it on the x/y-axis. Parameters ---------- top : None or int, optional Amount of pixels by which to shift the polygon from the top. right : None or int, optional Amount of pixels by which to shift the polygon from the right. bottom : None or int, optional Amount of pixels by which to shift the polygon from the bottom. left : None or int, optional Amount of pixels by which to shift the polygon from the left. Returns ------- imgaug.Polygon Shifted polygon. """ ls_shifted = self.to_line_string(closed=False).shift( top=top, right=right, bottom=bottom, left=left) return self.copy(exterior=ls_shifted.coords)
python
def shift(self, top=None, right=None, bottom=None, left=None): """ Shift the polygon from one or more image sides, i.e. move it on the x/y-axis. Parameters ---------- top : None or int, optional Amount of pixels by which to shift the polygon from the top. right : None or int, optional Amount of pixels by which to shift the polygon from the right. bottom : None or int, optional Amount of pixels by which to shift the polygon from the bottom. left : None or int, optional Amount of pixels by which to shift the polygon from the left. Returns ------- imgaug.Polygon Shifted polygon. """ ls_shifted = self.to_line_string(closed=False).shift( top=top, right=right, bottom=bottom, left=left) return self.copy(exterior=ls_shifted.coords)
[ "def", "shift", "(", "self", ",", "top", "=", "None", ",", "right", "=", "None", ",", "bottom", "=", "None", ",", "left", "=", "None", ")", ":", "ls_shifted", "=", "self", ".", "to_line_string", "(", "closed", "=", "False", ")", ".", "shift", "(", "top", "=", "top", ",", "right", "=", "right", ",", "bottom", "=", "bottom", ",", "left", "=", "left", ")", "return", "self", ".", "copy", "(", "exterior", "=", "ls_shifted", ".", "coords", ")" ]
Shift the polygon from one or more image sides, i.e. move it on the x/y-axis. Parameters ---------- top : None or int, optional Amount of pixels by which to shift the polygon from the top. right : None or int, optional Amount of pixels by which to shift the polygon from the right. bottom : None or int, optional Amount of pixels by which to shift the polygon from the bottom. left : None or int, optional Amount of pixels by which to shift the polygon from the left. Returns ------- imgaug.Polygon Shifted polygon.
[ "Shift", "the", "polygon", "from", "one", "or", "more", "image", "sides", "i", ".", "e", ".", "move", "it", "on", "the", "x", "/", "y", "-", "axis", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/augmentables/polys.py#L405-L431
valid
aleju/imgaug
imgaug/augmentables/polys.py
Polygon.draw_on_image
def draw_on_image(self, image, color=(0, 255, 0), color_face=None, color_lines=None, color_points=None, alpha=1.0, alpha_face=None, alpha_lines=None, alpha_points=None, size=1, size_lines=None, size_points=None, raise_if_out_of_image=False): """ Draw the polygon on an image. Parameters ---------- image : (H,W,C) ndarray The image onto which to draw the polygon. Usually expected to be of dtype ``uint8``, though other dtypes are also handled. color : iterable of int, optional The color to use for the whole polygon. Must correspond to the channel layout of the image. Usually RGB. The values for `color_face`, `color_lines` and `color_points` will be derived from this color if they are set to ``None``. This argument has no effect if `color_face`, `color_lines` and `color_points` are all set anything other than ``None``. color_face : None or iterable of int, optional The color to use for the inner polygon area (excluding perimeter). Must correspond to the channel layout of the image. Usually RGB. If this is ``None``, it will be derived from ``color * 1.0``. color_lines : None or iterable of int, optional The color to use for the line (aka perimeter/border) of the polygon. Must correspond to the channel layout of the image. Usually RGB. If this is ``None``, it will be derived from ``color * 0.5``. color_points : None or iterable of int, optional The color to use for the corner points of the polygon. Must correspond to the channel layout of the image. Usually RGB. If this is ``None``, it will be derived from ``color * 0.5``. alpha : float, optional The opacity of the whole polygon, where ``1.0`` denotes a completely visible polygon and ``0.0`` an invisible one. The values for `alpha_face`, `alpha_lines` and `alpha_points` will be derived from this alpha value if they are set to ``None``. This argument has no effect if `alpha_face`, `alpha_lines` and `alpha_points` are all set anything other than ``None``. alpha_face : None or number, optional The opacity of the polygon's inner area (excluding the perimeter), where ``1.0`` denotes a completely visible inner area and ``0.0`` an invisible one. If this is ``None``, it will be derived from ``alpha * 0.5``. alpha_lines : None or number, optional The opacity of the polygon's line (aka perimeter/border), where ``1.0`` denotes a completely visible line and ``0.0`` an invisible one. If this is ``None``, it will be derived from ``alpha * 1.0``. alpha_points : None or number, optional The opacity of the polygon's corner points, where ``1.0`` denotes completely visible corners and ``0.0`` invisible ones. If this is ``None``, it will be derived from ``alpha * 1.0``. size : int, optional Size of the polygon. The sizes of the line and points are derived from this value, unless they are set. size_lines : None or int, optional Thickness of the polygon's line (aka perimeter/border). If ``None``, this value is derived from `size`. size_points : int, optional Size of the points in pixels. If ``None``, this value is derived from ``3 * size``. raise_if_out_of_image : bool, optional Whether to raise an error if the polygon is fully outside of the image. If set to False, no error will be raised and only the parts inside the image will be drawn. Returns ------- result : (H,W,C) ndarray Image with polygon drawn on it. Result dtype is the same as the input dtype. """ assert color is not None assert alpha is not None assert size is not None color_face = color_face if color_face is not None else np.array(color) color_lines = color_lines if color_lines is not None else np.array(color) * 0.5 color_points = color_points if color_points is not None else np.array(color) * 0.5 alpha_face = alpha_face if alpha_face is not None else alpha * 0.5 alpha_lines = alpha_lines if alpha_lines is not None else alpha alpha_points = alpha_points if alpha_points is not None else alpha size_lines = size_lines if size_lines is not None else size size_points = size_points if size_points is not None else size * 3 if image.ndim == 2: assert ia.is_single_number(color_face), ( "Got a 2D image. Expected then 'color_face' to be a single " "number, but got %s." % (str(color_face),)) color_face = [color_face] elif image.ndim == 3 and ia.is_single_number(color_face): color_face = [color_face] * image.shape[-1] if alpha_face < 0.01: alpha_face = 0 elif alpha_face > 0.99: alpha_face = 1 if raise_if_out_of_image and self.is_out_of_image(image): raise Exception("Cannot draw polygon %s on image with shape %s." % ( str(self), image.shape )) # TODO np.clip to image plane if is_fully_within_image(), similar to how it is done for bounding boxes # TODO improve efficiency by only drawing in rectangle that covers poly instead of drawing in the whole image # TODO for a rectangular polygon, the face coordinates include the top/left boundary but not the right/bottom # boundary. This may be unintuitive when not drawing the boundary. Maybe somehow remove the boundary # coordinates from the face coordinates after generating both? input_dtype = image.dtype result = image.astype(np.float32) rr, cc = skimage.draw.polygon(self.yy_int, self.xx_int, shape=image.shape) if len(rr) > 0: if alpha_face == 1: result[rr, cc] = np.float32(color_face) elif alpha_face == 0: pass else: result[rr, cc] = ( (1 - alpha_face) * result[rr, cc, :] + alpha_face * np.float32(color_face) ) ls_open = self.to_line_string(closed=False) ls_closed = self.to_line_string(closed=True) result = ls_closed.draw_lines_on_image( result, color=color_lines, alpha=alpha_lines, size=size_lines, raise_if_out_of_image=raise_if_out_of_image) result = ls_open.draw_points_on_image( result, color=color_points, alpha=alpha_points, size=size_points, raise_if_out_of_image=raise_if_out_of_image) if input_dtype.type == np.uint8: result = np.clip(np.round(result), 0, 255).astype(input_dtype) # TODO make clipping more flexible else: result = result.astype(input_dtype) return result
python
def draw_on_image(self, image, color=(0, 255, 0), color_face=None, color_lines=None, color_points=None, alpha=1.0, alpha_face=None, alpha_lines=None, alpha_points=None, size=1, size_lines=None, size_points=None, raise_if_out_of_image=False): """ Draw the polygon on an image. Parameters ---------- image : (H,W,C) ndarray The image onto which to draw the polygon. Usually expected to be of dtype ``uint8``, though other dtypes are also handled. color : iterable of int, optional The color to use for the whole polygon. Must correspond to the channel layout of the image. Usually RGB. The values for `color_face`, `color_lines` and `color_points` will be derived from this color if they are set to ``None``. This argument has no effect if `color_face`, `color_lines` and `color_points` are all set anything other than ``None``. color_face : None or iterable of int, optional The color to use for the inner polygon area (excluding perimeter). Must correspond to the channel layout of the image. Usually RGB. If this is ``None``, it will be derived from ``color * 1.0``. color_lines : None or iterable of int, optional The color to use for the line (aka perimeter/border) of the polygon. Must correspond to the channel layout of the image. Usually RGB. If this is ``None``, it will be derived from ``color * 0.5``. color_points : None or iterable of int, optional The color to use for the corner points of the polygon. Must correspond to the channel layout of the image. Usually RGB. If this is ``None``, it will be derived from ``color * 0.5``. alpha : float, optional The opacity of the whole polygon, where ``1.0`` denotes a completely visible polygon and ``0.0`` an invisible one. The values for `alpha_face`, `alpha_lines` and `alpha_points` will be derived from this alpha value if they are set to ``None``. This argument has no effect if `alpha_face`, `alpha_lines` and `alpha_points` are all set anything other than ``None``. alpha_face : None or number, optional The opacity of the polygon's inner area (excluding the perimeter), where ``1.0`` denotes a completely visible inner area and ``0.0`` an invisible one. If this is ``None``, it will be derived from ``alpha * 0.5``. alpha_lines : None or number, optional The opacity of the polygon's line (aka perimeter/border), where ``1.0`` denotes a completely visible line and ``0.0`` an invisible one. If this is ``None``, it will be derived from ``alpha * 1.0``. alpha_points : None or number, optional The opacity of the polygon's corner points, where ``1.0`` denotes completely visible corners and ``0.0`` invisible ones. If this is ``None``, it will be derived from ``alpha * 1.0``. size : int, optional Size of the polygon. The sizes of the line and points are derived from this value, unless they are set. size_lines : None or int, optional Thickness of the polygon's line (aka perimeter/border). If ``None``, this value is derived from `size`. size_points : int, optional Size of the points in pixels. If ``None``, this value is derived from ``3 * size``. raise_if_out_of_image : bool, optional Whether to raise an error if the polygon is fully outside of the image. If set to False, no error will be raised and only the parts inside the image will be drawn. Returns ------- result : (H,W,C) ndarray Image with polygon drawn on it. Result dtype is the same as the input dtype. """ assert color is not None assert alpha is not None assert size is not None color_face = color_face if color_face is not None else np.array(color) color_lines = color_lines if color_lines is not None else np.array(color) * 0.5 color_points = color_points if color_points is not None else np.array(color) * 0.5 alpha_face = alpha_face if alpha_face is not None else alpha * 0.5 alpha_lines = alpha_lines if alpha_lines is not None else alpha alpha_points = alpha_points if alpha_points is not None else alpha size_lines = size_lines if size_lines is not None else size size_points = size_points if size_points is not None else size * 3 if image.ndim == 2: assert ia.is_single_number(color_face), ( "Got a 2D image. Expected then 'color_face' to be a single " "number, but got %s." % (str(color_face),)) color_face = [color_face] elif image.ndim == 3 and ia.is_single_number(color_face): color_face = [color_face] * image.shape[-1] if alpha_face < 0.01: alpha_face = 0 elif alpha_face > 0.99: alpha_face = 1 if raise_if_out_of_image and self.is_out_of_image(image): raise Exception("Cannot draw polygon %s on image with shape %s." % ( str(self), image.shape )) # TODO np.clip to image plane if is_fully_within_image(), similar to how it is done for bounding boxes # TODO improve efficiency by only drawing in rectangle that covers poly instead of drawing in the whole image # TODO for a rectangular polygon, the face coordinates include the top/left boundary but not the right/bottom # boundary. This may be unintuitive when not drawing the boundary. Maybe somehow remove the boundary # coordinates from the face coordinates after generating both? input_dtype = image.dtype result = image.astype(np.float32) rr, cc = skimage.draw.polygon(self.yy_int, self.xx_int, shape=image.shape) if len(rr) > 0: if alpha_face == 1: result[rr, cc] = np.float32(color_face) elif alpha_face == 0: pass else: result[rr, cc] = ( (1 - alpha_face) * result[rr, cc, :] + alpha_face * np.float32(color_face) ) ls_open = self.to_line_string(closed=False) ls_closed = self.to_line_string(closed=True) result = ls_closed.draw_lines_on_image( result, color=color_lines, alpha=alpha_lines, size=size_lines, raise_if_out_of_image=raise_if_out_of_image) result = ls_open.draw_points_on_image( result, color=color_points, alpha=alpha_points, size=size_points, raise_if_out_of_image=raise_if_out_of_image) if input_dtype.type == np.uint8: result = np.clip(np.round(result), 0, 255).astype(input_dtype) # TODO make clipping more flexible else: result = result.astype(input_dtype) return result
[ "def", "draw_on_image", "(", "self", ",", "image", ",", "color", "=", "(", "0", ",", "255", ",", "0", ")", ",", "color_face", "=", "None", ",", "color_lines", "=", "None", ",", "color_points", "=", "None", ",", "alpha", "=", "1.0", ",", "alpha_face", "=", "None", ",", "alpha_lines", "=", "None", ",", "alpha_points", "=", "None", ",", "size", "=", "1", ",", "size_lines", "=", "None", ",", "size_points", "=", "None", ",", "raise_if_out_of_image", "=", "False", ")", ":", "assert", "color", "is", "not", "None", "assert", "alpha", "is", "not", "None", "assert", "size", "is", "not", "None", "color_face", "=", "color_face", "if", "color_face", "is", "not", "None", "else", "np", ".", "array", "(", "color", ")", "color_lines", "=", "color_lines", "if", "color_lines", "is", "not", "None", "else", "np", ".", "array", "(", "color", ")", "*", "0.5", "color_points", "=", "color_points", "if", "color_points", "is", "not", "None", "else", "np", ".", "array", "(", "color", ")", "*", "0.5", "alpha_face", "=", "alpha_face", "if", "alpha_face", "is", "not", "None", "else", "alpha", "*", "0.5", "alpha_lines", "=", "alpha_lines", "if", "alpha_lines", "is", "not", "None", "else", "alpha", "alpha_points", "=", "alpha_points", "if", "alpha_points", "is", "not", "None", "else", "alpha", "size_lines", "=", "size_lines", "if", "size_lines", "is", "not", "None", "else", "size", "size_points", "=", "size_points", "if", "size_points", "is", "not", "None", "else", "size", "*", "3", "if", "image", ".", "ndim", "==", "2", ":", "assert", "ia", ".", "is_single_number", "(", "color_face", ")", ",", "(", "\"Got a 2D image. Expected then 'color_face' to be a single \"", "\"number, but got %s.\"", "%", "(", "str", "(", "color_face", ")", ",", ")", ")", "color_face", "=", "[", "color_face", "]", "elif", "image", ".", "ndim", "==", "3", "and", "ia", ".", "is_single_number", "(", "color_face", ")", ":", "color_face", "=", "[", "color_face", "]", "*", "image", ".", "shape", "[", "-", "1", "]", "if", "alpha_face", "<", "0.01", ":", "alpha_face", "=", "0", "elif", "alpha_face", ">", "0.99", ":", "alpha_face", "=", "1", "if", "raise_if_out_of_image", "and", "self", ".", "is_out_of_image", "(", "image", ")", ":", "raise", "Exception", "(", "\"Cannot draw polygon %s on image with shape %s.\"", "%", "(", "str", "(", "self", ")", ",", "image", ".", "shape", ")", ")", "# TODO np.clip to image plane if is_fully_within_image(), similar to how it is done for bounding boxes", "# TODO improve efficiency by only drawing in rectangle that covers poly instead of drawing in the whole image", "# TODO for a rectangular polygon, the face coordinates include the top/left boundary but not the right/bottom", "# boundary. This may be unintuitive when not drawing the boundary. Maybe somehow remove the boundary", "# coordinates from the face coordinates after generating both?", "input_dtype", "=", "image", ".", "dtype", "result", "=", "image", ".", "astype", "(", "np", ".", "float32", ")", "rr", ",", "cc", "=", "skimage", ".", "draw", ".", "polygon", "(", "self", ".", "yy_int", ",", "self", ".", "xx_int", ",", "shape", "=", "image", ".", "shape", ")", "if", "len", "(", "rr", ")", ">", "0", ":", "if", "alpha_face", "==", "1", ":", "result", "[", "rr", ",", "cc", "]", "=", "np", ".", "float32", "(", "color_face", ")", "elif", "alpha_face", "==", "0", ":", "pass", "else", ":", "result", "[", "rr", ",", "cc", "]", "=", "(", "(", "1", "-", "alpha_face", ")", "*", "result", "[", "rr", ",", "cc", ",", ":", "]", "+", "alpha_face", "*", "np", ".", "float32", "(", "color_face", ")", ")", "ls_open", "=", "self", ".", "to_line_string", "(", "closed", "=", "False", ")", "ls_closed", "=", "self", ".", "to_line_string", "(", "closed", "=", "True", ")", "result", "=", "ls_closed", ".", "draw_lines_on_image", "(", "result", ",", "color", "=", "color_lines", ",", "alpha", "=", "alpha_lines", ",", "size", "=", "size_lines", ",", "raise_if_out_of_image", "=", "raise_if_out_of_image", ")", "result", "=", "ls_open", ".", "draw_points_on_image", "(", "result", ",", "color", "=", "color_points", ",", "alpha", "=", "alpha_points", ",", "size", "=", "size_points", ",", "raise_if_out_of_image", "=", "raise_if_out_of_image", ")", "if", "input_dtype", ".", "type", "==", "np", ".", "uint8", ":", "result", "=", "np", ".", "clip", "(", "np", ".", "round", "(", "result", ")", ",", "0", ",", "255", ")", ".", "astype", "(", "input_dtype", ")", "# TODO make clipping more flexible", "else", ":", "result", "=", "result", ".", "astype", "(", "input_dtype", ")", "return", "result" ]
Draw the polygon on an image. Parameters ---------- image : (H,W,C) ndarray The image onto which to draw the polygon. Usually expected to be of dtype ``uint8``, though other dtypes are also handled. color : iterable of int, optional The color to use for the whole polygon. Must correspond to the channel layout of the image. Usually RGB. The values for `color_face`, `color_lines` and `color_points` will be derived from this color if they are set to ``None``. This argument has no effect if `color_face`, `color_lines` and `color_points` are all set anything other than ``None``. color_face : None or iterable of int, optional The color to use for the inner polygon area (excluding perimeter). Must correspond to the channel layout of the image. Usually RGB. If this is ``None``, it will be derived from ``color * 1.0``. color_lines : None or iterable of int, optional The color to use for the line (aka perimeter/border) of the polygon. Must correspond to the channel layout of the image. Usually RGB. If this is ``None``, it will be derived from ``color * 0.5``. color_points : None or iterable of int, optional The color to use for the corner points of the polygon. Must correspond to the channel layout of the image. Usually RGB. If this is ``None``, it will be derived from ``color * 0.5``. alpha : float, optional The opacity of the whole polygon, where ``1.0`` denotes a completely visible polygon and ``0.0`` an invisible one. The values for `alpha_face`, `alpha_lines` and `alpha_points` will be derived from this alpha value if they are set to ``None``. This argument has no effect if `alpha_face`, `alpha_lines` and `alpha_points` are all set anything other than ``None``. alpha_face : None or number, optional The opacity of the polygon's inner area (excluding the perimeter), where ``1.0`` denotes a completely visible inner area and ``0.0`` an invisible one. If this is ``None``, it will be derived from ``alpha * 0.5``. alpha_lines : None or number, optional The opacity of the polygon's line (aka perimeter/border), where ``1.0`` denotes a completely visible line and ``0.0`` an invisible one. If this is ``None``, it will be derived from ``alpha * 1.0``. alpha_points : None or number, optional The opacity of the polygon's corner points, where ``1.0`` denotes completely visible corners and ``0.0`` invisible ones. If this is ``None``, it will be derived from ``alpha * 1.0``. size : int, optional Size of the polygon. The sizes of the line and points are derived from this value, unless they are set. size_lines : None or int, optional Thickness of the polygon's line (aka perimeter/border). If ``None``, this value is derived from `size`. size_points : int, optional Size of the points in pixels. If ``None``, this value is derived from ``3 * size``. raise_if_out_of_image : bool, optional Whether to raise an error if the polygon is fully outside of the image. If set to False, no error will be raised and only the parts inside the image will be drawn. Returns ------- result : (H,W,C) ndarray Image with polygon drawn on it. Result dtype is the same as the input dtype.
[ "Draw", "the", "polygon", "on", "an", "image", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/augmentables/polys.py#L435-L591
valid
aleju/imgaug
imgaug/augmentables/polys.py
Polygon.extract_from_image
def extract_from_image(self, image): """ Extract the image pixels within the polygon. This function will zero-pad the image if the polygon is partially/fully outside of the image. Parameters ---------- image : (H,W) ndarray or (H,W,C) ndarray The image from which to extract the pixels within the polygon. Returns ------- result : (H',W') ndarray or (H',W',C) ndarray Pixels within the polygon. Zero-padded if the polygon is partially/fully outside of the image. """ ia.do_assert(image.ndim in [2, 3]) if len(self.exterior) <= 2: raise Exception("Polygon must be made up of at least 3 points to extract its area from an image.") bb = self.to_bounding_box() bb_area = bb.extract_from_image(image) if self.is_out_of_image(image, fully=True, partly=False): return bb_area xx = self.xx_int yy = self.yy_int xx_mask = xx - np.min(xx) yy_mask = yy - np.min(yy) height_mask = np.max(yy_mask) width_mask = np.max(xx_mask) rr_face, cc_face = skimage.draw.polygon(yy_mask, xx_mask, shape=(height_mask, width_mask)) mask = np.zeros((height_mask, width_mask), dtype=np.bool) mask[rr_face, cc_face] = True if image.ndim == 3: mask = np.tile(mask[:, :, np.newaxis], (1, 1, image.shape[2])) return bb_area * mask
python
def extract_from_image(self, image): """ Extract the image pixels within the polygon. This function will zero-pad the image if the polygon is partially/fully outside of the image. Parameters ---------- image : (H,W) ndarray or (H,W,C) ndarray The image from which to extract the pixels within the polygon. Returns ------- result : (H',W') ndarray or (H',W',C) ndarray Pixels within the polygon. Zero-padded if the polygon is partially/fully outside of the image. """ ia.do_assert(image.ndim in [2, 3]) if len(self.exterior) <= 2: raise Exception("Polygon must be made up of at least 3 points to extract its area from an image.") bb = self.to_bounding_box() bb_area = bb.extract_from_image(image) if self.is_out_of_image(image, fully=True, partly=False): return bb_area xx = self.xx_int yy = self.yy_int xx_mask = xx - np.min(xx) yy_mask = yy - np.min(yy) height_mask = np.max(yy_mask) width_mask = np.max(xx_mask) rr_face, cc_face = skimage.draw.polygon(yy_mask, xx_mask, shape=(height_mask, width_mask)) mask = np.zeros((height_mask, width_mask), dtype=np.bool) mask[rr_face, cc_face] = True if image.ndim == 3: mask = np.tile(mask[:, :, np.newaxis], (1, 1, image.shape[2])) return bb_area * mask
[ "def", "extract_from_image", "(", "self", ",", "image", ")", ":", "ia", ".", "do_assert", "(", "image", ".", "ndim", "in", "[", "2", ",", "3", "]", ")", "if", "len", "(", "self", ".", "exterior", ")", "<=", "2", ":", "raise", "Exception", "(", "\"Polygon must be made up of at least 3 points to extract its area from an image.\"", ")", "bb", "=", "self", ".", "to_bounding_box", "(", ")", "bb_area", "=", "bb", ".", "extract_from_image", "(", "image", ")", "if", "self", ".", "is_out_of_image", "(", "image", ",", "fully", "=", "True", ",", "partly", "=", "False", ")", ":", "return", "bb_area", "xx", "=", "self", ".", "xx_int", "yy", "=", "self", ".", "yy_int", "xx_mask", "=", "xx", "-", "np", ".", "min", "(", "xx", ")", "yy_mask", "=", "yy", "-", "np", ".", "min", "(", "yy", ")", "height_mask", "=", "np", ".", "max", "(", "yy_mask", ")", "width_mask", "=", "np", ".", "max", "(", "xx_mask", ")", "rr_face", ",", "cc_face", "=", "skimage", ".", "draw", ".", "polygon", "(", "yy_mask", ",", "xx_mask", ",", "shape", "=", "(", "height_mask", ",", "width_mask", ")", ")", "mask", "=", "np", ".", "zeros", "(", "(", "height_mask", ",", "width_mask", ")", ",", "dtype", "=", "np", ".", "bool", ")", "mask", "[", "rr_face", ",", "cc_face", "]", "=", "True", "if", "image", ".", "ndim", "==", "3", ":", "mask", "=", "np", ".", "tile", "(", "mask", "[", ":", ",", ":", ",", "np", ".", "newaxis", "]", ",", "(", "1", ",", "1", ",", "image", ".", "shape", "[", "2", "]", ")", ")", "return", "bb_area", "*", "mask" ]
Extract the image pixels within the polygon. This function will zero-pad the image if the polygon is partially/fully outside of the image. Parameters ---------- image : (H,W) ndarray or (H,W,C) ndarray The image from which to extract the pixels within the polygon. Returns ------- result : (H',W') ndarray or (H',W',C) ndarray Pixels within the polygon. Zero-padded if the polygon is partially/fully outside of the image.
[ "Extract", "the", "image", "pixels", "within", "the", "polygon", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/augmentables/polys.py#L593-L636
valid
aleju/imgaug
imgaug/augmentables/polys.py
Polygon.change_first_point_by_coords
def change_first_point_by_coords(self, x, y, max_distance=1e-4, raise_if_too_far_away=True): """ Set the first point of the exterior to the given point based on its coordinates. If multiple points are found, the closest one will be picked. If no matching points are found, an exception is raised. Note: This method does *not* work in-place. Parameters ---------- x : number X-coordinate of the point. y : number Y-coordinate of the point. max_distance : None or number, optional Maximum distance past which possible matches are ignored. If ``None`` the distance limit is deactivated. raise_if_too_far_away : bool, optional Whether to raise an exception if the closest found point is too far away (``True``) or simply return an unchanged copy if this object (``False``). Returns ------- imgaug.Polygon Copy of this polygon with the new point order. """ if len(self.exterior) == 0: raise Exception("Cannot reorder polygon points, because it contains no points.") closest_idx, closest_dist = self.find_closest_point_index(x=x, y=y, return_distance=True) if max_distance is not None and closest_dist > max_distance: if not raise_if_too_far_away: return self.deepcopy() closest_point = self.exterior[closest_idx, :] raise Exception( "Closest found point (%.9f, %.9f) exceeds max_distance of %.9f exceeded" % ( closest_point[0], closest_point[1], closest_dist) ) return self.change_first_point_by_index(closest_idx)
python
def change_first_point_by_coords(self, x, y, max_distance=1e-4, raise_if_too_far_away=True): """ Set the first point of the exterior to the given point based on its coordinates. If multiple points are found, the closest one will be picked. If no matching points are found, an exception is raised. Note: This method does *not* work in-place. Parameters ---------- x : number X-coordinate of the point. y : number Y-coordinate of the point. max_distance : None or number, optional Maximum distance past which possible matches are ignored. If ``None`` the distance limit is deactivated. raise_if_too_far_away : bool, optional Whether to raise an exception if the closest found point is too far away (``True``) or simply return an unchanged copy if this object (``False``). Returns ------- imgaug.Polygon Copy of this polygon with the new point order. """ if len(self.exterior) == 0: raise Exception("Cannot reorder polygon points, because it contains no points.") closest_idx, closest_dist = self.find_closest_point_index(x=x, y=y, return_distance=True) if max_distance is not None and closest_dist > max_distance: if not raise_if_too_far_away: return self.deepcopy() closest_point = self.exterior[closest_idx, :] raise Exception( "Closest found point (%.9f, %.9f) exceeds max_distance of %.9f exceeded" % ( closest_point[0], closest_point[1], closest_dist) ) return self.change_first_point_by_index(closest_idx)
[ "def", "change_first_point_by_coords", "(", "self", ",", "x", ",", "y", ",", "max_distance", "=", "1e-4", ",", "raise_if_too_far_away", "=", "True", ")", ":", "if", "len", "(", "self", ".", "exterior", ")", "==", "0", ":", "raise", "Exception", "(", "\"Cannot reorder polygon points, because it contains no points.\"", ")", "closest_idx", ",", "closest_dist", "=", "self", ".", "find_closest_point_index", "(", "x", "=", "x", ",", "y", "=", "y", ",", "return_distance", "=", "True", ")", "if", "max_distance", "is", "not", "None", "and", "closest_dist", ">", "max_distance", ":", "if", "not", "raise_if_too_far_away", ":", "return", "self", ".", "deepcopy", "(", ")", "closest_point", "=", "self", ".", "exterior", "[", "closest_idx", ",", ":", "]", "raise", "Exception", "(", "\"Closest found point (%.9f, %.9f) exceeds max_distance of %.9f exceeded\"", "%", "(", "closest_point", "[", "0", "]", ",", "closest_point", "[", "1", "]", ",", "closest_dist", ")", ")", "return", "self", ".", "change_first_point_by_index", "(", "closest_idx", ")" ]
Set the first point of the exterior to the given point based on its coordinates. If multiple points are found, the closest one will be picked. If no matching points are found, an exception is raised. Note: This method does *not* work in-place. Parameters ---------- x : number X-coordinate of the point. y : number Y-coordinate of the point. max_distance : None or number, optional Maximum distance past which possible matches are ignored. If ``None`` the distance limit is deactivated. raise_if_too_far_away : bool, optional Whether to raise an exception if the closest found point is too far away (``True``) or simply return an unchanged copy if this object (``False``). Returns ------- imgaug.Polygon Copy of this polygon with the new point order.
[ "Set", "the", "first", "point", "of", "the", "exterior", "to", "the", "given", "point", "based", "on", "its", "coordinates", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/augmentables/polys.py#L638-L684
valid
aleju/imgaug
imgaug/augmentables/polys.py
Polygon.change_first_point_by_index
def change_first_point_by_index(self, point_idx): """ Set the first point of the exterior to the given point based on its index. Note: This method does *not* work in-place. Parameters ---------- point_idx : int Index of the desired starting point. Returns ------- imgaug.Polygon Copy of this polygon with the new point order. """ ia.do_assert(0 <= point_idx < len(self.exterior)) if point_idx == 0: return self.deepcopy() exterior = np.concatenate( (self.exterior[point_idx:, :], self.exterior[:point_idx, :]), axis=0 ) return self.deepcopy(exterior=exterior)
python
def change_first_point_by_index(self, point_idx): """ Set the first point of the exterior to the given point based on its index. Note: This method does *not* work in-place. Parameters ---------- point_idx : int Index of the desired starting point. Returns ------- imgaug.Polygon Copy of this polygon with the new point order. """ ia.do_assert(0 <= point_idx < len(self.exterior)) if point_idx == 0: return self.deepcopy() exterior = np.concatenate( (self.exterior[point_idx:, :], self.exterior[:point_idx, :]), axis=0 ) return self.deepcopy(exterior=exterior)
[ "def", "change_first_point_by_index", "(", "self", ",", "point_idx", ")", ":", "ia", ".", "do_assert", "(", "0", "<=", "point_idx", "<", "len", "(", "self", ".", "exterior", ")", ")", "if", "point_idx", "==", "0", ":", "return", "self", ".", "deepcopy", "(", ")", "exterior", "=", "np", ".", "concatenate", "(", "(", "self", ".", "exterior", "[", "point_idx", ":", ",", ":", "]", ",", "self", ".", "exterior", "[", ":", "point_idx", ",", ":", "]", ")", ",", "axis", "=", "0", ")", "return", "self", ".", "deepcopy", "(", "exterior", "=", "exterior", ")" ]
Set the first point of the exterior to the given point based on its index. Note: This method does *not* work in-place. Parameters ---------- point_idx : int Index of the desired starting point. Returns ------- imgaug.Polygon Copy of this polygon with the new point order.
[ "Set", "the", "first", "point", "of", "the", "exterior", "to", "the", "given", "point", "based", "on", "its", "index", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/augmentables/polys.py#L686-L710
valid
aleju/imgaug
imgaug/augmentables/polys.py
Polygon.to_shapely_polygon
def to_shapely_polygon(self): """ Convert this polygon to a Shapely polygon. Returns ------- shapely.geometry.Polygon The Shapely polygon matching this polygon's exterior. """ # load shapely lazily, which makes the dependency more optional import shapely.geometry return shapely.geometry.Polygon([(point[0], point[1]) for point in self.exterior])
python
def to_shapely_polygon(self): """ Convert this polygon to a Shapely polygon. Returns ------- shapely.geometry.Polygon The Shapely polygon matching this polygon's exterior. """ # load shapely lazily, which makes the dependency more optional import shapely.geometry return shapely.geometry.Polygon([(point[0], point[1]) for point in self.exterior])
[ "def", "to_shapely_polygon", "(", "self", ")", ":", "# load shapely lazily, which makes the dependency more optional", "import", "shapely", ".", "geometry", "return", "shapely", ".", "geometry", ".", "Polygon", "(", "[", "(", "point", "[", "0", "]", ",", "point", "[", "1", "]", ")", "for", "point", "in", "self", ".", "exterior", "]", ")" ]
Convert this polygon to a Shapely polygon. Returns ------- shapely.geometry.Polygon The Shapely polygon matching this polygon's exterior.
[ "Convert", "this", "polygon", "to", "a", "Shapely", "polygon", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/augmentables/polys.py#L712-L725
valid
aleju/imgaug
imgaug/augmentables/polys.py
Polygon.to_shapely_line_string
def to_shapely_line_string(self, closed=False, interpolate=0): """ Convert this polygon to a Shapely LineString object. Parameters ---------- closed : bool, optional Whether to return the line string with the last point being identical to the first point. interpolate : int, optional Number of points to interpolate between any pair of two consecutive points. These points are added to the final line string. Returns ------- shapely.geometry.LineString The Shapely LineString matching the polygon's exterior. """ return _convert_points_to_shapely_line_string(self.exterior, closed=closed, interpolate=interpolate)
python
def to_shapely_line_string(self, closed=False, interpolate=0): """ Convert this polygon to a Shapely LineString object. Parameters ---------- closed : bool, optional Whether to return the line string with the last point being identical to the first point. interpolate : int, optional Number of points to interpolate between any pair of two consecutive points. These points are added to the final line string. Returns ------- shapely.geometry.LineString The Shapely LineString matching the polygon's exterior. """ return _convert_points_to_shapely_line_string(self.exterior, closed=closed, interpolate=interpolate)
[ "def", "to_shapely_line_string", "(", "self", ",", "closed", "=", "False", ",", "interpolate", "=", "0", ")", ":", "return", "_convert_points_to_shapely_line_string", "(", "self", ".", "exterior", ",", "closed", "=", "closed", ",", "interpolate", "=", "interpolate", ")" ]
Convert this polygon to a Shapely LineString object. Parameters ---------- closed : bool, optional Whether to return the line string with the last point being identical to the first point. interpolate : int, optional Number of points to interpolate between any pair of two consecutive points. These points are added to the final line string. Returns ------- shapely.geometry.LineString The Shapely LineString matching the polygon's exterior.
[ "Convert", "this", "polygon", "to", "a", "Shapely", "LineString", "object", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/augmentables/polys.py#L727-L746
valid
aleju/imgaug
imgaug/augmentables/polys.py
Polygon.to_bounding_box
def to_bounding_box(self): """ Convert this polygon to a bounding box tightly containing the whole polygon. Returns ------- imgaug.BoundingBox Tight bounding box around the polygon. """ # TODO get rid of this deferred import from imgaug.augmentables.bbs import BoundingBox xx = self.xx yy = self.yy return BoundingBox(x1=min(xx), x2=max(xx), y1=min(yy), y2=max(yy), label=self.label)
python
def to_bounding_box(self): """ Convert this polygon to a bounding box tightly containing the whole polygon. Returns ------- imgaug.BoundingBox Tight bounding box around the polygon. """ # TODO get rid of this deferred import from imgaug.augmentables.bbs import BoundingBox xx = self.xx yy = self.yy return BoundingBox(x1=min(xx), x2=max(xx), y1=min(yy), y2=max(yy), label=self.label)
[ "def", "to_bounding_box", "(", "self", ")", ":", "# TODO get rid of this deferred import", "from", "imgaug", ".", "augmentables", ".", "bbs", "import", "BoundingBox", "xx", "=", "self", ".", "xx", "yy", "=", "self", ".", "yy", "return", "BoundingBox", "(", "x1", "=", "min", "(", "xx", ")", ",", "x2", "=", "max", "(", "xx", ")", ",", "y1", "=", "min", "(", "yy", ")", ",", "y2", "=", "max", "(", "yy", ")", ",", "label", "=", "self", ".", "label", ")" ]
Convert this polygon to a bounding box tightly containing the whole polygon. Returns ------- imgaug.BoundingBox Tight bounding box around the polygon.
[ "Convert", "this", "polygon", "to", "a", "bounding", "box", "tightly", "containing", "the", "whole", "polygon", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/augmentables/polys.py#L748-L763
valid
aleju/imgaug
imgaug/augmentables/polys.py
Polygon.to_keypoints
def to_keypoints(self): """ Convert this polygon's `exterior` to ``Keypoint`` instances. Returns ------- list of imgaug.Keypoint Exterior vertices as ``Keypoint`` instances. """ # TODO get rid of this deferred import from imgaug.augmentables.kps import Keypoint return [Keypoint(x=point[0], y=point[1]) for point in self.exterior]
python
def to_keypoints(self): """ Convert this polygon's `exterior` to ``Keypoint`` instances. Returns ------- list of imgaug.Keypoint Exterior vertices as ``Keypoint`` instances. """ # TODO get rid of this deferred import from imgaug.augmentables.kps import Keypoint return [Keypoint(x=point[0], y=point[1]) for point in self.exterior]
[ "def", "to_keypoints", "(", "self", ")", ":", "# TODO get rid of this deferred import", "from", "imgaug", ".", "augmentables", ".", "kps", "import", "Keypoint", "return", "[", "Keypoint", "(", "x", "=", "point", "[", "0", "]", ",", "y", "=", "point", "[", "1", "]", ")", "for", "point", "in", "self", ".", "exterior", "]" ]
Convert this polygon's `exterior` to ``Keypoint`` instances. Returns ------- list of imgaug.Keypoint Exterior vertices as ``Keypoint`` instances.
[ "Convert", "this", "polygon", "s", "exterior", "to", "Keypoint", "instances", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/augmentables/polys.py#L765-L778
valid
aleju/imgaug
imgaug/augmentables/polys.py
Polygon.to_line_string
def to_line_string(self, closed=True): """ Convert this polygon's `exterior` to a ``LineString`` instance. Parameters ---------- closed : bool, optional Whether to close the line string, i.e. to add the first point of the `exterior` also as the last point at the end of the line string. This has no effect if the polygon has a single point or zero points. Returns ------- imgaug.augmentables.lines.LineString Exterior of the polygon as a line string. """ from imgaug.augmentables.lines import LineString if not closed or len(self.exterior) <= 1: return LineString(self.exterior, label=self.label) return LineString( np.concatenate([self.exterior, self.exterior[0:1, :]], axis=0), label=self.label)
python
def to_line_string(self, closed=True): """ Convert this polygon's `exterior` to a ``LineString`` instance. Parameters ---------- closed : bool, optional Whether to close the line string, i.e. to add the first point of the `exterior` also as the last point at the end of the line string. This has no effect if the polygon has a single point or zero points. Returns ------- imgaug.augmentables.lines.LineString Exterior of the polygon as a line string. """ from imgaug.augmentables.lines import LineString if not closed or len(self.exterior) <= 1: return LineString(self.exterior, label=self.label) return LineString( np.concatenate([self.exterior, self.exterior[0:1, :]], axis=0), label=self.label)
[ "def", "to_line_string", "(", "self", ",", "closed", "=", "True", ")", ":", "from", "imgaug", ".", "augmentables", ".", "lines", "import", "LineString", "if", "not", "closed", "or", "len", "(", "self", ".", "exterior", ")", "<=", "1", ":", "return", "LineString", "(", "self", ".", "exterior", ",", "label", "=", "self", ".", "label", ")", "return", "LineString", "(", "np", ".", "concatenate", "(", "[", "self", ".", "exterior", ",", "self", ".", "exterior", "[", "0", ":", "1", ",", ":", "]", "]", ",", "axis", "=", "0", ")", ",", "label", "=", "self", ".", "label", ")" ]
Convert this polygon's `exterior` to a ``LineString`` instance. Parameters ---------- closed : bool, optional Whether to close the line string, i.e. to add the first point of the `exterior` also as the last point at the end of the line string. This has no effect if the polygon has a single point or zero points. Returns ------- imgaug.augmentables.lines.LineString Exterior of the polygon as a line string.
[ "Convert", "this", "polygon", "s", "exterior", "to", "a", "LineString", "instance", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/augmentables/polys.py#L780-L803
valid
aleju/imgaug
imgaug/augmentables/polys.py
Polygon.from_shapely
def from_shapely(polygon_shapely, label=None): """ Create a polygon from a Shapely polygon. Note: This will remove any holes in the Shapely polygon. Parameters ---------- polygon_shapely : shapely.geometry.Polygon The shapely polygon. label : None or str, optional The label of the new polygon. Returns ------- imgaug.Polygon A polygon with the same exterior as the Shapely polygon. """ # load shapely lazily, which makes the dependency more optional import shapely.geometry ia.do_assert(isinstance(polygon_shapely, shapely.geometry.Polygon)) # polygon_shapely.exterior can be None if the polygon was instantiated without points if polygon_shapely.exterior is None or len(polygon_shapely.exterior.coords) == 0: return Polygon([], label=label) exterior = np.float32([[x, y] for (x, y) in polygon_shapely.exterior.coords]) return Polygon(exterior, label=label)
python
def from_shapely(polygon_shapely, label=None): """ Create a polygon from a Shapely polygon. Note: This will remove any holes in the Shapely polygon. Parameters ---------- polygon_shapely : shapely.geometry.Polygon The shapely polygon. label : None or str, optional The label of the new polygon. Returns ------- imgaug.Polygon A polygon with the same exterior as the Shapely polygon. """ # load shapely lazily, which makes the dependency more optional import shapely.geometry ia.do_assert(isinstance(polygon_shapely, shapely.geometry.Polygon)) # polygon_shapely.exterior can be None if the polygon was instantiated without points if polygon_shapely.exterior is None or len(polygon_shapely.exterior.coords) == 0: return Polygon([], label=label) exterior = np.float32([[x, y] for (x, y) in polygon_shapely.exterior.coords]) return Polygon(exterior, label=label)
[ "def", "from_shapely", "(", "polygon_shapely", ",", "label", "=", "None", ")", ":", "# load shapely lazily, which makes the dependency more optional", "import", "shapely", ".", "geometry", "ia", ".", "do_assert", "(", "isinstance", "(", "polygon_shapely", ",", "shapely", ".", "geometry", ".", "Polygon", ")", ")", "# polygon_shapely.exterior can be None if the polygon was instantiated without points", "if", "polygon_shapely", ".", "exterior", "is", "None", "or", "len", "(", "polygon_shapely", ".", "exterior", ".", "coords", ")", "==", "0", ":", "return", "Polygon", "(", "[", "]", ",", "label", "=", "label", ")", "exterior", "=", "np", ".", "float32", "(", "[", "[", "x", ",", "y", "]", "for", "(", "x", ",", "y", ")", "in", "polygon_shapely", ".", "exterior", ".", "coords", "]", ")", "return", "Polygon", "(", "exterior", ",", "label", "=", "label", ")" ]
Create a polygon from a Shapely polygon. Note: This will remove any holes in the Shapely polygon. Parameters ---------- polygon_shapely : shapely.geometry.Polygon The shapely polygon. label : None or str, optional The label of the new polygon. Returns ------- imgaug.Polygon A polygon with the same exterior as the Shapely polygon.
[ "Create", "a", "polygon", "from", "a", "Shapely", "polygon", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/augmentables/polys.py#L806-L834
valid
aleju/imgaug
imgaug/augmentables/polys.py
Polygon.exterior_almost_equals
def exterior_almost_equals(self, other, max_distance=1e-6, points_per_edge=8): """ Estimate if this and other polygon's exterior are almost identical. The two exteriors can have different numbers of points, but any point randomly sampled on the exterior of one polygon should be close to the closest point on the exterior of the other polygon. Note that this method works approximately. One can come up with polygons with fairly different shapes that will still be estimated as equal by this method. In practice however this should be unlikely to be the case. The probability for something like that goes down as the interpolation parameter is increased. Parameters ---------- other : imgaug.Polygon or (N,2) ndarray or list of tuple The other polygon with which to compare the exterior. If this is an ndarray, it is assumed to represent an exterior. It must then have dtype ``float32`` and shape ``(N,2)`` with the second dimension denoting xy-coordinates. If this is a list of tuples, it is assumed to represent an exterior. Each tuple then must contain exactly two numbers, denoting xy-coordinates. max_distance : number, optional The maximum euclidean distance between a point on one polygon and the closest point on the other polygon. If the distance is exceeded for any such pair, the two exteriors are not viewed as equal. The points are other the points contained in the polygon's exterior ndarray or interpolated points between these. points_per_edge : int, optional How many points to interpolate on each edge. Returns ------- bool Whether the two polygon's exteriors can be viewed as equal (approximate test). """ if isinstance(other, list): other = Polygon(np.float32(other)) elif ia.is_np_array(other): other = Polygon(other) else: assert isinstance(other, Polygon) other = other return self.to_line_string(closed=True).coords_almost_equals( other.to_line_string(closed=True), max_distance=max_distance, points_per_edge=points_per_edge )
python
def exterior_almost_equals(self, other, max_distance=1e-6, points_per_edge=8): """ Estimate if this and other polygon's exterior are almost identical. The two exteriors can have different numbers of points, but any point randomly sampled on the exterior of one polygon should be close to the closest point on the exterior of the other polygon. Note that this method works approximately. One can come up with polygons with fairly different shapes that will still be estimated as equal by this method. In practice however this should be unlikely to be the case. The probability for something like that goes down as the interpolation parameter is increased. Parameters ---------- other : imgaug.Polygon or (N,2) ndarray or list of tuple The other polygon with which to compare the exterior. If this is an ndarray, it is assumed to represent an exterior. It must then have dtype ``float32`` and shape ``(N,2)`` with the second dimension denoting xy-coordinates. If this is a list of tuples, it is assumed to represent an exterior. Each tuple then must contain exactly two numbers, denoting xy-coordinates. max_distance : number, optional The maximum euclidean distance between a point on one polygon and the closest point on the other polygon. If the distance is exceeded for any such pair, the two exteriors are not viewed as equal. The points are other the points contained in the polygon's exterior ndarray or interpolated points between these. points_per_edge : int, optional How many points to interpolate on each edge. Returns ------- bool Whether the two polygon's exteriors can be viewed as equal (approximate test). """ if isinstance(other, list): other = Polygon(np.float32(other)) elif ia.is_np_array(other): other = Polygon(other) else: assert isinstance(other, Polygon) other = other return self.to_line_string(closed=True).coords_almost_equals( other.to_line_string(closed=True), max_distance=max_distance, points_per_edge=points_per_edge )
[ "def", "exterior_almost_equals", "(", "self", ",", "other", ",", "max_distance", "=", "1e-6", ",", "points_per_edge", "=", "8", ")", ":", "if", "isinstance", "(", "other", ",", "list", ")", ":", "other", "=", "Polygon", "(", "np", ".", "float32", "(", "other", ")", ")", "elif", "ia", ".", "is_np_array", "(", "other", ")", ":", "other", "=", "Polygon", "(", "other", ")", "else", ":", "assert", "isinstance", "(", "other", ",", "Polygon", ")", "other", "=", "other", "return", "self", ".", "to_line_string", "(", "closed", "=", "True", ")", ".", "coords_almost_equals", "(", "other", ".", "to_line_string", "(", "closed", "=", "True", ")", ",", "max_distance", "=", "max_distance", ",", "points_per_edge", "=", "points_per_edge", ")" ]
Estimate if this and other polygon's exterior are almost identical. The two exteriors can have different numbers of points, but any point randomly sampled on the exterior of one polygon should be close to the closest point on the exterior of the other polygon. Note that this method works approximately. One can come up with polygons with fairly different shapes that will still be estimated as equal by this method. In practice however this should be unlikely to be the case. The probability for something like that goes down as the interpolation parameter is increased. Parameters ---------- other : imgaug.Polygon or (N,2) ndarray or list of tuple The other polygon with which to compare the exterior. If this is an ndarray, it is assumed to represent an exterior. It must then have dtype ``float32`` and shape ``(N,2)`` with the second dimension denoting xy-coordinates. If this is a list of tuples, it is assumed to represent an exterior. Each tuple then must contain exactly two numbers, denoting xy-coordinates. max_distance : number, optional The maximum euclidean distance between a point on one polygon and the closest point on the other polygon. If the distance is exceeded for any such pair, the two exteriors are not viewed as equal. The points are other the points contained in the polygon's exterior ndarray or interpolated points between these. points_per_edge : int, optional How many points to interpolate on each edge. Returns ------- bool Whether the two polygon's exteriors can be viewed as equal (approximate test).
[ "Estimate", "if", "this", "and", "other", "polygon", "s", "exterior", "are", "almost", "identical", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/augmentables/polys.py#L836-L890
valid
aleju/imgaug
imgaug/augmentables/polys.py
Polygon.almost_equals
def almost_equals(self, other, max_distance=1e-6, points_per_edge=8): """ Estimate if this polygon's and another's geometry/labels are similar. This is the same as :func:`imgaug.Polygon.exterior_almost_equals` but additionally compares the labels. Parameters ---------- other The object to compare against. If not a Polygon, then False will be returned. max_distance : float, optional See :func:`imgaug.augmentables.polys.Polygon.exterior_almost_equals`. points_per_edge : int, optional See :func:`imgaug.augmentables.polys.Polygon.exterior_almost_equals`. Returns ------- bool Whether the two polygons can be viewed as equal. In the case of the exteriors this is an approximate test. """ if not isinstance(other, Polygon): return False if self.label is not None or other.label is not None: if self.label is None: return False if other.label is None: return False if self.label != other.label: return False return self.exterior_almost_equals( other, max_distance=max_distance, points_per_edge=points_per_edge)
python
def almost_equals(self, other, max_distance=1e-6, points_per_edge=8): """ Estimate if this polygon's and another's geometry/labels are similar. This is the same as :func:`imgaug.Polygon.exterior_almost_equals` but additionally compares the labels. Parameters ---------- other The object to compare against. If not a Polygon, then False will be returned. max_distance : float, optional See :func:`imgaug.augmentables.polys.Polygon.exterior_almost_equals`. points_per_edge : int, optional See :func:`imgaug.augmentables.polys.Polygon.exterior_almost_equals`. Returns ------- bool Whether the two polygons can be viewed as equal. In the case of the exteriors this is an approximate test. """ if not isinstance(other, Polygon): return False if self.label is not None or other.label is not None: if self.label is None: return False if other.label is None: return False if self.label != other.label: return False return self.exterior_almost_equals( other, max_distance=max_distance, points_per_edge=points_per_edge)
[ "def", "almost_equals", "(", "self", ",", "other", ",", "max_distance", "=", "1e-6", ",", "points_per_edge", "=", "8", ")", ":", "if", "not", "isinstance", "(", "other", ",", "Polygon", ")", ":", "return", "False", "if", "self", ".", "label", "is", "not", "None", "or", "other", ".", "label", "is", "not", "None", ":", "if", "self", ".", "label", "is", "None", ":", "return", "False", "if", "other", ".", "label", "is", "None", ":", "return", "False", "if", "self", ".", "label", "!=", "other", ".", "label", ":", "return", "False", "return", "self", ".", "exterior_almost_equals", "(", "other", ",", "max_distance", "=", "max_distance", ",", "points_per_edge", "=", "points_per_edge", ")" ]
Estimate if this polygon's and another's geometry/labels are similar. This is the same as :func:`imgaug.Polygon.exterior_almost_equals` but additionally compares the labels. Parameters ---------- other The object to compare against. If not a Polygon, then False will be returned. max_distance : float, optional See :func:`imgaug.augmentables.polys.Polygon.exterior_almost_equals`. points_per_edge : int, optional See :func:`imgaug.augmentables.polys.Polygon.exterior_almost_equals`. Returns ------- bool Whether the two polygons can be viewed as equal. In the case of the exteriors this is an approximate test.
[ "Estimate", "if", "this", "polygon", "s", "and", "another", "s", "geometry", "/", "labels", "are", "similar", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/augmentables/polys.py#L892-L928
valid
aleju/imgaug
imgaug/augmentables/polys.py
Polygon.copy
def copy(self, exterior=None, label=None): """ Create a shallow copy of the Polygon object. Parameters ---------- exterior : list of imgaug.Keypoint or list of tuple or (N,2) ndarray, optional List of points defining the polygon. See :func:`imgaug.Polygon.__init__` for details. label : None or str, optional If not None, then the label of the copied object will be set to this value. Returns ------- imgaug.Polygon Shallow copy. """ return self.deepcopy(exterior=exterior, label=label)
python
def copy(self, exterior=None, label=None): """ Create a shallow copy of the Polygon object. Parameters ---------- exterior : list of imgaug.Keypoint or list of tuple or (N,2) ndarray, optional List of points defining the polygon. See :func:`imgaug.Polygon.__init__` for details. label : None or str, optional If not None, then the label of the copied object will be set to this value. Returns ------- imgaug.Polygon Shallow copy. """ return self.deepcopy(exterior=exterior, label=label)
[ "def", "copy", "(", "self", ",", "exterior", "=", "None", ",", "label", "=", "None", ")", ":", "return", "self", ".", "deepcopy", "(", "exterior", "=", "exterior", ",", "label", "=", "label", ")" ]
Create a shallow copy of the Polygon object. Parameters ---------- exterior : list of imgaug.Keypoint or list of tuple or (N,2) ndarray, optional List of points defining the polygon. See :func:`imgaug.Polygon.__init__` for details. label : None or str, optional If not None, then the label of the copied object will be set to this value. Returns ------- imgaug.Polygon Shallow copy.
[ "Create", "a", "shallow", "copy", "of", "the", "Polygon", "object", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/augmentables/polys.py#L930-L948
valid
aleju/imgaug
imgaug/augmentables/polys.py
Polygon.deepcopy
def deepcopy(self, exterior=None, label=None): """ Create a deep copy of the Polygon object. Parameters ---------- exterior : list of Keypoint or list of tuple or (N,2) ndarray, optional List of points defining the polygon. See `imgaug.Polygon.__init__` for details. label : None or str If not None, then the label of the copied object will be set to this value. Returns ------- imgaug.Polygon Deep copy. """ return Polygon( exterior=np.copy(self.exterior) if exterior is None else exterior, label=self.label if label is None else label )
python
def deepcopy(self, exterior=None, label=None): """ Create a deep copy of the Polygon object. Parameters ---------- exterior : list of Keypoint or list of tuple or (N,2) ndarray, optional List of points defining the polygon. See `imgaug.Polygon.__init__` for details. label : None or str If not None, then the label of the copied object will be set to this value. Returns ------- imgaug.Polygon Deep copy. """ return Polygon( exterior=np.copy(self.exterior) if exterior is None else exterior, label=self.label if label is None else label )
[ "def", "deepcopy", "(", "self", ",", "exterior", "=", "None", ",", "label", "=", "None", ")", ":", "return", "Polygon", "(", "exterior", "=", "np", ".", "copy", "(", "self", ".", "exterior", ")", "if", "exterior", "is", "None", "else", "exterior", ",", "label", "=", "self", ".", "label", "if", "label", "is", "None", "else", "label", ")" ]
Create a deep copy of the Polygon object. Parameters ---------- exterior : list of Keypoint or list of tuple or (N,2) ndarray, optional List of points defining the polygon. See `imgaug.Polygon.__init__` for details. label : None or str If not None, then the label of the copied object will be set to this value. Returns ------- imgaug.Polygon Deep copy.
[ "Create", "a", "deep", "copy", "of", "the", "Polygon", "object", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/augmentables/polys.py#L950-L971
valid
aleju/imgaug
imgaug/augmentables/polys.py
PolygonsOnImage.on
def on(self, image): """ Project polygons from one image to a new one. Parameters ---------- image : ndarray or tuple of int New image onto which the polygons are to be projected. May also simply be that new image's shape tuple. Returns ------- imgaug.PolygonsOnImage Object containing all projected polygons. """ shape = normalize_shape(image) if shape[0:2] == self.shape[0:2]: return self.deepcopy() polygons = [poly.project(self.shape, shape) for poly in self.polygons] # TODO use deepcopy() here return PolygonsOnImage(polygons, shape)
python
def on(self, image): """ Project polygons from one image to a new one. Parameters ---------- image : ndarray or tuple of int New image onto which the polygons are to be projected. May also simply be that new image's shape tuple. Returns ------- imgaug.PolygonsOnImage Object containing all projected polygons. """ shape = normalize_shape(image) if shape[0:2] == self.shape[0:2]: return self.deepcopy() polygons = [poly.project(self.shape, shape) for poly in self.polygons] # TODO use deepcopy() here return PolygonsOnImage(polygons, shape)
[ "def", "on", "(", "self", ",", "image", ")", ":", "shape", "=", "normalize_shape", "(", "image", ")", "if", "shape", "[", "0", ":", "2", "]", "==", "self", ".", "shape", "[", "0", ":", "2", "]", ":", "return", "self", ".", "deepcopy", "(", ")", "polygons", "=", "[", "poly", ".", "project", "(", "self", ".", "shape", ",", "shape", ")", "for", "poly", "in", "self", ".", "polygons", "]", "# TODO use deepcopy() here", "return", "PolygonsOnImage", "(", "polygons", ",", "shape", ")" ]
Project polygons from one image to a new one. Parameters ---------- image : ndarray or tuple of int New image onto which the polygons are to be projected. May also simply be that new image's shape tuple. Returns ------- imgaug.PolygonsOnImage Object containing all projected polygons.
[ "Project", "polygons", "from", "one", "image", "to", "a", "new", "one", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/augmentables/polys.py#L1024-L1045
valid
aleju/imgaug
imgaug/augmentables/polys.py
PolygonsOnImage.draw_on_image
def draw_on_image(self, image, color=(0, 255, 0), color_face=None, color_lines=None, color_points=None, alpha=1.0, alpha_face=None, alpha_lines=None, alpha_points=None, size=1, size_lines=None, size_points=None, raise_if_out_of_image=False): """ Draw all polygons onto a given image. Parameters ---------- image : (H,W,C) ndarray The image onto which to draw the bounding boxes. This image should usually have the same shape as set in ``PolygonsOnImage.shape``. color : iterable of int, optional The color to use for the whole polygons. Must correspond to the channel layout of the image. Usually RGB. The values for `color_face`, `color_lines` and `color_points` will be derived from this color if they are set to ``None``. This argument has no effect if `color_face`, `color_lines` and `color_points` are all set anything other than ``None``. color_face : None or iterable of int, optional The color to use for the inner polygon areas (excluding perimeters). Must correspond to the channel layout of the image. Usually RGB. If this is ``None``, it will be derived from ``color * 1.0``. color_lines : None or iterable of int, optional The color to use for the lines (aka perimeters/borders) of the polygons. Must correspond to the channel layout of the image. Usually RGB. If this is ``None``, it will be derived from ``color * 0.5``. color_points : None or iterable of int, optional The color to use for the corner points of the polygons. Must correspond to the channel layout of the image. Usually RGB. If this is ``None``, it will be derived from ``color * 0.5``. alpha : float, optional The opacity of the whole polygons, where ``1.0`` denotes completely visible polygons and ``0.0`` invisible ones. The values for `alpha_face`, `alpha_lines` and `alpha_points` will be derived from this alpha value if they are set to ``None``. This argument has no effect if `alpha_face`, `alpha_lines` and `alpha_points` are all set anything other than ``None``. alpha_face : None or number, optional The opacity of the polygon's inner areas (excluding the perimeters), where ``1.0`` denotes completely visible inner areas and ``0.0`` invisible ones. If this is ``None``, it will be derived from ``alpha * 0.5``. alpha_lines : None or number, optional The opacity of the polygon's lines (aka perimeters/borders), where ``1.0`` denotes completely visible perimeters and ``0.0`` invisible ones. If this is ``None``, it will be derived from ``alpha * 1.0``. alpha_points : None or number, optional The opacity of the polygon's corner points, where ``1.0`` denotes completely visible corners and ``0.0`` invisible ones. Currently this is an on/off choice, i.e. only ``0.0`` or ``1.0`` are allowed. If this is ``None``, it will be derived from ``alpha * 1.0``. size : int, optional Size of the polygons. The sizes of the line and points are derived from this value, unless they are set. size_lines : None or int, optional Thickness of the polygon lines (aka perimeter/border). If ``None``, this value is derived from `size`. size_points : int, optional The size of all corner points. If set to ``C``, each corner point will be drawn as a square of size ``C x C``. raise_if_out_of_image : bool, optional Whether to raise an error if any polygon is fully outside of the image. If set to False, no error will be raised and only the parts inside the image will be drawn. Returns ------- image : (H,W,C) ndarray Image with drawn polygons. """ for poly in self.polygons: image = poly.draw_on_image( image, color=color, color_face=color_face, color_lines=color_lines, color_points=color_points, alpha=alpha, alpha_face=alpha_face, alpha_lines=alpha_lines, alpha_points=alpha_points, size=size, size_lines=size_lines, size_points=size_points, raise_if_out_of_image=raise_if_out_of_image ) return image
python
def draw_on_image(self, image, color=(0, 255, 0), color_face=None, color_lines=None, color_points=None, alpha=1.0, alpha_face=None, alpha_lines=None, alpha_points=None, size=1, size_lines=None, size_points=None, raise_if_out_of_image=False): """ Draw all polygons onto a given image. Parameters ---------- image : (H,W,C) ndarray The image onto which to draw the bounding boxes. This image should usually have the same shape as set in ``PolygonsOnImage.shape``. color : iterable of int, optional The color to use for the whole polygons. Must correspond to the channel layout of the image. Usually RGB. The values for `color_face`, `color_lines` and `color_points` will be derived from this color if they are set to ``None``. This argument has no effect if `color_face`, `color_lines` and `color_points` are all set anything other than ``None``. color_face : None or iterable of int, optional The color to use for the inner polygon areas (excluding perimeters). Must correspond to the channel layout of the image. Usually RGB. If this is ``None``, it will be derived from ``color * 1.0``. color_lines : None or iterable of int, optional The color to use for the lines (aka perimeters/borders) of the polygons. Must correspond to the channel layout of the image. Usually RGB. If this is ``None``, it will be derived from ``color * 0.5``. color_points : None or iterable of int, optional The color to use for the corner points of the polygons. Must correspond to the channel layout of the image. Usually RGB. If this is ``None``, it will be derived from ``color * 0.5``. alpha : float, optional The opacity of the whole polygons, where ``1.0`` denotes completely visible polygons and ``0.0`` invisible ones. The values for `alpha_face`, `alpha_lines` and `alpha_points` will be derived from this alpha value if they are set to ``None``. This argument has no effect if `alpha_face`, `alpha_lines` and `alpha_points` are all set anything other than ``None``. alpha_face : None or number, optional The opacity of the polygon's inner areas (excluding the perimeters), where ``1.0`` denotes completely visible inner areas and ``0.0`` invisible ones. If this is ``None``, it will be derived from ``alpha * 0.5``. alpha_lines : None or number, optional The opacity of the polygon's lines (aka perimeters/borders), where ``1.0`` denotes completely visible perimeters and ``0.0`` invisible ones. If this is ``None``, it will be derived from ``alpha * 1.0``. alpha_points : None or number, optional The opacity of the polygon's corner points, where ``1.0`` denotes completely visible corners and ``0.0`` invisible ones. Currently this is an on/off choice, i.e. only ``0.0`` or ``1.0`` are allowed. If this is ``None``, it will be derived from ``alpha * 1.0``. size : int, optional Size of the polygons. The sizes of the line and points are derived from this value, unless they are set. size_lines : None or int, optional Thickness of the polygon lines (aka perimeter/border). If ``None``, this value is derived from `size`. size_points : int, optional The size of all corner points. If set to ``C``, each corner point will be drawn as a square of size ``C x C``. raise_if_out_of_image : bool, optional Whether to raise an error if any polygon is fully outside of the image. If set to False, no error will be raised and only the parts inside the image will be drawn. Returns ------- image : (H,W,C) ndarray Image with drawn polygons. """ for poly in self.polygons: image = poly.draw_on_image( image, color=color, color_face=color_face, color_lines=color_lines, color_points=color_points, alpha=alpha, alpha_face=alpha_face, alpha_lines=alpha_lines, alpha_points=alpha_points, size=size, size_lines=size_lines, size_points=size_points, raise_if_out_of_image=raise_if_out_of_image ) return image
[ "def", "draw_on_image", "(", "self", ",", "image", ",", "color", "=", "(", "0", ",", "255", ",", "0", ")", ",", "color_face", "=", "None", ",", "color_lines", "=", "None", ",", "color_points", "=", "None", ",", "alpha", "=", "1.0", ",", "alpha_face", "=", "None", ",", "alpha_lines", "=", "None", ",", "alpha_points", "=", "None", ",", "size", "=", "1", ",", "size_lines", "=", "None", ",", "size_points", "=", "None", ",", "raise_if_out_of_image", "=", "False", ")", ":", "for", "poly", "in", "self", ".", "polygons", ":", "image", "=", "poly", ".", "draw_on_image", "(", "image", ",", "color", "=", "color", ",", "color_face", "=", "color_face", ",", "color_lines", "=", "color_lines", ",", "color_points", "=", "color_points", ",", "alpha", "=", "alpha", ",", "alpha_face", "=", "alpha_face", ",", "alpha_lines", "=", "alpha_lines", ",", "alpha_points", "=", "alpha_points", ",", "size", "=", "size", ",", "size_lines", "=", "size_lines", ",", "size_points", "=", "size_points", ",", "raise_if_out_of_image", "=", "raise_if_out_of_image", ")", "return", "image" ]
Draw all polygons onto a given image. Parameters ---------- image : (H,W,C) ndarray The image onto which to draw the bounding boxes. This image should usually have the same shape as set in ``PolygonsOnImage.shape``. color : iterable of int, optional The color to use for the whole polygons. Must correspond to the channel layout of the image. Usually RGB. The values for `color_face`, `color_lines` and `color_points` will be derived from this color if they are set to ``None``. This argument has no effect if `color_face`, `color_lines` and `color_points` are all set anything other than ``None``. color_face : None or iterable of int, optional The color to use for the inner polygon areas (excluding perimeters). Must correspond to the channel layout of the image. Usually RGB. If this is ``None``, it will be derived from ``color * 1.0``. color_lines : None or iterable of int, optional The color to use for the lines (aka perimeters/borders) of the polygons. Must correspond to the channel layout of the image. Usually RGB. If this is ``None``, it will be derived from ``color * 0.5``. color_points : None or iterable of int, optional The color to use for the corner points of the polygons. Must correspond to the channel layout of the image. Usually RGB. If this is ``None``, it will be derived from ``color * 0.5``. alpha : float, optional The opacity of the whole polygons, where ``1.0`` denotes completely visible polygons and ``0.0`` invisible ones. The values for `alpha_face`, `alpha_lines` and `alpha_points` will be derived from this alpha value if they are set to ``None``. This argument has no effect if `alpha_face`, `alpha_lines` and `alpha_points` are all set anything other than ``None``. alpha_face : None or number, optional The opacity of the polygon's inner areas (excluding the perimeters), where ``1.0`` denotes completely visible inner areas and ``0.0`` invisible ones. If this is ``None``, it will be derived from ``alpha * 0.5``. alpha_lines : None or number, optional The opacity of the polygon's lines (aka perimeters/borders), where ``1.0`` denotes completely visible perimeters and ``0.0`` invisible ones. If this is ``None``, it will be derived from ``alpha * 1.0``. alpha_points : None or number, optional The opacity of the polygon's corner points, where ``1.0`` denotes completely visible corners and ``0.0`` invisible ones. Currently this is an on/off choice, i.e. only ``0.0`` or ``1.0`` are allowed. If this is ``None``, it will be derived from ``alpha * 1.0``. size : int, optional Size of the polygons. The sizes of the line and points are derived from this value, unless they are set. size_lines : None or int, optional Thickness of the polygon lines (aka perimeter/border). If ``None``, this value is derived from `size`. size_points : int, optional The size of all corner points. If set to ``C``, each corner point will be drawn as a square of size ``C x C``. raise_if_out_of_image : bool, optional Whether to raise an error if any polygon is fully outside of the image. If set to False, no error will be raised and only the parts inside the image will be drawn. Returns ------- image : (H,W,C) ndarray Image with drawn polygons.
[ "Draw", "all", "polygons", "onto", "a", "given", "image", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/augmentables/polys.py#L1047-L1156
valid
aleju/imgaug
imgaug/augmentables/polys.py
PolygonsOnImage.remove_out_of_image
def remove_out_of_image(self, fully=True, partly=False): """ Remove all polygons that are fully or partially outside of the image. Parameters ---------- fully : bool, optional Whether to remove polygons that are fully outside of the image. partly : bool, optional Whether to remove polygons that are partially outside of the image. Returns ------- imgaug.PolygonsOnImage Reduced set of polygons, with those that were fully/partially outside of the image removed. """ polys_clean = [ poly for poly in self.polygons if not poly.is_out_of_image(self.shape, fully=fully, partly=partly) ] # TODO use deepcopy() here return PolygonsOnImage(polys_clean, shape=self.shape)
python
def remove_out_of_image(self, fully=True, partly=False): """ Remove all polygons that are fully or partially outside of the image. Parameters ---------- fully : bool, optional Whether to remove polygons that are fully outside of the image. partly : bool, optional Whether to remove polygons that are partially outside of the image. Returns ------- imgaug.PolygonsOnImage Reduced set of polygons, with those that were fully/partially outside of the image removed. """ polys_clean = [ poly for poly in self.polygons if not poly.is_out_of_image(self.shape, fully=fully, partly=partly) ] # TODO use deepcopy() here return PolygonsOnImage(polys_clean, shape=self.shape)
[ "def", "remove_out_of_image", "(", "self", ",", "fully", "=", "True", ",", "partly", "=", "False", ")", ":", "polys_clean", "=", "[", "poly", "for", "poly", "in", "self", ".", "polygons", "if", "not", "poly", ".", "is_out_of_image", "(", "self", ".", "shape", ",", "fully", "=", "fully", ",", "partly", "=", "partly", ")", "]", "# TODO use deepcopy() here", "return", "PolygonsOnImage", "(", "polys_clean", ",", "shape", "=", "self", ".", "shape", ")" ]
Remove all polygons that are fully or partially outside of the image. Parameters ---------- fully : bool, optional Whether to remove polygons that are fully outside of the image. partly : bool, optional Whether to remove polygons that are partially outside of the image. Returns ------- imgaug.PolygonsOnImage Reduced set of polygons, with those that were fully/partially outside of the image removed.
[ "Remove", "all", "polygons", "that", "are", "fully", "or", "partially", "outside", "of", "the", "image", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/augmentables/polys.py#L1158-L1182
valid
aleju/imgaug
imgaug/augmentables/polys.py
PolygonsOnImage.clip_out_of_image
def clip_out_of_image(self): """ Clip off all parts from all polygons that are outside of the image. NOTE: The result can contain less polygons than the input did. That happens when a polygon is fully outside of the image plane. NOTE: The result can also contain *more* polygons than the input did. That happens when distinct parts of a polygon are only connected by areas that are outside of the image plane and hence will be clipped off, resulting in two or more unconnected polygon parts that are left in the image plane. Returns ------- imgaug.PolygonsOnImage Polygons, clipped to fall within the image dimensions. Count of output polygons may differ from the input count. """ polys_cut = [ poly.clip_out_of_image(self.shape) for poly in self.polygons if poly.is_partly_within_image(self.shape) ] polys_cut_flat = [poly for poly_lst in polys_cut for poly in poly_lst] # TODO use deepcopy() here return PolygonsOnImage(polys_cut_flat, shape=self.shape)
python
def clip_out_of_image(self): """ Clip off all parts from all polygons that are outside of the image. NOTE: The result can contain less polygons than the input did. That happens when a polygon is fully outside of the image plane. NOTE: The result can also contain *more* polygons than the input did. That happens when distinct parts of a polygon are only connected by areas that are outside of the image plane and hence will be clipped off, resulting in two or more unconnected polygon parts that are left in the image plane. Returns ------- imgaug.PolygonsOnImage Polygons, clipped to fall within the image dimensions. Count of output polygons may differ from the input count. """ polys_cut = [ poly.clip_out_of_image(self.shape) for poly in self.polygons if poly.is_partly_within_image(self.shape) ] polys_cut_flat = [poly for poly_lst in polys_cut for poly in poly_lst] # TODO use deepcopy() here return PolygonsOnImage(polys_cut_flat, shape=self.shape)
[ "def", "clip_out_of_image", "(", "self", ")", ":", "polys_cut", "=", "[", "poly", ".", "clip_out_of_image", "(", "self", ".", "shape", ")", "for", "poly", "in", "self", ".", "polygons", "if", "poly", ".", "is_partly_within_image", "(", "self", ".", "shape", ")", "]", "polys_cut_flat", "=", "[", "poly", "for", "poly_lst", "in", "polys_cut", "for", "poly", "in", "poly_lst", "]", "# TODO use deepcopy() here", "return", "PolygonsOnImage", "(", "polys_cut_flat", ",", "shape", "=", "self", ".", "shape", ")" ]
Clip off all parts from all polygons that are outside of the image. NOTE: The result can contain less polygons than the input did. That happens when a polygon is fully outside of the image plane. NOTE: The result can also contain *more* polygons than the input did. That happens when distinct parts of a polygon are only connected by areas that are outside of the image plane and hence will be clipped off, resulting in two or more unconnected polygon parts that are left in the image plane. Returns ------- imgaug.PolygonsOnImage Polygons, clipped to fall within the image dimensions. Count of output polygons may differ from the input count.
[ "Clip", "off", "all", "parts", "from", "all", "polygons", "that", "are", "outside", "of", "the", "image", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/augmentables/polys.py#L1184-L1212
valid
aleju/imgaug
imgaug/augmentables/polys.py
PolygonsOnImage.shift
def shift(self, top=None, right=None, bottom=None, left=None): """ Shift all polygons from one or more image sides, i.e. move them on the x/y-axis. Parameters ---------- top : None or int, optional Amount of pixels by which to shift all polygons from the top. right : None or int, optional Amount of pixels by which to shift all polygons from the right. bottom : None or int, optional Amount of pixels by which to shift all polygons from the bottom. left : None or int, optional Amount of pixels by which to shift all polygons from the left. Returns ------- imgaug.PolygonsOnImage Shifted polygons. """ polys_new = [ poly.shift(top=top, right=right, bottom=bottom, left=left) for poly in self.polygons ] return PolygonsOnImage(polys_new, shape=self.shape)
python
def shift(self, top=None, right=None, bottom=None, left=None): """ Shift all polygons from one or more image sides, i.e. move them on the x/y-axis. Parameters ---------- top : None or int, optional Amount of pixels by which to shift all polygons from the top. right : None or int, optional Amount of pixels by which to shift all polygons from the right. bottom : None or int, optional Amount of pixels by which to shift all polygons from the bottom. left : None or int, optional Amount of pixels by which to shift all polygons from the left. Returns ------- imgaug.PolygonsOnImage Shifted polygons. """ polys_new = [ poly.shift(top=top, right=right, bottom=bottom, left=left) for poly in self.polygons ] return PolygonsOnImage(polys_new, shape=self.shape)
[ "def", "shift", "(", "self", ",", "top", "=", "None", ",", "right", "=", "None", ",", "bottom", "=", "None", ",", "left", "=", "None", ")", ":", "polys_new", "=", "[", "poly", ".", "shift", "(", "top", "=", "top", ",", "right", "=", "right", ",", "bottom", "=", "bottom", ",", "left", "=", "left", ")", "for", "poly", "in", "self", ".", "polygons", "]", "return", "PolygonsOnImage", "(", "polys_new", ",", "shape", "=", "self", ".", "shape", ")" ]
Shift all polygons from one or more image sides, i.e. move them on the x/y-axis. Parameters ---------- top : None or int, optional Amount of pixels by which to shift all polygons from the top. right : None or int, optional Amount of pixels by which to shift all polygons from the right. bottom : None or int, optional Amount of pixels by which to shift all polygons from the bottom. left : None or int, optional Amount of pixels by which to shift all polygons from the left. Returns ------- imgaug.PolygonsOnImage Shifted polygons.
[ "Shift", "all", "polygons", "from", "one", "or", "more", "image", "sides", "i", ".", "e", ".", "move", "them", "on", "the", "x", "/", "y", "-", "axis", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/augmentables/polys.py#L1214-L1243
valid
aleju/imgaug
imgaug/augmentables/polys.py
PolygonsOnImage.deepcopy
def deepcopy(self): """ Create a deep copy of the PolygonsOnImage object. Returns ------- imgaug.PolygonsOnImage Deep copy. """ # Manual copy is far faster than deepcopy for PolygonsOnImage, # so use manual copy here too polys = [poly.deepcopy() for poly in self.polygons] return PolygonsOnImage(polys, tuple(self.shape))
python
def deepcopy(self): """ Create a deep copy of the PolygonsOnImage object. Returns ------- imgaug.PolygonsOnImage Deep copy. """ # Manual copy is far faster than deepcopy for PolygonsOnImage, # so use manual copy here too polys = [poly.deepcopy() for poly in self.polygons] return PolygonsOnImage(polys, tuple(self.shape))
[ "def", "deepcopy", "(", "self", ")", ":", "# Manual copy is far faster than deepcopy for PolygonsOnImage,", "# so use manual copy here too", "polys", "=", "[", "poly", ".", "deepcopy", "(", ")", "for", "poly", "in", "self", ".", "polygons", "]", "return", "PolygonsOnImage", "(", "polys", ",", "tuple", "(", "self", ".", "shape", ")", ")" ]
Create a deep copy of the PolygonsOnImage object. Returns ------- imgaug.PolygonsOnImage Deep copy.
[ "Create", "a", "deep", "copy", "of", "the", "PolygonsOnImage", "object", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/augmentables/polys.py#L1257-L1270
valid
aleju/imgaug
imgaug/augmentables/polys.py
MultiPolygon.from_shapely
def from_shapely(geometry, label=None): """ Create a MultiPolygon from a Shapely MultiPolygon, a Shapely Polygon or a Shapely GeometryCollection. This also creates all necessary Polygons contained by this MultiPolygon. Parameters ---------- geometry : shapely.geometry.MultiPolygon or shapely.geometry.Polygon\ or shapely.geometry.collection.GeometryCollection The object to convert to a MultiPolygon. label : None or str, optional A label assigned to all Polygons within the MultiPolygon. Returns ------- imgaug.MultiPolygon The derived MultiPolygon. """ # load shapely lazily, which makes the dependency more optional import shapely.geometry if isinstance(geometry, shapely.geometry.MultiPolygon): return MultiPolygon([Polygon.from_shapely(poly, label=label) for poly in geometry.geoms]) elif isinstance(geometry, shapely.geometry.Polygon): return MultiPolygon([Polygon.from_shapely(geometry, label=label)]) elif isinstance(geometry, shapely.geometry.collection.GeometryCollection): ia.do_assert(all([isinstance(poly, shapely.geometry.Polygon) for poly in geometry.geoms])) return MultiPolygon([Polygon.from_shapely(poly, label=label) for poly in geometry.geoms]) else: raise Exception("Unknown datatype '%s'. Expected shapely.geometry.Polygon or " "shapely.geometry.MultiPolygon or " "shapely.geometry.collections.GeometryCollection." % (type(geometry),))
python
def from_shapely(geometry, label=None): """ Create a MultiPolygon from a Shapely MultiPolygon, a Shapely Polygon or a Shapely GeometryCollection. This also creates all necessary Polygons contained by this MultiPolygon. Parameters ---------- geometry : shapely.geometry.MultiPolygon or shapely.geometry.Polygon\ or shapely.geometry.collection.GeometryCollection The object to convert to a MultiPolygon. label : None or str, optional A label assigned to all Polygons within the MultiPolygon. Returns ------- imgaug.MultiPolygon The derived MultiPolygon. """ # load shapely lazily, which makes the dependency more optional import shapely.geometry if isinstance(geometry, shapely.geometry.MultiPolygon): return MultiPolygon([Polygon.from_shapely(poly, label=label) for poly in geometry.geoms]) elif isinstance(geometry, shapely.geometry.Polygon): return MultiPolygon([Polygon.from_shapely(geometry, label=label)]) elif isinstance(geometry, shapely.geometry.collection.GeometryCollection): ia.do_assert(all([isinstance(poly, shapely.geometry.Polygon) for poly in geometry.geoms])) return MultiPolygon([Polygon.from_shapely(poly, label=label) for poly in geometry.geoms]) else: raise Exception("Unknown datatype '%s'. Expected shapely.geometry.Polygon or " "shapely.geometry.MultiPolygon or " "shapely.geometry.collections.GeometryCollection." % (type(geometry),))
[ "def", "from_shapely", "(", "geometry", ",", "label", "=", "None", ")", ":", "# load shapely lazily, which makes the dependency more optional", "import", "shapely", ".", "geometry", "if", "isinstance", "(", "geometry", ",", "shapely", ".", "geometry", ".", "MultiPolygon", ")", ":", "return", "MultiPolygon", "(", "[", "Polygon", ".", "from_shapely", "(", "poly", ",", "label", "=", "label", ")", "for", "poly", "in", "geometry", ".", "geoms", "]", ")", "elif", "isinstance", "(", "geometry", ",", "shapely", ".", "geometry", ".", "Polygon", ")", ":", "return", "MultiPolygon", "(", "[", "Polygon", ".", "from_shapely", "(", "geometry", ",", "label", "=", "label", ")", "]", ")", "elif", "isinstance", "(", "geometry", ",", "shapely", ".", "geometry", ".", "collection", ".", "GeometryCollection", ")", ":", "ia", ".", "do_assert", "(", "all", "(", "[", "isinstance", "(", "poly", ",", "shapely", ".", "geometry", ".", "Polygon", ")", "for", "poly", "in", "geometry", ".", "geoms", "]", ")", ")", "return", "MultiPolygon", "(", "[", "Polygon", ".", "from_shapely", "(", "poly", ",", "label", "=", "label", ")", "for", "poly", "in", "geometry", ".", "geoms", "]", ")", "else", ":", "raise", "Exception", "(", "\"Unknown datatype '%s'. Expected shapely.geometry.Polygon or \"", "\"shapely.geometry.MultiPolygon or \"", "\"shapely.geometry.collections.GeometryCollection.\"", "%", "(", "type", "(", "geometry", ")", ",", ")", ")" ]
Create a MultiPolygon from a Shapely MultiPolygon, a Shapely Polygon or a Shapely GeometryCollection. This also creates all necessary Polygons contained by this MultiPolygon. Parameters ---------- geometry : shapely.geometry.MultiPolygon or shapely.geometry.Polygon\ or shapely.geometry.collection.GeometryCollection The object to convert to a MultiPolygon. label : None or str, optional A label assigned to all Polygons within the MultiPolygon. Returns ------- imgaug.MultiPolygon The derived MultiPolygon.
[ "Create", "a", "MultiPolygon", "from", "a", "Shapely", "MultiPolygon", "a", "Shapely", "Polygon", "or", "a", "Shapely", "GeometryCollection", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/augmentables/polys.py#L1776-L1810
valid
aleju/imgaug
imgaug/augmenters/size.py
Pad
def Pad(px=None, percent=None, pad_mode="constant", pad_cval=0, keep_size=True, sample_independently=True, name=None, deterministic=False, random_state=None): """ Augmenter that pads images, i.e. adds columns/rows to them. dtype support:: See ``imgaug.augmenters.size.CropAndPad``. Parameters ---------- px : None or int or imgaug.parameters.StochasticParameter or tuple, optional The number of pixels to pad on each side of the image. Either this or the parameter `percent` may be set, not both at the same time. * If None, then pixel-based padding will not be used. * If int, then that exact number of pixels will always be padded. * If StochasticParameter, then that parameter will be used for each image. Four samples will be drawn per image (top, right, bottom, left). * If a tuple of two ints with values a and b, then each side will be padded by a random amount in the range ``a <= x <= b``. ``x`` is sampled per image side. * If a tuple of four entries, then the entries represent top, right, bottom, left. Each entry may be a single integer (always pad by exactly that value), a tuple of two ints ``a`` and ``b`` (pad by an amount ``a <= x <= b``), a list of ints (pad by a random value that is contained in the list) or a StochasticParameter (sample the amount to pad from that parameter). percent : None or int or float or imgaug.parameters.StochasticParameter \ or tuple, optional The number of pixels to pad on each side of the image given *in percent* of the image height/width. E.g. if this is set to 0.1, the augmenter will always add 10 percent of the image's height to the top, 10 percent of the width to the right, 10 percent of the height at the bottom and 10 percent of the width to the left. Either this or the parameter `px` may be set, not both at the same time. * If None, then percent-based padding will not be used. * If int, then expected to be 0 (no padding). * If float, then that percentage will always be padded. * If StochasticParameter, then that parameter will be used for each image. Four samples will be drawn per image (top, right, bottom, left). * If a tuple of two floats with values a and b, then each side will be padded by a random percentage in the range ``a <= x <= b``. ``x`` is sampled per image side. * If a tuple of four entries, then the entries represent top, right, bottom, left. Each entry may be a single float (always pad by exactly that percent value), a tuple of two floats ``a`` and ``b`` (pad by a percentage ``a <= x <= b``), a list of floats (pad by a random value that is contained in the list) or a StochasticParameter (sample the percentage to pad from that parameter). pad_mode : imgaug.ALL or str or list of str or \ imgaug.parameters.StochasticParameter, optional Padding mode to use. The available modes match the numpy padding modes, i.e. ``constant``, ``edge``, ``linear_ramp``, ``maximum``, ``median``, ``minimum``, ``reflect``, ``symmetric``, ``wrap``. The modes ``constant`` and ``linear_ramp`` use extra values, which are provided by ``pad_cval`` when necessary. See :func:`imgaug.imgaug.pad` for more details. * If ``imgaug.ALL``, then a random mode from all available modes will be sampled per image. * If a string, it will be used as the pad mode for all images. * If a list of strings, a random one of these will be sampled per image and used as the mode. * If StochasticParameter, a random mode will be sampled from this parameter per image. pad_cval : number or tuple of number list of number or \ imgaug.parameters.StochasticParameter, optional The constant value to use if the pad mode is ``constant`` or the end value to use if the mode is ``linear_ramp``. See :func:`imgaug.imgaug.pad` for more details. * If number, then that value will be used. * If a tuple of two numbers and at least one of them is a float, then a random number will be sampled from the continuous range ``a <= x <= b`` and used as the value. If both numbers are integers, the range is discrete. * If a list of number, then a random value will be chosen from the elements of the list and used as the value. * If StochasticParameter, a random value will be sampled from that parameter per image. keep_size : bool, optional After padding, the result image will usually have a different height/width compared to the original input image. If this parameter is set to True, then the padded image will be resized to the input image's size, i.e. the augmenter's output shape is always identical to the input shape. sample_independently : bool, optional If False AND the values for `px`/`percent` result in exactly one probability distribution for the amount to pad, only one single value will be sampled from that probability distribution and used for all sides. I.e. the pad amount then is the same for all sides. name : None or str, optional See :func:`imgaug.augmenters.meta.Augmenter.__init__`. deterministic : bool, optional See :func:`imgaug.augmenters.meta.Augmenter.__init__`. random_state : None or int or numpy.random.RandomState, optional See :func:`imgaug.augmenters.meta.Augmenter.__init__`. Examples -------- >>> aug = iaa.Pad(px=(0, 10)) pads each side by a random value from the range 0px to 10px (the value is sampled per side). The added rows/columns are filled with black pixels. >>> aug = iaa.Pad(px=(0, 10), sample_independently=False) samples one value v from the discrete range ``[0..10]`` and pads all sides by ``v`` pixels. >>> aug = iaa.Pad(px=(0, 10), keep_size=False) pads each side by a random value from the range 0px to 10px (the value is sampled per side). After padding, the images are NOT resized to their original size (i.e. the images may end up having different heights/widths). >>> aug = iaa.Pad(px=((0, 10), (0, 5), (0, 10), (0, 5))) pads the top and bottom by a random value from the range 0px to 10px and the left and right by a random value in the range 0px to 5px. >>> aug = iaa.Pad(percent=(0, 0.1)) pads each side by a random value from the range 0 percent to 10 percent. (Percent with respect to the side's size, e.g. for the top side it uses the image's height.) >>> aug = iaa.Pad(percent=([0.05, 0.1], [0.05, 0.1], [0.05, 0.1], [0.05, 0.1])) pads each side by either 5 percent or 10 percent. >>> aug = iaa.Pad(px=(0, 10), pad_mode="edge") pads each side by a random value from the range 0px to 10px (the values are sampled per side). The padding uses the ``edge`` mode from numpy's pad function. >>> aug = iaa.Pad(px=(0, 10), pad_mode=["constant", "edge"]) pads each side by a random value from the range 0px to 10px (the values are sampled per side). The padding uses randomly either the ``constant`` or ``edge`` mode from numpy's pad function. >>> aug = iaa.Pad(px=(0, 10), pad_mode=ia.ALL, pad_cval=(0, 255)) pads each side by a random value from the range 0px to 10px (the values are sampled per side). It uses a random mode for numpy's pad function. If the mode is ``constant`` or ``linear_ramp``, it samples a random value ``v`` from the range ``[0, 255]`` and uses that as the constant value (``mode=constant``) or end value (``mode=linear_ramp``). """ def recursive_validate(v): if v is None: return v elif ia.is_single_number(v): ia.do_assert(v >= 0) return v elif isinstance(v, iap.StochasticParameter): return v elif isinstance(v, tuple): return tuple([recursive_validate(v_) for v_ in v]) elif isinstance(v, list): return [recursive_validate(v_) for v_ in v] else: raise Exception("Expected None or int or float or StochasticParameter or list or tuple, got %s." % ( type(v),)) px = recursive_validate(px) percent = recursive_validate(percent) if name is None: name = "Unnamed%s" % (ia.caller_name(),) aug = CropAndPad( px=px, percent=percent, pad_mode=pad_mode, pad_cval=pad_cval, keep_size=keep_size, sample_independently=sample_independently, name=name, deterministic=deterministic, random_state=random_state ) return aug
python
def Pad(px=None, percent=None, pad_mode="constant", pad_cval=0, keep_size=True, sample_independently=True, name=None, deterministic=False, random_state=None): """ Augmenter that pads images, i.e. adds columns/rows to them. dtype support:: See ``imgaug.augmenters.size.CropAndPad``. Parameters ---------- px : None or int or imgaug.parameters.StochasticParameter or tuple, optional The number of pixels to pad on each side of the image. Either this or the parameter `percent` may be set, not both at the same time. * If None, then pixel-based padding will not be used. * If int, then that exact number of pixels will always be padded. * If StochasticParameter, then that parameter will be used for each image. Four samples will be drawn per image (top, right, bottom, left). * If a tuple of two ints with values a and b, then each side will be padded by a random amount in the range ``a <= x <= b``. ``x`` is sampled per image side. * If a tuple of four entries, then the entries represent top, right, bottom, left. Each entry may be a single integer (always pad by exactly that value), a tuple of two ints ``a`` and ``b`` (pad by an amount ``a <= x <= b``), a list of ints (pad by a random value that is contained in the list) or a StochasticParameter (sample the amount to pad from that parameter). percent : None or int or float or imgaug.parameters.StochasticParameter \ or tuple, optional The number of pixels to pad on each side of the image given *in percent* of the image height/width. E.g. if this is set to 0.1, the augmenter will always add 10 percent of the image's height to the top, 10 percent of the width to the right, 10 percent of the height at the bottom and 10 percent of the width to the left. Either this or the parameter `px` may be set, not both at the same time. * If None, then percent-based padding will not be used. * If int, then expected to be 0 (no padding). * If float, then that percentage will always be padded. * If StochasticParameter, then that parameter will be used for each image. Four samples will be drawn per image (top, right, bottom, left). * If a tuple of two floats with values a and b, then each side will be padded by a random percentage in the range ``a <= x <= b``. ``x`` is sampled per image side. * If a tuple of four entries, then the entries represent top, right, bottom, left. Each entry may be a single float (always pad by exactly that percent value), a tuple of two floats ``a`` and ``b`` (pad by a percentage ``a <= x <= b``), a list of floats (pad by a random value that is contained in the list) or a StochasticParameter (sample the percentage to pad from that parameter). pad_mode : imgaug.ALL or str or list of str or \ imgaug.parameters.StochasticParameter, optional Padding mode to use. The available modes match the numpy padding modes, i.e. ``constant``, ``edge``, ``linear_ramp``, ``maximum``, ``median``, ``minimum``, ``reflect``, ``symmetric``, ``wrap``. The modes ``constant`` and ``linear_ramp`` use extra values, which are provided by ``pad_cval`` when necessary. See :func:`imgaug.imgaug.pad` for more details. * If ``imgaug.ALL``, then a random mode from all available modes will be sampled per image. * If a string, it will be used as the pad mode for all images. * If a list of strings, a random one of these will be sampled per image and used as the mode. * If StochasticParameter, a random mode will be sampled from this parameter per image. pad_cval : number or tuple of number list of number or \ imgaug.parameters.StochasticParameter, optional The constant value to use if the pad mode is ``constant`` or the end value to use if the mode is ``linear_ramp``. See :func:`imgaug.imgaug.pad` for more details. * If number, then that value will be used. * If a tuple of two numbers and at least one of them is a float, then a random number will be sampled from the continuous range ``a <= x <= b`` and used as the value. If both numbers are integers, the range is discrete. * If a list of number, then a random value will be chosen from the elements of the list and used as the value. * If StochasticParameter, a random value will be sampled from that parameter per image. keep_size : bool, optional After padding, the result image will usually have a different height/width compared to the original input image. If this parameter is set to True, then the padded image will be resized to the input image's size, i.e. the augmenter's output shape is always identical to the input shape. sample_independently : bool, optional If False AND the values for `px`/`percent` result in exactly one probability distribution for the amount to pad, only one single value will be sampled from that probability distribution and used for all sides. I.e. the pad amount then is the same for all sides. name : None or str, optional See :func:`imgaug.augmenters.meta.Augmenter.__init__`. deterministic : bool, optional See :func:`imgaug.augmenters.meta.Augmenter.__init__`. random_state : None or int or numpy.random.RandomState, optional See :func:`imgaug.augmenters.meta.Augmenter.__init__`. Examples -------- >>> aug = iaa.Pad(px=(0, 10)) pads each side by a random value from the range 0px to 10px (the value is sampled per side). The added rows/columns are filled with black pixels. >>> aug = iaa.Pad(px=(0, 10), sample_independently=False) samples one value v from the discrete range ``[0..10]`` and pads all sides by ``v`` pixels. >>> aug = iaa.Pad(px=(0, 10), keep_size=False) pads each side by a random value from the range 0px to 10px (the value is sampled per side). After padding, the images are NOT resized to their original size (i.e. the images may end up having different heights/widths). >>> aug = iaa.Pad(px=((0, 10), (0, 5), (0, 10), (0, 5))) pads the top and bottom by a random value from the range 0px to 10px and the left and right by a random value in the range 0px to 5px. >>> aug = iaa.Pad(percent=(0, 0.1)) pads each side by a random value from the range 0 percent to 10 percent. (Percent with respect to the side's size, e.g. for the top side it uses the image's height.) >>> aug = iaa.Pad(percent=([0.05, 0.1], [0.05, 0.1], [0.05, 0.1], [0.05, 0.1])) pads each side by either 5 percent or 10 percent. >>> aug = iaa.Pad(px=(0, 10), pad_mode="edge") pads each side by a random value from the range 0px to 10px (the values are sampled per side). The padding uses the ``edge`` mode from numpy's pad function. >>> aug = iaa.Pad(px=(0, 10), pad_mode=["constant", "edge"]) pads each side by a random value from the range 0px to 10px (the values are sampled per side). The padding uses randomly either the ``constant`` or ``edge`` mode from numpy's pad function. >>> aug = iaa.Pad(px=(0, 10), pad_mode=ia.ALL, pad_cval=(0, 255)) pads each side by a random value from the range 0px to 10px (the values are sampled per side). It uses a random mode for numpy's pad function. If the mode is ``constant`` or ``linear_ramp``, it samples a random value ``v`` from the range ``[0, 255]`` and uses that as the constant value (``mode=constant``) or end value (``mode=linear_ramp``). """ def recursive_validate(v): if v is None: return v elif ia.is_single_number(v): ia.do_assert(v >= 0) return v elif isinstance(v, iap.StochasticParameter): return v elif isinstance(v, tuple): return tuple([recursive_validate(v_) for v_ in v]) elif isinstance(v, list): return [recursive_validate(v_) for v_ in v] else: raise Exception("Expected None or int or float or StochasticParameter or list or tuple, got %s." % ( type(v),)) px = recursive_validate(px) percent = recursive_validate(percent) if name is None: name = "Unnamed%s" % (ia.caller_name(),) aug = CropAndPad( px=px, percent=percent, pad_mode=pad_mode, pad_cval=pad_cval, keep_size=keep_size, sample_independently=sample_independently, name=name, deterministic=deterministic, random_state=random_state ) return aug
[ "def", "Pad", "(", "px", "=", "None", ",", "percent", "=", "None", ",", "pad_mode", "=", "\"constant\"", ",", "pad_cval", "=", "0", ",", "keep_size", "=", "True", ",", "sample_independently", "=", "True", ",", "name", "=", "None", ",", "deterministic", "=", "False", ",", "random_state", "=", "None", ")", ":", "def", "recursive_validate", "(", "v", ")", ":", "if", "v", "is", "None", ":", "return", "v", "elif", "ia", ".", "is_single_number", "(", "v", ")", ":", "ia", ".", "do_assert", "(", "v", ">=", "0", ")", "return", "v", "elif", "isinstance", "(", "v", ",", "iap", ".", "StochasticParameter", ")", ":", "return", "v", "elif", "isinstance", "(", "v", ",", "tuple", ")", ":", "return", "tuple", "(", "[", "recursive_validate", "(", "v_", ")", "for", "v_", "in", "v", "]", ")", "elif", "isinstance", "(", "v", ",", "list", ")", ":", "return", "[", "recursive_validate", "(", "v_", ")", "for", "v_", "in", "v", "]", "else", ":", "raise", "Exception", "(", "\"Expected None or int or float or StochasticParameter or list or tuple, got %s.\"", "%", "(", "type", "(", "v", ")", ",", ")", ")", "px", "=", "recursive_validate", "(", "px", ")", "percent", "=", "recursive_validate", "(", "percent", ")", "if", "name", "is", "None", ":", "name", "=", "\"Unnamed%s\"", "%", "(", "ia", ".", "caller_name", "(", ")", ",", ")", "aug", "=", "CropAndPad", "(", "px", "=", "px", ",", "percent", "=", "percent", ",", "pad_mode", "=", "pad_mode", ",", "pad_cval", "=", "pad_cval", ",", "keep_size", "=", "keep_size", ",", "sample_independently", "=", "sample_independently", ",", "name", "=", "name", ",", "deterministic", "=", "deterministic", ",", "random_state", "=", "random_state", ")", "return", "aug" ]
Augmenter that pads images, i.e. adds columns/rows to them. dtype support:: See ``imgaug.augmenters.size.CropAndPad``. Parameters ---------- px : None or int or imgaug.parameters.StochasticParameter or tuple, optional The number of pixels to pad on each side of the image. Either this or the parameter `percent` may be set, not both at the same time. * If None, then pixel-based padding will not be used. * If int, then that exact number of pixels will always be padded. * If StochasticParameter, then that parameter will be used for each image. Four samples will be drawn per image (top, right, bottom, left). * If a tuple of two ints with values a and b, then each side will be padded by a random amount in the range ``a <= x <= b``. ``x`` is sampled per image side. * If a tuple of four entries, then the entries represent top, right, bottom, left. Each entry may be a single integer (always pad by exactly that value), a tuple of two ints ``a`` and ``b`` (pad by an amount ``a <= x <= b``), a list of ints (pad by a random value that is contained in the list) or a StochasticParameter (sample the amount to pad from that parameter). percent : None or int or float or imgaug.parameters.StochasticParameter \ or tuple, optional The number of pixels to pad on each side of the image given *in percent* of the image height/width. E.g. if this is set to 0.1, the augmenter will always add 10 percent of the image's height to the top, 10 percent of the width to the right, 10 percent of the height at the bottom and 10 percent of the width to the left. Either this or the parameter `px` may be set, not both at the same time. * If None, then percent-based padding will not be used. * If int, then expected to be 0 (no padding). * If float, then that percentage will always be padded. * If StochasticParameter, then that parameter will be used for each image. Four samples will be drawn per image (top, right, bottom, left). * If a tuple of two floats with values a and b, then each side will be padded by a random percentage in the range ``a <= x <= b``. ``x`` is sampled per image side. * If a tuple of four entries, then the entries represent top, right, bottom, left. Each entry may be a single float (always pad by exactly that percent value), a tuple of two floats ``a`` and ``b`` (pad by a percentage ``a <= x <= b``), a list of floats (pad by a random value that is contained in the list) or a StochasticParameter (sample the percentage to pad from that parameter). pad_mode : imgaug.ALL or str or list of str or \ imgaug.parameters.StochasticParameter, optional Padding mode to use. The available modes match the numpy padding modes, i.e. ``constant``, ``edge``, ``linear_ramp``, ``maximum``, ``median``, ``minimum``, ``reflect``, ``symmetric``, ``wrap``. The modes ``constant`` and ``linear_ramp`` use extra values, which are provided by ``pad_cval`` when necessary. See :func:`imgaug.imgaug.pad` for more details. * If ``imgaug.ALL``, then a random mode from all available modes will be sampled per image. * If a string, it will be used as the pad mode for all images. * If a list of strings, a random one of these will be sampled per image and used as the mode. * If StochasticParameter, a random mode will be sampled from this parameter per image. pad_cval : number or tuple of number list of number or \ imgaug.parameters.StochasticParameter, optional The constant value to use if the pad mode is ``constant`` or the end value to use if the mode is ``linear_ramp``. See :func:`imgaug.imgaug.pad` for more details. * If number, then that value will be used. * If a tuple of two numbers and at least one of them is a float, then a random number will be sampled from the continuous range ``a <= x <= b`` and used as the value. If both numbers are integers, the range is discrete. * If a list of number, then a random value will be chosen from the elements of the list and used as the value. * If StochasticParameter, a random value will be sampled from that parameter per image. keep_size : bool, optional After padding, the result image will usually have a different height/width compared to the original input image. If this parameter is set to True, then the padded image will be resized to the input image's size, i.e. the augmenter's output shape is always identical to the input shape. sample_independently : bool, optional If False AND the values for `px`/`percent` result in exactly one probability distribution for the amount to pad, only one single value will be sampled from that probability distribution and used for all sides. I.e. the pad amount then is the same for all sides. name : None or str, optional See :func:`imgaug.augmenters.meta.Augmenter.__init__`. deterministic : bool, optional See :func:`imgaug.augmenters.meta.Augmenter.__init__`. random_state : None or int or numpy.random.RandomState, optional See :func:`imgaug.augmenters.meta.Augmenter.__init__`. Examples -------- >>> aug = iaa.Pad(px=(0, 10)) pads each side by a random value from the range 0px to 10px (the value is sampled per side). The added rows/columns are filled with black pixels. >>> aug = iaa.Pad(px=(0, 10), sample_independently=False) samples one value v from the discrete range ``[0..10]`` and pads all sides by ``v`` pixels. >>> aug = iaa.Pad(px=(0, 10), keep_size=False) pads each side by a random value from the range 0px to 10px (the value is sampled per side). After padding, the images are NOT resized to their original size (i.e. the images may end up having different heights/widths). >>> aug = iaa.Pad(px=((0, 10), (0, 5), (0, 10), (0, 5))) pads the top and bottom by a random value from the range 0px to 10px and the left and right by a random value in the range 0px to 5px. >>> aug = iaa.Pad(percent=(0, 0.1)) pads each side by a random value from the range 0 percent to 10 percent. (Percent with respect to the side's size, e.g. for the top side it uses the image's height.) >>> aug = iaa.Pad(percent=([0.05, 0.1], [0.05, 0.1], [0.05, 0.1], [0.05, 0.1])) pads each side by either 5 percent or 10 percent. >>> aug = iaa.Pad(px=(0, 10), pad_mode="edge") pads each side by a random value from the range 0px to 10px (the values are sampled per side). The padding uses the ``edge`` mode from numpy's pad function. >>> aug = iaa.Pad(px=(0, 10), pad_mode=["constant", "edge"]) pads each side by a random value from the range 0px to 10px (the values are sampled per side). The padding uses randomly either the ``constant`` or ``edge`` mode from numpy's pad function. >>> aug = iaa.Pad(px=(0, 10), pad_mode=ia.ALL, pad_cval=(0, 255)) pads each side by a random value from the range 0px to 10px (the values are sampled per side). It uses a random mode for numpy's pad function. If the mode is ``constant`` or ``linear_ramp``, it samples a random value ``v`` from the range ``[0, 255]`` and uses that as the constant value (``mode=constant``) or end value (``mode=linear_ramp``).
[ "Augmenter", "that", "pads", "images", "i", ".", "e", ".", "adds", "columns", "/", "rows", "to", "them", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/augmenters/size.py#L958-L1154
valid
aleju/imgaug
imgaug/augmenters/size.py
Crop
def Crop(px=None, percent=None, keep_size=True, sample_independently=True, name=None, deterministic=False, random_state=None): """ Augmenter that crops/cuts away pixels at the sides of the image. That allows to cut out subimages from given (full) input images. The number of pixels to cut off may be defined in absolute values or percent of the image sizes. dtype support:: See ``imgaug.augmenters.size.CropAndPad``. Parameters ---------- px : None or int or imgaug.parameters.StochasticParameter or tuple, optional The number of pixels to crop away (cut off) on each side of the image. Either this or the parameter `percent` may be set, not both at the same time. * If None, then pixel-based cropping will not be used. * If int, then that exact number of pixels will always be cropped. * If StochasticParameter, then that parameter will be used for each image. Four samples will be drawn per image (top, right, bottom, left). * If a tuple of two ints with values ``a`` and ``b``, then each side will be cropped by a random amount in the range ``a <= x <= b``. ``x`` is sampled per image side. * If a tuple of four entries, then the entries represent top, right, bottom, left. Each entry may be a single integer (always crop by exactly that value), a tuple of two ints ``a`` and ``b`` (crop by an amount ``a <= x <= b``), a list of ints (crop by a random value that is contained in the list) or a StochasticParameter (sample the amount to crop from that parameter). percent : None or int or float or imgaug.parameters.StochasticParameter \ or tuple, optional The number of pixels to crop away (cut off) on each side of the image given *in percent* of the image height/width. E.g. if this is set to 0.1, the augmenter will always crop away 10 percent of the image's height at the top, 10 percent of the width on the right, 10 percent of the height at the bottom and 10 percent of the width on the left. Either this or the parameter `px` may be set, not both at the same time. * If None, then percent-based cropping will not be used. * If int, then expected to be 0 (no cropping). * If float, then that percentage will always be cropped away. * If StochasticParameter, then that parameter will be used for each image. Four samples will be drawn per image (top, right, bottom, left). * If a tuple of two floats with values ``a`` and ``b``, then each side will be cropped by a random percentage in the range ``a <= x <= b``. ``x`` is sampled per image side. * If a tuple of four entries, then the entries represent top, right, bottom, left. Each entry may be a single float (always crop by exactly that percent value), a tuple of two floats a and ``b`` (crop by a percentage ``a <= x <= b``), a list of floats (crop by a random value that is contained in the list) or a StochasticParameter (sample the percentage to crop from that parameter). keep_size : bool, optional After cropping, the result image has a different height/width than the input image. If this parameter is set to True, then the cropped image will be resized to the input image's size, i.e. the image size is then not changed by the augmenter. sample_independently : bool, optional If False AND the values for `px`/`percent` result in exactly one probability distribution for the amount to crop, only one single value will be sampled from that probability distribution and used for all sides. I.e. the crop amount then is the same for all sides. name : None or str, optional See :func:`imgaug.augmenters.meta.Augmenter.__init__`. deterministic : bool, optional See :func:`imgaug.augmenters.meta.Augmenter.__init__`. random_state : None or int or numpy.random.RandomState, optional See :func:`imgaug.augmenters.meta.Augmenter.__init__`. Examples -------- >>> aug = iaa.Crop(px=(0, 10)) crops each side by a random value from the range 0px to 10px (the value is sampled per side). >>> aug = iaa.Crop(px=(0, 10), sample_independently=False) samples one value ``v`` from the discrete range ``[0..10]`` and crops all sides by ``v`` pixels. >>> aug = iaa.Crop(px=(0, 10), keep_size=False) crops each side by a random value from the range 0px to 10px (the value is sampled per side). After cropping, the images are NOT resized to their original size (i.e. the images may end up having different heights/widths). >>> aug = iaa.Crop(px=((0, 10), (0, 5), (0, 10), (0, 5))) crops the top and bottom by a random value from the range 0px to 10px and the left and right by a random value in the range 0px to 5px. >>> aug = iaa.Crop(percent=(0, 0.1)) crops each side by a random value from the range 0 percent to 10 percent. (Percent with respect to the side's size, e.g. for the top side it uses the image's height.) >>> aug = iaa.Crop(percent=([0.05, 0.1], [0.05, 0.1], [0.05, 0.1], [0.05, 0.1])) crops each side by either 5 percent or 10 percent. """ def recursive_negate(v): if v is None: return v elif ia.is_single_number(v): ia.do_assert(v >= 0) return -v elif isinstance(v, iap.StochasticParameter): return iap.Multiply(v, -1) elif isinstance(v, tuple): return tuple([recursive_negate(v_) for v_ in v]) elif isinstance(v, list): return [recursive_negate(v_) for v_ in v] else: raise Exception("Expected None or int or float or StochasticParameter or list or tuple, got %s." % ( type(v),)) px = recursive_negate(px) percent = recursive_negate(percent) if name is None: name = "Unnamed%s" % (ia.caller_name(),) aug = CropAndPad( px=px, percent=percent, keep_size=keep_size, sample_independently=sample_independently, name=name, deterministic=deterministic, random_state=random_state ) return aug
python
def Crop(px=None, percent=None, keep_size=True, sample_independently=True, name=None, deterministic=False, random_state=None): """ Augmenter that crops/cuts away pixels at the sides of the image. That allows to cut out subimages from given (full) input images. The number of pixels to cut off may be defined in absolute values or percent of the image sizes. dtype support:: See ``imgaug.augmenters.size.CropAndPad``. Parameters ---------- px : None or int or imgaug.parameters.StochasticParameter or tuple, optional The number of pixels to crop away (cut off) on each side of the image. Either this or the parameter `percent` may be set, not both at the same time. * If None, then pixel-based cropping will not be used. * If int, then that exact number of pixels will always be cropped. * If StochasticParameter, then that parameter will be used for each image. Four samples will be drawn per image (top, right, bottom, left). * If a tuple of two ints with values ``a`` and ``b``, then each side will be cropped by a random amount in the range ``a <= x <= b``. ``x`` is sampled per image side. * If a tuple of four entries, then the entries represent top, right, bottom, left. Each entry may be a single integer (always crop by exactly that value), a tuple of two ints ``a`` and ``b`` (crop by an amount ``a <= x <= b``), a list of ints (crop by a random value that is contained in the list) or a StochasticParameter (sample the amount to crop from that parameter). percent : None or int or float or imgaug.parameters.StochasticParameter \ or tuple, optional The number of pixels to crop away (cut off) on each side of the image given *in percent* of the image height/width. E.g. if this is set to 0.1, the augmenter will always crop away 10 percent of the image's height at the top, 10 percent of the width on the right, 10 percent of the height at the bottom and 10 percent of the width on the left. Either this or the parameter `px` may be set, not both at the same time. * If None, then percent-based cropping will not be used. * If int, then expected to be 0 (no cropping). * If float, then that percentage will always be cropped away. * If StochasticParameter, then that parameter will be used for each image. Four samples will be drawn per image (top, right, bottom, left). * If a tuple of two floats with values ``a`` and ``b``, then each side will be cropped by a random percentage in the range ``a <= x <= b``. ``x`` is sampled per image side. * If a tuple of four entries, then the entries represent top, right, bottom, left. Each entry may be a single float (always crop by exactly that percent value), a tuple of two floats a and ``b`` (crop by a percentage ``a <= x <= b``), a list of floats (crop by a random value that is contained in the list) or a StochasticParameter (sample the percentage to crop from that parameter). keep_size : bool, optional After cropping, the result image has a different height/width than the input image. If this parameter is set to True, then the cropped image will be resized to the input image's size, i.e. the image size is then not changed by the augmenter. sample_independently : bool, optional If False AND the values for `px`/`percent` result in exactly one probability distribution for the amount to crop, only one single value will be sampled from that probability distribution and used for all sides. I.e. the crop amount then is the same for all sides. name : None or str, optional See :func:`imgaug.augmenters.meta.Augmenter.__init__`. deterministic : bool, optional See :func:`imgaug.augmenters.meta.Augmenter.__init__`. random_state : None or int or numpy.random.RandomState, optional See :func:`imgaug.augmenters.meta.Augmenter.__init__`. Examples -------- >>> aug = iaa.Crop(px=(0, 10)) crops each side by a random value from the range 0px to 10px (the value is sampled per side). >>> aug = iaa.Crop(px=(0, 10), sample_independently=False) samples one value ``v`` from the discrete range ``[0..10]`` and crops all sides by ``v`` pixels. >>> aug = iaa.Crop(px=(0, 10), keep_size=False) crops each side by a random value from the range 0px to 10px (the value is sampled per side). After cropping, the images are NOT resized to their original size (i.e. the images may end up having different heights/widths). >>> aug = iaa.Crop(px=((0, 10), (0, 5), (0, 10), (0, 5))) crops the top and bottom by a random value from the range 0px to 10px and the left and right by a random value in the range 0px to 5px. >>> aug = iaa.Crop(percent=(0, 0.1)) crops each side by a random value from the range 0 percent to 10 percent. (Percent with respect to the side's size, e.g. for the top side it uses the image's height.) >>> aug = iaa.Crop(percent=([0.05, 0.1], [0.05, 0.1], [0.05, 0.1], [0.05, 0.1])) crops each side by either 5 percent or 10 percent. """ def recursive_negate(v): if v is None: return v elif ia.is_single_number(v): ia.do_assert(v >= 0) return -v elif isinstance(v, iap.StochasticParameter): return iap.Multiply(v, -1) elif isinstance(v, tuple): return tuple([recursive_negate(v_) for v_ in v]) elif isinstance(v, list): return [recursive_negate(v_) for v_ in v] else: raise Exception("Expected None or int or float or StochasticParameter or list or tuple, got %s." % ( type(v),)) px = recursive_negate(px) percent = recursive_negate(percent) if name is None: name = "Unnamed%s" % (ia.caller_name(),) aug = CropAndPad( px=px, percent=percent, keep_size=keep_size, sample_independently=sample_independently, name=name, deterministic=deterministic, random_state=random_state ) return aug
[ "def", "Crop", "(", "px", "=", "None", ",", "percent", "=", "None", ",", "keep_size", "=", "True", ",", "sample_independently", "=", "True", ",", "name", "=", "None", ",", "deterministic", "=", "False", ",", "random_state", "=", "None", ")", ":", "def", "recursive_negate", "(", "v", ")", ":", "if", "v", "is", "None", ":", "return", "v", "elif", "ia", ".", "is_single_number", "(", "v", ")", ":", "ia", ".", "do_assert", "(", "v", ">=", "0", ")", "return", "-", "v", "elif", "isinstance", "(", "v", ",", "iap", ".", "StochasticParameter", ")", ":", "return", "iap", ".", "Multiply", "(", "v", ",", "-", "1", ")", "elif", "isinstance", "(", "v", ",", "tuple", ")", ":", "return", "tuple", "(", "[", "recursive_negate", "(", "v_", ")", "for", "v_", "in", "v", "]", ")", "elif", "isinstance", "(", "v", ",", "list", ")", ":", "return", "[", "recursive_negate", "(", "v_", ")", "for", "v_", "in", "v", "]", "else", ":", "raise", "Exception", "(", "\"Expected None or int or float or StochasticParameter or list or tuple, got %s.\"", "%", "(", "type", "(", "v", ")", ",", ")", ")", "px", "=", "recursive_negate", "(", "px", ")", "percent", "=", "recursive_negate", "(", "percent", ")", "if", "name", "is", "None", ":", "name", "=", "\"Unnamed%s\"", "%", "(", "ia", ".", "caller_name", "(", ")", ",", ")", "aug", "=", "CropAndPad", "(", "px", "=", "px", ",", "percent", "=", "percent", ",", "keep_size", "=", "keep_size", ",", "sample_independently", "=", "sample_independently", ",", "name", "=", "name", ",", "deterministic", "=", "deterministic", ",", "random_state", "=", "random_state", ")", "return", "aug" ]
Augmenter that crops/cuts away pixels at the sides of the image. That allows to cut out subimages from given (full) input images. The number of pixels to cut off may be defined in absolute values or percent of the image sizes. dtype support:: See ``imgaug.augmenters.size.CropAndPad``. Parameters ---------- px : None or int or imgaug.parameters.StochasticParameter or tuple, optional The number of pixels to crop away (cut off) on each side of the image. Either this or the parameter `percent` may be set, not both at the same time. * If None, then pixel-based cropping will not be used. * If int, then that exact number of pixels will always be cropped. * If StochasticParameter, then that parameter will be used for each image. Four samples will be drawn per image (top, right, bottom, left). * If a tuple of two ints with values ``a`` and ``b``, then each side will be cropped by a random amount in the range ``a <= x <= b``. ``x`` is sampled per image side. * If a tuple of four entries, then the entries represent top, right, bottom, left. Each entry may be a single integer (always crop by exactly that value), a tuple of two ints ``a`` and ``b`` (crop by an amount ``a <= x <= b``), a list of ints (crop by a random value that is contained in the list) or a StochasticParameter (sample the amount to crop from that parameter). percent : None or int or float or imgaug.parameters.StochasticParameter \ or tuple, optional The number of pixels to crop away (cut off) on each side of the image given *in percent* of the image height/width. E.g. if this is set to 0.1, the augmenter will always crop away 10 percent of the image's height at the top, 10 percent of the width on the right, 10 percent of the height at the bottom and 10 percent of the width on the left. Either this or the parameter `px` may be set, not both at the same time. * If None, then percent-based cropping will not be used. * If int, then expected to be 0 (no cropping). * If float, then that percentage will always be cropped away. * If StochasticParameter, then that parameter will be used for each image. Four samples will be drawn per image (top, right, bottom, left). * If a tuple of two floats with values ``a`` and ``b``, then each side will be cropped by a random percentage in the range ``a <= x <= b``. ``x`` is sampled per image side. * If a tuple of four entries, then the entries represent top, right, bottom, left. Each entry may be a single float (always crop by exactly that percent value), a tuple of two floats a and ``b`` (crop by a percentage ``a <= x <= b``), a list of floats (crop by a random value that is contained in the list) or a StochasticParameter (sample the percentage to crop from that parameter). keep_size : bool, optional After cropping, the result image has a different height/width than the input image. If this parameter is set to True, then the cropped image will be resized to the input image's size, i.e. the image size is then not changed by the augmenter. sample_independently : bool, optional If False AND the values for `px`/`percent` result in exactly one probability distribution for the amount to crop, only one single value will be sampled from that probability distribution and used for all sides. I.e. the crop amount then is the same for all sides. name : None or str, optional See :func:`imgaug.augmenters.meta.Augmenter.__init__`. deterministic : bool, optional See :func:`imgaug.augmenters.meta.Augmenter.__init__`. random_state : None or int or numpy.random.RandomState, optional See :func:`imgaug.augmenters.meta.Augmenter.__init__`. Examples -------- >>> aug = iaa.Crop(px=(0, 10)) crops each side by a random value from the range 0px to 10px (the value is sampled per side). >>> aug = iaa.Crop(px=(0, 10), sample_independently=False) samples one value ``v`` from the discrete range ``[0..10]`` and crops all sides by ``v`` pixels. >>> aug = iaa.Crop(px=(0, 10), keep_size=False) crops each side by a random value from the range 0px to 10px (the value is sampled per side). After cropping, the images are NOT resized to their original size (i.e. the images may end up having different heights/widths). >>> aug = iaa.Crop(px=((0, 10), (0, 5), (0, 10), (0, 5))) crops the top and bottom by a random value from the range 0px to 10px and the left and right by a random value in the range 0px to 5px. >>> aug = iaa.Crop(percent=(0, 0.1)) crops each side by a random value from the range 0 percent to 10 percent. (Percent with respect to the side's size, e.g. for the top side it uses the image's height.) >>> aug = iaa.Crop(percent=([0.05, 0.1], [0.05, 0.1], [0.05, 0.1], [0.05, 0.1])) crops each side by either 5 percent or 10 percent.
[ "Augmenter", "that", "crops", "/", "cuts", "away", "pixels", "at", "the", "sides", "of", "the", "image", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/augmenters/size.py#L1157-L1303
valid
aleju/imgaug
imgaug/external/poly_point_isect_py2py3.py
isect_segments__naive
def isect_segments__naive(segments): """ Brute force O(n2) version of ``isect_segments`` for test validation. """ isect = [] # order points left -> right if Real is float: segments = [ (s[0], s[1]) if s[0][X] <= s[1][X] else (s[1], s[0]) for s in segments] else: segments = [ ( (Real(s[0][0]), Real(s[0][1])), (Real(s[1][0]), Real(s[1][1])), ) if (s[0] <= s[1]) else ( (Real(s[1][0]), Real(s[1][1])), (Real(s[0][0]), Real(s[0][1])), ) for s in segments] n = len(segments) for i in range(n): a0, a1 = segments[i] for j in range(i + 1, n): b0, b1 = segments[j] if a0 not in (b0, b1) and a1 not in (b0, b1): ix = isect_seg_seg_v2_point(a0, a1, b0, b1) if ix is not None: # USE_IGNORE_SEGMENT_ENDINGS handled already isect.append(ix) return isect
python
def isect_segments__naive(segments): """ Brute force O(n2) version of ``isect_segments`` for test validation. """ isect = [] # order points left -> right if Real is float: segments = [ (s[0], s[1]) if s[0][X] <= s[1][X] else (s[1], s[0]) for s in segments] else: segments = [ ( (Real(s[0][0]), Real(s[0][1])), (Real(s[1][0]), Real(s[1][1])), ) if (s[0] <= s[1]) else ( (Real(s[1][0]), Real(s[1][1])), (Real(s[0][0]), Real(s[0][1])), ) for s in segments] n = len(segments) for i in range(n): a0, a1 = segments[i] for j in range(i + 1, n): b0, b1 = segments[j] if a0 not in (b0, b1) and a1 not in (b0, b1): ix = isect_seg_seg_v2_point(a0, a1, b0, b1) if ix is not None: # USE_IGNORE_SEGMENT_ENDINGS handled already isect.append(ix) return isect
[ "def", "isect_segments__naive", "(", "segments", ")", ":", "isect", "=", "[", "]", "# order points left -> right", "if", "Real", "is", "float", ":", "segments", "=", "[", "(", "s", "[", "0", "]", ",", "s", "[", "1", "]", ")", "if", "s", "[", "0", "]", "[", "X", "]", "<=", "s", "[", "1", "]", "[", "X", "]", "else", "(", "s", "[", "1", "]", ",", "s", "[", "0", "]", ")", "for", "s", "in", "segments", "]", "else", ":", "segments", "=", "[", "(", "(", "Real", "(", "s", "[", "0", "]", "[", "0", "]", ")", ",", "Real", "(", "s", "[", "0", "]", "[", "1", "]", ")", ")", ",", "(", "Real", "(", "s", "[", "1", "]", "[", "0", "]", ")", ",", "Real", "(", "s", "[", "1", "]", "[", "1", "]", ")", ")", ",", ")", "if", "(", "s", "[", "0", "]", "<=", "s", "[", "1", "]", ")", "else", "(", "(", "Real", "(", "s", "[", "1", "]", "[", "0", "]", ")", ",", "Real", "(", "s", "[", "1", "]", "[", "1", "]", ")", ")", ",", "(", "Real", "(", "s", "[", "0", "]", "[", "0", "]", ")", ",", "Real", "(", "s", "[", "0", "]", "[", "1", "]", ")", ")", ",", ")", "for", "s", "in", "segments", "]", "n", "=", "len", "(", "segments", ")", "for", "i", "in", "range", "(", "n", ")", ":", "a0", ",", "a1", "=", "segments", "[", "i", "]", "for", "j", "in", "range", "(", "i", "+", "1", ",", "n", ")", ":", "b0", ",", "b1", "=", "segments", "[", "j", "]", "if", "a0", "not", "in", "(", "b0", ",", "b1", ")", "and", "a1", "not", "in", "(", "b0", ",", "b1", ")", ":", "ix", "=", "isect_seg_seg_v2_point", "(", "a0", ",", "a1", ",", "b0", ",", "b1", ")", "if", "ix", "is", "not", "None", ":", "# USE_IGNORE_SEGMENT_ENDINGS handled already", "isect", ".", "append", "(", "ix", ")", "return", "isect" ]
Brute force O(n2) version of ``isect_segments`` for test validation.
[ "Brute", "force", "O", "(", "n2", ")", "version", "of", "isect_segments", "for", "test", "validation", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/external/poly_point_isect_py2py3.py#L685-L721
valid
aleju/imgaug
imgaug/external/poly_point_isect_py2py3.py
isect_polygon__naive
def isect_polygon__naive(points): """ Brute force O(n2) version of ``isect_polygon`` for test validation. """ isect = [] n = len(points) if Real is float: pass else: points = [(Real(p[0]), Real(p[1])) for p in points] for i in range(n): a0, a1 = points[i], points[(i + 1) % n] for j in range(i + 1, n): b0, b1 = points[j], points[(j + 1) % n] if a0 not in (b0, b1) and a1 not in (b0, b1): ix = isect_seg_seg_v2_point(a0, a1, b0, b1) if ix is not None: if USE_IGNORE_SEGMENT_ENDINGS: if ((len_squared_v2v2(ix, a0) < NUM_EPS_SQ or len_squared_v2v2(ix, a1) < NUM_EPS_SQ) and (len_squared_v2v2(ix, b0) < NUM_EPS_SQ or len_squared_v2v2(ix, b1) < NUM_EPS_SQ)): continue isect.append(ix) return isect
python
def isect_polygon__naive(points): """ Brute force O(n2) version of ``isect_polygon`` for test validation. """ isect = [] n = len(points) if Real is float: pass else: points = [(Real(p[0]), Real(p[1])) for p in points] for i in range(n): a0, a1 = points[i], points[(i + 1) % n] for j in range(i + 1, n): b0, b1 = points[j], points[(j + 1) % n] if a0 not in (b0, b1) and a1 not in (b0, b1): ix = isect_seg_seg_v2_point(a0, a1, b0, b1) if ix is not None: if USE_IGNORE_SEGMENT_ENDINGS: if ((len_squared_v2v2(ix, a0) < NUM_EPS_SQ or len_squared_v2v2(ix, a1) < NUM_EPS_SQ) and (len_squared_v2v2(ix, b0) < NUM_EPS_SQ or len_squared_v2v2(ix, b1) < NUM_EPS_SQ)): continue isect.append(ix) return isect
[ "def", "isect_polygon__naive", "(", "points", ")", ":", "isect", "=", "[", "]", "n", "=", "len", "(", "points", ")", "if", "Real", "is", "float", ":", "pass", "else", ":", "points", "=", "[", "(", "Real", "(", "p", "[", "0", "]", ")", ",", "Real", "(", "p", "[", "1", "]", ")", ")", "for", "p", "in", "points", "]", "for", "i", "in", "range", "(", "n", ")", ":", "a0", ",", "a1", "=", "points", "[", "i", "]", ",", "points", "[", "(", "i", "+", "1", ")", "%", "n", "]", "for", "j", "in", "range", "(", "i", "+", "1", ",", "n", ")", ":", "b0", ",", "b1", "=", "points", "[", "j", "]", ",", "points", "[", "(", "j", "+", "1", ")", "%", "n", "]", "if", "a0", "not", "in", "(", "b0", ",", "b1", ")", "and", "a1", "not", "in", "(", "b0", ",", "b1", ")", ":", "ix", "=", "isect_seg_seg_v2_point", "(", "a0", ",", "a1", ",", "b0", ",", "b1", ")", "if", "ix", "is", "not", "None", ":", "if", "USE_IGNORE_SEGMENT_ENDINGS", ":", "if", "(", "(", "len_squared_v2v2", "(", "ix", ",", "a0", ")", "<", "NUM_EPS_SQ", "or", "len_squared_v2v2", "(", "ix", ",", "a1", ")", "<", "NUM_EPS_SQ", ")", "and", "(", "len_squared_v2v2", "(", "ix", ",", "b0", ")", "<", "NUM_EPS_SQ", "or", "len_squared_v2v2", "(", "ix", ",", "b1", ")", "<", "NUM_EPS_SQ", ")", ")", ":", "continue", "isect", ".", "append", "(", "ix", ")", "return", "isect" ]
Brute force O(n2) version of ``isect_polygon`` for test validation.
[ "Brute", "force", "O", "(", "n2", ")", "version", "of", "isect_polygon", "for", "test", "validation", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/external/poly_point_isect_py2py3.py#L724-L755
valid
aleju/imgaug
imgaug/external/poly_point_isect_py2py3.py
SweepLine.get_intersections
def get_intersections(self): """ Return a list of unordered intersection points. """ if Real is float: return list(self.intersections.keys()) else: return [(float(p[0]), float(p[1])) for p in self.intersections.keys()]
python
def get_intersections(self): """ Return a list of unordered intersection points. """ if Real is float: return list(self.intersections.keys()) else: return [(float(p[0]), float(p[1])) for p in self.intersections.keys()]
[ "def", "get_intersections", "(", "self", ")", ":", "if", "Real", "is", "float", ":", "return", "list", "(", "self", ".", "intersections", ".", "keys", "(", ")", ")", "else", ":", "return", "[", "(", "float", "(", "p", "[", "0", "]", ")", ",", "float", "(", "p", "[", "1", "]", ")", ")", "for", "p", "in", "self", ".", "intersections", ".", "keys", "(", ")", "]" ]
Return a list of unordered intersection points.
[ "Return", "a", "list", "of", "unordered", "intersection", "points", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/external/poly_point_isect_py2py3.py#L237-L244
valid
aleju/imgaug
imgaug/external/poly_point_isect_py2py3.py
SweepLine.get_intersections_with_segments
def get_intersections_with_segments(self): """ Return a list of unordered intersection '(point, segment)' pairs, where segments may contain 2 or more values. """ if Real is float: return [ (p, [event.segment for event in event_set]) for p, event_set in self.intersections.items() ] else: return [ ( (float(p[0]), float(p[1])), [((float(event.segment[0][0]), float(event.segment[0][1])), (float(event.segment[1][0]), float(event.segment[1][1]))) for event in event_set], ) for p, event_set in self.intersections.items() ]
python
def get_intersections_with_segments(self): """ Return a list of unordered intersection '(point, segment)' pairs, where segments may contain 2 or more values. """ if Real is float: return [ (p, [event.segment for event in event_set]) for p, event_set in self.intersections.items() ] else: return [ ( (float(p[0]), float(p[1])), [((float(event.segment[0][0]), float(event.segment[0][1])), (float(event.segment[1][0]), float(event.segment[1][1]))) for event in event_set], ) for p, event_set in self.intersections.items() ]
[ "def", "get_intersections_with_segments", "(", "self", ")", ":", "if", "Real", "is", "float", ":", "return", "[", "(", "p", ",", "[", "event", ".", "segment", "for", "event", "in", "event_set", "]", ")", "for", "p", ",", "event_set", "in", "self", ".", "intersections", ".", "items", "(", ")", "]", "else", ":", "return", "[", "(", "(", "float", "(", "p", "[", "0", "]", ")", ",", "float", "(", "p", "[", "1", "]", ")", ")", ",", "[", "(", "(", "float", "(", "event", ".", "segment", "[", "0", "]", "[", "0", "]", ")", ",", "float", "(", "event", ".", "segment", "[", "0", "]", "[", "1", "]", ")", ")", ",", "(", "float", "(", "event", ".", "segment", "[", "1", "]", "[", "0", "]", ")", ",", "float", "(", "event", ".", "segment", "[", "1", "]", "[", "1", "]", ")", ")", ")", "for", "event", "in", "event_set", "]", ",", ")", "for", "p", ",", "event_set", "in", "self", ".", "intersections", ".", "items", "(", ")", "]" ]
Return a list of unordered intersection '(point, segment)' pairs, where segments may contain 2 or more values.
[ "Return", "a", "list", "of", "unordered", "intersection", "(", "point", "segment", ")", "pairs", "where", "segments", "may", "contain", "2", "or", "more", "values", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/external/poly_point_isect_py2py3.py#L247-L266
valid
aleju/imgaug
imgaug/external/poly_point_isect_py2py3.py
EventQueue.poll
def poll(self): """ Get, and remove, the first (lowest) item from this queue. :return: the first (lowest) item from this queue. :rtype: Point, Event pair. """ assert(len(self.events_scan) != 0) p, events_current = self.events_scan.pop_min() return p, events_current
python
def poll(self): """ Get, and remove, the first (lowest) item from this queue. :return: the first (lowest) item from this queue. :rtype: Point, Event pair. """ assert(len(self.events_scan) != 0) p, events_current = self.events_scan.pop_min() return p, events_current
[ "def", "poll", "(", "self", ")", ":", "assert", "(", "len", "(", "self", ".", "events_scan", ")", "!=", "0", ")", "p", ",", "events_current", "=", "self", ".", "events_scan", ".", "pop_min", "(", ")", "return", "p", ",", "events_current" ]
Get, and remove, the first (lowest) item from this queue. :return: the first (lowest) item from this queue. :rtype: Point, Event pair.
[ "Get", "and", "remove", "the", "first", "(", "lowest", ")", "item", "from", "this", "queue", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/external/poly_point_isect_py2py3.py#L532-L541
valid
aleju/imgaug
imgaug/external/poly_point_isect_py2py3.py
_ABCTree.clear
def clear(self): """T.clear() -> None. Remove all items from T.""" def _clear(node): if node is not None: _clear(node.left) _clear(node.right) node.free() _clear(self._root) self._count = 0 self._root = None
python
def clear(self): """T.clear() -> None. Remove all items from T.""" def _clear(node): if node is not None: _clear(node.left) _clear(node.right) node.free() _clear(self._root) self._count = 0 self._root = None
[ "def", "clear", "(", "self", ")", ":", "def", "_clear", "(", "node", ")", ":", "if", "node", "is", "not", "None", ":", "_clear", "(", "node", ".", "left", ")", "_clear", "(", "node", ".", "right", ")", "node", ".", "free", "(", ")", "_clear", "(", "self", ".", "_root", ")", "self", ".", "_count", "=", "0", "self", ".", "_root", "=", "None" ]
T.clear() -> None. Remove all items from T.
[ "T", ".", "clear", "()", "-", ">", "None", ".", "Remove", "all", "items", "from", "T", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/external/poly_point_isect_py2py3.py#L796-L805
valid
aleju/imgaug
imgaug/external/poly_point_isect_py2py3.py
_ABCTree.pop_item
def pop_item(self): """T.pop_item() -> (k, v), remove and return some (key, value) pair as a 2-tuple; but raise KeyError if T is empty. """ if self.is_empty(): raise KeyError("pop_item(): tree is empty") node = self._root while True: if node.left is not None: node = node.left elif node.right is not None: node = node.right else: break key = node.key value = node.value self.remove(key) return key, value
python
def pop_item(self): """T.pop_item() -> (k, v), remove and return some (key, value) pair as a 2-tuple; but raise KeyError if T is empty. """ if self.is_empty(): raise KeyError("pop_item(): tree is empty") node = self._root while True: if node.left is not None: node = node.left elif node.right is not None: node = node.right else: break key = node.key value = node.value self.remove(key) return key, value
[ "def", "pop_item", "(", "self", ")", ":", "if", "self", ".", "is_empty", "(", ")", ":", "raise", "KeyError", "(", "\"pop_item(): tree is empty\"", ")", "node", "=", "self", ".", "_root", "while", "True", ":", "if", "node", ".", "left", "is", "not", "None", ":", "node", "=", "node", ".", "left", "elif", "node", ".", "right", "is", "not", "None", ":", "node", "=", "node", ".", "right", "else", ":", "break", "key", "=", "node", ".", "key", "value", "=", "node", ".", "value", "self", ".", "remove", "(", "key", ")", "return", "key", ",", "value" ]
T.pop_item() -> (k, v), remove and return some (key, value) pair as a 2-tuple; but raise KeyError if T is empty.
[ "T", ".", "pop_item", "()", "-", ">", "(", "k", "v", ")", "remove", "and", "return", "some", "(", "key", "value", ")", "pair", "as", "a", "2", "-", "tuple", ";", "but", "raise", "KeyError", "if", "T", "is", "empty", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/external/poly_point_isect_py2py3.py#L824-L841
valid
aleju/imgaug
imgaug/external/poly_point_isect_py2py3.py
_ABCTree.min_item
def min_item(self): """Get item with min key of tree, raises ValueError if tree is empty.""" if self.is_empty(): raise ValueError("Tree is empty") node = self._root while node.left is not None: node = node.left return node.key, node.value
python
def min_item(self): """Get item with min key of tree, raises ValueError if tree is empty.""" if self.is_empty(): raise ValueError("Tree is empty") node = self._root while node.left is not None: node = node.left return node.key, node.value
[ "def", "min_item", "(", "self", ")", ":", "if", "self", ".", "is_empty", "(", ")", ":", "raise", "ValueError", "(", "\"Tree is empty\"", ")", "node", "=", "self", ".", "_root", "while", "node", ".", "left", "is", "not", "None", ":", "node", "=", "node", ".", "left", "return", "node", ".", "key", ",", "node", ".", "value" ]
Get item with min key of tree, raises ValueError if tree is empty.
[ "Get", "item", "with", "min", "key", "of", "tree", "raises", "ValueError", "if", "tree", "is", "empty", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/external/poly_point_isect_py2py3.py#L844-L851
valid
aleju/imgaug
imgaug/external/poly_point_isect_py2py3.py
_ABCTree.max_item
def max_item(self): """Get item with max key of tree, raises ValueError if tree is empty.""" if self.is_empty(): raise ValueError("Tree is empty") node = self._root while node.right is not None: node = node.right return node.key, node.value
python
def max_item(self): """Get item with max key of tree, raises ValueError if tree is empty.""" if self.is_empty(): raise ValueError("Tree is empty") node = self._root while node.right is not None: node = node.right return node.key, node.value
[ "def", "max_item", "(", "self", ")", ":", "if", "self", ".", "is_empty", "(", ")", ":", "raise", "ValueError", "(", "\"Tree is empty\"", ")", "node", "=", "self", ".", "_root", "while", "node", ".", "right", "is", "not", "None", ":", "node", "=", "node", ".", "right", "return", "node", ".", "key", ",", "node", ".", "value" ]
Get item with max key of tree, raises ValueError if tree is empty.
[ "Get", "item", "with", "max", "key", "of", "tree", "raises", "ValueError", "if", "tree", "is", "empty", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/external/poly_point_isect_py2py3.py#L853-L860
valid
aleju/imgaug
imgaug/external/poly_point_isect_py2py3.py
_ABCTree.succ_item
def succ_item(self, key, default=_sentinel): """Get successor (k,v) pair of key, raises KeyError if key is max key or key does not exist. optimized for pypy. """ # removed graingets version, because it was little slower on CPython and much slower on pypy # this version runs about 4x faster with pypy than the Cython version # Note: Code sharing of succ_item() and ceiling_item() is possible, but has always a speed penalty. node = self._root succ_node = None while node is not None: cmp = self._cmp(self._cmp_data, key, node.key) if cmp == 0: break elif cmp < 0: if (succ_node is None) or self._cmp(self._cmp_data, node.key, succ_node.key) < 0: succ_node = node node = node.left else: node = node.right if node is None: # stay at dead end if default is _sentinel: raise KeyError(str(key)) return default # found node of key if node.right is not None: # find smallest node of right subtree node = node.right while node.left is not None: node = node.left if succ_node is None: succ_node = node elif self._cmp(self._cmp_data, node.key, succ_node.key) < 0: succ_node = node elif succ_node is None: # given key is biggest in tree if default is _sentinel: raise KeyError(str(key)) return default return succ_node.key, succ_node.value
python
def succ_item(self, key, default=_sentinel): """Get successor (k,v) pair of key, raises KeyError if key is max key or key does not exist. optimized for pypy. """ # removed graingets version, because it was little slower on CPython and much slower on pypy # this version runs about 4x faster with pypy than the Cython version # Note: Code sharing of succ_item() and ceiling_item() is possible, but has always a speed penalty. node = self._root succ_node = None while node is not None: cmp = self._cmp(self._cmp_data, key, node.key) if cmp == 0: break elif cmp < 0: if (succ_node is None) or self._cmp(self._cmp_data, node.key, succ_node.key) < 0: succ_node = node node = node.left else: node = node.right if node is None: # stay at dead end if default is _sentinel: raise KeyError(str(key)) return default # found node of key if node.right is not None: # find smallest node of right subtree node = node.right while node.left is not None: node = node.left if succ_node is None: succ_node = node elif self._cmp(self._cmp_data, node.key, succ_node.key) < 0: succ_node = node elif succ_node is None: # given key is biggest in tree if default is _sentinel: raise KeyError(str(key)) return default return succ_node.key, succ_node.value
[ "def", "succ_item", "(", "self", ",", "key", ",", "default", "=", "_sentinel", ")", ":", "# removed graingets version, because it was little slower on CPython and much slower on pypy", "# this version runs about 4x faster with pypy than the Cython version", "# Note: Code sharing of succ_item() and ceiling_item() is possible, but has always a speed penalty.", "node", "=", "self", ".", "_root", "succ_node", "=", "None", "while", "node", "is", "not", "None", ":", "cmp", "=", "self", ".", "_cmp", "(", "self", ".", "_cmp_data", ",", "key", ",", "node", ".", "key", ")", "if", "cmp", "==", "0", ":", "break", "elif", "cmp", "<", "0", ":", "if", "(", "succ_node", "is", "None", ")", "or", "self", ".", "_cmp", "(", "self", ".", "_cmp_data", ",", "node", ".", "key", ",", "succ_node", ".", "key", ")", "<", "0", ":", "succ_node", "=", "node", "node", "=", "node", ".", "left", "else", ":", "node", "=", "node", ".", "right", "if", "node", "is", "None", ":", "# stay at dead end", "if", "default", "is", "_sentinel", ":", "raise", "KeyError", "(", "str", "(", "key", ")", ")", "return", "default", "# found node of key", "if", "node", ".", "right", "is", "not", "None", ":", "# find smallest node of right subtree", "node", "=", "node", ".", "right", "while", "node", ".", "left", "is", "not", "None", ":", "node", "=", "node", ".", "left", "if", "succ_node", "is", "None", ":", "succ_node", "=", "node", "elif", "self", ".", "_cmp", "(", "self", ".", "_cmp_data", ",", "node", ".", "key", ",", "succ_node", ".", "key", ")", "<", "0", ":", "succ_node", "=", "node", "elif", "succ_node", "is", "None", ":", "# given key is biggest in tree", "if", "default", "is", "_sentinel", ":", "raise", "KeyError", "(", "str", "(", "key", ")", ")", "return", "default", "return", "succ_node", ".", "key", ",", "succ_node", ".", "value" ]
Get successor (k,v) pair of key, raises KeyError if key is max key or key does not exist. optimized for pypy.
[ "Get", "successor", "(", "k", "v", ")", "pair", "of", "key", "raises", "KeyError", "if", "key", "is", "max", "key", "or", "key", "does", "not", "exist", ".", "optimized", "for", "pypy", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/external/poly_point_isect_py2py3.py#L862-L900
valid
aleju/imgaug
imgaug/external/poly_point_isect_py2py3.py
_ABCTree.prev_item
def prev_item(self, key, default=_sentinel): """Get predecessor (k,v) pair of key, raises KeyError if key is min key or key does not exist. optimized for pypy. """ # removed graingets version, because it was little slower on CPython and much slower on pypy # this version runs about 4x faster with pypy than the Cython version # Note: Code sharing of prev_item() and floor_item() is possible, but has always a speed penalty. node = self._root prev_node = None while node is not None: cmp = self._cmp(self._cmp_data, key, node.key) if cmp == 0: break elif cmp < 0: node = node.left else: if (prev_node is None) or self._cmp(self._cmp_data, prev_node.key, node.key) < 0: prev_node = node node = node.right if node is None: # stay at dead end (None) if default is _sentinel: raise KeyError(str(key)) return default # found node of key if node.left is not None: # find biggest node of left subtree node = node.left while node.right is not None: node = node.right if prev_node is None: prev_node = node elif self._cmp(self._cmp_data, prev_node.key, node.key) < 0: prev_node = node elif prev_node is None: # given key is smallest in tree if default is _sentinel: raise KeyError(str(key)) return default return prev_node.key, prev_node.value
python
def prev_item(self, key, default=_sentinel): """Get predecessor (k,v) pair of key, raises KeyError if key is min key or key does not exist. optimized for pypy. """ # removed graingets version, because it was little slower on CPython and much slower on pypy # this version runs about 4x faster with pypy than the Cython version # Note: Code sharing of prev_item() and floor_item() is possible, but has always a speed penalty. node = self._root prev_node = None while node is not None: cmp = self._cmp(self._cmp_data, key, node.key) if cmp == 0: break elif cmp < 0: node = node.left else: if (prev_node is None) or self._cmp(self._cmp_data, prev_node.key, node.key) < 0: prev_node = node node = node.right if node is None: # stay at dead end (None) if default is _sentinel: raise KeyError(str(key)) return default # found node of key if node.left is not None: # find biggest node of left subtree node = node.left while node.right is not None: node = node.right if prev_node is None: prev_node = node elif self._cmp(self._cmp_data, prev_node.key, node.key) < 0: prev_node = node elif prev_node is None: # given key is smallest in tree if default is _sentinel: raise KeyError(str(key)) return default return prev_node.key, prev_node.value
[ "def", "prev_item", "(", "self", ",", "key", ",", "default", "=", "_sentinel", ")", ":", "# removed graingets version, because it was little slower on CPython and much slower on pypy", "# this version runs about 4x faster with pypy than the Cython version", "# Note: Code sharing of prev_item() and floor_item() is possible, but has always a speed penalty.", "node", "=", "self", ".", "_root", "prev_node", "=", "None", "while", "node", "is", "not", "None", ":", "cmp", "=", "self", ".", "_cmp", "(", "self", ".", "_cmp_data", ",", "key", ",", "node", ".", "key", ")", "if", "cmp", "==", "0", ":", "break", "elif", "cmp", "<", "0", ":", "node", "=", "node", ".", "left", "else", ":", "if", "(", "prev_node", "is", "None", ")", "or", "self", ".", "_cmp", "(", "self", ".", "_cmp_data", ",", "prev_node", ".", "key", ",", "node", ".", "key", ")", "<", "0", ":", "prev_node", "=", "node", "node", "=", "node", ".", "right", "if", "node", "is", "None", ":", "# stay at dead end (None)", "if", "default", "is", "_sentinel", ":", "raise", "KeyError", "(", "str", "(", "key", ")", ")", "return", "default", "# found node of key", "if", "node", ".", "left", "is", "not", "None", ":", "# find biggest node of left subtree", "node", "=", "node", ".", "left", "while", "node", ".", "right", "is", "not", "None", ":", "node", "=", "node", ".", "right", "if", "prev_node", "is", "None", ":", "prev_node", "=", "node", "elif", "self", ".", "_cmp", "(", "self", ".", "_cmp_data", ",", "prev_node", ".", "key", ",", "node", ".", "key", ")", "<", "0", ":", "prev_node", "=", "node", "elif", "prev_node", "is", "None", ":", "# given key is smallest in tree", "if", "default", "is", "_sentinel", ":", "raise", "KeyError", "(", "str", "(", "key", ")", ")", "return", "default", "return", "prev_node", ".", "key", ",", "prev_node", ".", "value" ]
Get predecessor (k,v) pair of key, raises KeyError if key is min key or key does not exist. optimized for pypy.
[ "Get", "predecessor", "(", "k", "v", ")", "pair", "of", "key", "raises", "KeyError", "if", "key", "is", "min", "key", "or", "key", "does", "not", "exist", ".", "optimized", "for", "pypy", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/external/poly_point_isect_py2py3.py#L902-L941
valid
aleju/imgaug
imgaug/external/poly_point_isect_py2py3.py
_ABCTree.set_default
def set_default(self, key, default=None): """T.set_default(k[,d]) -> T.get(k,d), also set T[k]=d if k not in T""" try: return self.get_value(key) except KeyError: self.insert(key, default) return default
python
def set_default(self, key, default=None): """T.set_default(k[,d]) -> T.get(k,d), also set T[k]=d if k not in T""" try: return self.get_value(key) except KeyError: self.insert(key, default) return default
[ "def", "set_default", "(", "self", ",", "key", ",", "default", "=", "None", ")", ":", "try", ":", "return", "self", ".", "get_value", "(", "key", ")", "except", "KeyError", ":", "self", ".", "insert", "(", "key", ",", "default", ")", "return", "default" ]
T.set_default(k[,d]) -> T.get(k,d), also set T[k]=d if k not in T
[ "T", ".", "set_default", "(", "k", "[", "d", "]", ")", "-", ">", "T", ".", "get", "(", "k", "d", ")", "also", "set", "T", "[", "k", "]", "=", "d", "if", "k", "not", "in", "T" ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/external/poly_point_isect_py2py3.py#L964-L970
valid
aleju/imgaug
imgaug/external/poly_point_isect_py2py3.py
_ABCTree.pop
def pop(self, key, *args): """T.pop(k[,d]) -> v, remove specified key and return the corresponding value. If key is not found, d is returned if given, otherwise KeyError is raised """ if len(args) > 1: raise TypeError("pop expected at most 2 arguments, got %d" % (1 + len(args))) try: value = self.get_value(key) self.remove(key) return value except KeyError: if len(args) == 0: raise else: return args[0]
python
def pop(self, key, *args): """T.pop(k[,d]) -> v, remove specified key and return the corresponding value. If key is not found, d is returned if given, otherwise KeyError is raised """ if len(args) > 1: raise TypeError("pop expected at most 2 arguments, got %d" % (1 + len(args))) try: value = self.get_value(key) self.remove(key) return value except KeyError: if len(args) == 0: raise else: return args[0]
[ "def", "pop", "(", "self", ",", "key", ",", "*", "args", ")", ":", "if", "len", "(", "args", ")", ">", "1", ":", "raise", "TypeError", "(", "\"pop expected at most 2 arguments, got %d\"", "%", "(", "1", "+", "len", "(", "args", ")", ")", ")", "try", ":", "value", "=", "self", ".", "get_value", "(", "key", ")", "self", ".", "remove", "(", "key", ")", "return", "value", "except", "KeyError", ":", "if", "len", "(", "args", ")", "==", "0", ":", "raise", "else", ":", "return", "args", "[", "0", "]" ]
T.pop(k[,d]) -> v, remove specified key and return the corresponding value. If key is not found, d is returned if given, otherwise KeyError is raised
[ "T", ".", "pop", "(", "k", "[", "d", "]", ")", "-", ">", "v", "remove", "specified", "key", "and", "return", "the", "corresponding", "value", ".", "If", "key", "is", "not", "found", "d", "is", "returned", "if", "given", "otherwise", "KeyError", "is", "raised" ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/external/poly_point_isect_py2py3.py#L980-L994
valid
aleju/imgaug
imgaug/external/poly_point_isect_py2py3.py
_ABCTree.prev_key
def prev_key(self, key, default=_sentinel): """Get predecessor to key, raises KeyError if key is min key or key does not exist. """ item = self.prev_item(key, default) return default if item is default else item[0]
python
def prev_key(self, key, default=_sentinel): """Get predecessor to key, raises KeyError if key is min key or key does not exist. """ item = self.prev_item(key, default) return default if item is default else item[0]
[ "def", "prev_key", "(", "self", ",", "key", ",", "default", "=", "_sentinel", ")", ":", "item", "=", "self", ".", "prev_item", "(", "key", ",", "default", ")", "return", "default", "if", "item", "is", "default", "else", "item", "[", "0", "]" ]
Get predecessor to key, raises KeyError if key is min key or key does not exist.
[ "Get", "predecessor", "to", "key", "raises", "KeyError", "if", "key", "is", "min", "key", "or", "key", "does", "not", "exist", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/external/poly_point_isect_py2py3.py#L996-L1001
valid
aleju/imgaug
imgaug/external/poly_point_isect_py2py3.py
_ABCTree.succ_key
def succ_key(self, key, default=_sentinel): """Get successor to key, raises KeyError if key is max key or key does not exist. """ item = self.succ_item(key, default) return default if item is default else item[0]
python
def succ_key(self, key, default=_sentinel): """Get successor to key, raises KeyError if key is max key or key does not exist. """ item = self.succ_item(key, default) return default if item is default else item[0]
[ "def", "succ_key", "(", "self", ",", "key", ",", "default", "=", "_sentinel", ")", ":", "item", "=", "self", ".", "succ_item", "(", "key", ",", "default", ")", "return", "default", "if", "item", "is", "default", "else", "item", "[", "0", "]" ]
Get successor to key, raises KeyError if key is max key or key does not exist.
[ "Get", "successor", "to", "key", "raises", "KeyError", "if", "key", "is", "max", "key", "or", "key", "does", "not", "exist", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/external/poly_point_isect_py2py3.py#L1003-L1008
valid
aleju/imgaug
imgaug/external/poly_point_isect_py2py3.py
_ABCTree.key_slice
def key_slice(self, start_key, end_key, reverse=False): """T.key_slice(start_key, end_key) -> key iterator: start_key <= key < end_key. Yields keys in ascending order if reverse is False else in descending order. """ return (k for k, v in self.iter_items(start_key, end_key, reverse=reverse))
python
def key_slice(self, start_key, end_key, reverse=False): """T.key_slice(start_key, end_key) -> key iterator: start_key <= key < end_key. Yields keys in ascending order if reverse is False else in descending order. """ return (k for k, v in self.iter_items(start_key, end_key, reverse=reverse))
[ "def", "key_slice", "(", "self", ",", "start_key", ",", "end_key", ",", "reverse", "=", "False", ")", ":", "return", "(", "k", "for", "k", ",", "v", "in", "self", ".", "iter_items", "(", "start_key", ",", "end_key", ",", "reverse", "=", "reverse", ")", ")" ]
T.key_slice(start_key, end_key) -> key iterator: start_key <= key < end_key. Yields keys in ascending order if reverse is False else in descending order.
[ "T", ".", "key_slice", "(", "start_key", "end_key", ")", "-", ">", "key", "iterator", ":", "start_key", "<", "=", "key", "<", "end_key", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/external/poly_point_isect_py2py3.py#L1034-L1040
valid
aleju/imgaug
imgaug/external/poly_point_isect_py2py3.py
_ABCTree.iter_items
def iter_items(self, start_key=None, end_key=None, reverse=False): """Iterates over the (key, value) items of the associated tree, in ascending order if reverse is True, iterate in descending order, reverse defaults to False""" # optimized iterator (reduced method calls) - faster on CPython but slower on pypy if self.is_empty(): return [] if reverse: return self._iter_items_backward(start_key, end_key) else: return self._iter_items_forward(start_key, end_key)
python
def iter_items(self, start_key=None, end_key=None, reverse=False): """Iterates over the (key, value) items of the associated tree, in ascending order if reverse is True, iterate in descending order, reverse defaults to False""" # optimized iterator (reduced method calls) - faster on CPython but slower on pypy if self.is_empty(): return [] if reverse: return self._iter_items_backward(start_key, end_key) else: return self._iter_items_forward(start_key, end_key)
[ "def", "iter_items", "(", "self", ",", "start_key", "=", "None", ",", "end_key", "=", "None", ",", "reverse", "=", "False", ")", ":", "# optimized iterator (reduced method calls) - faster on CPython but slower on pypy", "if", "self", ".", "is_empty", "(", ")", ":", "return", "[", "]", "if", "reverse", ":", "return", "self", ".", "_iter_items_backward", "(", "start_key", ",", "end_key", ")", "else", ":", "return", "self", ".", "_iter_items_forward", "(", "start_key", ",", "end_key", ")" ]
Iterates over the (key, value) items of the associated tree, in ascending order if reverse is True, iterate in descending order, reverse defaults to False
[ "Iterates", "over", "the", "(", "key", "value", ")", "items", "of", "the", "associated", "tree", "in", "ascending", "order", "if", "reverse", "is", "True", "iterate", "in", "descending", "order", "reverse", "defaults", "to", "False" ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/external/poly_point_isect_py2py3.py#L1042-L1053
valid
aleju/imgaug
imgaug/external/poly_point_isect_py2py3.py
RBTree.insert
def insert(self, key, value): """T.insert(key, value) <==> T[key] = value, insert key, value into tree.""" if self._root is None: # Empty tree case self._root = self._new_node(key, value) self._root.red = False # make root black return head = Node() # False tree root grand_parent = None grand_grand_parent = head parent = None # parent direction = 0 last = 0 # Set up helpers grand_grand_parent.right = self._root node = grand_grand_parent.right # Search down the tree while True: if node is None: # Insert new node at the bottom node = self._new_node(key, value) parent[direction] = node elif RBTree.is_red(node.left) and RBTree.is_red(node.right): # Color flip node.red = True node.left.red = False node.right.red = False # Fix red violation if RBTree.is_red(node) and RBTree.is_red(parent): direction2 = 1 if grand_grand_parent.right is grand_parent else 0 if node is parent[last]: grand_grand_parent[direction2] = RBTree.jsw_single(grand_parent, 1 - last) else: grand_grand_parent[direction2] = RBTree.jsw_double(grand_parent, 1 - last) # Stop if found if self._cmp(self._cmp_data, key, node.key) == 0: node.value = value # set new value for key break last = direction direction = 0 if (self._cmp(self._cmp_data, key, node.key) < 0) else 1 # Update helpers if grand_parent is not None: grand_grand_parent = grand_parent grand_parent = parent parent = node node = node[direction] self._root = head.right # Update root self._root.red = False
python
def insert(self, key, value): """T.insert(key, value) <==> T[key] = value, insert key, value into tree.""" if self._root is None: # Empty tree case self._root = self._new_node(key, value) self._root.red = False # make root black return head = Node() # False tree root grand_parent = None grand_grand_parent = head parent = None # parent direction = 0 last = 0 # Set up helpers grand_grand_parent.right = self._root node = grand_grand_parent.right # Search down the tree while True: if node is None: # Insert new node at the bottom node = self._new_node(key, value) parent[direction] = node elif RBTree.is_red(node.left) and RBTree.is_red(node.right): # Color flip node.red = True node.left.red = False node.right.red = False # Fix red violation if RBTree.is_red(node) and RBTree.is_red(parent): direction2 = 1 if grand_grand_parent.right is grand_parent else 0 if node is parent[last]: grand_grand_parent[direction2] = RBTree.jsw_single(grand_parent, 1 - last) else: grand_grand_parent[direction2] = RBTree.jsw_double(grand_parent, 1 - last) # Stop if found if self._cmp(self._cmp_data, key, node.key) == 0: node.value = value # set new value for key break last = direction direction = 0 if (self._cmp(self._cmp_data, key, node.key) < 0) else 1 # Update helpers if grand_parent is not None: grand_grand_parent = grand_parent grand_parent = parent parent = node node = node[direction] self._root = head.right # Update root self._root.red = False
[ "def", "insert", "(", "self", ",", "key", ",", "value", ")", ":", "if", "self", ".", "_root", "is", "None", ":", "# Empty tree case", "self", ".", "_root", "=", "self", ".", "_new_node", "(", "key", ",", "value", ")", "self", ".", "_root", ".", "red", "=", "False", "# make root black", "return", "head", "=", "Node", "(", ")", "# False tree root", "grand_parent", "=", "None", "grand_grand_parent", "=", "head", "parent", "=", "None", "# parent", "direction", "=", "0", "last", "=", "0", "# Set up helpers", "grand_grand_parent", ".", "right", "=", "self", ".", "_root", "node", "=", "grand_grand_parent", ".", "right", "# Search down the tree", "while", "True", ":", "if", "node", "is", "None", ":", "# Insert new node at the bottom", "node", "=", "self", ".", "_new_node", "(", "key", ",", "value", ")", "parent", "[", "direction", "]", "=", "node", "elif", "RBTree", ".", "is_red", "(", "node", ".", "left", ")", "and", "RBTree", ".", "is_red", "(", "node", ".", "right", ")", ":", "# Color flip", "node", ".", "red", "=", "True", "node", ".", "left", ".", "red", "=", "False", "node", ".", "right", ".", "red", "=", "False", "# Fix red violation", "if", "RBTree", ".", "is_red", "(", "node", ")", "and", "RBTree", ".", "is_red", "(", "parent", ")", ":", "direction2", "=", "1", "if", "grand_grand_parent", ".", "right", "is", "grand_parent", "else", "0", "if", "node", "is", "parent", "[", "last", "]", ":", "grand_grand_parent", "[", "direction2", "]", "=", "RBTree", ".", "jsw_single", "(", "grand_parent", ",", "1", "-", "last", ")", "else", ":", "grand_grand_parent", "[", "direction2", "]", "=", "RBTree", ".", "jsw_double", "(", "grand_parent", ",", "1", "-", "last", ")", "# Stop if found", "if", "self", ".", "_cmp", "(", "self", ".", "_cmp_data", ",", "key", ",", "node", ".", "key", ")", "==", "0", ":", "node", ".", "value", "=", "value", "# set new value for key", "break", "last", "=", "direction", "direction", "=", "0", "if", "(", "self", ".", "_cmp", "(", "self", ".", "_cmp_data", ",", "key", ",", "node", ".", "key", ")", "<", "0", ")", "else", "1", "# Update helpers", "if", "grand_parent", "is", "not", "None", ":", "grand_grand_parent", "=", "grand_parent", "grand_parent", "=", "parent", "parent", "=", "node", "node", "=", "node", "[", "direction", "]", "self", ".", "_root", "=", "head", ".", "right", "# Update root", "self", ".", "_root", ".", "red", "=", "False" ]
T.insert(key, value) <==> T[key] = value, insert key, value into tree.
[ "T", ".", "insert", "(", "key", "value", ")", "<", "==", ">", "T", "[", "key", "]", "=", "value", "insert", "key", "value", "into", "tree", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/external/poly_point_isect_py2py3.py#L1166-L1216
valid
aleju/imgaug
imgaug/external/poly_point_isect_py2py3.py
RBTree.remove
def remove(self, key): """T.remove(key) <==> del T[key], remove item <key> from tree.""" if self._root is None: raise KeyError(str(key)) head = Node() # False tree root node = head node.right = self._root parent = None grand_parent = None found = None # Found item direction = 1 # Search and push a red down while node[direction] is not None: last = direction # Update helpers grand_parent = parent parent = node node = node[direction] direction = 1 if (self._cmp(self._cmp_data, node.key, key) < 0) else 0 # Save found node if self._cmp(self._cmp_data, key, node.key) == 0: found = node # Push the red node down if not RBTree.is_red(node) and not RBTree.is_red(node[direction]): if RBTree.is_red(node[1 - direction]): parent[last] = RBTree.jsw_single(node, direction) parent = parent[last] elif not RBTree.is_red(node[1 - direction]): sibling = parent[1 - last] if sibling is not None: if (not RBTree.is_red(sibling[1 - last])) and (not RBTree.is_red(sibling[last])): # Color flip parent.red = False sibling.red = True node.red = True else: direction2 = 1 if grand_parent.right is parent else 0 if RBTree.is_red(sibling[last]): grand_parent[direction2] = RBTree.jsw_double(parent, last) elif RBTree.is_red(sibling[1-last]): grand_parent[direction2] = RBTree.jsw_single(parent, last) # Ensure correct coloring grand_parent[direction2].red = True node.red = True grand_parent[direction2].left.red = False grand_parent[direction2].right.red = False # Replace and remove if found if found is not None: found.key = node.key found.value = node.value parent[int(parent.right is node)] = node[int(node.left is None)] node.free() self._count -= 1 # Update root and make it black self._root = head.right if self._root is not None: self._root.red = False if not found: raise KeyError(str(key))
python
def remove(self, key): """T.remove(key) <==> del T[key], remove item <key> from tree.""" if self._root is None: raise KeyError(str(key)) head = Node() # False tree root node = head node.right = self._root parent = None grand_parent = None found = None # Found item direction = 1 # Search and push a red down while node[direction] is not None: last = direction # Update helpers grand_parent = parent parent = node node = node[direction] direction = 1 if (self._cmp(self._cmp_data, node.key, key) < 0) else 0 # Save found node if self._cmp(self._cmp_data, key, node.key) == 0: found = node # Push the red node down if not RBTree.is_red(node) and not RBTree.is_red(node[direction]): if RBTree.is_red(node[1 - direction]): parent[last] = RBTree.jsw_single(node, direction) parent = parent[last] elif not RBTree.is_red(node[1 - direction]): sibling = parent[1 - last] if sibling is not None: if (not RBTree.is_red(sibling[1 - last])) and (not RBTree.is_red(sibling[last])): # Color flip parent.red = False sibling.red = True node.red = True else: direction2 = 1 if grand_parent.right is parent else 0 if RBTree.is_red(sibling[last]): grand_parent[direction2] = RBTree.jsw_double(parent, last) elif RBTree.is_red(sibling[1-last]): grand_parent[direction2] = RBTree.jsw_single(parent, last) # Ensure correct coloring grand_parent[direction2].red = True node.red = True grand_parent[direction2].left.red = False grand_parent[direction2].right.red = False # Replace and remove if found if found is not None: found.key = node.key found.value = node.value parent[int(parent.right is node)] = node[int(node.left is None)] node.free() self._count -= 1 # Update root and make it black self._root = head.right if self._root is not None: self._root.red = False if not found: raise KeyError(str(key))
[ "def", "remove", "(", "self", ",", "key", ")", ":", "if", "self", ".", "_root", "is", "None", ":", "raise", "KeyError", "(", "str", "(", "key", ")", ")", "head", "=", "Node", "(", ")", "# False tree root", "node", "=", "head", "node", ".", "right", "=", "self", ".", "_root", "parent", "=", "None", "grand_parent", "=", "None", "found", "=", "None", "# Found item", "direction", "=", "1", "# Search and push a red down", "while", "node", "[", "direction", "]", "is", "not", "None", ":", "last", "=", "direction", "# Update helpers", "grand_parent", "=", "parent", "parent", "=", "node", "node", "=", "node", "[", "direction", "]", "direction", "=", "1", "if", "(", "self", ".", "_cmp", "(", "self", ".", "_cmp_data", ",", "node", ".", "key", ",", "key", ")", "<", "0", ")", "else", "0", "# Save found node", "if", "self", ".", "_cmp", "(", "self", ".", "_cmp_data", ",", "key", ",", "node", ".", "key", ")", "==", "0", ":", "found", "=", "node", "# Push the red node down", "if", "not", "RBTree", ".", "is_red", "(", "node", ")", "and", "not", "RBTree", ".", "is_red", "(", "node", "[", "direction", "]", ")", ":", "if", "RBTree", ".", "is_red", "(", "node", "[", "1", "-", "direction", "]", ")", ":", "parent", "[", "last", "]", "=", "RBTree", ".", "jsw_single", "(", "node", ",", "direction", ")", "parent", "=", "parent", "[", "last", "]", "elif", "not", "RBTree", ".", "is_red", "(", "node", "[", "1", "-", "direction", "]", ")", ":", "sibling", "=", "parent", "[", "1", "-", "last", "]", "if", "sibling", "is", "not", "None", ":", "if", "(", "not", "RBTree", ".", "is_red", "(", "sibling", "[", "1", "-", "last", "]", ")", ")", "and", "(", "not", "RBTree", ".", "is_red", "(", "sibling", "[", "last", "]", ")", ")", ":", "# Color flip", "parent", ".", "red", "=", "False", "sibling", ".", "red", "=", "True", "node", ".", "red", "=", "True", "else", ":", "direction2", "=", "1", "if", "grand_parent", ".", "right", "is", "parent", "else", "0", "if", "RBTree", ".", "is_red", "(", "sibling", "[", "last", "]", ")", ":", "grand_parent", "[", "direction2", "]", "=", "RBTree", ".", "jsw_double", "(", "parent", ",", "last", ")", "elif", "RBTree", ".", "is_red", "(", "sibling", "[", "1", "-", "last", "]", ")", ":", "grand_parent", "[", "direction2", "]", "=", "RBTree", ".", "jsw_single", "(", "parent", ",", "last", ")", "# Ensure correct coloring", "grand_parent", "[", "direction2", "]", ".", "red", "=", "True", "node", ".", "red", "=", "True", "grand_parent", "[", "direction2", "]", ".", "left", ".", "red", "=", "False", "grand_parent", "[", "direction2", "]", ".", "right", ".", "red", "=", "False", "# Replace and remove if found", "if", "found", "is", "not", "None", ":", "found", ".", "key", "=", "node", ".", "key", "found", ".", "value", "=", "node", ".", "value", "parent", "[", "int", "(", "parent", ".", "right", "is", "node", ")", "]", "=", "node", "[", "int", "(", "node", ".", "left", "is", "None", ")", "]", "node", ".", "free", "(", ")", "self", ".", "_count", "-=", "1", "# Update root and make it black", "self", ".", "_root", "=", "head", ".", "right", "if", "self", ".", "_root", "is", "not", "None", ":", "self", ".", "_root", ".", "red", "=", "False", "if", "not", "found", ":", "raise", "KeyError", "(", "str", "(", "key", ")", ")" ]
T.remove(key) <==> del T[key], remove item <key> from tree.
[ "T", ".", "remove", "(", "key", ")", "<", "==", ">", "del", "T", "[", "key", "]", "remove", "item", "<key", ">", "from", "tree", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/external/poly_point_isect_py2py3.py#L1218-L1283
valid
aleju/imgaug
imgaug/external/opensimplex.py
OpenSimplex.noise2d
def noise2d(self, x, y): """ Generate 2D OpenSimplex noise from X,Y coordinates. """ # Place input coordinates onto grid. stretch_offset = (x + y) * STRETCH_CONSTANT_2D xs = x + stretch_offset ys = y + stretch_offset # Floor to get grid coordinates of rhombus (stretched square) super-cell origin. xsb = floor(xs) ysb = floor(ys) # Skew out to get actual coordinates of rhombus origin. We'll need these later. squish_offset = (xsb + ysb) * SQUISH_CONSTANT_2D xb = xsb + squish_offset yb = ysb + squish_offset # Compute grid coordinates relative to rhombus origin. xins = xs - xsb yins = ys - ysb # Sum those together to get a value that determines which region we're in. in_sum = xins + yins # Positions relative to origin point. dx0 = x - xb dy0 = y - yb value = 0 # Contribution (1,0) dx1 = dx0 - 1 - SQUISH_CONSTANT_2D dy1 = dy0 - 0 - SQUISH_CONSTANT_2D attn1 = 2 - dx1 * dx1 - dy1 * dy1 extrapolate = self._extrapolate2d if attn1 > 0: attn1 *= attn1 value += attn1 * attn1 * extrapolate(xsb + 1, ysb + 0, dx1, dy1) # Contribution (0,1) dx2 = dx0 - 0 - SQUISH_CONSTANT_2D dy2 = dy0 - 1 - SQUISH_CONSTANT_2D attn2 = 2 - dx2 * dx2 - dy2 * dy2 if attn2 > 0: attn2 *= attn2 value += attn2 * attn2 * extrapolate(xsb + 0, ysb + 1, dx2, dy2) if in_sum <= 1: # We're inside the triangle (2-Simplex) at (0,0) zins = 1 - in_sum if zins > xins or zins > yins: # (0,0) is one of the closest two triangular vertices if xins > yins: xsv_ext = xsb + 1 ysv_ext = ysb - 1 dx_ext = dx0 - 1 dy_ext = dy0 + 1 else: xsv_ext = xsb - 1 ysv_ext = ysb + 1 dx_ext = dx0 + 1 dy_ext = dy0 - 1 else: # (1,0) and (0,1) are the closest two vertices. xsv_ext = xsb + 1 ysv_ext = ysb + 1 dx_ext = dx0 - 1 - 2 * SQUISH_CONSTANT_2D dy_ext = dy0 - 1 - 2 * SQUISH_CONSTANT_2D else: # We're inside the triangle (2-Simplex) at (1,1) zins = 2 - in_sum if zins < xins or zins < yins: # (0,0) is one of the closest two triangular vertices if xins > yins: xsv_ext = xsb + 2 ysv_ext = ysb + 0 dx_ext = dx0 - 2 - 2 * SQUISH_CONSTANT_2D dy_ext = dy0 + 0 - 2 * SQUISH_CONSTANT_2D else: xsv_ext = xsb + 0 ysv_ext = ysb + 2 dx_ext = dx0 + 0 - 2 * SQUISH_CONSTANT_2D dy_ext = dy0 - 2 - 2 * SQUISH_CONSTANT_2D else: # (1,0) and (0,1) are the closest two vertices. dx_ext = dx0 dy_ext = dy0 xsv_ext = xsb ysv_ext = ysb xsb += 1 ysb += 1 dx0 = dx0 - 1 - 2 * SQUISH_CONSTANT_2D dy0 = dy0 - 1 - 2 * SQUISH_CONSTANT_2D # Contribution (0,0) or (1,1) attn0 = 2 - dx0 * dx0 - dy0 * dy0 if attn0 > 0: attn0 *= attn0 value += attn0 * attn0 * extrapolate(xsb, ysb, dx0, dy0) # Extra Vertex attn_ext = 2 - dx_ext * dx_ext - dy_ext * dy_ext if attn_ext > 0: attn_ext *= attn_ext value += attn_ext * attn_ext * extrapolate(xsv_ext, ysv_ext, dx_ext, dy_ext) return value / NORM_CONSTANT_2D
python
def noise2d(self, x, y): """ Generate 2D OpenSimplex noise from X,Y coordinates. """ # Place input coordinates onto grid. stretch_offset = (x + y) * STRETCH_CONSTANT_2D xs = x + stretch_offset ys = y + stretch_offset # Floor to get grid coordinates of rhombus (stretched square) super-cell origin. xsb = floor(xs) ysb = floor(ys) # Skew out to get actual coordinates of rhombus origin. We'll need these later. squish_offset = (xsb + ysb) * SQUISH_CONSTANT_2D xb = xsb + squish_offset yb = ysb + squish_offset # Compute grid coordinates relative to rhombus origin. xins = xs - xsb yins = ys - ysb # Sum those together to get a value that determines which region we're in. in_sum = xins + yins # Positions relative to origin point. dx0 = x - xb dy0 = y - yb value = 0 # Contribution (1,0) dx1 = dx0 - 1 - SQUISH_CONSTANT_2D dy1 = dy0 - 0 - SQUISH_CONSTANT_2D attn1 = 2 - dx1 * dx1 - dy1 * dy1 extrapolate = self._extrapolate2d if attn1 > 0: attn1 *= attn1 value += attn1 * attn1 * extrapolate(xsb + 1, ysb + 0, dx1, dy1) # Contribution (0,1) dx2 = dx0 - 0 - SQUISH_CONSTANT_2D dy2 = dy0 - 1 - SQUISH_CONSTANT_2D attn2 = 2 - dx2 * dx2 - dy2 * dy2 if attn2 > 0: attn2 *= attn2 value += attn2 * attn2 * extrapolate(xsb + 0, ysb + 1, dx2, dy2) if in_sum <= 1: # We're inside the triangle (2-Simplex) at (0,0) zins = 1 - in_sum if zins > xins or zins > yins: # (0,0) is one of the closest two triangular vertices if xins > yins: xsv_ext = xsb + 1 ysv_ext = ysb - 1 dx_ext = dx0 - 1 dy_ext = dy0 + 1 else: xsv_ext = xsb - 1 ysv_ext = ysb + 1 dx_ext = dx0 + 1 dy_ext = dy0 - 1 else: # (1,0) and (0,1) are the closest two vertices. xsv_ext = xsb + 1 ysv_ext = ysb + 1 dx_ext = dx0 - 1 - 2 * SQUISH_CONSTANT_2D dy_ext = dy0 - 1 - 2 * SQUISH_CONSTANT_2D else: # We're inside the triangle (2-Simplex) at (1,1) zins = 2 - in_sum if zins < xins or zins < yins: # (0,0) is one of the closest two triangular vertices if xins > yins: xsv_ext = xsb + 2 ysv_ext = ysb + 0 dx_ext = dx0 - 2 - 2 * SQUISH_CONSTANT_2D dy_ext = dy0 + 0 - 2 * SQUISH_CONSTANT_2D else: xsv_ext = xsb + 0 ysv_ext = ysb + 2 dx_ext = dx0 + 0 - 2 * SQUISH_CONSTANT_2D dy_ext = dy0 - 2 - 2 * SQUISH_CONSTANT_2D else: # (1,0) and (0,1) are the closest two vertices. dx_ext = dx0 dy_ext = dy0 xsv_ext = xsb ysv_ext = ysb xsb += 1 ysb += 1 dx0 = dx0 - 1 - 2 * SQUISH_CONSTANT_2D dy0 = dy0 - 1 - 2 * SQUISH_CONSTANT_2D # Contribution (0,0) or (1,1) attn0 = 2 - dx0 * dx0 - dy0 * dy0 if attn0 > 0: attn0 *= attn0 value += attn0 * attn0 * extrapolate(xsb, ysb, dx0, dy0) # Extra Vertex attn_ext = 2 - dx_ext * dx_ext - dy_ext * dy_ext if attn_ext > 0: attn_ext *= attn_ext value += attn_ext * attn_ext * extrapolate(xsv_ext, ysv_ext, dx_ext, dy_ext) return value / NORM_CONSTANT_2D
[ "def", "noise2d", "(", "self", ",", "x", ",", "y", ")", ":", "# Place input coordinates onto grid.", "stretch_offset", "=", "(", "x", "+", "y", ")", "*", "STRETCH_CONSTANT_2D", "xs", "=", "x", "+", "stretch_offset", "ys", "=", "y", "+", "stretch_offset", "# Floor to get grid coordinates of rhombus (stretched square) super-cell origin.", "xsb", "=", "floor", "(", "xs", ")", "ysb", "=", "floor", "(", "ys", ")", "# Skew out to get actual coordinates of rhombus origin. We'll need these later.", "squish_offset", "=", "(", "xsb", "+", "ysb", ")", "*", "SQUISH_CONSTANT_2D", "xb", "=", "xsb", "+", "squish_offset", "yb", "=", "ysb", "+", "squish_offset", "# Compute grid coordinates relative to rhombus origin.", "xins", "=", "xs", "-", "xsb", "yins", "=", "ys", "-", "ysb", "# Sum those together to get a value that determines which region we're in.", "in_sum", "=", "xins", "+", "yins", "# Positions relative to origin point.", "dx0", "=", "x", "-", "xb", "dy0", "=", "y", "-", "yb", "value", "=", "0", "# Contribution (1,0)", "dx1", "=", "dx0", "-", "1", "-", "SQUISH_CONSTANT_2D", "dy1", "=", "dy0", "-", "0", "-", "SQUISH_CONSTANT_2D", "attn1", "=", "2", "-", "dx1", "*", "dx1", "-", "dy1", "*", "dy1", "extrapolate", "=", "self", ".", "_extrapolate2d", "if", "attn1", ">", "0", ":", "attn1", "*=", "attn1", "value", "+=", "attn1", "*", "attn1", "*", "extrapolate", "(", "xsb", "+", "1", ",", "ysb", "+", "0", ",", "dx1", ",", "dy1", ")", "# Contribution (0,1)", "dx2", "=", "dx0", "-", "0", "-", "SQUISH_CONSTANT_2D", "dy2", "=", "dy0", "-", "1", "-", "SQUISH_CONSTANT_2D", "attn2", "=", "2", "-", "dx2", "*", "dx2", "-", "dy2", "*", "dy2", "if", "attn2", ">", "0", ":", "attn2", "*=", "attn2", "value", "+=", "attn2", "*", "attn2", "*", "extrapolate", "(", "xsb", "+", "0", ",", "ysb", "+", "1", ",", "dx2", ",", "dy2", ")", "if", "in_sum", "<=", "1", ":", "# We're inside the triangle (2-Simplex) at (0,0)", "zins", "=", "1", "-", "in_sum", "if", "zins", ">", "xins", "or", "zins", ">", "yins", ":", "# (0,0) is one of the closest two triangular vertices", "if", "xins", ">", "yins", ":", "xsv_ext", "=", "xsb", "+", "1", "ysv_ext", "=", "ysb", "-", "1", "dx_ext", "=", "dx0", "-", "1", "dy_ext", "=", "dy0", "+", "1", "else", ":", "xsv_ext", "=", "xsb", "-", "1", "ysv_ext", "=", "ysb", "+", "1", "dx_ext", "=", "dx0", "+", "1", "dy_ext", "=", "dy0", "-", "1", "else", ":", "# (1,0) and (0,1) are the closest two vertices.", "xsv_ext", "=", "xsb", "+", "1", "ysv_ext", "=", "ysb", "+", "1", "dx_ext", "=", "dx0", "-", "1", "-", "2", "*", "SQUISH_CONSTANT_2D", "dy_ext", "=", "dy0", "-", "1", "-", "2", "*", "SQUISH_CONSTANT_2D", "else", ":", "# We're inside the triangle (2-Simplex) at (1,1)", "zins", "=", "2", "-", "in_sum", "if", "zins", "<", "xins", "or", "zins", "<", "yins", ":", "# (0,0) is one of the closest two triangular vertices", "if", "xins", ">", "yins", ":", "xsv_ext", "=", "xsb", "+", "2", "ysv_ext", "=", "ysb", "+", "0", "dx_ext", "=", "dx0", "-", "2", "-", "2", "*", "SQUISH_CONSTANT_2D", "dy_ext", "=", "dy0", "+", "0", "-", "2", "*", "SQUISH_CONSTANT_2D", "else", ":", "xsv_ext", "=", "xsb", "+", "0", "ysv_ext", "=", "ysb", "+", "2", "dx_ext", "=", "dx0", "+", "0", "-", "2", "*", "SQUISH_CONSTANT_2D", "dy_ext", "=", "dy0", "-", "2", "-", "2", "*", "SQUISH_CONSTANT_2D", "else", ":", "# (1,0) and (0,1) are the closest two vertices.", "dx_ext", "=", "dx0", "dy_ext", "=", "dy0", "xsv_ext", "=", "xsb", "ysv_ext", "=", "ysb", "xsb", "+=", "1", "ysb", "+=", "1", "dx0", "=", "dx0", "-", "1", "-", "2", "*", "SQUISH_CONSTANT_2D", "dy0", "=", "dy0", "-", "1", "-", "2", "*", "SQUISH_CONSTANT_2D", "# Contribution (0,0) or (1,1)", "attn0", "=", "2", "-", "dx0", "*", "dx0", "-", "dy0", "*", "dy0", "if", "attn0", ">", "0", ":", "attn0", "*=", "attn0", "value", "+=", "attn0", "*", "attn0", "*", "extrapolate", "(", "xsb", ",", "ysb", ",", "dx0", ",", "dy0", ")", "# Extra Vertex", "attn_ext", "=", "2", "-", "dx_ext", "*", "dx_ext", "-", "dy_ext", "*", "dy_ext", "if", "attn_ext", ">", "0", ":", "attn_ext", "*=", "attn_ext", "value", "+=", "attn_ext", "*", "attn_ext", "*", "extrapolate", "(", "xsv_ext", ",", "ysv_ext", ",", "dx_ext", ",", "dy_ext", ")", "return", "value", "/", "NORM_CONSTANT_2D" ]
Generate 2D OpenSimplex noise from X,Y coordinates.
[ "Generate", "2D", "OpenSimplex", "noise", "from", "X", "Y", "coordinates", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/external/opensimplex.py#L143-L244
valid
aleju/imgaug
imgaug/external/opensimplex.py
OpenSimplex.noise3d
def noise3d(self, x, y, z): """ Generate 3D OpenSimplex noise from X,Y,Z coordinates. """ # Place input coordinates on simplectic honeycomb. stretch_offset = (x + y + z) * STRETCH_CONSTANT_3D xs = x + stretch_offset ys = y + stretch_offset zs = z + stretch_offset # Floor to get simplectic honeycomb coordinates of rhombohedron (stretched cube) super-cell origin. xsb = floor(xs) ysb = floor(ys) zsb = floor(zs) # Skew out to get actual coordinates of rhombohedron origin. We'll need these later. squish_offset = (xsb + ysb + zsb) * SQUISH_CONSTANT_3D xb = xsb + squish_offset yb = ysb + squish_offset zb = zsb + squish_offset # Compute simplectic honeycomb coordinates relative to rhombohedral origin. xins = xs - xsb yins = ys - ysb zins = zs - zsb # Sum those together to get a value that determines which region we're in. in_sum = xins + yins + zins # Positions relative to origin point. dx0 = x - xb dy0 = y - yb dz0 = z - zb value = 0 extrapolate = self._extrapolate3d if in_sum <= 1: # We're inside the tetrahedron (3-Simplex) at (0,0,0) # Determine which two of (0,0,1), (0,1,0), (1,0,0) are closest. a_point = 0x01 a_score = xins b_point = 0x02 b_score = yins if a_score >= b_score and zins > b_score: b_score = zins b_point = 0x04 elif a_score < b_score and zins > a_score: a_score = zins a_point = 0x04 # Now we determine the two lattice points not part of the tetrahedron that may contribute. # This depends on the closest two tetrahedral vertices, including (0,0,0) wins = 1 - in_sum if wins > a_score or wins > b_score: # (0,0,0) is one of the closest two tetrahedral vertices. c = b_point if (b_score > a_score) else a_point # Our other closest vertex is the closest out of a and b. if (c & 0x01) == 0: xsv_ext0 = xsb - 1 xsv_ext1 = xsb dx_ext0 = dx0 + 1 dx_ext1 = dx0 else: xsv_ext0 = xsv_ext1 = xsb + 1 dx_ext0 = dx_ext1 = dx0 - 1 if (c & 0x02) == 0: ysv_ext0 = ysv_ext1 = ysb dy_ext0 = dy_ext1 = dy0 if (c & 0x01) == 0: ysv_ext1 -= 1 dy_ext1 += 1 else: ysv_ext0 -= 1 dy_ext0 += 1 else: ysv_ext0 = ysv_ext1 = ysb + 1 dy_ext0 = dy_ext1 = dy0 - 1 if (c & 0x04) == 0: zsv_ext0 = zsb zsv_ext1 = zsb - 1 dz_ext0 = dz0 dz_ext1 = dz0 + 1 else: zsv_ext0 = zsv_ext1 = zsb + 1 dz_ext0 = dz_ext1 = dz0 - 1 else: # (0,0,0) is not one of the closest two tetrahedral vertices. c = (a_point | b_point) # Our two extra vertices are determined by the closest two. if (c & 0x01) == 0: xsv_ext0 = xsb xsv_ext1 = xsb - 1 dx_ext0 = dx0 - 2 * SQUISH_CONSTANT_3D dx_ext1 = dx0 + 1 - SQUISH_CONSTANT_3D else: xsv_ext0 = xsv_ext1 = xsb + 1 dx_ext0 = dx0 - 1 - 2 * SQUISH_CONSTANT_3D dx_ext1 = dx0 - 1 - SQUISH_CONSTANT_3D if (c & 0x02) == 0: ysv_ext0 = ysb ysv_ext1 = ysb - 1 dy_ext0 = dy0 - 2 * SQUISH_CONSTANT_3D dy_ext1 = dy0 + 1 - SQUISH_CONSTANT_3D else: ysv_ext0 = ysv_ext1 = ysb + 1 dy_ext0 = dy0 - 1 - 2 * SQUISH_CONSTANT_3D dy_ext1 = dy0 - 1 - SQUISH_CONSTANT_3D if (c & 0x04) == 0: zsv_ext0 = zsb zsv_ext1 = zsb - 1 dz_ext0 = dz0 - 2 * SQUISH_CONSTANT_3D dz_ext1 = dz0 + 1 - SQUISH_CONSTANT_3D else: zsv_ext0 = zsv_ext1 = zsb + 1 dz_ext0 = dz0 - 1 - 2 * SQUISH_CONSTANT_3D dz_ext1 = dz0 - 1 - SQUISH_CONSTANT_3D # Contribution (0,0,0) attn0 = 2 - dx0 * dx0 - dy0 * dy0 - dz0 * dz0 if attn0 > 0: attn0 *= attn0 value += attn0 * attn0 * extrapolate(xsb + 0, ysb + 0, zsb + 0, dx0, dy0, dz0) # Contribution (1,0,0) dx1 = dx0 - 1 - SQUISH_CONSTANT_3D dy1 = dy0 - 0 - SQUISH_CONSTANT_3D dz1 = dz0 - 0 - SQUISH_CONSTANT_3D attn1 = 2 - dx1 * dx1 - dy1 * dy1 - dz1 * dz1 if attn1 > 0: attn1 *= attn1 value += attn1 * attn1 * extrapolate(xsb + 1, ysb + 0, zsb + 0, dx1, dy1, dz1) # Contribution (0,1,0) dx2 = dx0 - 0 - SQUISH_CONSTANT_3D dy2 = dy0 - 1 - SQUISH_CONSTANT_3D dz2 = dz1 attn2 = 2 - dx2 * dx2 - dy2 * dy2 - dz2 * dz2 if attn2 > 0: attn2 *= attn2 value += attn2 * attn2 * extrapolate(xsb + 0, ysb + 1, zsb + 0, dx2, dy2, dz2) # Contribution (0,0,1) dx3 = dx2 dy3 = dy1 dz3 = dz0 - 1 - SQUISH_CONSTANT_3D attn3 = 2 - dx3 * dx3 - dy3 * dy3 - dz3 * dz3 if attn3 > 0: attn3 *= attn3 value += attn3 * attn3 * extrapolate(xsb + 0, ysb + 0, zsb + 1, dx3, dy3, dz3) elif in_sum >= 2: # We're inside the tetrahedron (3-Simplex) at (1,1,1) # Determine which two tetrahedral vertices are the closest, out of (1,1,0), (1,0,1), (0,1,1) but not (1,1,1). a_point = 0x06 a_score = xins b_point = 0x05 b_score = yins if a_score <= b_score and zins < b_score: b_score = zins b_point = 0x03 elif a_score > b_score and zins < a_score: a_score = zins a_point = 0x03 # Now we determine the two lattice points not part of the tetrahedron that may contribute. # This depends on the closest two tetrahedral vertices, including (1,1,1) wins = 3 - in_sum if wins < a_score or wins < b_score: # (1,1,1) is one of the closest two tetrahedral vertices. c = b_point if (b_score < a_score) else a_point # Our other closest vertex is the closest out of a and b. if (c & 0x01) != 0: xsv_ext0 = xsb + 2 xsv_ext1 = xsb + 1 dx_ext0 = dx0 - 2 - 3 * SQUISH_CONSTANT_3D dx_ext1 = dx0 - 1 - 3 * SQUISH_CONSTANT_3D else: xsv_ext0 = xsv_ext1 = xsb dx_ext0 = dx_ext1 = dx0 - 3 * SQUISH_CONSTANT_3D if (c & 0x02) != 0: ysv_ext0 = ysv_ext1 = ysb + 1 dy_ext0 = dy_ext1 = dy0 - 1 - 3 * SQUISH_CONSTANT_3D if (c & 0x01) != 0: ysv_ext1 += 1 dy_ext1 -= 1 else: ysv_ext0 += 1 dy_ext0 -= 1 else: ysv_ext0 = ysv_ext1 = ysb dy_ext0 = dy_ext1 = dy0 - 3 * SQUISH_CONSTANT_3D if (c & 0x04) != 0: zsv_ext0 = zsb + 1 zsv_ext1 = zsb + 2 dz_ext0 = dz0 - 1 - 3 * SQUISH_CONSTANT_3D dz_ext1 = dz0 - 2 - 3 * SQUISH_CONSTANT_3D else: zsv_ext0 = zsv_ext1 = zsb dz_ext0 = dz_ext1 = dz0 - 3 * SQUISH_CONSTANT_3D else: # (1,1,1) is not one of the closest two tetrahedral vertices. c = (a_point & b_point) # Our two extra vertices are determined by the closest two. if (c & 0x01) != 0: xsv_ext0 = xsb + 1 xsv_ext1 = xsb + 2 dx_ext0 = dx0 - 1 - SQUISH_CONSTANT_3D dx_ext1 = dx0 - 2 - 2 * SQUISH_CONSTANT_3D else: xsv_ext0 = xsv_ext1 = xsb dx_ext0 = dx0 - SQUISH_CONSTANT_3D dx_ext1 = dx0 - 2 * SQUISH_CONSTANT_3D if (c & 0x02) != 0: ysv_ext0 = ysb + 1 ysv_ext1 = ysb + 2 dy_ext0 = dy0 - 1 - SQUISH_CONSTANT_3D dy_ext1 = dy0 - 2 - 2 * SQUISH_CONSTANT_3D else: ysv_ext0 = ysv_ext1 = ysb dy_ext0 = dy0 - SQUISH_CONSTANT_3D dy_ext1 = dy0 - 2 * SQUISH_CONSTANT_3D if (c & 0x04) != 0: zsv_ext0 = zsb + 1 zsv_ext1 = zsb + 2 dz_ext0 = dz0 - 1 - SQUISH_CONSTANT_3D dz_ext1 = dz0 - 2 - 2 * SQUISH_CONSTANT_3D else: zsv_ext0 = zsv_ext1 = zsb dz_ext0 = dz0 - SQUISH_CONSTANT_3D dz_ext1 = dz0 - 2 * SQUISH_CONSTANT_3D # Contribution (1,1,0) dx3 = dx0 - 1 - 2 * SQUISH_CONSTANT_3D dy3 = dy0 - 1 - 2 * SQUISH_CONSTANT_3D dz3 = dz0 - 0 - 2 * SQUISH_CONSTANT_3D attn3 = 2 - dx3 * dx3 - dy3 * dy3 - dz3 * dz3 if attn3 > 0: attn3 *= attn3 value += attn3 * attn3 * extrapolate(xsb + 1, ysb + 1, zsb + 0, dx3, dy3, dz3) # Contribution (1,0,1) dx2 = dx3 dy2 = dy0 - 0 - 2 * SQUISH_CONSTANT_3D dz2 = dz0 - 1 - 2 * SQUISH_CONSTANT_3D attn2 = 2 - dx2 * dx2 - dy2 * dy2 - dz2 * dz2 if attn2 > 0: attn2 *= attn2 value += attn2 * attn2 * extrapolate(xsb + 1, ysb + 0, zsb + 1, dx2, dy2, dz2) # Contribution (0,1,1) dx1 = dx0 - 0 - 2 * SQUISH_CONSTANT_3D dy1 = dy3 dz1 = dz2 attn1 = 2 - dx1 * dx1 - dy1 * dy1 - dz1 * dz1 if attn1 > 0: attn1 *= attn1 value += attn1 * attn1 * extrapolate(xsb + 0, ysb + 1, zsb + 1, dx1, dy1, dz1) # Contribution (1,1,1) dx0 = dx0 - 1 - 3 * SQUISH_CONSTANT_3D dy0 = dy0 - 1 - 3 * SQUISH_CONSTANT_3D dz0 = dz0 - 1 - 3 * SQUISH_CONSTANT_3D attn0 = 2 - dx0 * dx0 - dy0 * dy0 - dz0 * dz0 if attn0 > 0: attn0 *= attn0 value += attn0 * attn0 * extrapolate(xsb + 1, ysb + 1, zsb + 1, dx0, dy0, dz0) else: # We're inside the octahedron (Rectified 3-Simplex) in between. # Decide between point (0,0,1) and (1,1,0) as closest p1 = xins + yins if p1 > 1: a_score = p1 - 1 a_point = 0x03 a_is_further_side = True else: a_score = 1 - p1 a_point = 0x04 a_is_further_side = False # Decide between point (0,1,0) and (1,0,1) as closest p2 = xins + zins if p2 > 1: b_score = p2 - 1 b_point = 0x05 b_is_further_side = True else: b_score = 1 - p2 b_point = 0x02 b_is_further_side = False # The closest out of the two (1,0,0) and (0,1,1) will replace the furthest out of the two decided above, if closer. p3 = yins + zins if p3 > 1: score = p3 - 1 if a_score <= b_score and a_score < score: a_point = 0x06 a_is_further_side = True elif a_score > b_score and b_score < score: b_point = 0x06 b_is_further_side = True else: score = 1 - p3 if a_score <= b_score and a_score < score: a_point = 0x01 a_is_further_side = False elif a_score > b_score and b_score < score: b_point = 0x01 b_is_further_side = False # Where each of the two closest points are determines how the extra two vertices are calculated. if a_is_further_side == b_is_further_side: if a_is_further_side: # Both closest points on (1,1,1) side # One of the two extra points is (1,1,1) dx_ext0 = dx0 - 1 - 3 * SQUISH_CONSTANT_3D dy_ext0 = dy0 - 1 - 3 * SQUISH_CONSTANT_3D dz_ext0 = dz0 - 1 - 3 * SQUISH_CONSTANT_3D xsv_ext0 = xsb + 1 ysv_ext0 = ysb + 1 zsv_ext0 = zsb + 1 # Other extra point is based on the shared axis. c = (a_point & b_point) if (c & 0x01) != 0: dx_ext1 = dx0 - 2 - 2 * SQUISH_CONSTANT_3D dy_ext1 = dy0 - 2 * SQUISH_CONSTANT_3D dz_ext1 = dz0 - 2 * SQUISH_CONSTANT_3D xsv_ext1 = xsb + 2 ysv_ext1 = ysb zsv_ext1 = zsb elif (c & 0x02) != 0: dx_ext1 = dx0 - 2 * SQUISH_CONSTANT_3D dy_ext1 = dy0 - 2 - 2 * SQUISH_CONSTANT_3D dz_ext1 = dz0 - 2 * SQUISH_CONSTANT_3D xsv_ext1 = xsb ysv_ext1 = ysb + 2 zsv_ext1 = zsb else: dx_ext1 = dx0 - 2 * SQUISH_CONSTANT_3D dy_ext1 = dy0 - 2 * SQUISH_CONSTANT_3D dz_ext1 = dz0 - 2 - 2 * SQUISH_CONSTANT_3D xsv_ext1 = xsb ysv_ext1 = ysb zsv_ext1 = zsb + 2 else:# Both closest points on (0,0,0) side # One of the two extra points is (0,0,0) dx_ext0 = dx0 dy_ext0 = dy0 dz_ext0 = dz0 xsv_ext0 = xsb ysv_ext0 = ysb zsv_ext0 = zsb # Other extra point is based on the omitted axis. c = (a_point | b_point) if (c & 0x01) == 0: dx_ext1 = dx0 + 1 - SQUISH_CONSTANT_3D dy_ext1 = dy0 - 1 - SQUISH_CONSTANT_3D dz_ext1 = dz0 - 1 - SQUISH_CONSTANT_3D xsv_ext1 = xsb - 1 ysv_ext1 = ysb + 1 zsv_ext1 = zsb + 1 elif (c & 0x02) == 0: dx_ext1 = dx0 - 1 - SQUISH_CONSTANT_3D dy_ext1 = dy0 + 1 - SQUISH_CONSTANT_3D dz_ext1 = dz0 - 1 - SQUISH_CONSTANT_3D xsv_ext1 = xsb + 1 ysv_ext1 = ysb - 1 zsv_ext1 = zsb + 1 else: dx_ext1 = dx0 - 1 - SQUISH_CONSTANT_3D dy_ext1 = dy0 - 1 - SQUISH_CONSTANT_3D dz_ext1 = dz0 + 1 - SQUISH_CONSTANT_3D xsv_ext1 = xsb + 1 ysv_ext1 = ysb + 1 zsv_ext1 = zsb - 1 else: # One point on (0,0,0) side, one point on (1,1,1) side if a_is_further_side: c1 = a_point c2 = b_point else: c1 = b_point c2 = a_point # One contribution is a _permutation of (1,1,-1) if (c1 & 0x01) == 0: dx_ext0 = dx0 + 1 - SQUISH_CONSTANT_3D dy_ext0 = dy0 - 1 - SQUISH_CONSTANT_3D dz_ext0 = dz0 - 1 - SQUISH_CONSTANT_3D xsv_ext0 = xsb - 1 ysv_ext0 = ysb + 1 zsv_ext0 = zsb + 1 elif (c1 & 0x02) == 0: dx_ext0 = dx0 - 1 - SQUISH_CONSTANT_3D dy_ext0 = dy0 + 1 - SQUISH_CONSTANT_3D dz_ext0 = dz0 - 1 - SQUISH_CONSTANT_3D xsv_ext0 = xsb + 1 ysv_ext0 = ysb - 1 zsv_ext0 = zsb + 1 else: dx_ext0 = dx0 - 1 - SQUISH_CONSTANT_3D dy_ext0 = dy0 - 1 - SQUISH_CONSTANT_3D dz_ext0 = dz0 + 1 - SQUISH_CONSTANT_3D xsv_ext0 = xsb + 1 ysv_ext0 = ysb + 1 zsv_ext0 = zsb - 1 # One contribution is a _permutation of (0,0,2) dx_ext1 = dx0 - 2 * SQUISH_CONSTANT_3D dy_ext1 = dy0 - 2 * SQUISH_CONSTANT_3D dz_ext1 = dz0 - 2 * SQUISH_CONSTANT_3D xsv_ext1 = xsb ysv_ext1 = ysb zsv_ext1 = zsb if (c2 & 0x01) != 0: dx_ext1 -= 2 xsv_ext1 += 2 elif (c2 & 0x02) != 0: dy_ext1 -= 2 ysv_ext1 += 2 else: dz_ext1 -= 2 zsv_ext1 += 2 # Contribution (1,0,0) dx1 = dx0 - 1 - SQUISH_CONSTANT_3D dy1 = dy0 - 0 - SQUISH_CONSTANT_3D dz1 = dz0 - 0 - SQUISH_CONSTANT_3D attn1 = 2 - dx1 * dx1 - dy1 * dy1 - dz1 * dz1 if attn1 > 0: attn1 *= attn1 value += attn1 * attn1 * extrapolate(xsb + 1, ysb + 0, zsb + 0, dx1, dy1, dz1) # Contribution (0,1,0) dx2 = dx0 - 0 - SQUISH_CONSTANT_3D dy2 = dy0 - 1 - SQUISH_CONSTANT_3D dz2 = dz1 attn2 = 2 - dx2 * dx2 - dy2 * dy2 - dz2 * dz2 if attn2 > 0: attn2 *= attn2 value += attn2 * attn2 * extrapolate(xsb + 0, ysb + 1, zsb + 0, dx2, dy2, dz2) # Contribution (0,0,1) dx3 = dx2 dy3 = dy1 dz3 = dz0 - 1 - SQUISH_CONSTANT_3D attn3 = 2 - dx3 * dx3 - dy3 * dy3 - dz3 * dz3 if attn3 > 0: attn3 *= attn3 value += attn3 * attn3 * extrapolate(xsb + 0, ysb + 0, zsb + 1, dx3, dy3, dz3) # Contribution (1,1,0) dx4 = dx0 - 1 - 2 * SQUISH_CONSTANT_3D dy4 = dy0 - 1 - 2 * SQUISH_CONSTANT_3D dz4 = dz0 - 0 - 2 * SQUISH_CONSTANT_3D attn4 = 2 - dx4 * dx4 - dy4 * dy4 - dz4 * dz4 if attn4 > 0: attn4 *= attn4 value += attn4 * attn4 * extrapolate(xsb + 1, ysb + 1, zsb + 0, dx4, dy4, dz4) # Contribution (1,0,1) dx5 = dx4 dy5 = dy0 - 0 - 2 * SQUISH_CONSTANT_3D dz5 = dz0 - 1 - 2 * SQUISH_CONSTANT_3D attn5 = 2 - dx5 * dx5 - dy5 * dy5 - dz5 * dz5 if attn5 > 0: attn5 *= attn5 value += attn5 * attn5 * extrapolate(xsb + 1, ysb + 0, zsb + 1, dx5, dy5, dz5) # Contribution (0,1,1) dx6 = dx0 - 0 - 2 * SQUISH_CONSTANT_3D dy6 = dy4 dz6 = dz5 attn6 = 2 - dx6 * dx6 - dy6 * dy6 - dz6 * dz6 if attn6 > 0: attn6 *= attn6 value += attn6 * attn6 * extrapolate(xsb + 0, ysb + 1, zsb + 1, dx6, dy6, dz6) # First extra vertex attn_ext0 = 2 - dx_ext0 * dx_ext0 - dy_ext0 * dy_ext0 - dz_ext0 * dz_ext0 if attn_ext0 > 0: attn_ext0 *= attn_ext0 value += attn_ext0 * attn_ext0 * extrapolate(xsv_ext0, ysv_ext0, zsv_ext0, dx_ext0, dy_ext0, dz_ext0) # Second extra vertex attn_ext1 = 2 - dx_ext1 * dx_ext1 - dy_ext1 * dy_ext1 - dz_ext1 * dz_ext1 if attn_ext1 > 0: attn_ext1 *= attn_ext1 value += attn_ext1 * attn_ext1 * extrapolate(xsv_ext1, ysv_ext1, zsv_ext1, dx_ext1, dy_ext1, dz_ext1) return value / NORM_CONSTANT_3D
python
def noise3d(self, x, y, z): """ Generate 3D OpenSimplex noise from X,Y,Z coordinates. """ # Place input coordinates on simplectic honeycomb. stretch_offset = (x + y + z) * STRETCH_CONSTANT_3D xs = x + stretch_offset ys = y + stretch_offset zs = z + stretch_offset # Floor to get simplectic honeycomb coordinates of rhombohedron (stretched cube) super-cell origin. xsb = floor(xs) ysb = floor(ys) zsb = floor(zs) # Skew out to get actual coordinates of rhombohedron origin. We'll need these later. squish_offset = (xsb + ysb + zsb) * SQUISH_CONSTANT_3D xb = xsb + squish_offset yb = ysb + squish_offset zb = zsb + squish_offset # Compute simplectic honeycomb coordinates relative to rhombohedral origin. xins = xs - xsb yins = ys - ysb zins = zs - zsb # Sum those together to get a value that determines which region we're in. in_sum = xins + yins + zins # Positions relative to origin point. dx0 = x - xb dy0 = y - yb dz0 = z - zb value = 0 extrapolate = self._extrapolate3d if in_sum <= 1: # We're inside the tetrahedron (3-Simplex) at (0,0,0) # Determine which two of (0,0,1), (0,1,0), (1,0,0) are closest. a_point = 0x01 a_score = xins b_point = 0x02 b_score = yins if a_score >= b_score and zins > b_score: b_score = zins b_point = 0x04 elif a_score < b_score and zins > a_score: a_score = zins a_point = 0x04 # Now we determine the two lattice points not part of the tetrahedron that may contribute. # This depends on the closest two tetrahedral vertices, including (0,0,0) wins = 1 - in_sum if wins > a_score or wins > b_score: # (0,0,0) is one of the closest two tetrahedral vertices. c = b_point if (b_score > a_score) else a_point # Our other closest vertex is the closest out of a and b. if (c & 0x01) == 0: xsv_ext0 = xsb - 1 xsv_ext1 = xsb dx_ext0 = dx0 + 1 dx_ext1 = dx0 else: xsv_ext0 = xsv_ext1 = xsb + 1 dx_ext0 = dx_ext1 = dx0 - 1 if (c & 0x02) == 0: ysv_ext0 = ysv_ext1 = ysb dy_ext0 = dy_ext1 = dy0 if (c & 0x01) == 0: ysv_ext1 -= 1 dy_ext1 += 1 else: ysv_ext0 -= 1 dy_ext0 += 1 else: ysv_ext0 = ysv_ext1 = ysb + 1 dy_ext0 = dy_ext1 = dy0 - 1 if (c & 0x04) == 0: zsv_ext0 = zsb zsv_ext1 = zsb - 1 dz_ext0 = dz0 dz_ext1 = dz0 + 1 else: zsv_ext0 = zsv_ext1 = zsb + 1 dz_ext0 = dz_ext1 = dz0 - 1 else: # (0,0,0) is not one of the closest two tetrahedral vertices. c = (a_point | b_point) # Our two extra vertices are determined by the closest two. if (c & 0x01) == 0: xsv_ext0 = xsb xsv_ext1 = xsb - 1 dx_ext0 = dx0 - 2 * SQUISH_CONSTANT_3D dx_ext1 = dx0 + 1 - SQUISH_CONSTANT_3D else: xsv_ext0 = xsv_ext1 = xsb + 1 dx_ext0 = dx0 - 1 - 2 * SQUISH_CONSTANT_3D dx_ext1 = dx0 - 1 - SQUISH_CONSTANT_3D if (c & 0x02) == 0: ysv_ext0 = ysb ysv_ext1 = ysb - 1 dy_ext0 = dy0 - 2 * SQUISH_CONSTANT_3D dy_ext1 = dy0 + 1 - SQUISH_CONSTANT_3D else: ysv_ext0 = ysv_ext1 = ysb + 1 dy_ext0 = dy0 - 1 - 2 * SQUISH_CONSTANT_3D dy_ext1 = dy0 - 1 - SQUISH_CONSTANT_3D if (c & 0x04) == 0: zsv_ext0 = zsb zsv_ext1 = zsb - 1 dz_ext0 = dz0 - 2 * SQUISH_CONSTANT_3D dz_ext1 = dz0 + 1 - SQUISH_CONSTANT_3D else: zsv_ext0 = zsv_ext1 = zsb + 1 dz_ext0 = dz0 - 1 - 2 * SQUISH_CONSTANT_3D dz_ext1 = dz0 - 1 - SQUISH_CONSTANT_3D # Contribution (0,0,0) attn0 = 2 - dx0 * dx0 - dy0 * dy0 - dz0 * dz0 if attn0 > 0: attn0 *= attn0 value += attn0 * attn0 * extrapolate(xsb + 0, ysb + 0, zsb + 0, dx0, dy0, dz0) # Contribution (1,0,0) dx1 = dx0 - 1 - SQUISH_CONSTANT_3D dy1 = dy0 - 0 - SQUISH_CONSTANT_3D dz1 = dz0 - 0 - SQUISH_CONSTANT_3D attn1 = 2 - dx1 * dx1 - dy1 * dy1 - dz1 * dz1 if attn1 > 0: attn1 *= attn1 value += attn1 * attn1 * extrapolate(xsb + 1, ysb + 0, zsb + 0, dx1, dy1, dz1) # Contribution (0,1,0) dx2 = dx0 - 0 - SQUISH_CONSTANT_3D dy2 = dy0 - 1 - SQUISH_CONSTANT_3D dz2 = dz1 attn2 = 2 - dx2 * dx2 - dy2 * dy2 - dz2 * dz2 if attn2 > 0: attn2 *= attn2 value += attn2 * attn2 * extrapolate(xsb + 0, ysb + 1, zsb + 0, dx2, dy2, dz2) # Contribution (0,0,1) dx3 = dx2 dy3 = dy1 dz3 = dz0 - 1 - SQUISH_CONSTANT_3D attn3 = 2 - dx3 * dx3 - dy3 * dy3 - dz3 * dz3 if attn3 > 0: attn3 *= attn3 value += attn3 * attn3 * extrapolate(xsb + 0, ysb + 0, zsb + 1, dx3, dy3, dz3) elif in_sum >= 2: # We're inside the tetrahedron (3-Simplex) at (1,1,1) # Determine which two tetrahedral vertices are the closest, out of (1,1,0), (1,0,1), (0,1,1) but not (1,1,1). a_point = 0x06 a_score = xins b_point = 0x05 b_score = yins if a_score <= b_score and zins < b_score: b_score = zins b_point = 0x03 elif a_score > b_score and zins < a_score: a_score = zins a_point = 0x03 # Now we determine the two lattice points not part of the tetrahedron that may contribute. # This depends on the closest two tetrahedral vertices, including (1,1,1) wins = 3 - in_sum if wins < a_score or wins < b_score: # (1,1,1) is one of the closest two tetrahedral vertices. c = b_point if (b_score < a_score) else a_point # Our other closest vertex is the closest out of a and b. if (c & 0x01) != 0: xsv_ext0 = xsb + 2 xsv_ext1 = xsb + 1 dx_ext0 = dx0 - 2 - 3 * SQUISH_CONSTANT_3D dx_ext1 = dx0 - 1 - 3 * SQUISH_CONSTANT_3D else: xsv_ext0 = xsv_ext1 = xsb dx_ext0 = dx_ext1 = dx0 - 3 * SQUISH_CONSTANT_3D if (c & 0x02) != 0: ysv_ext0 = ysv_ext1 = ysb + 1 dy_ext0 = dy_ext1 = dy0 - 1 - 3 * SQUISH_CONSTANT_3D if (c & 0x01) != 0: ysv_ext1 += 1 dy_ext1 -= 1 else: ysv_ext0 += 1 dy_ext0 -= 1 else: ysv_ext0 = ysv_ext1 = ysb dy_ext0 = dy_ext1 = dy0 - 3 * SQUISH_CONSTANT_3D if (c & 0x04) != 0: zsv_ext0 = zsb + 1 zsv_ext1 = zsb + 2 dz_ext0 = dz0 - 1 - 3 * SQUISH_CONSTANT_3D dz_ext1 = dz0 - 2 - 3 * SQUISH_CONSTANT_3D else: zsv_ext0 = zsv_ext1 = zsb dz_ext0 = dz_ext1 = dz0 - 3 * SQUISH_CONSTANT_3D else: # (1,1,1) is not one of the closest two tetrahedral vertices. c = (a_point & b_point) # Our two extra vertices are determined by the closest two. if (c & 0x01) != 0: xsv_ext0 = xsb + 1 xsv_ext1 = xsb + 2 dx_ext0 = dx0 - 1 - SQUISH_CONSTANT_3D dx_ext1 = dx0 - 2 - 2 * SQUISH_CONSTANT_3D else: xsv_ext0 = xsv_ext1 = xsb dx_ext0 = dx0 - SQUISH_CONSTANT_3D dx_ext1 = dx0 - 2 * SQUISH_CONSTANT_3D if (c & 0x02) != 0: ysv_ext0 = ysb + 1 ysv_ext1 = ysb + 2 dy_ext0 = dy0 - 1 - SQUISH_CONSTANT_3D dy_ext1 = dy0 - 2 - 2 * SQUISH_CONSTANT_3D else: ysv_ext0 = ysv_ext1 = ysb dy_ext0 = dy0 - SQUISH_CONSTANT_3D dy_ext1 = dy0 - 2 * SQUISH_CONSTANT_3D if (c & 0x04) != 0: zsv_ext0 = zsb + 1 zsv_ext1 = zsb + 2 dz_ext0 = dz0 - 1 - SQUISH_CONSTANT_3D dz_ext1 = dz0 - 2 - 2 * SQUISH_CONSTANT_3D else: zsv_ext0 = zsv_ext1 = zsb dz_ext0 = dz0 - SQUISH_CONSTANT_3D dz_ext1 = dz0 - 2 * SQUISH_CONSTANT_3D # Contribution (1,1,0) dx3 = dx0 - 1 - 2 * SQUISH_CONSTANT_3D dy3 = dy0 - 1 - 2 * SQUISH_CONSTANT_3D dz3 = dz0 - 0 - 2 * SQUISH_CONSTANT_3D attn3 = 2 - dx3 * dx3 - dy3 * dy3 - dz3 * dz3 if attn3 > 0: attn3 *= attn3 value += attn3 * attn3 * extrapolate(xsb + 1, ysb + 1, zsb + 0, dx3, dy3, dz3) # Contribution (1,0,1) dx2 = dx3 dy2 = dy0 - 0 - 2 * SQUISH_CONSTANT_3D dz2 = dz0 - 1 - 2 * SQUISH_CONSTANT_3D attn2 = 2 - dx2 * dx2 - dy2 * dy2 - dz2 * dz2 if attn2 > 0: attn2 *= attn2 value += attn2 * attn2 * extrapolate(xsb + 1, ysb + 0, zsb + 1, dx2, dy2, dz2) # Contribution (0,1,1) dx1 = dx0 - 0 - 2 * SQUISH_CONSTANT_3D dy1 = dy3 dz1 = dz2 attn1 = 2 - dx1 * dx1 - dy1 * dy1 - dz1 * dz1 if attn1 > 0: attn1 *= attn1 value += attn1 * attn1 * extrapolate(xsb + 0, ysb + 1, zsb + 1, dx1, dy1, dz1) # Contribution (1,1,1) dx0 = dx0 - 1 - 3 * SQUISH_CONSTANT_3D dy0 = dy0 - 1 - 3 * SQUISH_CONSTANT_3D dz0 = dz0 - 1 - 3 * SQUISH_CONSTANT_3D attn0 = 2 - dx0 * dx0 - dy0 * dy0 - dz0 * dz0 if attn0 > 0: attn0 *= attn0 value += attn0 * attn0 * extrapolate(xsb + 1, ysb + 1, zsb + 1, dx0, dy0, dz0) else: # We're inside the octahedron (Rectified 3-Simplex) in between. # Decide between point (0,0,1) and (1,1,0) as closest p1 = xins + yins if p1 > 1: a_score = p1 - 1 a_point = 0x03 a_is_further_side = True else: a_score = 1 - p1 a_point = 0x04 a_is_further_side = False # Decide between point (0,1,0) and (1,0,1) as closest p2 = xins + zins if p2 > 1: b_score = p2 - 1 b_point = 0x05 b_is_further_side = True else: b_score = 1 - p2 b_point = 0x02 b_is_further_side = False # The closest out of the two (1,0,0) and (0,1,1) will replace the furthest out of the two decided above, if closer. p3 = yins + zins if p3 > 1: score = p3 - 1 if a_score <= b_score and a_score < score: a_point = 0x06 a_is_further_side = True elif a_score > b_score and b_score < score: b_point = 0x06 b_is_further_side = True else: score = 1 - p3 if a_score <= b_score and a_score < score: a_point = 0x01 a_is_further_side = False elif a_score > b_score and b_score < score: b_point = 0x01 b_is_further_side = False # Where each of the two closest points are determines how the extra two vertices are calculated. if a_is_further_side == b_is_further_side: if a_is_further_side: # Both closest points on (1,1,1) side # One of the two extra points is (1,1,1) dx_ext0 = dx0 - 1 - 3 * SQUISH_CONSTANT_3D dy_ext0 = dy0 - 1 - 3 * SQUISH_CONSTANT_3D dz_ext0 = dz0 - 1 - 3 * SQUISH_CONSTANT_3D xsv_ext0 = xsb + 1 ysv_ext0 = ysb + 1 zsv_ext0 = zsb + 1 # Other extra point is based on the shared axis. c = (a_point & b_point) if (c & 0x01) != 0: dx_ext1 = dx0 - 2 - 2 * SQUISH_CONSTANT_3D dy_ext1 = dy0 - 2 * SQUISH_CONSTANT_3D dz_ext1 = dz0 - 2 * SQUISH_CONSTANT_3D xsv_ext1 = xsb + 2 ysv_ext1 = ysb zsv_ext1 = zsb elif (c & 0x02) != 0: dx_ext1 = dx0 - 2 * SQUISH_CONSTANT_3D dy_ext1 = dy0 - 2 - 2 * SQUISH_CONSTANT_3D dz_ext1 = dz0 - 2 * SQUISH_CONSTANT_3D xsv_ext1 = xsb ysv_ext1 = ysb + 2 zsv_ext1 = zsb else: dx_ext1 = dx0 - 2 * SQUISH_CONSTANT_3D dy_ext1 = dy0 - 2 * SQUISH_CONSTANT_3D dz_ext1 = dz0 - 2 - 2 * SQUISH_CONSTANT_3D xsv_ext1 = xsb ysv_ext1 = ysb zsv_ext1 = zsb + 2 else:# Both closest points on (0,0,0) side # One of the two extra points is (0,0,0) dx_ext0 = dx0 dy_ext0 = dy0 dz_ext0 = dz0 xsv_ext0 = xsb ysv_ext0 = ysb zsv_ext0 = zsb # Other extra point is based on the omitted axis. c = (a_point | b_point) if (c & 0x01) == 0: dx_ext1 = dx0 + 1 - SQUISH_CONSTANT_3D dy_ext1 = dy0 - 1 - SQUISH_CONSTANT_3D dz_ext1 = dz0 - 1 - SQUISH_CONSTANT_3D xsv_ext1 = xsb - 1 ysv_ext1 = ysb + 1 zsv_ext1 = zsb + 1 elif (c & 0x02) == 0: dx_ext1 = dx0 - 1 - SQUISH_CONSTANT_3D dy_ext1 = dy0 + 1 - SQUISH_CONSTANT_3D dz_ext1 = dz0 - 1 - SQUISH_CONSTANT_3D xsv_ext1 = xsb + 1 ysv_ext1 = ysb - 1 zsv_ext1 = zsb + 1 else: dx_ext1 = dx0 - 1 - SQUISH_CONSTANT_3D dy_ext1 = dy0 - 1 - SQUISH_CONSTANT_3D dz_ext1 = dz0 + 1 - SQUISH_CONSTANT_3D xsv_ext1 = xsb + 1 ysv_ext1 = ysb + 1 zsv_ext1 = zsb - 1 else: # One point on (0,0,0) side, one point on (1,1,1) side if a_is_further_side: c1 = a_point c2 = b_point else: c1 = b_point c2 = a_point # One contribution is a _permutation of (1,1,-1) if (c1 & 0x01) == 0: dx_ext0 = dx0 + 1 - SQUISH_CONSTANT_3D dy_ext0 = dy0 - 1 - SQUISH_CONSTANT_3D dz_ext0 = dz0 - 1 - SQUISH_CONSTANT_3D xsv_ext0 = xsb - 1 ysv_ext0 = ysb + 1 zsv_ext0 = zsb + 1 elif (c1 & 0x02) == 0: dx_ext0 = dx0 - 1 - SQUISH_CONSTANT_3D dy_ext0 = dy0 + 1 - SQUISH_CONSTANT_3D dz_ext0 = dz0 - 1 - SQUISH_CONSTANT_3D xsv_ext0 = xsb + 1 ysv_ext0 = ysb - 1 zsv_ext0 = zsb + 1 else: dx_ext0 = dx0 - 1 - SQUISH_CONSTANT_3D dy_ext0 = dy0 - 1 - SQUISH_CONSTANT_3D dz_ext0 = dz0 + 1 - SQUISH_CONSTANT_3D xsv_ext0 = xsb + 1 ysv_ext0 = ysb + 1 zsv_ext0 = zsb - 1 # One contribution is a _permutation of (0,0,2) dx_ext1 = dx0 - 2 * SQUISH_CONSTANT_3D dy_ext1 = dy0 - 2 * SQUISH_CONSTANT_3D dz_ext1 = dz0 - 2 * SQUISH_CONSTANT_3D xsv_ext1 = xsb ysv_ext1 = ysb zsv_ext1 = zsb if (c2 & 0x01) != 0: dx_ext1 -= 2 xsv_ext1 += 2 elif (c2 & 0x02) != 0: dy_ext1 -= 2 ysv_ext1 += 2 else: dz_ext1 -= 2 zsv_ext1 += 2 # Contribution (1,0,0) dx1 = dx0 - 1 - SQUISH_CONSTANT_3D dy1 = dy0 - 0 - SQUISH_CONSTANT_3D dz1 = dz0 - 0 - SQUISH_CONSTANT_3D attn1 = 2 - dx1 * dx1 - dy1 * dy1 - dz1 * dz1 if attn1 > 0: attn1 *= attn1 value += attn1 * attn1 * extrapolate(xsb + 1, ysb + 0, zsb + 0, dx1, dy1, dz1) # Contribution (0,1,0) dx2 = dx0 - 0 - SQUISH_CONSTANT_3D dy2 = dy0 - 1 - SQUISH_CONSTANT_3D dz2 = dz1 attn2 = 2 - dx2 * dx2 - dy2 * dy2 - dz2 * dz2 if attn2 > 0: attn2 *= attn2 value += attn2 * attn2 * extrapolate(xsb + 0, ysb + 1, zsb + 0, dx2, dy2, dz2) # Contribution (0,0,1) dx3 = dx2 dy3 = dy1 dz3 = dz0 - 1 - SQUISH_CONSTANT_3D attn3 = 2 - dx3 * dx3 - dy3 * dy3 - dz3 * dz3 if attn3 > 0: attn3 *= attn3 value += attn3 * attn3 * extrapolate(xsb + 0, ysb + 0, zsb + 1, dx3, dy3, dz3) # Contribution (1,1,0) dx4 = dx0 - 1 - 2 * SQUISH_CONSTANT_3D dy4 = dy0 - 1 - 2 * SQUISH_CONSTANT_3D dz4 = dz0 - 0 - 2 * SQUISH_CONSTANT_3D attn4 = 2 - dx4 * dx4 - dy4 * dy4 - dz4 * dz4 if attn4 > 0: attn4 *= attn4 value += attn4 * attn4 * extrapolate(xsb + 1, ysb + 1, zsb + 0, dx4, dy4, dz4) # Contribution (1,0,1) dx5 = dx4 dy5 = dy0 - 0 - 2 * SQUISH_CONSTANT_3D dz5 = dz0 - 1 - 2 * SQUISH_CONSTANT_3D attn5 = 2 - dx5 * dx5 - dy5 * dy5 - dz5 * dz5 if attn5 > 0: attn5 *= attn5 value += attn5 * attn5 * extrapolate(xsb + 1, ysb + 0, zsb + 1, dx5, dy5, dz5) # Contribution (0,1,1) dx6 = dx0 - 0 - 2 * SQUISH_CONSTANT_3D dy6 = dy4 dz6 = dz5 attn6 = 2 - dx6 * dx6 - dy6 * dy6 - dz6 * dz6 if attn6 > 0: attn6 *= attn6 value += attn6 * attn6 * extrapolate(xsb + 0, ysb + 1, zsb + 1, dx6, dy6, dz6) # First extra vertex attn_ext0 = 2 - dx_ext0 * dx_ext0 - dy_ext0 * dy_ext0 - dz_ext0 * dz_ext0 if attn_ext0 > 0: attn_ext0 *= attn_ext0 value += attn_ext0 * attn_ext0 * extrapolate(xsv_ext0, ysv_ext0, zsv_ext0, dx_ext0, dy_ext0, dz_ext0) # Second extra vertex attn_ext1 = 2 - dx_ext1 * dx_ext1 - dy_ext1 * dy_ext1 - dz_ext1 * dz_ext1 if attn_ext1 > 0: attn_ext1 *= attn_ext1 value += attn_ext1 * attn_ext1 * extrapolate(xsv_ext1, ysv_ext1, zsv_ext1, dx_ext1, dy_ext1, dz_ext1) return value / NORM_CONSTANT_3D
[ "def", "noise3d", "(", "self", ",", "x", ",", "y", ",", "z", ")", ":", "# Place input coordinates on simplectic honeycomb.", "stretch_offset", "=", "(", "x", "+", "y", "+", "z", ")", "*", "STRETCH_CONSTANT_3D", "xs", "=", "x", "+", "stretch_offset", "ys", "=", "y", "+", "stretch_offset", "zs", "=", "z", "+", "stretch_offset", "# Floor to get simplectic honeycomb coordinates of rhombohedron (stretched cube) super-cell origin.", "xsb", "=", "floor", "(", "xs", ")", "ysb", "=", "floor", "(", "ys", ")", "zsb", "=", "floor", "(", "zs", ")", "# Skew out to get actual coordinates of rhombohedron origin. We'll need these later.", "squish_offset", "=", "(", "xsb", "+", "ysb", "+", "zsb", ")", "*", "SQUISH_CONSTANT_3D", "xb", "=", "xsb", "+", "squish_offset", "yb", "=", "ysb", "+", "squish_offset", "zb", "=", "zsb", "+", "squish_offset", "# Compute simplectic honeycomb coordinates relative to rhombohedral origin.", "xins", "=", "xs", "-", "xsb", "yins", "=", "ys", "-", "ysb", "zins", "=", "zs", "-", "zsb", "# Sum those together to get a value that determines which region we're in.", "in_sum", "=", "xins", "+", "yins", "+", "zins", "# Positions relative to origin point.", "dx0", "=", "x", "-", "xb", "dy0", "=", "y", "-", "yb", "dz0", "=", "z", "-", "zb", "value", "=", "0", "extrapolate", "=", "self", ".", "_extrapolate3d", "if", "in_sum", "<=", "1", ":", "# We're inside the tetrahedron (3-Simplex) at (0,0,0)", "# Determine which two of (0,0,1), (0,1,0), (1,0,0) are closest.", "a_point", "=", "0x01", "a_score", "=", "xins", "b_point", "=", "0x02", "b_score", "=", "yins", "if", "a_score", ">=", "b_score", "and", "zins", ">", "b_score", ":", "b_score", "=", "zins", "b_point", "=", "0x04", "elif", "a_score", "<", "b_score", "and", "zins", ">", "a_score", ":", "a_score", "=", "zins", "a_point", "=", "0x04", "# Now we determine the two lattice points not part of the tetrahedron that may contribute.", "# This depends on the closest two tetrahedral vertices, including (0,0,0)", "wins", "=", "1", "-", "in_sum", "if", "wins", ">", "a_score", "or", "wins", ">", "b_score", ":", "# (0,0,0) is one of the closest two tetrahedral vertices.", "c", "=", "b_point", "if", "(", "b_score", ">", "a_score", ")", "else", "a_point", "# Our other closest vertex is the closest out of a and b.", "if", "(", "c", "&", "0x01", ")", "==", "0", ":", "xsv_ext0", "=", "xsb", "-", "1", "xsv_ext1", "=", "xsb", "dx_ext0", "=", "dx0", "+", "1", "dx_ext1", "=", "dx0", "else", ":", "xsv_ext0", "=", "xsv_ext1", "=", "xsb", "+", "1", "dx_ext0", "=", "dx_ext1", "=", "dx0", "-", "1", "if", "(", "c", "&", "0x02", ")", "==", "0", ":", "ysv_ext0", "=", "ysv_ext1", "=", "ysb", "dy_ext0", "=", "dy_ext1", "=", "dy0", "if", "(", "c", "&", "0x01", ")", "==", "0", ":", "ysv_ext1", "-=", "1", "dy_ext1", "+=", "1", "else", ":", "ysv_ext0", "-=", "1", "dy_ext0", "+=", "1", "else", ":", "ysv_ext0", "=", "ysv_ext1", "=", "ysb", "+", "1", "dy_ext0", "=", "dy_ext1", "=", "dy0", "-", "1", "if", "(", "c", "&", "0x04", ")", "==", "0", ":", "zsv_ext0", "=", "zsb", "zsv_ext1", "=", "zsb", "-", "1", "dz_ext0", "=", "dz0", "dz_ext1", "=", "dz0", "+", "1", "else", ":", "zsv_ext0", "=", "zsv_ext1", "=", "zsb", "+", "1", "dz_ext0", "=", "dz_ext1", "=", "dz0", "-", "1", "else", ":", "# (0,0,0) is not one of the closest two tetrahedral vertices.", "c", "=", "(", "a_point", "|", "b_point", ")", "# Our two extra vertices are determined by the closest two.", "if", "(", "c", "&", "0x01", ")", "==", "0", ":", "xsv_ext0", "=", "xsb", "xsv_ext1", "=", "xsb", "-", "1", "dx_ext0", "=", "dx0", "-", "2", "*", "SQUISH_CONSTANT_3D", "dx_ext1", "=", "dx0", "+", "1", "-", "SQUISH_CONSTANT_3D", "else", ":", "xsv_ext0", "=", "xsv_ext1", "=", "xsb", "+", "1", "dx_ext0", "=", "dx0", "-", "1", "-", "2", "*", "SQUISH_CONSTANT_3D", "dx_ext1", "=", "dx0", "-", "1", "-", "SQUISH_CONSTANT_3D", "if", "(", "c", "&", "0x02", ")", "==", "0", ":", "ysv_ext0", "=", "ysb", "ysv_ext1", "=", "ysb", "-", "1", "dy_ext0", "=", "dy0", "-", "2", "*", "SQUISH_CONSTANT_3D", "dy_ext1", "=", "dy0", "+", "1", "-", "SQUISH_CONSTANT_3D", "else", ":", "ysv_ext0", "=", "ysv_ext1", "=", "ysb", "+", "1", "dy_ext0", "=", "dy0", "-", "1", "-", "2", "*", "SQUISH_CONSTANT_3D", "dy_ext1", "=", "dy0", "-", "1", "-", "SQUISH_CONSTANT_3D", "if", "(", "c", "&", "0x04", ")", "==", "0", ":", "zsv_ext0", "=", "zsb", "zsv_ext1", "=", "zsb", "-", "1", "dz_ext0", "=", "dz0", "-", "2", "*", "SQUISH_CONSTANT_3D", "dz_ext1", "=", "dz0", "+", "1", "-", "SQUISH_CONSTANT_3D", "else", ":", "zsv_ext0", "=", "zsv_ext1", "=", "zsb", "+", "1", "dz_ext0", "=", "dz0", "-", "1", "-", "2", "*", "SQUISH_CONSTANT_3D", "dz_ext1", "=", "dz0", "-", "1", "-", "SQUISH_CONSTANT_3D", "# Contribution (0,0,0)", "attn0", "=", "2", "-", "dx0", "*", "dx0", "-", "dy0", "*", "dy0", "-", "dz0", "*", "dz0", "if", "attn0", ">", "0", ":", "attn0", "*=", "attn0", "value", "+=", "attn0", "*", "attn0", "*", "extrapolate", "(", "xsb", "+", "0", ",", "ysb", "+", "0", ",", "zsb", "+", "0", ",", "dx0", ",", "dy0", ",", "dz0", ")", "# Contribution (1,0,0)", "dx1", "=", "dx0", "-", "1", "-", "SQUISH_CONSTANT_3D", "dy1", "=", "dy0", "-", "0", "-", "SQUISH_CONSTANT_3D", "dz1", "=", "dz0", "-", "0", "-", "SQUISH_CONSTANT_3D", "attn1", "=", "2", "-", "dx1", "*", "dx1", "-", "dy1", "*", "dy1", "-", "dz1", "*", "dz1", "if", "attn1", ">", "0", ":", "attn1", "*=", "attn1", "value", "+=", "attn1", "*", "attn1", "*", "extrapolate", "(", "xsb", "+", "1", ",", "ysb", "+", "0", ",", "zsb", "+", "0", ",", "dx1", ",", "dy1", ",", "dz1", ")", "# Contribution (0,1,0)", "dx2", "=", "dx0", "-", "0", "-", "SQUISH_CONSTANT_3D", "dy2", "=", "dy0", "-", "1", "-", "SQUISH_CONSTANT_3D", "dz2", "=", "dz1", "attn2", "=", "2", "-", "dx2", "*", "dx2", "-", "dy2", "*", "dy2", "-", "dz2", "*", "dz2", "if", "attn2", ">", "0", ":", "attn2", "*=", "attn2", "value", "+=", "attn2", "*", "attn2", "*", "extrapolate", "(", "xsb", "+", "0", ",", "ysb", "+", "1", ",", "zsb", "+", "0", ",", "dx2", ",", "dy2", ",", "dz2", ")", "# Contribution (0,0,1)", "dx3", "=", "dx2", "dy3", "=", "dy1", "dz3", "=", "dz0", "-", "1", "-", "SQUISH_CONSTANT_3D", "attn3", "=", "2", "-", "dx3", "*", "dx3", "-", "dy3", "*", "dy3", "-", "dz3", "*", "dz3", "if", "attn3", ">", "0", ":", "attn3", "*=", "attn3", "value", "+=", "attn3", "*", "attn3", "*", "extrapolate", "(", "xsb", "+", "0", ",", "ysb", "+", "0", ",", "zsb", "+", "1", ",", "dx3", ",", "dy3", ",", "dz3", ")", "elif", "in_sum", ">=", "2", ":", "# We're inside the tetrahedron (3-Simplex) at (1,1,1)", "# Determine which two tetrahedral vertices are the closest, out of (1,1,0), (1,0,1), (0,1,1) but not (1,1,1).", "a_point", "=", "0x06", "a_score", "=", "xins", "b_point", "=", "0x05", "b_score", "=", "yins", "if", "a_score", "<=", "b_score", "and", "zins", "<", "b_score", ":", "b_score", "=", "zins", "b_point", "=", "0x03", "elif", "a_score", ">", "b_score", "and", "zins", "<", "a_score", ":", "a_score", "=", "zins", "a_point", "=", "0x03", "# Now we determine the two lattice points not part of the tetrahedron that may contribute.", "# This depends on the closest two tetrahedral vertices, including (1,1,1)", "wins", "=", "3", "-", "in_sum", "if", "wins", "<", "a_score", "or", "wins", "<", "b_score", ":", "# (1,1,1) is one of the closest two tetrahedral vertices.", "c", "=", "b_point", "if", "(", "b_score", "<", "a_score", ")", "else", "a_point", "# Our other closest vertex is the closest out of a and b.", "if", "(", "c", "&", "0x01", ")", "!=", "0", ":", "xsv_ext0", "=", "xsb", "+", "2", "xsv_ext1", "=", "xsb", "+", "1", "dx_ext0", "=", "dx0", "-", "2", "-", "3", "*", "SQUISH_CONSTANT_3D", "dx_ext1", "=", "dx0", "-", "1", "-", "3", "*", "SQUISH_CONSTANT_3D", "else", ":", "xsv_ext0", "=", "xsv_ext1", "=", "xsb", "dx_ext0", "=", "dx_ext1", "=", "dx0", "-", "3", "*", "SQUISH_CONSTANT_3D", "if", "(", "c", "&", "0x02", ")", "!=", "0", ":", "ysv_ext0", "=", "ysv_ext1", "=", "ysb", "+", "1", "dy_ext0", "=", "dy_ext1", "=", "dy0", "-", "1", "-", "3", "*", "SQUISH_CONSTANT_3D", "if", "(", "c", "&", "0x01", ")", "!=", "0", ":", "ysv_ext1", "+=", "1", "dy_ext1", "-=", "1", "else", ":", "ysv_ext0", "+=", "1", "dy_ext0", "-=", "1", "else", ":", "ysv_ext0", "=", "ysv_ext1", "=", "ysb", "dy_ext0", "=", "dy_ext1", "=", "dy0", "-", "3", "*", "SQUISH_CONSTANT_3D", "if", "(", "c", "&", "0x04", ")", "!=", "0", ":", "zsv_ext0", "=", "zsb", "+", "1", "zsv_ext1", "=", "zsb", "+", "2", "dz_ext0", "=", "dz0", "-", "1", "-", "3", "*", "SQUISH_CONSTANT_3D", "dz_ext1", "=", "dz0", "-", "2", "-", "3", "*", "SQUISH_CONSTANT_3D", "else", ":", "zsv_ext0", "=", "zsv_ext1", "=", "zsb", "dz_ext0", "=", "dz_ext1", "=", "dz0", "-", "3", "*", "SQUISH_CONSTANT_3D", "else", ":", "# (1,1,1) is not one of the closest two tetrahedral vertices.", "c", "=", "(", "a_point", "&", "b_point", ")", "# Our two extra vertices are determined by the closest two.", "if", "(", "c", "&", "0x01", ")", "!=", "0", ":", "xsv_ext0", "=", "xsb", "+", "1", "xsv_ext1", "=", "xsb", "+", "2", "dx_ext0", "=", "dx0", "-", "1", "-", "SQUISH_CONSTANT_3D", "dx_ext1", "=", "dx0", "-", "2", "-", "2", "*", "SQUISH_CONSTANT_3D", "else", ":", "xsv_ext0", "=", "xsv_ext1", "=", "xsb", "dx_ext0", "=", "dx0", "-", "SQUISH_CONSTANT_3D", "dx_ext1", "=", "dx0", "-", "2", "*", "SQUISH_CONSTANT_3D", "if", "(", "c", "&", "0x02", ")", "!=", "0", ":", "ysv_ext0", "=", "ysb", "+", "1", "ysv_ext1", "=", "ysb", "+", "2", "dy_ext0", "=", "dy0", "-", "1", "-", "SQUISH_CONSTANT_3D", "dy_ext1", "=", "dy0", "-", "2", "-", "2", "*", "SQUISH_CONSTANT_3D", "else", ":", "ysv_ext0", "=", "ysv_ext1", "=", "ysb", "dy_ext0", "=", "dy0", "-", "SQUISH_CONSTANT_3D", "dy_ext1", "=", "dy0", "-", "2", "*", "SQUISH_CONSTANT_3D", "if", "(", "c", "&", "0x04", ")", "!=", "0", ":", "zsv_ext0", "=", "zsb", "+", "1", "zsv_ext1", "=", "zsb", "+", "2", "dz_ext0", "=", "dz0", "-", "1", "-", "SQUISH_CONSTANT_3D", "dz_ext1", "=", "dz0", "-", "2", "-", "2", "*", "SQUISH_CONSTANT_3D", "else", ":", "zsv_ext0", "=", "zsv_ext1", "=", "zsb", "dz_ext0", "=", "dz0", "-", "SQUISH_CONSTANT_3D", "dz_ext1", "=", "dz0", "-", "2", "*", "SQUISH_CONSTANT_3D", "# Contribution (1,1,0)", "dx3", "=", "dx0", "-", "1", "-", "2", "*", "SQUISH_CONSTANT_3D", "dy3", "=", "dy0", "-", "1", "-", "2", "*", "SQUISH_CONSTANT_3D", "dz3", "=", "dz0", "-", "0", "-", "2", "*", "SQUISH_CONSTANT_3D", "attn3", "=", "2", "-", "dx3", "*", "dx3", "-", "dy3", "*", "dy3", "-", "dz3", "*", "dz3", "if", "attn3", ">", "0", ":", "attn3", "*=", "attn3", "value", "+=", "attn3", "*", "attn3", "*", "extrapolate", "(", "xsb", "+", "1", ",", "ysb", "+", "1", ",", "zsb", "+", "0", ",", "dx3", ",", "dy3", ",", "dz3", ")", "# Contribution (1,0,1)", "dx2", "=", "dx3", "dy2", "=", "dy0", "-", "0", "-", "2", "*", "SQUISH_CONSTANT_3D", "dz2", "=", "dz0", "-", "1", "-", "2", "*", "SQUISH_CONSTANT_3D", "attn2", "=", "2", "-", "dx2", "*", "dx2", "-", "dy2", "*", "dy2", "-", "dz2", "*", "dz2", "if", "attn2", ">", "0", ":", "attn2", "*=", "attn2", "value", "+=", "attn2", "*", "attn2", "*", "extrapolate", "(", "xsb", "+", "1", ",", "ysb", "+", "0", ",", "zsb", "+", "1", ",", "dx2", ",", "dy2", ",", "dz2", ")", "# Contribution (0,1,1)", "dx1", "=", "dx0", "-", "0", "-", "2", "*", "SQUISH_CONSTANT_3D", "dy1", "=", "dy3", "dz1", "=", "dz2", "attn1", "=", "2", "-", "dx1", "*", "dx1", "-", "dy1", "*", "dy1", "-", "dz1", "*", "dz1", "if", "attn1", ">", "0", ":", "attn1", "*=", "attn1", "value", "+=", "attn1", "*", "attn1", "*", "extrapolate", "(", "xsb", "+", "0", ",", "ysb", "+", "1", ",", "zsb", "+", "1", ",", "dx1", ",", "dy1", ",", "dz1", ")", "# Contribution (1,1,1)", "dx0", "=", "dx0", "-", "1", "-", "3", "*", "SQUISH_CONSTANT_3D", "dy0", "=", "dy0", "-", "1", "-", "3", "*", "SQUISH_CONSTANT_3D", "dz0", "=", "dz0", "-", "1", "-", "3", "*", "SQUISH_CONSTANT_3D", "attn0", "=", "2", "-", "dx0", "*", "dx0", "-", "dy0", "*", "dy0", "-", "dz0", "*", "dz0", "if", "attn0", ">", "0", ":", "attn0", "*=", "attn0", "value", "+=", "attn0", "*", "attn0", "*", "extrapolate", "(", "xsb", "+", "1", ",", "ysb", "+", "1", ",", "zsb", "+", "1", ",", "dx0", ",", "dy0", ",", "dz0", ")", "else", ":", "# We're inside the octahedron (Rectified 3-Simplex) in between.", "# Decide between point (0,0,1) and (1,1,0) as closest", "p1", "=", "xins", "+", "yins", "if", "p1", ">", "1", ":", "a_score", "=", "p1", "-", "1", "a_point", "=", "0x03", "a_is_further_side", "=", "True", "else", ":", "a_score", "=", "1", "-", "p1", "a_point", "=", "0x04", "a_is_further_side", "=", "False", "# Decide between point (0,1,0) and (1,0,1) as closest", "p2", "=", "xins", "+", "zins", "if", "p2", ">", "1", ":", "b_score", "=", "p2", "-", "1", "b_point", "=", "0x05", "b_is_further_side", "=", "True", "else", ":", "b_score", "=", "1", "-", "p2", "b_point", "=", "0x02", "b_is_further_side", "=", "False", "# The closest out of the two (1,0,0) and (0,1,1) will replace the furthest out of the two decided above, if closer.", "p3", "=", "yins", "+", "zins", "if", "p3", ">", "1", ":", "score", "=", "p3", "-", "1", "if", "a_score", "<=", "b_score", "and", "a_score", "<", "score", ":", "a_point", "=", "0x06", "a_is_further_side", "=", "True", "elif", "a_score", ">", "b_score", "and", "b_score", "<", "score", ":", "b_point", "=", "0x06", "b_is_further_side", "=", "True", "else", ":", "score", "=", "1", "-", "p3", "if", "a_score", "<=", "b_score", "and", "a_score", "<", "score", ":", "a_point", "=", "0x01", "a_is_further_side", "=", "False", "elif", "a_score", ">", "b_score", "and", "b_score", "<", "score", ":", "b_point", "=", "0x01", "b_is_further_side", "=", "False", "# Where each of the two closest points are determines how the extra two vertices are calculated.", "if", "a_is_further_side", "==", "b_is_further_side", ":", "if", "a_is_further_side", ":", "# Both closest points on (1,1,1) side", "# One of the two extra points is (1,1,1)", "dx_ext0", "=", "dx0", "-", "1", "-", "3", "*", "SQUISH_CONSTANT_3D", "dy_ext0", "=", "dy0", "-", "1", "-", "3", "*", "SQUISH_CONSTANT_3D", "dz_ext0", "=", "dz0", "-", "1", "-", "3", "*", "SQUISH_CONSTANT_3D", "xsv_ext0", "=", "xsb", "+", "1", "ysv_ext0", "=", "ysb", "+", "1", "zsv_ext0", "=", "zsb", "+", "1", "# Other extra point is based on the shared axis.", "c", "=", "(", "a_point", "&", "b_point", ")", "if", "(", "c", "&", "0x01", ")", "!=", "0", ":", "dx_ext1", "=", "dx0", "-", "2", "-", "2", "*", "SQUISH_CONSTANT_3D", "dy_ext1", "=", "dy0", "-", "2", "*", "SQUISH_CONSTANT_3D", "dz_ext1", "=", "dz0", "-", "2", "*", "SQUISH_CONSTANT_3D", "xsv_ext1", "=", "xsb", "+", "2", "ysv_ext1", "=", "ysb", "zsv_ext1", "=", "zsb", "elif", "(", "c", "&", "0x02", ")", "!=", "0", ":", "dx_ext1", "=", "dx0", "-", "2", "*", "SQUISH_CONSTANT_3D", "dy_ext1", "=", "dy0", "-", "2", "-", "2", "*", "SQUISH_CONSTANT_3D", "dz_ext1", "=", "dz0", "-", "2", "*", "SQUISH_CONSTANT_3D", "xsv_ext1", "=", "xsb", "ysv_ext1", "=", "ysb", "+", "2", "zsv_ext1", "=", "zsb", "else", ":", "dx_ext1", "=", "dx0", "-", "2", "*", "SQUISH_CONSTANT_3D", "dy_ext1", "=", "dy0", "-", "2", "*", "SQUISH_CONSTANT_3D", "dz_ext1", "=", "dz0", "-", "2", "-", "2", "*", "SQUISH_CONSTANT_3D", "xsv_ext1", "=", "xsb", "ysv_ext1", "=", "ysb", "zsv_ext1", "=", "zsb", "+", "2", "else", ":", "# Both closest points on (0,0,0) side", "# One of the two extra points is (0,0,0)", "dx_ext0", "=", "dx0", "dy_ext0", "=", "dy0", "dz_ext0", "=", "dz0", "xsv_ext0", "=", "xsb", "ysv_ext0", "=", "ysb", "zsv_ext0", "=", "zsb", "# Other extra point is based on the omitted axis.", "c", "=", "(", "a_point", "|", "b_point", ")", "if", "(", "c", "&", "0x01", ")", "==", "0", ":", "dx_ext1", "=", "dx0", "+", "1", "-", "SQUISH_CONSTANT_3D", "dy_ext1", "=", "dy0", "-", "1", "-", "SQUISH_CONSTANT_3D", "dz_ext1", "=", "dz0", "-", "1", "-", "SQUISH_CONSTANT_3D", "xsv_ext1", "=", "xsb", "-", "1", "ysv_ext1", "=", "ysb", "+", "1", "zsv_ext1", "=", "zsb", "+", "1", "elif", "(", "c", "&", "0x02", ")", "==", "0", ":", "dx_ext1", "=", "dx0", "-", "1", "-", "SQUISH_CONSTANT_3D", "dy_ext1", "=", "dy0", "+", "1", "-", "SQUISH_CONSTANT_3D", "dz_ext1", "=", "dz0", "-", "1", "-", "SQUISH_CONSTANT_3D", "xsv_ext1", "=", "xsb", "+", "1", "ysv_ext1", "=", "ysb", "-", "1", "zsv_ext1", "=", "zsb", "+", "1", "else", ":", "dx_ext1", "=", "dx0", "-", "1", "-", "SQUISH_CONSTANT_3D", "dy_ext1", "=", "dy0", "-", "1", "-", "SQUISH_CONSTANT_3D", "dz_ext1", "=", "dz0", "+", "1", "-", "SQUISH_CONSTANT_3D", "xsv_ext1", "=", "xsb", "+", "1", "ysv_ext1", "=", "ysb", "+", "1", "zsv_ext1", "=", "zsb", "-", "1", "else", ":", "# One point on (0,0,0) side, one point on (1,1,1) side", "if", "a_is_further_side", ":", "c1", "=", "a_point", "c2", "=", "b_point", "else", ":", "c1", "=", "b_point", "c2", "=", "a_point", "# One contribution is a _permutation of (1,1,-1)", "if", "(", "c1", "&", "0x01", ")", "==", "0", ":", "dx_ext0", "=", "dx0", "+", "1", "-", "SQUISH_CONSTANT_3D", "dy_ext0", "=", "dy0", "-", "1", "-", "SQUISH_CONSTANT_3D", "dz_ext0", "=", "dz0", "-", "1", "-", "SQUISH_CONSTANT_3D", "xsv_ext0", "=", "xsb", "-", "1", "ysv_ext0", "=", "ysb", "+", "1", "zsv_ext0", "=", "zsb", "+", "1", "elif", "(", "c1", "&", "0x02", ")", "==", "0", ":", "dx_ext0", "=", "dx0", "-", "1", "-", "SQUISH_CONSTANT_3D", "dy_ext0", "=", "dy0", "+", "1", "-", "SQUISH_CONSTANT_3D", "dz_ext0", "=", "dz0", "-", "1", "-", "SQUISH_CONSTANT_3D", "xsv_ext0", "=", "xsb", "+", "1", "ysv_ext0", "=", "ysb", "-", "1", "zsv_ext0", "=", "zsb", "+", "1", "else", ":", "dx_ext0", "=", "dx0", "-", "1", "-", "SQUISH_CONSTANT_3D", "dy_ext0", "=", "dy0", "-", "1", "-", "SQUISH_CONSTANT_3D", "dz_ext0", "=", "dz0", "+", "1", "-", "SQUISH_CONSTANT_3D", "xsv_ext0", "=", "xsb", "+", "1", "ysv_ext0", "=", "ysb", "+", "1", "zsv_ext0", "=", "zsb", "-", "1", "# One contribution is a _permutation of (0,0,2)", "dx_ext1", "=", "dx0", "-", "2", "*", "SQUISH_CONSTANT_3D", "dy_ext1", "=", "dy0", "-", "2", "*", "SQUISH_CONSTANT_3D", "dz_ext1", "=", "dz0", "-", "2", "*", "SQUISH_CONSTANT_3D", "xsv_ext1", "=", "xsb", "ysv_ext1", "=", "ysb", "zsv_ext1", "=", "zsb", "if", "(", "c2", "&", "0x01", ")", "!=", "0", ":", "dx_ext1", "-=", "2", "xsv_ext1", "+=", "2", "elif", "(", "c2", "&", "0x02", ")", "!=", "0", ":", "dy_ext1", "-=", "2", "ysv_ext1", "+=", "2", "else", ":", "dz_ext1", "-=", "2", "zsv_ext1", "+=", "2", "# Contribution (1,0,0)", "dx1", "=", "dx0", "-", "1", "-", "SQUISH_CONSTANT_3D", "dy1", "=", "dy0", "-", "0", "-", "SQUISH_CONSTANT_3D", "dz1", "=", "dz0", "-", "0", "-", "SQUISH_CONSTANT_3D", "attn1", "=", "2", "-", "dx1", "*", "dx1", "-", "dy1", "*", "dy1", "-", "dz1", "*", "dz1", "if", "attn1", ">", "0", ":", "attn1", "*=", "attn1", "value", "+=", "attn1", "*", "attn1", "*", "extrapolate", "(", "xsb", "+", "1", ",", "ysb", "+", "0", ",", "zsb", "+", "0", ",", "dx1", ",", "dy1", ",", "dz1", ")", "# Contribution (0,1,0)", "dx2", "=", "dx0", "-", "0", "-", "SQUISH_CONSTANT_3D", "dy2", "=", "dy0", "-", "1", "-", "SQUISH_CONSTANT_3D", "dz2", "=", "dz1", "attn2", "=", "2", "-", "dx2", "*", "dx2", "-", "dy2", "*", "dy2", "-", "dz2", "*", "dz2", "if", "attn2", ">", "0", ":", "attn2", "*=", "attn2", "value", "+=", "attn2", "*", "attn2", "*", "extrapolate", "(", "xsb", "+", "0", ",", "ysb", "+", "1", ",", "zsb", "+", "0", ",", "dx2", ",", "dy2", ",", "dz2", ")", "# Contribution (0,0,1)", "dx3", "=", "dx2", "dy3", "=", "dy1", "dz3", "=", "dz0", "-", "1", "-", "SQUISH_CONSTANT_3D", "attn3", "=", "2", "-", "dx3", "*", "dx3", "-", "dy3", "*", "dy3", "-", "dz3", "*", "dz3", "if", "attn3", ">", "0", ":", "attn3", "*=", "attn3", "value", "+=", "attn3", "*", "attn3", "*", "extrapolate", "(", "xsb", "+", "0", ",", "ysb", "+", "0", ",", "zsb", "+", "1", ",", "dx3", ",", "dy3", ",", "dz3", ")", "# Contribution (1,1,0)", "dx4", "=", "dx0", "-", "1", "-", "2", "*", "SQUISH_CONSTANT_3D", "dy4", "=", "dy0", "-", "1", "-", "2", "*", "SQUISH_CONSTANT_3D", "dz4", "=", "dz0", "-", "0", "-", "2", "*", "SQUISH_CONSTANT_3D", "attn4", "=", "2", "-", "dx4", "*", "dx4", "-", "dy4", "*", "dy4", "-", "dz4", "*", "dz4", "if", "attn4", ">", "0", ":", "attn4", "*=", "attn4", "value", "+=", "attn4", "*", "attn4", "*", "extrapolate", "(", "xsb", "+", "1", ",", "ysb", "+", "1", ",", "zsb", "+", "0", ",", "dx4", ",", "dy4", ",", "dz4", ")", "# Contribution (1,0,1)", "dx5", "=", "dx4", "dy5", "=", "dy0", "-", "0", "-", "2", "*", "SQUISH_CONSTANT_3D", "dz5", "=", "dz0", "-", "1", "-", "2", "*", "SQUISH_CONSTANT_3D", "attn5", "=", "2", "-", "dx5", "*", "dx5", "-", "dy5", "*", "dy5", "-", "dz5", "*", "dz5", "if", "attn5", ">", "0", ":", "attn5", "*=", "attn5", "value", "+=", "attn5", "*", "attn5", "*", "extrapolate", "(", "xsb", "+", "1", ",", "ysb", "+", "0", ",", "zsb", "+", "1", ",", "dx5", ",", "dy5", ",", "dz5", ")", "# Contribution (0,1,1)", "dx6", "=", "dx0", "-", "0", "-", "2", "*", "SQUISH_CONSTANT_3D", "dy6", "=", "dy4", "dz6", "=", "dz5", "attn6", "=", "2", "-", "dx6", "*", "dx6", "-", "dy6", "*", "dy6", "-", "dz6", "*", "dz6", "if", "attn6", ">", "0", ":", "attn6", "*=", "attn6", "value", "+=", "attn6", "*", "attn6", "*", "extrapolate", "(", "xsb", "+", "0", ",", "ysb", "+", "1", ",", "zsb", "+", "1", ",", "dx6", ",", "dy6", ",", "dz6", ")", "# First extra vertex", "attn_ext0", "=", "2", "-", "dx_ext0", "*", "dx_ext0", "-", "dy_ext0", "*", "dy_ext0", "-", "dz_ext0", "*", "dz_ext0", "if", "attn_ext0", ">", "0", ":", "attn_ext0", "*=", "attn_ext0", "value", "+=", "attn_ext0", "*", "attn_ext0", "*", "extrapolate", "(", "xsv_ext0", ",", "ysv_ext0", ",", "zsv_ext0", ",", "dx_ext0", ",", "dy_ext0", ",", "dz_ext0", ")", "# Second extra vertex", "attn_ext1", "=", "2", "-", "dx_ext1", "*", "dx_ext1", "-", "dy_ext1", "*", "dy_ext1", "-", "dz_ext1", "*", "dz_ext1", "if", "attn_ext1", ">", "0", ":", "attn_ext1", "*=", "attn_ext1", "value", "+=", "attn_ext1", "*", "attn_ext1", "*", "extrapolate", "(", "xsv_ext1", ",", "ysv_ext1", ",", "zsv_ext1", ",", "dx_ext1", ",", "dy_ext1", ",", "dz_ext1", ")", "return", "value", "/", "NORM_CONSTANT_3D" ]
Generate 3D OpenSimplex noise from X,Y,Z coordinates.
[ "Generate", "3D", "OpenSimplex", "noise", "from", "X", "Y", "Z", "coordinates", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/external/opensimplex.py#L247-L740
valid
aleju/imgaug
imgaug/external/opensimplex.py
OpenSimplex.noise4d
def noise4d(self, x, y, z, w): """ Generate 4D OpenSimplex noise from X,Y,Z,W coordinates. """ # Place input coordinates on simplectic honeycomb. stretch_offset = (x + y + z + w) * STRETCH_CONSTANT_4D xs = x + stretch_offset ys = y + stretch_offset zs = z + stretch_offset ws = w + stretch_offset # Floor to get simplectic honeycomb coordinates of rhombo-hypercube super-cell origin. xsb = floor(xs) ysb = floor(ys) zsb = floor(zs) wsb = floor(ws) # Skew out to get actual coordinates of stretched rhombo-hypercube origin. We'll need these later. squish_offset = (xsb + ysb + zsb + wsb) * SQUISH_CONSTANT_4D xb = xsb + squish_offset yb = ysb + squish_offset zb = zsb + squish_offset wb = wsb + squish_offset # Compute simplectic honeycomb coordinates relative to rhombo-hypercube origin. xins = xs - xsb yins = ys - ysb zins = zs - zsb wins = ws - wsb # Sum those together to get a value that determines which region we're in. in_sum = xins + yins + zins + wins # Positions relative to origin po. dx0 = x - xb dy0 = y - yb dz0 = z - zb dw0 = w - wb value = 0 extrapolate = self._extrapolate4d if in_sum <= 1: # We're inside the pentachoron (4-Simplex) at (0,0,0,0) # Determine which two of (0,0,0,1), (0,0,1,0), (0,1,0,0), (1,0,0,0) are closest. a_po = 0x01 a_score = xins b_po = 0x02 b_score = yins if a_score >= b_score and zins > b_score: b_score = zins b_po = 0x04 elif a_score < b_score and zins > a_score: a_score = zins a_po = 0x04 if a_score >= b_score and wins > b_score: b_score = wins b_po = 0x08 elif a_score < b_score and wins > a_score: a_score = wins a_po = 0x08 # Now we determine the three lattice pos not part of the pentachoron that may contribute. # This depends on the closest two pentachoron vertices, including (0,0,0,0) uins = 1 - in_sum if uins > a_score or uins > b_score: # (0,0,0,0) is one of the closest two pentachoron vertices. c = b_po if (b_score > a_score) else a_po # Our other closest vertex is the closest out of a and b. if (c & 0x01) == 0: xsv_ext0 = xsb - 1 xsv_ext1 = xsv_ext2 = xsb dx_ext0 = dx0 + 1 dx_ext1 = dx_ext2 = dx0 else: xsv_ext0 = xsv_ext1 = xsv_ext2 = xsb + 1 dx_ext0 = dx_ext1 = dx_ext2 = dx0 - 1 if (c & 0x02) == 0: ysv_ext0 = ysv_ext1 = ysv_ext2 = ysb dy_ext0 = dy_ext1 = dy_ext2 = dy0 if (c & 0x01) == 0x01: ysv_ext0 -= 1 dy_ext0 += 1 else: ysv_ext1 -= 1 dy_ext1 += 1 else: ysv_ext0 = ysv_ext1 = ysv_ext2 = ysb + 1 dy_ext0 = dy_ext1 = dy_ext2 = dy0 - 1 if (c & 0x04) == 0: zsv_ext0 = zsv_ext1 = zsv_ext2 = zsb dz_ext0 = dz_ext1 = dz_ext2 = dz0 if (c & 0x03) != 0: if (c & 0x03) == 0x03: zsv_ext0 -= 1 dz_ext0 += 1 else: zsv_ext1 -= 1 dz_ext1 += 1 else: zsv_ext2 -= 1 dz_ext2 += 1 else: zsv_ext0 = zsv_ext1 = zsv_ext2 = zsb + 1 dz_ext0 = dz_ext1 = dz_ext2 = dz0 - 1 if (c & 0x08) == 0: wsv_ext0 = wsv_ext1 = wsb wsv_ext2 = wsb - 1 dw_ext0 = dw_ext1 = dw0 dw_ext2 = dw0 + 1 else: wsv_ext0 = wsv_ext1 = wsv_ext2 = wsb + 1 dw_ext0 = dw_ext1 = dw_ext2 = dw0 - 1 else: # (0,0,0,0) is not one of the closest two pentachoron vertices. c = (a_po | b_po) # Our three extra vertices are determined by the closest two. if (c & 0x01) == 0: xsv_ext0 = xsv_ext2 = xsb xsv_ext1 = xsb - 1 dx_ext0 = dx0 - 2 * SQUISH_CONSTANT_4D dx_ext1 = dx0 + 1 - SQUISH_CONSTANT_4D dx_ext2 = dx0 - SQUISH_CONSTANT_4D else: xsv_ext0 = xsv_ext1 = xsv_ext2 = xsb + 1 dx_ext0 = dx0 - 1 - 2 * SQUISH_CONSTANT_4D dx_ext1 = dx_ext2 = dx0 - 1 - SQUISH_CONSTANT_4D if (c & 0x02) == 0: ysv_ext0 = ysv_ext1 = ysv_ext2 = ysb dy_ext0 = dy0 - 2 * SQUISH_CONSTANT_4D dy_ext1 = dy_ext2 = dy0 - SQUISH_CONSTANT_4D if (c & 0x01) == 0x01: ysv_ext1 -= 1 dy_ext1 += 1 else: ysv_ext2 -= 1 dy_ext2 += 1 else: ysv_ext0 = ysv_ext1 = ysv_ext2 = ysb + 1 dy_ext0 = dy0 - 1 - 2 * SQUISH_CONSTANT_4D dy_ext1 = dy_ext2 = dy0 - 1 - SQUISH_CONSTANT_4D if (c & 0x04) == 0: zsv_ext0 = zsv_ext1 = zsv_ext2 = zsb dz_ext0 = dz0 - 2 * SQUISH_CONSTANT_4D dz_ext1 = dz_ext2 = dz0 - SQUISH_CONSTANT_4D if (c & 0x03) == 0x03: zsv_ext1 -= 1 dz_ext1 += 1 else: zsv_ext2 -= 1 dz_ext2 += 1 else: zsv_ext0 = zsv_ext1 = zsv_ext2 = zsb + 1 dz_ext0 = dz0 - 1 - 2 * SQUISH_CONSTANT_4D dz_ext1 = dz_ext2 = dz0 - 1 - SQUISH_CONSTANT_4D if (c & 0x08) == 0: wsv_ext0 = wsv_ext1 = wsb wsv_ext2 = wsb - 1 dw_ext0 = dw0 - 2 * SQUISH_CONSTANT_4D dw_ext1 = dw0 - SQUISH_CONSTANT_4D dw_ext2 = dw0 + 1 - SQUISH_CONSTANT_4D else: wsv_ext0 = wsv_ext1 = wsv_ext2 = wsb + 1 dw_ext0 = dw0 - 1 - 2 * SQUISH_CONSTANT_4D dw_ext1 = dw_ext2 = dw0 - 1 - SQUISH_CONSTANT_4D # Contribution (0,0,0,0) attn0 = 2 - dx0 * dx0 - dy0 * dy0 - dz0 * dz0 - dw0 * dw0 if attn0 > 0: attn0 *= attn0 value += attn0 * attn0 * extrapolate(xsb + 0, ysb + 0, zsb + 0, wsb + 0, dx0, dy0, dz0, dw0) # Contribution (1,0,0,0) dx1 = dx0 - 1 - SQUISH_CONSTANT_4D dy1 = dy0 - 0 - SQUISH_CONSTANT_4D dz1 = dz0 - 0 - SQUISH_CONSTANT_4D dw1 = dw0 - 0 - SQUISH_CONSTANT_4D attn1 = 2 - dx1 * dx1 - dy1 * dy1 - dz1 * dz1 - dw1 * dw1 if attn1 > 0: attn1 *= attn1 value += attn1 * attn1 * extrapolate(xsb + 1, ysb + 0, zsb + 0, wsb + 0, dx1, dy1, dz1, dw1) # Contribution (0,1,0,0) dx2 = dx0 - 0 - SQUISH_CONSTANT_4D dy2 = dy0 - 1 - SQUISH_CONSTANT_4D dz2 = dz1 dw2 = dw1 attn2 = 2 - dx2 * dx2 - dy2 * dy2 - dz2 * dz2 - dw2 * dw2 if attn2 > 0: attn2 *= attn2 value += attn2 * attn2 * extrapolate(xsb + 0, ysb + 1, zsb + 0, wsb + 0, dx2, dy2, dz2, dw2) # Contribution (0,0,1,0) dx3 = dx2 dy3 = dy1 dz3 = dz0 - 1 - SQUISH_CONSTANT_4D dw3 = dw1 attn3 = 2 - dx3 * dx3 - dy3 * dy3 - dz3 * dz3 - dw3 * dw3 if attn3 > 0: attn3 *= attn3 value += attn3 * attn3 * extrapolate(xsb + 0, ysb + 0, zsb + 1, wsb + 0, dx3, dy3, dz3, dw3) # Contribution (0,0,0,1) dx4 = dx2 dy4 = dy1 dz4 = dz1 dw4 = dw0 - 1 - SQUISH_CONSTANT_4D attn4 = 2 - dx4 * dx4 - dy4 * dy4 - dz4 * dz4 - dw4 * dw4 if attn4 > 0: attn4 *= attn4 value += attn4 * attn4 * extrapolate(xsb + 0, ysb + 0, zsb + 0, wsb + 1, dx4, dy4, dz4, dw4) elif in_sum >= 3: # We're inside the pentachoron (4-Simplex) at (1,1,1,1) # Determine which two of (1,1,1,0), (1,1,0,1), (1,0,1,1), (0,1,1,1) are closest. a_po = 0x0E a_score = xins b_po = 0x0D b_score = yins if a_score <= b_score and zins < b_score: b_score = zins b_po = 0x0B elif a_score > b_score and zins < a_score: a_score = zins a_po = 0x0B if a_score <= b_score and wins < b_score: b_score = wins b_po = 0x07 elif a_score > b_score and wins < a_score: a_score = wins a_po = 0x07 # Now we determine the three lattice pos not part of the pentachoron that may contribute. # This depends on the closest two pentachoron vertices, including (0,0,0,0) uins = 4 - in_sum if uins < a_score or uins < b_score: # (1,1,1,1) is one of the closest two pentachoron vertices. c = b_po if (b_score < a_score) else a_po # Our other closest vertex is the closest out of a and b. if (c & 0x01) != 0: xsv_ext0 = xsb + 2 xsv_ext1 = xsv_ext2 = xsb + 1 dx_ext0 = dx0 - 2 - 4 * SQUISH_CONSTANT_4D dx_ext1 = dx_ext2 = dx0 - 1 - 4 * SQUISH_CONSTANT_4D else: xsv_ext0 = xsv_ext1 = xsv_ext2 = xsb dx_ext0 = dx_ext1 = dx_ext2 = dx0 - 4 * SQUISH_CONSTANT_4D if (c & 0x02) != 0: ysv_ext0 = ysv_ext1 = ysv_ext2 = ysb + 1 dy_ext0 = dy_ext1 = dy_ext2 = dy0 - 1 - 4 * SQUISH_CONSTANT_4D if (c & 0x01) != 0: ysv_ext1 += 1 dy_ext1 -= 1 else: ysv_ext0 += 1 dy_ext0 -= 1 else: ysv_ext0 = ysv_ext1 = ysv_ext2 = ysb dy_ext0 = dy_ext1 = dy_ext2 = dy0 - 4 * SQUISH_CONSTANT_4D if (c & 0x04) != 0: zsv_ext0 = zsv_ext1 = zsv_ext2 = zsb + 1 dz_ext0 = dz_ext1 = dz_ext2 = dz0 - 1 - 4 * SQUISH_CONSTANT_4D if (c & 0x03) != 0x03: if (c & 0x03) == 0: zsv_ext0 += 1 dz_ext0 -= 1 else: zsv_ext1 += 1 dz_ext1 -= 1 else: zsv_ext2 += 1 dz_ext2 -= 1 else: zsv_ext0 = zsv_ext1 = zsv_ext2 = zsb dz_ext0 = dz_ext1 = dz_ext2 = dz0 - 4 * SQUISH_CONSTANT_4D if (c & 0x08) != 0: wsv_ext0 = wsv_ext1 = wsb + 1 wsv_ext2 = wsb + 2 dw_ext0 = dw_ext1 = dw0 - 1 - 4 * SQUISH_CONSTANT_4D dw_ext2 = dw0 - 2 - 4 * SQUISH_CONSTANT_4D else: wsv_ext0 = wsv_ext1 = wsv_ext2 = wsb dw_ext0 = dw_ext1 = dw_ext2 = dw0 - 4 * SQUISH_CONSTANT_4D else: # (1,1,1,1) is not one of the closest two pentachoron vertices. c = (a_po & b_po) # Our three extra vertices are determined by the closest two. if (c & 0x01) != 0: xsv_ext0 = xsv_ext2 = xsb + 1 xsv_ext1 = xsb + 2 dx_ext0 = dx0 - 1 - 2 * SQUISH_CONSTANT_4D dx_ext1 = dx0 - 2 - 3 * SQUISH_CONSTANT_4D dx_ext2 = dx0 - 1 - 3 * SQUISH_CONSTANT_4D else: xsv_ext0 = xsv_ext1 = xsv_ext2 = xsb dx_ext0 = dx0 - 2 * SQUISH_CONSTANT_4D dx_ext1 = dx_ext2 = dx0 - 3 * SQUISH_CONSTANT_4D if (c & 0x02) != 0: ysv_ext0 = ysv_ext1 = ysv_ext2 = ysb + 1 dy_ext0 = dy0 - 1 - 2 * SQUISH_CONSTANT_4D dy_ext1 = dy_ext2 = dy0 - 1 - 3 * SQUISH_CONSTANT_4D if (c & 0x01) != 0: ysv_ext2 += 1 dy_ext2 -= 1 else: ysv_ext1 += 1 dy_ext1 -= 1 else: ysv_ext0 = ysv_ext1 = ysv_ext2 = ysb dy_ext0 = dy0 - 2 * SQUISH_CONSTANT_4D dy_ext1 = dy_ext2 = dy0 - 3 * SQUISH_CONSTANT_4D if (c & 0x04) != 0: zsv_ext0 = zsv_ext1 = zsv_ext2 = zsb + 1 dz_ext0 = dz0 - 1 - 2 * SQUISH_CONSTANT_4D dz_ext1 = dz_ext2 = dz0 - 1 - 3 * SQUISH_CONSTANT_4D if (c & 0x03) != 0: zsv_ext2 += 1 dz_ext2 -= 1 else: zsv_ext1 += 1 dz_ext1 -= 1 else: zsv_ext0 = zsv_ext1 = zsv_ext2 = zsb dz_ext0 = dz0 - 2 * SQUISH_CONSTANT_4D dz_ext1 = dz_ext2 = dz0 - 3 * SQUISH_CONSTANT_4D if (c & 0x08) != 0: wsv_ext0 = wsv_ext1 = wsb + 1 wsv_ext2 = wsb + 2 dw_ext0 = dw0 - 1 - 2 * SQUISH_CONSTANT_4D dw_ext1 = dw0 - 1 - 3 * SQUISH_CONSTANT_4D dw_ext2 = dw0 - 2 - 3 * SQUISH_CONSTANT_4D else: wsv_ext0 = wsv_ext1 = wsv_ext2 = wsb dw_ext0 = dw0 - 2 * SQUISH_CONSTANT_4D dw_ext1 = dw_ext2 = dw0 - 3 * SQUISH_CONSTANT_4D # Contribution (1,1,1,0) dx4 = dx0 - 1 - 3 * SQUISH_CONSTANT_4D dy4 = dy0 - 1 - 3 * SQUISH_CONSTANT_4D dz4 = dz0 - 1 - 3 * SQUISH_CONSTANT_4D dw4 = dw0 - 3 * SQUISH_CONSTANT_4D attn4 = 2 - dx4 * dx4 - dy4 * dy4 - dz4 * dz4 - dw4 * dw4 if attn4 > 0: attn4 *= attn4 value += attn4 * attn4 * extrapolate(xsb + 1, ysb + 1, zsb + 1, wsb + 0, dx4, dy4, dz4, dw4) # Contribution (1,1,0,1) dx3 = dx4 dy3 = dy4 dz3 = dz0 - 3 * SQUISH_CONSTANT_4D dw3 = dw0 - 1 - 3 * SQUISH_CONSTANT_4D attn3 = 2 - dx3 * dx3 - dy3 * dy3 - dz3 * dz3 - dw3 * dw3 if attn3 > 0: attn3 *= attn3 value += attn3 * attn3 * extrapolate(xsb + 1, ysb + 1, zsb + 0, wsb + 1, dx3, dy3, dz3, dw3) # Contribution (1,0,1,1) dx2 = dx4 dy2 = dy0 - 3 * SQUISH_CONSTANT_4D dz2 = dz4 dw2 = dw3 attn2 = 2 - dx2 * dx2 - dy2 * dy2 - dz2 * dz2 - dw2 * dw2 if attn2 > 0: attn2 *= attn2 value += attn2 * attn2 * extrapolate(xsb + 1, ysb + 0, zsb + 1, wsb + 1, dx2, dy2, dz2, dw2) # Contribution (0,1,1,1) dx1 = dx0 - 3 * SQUISH_CONSTANT_4D dz1 = dz4 dy1 = dy4 dw1 = dw3 attn1 = 2 - dx1 * dx1 - dy1 * dy1 - dz1 * dz1 - dw1 * dw1 if attn1 > 0: attn1 *= attn1 value += attn1 * attn1 * extrapolate(xsb + 0, ysb + 1, zsb + 1, wsb + 1, dx1, dy1, dz1, dw1) # Contribution (1,1,1,1) dx0 = dx0 - 1 - 4 * SQUISH_CONSTANT_4D dy0 = dy0 - 1 - 4 * SQUISH_CONSTANT_4D dz0 = dz0 - 1 - 4 * SQUISH_CONSTANT_4D dw0 = dw0 - 1 - 4 * SQUISH_CONSTANT_4D attn0 = 2 - dx0 * dx0 - dy0 * dy0 - dz0 * dz0 - dw0 * dw0 if attn0 > 0: attn0 *= attn0 value += attn0 * attn0 * extrapolate(xsb + 1, ysb + 1, zsb + 1, wsb + 1, dx0, dy0, dz0, dw0) elif in_sum <= 2: # We're inside the first dispentachoron (Rectified 4-Simplex) a_is_bigger_side = True b_is_bigger_side = True # Decide between (1,1,0,0) and (0,0,1,1) if xins + yins > zins + wins: a_score = xins + yins a_po = 0x03 else: a_score = zins + wins a_po = 0x0C # Decide between (1,0,1,0) and (0,1,0,1) if xins + zins > yins + wins: b_score = xins + zins b_po = 0x05 else: b_score = yins + wins b_po = 0x0A # Closer between (1,0,0,1) and (0,1,1,0) will replace the further of a and b, if closer. if xins + wins > yins + zins: score = xins + wins if a_score >= b_score and score > b_score: b_score = score b_po = 0x09 elif a_score < b_score and score > a_score: a_score = score a_po = 0x09 else: score = yins + zins if a_score >= b_score and score > b_score: b_score = score b_po = 0x06 elif a_score < b_score and score > a_score: a_score = score a_po = 0x06 # Decide if (1,0,0,0) is closer. p1 = 2 - in_sum + xins if a_score >= b_score and p1 > b_score: b_score = p1 b_po = 0x01 b_is_bigger_side = False elif a_score < b_score and p1 > a_score: a_score = p1 a_po = 0x01 a_is_bigger_side = False # Decide if (0,1,0,0) is closer. p2 = 2 - in_sum + yins if a_score >= b_score and p2 > b_score: b_score = p2 b_po = 0x02 b_is_bigger_side = False elif a_score < b_score and p2 > a_score: a_score = p2 a_po = 0x02 a_is_bigger_side = False # Decide if (0,0,1,0) is closer. p3 = 2 - in_sum + zins if a_score >= b_score and p3 > b_score: b_score = p3 b_po = 0x04 b_is_bigger_side = False elif a_score < b_score and p3 > a_score: a_score = p3 a_po = 0x04 a_is_bigger_side = False # Decide if (0,0,0,1) is closer. p4 = 2 - in_sum + wins if a_score >= b_score and p4 > b_score: b_po = 0x08 b_is_bigger_side = False elif a_score < b_score and p4 > a_score: a_po = 0x08 a_is_bigger_side = False # Where each of the two closest pos are determines how the extra three vertices are calculated. if a_is_bigger_side == b_is_bigger_side: if a_is_bigger_side: # Both closest pos on the bigger side c1 = (a_po | b_po) c2 = (a_po & b_po) if (c1 & 0x01) == 0: xsv_ext0 = xsb xsv_ext1 = xsb - 1 dx_ext0 = dx0 - 3 * SQUISH_CONSTANT_4D dx_ext1 = dx0 + 1 - 2 * SQUISH_CONSTANT_4D else: xsv_ext0 = xsv_ext1 = xsb + 1 dx_ext0 = dx0 - 1 - 3 * SQUISH_CONSTANT_4D dx_ext1 = dx0 - 1 - 2 * SQUISH_CONSTANT_4D if (c1 & 0x02) == 0: ysv_ext0 = ysb ysv_ext1 = ysb - 1 dy_ext0 = dy0 - 3 * SQUISH_CONSTANT_4D dy_ext1 = dy0 + 1 - 2 * SQUISH_CONSTANT_4D else: ysv_ext0 = ysv_ext1 = ysb + 1 dy_ext0 = dy0 - 1 - 3 * SQUISH_CONSTANT_4D dy_ext1 = dy0 - 1 - 2 * SQUISH_CONSTANT_4D if (c1 & 0x04) == 0: zsv_ext0 = zsb zsv_ext1 = zsb - 1 dz_ext0 = dz0 - 3 * SQUISH_CONSTANT_4D dz_ext1 = dz0 + 1 - 2 * SQUISH_CONSTANT_4D else: zsv_ext0 = zsv_ext1 = zsb + 1 dz_ext0 = dz0 - 1 - 3 * SQUISH_CONSTANT_4D dz_ext1 = dz0 - 1 - 2 * SQUISH_CONSTANT_4D if (c1 & 0x08) == 0: wsv_ext0 = wsb wsv_ext1 = wsb - 1 dw_ext0 = dw0 - 3 * SQUISH_CONSTANT_4D dw_ext1 = dw0 + 1 - 2 * SQUISH_CONSTANT_4D else: wsv_ext0 = wsv_ext1 = wsb + 1 dw_ext0 = dw0 - 1 - 3 * SQUISH_CONSTANT_4D dw_ext1 = dw0 - 1 - 2 * SQUISH_CONSTANT_4D # One combination is a _permutation of (0,0,0,2) based on c2 xsv_ext2 = xsb ysv_ext2 = ysb zsv_ext2 = zsb wsv_ext2 = wsb dx_ext2 = dx0 - 2 * SQUISH_CONSTANT_4D dy_ext2 = dy0 - 2 * SQUISH_CONSTANT_4D dz_ext2 = dz0 - 2 * SQUISH_CONSTANT_4D dw_ext2 = dw0 - 2 * SQUISH_CONSTANT_4D if (c2 & 0x01) != 0: xsv_ext2 += 2 dx_ext2 -= 2 elif (c2 & 0x02) != 0: ysv_ext2 += 2 dy_ext2 -= 2 elif (c2 & 0x04) != 0: zsv_ext2 += 2 dz_ext2 -= 2 else: wsv_ext2 += 2 dw_ext2 -= 2 else: # Both closest pos on the smaller side # One of the two extra pos is (0,0,0,0) xsv_ext2 = xsb ysv_ext2 = ysb zsv_ext2 = zsb wsv_ext2 = wsb dx_ext2 = dx0 dy_ext2 = dy0 dz_ext2 = dz0 dw_ext2 = dw0 # Other two pos are based on the omitted axes. c = (a_po | b_po) if (c & 0x01) == 0: xsv_ext0 = xsb - 1 xsv_ext1 = xsb dx_ext0 = dx0 + 1 - SQUISH_CONSTANT_4D dx_ext1 = dx0 - SQUISH_CONSTANT_4D else: xsv_ext0 = xsv_ext1 = xsb + 1 dx_ext0 = dx_ext1 = dx0 - 1 - SQUISH_CONSTANT_4D if (c & 0x02) == 0: ysv_ext0 = ysv_ext1 = ysb dy_ext0 = dy_ext1 = dy0 - SQUISH_CONSTANT_4D if (c & 0x01) == 0x01: ysv_ext0 -= 1 dy_ext0 += 1 else: ysv_ext1 -= 1 dy_ext1 += 1 else: ysv_ext0 = ysv_ext1 = ysb + 1 dy_ext0 = dy_ext1 = dy0 - 1 - SQUISH_CONSTANT_4D if (c & 0x04) == 0: zsv_ext0 = zsv_ext1 = zsb dz_ext0 = dz_ext1 = dz0 - SQUISH_CONSTANT_4D if (c & 0x03) == 0x03: zsv_ext0 -= 1 dz_ext0 += 1 else: zsv_ext1 -= 1 dz_ext1 += 1 else: zsv_ext0 = zsv_ext1 = zsb + 1 dz_ext0 = dz_ext1 = dz0 - 1 - SQUISH_CONSTANT_4D if (c & 0x08) == 0: wsv_ext0 = wsb wsv_ext1 = wsb - 1 dw_ext0 = dw0 - SQUISH_CONSTANT_4D dw_ext1 = dw0 + 1 - SQUISH_CONSTANT_4D else: wsv_ext0 = wsv_ext1 = wsb + 1 dw_ext0 = dw_ext1 = dw0 - 1 - SQUISH_CONSTANT_4D else: # One po on each "side" if a_is_bigger_side: c1 = a_po c2 = b_po else: c1 = b_po c2 = a_po # Two contributions are the bigger-sided po with each 0 replaced with -1. if (c1 & 0x01) == 0: xsv_ext0 = xsb - 1 xsv_ext1 = xsb dx_ext0 = dx0 + 1 - SQUISH_CONSTANT_4D dx_ext1 = dx0 - SQUISH_CONSTANT_4D else: xsv_ext0 = xsv_ext1 = xsb + 1 dx_ext0 = dx_ext1 = dx0 - 1 - SQUISH_CONSTANT_4D if (c1 & 0x02) == 0: ysv_ext0 = ysv_ext1 = ysb dy_ext0 = dy_ext1 = dy0 - SQUISH_CONSTANT_4D if (c1 & 0x01) == 0x01: ysv_ext0 -= 1 dy_ext0 += 1 else: ysv_ext1 -= 1 dy_ext1 += 1 else: ysv_ext0 = ysv_ext1 = ysb + 1 dy_ext0 = dy_ext1 = dy0 - 1 - SQUISH_CONSTANT_4D if (c1 & 0x04) == 0: zsv_ext0 = zsv_ext1 = zsb dz_ext0 = dz_ext1 = dz0 - SQUISH_CONSTANT_4D if (c1 & 0x03) == 0x03: zsv_ext0 -= 1 dz_ext0 += 1 else: zsv_ext1 -= 1 dz_ext1 += 1 else: zsv_ext0 = zsv_ext1 = zsb + 1 dz_ext0 = dz_ext1 = dz0 - 1 - SQUISH_CONSTANT_4D if (c1 & 0x08) == 0: wsv_ext0 = wsb wsv_ext1 = wsb - 1 dw_ext0 = dw0 - SQUISH_CONSTANT_4D dw_ext1 = dw0 + 1 - SQUISH_CONSTANT_4D else: wsv_ext0 = wsv_ext1 = wsb + 1 dw_ext0 = dw_ext1 = dw0 - 1 - SQUISH_CONSTANT_4D # One contribution is a _permutation of (0,0,0,2) based on the smaller-sided po xsv_ext2 = xsb ysv_ext2 = ysb zsv_ext2 = zsb wsv_ext2 = wsb dx_ext2 = dx0 - 2 * SQUISH_CONSTANT_4D dy_ext2 = dy0 - 2 * SQUISH_CONSTANT_4D dz_ext2 = dz0 - 2 * SQUISH_CONSTANT_4D dw_ext2 = dw0 - 2 * SQUISH_CONSTANT_4D if (c2 & 0x01) != 0: xsv_ext2 += 2 dx_ext2 -= 2 elif (c2 & 0x02) != 0: ysv_ext2 += 2 dy_ext2 -= 2 elif (c2 & 0x04) != 0: zsv_ext2 += 2 dz_ext2 -= 2 else: wsv_ext2 += 2 dw_ext2 -= 2 # Contribution (1,0,0,0) dx1 = dx0 - 1 - SQUISH_CONSTANT_4D dy1 = dy0 - 0 - SQUISH_CONSTANT_4D dz1 = dz0 - 0 - SQUISH_CONSTANT_4D dw1 = dw0 - 0 - SQUISH_CONSTANT_4D attn1 = 2 - dx1 * dx1 - dy1 * dy1 - dz1 * dz1 - dw1 * dw1 if attn1 > 0: attn1 *= attn1 value += attn1 * attn1 * extrapolate(xsb + 1, ysb + 0, zsb + 0, wsb + 0, dx1, dy1, dz1, dw1) # Contribution (0,1,0,0) dx2 = dx0 - 0 - SQUISH_CONSTANT_4D dy2 = dy0 - 1 - SQUISH_CONSTANT_4D dz2 = dz1 dw2 = dw1 attn2 = 2 - dx2 * dx2 - dy2 * dy2 - dz2 * dz2 - dw2 * dw2 if attn2 > 0: attn2 *= attn2 value += attn2 * attn2 * extrapolate(xsb + 0, ysb + 1, zsb + 0, wsb + 0, dx2, dy2, dz2, dw2) # Contribution (0,0,1,0) dx3 = dx2 dy3 = dy1 dz3 = dz0 - 1 - SQUISH_CONSTANT_4D dw3 = dw1 attn3 = 2 - dx3 * dx3 - dy3 * dy3 - dz3 * dz3 - dw3 * dw3 if attn3 > 0: attn3 *= attn3 value += attn3 * attn3 * extrapolate(xsb + 0, ysb + 0, zsb + 1, wsb + 0, dx3, dy3, dz3, dw3) # Contribution (0,0,0,1) dx4 = dx2 dy4 = dy1 dz4 = dz1 dw4 = dw0 - 1 - SQUISH_CONSTANT_4D attn4 = 2 - dx4 * dx4 - dy4 * dy4 - dz4 * dz4 - dw4 * dw4 if attn4 > 0: attn4 *= attn4 value += attn4 * attn4 * extrapolate(xsb + 0, ysb + 0, zsb + 0, wsb + 1, dx4, dy4, dz4, dw4) # Contribution (1,1,0,0) dx5 = dx0 - 1 - 2 * SQUISH_CONSTANT_4D dy5 = dy0 - 1 - 2 * SQUISH_CONSTANT_4D dz5 = dz0 - 0 - 2 * SQUISH_CONSTANT_4D dw5 = dw0 - 0 - 2 * SQUISH_CONSTANT_4D attn5 = 2 - dx5 * dx5 - dy5 * dy5 - dz5 * dz5 - dw5 * dw5 if attn5 > 0: attn5 *= attn5 value += attn5 * attn5 * extrapolate(xsb + 1, ysb + 1, zsb + 0, wsb + 0, dx5, dy5, dz5, dw5) # Contribution (1,0,1,0) dx6 = dx0 - 1 - 2 * SQUISH_CONSTANT_4D dy6 = dy0 - 0 - 2 * SQUISH_CONSTANT_4D dz6 = dz0 - 1 - 2 * SQUISH_CONSTANT_4D dw6 = dw0 - 0 - 2 * SQUISH_CONSTANT_4D attn6 = 2 - dx6 * dx6 - dy6 * dy6 - dz6 * dz6 - dw6 * dw6 if attn6 > 0: attn6 *= attn6 value += attn6 * attn6 * extrapolate(xsb + 1, ysb + 0, zsb + 1, wsb + 0, dx6, dy6, dz6, dw6) # Contribution (1,0,0,1) dx7 = dx0 - 1 - 2 * SQUISH_CONSTANT_4D dy7 = dy0 - 0 - 2 * SQUISH_CONSTANT_4D dz7 = dz0 - 0 - 2 * SQUISH_CONSTANT_4D dw7 = dw0 - 1 - 2 * SQUISH_CONSTANT_4D attn7 = 2 - dx7 * dx7 - dy7 * dy7 - dz7 * dz7 - dw7 * dw7 if attn7 > 0: attn7 *= attn7 value += attn7 * attn7 * extrapolate(xsb + 1, ysb + 0, zsb + 0, wsb + 1, dx7, dy7, dz7, dw7) # Contribution (0,1,1,0) dx8 = dx0 - 0 - 2 * SQUISH_CONSTANT_4D dy8 = dy0 - 1 - 2 * SQUISH_CONSTANT_4D dz8 = dz0 - 1 - 2 * SQUISH_CONSTANT_4D dw8 = dw0 - 0 - 2 * SQUISH_CONSTANT_4D attn8 = 2 - dx8 * dx8 - dy8 * dy8 - dz8 * dz8 - dw8 * dw8 if attn8 > 0: attn8 *= attn8 value += attn8 * attn8 * extrapolate(xsb + 0, ysb + 1, zsb + 1, wsb + 0, dx8, dy8, dz8, dw8) # Contribution (0,1,0,1) dx9 = dx0 - 0 - 2 * SQUISH_CONSTANT_4D dy9 = dy0 - 1 - 2 * SQUISH_CONSTANT_4D dz9 = dz0 - 0 - 2 * SQUISH_CONSTANT_4D dw9 = dw0 - 1 - 2 * SQUISH_CONSTANT_4D attn9 = 2 - dx9 * dx9 - dy9 * dy9 - dz9 * dz9 - dw9 * dw9 if attn9 > 0: attn9 *= attn9 value += attn9 * attn9 * extrapolate(xsb + 0, ysb + 1, zsb + 0, wsb + 1, dx9, dy9, dz9, dw9) # Contribution (0,0,1,1) dx10 = dx0 - 0 - 2 * SQUISH_CONSTANT_4D dy10 = dy0 - 0 - 2 * SQUISH_CONSTANT_4D dz10 = dz0 - 1 - 2 * SQUISH_CONSTANT_4D dw10 = dw0 - 1 - 2 * SQUISH_CONSTANT_4D attn10 = 2 - dx10 * dx10 - dy10 * dy10 - dz10 * dz10 - dw10 * dw10 if attn10 > 0: attn10 *= attn10 value += attn10 * attn10 * extrapolate(xsb + 0, ysb + 0, zsb + 1, wsb + 1, dx10, dy10, dz10, dw10) else: # We're inside the second dispentachoron (Rectified 4-Simplex) a_is_bigger_side = True b_is_bigger_side = True # Decide between (0,0,1,1) and (1,1,0,0) if xins + yins < zins + wins: a_score = xins + yins a_po = 0x0C else: a_score = zins + wins a_po = 0x03 # Decide between (0,1,0,1) and (1,0,1,0) if xins + zins < yins + wins: b_score = xins + zins b_po = 0x0A else: b_score = yins + wins b_po = 0x05 # Closer between (0,1,1,0) and (1,0,0,1) will replace the further of a and b, if closer. if xins + wins < yins + zins: score = xins + wins if a_score <= b_score and score < b_score: b_score = score b_po = 0x06 elif a_score > b_score and score < a_score: a_score = score a_po = 0x06 else: score = yins + zins if a_score <= b_score and score < b_score: b_score = score b_po = 0x09 elif a_score > b_score and score < a_score: a_score = score a_po = 0x09 # Decide if (0,1,1,1) is closer. p1 = 3 - in_sum + xins if a_score <= b_score and p1 < b_score: b_score = p1 b_po = 0x0E b_is_bigger_side = False elif a_score > b_score and p1 < a_score: a_score = p1 a_po = 0x0E a_is_bigger_side = False # Decide if (1,0,1,1) is closer. p2 = 3 - in_sum + yins if a_score <= b_score and p2 < b_score: b_score = p2 b_po = 0x0D b_is_bigger_side = False elif a_score > b_score and p2 < a_score: a_score = p2 a_po = 0x0D a_is_bigger_side = False # Decide if (1,1,0,1) is closer. p3 = 3 - in_sum + zins if a_score <= b_score and p3 < b_score: b_score = p3 b_po = 0x0B b_is_bigger_side = False elif a_score > b_score and p3 < a_score: a_score = p3 a_po = 0x0B a_is_bigger_side = False # Decide if (1,1,1,0) is closer. p4 = 3 - in_sum + wins if a_score <= b_score and p4 < b_score: b_po = 0x07 b_is_bigger_side = False elif a_score > b_score and p4 < a_score: a_po = 0x07 a_is_bigger_side = False # Where each of the two closest pos are determines how the extra three vertices are calculated. if a_is_bigger_side == b_is_bigger_side: if a_is_bigger_side: # Both closest pos on the bigger side c1 = (a_po & b_po) c2 = (a_po | b_po) # Two contributions are _permutations of (0,0,0,1) and (0,0,0,2) based on c1 xsv_ext0 = xsv_ext1 = xsb ysv_ext0 = ysv_ext1 = ysb zsv_ext0 = zsv_ext1 = zsb wsv_ext0 = wsv_ext1 = wsb dx_ext0 = dx0 - SQUISH_CONSTANT_4D dy_ext0 = dy0 - SQUISH_CONSTANT_4D dz_ext0 = dz0 - SQUISH_CONSTANT_4D dw_ext0 = dw0 - SQUISH_CONSTANT_4D dx_ext1 = dx0 - 2 * SQUISH_CONSTANT_4D dy_ext1 = dy0 - 2 * SQUISH_CONSTANT_4D dz_ext1 = dz0 - 2 * SQUISH_CONSTANT_4D dw_ext1 = dw0 - 2 * SQUISH_CONSTANT_4D if (c1 & 0x01) != 0: xsv_ext0 += 1 dx_ext0 -= 1 xsv_ext1 += 2 dx_ext1 -= 2 elif (c1 & 0x02) != 0: ysv_ext0 += 1 dy_ext0 -= 1 ysv_ext1 += 2 dy_ext1 -= 2 elif (c1 & 0x04) != 0: zsv_ext0 += 1 dz_ext0 -= 1 zsv_ext1 += 2 dz_ext1 -= 2 else: wsv_ext0 += 1 dw_ext0 -= 1 wsv_ext1 += 2 dw_ext1 -= 2 # One contribution is a _permutation of (1,1,1,-1) based on c2 xsv_ext2 = xsb + 1 ysv_ext2 = ysb + 1 zsv_ext2 = zsb + 1 wsv_ext2 = wsb + 1 dx_ext2 = dx0 - 1 - 2 * SQUISH_CONSTANT_4D dy_ext2 = dy0 - 1 - 2 * SQUISH_CONSTANT_4D dz_ext2 = dz0 - 1 - 2 * SQUISH_CONSTANT_4D dw_ext2 = dw0 - 1 - 2 * SQUISH_CONSTANT_4D if (c2 & 0x01) == 0: xsv_ext2 -= 2 dx_ext2 += 2 elif (c2 & 0x02) == 0: ysv_ext2 -= 2 dy_ext2 += 2 elif (c2 & 0x04) == 0: zsv_ext2 -= 2 dz_ext2 += 2 else: wsv_ext2 -= 2 dw_ext2 += 2 else: # Both closest pos on the smaller side # One of the two extra pos is (1,1,1,1) xsv_ext2 = xsb + 1 ysv_ext2 = ysb + 1 zsv_ext2 = zsb + 1 wsv_ext2 = wsb + 1 dx_ext2 = dx0 - 1 - 4 * SQUISH_CONSTANT_4D dy_ext2 = dy0 - 1 - 4 * SQUISH_CONSTANT_4D dz_ext2 = dz0 - 1 - 4 * SQUISH_CONSTANT_4D dw_ext2 = dw0 - 1 - 4 * SQUISH_CONSTANT_4D # Other two pos are based on the shared axes. c = (a_po & b_po) if (c & 0x01) != 0: xsv_ext0 = xsb + 2 xsv_ext1 = xsb + 1 dx_ext0 = dx0 - 2 - 3 * SQUISH_CONSTANT_4D dx_ext1 = dx0 - 1 - 3 * SQUISH_CONSTANT_4D else: xsv_ext0 = xsv_ext1 = xsb dx_ext0 = dx_ext1 = dx0 - 3 * SQUISH_CONSTANT_4D if (c & 0x02) != 0: ysv_ext0 = ysv_ext1 = ysb + 1 dy_ext0 = dy_ext1 = dy0 - 1 - 3 * SQUISH_CONSTANT_4D if (c & 0x01) == 0: ysv_ext0 += 1 dy_ext0 -= 1 else: ysv_ext1 += 1 dy_ext1 -= 1 else: ysv_ext0 = ysv_ext1 = ysb dy_ext0 = dy_ext1 = dy0 - 3 * SQUISH_CONSTANT_4D if (c & 0x04) != 0: zsv_ext0 = zsv_ext1 = zsb + 1 dz_ext0 = dz_ext1 = dz0 - 1 - 3 * SQUISH_CONSTANT_4D if (c & 0x03) == 0: zsv_ext0 += 1 dz_ext0 -= 1 else: zsv_ext1 += 1 dz_ext1 -= 1 else: zsv_ext0 = zsv_ext1 = zsb dz_ext0 = dz_ext1 = dz0 - 3 * SQUISH_CONSTANT_4D if (c & 0x08) != 0: wsv_ext0 = wsb + 1 wsv_ext1 = wsb + 2 dw_ext0 = dw0 - 1 - 3 * SQUISH_CONSTANT_4D dw_ext1 = dw0 - 2 - 3 * SQUISH_CONSTANT_4D else: wsv_ext0 = wsv_ext1 = wsb dw_ext0 = dw_ext1 = dw0 - 3 * SQUISH_CONSTANT_4D else: # One po on each "side" if a_is_bigger_side: c1 = a_po c2 = b_po else: c1 = b_po c2 = a_po # Two contributions are the bigger-sided po with each 1 replaced with 2. if (c1 & 0x01) != 0: xsv_ext0 = xsb + 2 xsv_ext1 = xsb + 1 dx_ext0 = dx0 - 2 - 3 * SQUISH_CONSTANT_4D dx_ext1 = dx0 - 1 - 3 * SQUISH_CONSTANT_4D else: xsv_ext0 = xsv_ext1 = xsb dx_ext0 = dx_ext1 = dx0 - 3 * SQUISH_CONSTANT_4D if (c1 & 0x02) != 0: ysv_ext0 = ysv_ext1 = ysb + 1 dy_ext0 = dy_ext1 = dy0 - 1 - 3 * SQUISH_CONSTANT_4D if (c1 & 0x01) == 0: ysv_ext0 += 1 dy_ext0 -= 1 else: ysv_ext1 += 1 dy_ext1 -= 1 else: ysv_ext0 = ysv_ext1 = ysb dy_ext0 = dy_ext1 = dy0 - 3 * SQUISH_CONSTANT_4D if (c1 & 0x04) != 0: zsv_ext0 = zsv_ext1 = zsb + 1 dz_ext0 = dz_ext1 = dz0 - 1 - 3 * SQUISH_CONSTANT_4D if (c1 & 0x03) == 0: zsv_ext0 += 1 dz_ext0 -= 1 else: zsv_ext1 += 1 dz_ext1 -= 1 else: zsv_ext0 = zsv_ext1 = zsb dz_ext0 = dz_ext1 = dz0 - 3 * SQUISH_CONSTANT_4D if (c1 & 0x08) != 0: wsv_ext0 = wsb + 1 wsv_ext1 = wsb + 2 dw_ext0 = dw0 - 1 - 3 * SQUISH_CONSTANT_4D dw_ext1 = dw0 - 2 - 3 * SQUISH_CONSTANT_4D else: wsv_ext0 = wsv_ext1 = wsb dw_ext0 = dw_ext1 = dw0 - 3 * SQUISH_CONSTANT_4D # One contribution is a _permutation of (1,1,1,-1) based on the smaller-sided po xsv_ext2 = xsb + 1 ysv_ext2 = ysb + 1 zsv_ext2 = zsb + 1 wsv_ext2 = wsb + 1 dx_ext2 = dx0 - 1 - 2 * SQUISH_CONSTANT_4D dy_ext2 = dy0 - 1 - 2 * SQUISH_CONSTANT_4D dz_ext2 = dz0 - 1 - 2 * SQUISH_CONSTANT_4D dw_ext2 = dw0 - 1 - 2 * SQUISH_CONSTANT_4D if (c2 & 0x01) == 0: xsv_ext2 -= 2 dx_ext2 += 2 elif (c2 & 0x02) == 0: ysv_ext2 -= 2 dy_ext2 += 2 elif (c2 & 0x04) == 0: zsv_ext2 -= 2 dz_ext2 += 2 else: wsv_ext2 -= 2 dw_ext2 += 2 # Contribution (1,1,1,0) dx4 = dx0 - 1 - 3 * SQUISH_CONSTANT_4D dy4 = dy0 - 1 - 3 * SQUISH_CONSTANT_4D dz4 = dz0 - 1 - 3 * SQUISH_CONSTANT_4D dw4 = dw0 - 3 * SQUISH_CONSTANT_4D attn4 = 2 - dx4 * dx4 - dy4 * dy4 - dz4 * dz4 - dw4 * dw4 if attn4 > 0: attn4 *= attn4 value += attn4 * attn4 * extrapolate(xsb + 1, ysb + 1, zsb + 1, wsb + 0, dx4, dy4, dz4, dw4) # Contribution (1,1,0,1) dx3 = dx4 dy3 = dy4 dz3 = dz0 - 3 * SQUISH_CONSTANT_4D dw3 = dw0 - 1 - 3 * SQUISH_CONSTANT_4D attn3 = 2 - dx3 * dx3 - dy3 * dy3 - dz3 * dz3 - dw3 * dw3 if attn3 > 0: attn3 *= attn3 value += attn3 * attn3 * extrapolate(xsb + 1, ysb + 1, zsb + 0, wsb + 1, dx3, dy3, dz3, dw3) # Contribution (1,0,1,1) dx2 = dx4 dy2 = dy0 - 3 * SQUISH_CONSTANT_4D dz2 = dz4 dw2 = dw3 attn2 = 2 - dx2 * dx2 - dy2 * dy2 - dz2 * dz2 - dw2 * dw2 if attn2 > 0: attn2 *= attn2 value += attn2 * attn2 * extrapolate(xsb + 1, ysb + 0, zsb + 1, wsb + 1, dx2, dy2, dz2, dw2) # Contribution (0,1,1,1) dx1 = dx0 - 3 * SQUISH_CONSTANT_4D dz1 = dz4 dy1 = dy4 dw1 = dw3 attn1 = 2 - dx1 * dx1 - dy1 * dy1 - dz1 * dz1 - dw1 * dw1 if attn1 > 0: attn1 *= attn1 value += attn1 * attn1 * extrapolate(xsb + 0, ysb + 1, zsb + 1, wsb + 1, dx1, dy1, dz1, dw1) # Contribution (1,1,0,0) dx5 = dx0 - 1 - 2 * SQUISH_CONSTANT_4D dy5 = dy0 - 1 - 2 * SQUISH_CONSTANT_4D dz5 = dz0 - 0 - 2 * SQUISH_CONSTANT_4D dw5 = dw0 - 0 - 2 * SQUISH_CONSTANT_4D attn5 = 2 - dx5 * dx5 - dy5 * dy5 - dz5 * dz5 - dw5 * dw5 if attn5 > 0: attn5 *= attn5 value += attn5 * attn5 * extrapolate(xsb + 1, ysb + 1, zsb + 0, wsb + 0, dx5, dy5, dz5, dw5) # Contribution (1,0,1,0) dx6 = dx0 - 1 - 2 * SQUISH_CONSTANT_4D dy6 = dy0 - 0 - 2 * SQUISH_CONSTANT_4D dz6 = dz0 - 1 - 2 * SQUISH_CONSTANT_4D dw6 = dw0 - 0 - 2 * SQUISH_CONSTANT_4D attn6 = 2 - dx6 * dx6 - dy6 * dy6 - dz6 * dz6 - dw6 * dw6 if attn6 > 0: attn6 *= attn6 value += attn6 * attn6 * extrapolate(xsb + 1, ysb + 0, zsb + 1, wsb + 0, dx6, dy6, dz6, dw6) # Contribution (1,0,0,1) dx7 = dx0 - 1 - 2 * SQUISH_CONSTANT_4D dy7 = dy0 - 0 - 2 * SQUISH_CONSTANT_4D dz7 = dz0 - 0 - 2 * SQUISH_CONSTANT_4D dw7 = dw0 - 1 - 2 * SQUISH_CONSTANT_4D attn7 = 2 - dx7 * dx7 - dy7 * dy7 - dz7 * dz7 - dw7 * dw7 if attn7 > 0: attn7 *= attn7 value += attn7 * attn7 * extrapolate(xsb + 1, ysb + 0, zsb + 0, wsb + 1, dx7, dy7, dz7, dw7) # Contribution (0,1,1,0) dx8 = dx0 - 0 - 2 * SQUISH_CONSTANT_4D dy8 = dy0 - 1 - 2 * SQUISH_CONSTANT_4D dz8 = dz0 - 1 - 2 * SQUISH_CONSTANT_4D dw8 = dw0 - 0 - 2 * SQUISH_CONSTANT_4D attn8 = 2 - dx8 * dx8 - dy8 * dy8 - dz8 * dz8 - dw8 * dw8 if attn8 > 0: attn8 *= attn8 value += attn8 * attn8 * extrapolate(xsb + 0, ysb + 1, zsb + 1, wsb + 0, dx8, dy8, dz8, dw8) # Contribution (0,1,0,1) dx9 = dx0 - 0 - 2 * SQUISH_CONSTANT_4D dy9 = dy0 - 1 - 2 * SQUISH_CONSTANT_4D dz9 = dz0 - 0 - 2 * SQUISH_CONSTANT_4D dw9 = dw0 - 1 - 2 * SQUISH_CONSTANT_4D attn9 = 2 - dx9 * dx9 - dy9 * dy9 - dz9 * dz9 - dw9 * dw9 if attn9 > 0: attn9 *= attn9 value += attn9 * attn9 * extrapolate(xsb + 0, ysb + 1, zsb + 0, wsb + 1, dx9, dy9, dz9, dw9) # Contribution (0,0,1,1) dx10 = dx0 - 0 - 2 * SQUISH_CONSTANT_4D dy10 = dy0 - 0 - 2 * SQUISH_CONSTANT_4D dz10 = dz0 - 1 - 2 * SQUISH_CONSTANT_4D dw10 = dw0 - 1 - 2 * SQUISH_CONSTANT_4D attn10 = 2 - dx10 * dx10 - dy10 * dy10 - dz10 * dz10 - dw10 * dw10 if attn10 > 0: attn10 *= attn10 value += attn10 * attn10 * extrapolate(xsb + 0, ysb + 0, zsb + 1, wsb + 1, dx10, dy10, dz10, dw10) # First extra vertex attn_ext0 = 2 - dx_ext0 * dx_ext0 - dy_ext0 * dy_ext0 - dz_ext0 * dz_ext0 - dw_ext0 * dw_ext0 if attn_ext0 > 0: attn_ext0 *= attn_ext0 value += attn_ext0 * attn_ext0 * extrapolate(xsv_ext0, ysv_ext0, zsv_ext0, wsv_ext0, dx_ext0, dy_ext0, dz_ext0, dw_ext0) # Second extra vertex attn_ext1 = 2 - dx_ext1 * dx_ext1 - dy_ext1 * dy_ext1 - dz_ext1 * dz_ext1 - dw_ext1 * dw_ext1 if attn_ext1 > 0: attn_ext1 *= attn_ext1 value += attn_ext1 * attn_ext1 * extrapolate(xsv_ext1, ysv_ext1, zsv_ext1, wsv_ext1, dx_ext1, dy_ext1, dz_ext1, dw_ext1) # Third extra vertex attn_ext2 = 2 - dx_ext2 * dx_ext2 - dy_ext2 * dy_ext2 - dz_ext2 * dz_ext2 - dw_ext2 * dw_ext2 if attn_ext2 > 0: attn_ext2 *= attn_ext2 value += attn_ext2 * attn_ext2 * extrapolate(xsv_ext2, ysv_ext2, zsv_ext2, wsv_ext2, dx_ext2, dy_ext2, dz_ext2, dw_ext2) return value / NORM_CONSTANT_4D
python
def noise4d(self, x, y, z, w): """ Generate 4D OpenSimplex noise from X,Y,Z,W coordinates. """ # Place input coordinates on simplectic honeycomb. stretch_offset = (x + y + z + w) * STRETCH_CONSTANT_4D xs = x + stretch_offset ys = y + stretch_offset zs = z + stretch_offset ws = w + stretch_offset # Floor to get simplectic honeycomb coordinates of rhombo-hypercube super-cell origin. xsb = floor(xs) ysb = floor(ys) zsb = floor(zs) wsb = floor(ws) # Skew out to get actual coordinates of stretched rhombo-hypercube origin. We'll need these later. squish_offset = (xsb + ysb + zsb + wsb) * SQUISH_CONSTANT_4D xb = xsb + squish_offset yb = ysb + squish_offset zb = zsb + squish_offset wb = wsb + squish_offset # Compute simplectic honeycomb coordinates relative to rhombo-hypercube origin. xins = xs - xsb yins = ys - ysb zins = zs - zsb wins = ws - wsb # Sum those together to get a value that determines which region we're in. in_sum = xins + yins + zins + wins # Positions relative to origin po. dx0 = x - xb dy0 = y - yb dz0 = z - zb dw0 = w - wb value = 0 extrapolate = self._extrapolate4d if in_sum <= 1: # We're inside the pentachoron (4-Simplex) at (0,0,0,0) # Determine which two of (0,0,0,1), (0,0,1,0), (0,1,0,0), (1,0,0,0) are closest. a_po = 0x01 a_score = xins b_po = 0x02 b_score = yins if a_score >= b_score and zins > b_score: b_score = zins b_po = 0x04 elif a_score < b_score and zins > a_score: a_score = zins a_po = 0x04 if a_score >= b_score and wins > b_score: b_score = wins b_po = 0x08 elif a_score < b_score and wins > a_score: a_score = wins a_po = 0x08 # Now we determine the three lattice pos not part of the pentachoron that may contribute. # This depends on the closest two pentachoron vertices, including (0,0,0,0) uins = 1 - in_sum if uins > a_score or uins > b_score: # (0,0,0,0) is one of the closest two pentachoron vertices. c = b_po if (b_score > a_score) else a_po # Our other closest vertex is the closest out of a and b. if (c & 0x01) == 0: xsv_ext0 = xsb - 1 xsv_ext1 = xsv_ext2 = xsb dx_ext0 = dx0 + 1 dx_ext1 = dx_ext2 = dx0 else: xsv_ext0 = xsv_ext1 = xsv_ext2 = xsb + 1 dx_ext0 = dx_ext1 = dx_ext2 = dx0 - 1 if (c & 0x02) == 0: ysv_ext0 = ysv_ext1 = ysv_ext2 = ysb dy_ext0 = dy_ext1 = dy_ext2 = dy0 if (c & 0x01) == 0x01: ysv_ext0 -= 1 dy_ext0 += 1 else: ysv_ext1 -= 1 dy_ext1 += 1 else: ysv_ext0 = ysv_ext1 = ysv_ext2 = ysb + 1 dy_ext0 = dy_ext1 = dy_ext2 = dy0 - 1 if (c & 0x04) == 0: zsv_ext0 = zsv_ext1 = zsv_ext2 = zsb dz_ext0 = dz_ext1 = dz_ext2 = dz0 if (c & 0x03) != 0: if (c & 0x03) == 0x03: zsv_ext0 -= 1 dz_ext0 += 1 else: zsv_ext1 -= 1 dz_ext1 += 1 else: zsv_ext2 -= 1 dz_ext2 += 1 else: zsv_ext0 = zsv_ext1 = zsv_ext2 = zsb + 1 dz_ext0 = dz_ext1 = dz_ext2 = dz0 - 1 if (c & 0x08) == 0: wsv_ext0 = wsv_ext1 = wsb wsv_ext2 = wsb - 1 dw_ext0 = dw_ext1 = dw0 dw_ext2 = dw0 + 1 else: wsv_ext0 = wsv_ext1 = wsv_ext2 = wsb + 1 dw_ext0 = dw_ext1 = dw_ext2 = dw0 - 1 else: # (0,0,0,0) is not one of the closest two pentachoron vertices. c = (a_po | b_po) # Our three extra vertices are determined by the closest two. if (c & 0x01) == 0: xsv_ext0 = xsv_ext2 = xsb xsv_ext1 = xsb - 1 dx_ext0 = dx0 - 2 * SQUISH_CONSTANT_4D dx_ext1 = dx0 + 1 - SQUISH_CONSTANT_4D dx_ext2 = dx0 - SQUISH_CONSTANT_4D else: xsv_ext0 = xsv_ext1 = xsv_ext2 = xsb + 1 dx_ext0 = dx0 - 1 - 2 * SQUISH_CONSTANT_4D dx_ext1 = dx_ext2 = dx0 - 1 - SQUISH_CONSTANT_4D if (c & 0x02) == 0: ysv_ext0 = ysv_ext1 = ysv_ext2 = ysb dy_ext0 = dy0 - 2 * SQUISH_CONSTANT_4D dy_ext1 = dy_ext2 = dy0 - SQUISH_CONSTANT_4D if (c & 0x01) == 0x01: ysv_ext1 -= 1 dy_ext1 += 1 else: ysv_ext2 -= 1 dy_ext2 += 1 else: ysv_ext0 = ysv_ext1 = ysv_ext2 = ysb + 1 dy_ext0 = dy0 - 1 - 2 * SQUISH_CONSTANT_4D dy_ext1 = dy_ext2 = dy0 - 1 - SQUISH_CONSTANT_4D if (c & 0x04) == 0: zsv_ext0 = zsv_ext1 = zsv_ext2 = zsb dz_ext0 = dz0 - 2 * SQUISH_CONSTANT_4D dz_ext1 = dz_ext2 = dz0 - SQUISH_CONSTANT_4D if (c & 0x03) == 0x03: zsv_ext1 -= 1 dz_ext1 += 1 else: zsv_ext2 -= 1 dz_ext2 += 1 else: zsv_ext0 = zsv_ext1 = zsv_ext2 = zsb + 1 dz_ext0 = dz0 - 1 - 2 * SQUISH_CONSTANT_4D dz_ext1 = dz_ext2 = dz0 - 1 - SQUISH_CONSTANT_4D if (c & 0x08) == 0: wsv_ext0 = wsv_ext1 = wsb wsv_ext2 = wsb - 1 dw_ext0 = dw0 - 2 * SQUISH_CONSTANT_4D dw_ext1 = dw0 - SQUISH_CONSTANT_4D dw_ext2 = dw0 + 1 - SQUISH_CONSTANT_4D else: wsv_ext0 = wsv_ext1 = wsv_ext2 = wsb + 1 dw_ext0 = dw0 - 1 - 2 * SQUISH_CONSTANT_4D dw_ext1 = dw_ext2 = dw0 - 1 - SQUISH_CONSTANT_4D # Contribution (0,0,0,0) attn0 = 2 - dx0 * dx0 - dy0 * dy0 - dz0 * dz0 - dw0 * dw0 if attn0 > 0: attn0 *= attn0 value += attn0 * attn0 * extrapolate(xsb + 0, ysb + 0, zsb + 0, wsb + 0, dx0, dy0, dz0, dw0) # Contribution (1,0,0,0) dx1 = dx0 - 1 - SQUISH_CONSTANT_4D dy1 = dy0 - 0 - SQUISH_CONSTANT_4D dz1 = dz0 - 0 - SQUISH_CONSTANT_4D dw1 = dw0 - 0 - SQUISH_CONSTANT_4D attn1 = 2 - dx1 * dx1 - dy1 * dy1 - dz1 * dz1 - dw1 * dw1 if attn1 > 0: attn1 *= attn1 value += attn1 * attn1 * extrapolate(xsb + 1, ysb + 0, zsb + 0, wsb + 0, dx1, dy1, dz1, dw1) # Contribution (0,1,0,0) dx2 = dx0 - 0 - SQUISH_CONSTANT_4D dy2 = dy0 - 1 - SQUISH_CONSTANT_4D dz2 = dz1 dw2 = dw1 attn2 = 2 - dx2 * dx2 - dy2 * dy2 - dz2 * dz2 - dw2 * dw2 if attn2 > 0: attn2 *= attn2 value += attn2 * attn2 * extrapolate(xsb + 0, ysb + 1, zsb + 0, wsb + 0, dx2, dy2, dz2, dw2) # Contribution (0,0,1,0) dx3 = dx2 dy3 = dy1 dz3 = dz0 - 1 - SQUISH_CONSTANT_4D dw3 = dw1 attn3 = 2 - dx3 * dx3 - dy3 * dy3 - dz3 * dz3 - dw3 * dw3 if attn3 > 0: attn3 *= attn3 value += attn3 * attn3 * extrapolate(xsb + 0, ysb + 0, zsb + 1, wsb + 0, dx3, dy3, dz3, dw3) # Contribution (0,0,0,1) dx4 = dx2 dy4 = dy1 dz4 = dz1 dw4 = dw0 - 1 - SQUISH_CONSTANT_4D attn4 = 2 - dx4 * dx4 - dy4 * dy4 - dz4 * dz4 - dw4 * dw4 if attn4 > 0: attn4 *= attn4 value += attn4 * attn4 * extrapolate(xsb + 0, ysb + 0, zsb + 0, wsb + 1, dx4, dy4, dz4, dw4) elif in_sum >= 3: # We're inside the pentachoron (4-Simplex) at (1,1,1,1) # Determine which two of (1,1,1,0), (1,1,0,1), (1,0,1,1), (0,1,1,1) are closest. a_po = 0x0E a_score = xins b_po = 0x0D b_score = yins if a_score <= b_score and zins < b_score: b_score = zins b_po = 0x0B elif a_score > b_score and zins < a_score: a_score = zins a_po = 0x0B if a_score <= b_score and wins < b_score: b_score = wins b_po = 0x07 elif a_score > b_score and wins < a_score: a_score = wins a_po = 0x07 # Now we determine the three lattice pos not part of the pentachoron that may contribute. # This depends on the closest two pentachoron vertices, including (0,0,0,0) uins = 4 - in_sum if uins < a_score or uins < b_score: # (1,1,1,1) is one of the closest two pentachoron vertices. c = b_po if (b_score < a_score) else a_po # Our other closest vertex is the closest out of a and b. if (c & 0x01) != 0: xsv_ext0 = xsb + 2 xsv_ext1 = xsv_ext2 = xsb + 1 dx_ext0 = dx0 - 2 - 4 * SQUISH_CONSTANT_4D dx_ext1 = dx_ext2 = dx0 - 1 - 4 * SQUISH_CONSTANT_4D else: xsv_ext0 = xsv_ext1 = xsv_ext2 = xsb dx_ext0 = dx_ext1 = dx_ext2 = dx0 - 4 * SQUISH_CONSTANT_4D if (c & 0x02) != 0: ysv_ext0 = ysv_ext1 = ysv_ext2 = ysb + 1 dy_ext0 = dy_ext1 = dy_ext2 = dy0 - 1 - 4 * SQUISH_CONSTANT_4D if (c & 0x01) != 0: ysv_ext1 += 1 dy_ext1 -= 1 else: ysv_ext0 += 1 dy_ext0 -= 1 else: ysv_ext0 = ysv_ext1 = ysv_ext2 = ysb dy_ext0 = dy_ext1 = dy_ext2 = dy0 - 4 * SQUISH_CONSTANT_4D if (c & 0x04) != 0: zsv_ext0 = zsv_ext1 = zsv_ext2 = zsb + 1 dz_ext0 = dz_ext1 = dz_ext2 = dz0 - 1 - 4 * SQUISH_CONSTANT_4D if (c & 0x03) != 0x03: if (c & 0x03) == 0: zsv_ext0 += 1 dz_ext0 -= 1 else: zsv_ext1 += 1 dz_ext1 -= 1 else: zsv_ext2 += 1 dz_ext2 -= 1 else: zsv_ext0 = zsv_ext1 = zsv_ext2 = zsb dz_ext0 = dz_ext1 = dz_ext2 = dz0 - 4 * SQUISH_CONSTANT_4D if (c & 0x08) != 0: wsv_ext0 = wsv_ext1 = wsb + 1 wsv_ext2 = wsb + 2 dw_ext0 = dw_ext1 = dw0 - 1 - 4 * SQUISH_CONSTANT_4D dw_ext2 = dw0 - 2 - 4 * SQUISH_CONSTANT_4D else: wsv_ext0 = wsv_ext1 = wsv_ext2 = wsb dw_ext0 = dw_ext1 = dw_ext2 = dw0 - 4 * SQUISH_CONSTANT_4D else: # (1,1,1,1) is not one of the closest two pentachoron vertices. c = (a_po & b_po) # Our three extra vertices are determined by the closest two. if (c & 0x01) != 0: xsv_ext0 = xsv_ext2 = xsb + 1 xsv_ext1 = xsb + 2 dx_ext0 = dx0 - 1 - 2 * SQUISH_CONSTANT_4D dx_ext1 = dx0 - 2 - 3 * SQUISH_CONSTANT_4D dx_ext2 = dx0 - 1 - 3 * SQUISH_CONSTANT_4D else: xsv_ext0 = xsv_ext1 = xsv_ext2 = xsb dx_ext0 = dx0 - 2 * SQUISH_CONSTANT_4D dx_ext1 = dx_ext2 = dx0 - 3 * SQUISH_CONSTANT_4D if (c & 0x02) != 0: ysv_ext0 = ysv_ext1 = ysv_ext2 = ysb + 1 dy_ext0 = dy0 - 1 - 2 * SQUISH_CONSTANT_4D dy_ext1 = dy_ext2 = dy0 - 1 - 3 * SQUISH_CONSTANT_4D if (c & 0x01) != 0: ysv_ext2 += 1 dy_ext2 -= 1 else: ysv_ext1 += 1 dy_ext1 -= 1 else: ysv_ext0 = ysv_ext1 = ysv_ext2 = ysb dy_ext0 = dy0 - 2 * SQUISH_CONSTANT_4D dy_ext1 = dy_ext2 = dy0 - 3 * SQUISH_CONSTANT_4D if (c & 0x04) != 0: zsv_ext0 = zsv_ext1 = zsv_ext2 = zsb + 1 dz_ext0 = dz0 - 1 - 2 * SQUISH_CONSTANT_4D dz_ext1 = dz_ext2 = dz0 - 1 - 3 * SQUISH_CONSTANT_4D if (c & 0x03) != 0: zsv_ext2 += 1 dz_ext2 -= 1 else: zsv_ext1 += 1 dz_ext1 -= 1 else: zsv_ext0 = zsv_ext1 = zsv_ext2 = zsb dz_ext0 = dz0 - 2 * SQUISH_CONSTANT_4D dz_ext1 = dz_ext2 = dz0 - 3 * SQUISH_CONSTANT_4D if (c & 0x08) != 0: wsv_ext0 = wsv_ext1 = wsb + 1 wsv_ext2 = wsb + 2 dw_ext0 = dw0 - 1 - 2 * SQUISH_CONSTANT_4D dw_ext1 = dw0 - 1 - 3 * SQUISH_CONSTANT_4D dw_ext2 = dw0 - 2 - 3 * SQUISH_CONSTANT_4D else: wsv_ext0 = wsv_ext1 = wsv_ext2 = wsb dw_ext0 = dw0 - 2 * SQUISH_CONSTANT_4D dw_ext1 = dw_ext2 = dw0 - 3 * SQUISH_CONSTANT_4D # Contribution (1,1,1,0) dx4 = dx0 - 1 - 3 * SQUISH_CONSTANT_4D dy4 = dy0 - 1 - 3 * SQUISH_CONSTANT_4D dz4 = dz0 - 1 - 3 * SQUISH_CONSTANT_4D dw4 = dw0 - 3 * SQUISH_CONSTANT_4D attn4 = 2 - dx4 * dx4 - dy4 * dy4 - dz4 * dz4 - dw4 * dw4 if attn4 > 0: attn4 *= attn4 value += attn4 * attn4 * extrapolate(xsb + 1, ysb + 1, zsb + 1, wsb + 0, dx4, dy4, dz4, dw4) # Contribution (1,1,0,1) dx3 = dx4 dy3 = dy4 dz3 = dz0 - 3 * SQUISH_CONSTANT_4D dw3 = dw0 - 1 - 3 * SQUISH_CONSTANT_4D attn3 = 2 - dx3 * dx3 - dy3 * dy3 - dz3 * dz3 - dw3 * dw3 if attn3 > 0: attn3 *= attn3 value += attn3 * attn3 * extrapolate(xsb + 1, ysb + 1, zsb + 0, wsb + 1, dx3, dy3, dz3, dw3) # Contribution (1,0,1,1) dx2 = dx4 dy2 = dy0 - 3 * SQUISH_CONSTANT_4D dz2 = dz4 dw2 = dw3 attn2 = 2 - dx2 * dx2 - dy2 * dy2 - dz2 * dz2 - dw2 * dw2 if attn2 > 0: attn2 *= attn2 value += attn2 * attn2 * extrapolate(xsb + 1, ysb + 0, zsb + 1, wsb + 1, dx2, dy2, dz2, dw2) # Contribution (0,1,1,1) dx1 = dx0 - 3 * SQUISH_CONSTANT_4D dz1 = dz4 dy1 = dy4 dw1 = dw3 attn1 = 2 - dx1 * dx1 - dy1 * dy1 - dz1 * dz1 - dw1 * dw1 if attn1 > 0: attn1 *= attn1 value += attn1 * attn1 * extrapolate(xsb + 0, ysb + 1, zsb + 1, wsb + 1, dx1, dy1, dz1, dw1) # Contribution (1,1,1,1) dx0 = dx0 - 1 - 4 * SQUISH_CONSTANT_4D dy0 = dy0 - 1 - 4 * SQUISH_CONSTANT_4D dz0 = dz0 - 1 - 4 * SQUISH_CONSTANT_4D dw0 = dw0 - 1 - 4 * SQUISH_CONSTANT_4D attn0 = 2 - dx0 * dx0 - dy0 * dy0 - dz0 * dz0 - dw0 * dw0 if attn0 > 0: attn0 *= attn0 value += attn0 * attn0 * extrapolate(xsb + 1, ysb + 1, zsb + 1, wsb + 1, dx0, dy0, dz0, dw0) elif in_sum <= 2: # We're inside the first dispentachoron (Rectified 4-Simplex) a_is_bigger_side = True b_is_bigger_side = True # Decide between (1,1,0,0) and (0,0,1,1) if xins + yins > zins + wins: a_score = xins + yins a_po = 0x03 else: a_score = zins + wins a_po = 0x0C # Decide between (1,0,1,0) and (0,1,0,1) if xins + zins > yins + wins: b_score = xins + zins b_po = 0x05 else: b_score = yins + wins b_po = 0x0A # Closer between (1,0,0,1) and (0,1,1,0) will replace the further of a and b, if closer. if xins + wins > yins + zins: score = xins + wins if a_score >= b_score and score > b_score: b_score = score b_po = 0x09 elif a_score < b_score and score > a_score: a_score = score a_po = 0x09 else: score = yins + zins if a_score >= b_score and score > b_score: b_score = score b_po = 0x06 elif a_score < b_score and score > a_score: a_score = score a_po = 0x06 # Decide if (1,0,0,0) is closer. p1 = 2 - in_sum + xins if a_score >= b_score and p1 > b_score: b_score = p1 b_po = 0x01 b_is_bigger_side = False elif a_score < b_score and p1 > a_score: a_score = p1 a_po = 0x01 a_is_bigger_side = False # Decide if (0,1,0,0) is closer. p2 = 2 - in_sum + yins if a_score >= b_score and p2 > b_score: b_score = p2 b_po = 0x02 b_is_bigger_side = False elif a_score < b_score and p2 > a_score: a_score = p2 a_po = 0x02 a_is_bigger_side = False # Decide if (0,0,1,0) is closer. p3 = 2 - in_sum + zins if a_score >= b_score and p3 > b_score: b_score = p3 b_po = 0x04 b_is_bigger_side = False elif a_score < b_score and p3 > a_score: a_score = p3 a_po = 0x04 a_is_bigger_side = False # Decide if (0,0,0,1) is closer. p4 = 2 - in_sum + wins if a_score >= b_score and p4 > b_score: b_po = 0x08 b_is_bigger_side = False elif a_score < b_score and p4 > a_score: a_po = 0x08 a_is_bigger_side = False # Where each of the two closest pos are determines how the extra three vertices are calculated. if a_is_bigger_side == b_is_bigger_side: if a_is_bigger_side: # Both closest pos on the bigger side c1 = (a_po | b_po) c2 = (a_po & b_po) if (c1 & 0x01) == 0: xsv_ext0 = xsb xsv_ext1 = xsb - 1 dx_ext0 = dx0 - 3 * SQUISH_CONSTANT_4D dx_ext1 = dx0 + 1 - 2 * SQUISH_CONSTANT_4D else: xsv_ext0 = xsv_ext1 = xsb + 1 dx_ext0 = dx0 - 1 - 3 * SQUISH_CONSTANT_4D dx_ext1 = dx0 - 1 - 2 * SQUISH_CONSTANT_4D if (c1 & 0x02) == 0: ysv_ext0 = ysb ysv_ext1 = ysb - 1 dy_ext0 = dy0 - 3 * SQUISH_CONSTANT_4D dy_ext1 = dy0 + 1 - 2 * SQUISH_CONSTANT_4D else: ysv_ext0 = ysv_ext1 = ysb + 1 dy_ext0 = dy0 - 1 - 3 * SQUISH_CONSTANT_4D dy_ext1 = dy0 - 1 - 2 * SQUISH_CONSTANT_4D if (c1 & 0x04) == 0: zsv_ext0 = zsb zsv_ext1 = zsb - 1 dz_ext0 = dz0 - 3 * SQUISH_CONSTANT_4D dz_ext1 = dz0 + 1 - 2 * SQUISH_CONSTANT_4D else: zsv_ext0 = zsv_ext1 = zsb + 1 dz_ext0 = dz0 - 1 - 3 * SQUISH_CONSTANT_4D dz_ext1 = dz0 - 1 - 2 * SQUISH_CONSTANT_4D if (c1 & 0x08) == 0: wsv_ext0 = wsb wsv_ext1 = wsb - 1 dw_ext0 = dw0 - 3 * SQUISH_CONSTANT_4D dw_ext1 = dw0 + 1 - 2 * SQUISH_CONSTANT_4D else: wsv_ext0 = wsv_ext1 = wsb + 1 dw_ext0 = dw0 - 1 - 3 * SQUISH_CONSTANT_4D dw_ext1 = dw0 - 1 - 2 * SQUISH_CONSTANT_4D # One combination is a _permutation of (0,0,0,2) based on c2 xsv_ext2 = xsb ysv_ext2 = ysb zsv_ext2 = zsb wsv_ext2 = wsb dx_ext2 = dx0 - 2 * SQUISH_CONSTANT_4D dy_ext2 = dy0 - 2 * SQUISH_CONSTANT_4D dz_ext2 = dz0 - 2 * SQUISH_CONSTANT_4D dw_ext2 = dw0 - 2 * SQUISH_CONSTANT_4D if (c2 & 0x01) != 0: xsv_ext2 += 2 dx_ext2 -= 2 elif (c2 & 0x02) != 0: ysv_ext2 += 2 dy_ext2 -= 2 elif (c2 & 0x04) != 0: zsv_ext2 += 2 dz_ext2 -= 2 else: wsv_ext2 += 2 dw_ext2 -= 2 else: # Both closest pos on the smaller side # One of the two extra pos is (0,0,0,0) xsv_ext2 = xsb ysv_ext2 = ysb zsv_ext2 = zsb wsv_ext2 = wsb dx_ext2 = dx0 dy_ext2 = dy0 dz_ext2 = dz0 dw_ext2 = dw0 # Other two pos are based on the omitted axes. c = (a_po | b_po) if (c & 0x01) == 0: xsv_ext0 = xsb - 1 xsv_ext1 = xsb dx_ext0 = dx0 + 1 - SQUISH_CONSTANT_4D dx_ext1 = dx0 - SQUISH_CONSTANT_4D else: xsv_ext0 = xsv_ext1 = xsb + 1 dx_ext0 = dx_ext1 = dx0 - 1 - SQUISH_CONSTANT_4D if (c & 0x02) == 0: ysv_ext0 = ysv_ext1 = ysb dy_ext0 = dy_ext1 = dy0 - SQUISH_CONSTANT_4D if (c & 0x01) == 0x01: ysv_ext0 -= 1 dy_ext0 += 1 else: ysv_ext1 -= 1 dy_ext1 += 1 else: ysv_ext0 = ysv_ext1 = ysb + 1 dy_ext0 = dy_ext1 = dy0 - 1 - SQUISH_CONSTANT_4D if (c & 0x04) == 0: zsv_ext0 = zsv_ext1 = zsb dz_ext0 = dz_ext1 = dz0 - SQUISH_CONSTANT_4D if (c & 0x03) == 0x03: zsv_ext0 -= 1 dz_ext0 += 1 else: zsv_ext1 -= 1 dz_ext1 += 1 else: zsv_ext0 = zsv_ext1 = zsb + 1 dz_ext0 = dz_ext1 = dz0 - 1 - SQUISH_CONSTANT_4D if (c & 0x08) == 0: wsv_ext0 = wsb wsv_ext1 = wsb - 1 dw_ext0 = dw0 - SQUISH_CONSTANT_4D dw_ext1 = dw0 + 1 - SQUISH_CONSTANT_4D else: wsv_ext0 = wsv_ext1 = wsb + 1 dw_ext0 = dw_ext1 = dw0 - 1 - SQUISH_CONSTANT_4D else: # One po on each "side" if a_is_bigger_side: c1 = a_po c2 = b_po else: c1 = b_po c2 = a_po # Two contributions are the bigger-sided po with each 0 replaced with -1. if (c1 & 0x01) == 0: xsv_ext0 = xsb - 1 xsv_ext1 = xsb dx_ext0 = dx0 + 1 - SQUISH_CONSTANT_4D dx_ext1 = dx0 - SQUISH_CONSTANT_4D else: xsv_ext0 = xsv_ext1 = xsb + 1 dx_ext0 = dx_ext1 = dx0 - 1 - SQUISH_CONSTANT_4D if (c1 & 0x02) == 0: ysv_ext0 = ysv_ext1 = ysb dy_ext0 = dy_ext1 = dy0 - SQUISH_CONSTANT_4D if (c1 & 0x01) == 0x01: ysv_ext0 -= 1 dy_ext0 += 1 else: ysv_ext1 -= 1 dy_ext1 += 1 else: ysv_ext0 = ysv_ext1 = ysb + 1 dy_ext0 = dy_ext1 = dy0 - 1 - SQUISH_CONSTANT_4D if (c1 & 0x04) == 0: zsv_ext0 = zsv_ext1 = zsb dz_ext0 = dz_ext1 = dz0 - SQUISH_CONSTANT_4D if (c1 & 0x03) == 0x03: zsv_ext0 -= 1 dz_ext0 += 1 else: zsv_ext1 -= 1 dz_ext1 += 1 else: zsv_ext0 = zsv_ext1 = zsb + 1 dz_ext0 = dz_ext1 = dz0 - 1 - SQUISH_CONSTANT_4D if (c1 & 0x08) == 0: wsv_ext0 = wsb wsv_ext1 = wsb - 1 dw_ext0 = dw0 - SQUISH_CONSTANT_4D dw_ext1 = dw0 + 1 - SQUISH_CONSTANT_4D else: wsv_ext0 = wsv_ext1 = wsb + 1 dw_ext0 = dw_ext1 = dw0 - 1 - SQUISH_CONSTANT_4D # One contribution is a _permutation of (0,0,0,2) based on the smaller-sided po xsv_ext2 = xsb ysv_ext2 = ysb zsv_ext2 = zsb wsv_ext2 = wsb dx_ext2 = dx0 - 2 * SQUISH_CONSTANT_4D dy_ext2 = dy0 - 2 * SQUISH_CONSTANT_4D dz_ext2 = dz0 - 2 * SQUISH_CONSTANT_4D dw_ext2 = dw0 - 2 * SQUISH_CONSTANT_4D if (c2 & 0x01) != 0: xsv_ext2 += 2 dx_ext2 -= 2 elif (c2 & 0x02) != 0: ysv_ext2 += 2 dy_ext2 -= 2 elif (c2 & 0x04) != 0: zsv_ext2 += 2 dz_ext2 -= 2 else: wsv_ext2 += 2 dw_ext2 -= 2 # Contribution (1,0,0,0) dx1 = dx0 - 1 - SQUISH_CONSTANT_4D dy1 = dy0 - 0 - SQUISH_CONSTANT_4D dz1 = dz0 - 0 - SQUISH_CONSTANT_4D dw1 = dw0 - 0 - SQUISH_CONSTANT_4D attn1 = 2 - dx1 * dx1 - dy1 * dy1 - dz1 * dz1 - dw1 * dw1 if attn1 > 0: attn1 *= attn1 value += attn1 * attn1 * extrapolate(xsb + 1, ysb + 0, zsb + 0, wsb + 0, dx1, dy1, dz1, dw1) # Contribution (0,1,0,0) dx2 = dx0 - 0 - SQUISH_CONSTANT_4D dy2 = dy0 - 1 - SQUISH_CONSTANT_4D dz2 = dz1 dw2 = dw1 attn2 = 2 - dx2 * dx2 - dy2 * dy2 - dz2 * dz2 - dw2 * dw2 if attn2 > 0: attn2 *= attn2 value += attn2 * attn2 * extrapolate(xsb + 0, ysb + 1, zsb + 0, wsb + 0, dx2, dy2, dz2, dw2) # Contribution (0,0,1,0) dx3 = dx2 dy3 = dy1 dz3 = dz0 - 1 - SQUISH_CONSTANT_4D dw3 = dw1 attn3 = 2 - dx3 * dx3 - dy3 * dy3 - dz3 * dz3 - dw3 * dw3 if attn3 > 0: attn3 *= attn3 value += attn3 * attn3 * extrapolate(xsb + 0, ysb + 0, zsb + 1, wsb + 0, dx3, dy3, dz3, dw3) # Contribution (0,0,0,1) dx4 = dx2 dy4 = dy1 dz4 = dz1 dw4 = dw0 - 1 - SQUISH_CONSTANT_4D attn4 = 2 - dx4 * dx4 - dy4 * dy4 - dz4 * dz4 - dw4 * dw4 if attn4 > 0: attn4 *= attn4 value += attn4 * attn4 * extrapolate(xsb + 0, ysb + 0, zsb + 0, wsb + 1, dx4, dy4, dz4, dw4) # Contribution (1,1,0,0) dx5 = dx0 - 1 - 2 * SQUISH_CONSTANT_4D dy5 = dy0 - 1 - 2 * SQUISH_CONSTANT_4D dz5 = dz0 - 0 - 2 * SQUISH_CONSTANT_4D dw5 = dw0 - 0 - 2 * SQUISH_CONSTANT_4D attn5 = 2 - dx5 * dx5 - dy5 * dy5 - dz5 * dz5 - dw5 * dw5 if attn5 > 0: attn5 *= attn5 value += attn5 * attn5 * extrapolate(xsb + 1, ysb + 1, zsb + 0, wsb + 0, dx5, dy5, dz5, dw5) # Contribution (1,0,1,0) dx6 = dx0 - 1 - 2 * SQUISH_CONSTANT_4D dy6 = dy0 - 0 - 2 * SQUISH_CONSTANT_4D dz6 = dz0 - 1 - 2 * SQUISH_CONSTANT_4D dw6 = dw0 - 0 - 2 * SQUISH_CONSTANT_4D attn6 = 2 - dx6 * dx6 - dy6 * dy6 - dz6 * dz6 - dw6 * dw6 if attn6 > 0: attn6 *= attn6 value += attn6 * attn6 * extrapolate(xsb + 1, ysb + 0, zsb + 1, wsb + 0, dx6, dy6, dz6, dw6) # Contribution (1,0,0,1) dx7 = dx0 - 1 - 2 * SQUISH_CONSTANT_4D dy7 = dy0 - 0 - 2 * SQUISH_CONSTANT_4D dz7 = dz0 - 0 - 2 * SQUISH_CONSTANT_4D dw7 = dw0 - 1 - 2 * SQUISH_CONSTANT_4D attn7 = 2 - dx7 * dx7 - dy7 * dy7 - dz7 * dz7 - dw7 * dw7 if attn7 > 0: attn7 *= attn7 value += attn7 * attn7 * extrapolate(xsb + 1, ysb + 0, zsb + 0, wsb + 1, dx7, dy7, dz7, dw7) # Contribution (0,1,1,0) dx8 = dx0 - 0 - 2 * SQUISH_CONSTANT_4D dy8 = dy0 - 1 - 2 * SQUISH_CONSTANT_4D dz8 = dz0 - 1 - 2 * SQUISH_CONSTANT_4D dw8 = dw0 - 0 - 2 * SQUISH_CONSTANT_4D attn8 = 2 - dx8 * dx8 - dy8 * dy8 - dz8 * dz8 - dw8 * dw8 if attn8 > 0: attn8 *= attn8 value += attn8 * attn8 * extrapolate(xsb + 0, ysb + 1, zsb + 1, wsb + 0, dx8, dy8, dz8, dw8) # Contribution (0,1,0,1) dx9 = dx0 - 0 - 2 * SQUISH_CONSTANT_4D dy9 = dy0 - 1 - 2 * SQUISH_CONSTANT_4D dz9 = dz0 - 0 - 2 * SQUISH_CONSTANT_4D dw9 = dw0 - 1 - 2 * SQUISH_CONSTANT_4D attn9 = 2 - dx9 * dx9 - dy9 * dy9 - dz9 * dz9 - dw9 * dw9 if attn9 > 0: attn9 *= attn9 value += attn9 * attn9 * extrapolate(xsb + 0, ysb + 1, zsb + 0, wsb + 1, dx9, dy9, dz9, dw9) # Contribution (0,0,1,1) dx10 = dx0 - 0 - 2 * SQUISH_CONSTANT_4D dy10 = dy0 - 0 - 2 * SQUISH_CONSTANT_4D dz10 = dz0 - 1 - 2 * SQUISH_CONSTANT_4D dw10 = dw0 - 1 - 2 * SQUISH_CONSTANT_4D attn10 = 2 - dx10 * dx10 - dy10 * dy10 - dz10 * dz10 - dw10 * dw10 if attn10 > 0: attn10 *= attn10 value += attn10 * attn10 * extrapolate(xsb + 0, ysb + 0, zsb + 1, wsb + 1, dx10, dy10, dz10, dw10) else: # We're inside the second dispentachoron (Rectified 4-Simplex) a_is_bigger_side = True b_is_bigger_side = True # Decide between (0,0,1,1) and (1,1,0,0) if xins + yins < zins + wins: a_score = xins + yins a_po = 0x0C else: a_score = zins + wins a_po = 0x03 # Decide between (0,1,0,1) and (1,0,1,0) if xins + zins < yins + wins: b_score = xins + zins b_po = 0x0A else: b_score = yins + wins b_po = 0x05 # Closer between (0,1,1,0) and (1,0,0,1) will replace the further of a and b, if closer. if xins + wins < yins + zins: score = xins + wins if a_score <= b_score and score < b_score: b_score = score b_po = 0x06 elif a_score > b_score and score < a_score: a_score = score a_po = 0x06 else: score = yins + zins if a_score <= b_score and score < b_score: b_score = score b_po = 0x09 elif a_score > b_score and score < a_score: a_score = score a_po = 0x09 # Decide if (0,1,1,1) is closer. p1 = 3 - in_sum + xins if a_score <= b_score and p1 < b_score: b_score = p1 b_po = 0x0E b_is_bigger_side = False elif a_score > b_score and p1 < a_score: a_score = p1 a_po = 0x0E a_is_bigger_side = False # Decide if (1,0,1,1) is closer. p2 = 3 - in_sum + yins if a_score <= b_score and p2 < b_score: b_score = p2 b_po = 0x0D b_is_bigger_side = False elif a_score > b_score and p2 < a_score: a_score = p2 a_po = 0x0D a_is_bigger_side = False # Decide if (1,1,0,1) is closer. p3 = 3 - in_sum + zins if a_score <= b_score and p3 < b_score: b_score = p3 b_po = 0x0B b_is_bigger_side = False elif a_score > b_score and p3 < a_score: a_score = p3 a_po = 0x0B a_is_bigger_side = False # Decide if (1,1,1,0) is closer. p4 = 3 - in_sum + wins if a_score <= b_score and p4 < b_score: b_po = 0x07 b_is_bigger_side = False elif a_score > b_score and p4 < a_score: a_po = 0x07 a_is_bigger_side = False # Where each of the two closest pos are determines how the extra three vertices are calculated. if a_is_bigger_side == b_is_bigger_side: if a_is_bigger_side: # Both closest pos on the bigger side c1 = (a_po & b_po) c2 = (a_po | b_po) # Two contributions are _permutations of (0,0,0,1) and (0,0,0,2) based on c1 xsv_ext0 = xsv_ext1 = xsb ysv_ext0 = ysv_ext1 = ysb zsv_ext0 = zsv_ext1 = zsb wsv_ext0 = wsv_ext1 = wsb dx_ext0 = dx0 - SQUISH_CONSTANT_4D dy_ext0 = dy0 - SQUISH_CONSTANT_4D dz_ext0 = dz0 - SQUISH_CONSTANT_4D dw_ext0 = dw0 - SQUISH_CONSTANT_4D dx_ext1 = dx0 - 2 * SQUISH_CONSTANT_4D dy_ext1 = dy0 - 2 * SQUISH_CONSTANT_4D dz_ext1 = dz0 - 2 * SQUISH_CONSTANT_4D dw_ext1 = dw0 - 2 * SQUISH_CONSTANT_4D if (c1 & 0x01) != 0: xsv_ext0 += 1 dx_ext0 -= 1 xsv_ext1 += 2 dx_ext1 -= 2 elif (c1 & 0x02) != 0: ysv_ext0 += 1 dy_ext0 -= 1 ysv_ext1 += 2 dy_ext1 -= 2 elif (c1 & 0x04) != 0: zsv_ext0 += 1 dz_ext0 -= 1 zsv_ext1 += 2 dz_ext1 -= 2 else: wsv_ext0 += 1 dw_ext0 -= 1 wsv_ext1 += 2 dw_ext1 -= 2 # One contribution is a _permutation of (1,1,1,-1) based on c2 xsv_ext2 = xsb + 1 ysv_ext2 = ysb + 1 zsv_ext2 = zsb + 1 wsv_ext2 = wsb + 1 dx_ext2 = dx0 - 1 - 2 * SQUISH_CONSTANT_4D dy_ext2 = dy0 - 1 - 2 * SQUISH_CONSTANT_4D dz_ext2 = dz0 - 1 - 2 * SQUISH_CONSTANT_4D dw_ext2 = dw0 - 1 - 2 * SQUISH_CONSTANT_4D if (c2 & 0x01) == 0: xsv_ext2 -= 2 dx_ext2 += 2 elif (c2 & 0x02) == 0: ysv_ext2 -= 2 dy_ext2 += 2 elif (c2 & 0x04) == 0: zsv_ext2 -= 2 dz_ext2 += 2 else: wsv_ext2 -= 2 dw_ext2 += 2 else: # Both closest pos on the smaller side # One of the two extra pos is (1,1,1,1) xsv_ext2 = xsb + 1 ysv_ext2 = ysb + 1 zsv_ext2 = zsb + 1 wsv_ext2 = wsb + 1 dx_ext2 = dx0 - 1 - 4 * SQUISH_CONSTANT_4D dy_ext2 = dy0 - 1 - 4 * SQUISH_CONSTANT_4D dz_ext2 = dz0 - 1 - 4 * SQUISH_CONSTANT_4D dw_ext2 = dw0 - 1 - 4 * SQUISH_CONSTANT_4D # Other two pos are based on the shared axes. c = (a_po & b_po) if (c & 0x01) != 0: xsv_ext0 = xsb + 2 xsv_ext1 = xsb + 1 dx_ext0 = dx0 - 2 - 3 * SQUISH_CONSTANT_4D dx_ext1 = dx0 - 1 - 3 * SQUISH_CONSTANT_4D else: xsv_ext0 = xsv_ext1 = xsb dx_ext0 = dx_ext1 = dx0 - 3 * SQUISH_CONSTANT_4D if (c & 0x02) != 0: ysv_ext0 = ysv_ext1 = ysb + 1 dy_ext0 = dy_ext1 = dy0 - 1 - 3 * SQUISH_CONSTANT_4D if (c & 0x01) == 0: ysv_ext0 += 1 dy_ext0 -= 1 else: ysv_ext1 += 1 dy_ext1 -= 1 else: ysv_ext0 = ysv_ext1 = ysb dy_ext0 = dy_ext1 = dy0 - 3 * SQUISH_CONSTANT_4D if (c & 0x04) != 0: zsv_ext0 = zsv_ext1 = zsb + 1 dz_ext0 = dz_ext1 = dz0 - 1 - 3 * SQUISH_CONSTANT_4D if (c & 0x03) == 0: zsv_ext0 += 1 dz_ext0 -= 1 else: zsv_ext1 += 1 dz_ext1 -= 1 else: zsv_ext0 = zsv_ext1 = zsb dz_ext0 = dz_ext1 = dz0 - 3 * SQUISH_CONSTANT_4D if (c & 0x08) != 0: wsv_ext0 = wsb + 1 wsv_ext1 = wsb + 2 dw_ext0 = dw0 - 1 - 3 * SQUISH_CONSTANT_4D dw_ext1 = dw0 - 2 - 3 * SQUISH_CONSTANT_4D else: wsv_ext0 = wsv_ext1 = wsb dw_ext0 = dw_ext1 = dw0 - 3 * SQUISH_CONSTANT_4D else: # One po on each "side" if a_is_bigger_side: c1 = a_po c2 = b_po else: c1 = b_po c2 = a_po # Two contributions are the bigger-sided po with each 1 replaced with 2. if (c1 & 0x01) != 0: xsv_ext0 = xsb + 2 xsv_ext1 = xsb + 1 dx_ext0 = dx0 - 2 - 3 * SQUISH_CONSTANT_4D dx_ext1 = dx0 - 1 - 3 * SQUISH_CONSTANT_4D else: xsv_ext0 = xsv_ext1 = xsb dx_ext0 = dx_ext1 = dx0 - 3 * SQUISH_CONSTANT_4D if (c1 & 0x02) != 0: ysv_ext0 = ysv_ext1 = ysb + 1 dy_ext0 = dy_ext1 = dy0 - 1 - 3 * SQUISH_CONSTANT_4D if (c1 & 0x01) == 0: ysv_ext0 += 1 dy_ext0 -= 1 else: ysv_ext1 += 1 dy_ext1 -= 1 else: ysv_ext0 = ysv_ext1 = ysb dy_ext0 = dy_ext1 = dy0 - 3 * SQUISH_CONSTANT_4D if (c1 & 0x04) != 0: zsv_ext0 = zsv_ext1 = zsb + 1 dz_ext0 = dz_ext1 = dz0 - 1 - 3 * SQUISH_CONSTANT_4D if (c1 & 0x03) == 0: zsv_ext0 += 1 dz_ext0 -= 1 else: zsv_ext1 += 1 dz_ext1 -= 1 else: zsv_ext0 = zsv_ext1 = zsb dz_ext0 = dz_ext1 = dz0 - 3 * SQUISH_CONSTANT_4D if (c1 & 0x08) != 0: wsv_ext0 = wsb + 1 wsv_ext1 = wsb + 2 dw_ext0 = dw0 - 1 - 3 * SQUISH_CONSTANT_4D dw_ext1 = dw0 - 2 - 3 * SQUISH_CONSTANT_4D else: wsv_ext0 = wsv_ext1 = wsb dw_ext0 = dw_ext1 = dw0 - 3 * SQUISH_CONSTANT_4D # One contribution is a _permutation of (1,1,1,-1) based on the smaller-sided po xsv_ext2 = xsb + 1 ysv_ext2 = ysb + 1 zsv_ext2 = zsb + 1 wsv_ext2 = wsb + 1 dx_ext2 = dx0 - 1 - 2 * SQUISH_CONSTANT_4D dy_ext2 = dy0 - 1 - 2 * SQUISH_CONSTANT_4D dz_ext2 = dz0 - 1 - 2 * SQUISH_CONSTANT_4D dw_ext2 = dw0 - 1 - 2 * SQUISH_CONSTANT_4D if (c2 & 0x01) == 0: xsv_ext2 -= 2 dx_ext2 += 2 elif (c2 & 0x02) == 0: ysv_ext2 -= 2 dy_ext2 += 2 elif (c2 & 0x04) == 0: zsv_ext2 -= 2 dz_ext2 += 2 else: wsv_ext2 -= 2 dw_ext2 += 2 # Contribution (1,1,1,0) dx4 = dx0 - 1 - 3 * SQUISH_CONSTANT_4D dy4 = dy0 - 1 - 3 * SQUISH_CONSTANT_4D dz4 = dz0 - 1 - 3 * SQUISH_CONSTANT_4D dw4 = dw0 - 3 * SQUISH_CONSTANT_4D attn4 = 2 - dx4 * dx4 - dy4 * dy4 - dz4 * dz4 - dw4 * dw4 if attn4 > 0: attn4 *= attn4 value += attn4 * attn4 * extrapolate(xsb + 1, ysb + 1, zsb + 1, wsb + 0, dx4, dy4, dz4, dw4) # Contribution (1,1,0,1) dx3 = dx4 dy3 = dy4 dz3 = dz0 - 3 * SQUISH_CONSTANT_4D dw3 = dw0 - 1 - 3 * SQUISH_CONSTANT_4D attn3 = 2 - dx3 * dx3 - dy3 * dy3 - dz3 * dz3 - dw3 * dw3 if attn3 > 0: attn3 *= attn3 value += attn3 * attn3 * extrapolate(xsb + 1, ysb + 1, zsb + 0, wsb + 1, dx3, dy3, dz3, dw3) # Contribution (1,0,1,1) dx2 = dx4 dy2 = dy0 - 3 * SQUISH_CONSTANT_4D dz2 = dz4 dw2 = dw3 attn2 = 2 - dx2 * dx2 - dy2 * dy2 - dz2 * dz2 - dw2 * dw2 if attn2 > 0: attn2 *= attn2 value += attn2 * attn2 * extrapolate(xsb + 1, ysb + 0, zsb + 1, wsb + 1, dx2, dy2, dz2, dw2) # Contribution (0,1,1,1) dx1 = dx0 - 3 * SQUISH_CONSTANT_4D dz1 = dz4 dy1 = dy4 dw1 = dw3 attn1 = 2 - dx1 * dx1 - dy1 * dy1 - dz1 * dz1 - dw1 * dw1 if attn1 > 0: attn1 *= attn1 value += attn1 * attn1 * extrapolate(xsb + 0, ysb + 1, zsb + 1, wsb + 1, dx1, dy1, dz1, dw1) # Contribution (1,1,0,0) dx5 = dx0 - 1 - 2 * SQUISH_CONSTANT_4D dy5 = dy0 - 1 - 2 * SQUISH_CONSTANT_4D dz5 = dz0 - 0 - 2 * SQUISH_CONSTANT_4D dw5 = dw0 - 0 - 2 * SQUISH_CONSTANT_4D attn5 = 2 - dx5 * dx5 - dy5 * dy5 - dz5 * dz5 - dw5 * dw5 if attn5 > 0: attn5 *= attn5 value += attn5 * attn5 * extrapolate(xsb + 1, ysb + 1, zsb + 0, wsb + 0, dx5, dy5, dz5, dw5) # Contribution (1,0,1,0) dx6 = dx0 - 1 - 2 * SQUISH_CONSTANT_4D dy6 = dy0 - 0 - 2 * SQUISH_CONSTANT_4D dz6 = dz0 - 1 - 2 * SQUISH_CONSTANT_4D dw6 = dw0 - 0 - 2 * SQUISH_CONSTANT_4D attn6 = 2 - dx6 * dx6 - dy6 * dy6 - dz6 * dz6 - dw6 * dw6 if attn6 > 0: attn6 *= attn6 value += attn6 * attn6 * extrapolate(xsb + 1, ysb + 0, zsb + 1, wsb + 0, dx6, dy6, dz6, dw6) # Contribution (1,0,0,1) dx7 = dx0 - 1 - 2 * SQUISH_CONSTANT_4D dy7 = dy0 - 0 - 2 * SQUISH_CONSTANT_4D dz7 = dz0 - 0 - 2 * SQUISH_CONSTANT_4D dw7 = dw0 - 1 - 2 * SQUISH_CONSTANT_4D attn7 = 2 - dx7 * dx7 - dy7 * dy7 - dz7 * dz7 - dw7 * dw7 if attn7 > 0: attn7 *= attn7 value += attn7 * attn7 * extrapolate(xsb + 1, ysb + 0, zsb + 0, wsb + 1, dx7, dy7, dz7, dw7) # Contribution (0,1,1,0) dx8 = dx0 - 0 - 2 * SQUISH_CONSTANT_4D dy8 = dy0 - 1 - 2 * SQUISH_CONSTANT_4D dz8 = dz0 - 1 - 2 * SQUISH_CONSTANT_4D dw8 = dw0 - 0 - 2 * SQUISH_CONSTANT_4D attn8 = 2 - dx8 * dx8 - dy8 * dy8 - dz8 * dz8 - dw8 * dw8 if attn8 > 0: attn8 *= attn8 value += attn8 * attn8 * extrapolate(xsb + 0, ysb + 1, zsb + 1, wsb + 0, dx8, dy8, dz8, dw8) # Contribution (0,1,0,1) dx9 = dx0 - 0 - 2 * SQUISH_CONSTANT_4D dy9 = dy0 - 1 - 2 * SQUISH_CONSTANT_4D dz9 = dz0 - 0 - 2 * SQUISH_CONSTANT_4D dw9 = dw0 - 1 - 2 * SQUISH_CONSTANT_4D attn9 = 2 - dx9 * dx9 - dy9 * dy9 - dz9 * dz9 - dw9 * dw9 if attn9 > 0: attn9 *= attn9 value += attn9 * attn9 * extrapolate(xsb + 0, ysb + 1, zsb + 0, wsb + 1, dx9, dy9, dz9, dw9) # Contribution (0,0,1,1) dx10 = dx0 - 0 - 2 * SQUISH_CONSTANT_4D dy10 = dy0 - 0 - 2 * SQUISH_CONSTANT_4D dz10 = dz0 - 1 - 2 * SQUISH_CONSTANT_4D dw10 = dw0 - 1 - 2 * SQUISH_CONSTANT_4D attn10 = 2 - dx10 * dx10 - dy10 * dy10 - dz10 * dz10 - dw10 * dw10 if attn10 > 0: attn10 *= attn10 value += attn10 * attn10 * extrapolate(xsb + 0, ysb + 0, zsb + 1, wsb + 1, dx10, dy10, dz10, dw10) # First extra vertex attn_ext0 = 2 - dx_ext0 * dx_ext0 - dy_ext0 * dy_ext0 - dz_ext0 * dz_ext0 - dw_ext0 * dw_ext0 if attn_ext0 > 0: attn_ext0 *= attn_ext0 value += attn_ext0 * attn_ext0 * extrapolate(xsv_ext0, ysv_ext0, zsv_ext0, wsv_ext0, dx_ext0, dy_ext0, dz_ext0, dw_ext0) # Second extra vertex attn_ext1 = 2 - dx_ext1 * dx_ext1 - dy_ext1 * dy_ext1 - dz_ext1 * dz_ext1 - dw_ext1 * dw_ext1 if attn_ext1 > 0: attn_ext1 *= attn_ext1 value += attn_ext1 * attn_ext1 * extrapolate(xsv_ext1, ysv_ext1, zsv_ext1, wsv_ext1, dx_ext1, dy_ext1, dz_ext1, dw_ext1) # Third extra vertex attn_ext2 = 2 - dx_ext2 * dx_ext2 - dy_ext2 * dy_ext2 - dz_ext2 * dz_ext2 - dw_ext2 * dw_ext2 if attn_ext2 > 0: attn_ext2 *= attn_ext2 value += attn_ext2 * attn_ext2 * extrapolate(xsv_ext2, ysv_ext2, zsv_ext2, wsv_ext2, dx_ext2, dy_ext2, dz_ext2, dw_ext2) return value / NORM_CONSTANT_4D
[ "def", "noise4d", "(", "self", ",", "x", ",", "y", ",", "z", ",", "w", ")", ":", "# Place input coordinates on simplectic honeycomb.", "stretch_offset", "=", "(", "x", "+", "y", "+", "z", "+", "w", ")", "*", "STRETCH_CONSTANT_4D", "xs", "=", "x", "+", "stretch_offset", "ys", "=", "y", "+", "stretch_offset", "zs", "=", "z", "+", "stretch_offset", "ws", "=", "w", "+", "stretch_offset", "# Floor to get simplectic honeycomb coordinates of rhombo-hypercube super-cell origin.", "xsb", "=", "floor", "(", "xs", ")", "ysb", "=", "floor", "(", "ys", ")", "zsb", "=", "floor", "(", "zs", ")", "wsb", "=", "floor", "(", "ws", ")", "# Skew out to get actual coordinates of stretched rhombo-hypercube origin. We'll need these later.", "squish_offset", "=", "(", "xsb", "+", "ysb", "+", "zsb", "+", "wsb", ")", "*", "SQUISH_CONSTANT_4D", "xb", "=", "xsb", "+", "squish_offset", "yb", "=", "ysb", "+", "squish_offset", "zb", "=", "zsb", "+", "squish_offset", "wb", "=", "wsb", "+", "squish_offset", "# Compute simplectic honeycomb coordinates relative to rhombo-hypercube origin.", "xins", "=", "xs", "-", "xsb", "yins", "=", "ys", "-", "ysb", "zins", "=", "zs", "-", "zsb", "wins", "=", "ws", "-", "wsb", "# Sum those together to get a value that determines which region we're in.", "in_sum", "=", "xins", "+", "yins", "+", "zins", "+", "wins", "# Positions relative to origin po.", "dx0", "=", "x", "-", "xb", "dy0", "=", "y", "-", "yb", "dz0", "=", "z", "-", "zb", "dw0", "=", "w", "-", "wb", "value", "=", "0", "extrapolate", "=", "self", ".", "_extrapolate4d", "if", "in_sum", "<=", "1", ":", "# We're inside the pentachoron (4-Simplex) at (0,0,0,0)", "# Determine which two of (0,0,0,1), (0,0,1,0), (0,1,0,0), (1,0,0,0) are closest.", "a_po", "=", "0x01", "a_score", "=", "xins", "b_po", "=", "0x02", "b_score", "=", "yins", "if", "a_score", ">=", "b_score", "and", "zins", ">", "b_score", ":", "b_score", "=", "zins", "b_po", "=", "0x04", "elif", "a_score", "<", "b_score", "and", "zins", ">", "a_score", ":", "a_score", "=", "zins", "a_po", "=", "0x04", "if", "a_score", ">=", "b_score", "and", "wins", ">", "b_score", ":", "b_score", "=", "wins", "b_po", "=", "0x08", "elif", "a_score", "<", "b_score", "and", "wins", ">", "a_score", ":", "a_score", "=", "wins", "a_po", "=", "0x08", "# Now we determine the three lattice pos not part of the pentachoron that may contribute.", "# This depends on the closest two pentachoron vertices, including (0,0,0,0)", "uins", "=", "1", "-", "in_sum", "if", "uins", ">", "a_score", "or", "uins", ">", "b_score", ":", "# (0,0,0,0) is one of the closest two pentachoron vertices.", "c", "=", "b_po", "if", "(", "b_score", ">", "a_score", ")", "else", "a_po", "# Our other closest vertex is the closest out of a and b.", "if", "(", "c", "&", "0x01", ")", "==", "0", ":", "xsv_ext0", "=", "xsb", "-", "1", "xsv_ext1", "=", "xsv_ext2", "=", "xsb", "dx_ext0", "=", "dx0", "+", "1", "dx_ext1", "=", "dx_ext2", "=", "dx0", "else", ":", "xsv_ext0", "=", "xsv_ext1", "=", "xsv_ext2", "=", "xsb", "+", "1", "dx_ext0", "=", "dx_ext1", "=", "dx_ext2", "=", "dx0", "-", "1", "if", "(", "c", "&", "0x02", ")", "==", "0", ":", "ysv_ext0", "=", "ysv_ext1", "=", "ysv_ext2", "=", "ysb", "dy_ext0", "=", "dy_ext1", "=", "dy_ext2", "=", "dy0", "if", "(", "c", "&", "0x01", ")", "==", "0x01", ":", "ysv_ext0", "-=", "1", "dy_ext0", "+=", "1", "else", ":", "ysv_ext1", "-=", "1", "dy_ext1", "+=", "1", "else", ":", "ysv_ext0", "=", "ysv_ext1", "=", "ysv_ext2", "=", "ysb", "+", "1", "dy_ext0", "=", "dy_ext1", "=", "dy_ext2", "=", "dy0", "-", "1", "if", "(", "c", "&", "0x04", ")", "==", "0", ":", "zsv_ext0", "=", "zsv_ext1", "=", "zsv_ext2", "=", "zsb", "dz_ext0", "=", "dz_ext1", "=", "dz_ext2", "=", "dz0", "if", "(", "c", "&", "0x03", ")", "!=", "0", ":", "if", "(", "c", "&", "0x03", ")", "==", "0x03", ":", "zsv_ext0", "-=", "1", "dz_ext0", "+=", "1", "else", ":", "zsv_ext1", "-=", "1", "dz_ext1", "+=", "1", "else", ":", "zsv_ext2", "-=", "1", "dz_ext2", "+=", "1", "else", ":", "zsv_ext0", "=", "zsv_ext1", "=", "zsv_ext2", "=", "zsb", "+", "1", "dz_ext0", "=", "dz_ext1", "=", "dz_ext2", "=", "dz0", "-", "1", "if", "(", "c", "&", "0x08", ")", "==", "0", ":", "wsv_ext0", "=", "wsv_ext1", "=", "wsb", "wsv_ext2", "=", "wsb", "-", "1", "dw_ext0", "=", "dw_ext1", "=", "dw0", "dw_ext2", "=", "dw0", "+", "1", "else", ":", "wsv_ext0", "=", "wsv_ext1", "=", "wsv_ext2", "=", "wsb", "+", "1", "dw_ext0", "=", "dw_ext1", "=", "dw_ext2", "=", "dw0", "-", "1", "else", ":", "# (0,0,0,0) is not one of the closest two pentachoron vertices.", "c", "=", "(", "a_po", "|", "b_po", ")", "# Our three extra vertices are determined by the closest two.", "if", "(", "c", "&", "0x01", ")", "==", "0", ":", "xsv_ext0", "=", "xsv_ext2", "=", "xsb", "xsv_ext1", "=", "xsb", "-", "1", "dx_ext0", "=", "dx0", "-", "2", "*", "SQUISH_CONSTANT_4D", "dx_ext1", "=", "dx0", "+", "1", "-", "SQUISH_CONSTANT_4D", "dx_ext2", "=", "dx0", "-", "SQUISH_CONSTANT_4D", "else", ":", "xsv_ext0", "=", "xsv_ext1", "=", "xsv_ext2", "=", "xsb", "+", "1", "dx_ext0", "=", "dx0", "-", "1", "-", "2", "*", "SQUISH_CONSTANT_4D", "dx_ext1", "=", "dx_ext2", "=", "dx0", "-", "1", "-", "SQUISH_CONSTANT_4D", "if", "(", "c", "&", "0x02", ")", "==", "0", ":", "ysv_ext0", "=", "ysv_ext1", "=", "ysv_ext2", "=", "ysb", "dy_ext0", "=", "dy0", "-", "2", "*", "SQUISH_CONSTANT_4D", "dy_ext1", "=", "dy_ext2", "=", "dy0", "-", "SQUISH_CONSTANT_4D", "if", "(", "c", "&", "0x01", ")", "==", "0x01", ":", "ysv_ext1", "-=", "1", "dy_ext1", "+=", "1", "else", ":", "ysv_ext2", "-=", "1", "dy_ext2", "+=", "1", "else", ":", "ysv_ext0", "=", "ysv_ext1", "=", "ysv_ext2", "=", "ysb", "+", "1", "dy_ext0", "=", "dy0", "-", "1", "-", "2", "*", "SQUISH_CONSTANT_4D", "dy_ext1", "=", "dy_ext2", "=", "dy0", "-", "1", "-", "SQUISH_CONSTANT_4D", "if", "(", "c", "&", "0x04", ")", "==", "0", ":", "zsv_ext0", "=", "zsv_ext1", "=", "zsv_ext2", "=", "zsb", "dz_ext0", "=", "dz0", "-", "2", "*", "SQUISH_CONSTANT_4D", "dz_ext1", "=", "dz_ext2", "=", "dz0", "-", "SQUISH_CONSTANT_4D", "if", "(", "c", "&", "0x03", ")", "==", "0x03", ":", "zsv_ext1", "-=", "1", "dz_ext1", "+=", "1", "else", ":", "zsv_ext2", "-=", "1", "dz_ext2", "+=", "1", "else", ":", "zsv_ext0", "=", "zsv_ext1", "=", "zsv_ext2", "=", "zsb", "+", "1", "dz_ext0", "=", "dz0", "-", "1", "-", "2", "*", "SQUISH_CONSTANT_4D", "dz_ext1", "=", "dz_ext2", "=", "dz0", "-", "1", "-", "SQUISH_CONSTANT_4D", "if", "(", "c", "&", "0x08", ")", "==", "0", ":", "wsv_ext0", "=", "wsv_ext1", "=", "wsb", "wsv_ext2", "=", "wsb", "-", "1", "dw_ext0", "=", "dw0", "-", "2", "*", "SQUISH_CONSTANT_4D", "dw_ext1", "=", "dw0", "-", "SQUISH_CONSTANT_4D", "dw_ext2", "=", "dw0", "+", "1", "-", "SQUISH_CONSTANT_4D", "else", ":", "wsv_ext0", "=", "wsv_ext1", "=", "wsv_ext2", "=", "wsb", "+", "1", "dw_ext0", "=", "dw0", "-", "1", "-", "2", "*", "SQUISH_CONSTANT_4D", "dw_ext1", "=", "dw_ext2", "=", "dw0", "-", "1", "-", "SQUISH_CONSTANT_4D", "# Contribution (0,0,0,0)", "attn0", "=", "2", "-", "dx0", "*", "dx0", "-", "dy0", "*", "dy0", "-", "dz0", "*", "dz0", "-", "dw0", "*", "dw0", "if", "attn0", ">", "0", ":", "attn0", "*=", "attn0", "value", "+=", "attn0", "*", "attn0", "*", "extrapolate", "(", "xsb", "+", "0", ",", "ysb", "+", "0", ",", "zsb", "+", "0", ",", "wsb", "+", "0", ",", "dx0", ",", "dy0", ",", "dz0", ",", "dw0", ")", "# Contribution (1,0,0,0)", "dx1", "=", "dx0", "-", "1", "-", "SQUISH_CONSTANT_4D", "dy1", "=", "dy0", "-", "0", "-", "SQUISH_CONSTANT_4D", "dz1", "=", "dz0", "-", "0", "-", "SQUISH_CONSTANT_4D", "dw1", "=", "dw0", "-", "0", "-", "SQUISH_CONSTANT_4D", "attn1", "=", "2", "-", "dx1", "*", "dx1", "-", "dy1", "*", "dy1", "-", "dz1", "*", "dz1", "-", "dw1", "*", "dw1", "if", "attn1", ">", "0", ":", "attn1", "*=", "attn1", "value", "+=", "attn1", "*", "attn1", "*", "extrapolate", "(", "xsb", "+", "1", ",", "ysb", "+", "0", ",", "zsb", "+", "0", ",", "wsb", "+", "0", ",", "dx1", ",", "dy1", ",", "dz1", ",", "dw1", ")", "# Contribution (0,1,0,0)", "dx2", "=", "dx0", "-", "0", "-", "SQUISH_CONSTANT_4D", "dy2", "=", "dy0", "-", "1", "-", "SQUISH_CONSTANT_4D", "dz2", "=", "dz1", "dw2", "=", "dw1", "attn2", "=", "2", "-", "dx2", "*", "dx2", "-", "dy2", "*", "dy2", "-", "dz2", "*", "dz2", "-", "dw2", "*", "dw2", "if", "attn2", ">", "0", ":", "attn2", "*=", "attn2", "value", "+=", "attn2", "*", "attn2", "*", "extrapolate", "(", "xsb", "+", "0", ",", "ysb", "+", "1", ",", "zsb", "+", "0", ",", "wsb", "+", "0", ",", "dx2", ",", "dy2", ",", "dz2", ",", "dw2", ")", "# Contribution (0,0,1,0)", "dx3", "=", "dx2", "dy3", "=", "dy1", "dz3", "=", "dz0", "-", "1", "-", "SQUISH_CONSTANT_4D", "dw3", "=", "dw1", "attn3", "=", "2", "-", "dx3", "*", "dx3", "-", "dy3", "*", "dy3", "-", "dz3", "*", "dz3", "-", "dw3", "*", "dw3", "if", "attn3", ">", "0", ":", "attn3", "*=", "attn3", "value", "+=", "attn3", "*", "attn3", "*", "extrapolate", "(", "xsb", "+", "0", ",", "ysb", "+", "0", ",", "zsb", "+", "1", ",", "wsb", "+", "0", ",", "dx3", ",", "dy3", ",", "dz3", ",", "dw3", ")", "# Contribution (0,0,0,1)", "dx4", "=", "dx2", "dy4", "=", "dy1", "dz4", "=", "dz1", "dw4", "=", "dw0", "-", "1", "-", "SQUISH_CONSTANT_4D", "attn4", "=", "2", "-", "dx4", "*", "dx4", "-", "dy4", "*", "dy4", "-", "dz4", "*", "dz4", "-", "dw4", "*", "dw4", "if", "attn4", ">", "0", ":", "attn4", "*=", "attn4", "value", "+=", "attn4", "*", "attn4", "*", "extrapolate", "(", "xsb", "+", "0", ",", "ysb", "+", "0", ",", "zsb", "+", "0", ",", "wsb", "+", "1", ",", "dx4", ",", "dy4", ",", "dz4", ",", "dw4", ")", "elif", "in_sum", ">=", "3", ":", "# We're inside the pentachoron (4-Simplex) at (1,1,1,1)", "# Determine which two of (1,1,1,0), (1,1,0,1), (1,0,1,1), (0,1,1,1) are closest.", "a_po", "=", "0x0E", "a_score", "=", "xins", "b_po", "=", "0x0D", "b_score", "=", "yins", "if", "a_score", "<=", "b_score", "and", "zins", "<", "b_score", ":", "b_score", "=", "zins", "b_po", "=", "0x0B", "elif", "a_score", ">", "b_score", "and", "zins", "<", "a_score", ":", "a_score", "=", "zins", "a_po", "=", "0x0B", "if", "a_score", "<=", "b_score", "and", "wins", "<", "b_score", ":", "b_score", "=", "wins", "b_po", "=", "0x07", "elif", "a_score", ">", "b_score", "and", "wins", "<", "a_score", ":", "a_score", "=", "wins", "a_po", "=", "0x07", "# Now we determine the three lattice pos not part of the pentachoron that may contribute.", "# This depends on the closest two pentachoron vertices, including (0,0,0,0)", "uins", "=", "4", "-", "in_sum", "if", "uins", "<", "a_score", "or", "uins", "<", "b_score", ":", "# (1,1,1,1) is one of the closest two pentachoron vertices.", "c", "=", "b_po", "if", "(", "b_score", "<", "a_score", ")", "else", "a_po", "# Our other closest vertex is the closest out of a and b.", "if", "(", "c", "&", "0x01", ")", "!=", "0", ":", "xsv_ext0", "=", "xsb", "+", "2", "xsv_ext1", "=", "xsv_ext2", "=", "xsb", "+", "1", "dx_ext0", "=", "dx0", "-", "2", "-", "4", "*", "SQUISH_CONSTANT_4D", "dx_ext1", "=", "dx_ext2", "=", "dx0", "-", "1", "-", "4", "*", "SQUISH_CONSTANT_4D", "else", ":", "xsv_ext0", "=", "xsv_ext1", "=", "xsv_ext2", "=", "xsb", "dx_ext0", "=", "dx_ext1", "=", "dx_ext2", "=", "dx0", "-", "4", "*", "SQUISH_CONSTANT_4D", "if", "(", "c", "&", "0x02", ")", "!=", "0", ":", "ysv_ext0", "=", "ysv_ext1", "=", "ysv_ext2", "=", "ysb", "+", "1", "dy_ext0", "=", "dy_ext1", "=", "dy_ext2", "=", "dy0", "-", "1", "-", "4", "*", "SQUISH_CONSTANT_4D", "if", "(", "c", "&", "0x01", ")", "!=", "0", ":", "ysv_ext1", "+=", "1", "dy_ext1", "-=", "1", "else", ":", "ysv_ext0", "+=", "1", "dy_ext0", "-=", "1", "else", ":", "ysv_ext0", "=", "ysv_ext1", "=", "ysv_ext2", "=", "ysb", "dy_ext0", "=", "dy_ext1", "=", "dy_ext2", "=", "dy0", "-", "4", "*", "SQUISH_CONSTANT_4D", "if", "(", "c", "&", "0x04", ")", "!=", "0", ":", "zsv_ext0", "=", "zsv_ext1", "=", "zsv_ext2", "=", "zsb", "+", "1", "dz_ext0", "=", "dz_ext1", "=", "dz_ext2", "=", "dz0", "-", "1", "-", "4", "*", "SQUISH_CONSTANT_4D", "if", "(", "c", "&", "0x03", ")", "!=", "0x03", ":", "if", "(", "c", "&", "0x03", ")", "==", "0", ":", "zsv_ext0", "+=", "1", "dz_ext0", "-=", "1", "else", ":", "zsv_ext1", "+=", "1", "dz_ext1", "-=", "1", "else", ":", "zsv_ext2", "+=", "1", "dz_ext2", "-=", "1", "else", ":", "zsv_ext0", "=", "zsv_ext1", "=", "zsv_ext2", "=", "zsb", "dz_ext0", "=", "dz_ext1", "=", "dz_ext2", "=", "dz0", "-", "4", "*", "SQUISH_CONSTANT_4D", "if", "(", "c", "&", "0x08", ")", "!=", "0", ":", "wsv_ext0", "=", "wsv_ext1", "=", "wsb", "+", "1", "wsv_ext2", "=", "wsb", "+", "2", "dw_ext0", "=", "dw_ext1", "=", "dw0", "-", "1", "-", "4", "*", "SQUISH_CONSTANT_4D", "dw_ext2", "=", "dw0", "-", "2", "-", "4", "*", "SQUISH_CONSTANT_4D", "else", ":", "wsv_ext0", "=", "wsv_ext1", "=", "wsv_ext2", "=", "wsb", "dw_ext0", "=", "dw_ext1", "=", "dw_ext2", "=", "dw0", "-", "4", "*", "SQUISH_CONSTANT_4D", "else", ":", "# (1,1,1,1) is not one of the closest two pentachoron vertices.", "c", "=", "(", "a_po", "&", "b_po", ")", "# Our three extra vertices are determined by the closest two.", "if", "(", "c", "&", "0x01", ")", "!=", "0", ":", "xsv_ext0", "=", "xsv_ext2", "=", "xsb", "+", "1", "xsv_ext1", "=", "xsb", "+", "2", "dx_ext0", "=", "dx0", "-", "1", "-", "2", "*", "SQUISH_CONSTANT_4D", "dx_ext1", "=", "dx0", "-", "2", "-", "3", "*", "SQUISH_CONSTANT_4D", "dx_ext2", "=", "dx0", "-", "1", "-", "3", "*", "SQUISH_CONSTANT_4D", "else", ":", "xsv_ext0", "=", "xsv_ext1", "=", "xsv_ext2", "=", "xsb", "dx_ext0", "=", "dx0", "-", "2", "*", "SQUISH_CONSTANT_4D", "dx_ext1", "=", "dx_ext2", "=", "dx0", "-", "3", "*", "SQUISH_CONSTANT_4D", "if", "(", "c", "&", "0x02", ")", "!=", "0", ":", "ysv_ext0", "=", "ysv_ext1", "=", "ysv_ext2", "=", "ysb", "+", "1", "dy_ext0", "=", "dy0", "-", "1", "-", "2", "*", "SQUISH_CONSTANT_4D", "dy_ext1", "=", "dy_ext2", "=", "dy0", "-", "1", "-", "3", "*", "SQUISH_CONSTANT_4D", "if", "(", "c", "&", "0x01", ")", "!=", "0", ":", "ysv_ext2", "+=", "1", "dy_ext2", "-=", "1", "else", ":", "ysv_ext1", "+=", "1", "dy_ext1", "-=", "1", "else", ":", "ysv_ext0", "=", "ysv_ext1", "=", "ysv_ext2", "=", "ysb", "dy_ext0", "=", "dy0", "-", "2", "*", "SQUISH_CONSTANT_4D", "dy_ext1", "=", "dy_ext2", "=", "dy0", "-", "3", "*", "SQUISH_CONSTANT_4D", "if", "(", "c", "&", "0x04", ")", "!=", "0", ":", "zsv_ext0", "=", "zsv_ext1", "=", "zsv_ext2", "=", "zsb", "+", "1", "dz_ext0", "=", "dz0", "-", "1", "-", "2", "*", "SQUISH_CONSTANT_4D", "dz_ext1", "=", "dz_ext2", "=", "dz0", "-", "1", "-", "3", "*", "SQUISH_CONSTANT_4D", "if", "(", "c", "&", "0x03", ")", "!=", "0", ":", "zsv_ext2", "+=", "1", "dz_ext2", "-=", "1", "else", ":", "zsv_ext1", "+=", "1", "dz_ext1", "-=", "1", "else", ":", "zsv_ext0", "=", "zsv_ext1", "=", "zsv_ext2", "=", "zsb", "dz_ext0", "=", "dz0", "-", "2", "*", "SQUISH_CONSTANT_4D", "dz_ext1", "=", "dz_ext2", "=", "dz0", "-", "3", "*", "SQUISH_CONSTANT_4D", "if", "(", "c", "&", "0x08", ")", "!=", "0", ":", "wsv_ext0", "=", "wsv_ext1", "=", "wsb", "+", "1", "wsv_ext2", "=", "wsb", "+", "2", "dw_ext0", "=", "dw0", "-", "1", "-", "2", "*", "SQUISH_CONSTANT_4D", "dw_ext1", "=", "dw0", "-", "1", "-", "3", "*", "SQUISH_CONSTANT_4D", "dw_ext2", "=", "dw0", "-", "2", "-", "3", "*", "SQUISH_CONSTANT_4D", "else", ":", "wsv_ext0", "=", "wsv_ext1", "=", "wsv_ext2", "=", "wsb", "dw_ext0", "=", "dw0", "-", "2", "*", "SQUISH_CONSTANT_4D", "dw_ext1", "=", "dw_ext2", "=", "dw0", "-", "3", "*", "SQUISH_CONSTANT_4D", "# Contribution (1,1,1,0)", "dx4", "=", "dx0", "-", "1", "-", "3", "*", "SQUISH_CONSTANT_4D", "dy4", "=", "dy0", "-", "1", "-", "3", "*", "SQUISH_CONSTANT_4D", "dz4", "=", "dz0", "-", "1", "-", "3", "*", "SQUISH_CONSTANT_4D", "dw4", "=", "dw0", "-", "3", "*", "SQUISH_CONSTANT_4D", "attn4", "=", "2", "-", "dx4", "*", "dx4", "-", "dy4", "*", "dy4", "-", "dz4", "*", "dz4", "-", "dw4", "*", "dw4", "if", "attn4", ">", "0", ":", "attn4", "*=", "attn4", "value", "+=", "attn4", "*", "attn4", "*", "extrapolate", "(", "xsb", "+", "1", ",", "ysb", "+", "1", ",", "zsb", "+", "1", ",", "wsb", "+", "0", ",", "dx4", ",", "dy4", ",", "dz4", ",", "dw4", ")", "# Contribution (1,1,0,1)", "dx3", "=", "dx4", "dy3", "=", "dy4", "dz3", "=", "dz0", "-", "3", "*", "SQUISH_CONSTANT_4D", "dw3", "=", "dw0", "-", "1", "-", "3", "*", "SQUISH_CONSTANT_4D", "attn3", "=", "2", "-", "dx3", "*", "dx3", "-", "dy3", "*", "dy3", "-", "dz3", "*", "dz3", "-", "dw3", "*", "dw3", "if", "attn3", ">", "0", ":", "attn3", "*=", "attn3", "value", "+=", "attn3", "*", "attn3", "*", "extrapolate", "(", "xsb", "+", "1", ",", "ysb", "+", "1", ",", "zsb", "+", "0", ",", "wsb", "+", "1", ",", "dx3", ",", "dy3", ",", "dz3", ",", "dw3", ")", "# Contribution (1,0,1,1)", "dx2", "=", "dx4", "dy2", "=", "dy0", "-", "3", "*", "SQUISH_CONSTANT_4D", "dz2", "=", "dz4", "dw2", "=", "dw3", "attn2", "=", "2", "-", "dx2", "*", "dx2", "-", "dy2", "*", "dy2", "-", "dz2", "*", "dz2", "-", "dw2", "*", "dw2", "if", "attn2", ">", "0", ":", "attn2", "*=", "attn2", "value", "+=", "attn2", "*", "attn2", "*", "extrapolate", "(", "xsb", "+", "1", ",", "ysb", "+", "0", ",", "zsb", "+", "1", ",", "wsb", "+", "1", ",", "dx2", ",", "dy2", ",", "dz2", ",", "dw2", ")", "# Contribution (0,1,1,1)", "dx1", "=", "dx0", "-", "3", "*", "SQUISH_CONSTANT_4D", "dz1", "=", "dz4", "dy1", "=", "dy4", "dw1", "=", "dw3", "attn1", "=", "2", "-", "dx1", "*", "dx1", "-", "dy1", "*", "dy1", "-", "dz1", "*", "dz1", "-", "dw1", "*", "dw1", "if", "attn1", ">", "0", ":", "attn1", "*=", "attn1", "value", "+=", "attn1", "*", "attn1", "*", "extrapolate", "(", "xsb", "+", "0", ",", "ysb", "+", "1", ",", "zsb", "+", "1", ",", "wsb", "+", "1", ",", "dx1", ",", "dy1", ",", "dz1", ",", "dw1", ")", "# Contribution (1,1,1,1)", "dx0", "=", "dx0", "-", "1", "-", "4", "*", "SQUISH_CONSTANT_4D", "dy0", "=", "dy0", "-", "1", "-", "4", "*", "SQUISH_CONSTANT_4D", "dz0", "=", "dz0", "-", "1", "-", "4", "*", "SQUISH_CONSTANT_4D", "dw0", "=", "dw0", "-", "1", "-", "4", "*", "SQUISH_CONSTANT_4D", "attn0", "=", "2", "-", "dx0", "*", "dx0", "-", "dy0", "*", "dy0", "-", "dz0", "*", "dz0", "-", "dw0", "*", "dw0", "if", "attn0", ">", "0", ":", "attn0", "*=", "attn0", "value", "+=", "attn0", "*", "attn0", "*", "extrapolate", "(", "xsb", "+", "1", ",", "ysb", "+", "1", ",", "zsb", "+", "1", ",", "wsb", "+", "1", ",", "dx0", ",", "dy0", ",", "dz0", ",", "dw0", ")", "elif", "in_sum", "<=", "2", ":", "# We're inside the first dispentachoron (Rectified 4-Simplex)", "a_is_bigger_side", "=", "True", "b_is_bigger_side", "=", "True", "# Decide between (1,1,0,0) and (0,0,1,1)", "if", "xins", "+", "yins", ">", "zins", "+", "wins", ":", "a_score", "=", "xins", "+", "yins", "a_po", "=", "0x03", "else", ":", "a_score", "=", "zins", "+", "wins", "a_po", "=", "0x0C", "# Decide between (1,0,1,0) and (0,1,0,1)", "if", "xins", "+", "zins", ">", "yins", "+", "wins", ":", "b_score", "=", "xins", "+", "zins", "b_po", "=", "0x05", "else", ":", "b_score", "=", "yins", "+", "wins", "b_po", "=", "0x0A", "# Closer between (1,0,0,1) and (0,1,1,0) will replace the further of a and b, if closer.", "if", "xins", "+", "wins", ">", "yins", "+", "zins", ":", "score", "=", "xins", "+", "wins", "if", "a_score", ">=", "b_score", "and", "score", ">", "b_score", ":", "b_score", "=", "score", "b_po", "=", "0x09", "elif", "a_score", "<", "b_score", "and", "score", ">", "a_score", ":", "a_score", "=", "score", "a_po", "=", "0x09", "else", ":", "score", "=", "yins", "+", "zins", "if", "a_score", ">=", "b_score", "and", "score", ">", "b_score", ":", "b_score", "=", "score", "b_po", "=", "0x06", "elif", "a_score", "<", "b_score", "and", "score", ">", "a_score", ":", "a_score", "=", "score", "a_po", "=", "0x06", "# Decide if (1,0,0,0) is closer.", "p1", "=", "2", "-", "in_sum", "+", "xins", "if", "a_score", ">=", "b_score", "and", "p1", ">", "b_score", ":", "b_score", "=", "p1", "b_po", "=", "0x01", "b_is_bigger_side", "=", "False", "elif", "a_score", "<", "b_score", "and", "p1", ">", "a_score", ":", "a_score", "=", "p1", "a_po", "=", "0x01", "a_is_bigger_side", "=", "False", "# Decide if (0,1,0,0) is closer.", "p2", "=", "2", "-", "in_sum", "+", "yins", "if", "a_score", ">=", "b_score", "and", "p2", ">", "b_score", ":", "b_score", "=", "p2", "b_po", "=", "0x02", "b_is_bigger_side", "=", "False", "elif", "a_score", "<", "b_score", "and", "p2", ">", "a_score", ":", "a_score", "=", "p2", "a_po", "=", "0x02", "a_is_bigger_side", "=", "False", "# Decide if (0,0,1,0) is closer.", "p3", "=", "2", "-", "in_sum", "+", "zins", "if", "a_score", ">=", "b_score", "and", "p3", ">", "b_score", ":", "b_score", "=", "p3", "b_po", "=", "0x04", "b_is_bigger_side", "=", "False", "elif", "a_score", "<", "b_score", "and", "p3", ">", "a_score", ":", "a_score", "=", "p3", "a_po", "=", "0x04", "a_is_bigger_side", "=", "False", "# Decide if (0,0,0,1) is closer.", "p4", "=", "2", "-", "in_sum", "+", "wins", "if", "a_score", ">=", "b_score", "and", "p4", ">", "b_score", ":", "b_po", "=", "0x08", "b_is_bigger_side", "=", "False", "elif", "a_score", "<", "b_score", "and", "p4", ">", "a_score", ":", "a_po", "=", "0x08", "a_is_bigger_side", "=", "False", "# Where each of the two closest pos are determines how the extra three vertices are calculated.", "if", "a_is_bigger_side", "==", "b_is_bigger_side", ":", "if", "a_is_bigger_side", ":", "# Both closest pos on the bigger side", "c1", "=", "(", "a_po", "|", "b_po", ")", "c2", "=", "(", "a_po", "&", "b_po", ")", "if", "(", "c1", "&", "0x01", ")", "==", "0", ":", "xsv_ext0", "=", "xsb", "xsv_ext1", "=", "xsb", "-", "1", "dx_ext0", "=", "dx0", "-", "3", "*", "SQUISH_CONSTANT_4D", "dx_ext1", "=", "dx0", "+", "1", "-", "2", "*", "SQUISH_CONSTANT_4D", "else", ":", "xsv_ext0", "=", "xsv_ext1", "=", "xsb", "+", "1", "dx_ext0", "=", "dx0", "-", "1", "-", "3", "*", "SQUISH_CONSTANT_4D", "dx_ext1", "=", "dx0", "-", "1", "-", "2", "*", "SQUISH_CONSTANT_4D", "if", "(", "c1", "&", "0x02", ")", "==", "0", ":", "ysv_ext0", "=", "ysb", "ysv_ext1", "=", "ysb", "-", "1", "dy_ext0", "=", "dy0", "-", "3", "*", "SQUISH_CONSTANT_4D", "dy_ext1", "=", "dy0", "+", "1", "-", "2", "*", "SQUISH_CONSTANT_4D", "else", ":", "ysv_ext0", "=", "ysv_ext1", "=", "ysb", "+", "1", "dy_ext0", "=", "dy0", "-", "1", "-", "3", "*", "SQUISH_CONSTANT_4D", "dy_ext1", "=", "dy0", "-", "1", "-", "2", "*", "SQUISH_CONSTANT_4D", "if", "(", "c1", "&", "0x04", ")", "==", "0", ":", "zsv_ext0", "=", "zsb", "zsv_ext1", "=", "zsb", "-", "1", "dz_ext0", "=", "dz0", "-", "3", "*", "SQUISH_CONSTANT_4D", "dz_ext1", "=", "dz0", "+", "1", "-", "2", "*", "SQUISH_CONSTANT_4D", "else", ":", "zsv_ext0", "=", "zsv_ext1", "=", "zsb", "+", "1", "dz_ext0", "=", "dz0", "-", "1", "-", "3", "*", "SQUISH_CONSTANT_4D", "dz_ext1", "=", "dz0", "-", "1", "-", "2", "*", "SQUISH_CONSTANT_4D", "if", "(", "c1", "&", "0x08", ")", "==", "0", ":", "wsv_ext0", "=", "wsb", "wsv_ext1", "=", "wsb", "-", "1", "dw_ext0", "=", "dw0", "-", "3", "*", "SQUISH_CONSTANT_4D", "dw_ext1", "=", "dw0", "+", "1", "-", "2", "*", "SQUISH_CONSTANT_4D", "else", ":", "wsv_ext0", "=", "wsv_ext1", "=", "wsb", "+", "1", "dw_ext0", "=", "dw0", "-", "1", "-", "3", "*", "SQUISH_CONSTANT_4D", "dw_ext1", "=", "dw0", "-", "1", "-", "2", "*", "SQUISH_CONSTANT_4D", "# One combination is a _permutation of (0,0,0,2) based on c2", "xsv_ext2", "=", "xsb", "ysv_ext2", "=", "ysb", "zsv_ext2", "=", "zsb", "wsv_ext2", "=", "wsb", "dx_ext2", "=", "dx0", "-", "2", "*", "SQUISH_CONSTANT_4D", "dy_ext2", "=", "dy0", "-", "2", "*", "SQUISH_CONSTANT_4D", "dz_ext2", "=", "dz0", "-", "2", "*", "SQUISH_CONSTANT_4D", "dw_ext2", "=", "dw0", "-", "2", "*", "SQUISH_CONSTANT_4D", "if", "(", "c2", "&", "0x01", ")", "!=", "0", ":", "xsv_ext2", "+=", "2", "dx_ext2", "-=", "2", "elif", "(", "c2", "&", "0x02", ")", "!=", "0", ":", "ysv_ext2", "+=", "2", "dy_ext2", "-=", "2", "elif", "(", "c2", "&", "0x04", ")", "!=", "0", ":", "zsv_ext2", "+=", "2", "dz_ext2", "-=", "2", "else", ":", "wsv_ext2", "+=", "2", "dw_ext2", "-=", "2", "else", ":", "# Both closest pos on the smaller side", "# One of the two extra pos is (0,0,0,0)", "xsv_ext2", "=", "xsb", "ysv_ext2", "=", "ysb", "zsv_ext2", "=", "zsb", "wsv_ext2", "=", "wsb", "dx_ext2", "=", "dx0", "dy_ext2", "=", "dy0", "dz_ext2", "=", "dz0", "dw_ext2", "=", "dw0", "# Other two pos are based on the omitted axes.", "c", "=", "(", "a_po", "|", "b_po", ")", "if", "(", "c", "&", "0x01", ")", "==", "0", ":", "xsv_ext0", "=", "xsb", "-", "1", "xsv_ext1", "=", "xsb", "dx_ext0", "=", "dx0", "+", "1", "-", "SQUISH_CONSTANT_4D", "dx_ext1", "=", "dx0", "-", "SQUISH_CONSTANT_4D", "else", ":", "xsv_ext0", "=", "xsv_ext1", "=", "xsb", "+", "1", "dx_ext0", "=", "dx_ext1", "=", "dx0", "-", "1", "-", "SQUISH_CONSTANT_4D", "if", "(", "c", "&", "0x02", ")", "==", "0", ":", "ysv_ext0", "=", "ysv_ext1", "=", "ysb", "dy_ext0", "=", "dy_ext1", "=", "dy0", "-", "SQUISH_CONSTANT_4D", "if", "(", "c", "&", "0x01", ")", "==", "0x01", ":", "ysv_ext0", "-=", "1", "dy_ext0", "+=", "1", "else", ":", "ysv_ext1", "-=", "1", "dy_ext1", "+=", "1", "else", ":", "ysv_ext0", "=", "ysv_ext1", "=", "ysb", "+", "1", "dy_ext0", "=", "dy_ext1", "=", "dy0", "-", "1", "-", "SQUISH_CONSTANT_4D", "if", "(", "c", "&", "0x04", ")", "==", "0", ":", "zsv_ext0", "=", "zsv_ext1", "=", "zsb", "dz_ext0", "=", "dz_ext1", "=", "dz0", "-", "SQUISH_CONSTANT_4D", "if", "(", "c", "&", "0x03", ")", "==", "0x03", ":", "zsv_ext0", "-=", "1", "dz_ext0", "+=", "1", "else", ":", "zsv_ext1", "-=", "1", "dz_ext1", "+=", "1", "else", ":", "zsv_ext0", "=", "zsv_ext1", "=", "zsb", "+", "1", "dz_ext0", "=", "dz_ext1", "=", "dz0", "-", "1", "-", "SQUISH_CONSTANT_4D", "if", "(", "c", "&", "0x08", ")", "==", "0", ":", "wsv_ext0", "=", "wsb", "wsv_ext1", "=", "wsb", "-", "1", "dw_ext0", "=", "dw0", "-", "SQUISH_CONSTANT_4D", "dw_ext1", "=", "dw0", "+", "1", "-", "SQUISH_CONSTANT_4D", "else", ":", "wsv_ext0", "=", "wsv_ext1", "=", "wsb", "+", "1", "dw_ext0", "=", "dw_ext1", "=", "dw0", "-", "1", "-", "SQUISH_CONSTANT_4D", "else", ":", "# One po on each \"side\"", "if", "a_is_bigger_side", ":", "c1", "=", "a_po", "c2", "=", "b_po", "else", ":", "c1", "=", "b_po", "c2", "=", "a_po", "# Two contributions are the bigger-sided po with each 0 replaced with -1.", "if", "(", "c1", "&", "0x01", ")", "==", "0", ":", "xsv_ext0", "=", "xsb", "-", "1", "xsv_ext1", "=", "xsb", "dx_ext0", "=", "dx0", "+", "1", "-", "SQUISH_CONSTANT_4D", "dx_ext1", "=", "dx0", "-", "SQUISH_CONSTANT_4D", "else", ":", "xsv_ext0", "=", "xsv_ext1", "=", "xsb", "+", "1", "dx_ext0", "=", "dx_ext1", "=", "dx0", "-", "1", "-", "SQUISH_CONSTANT_4D", "if", "(", "c1", "&", "0x02", ")", "==", "0", ":", "ysv_ext0", "=", "ysv_ext1", "=", "ysb", "dy_ext0", "=", "dy_ext1", "=", "dy0", "-", "SQUISH_CONSTANT_4D", "if", "(", "c1", "&", "0x01", ")", "==", "0x01", ":", "ysv_ext0", "-=", "1", "dy_ext0", "+=", "1", "else", ":", "ysv_ext1", "-=", "1", "dy_ext1", "+=", "1", "else", ":", "ysv_ext0", "=", "ysv_ext1", "=", "ysb", "+", "1", "dy_ext0", "=", "dy_ext1", "=", "dy0", "-", "1", "-", "SQUISH_CONSTANT_4D", "if", "(", "c1", "&", "0x04", ")", "==", "0", ":", "zsv_ext0", "=", "zsv_ext1", "=", "zsb", "dz_ext0", "=", "dz_ext1", "=", "dz0", "-", "SQUISH_CONSTANT_4D", "if", "(", "c1", "&", "0x03", ")", "==", "0x03", ":", "zsv_ext0", "-=", "1", "dz_ext0", "+=", "1", "else", ":", "zsv_ext1", "-=", "1", "dz_ext1", "+=", "1", "else", ":", "zsv_ext0", "=", "zsv_ext1", "=", "zsb", "+", "1", "dz_ext0", "=", "dz_ext1", "=", "dz0", "-", "1", "-", "SQUISH_CONSTANT_4D", "if", "(", "c1", "&", "0x08", ")", "==", "0", ":", "wsv_ext0", "=", "wsb", "wsv_ext1", "=", "wsb", "-", "1", "dw_ext0", "=", "dw0", "-", "SQUISH_CONSTANT_4D", "dw_ext1", "=", "dw0", "+", "1", "-", "SQUISH_CONSTANT_4D", "else", ":", "wsv_ext0", "=", "wsv_ext1", "=", "wsb", "+", "1", "dw_ext0", "=", "dw_ext1", "=", "dw0", "-", "1", "-", "SQUISH_CONSTANT_4D", "# One contribution is a _permutation of (0,0,0,2) based on the smaller-sided po", "xsv_ext2", "=", "xsb", "ysv_ext2", "=", "ysb", "zsv_ext2", "=", "zsb", "wsv_ext2", "=", "wsb", "dx_ext2", "=", "dx0", "-", "2", "*", "SQUISH_CONSTANT_4D", "dy_ext2", "=", "dy0", "-", "2", "*", "SQUISH_CONSTANT_4D", "dz_ext2", "=", "dz0", "-", "2", "*", "SQUISH_CONSTANT_4D", "dw_ext2", "=", "dw0", "-", "2", "*", "SQUISH_CONSTANT_4D", "if", "(", "c2", "&", "0x01", ")", "!=", "0", ":", "xsv_ext2", "+=", "2", "dx_ext2", "-=", "2", "elif", "(", "c2", "&", "0x02", ")", "!=", "0", ":", "ysv_ext2", "+=", "2", "dy_ext2", "-=", "2", "elif", "(", "c2", "&", "0x04", ")", "!=", "0", ":", "zsv_ext2", "+=", "2", "dz_ext2", "-=", "2", "else", ":", "wsv_ext2", "+=", "2", "dw_ext2", "-=", "2", "# Contribution (1,0,0,0)", "dx1", "=", "dx0", "-", "1", "-", "SQUISH_CONSTANT_4D", "dy1", "=", "dy0", "-", "0", "-", "SQUISH_CONSTANT_4D", "dz1", "=", "dz0", "-", "0", "-", "SQUISH_CONSTANT_4D", "dw1", "=", "dw0", "-", "0", "-", "SQUISH_CONSTANT_4D", "attn1", "=", "2", "-", "dx1", "*", "dx1", "-", "dy1", "*", "dy1", "-", "dz1", "*", "dz1", "-", "dw1", "*", "dw1", "if", "attn1", ">", "0", ":", "attn1", "*=", "attn1", "value", "+=", "attn1", "*", "attn1", "*", "extrapolate", "(", "xsb", "+", "1", ",", "ysb", "+", "0", ",", "zsb", "+", "0", ",", "wsb", "+", "0", ",", "dx1", ",", "dy1", ",", "dz1", ",", "dw1", ")", "# Contribution (0,1,0,0)", "dx2", "=", "dx0", "-", "0", "-", "SQUISH_CONSTANT_4D", "dy2", "=", "dy0", "-", "1", "-", "SQUISH_CONSTANT_4D", "dz2", "=", "dz1", "dw2", "=", "dw1", "attn2", "=", "2", "-", "dx2", "*", "dx2", "-", "dy2", "*", "dy2", "-", "dz2", "*", "dz2", "-", "dw2", "*", "dw2", "if", "attn2", ">", "0", ":", "attn2", "*=", "attn2", "value", "+=", "attn2", "*", "attn2", "*", "extrapolate", "(", "xsb", "+", "0", ",", "ysb", "+", "1", ",", "zsb", "+", "0", ",", "wsb", "+", "0", ",", "dx2", ",", "dy2", ",", "dz2", ",", "dw2", ")", "# Contribution (0,0,1,0)", "dx3", "=", "dx2", "dy3", "=", "dy1", "dz3", "=", "dz0", "-", "1", "-", "SQUISH_CONSTANT_4D", "dw3", "=", "dw1", "attn3", "=", "2", "-", "dx3", "*", "dx3", "-", "dy3", "*", "dy3", "-", "dz3", "*", "dz3", "-", "dw3", "*", "dw3", "if", "attn3", ">", "0", ":", "attn3", "*=", "attn3", "value", "+=", "attn3", "*", "attn3", "*", "extrapolate", "(", "xsb", "+", "0", ",", "ysb", "+", "0", ",", "zsb", "+", "1", ",", "wsb", "+", "0", ",", "dx3", ",", "dy3", ",", "dz3", ",", "dw3", ")", "# Contribution (0,0,0,1)", "dx4", "=", "dx2", "dy4", "=", "dy1", "dz4", "=", "dz1", "dw4", "=", "dw0", "-", "1", "-", "SQUISH_CONSTANT_4D", "attn4", "=", "2", "-", "dx4", "*", "dx4", "-", "dy4", "*", "dy4", "-", "dz4", "*", "dz4", "-", "dw4", "*", "dw4", "if", "attn4", ">", "0", ":", "attn4", "*=", "attn4", "value", "+=", "attn4", "*", "attn4", "*", "extrapolate", "(", "xsb", "+", "0", ",", "ysb", "+", "0", ",", "zsb", "+", "0", ",", "wsb", "+", "1", ",", "dx4", ",", "dy4", ",", "dz4", ",", "dw4", ")", "# Contribution (1,1,0,0)", "dx5", "=", "dx0", "-", "1", "-", "2", "*", "SQUISH_CONSTANT_4D", "dy5", "=", "dy0", "-", "1", "-", "2", "*", "SQUISH_CONSTANT_4D", "dz5", "=", "dz0", "-", "0", "-", "2", "*", "SQUISH_CONSTANT_4D", "dw5", "=", "dw0", "-", "0", "-", "2", "*", "SQUISH_CONSTANT_4D", "attn5", "=", "2", "-", "dx5", "*", "dx5", "-", "dy5", "*", "dy5", "-", "dz5", "*", "dz5", "-", "dw5", "*", "dw5", "if", "attn5", ">", "0", ":", "attn5", "*=", "attn5", "value", "+=", "attn5", "*", "attn5", "*", "extrapolate", "(", "xsb", "+", "1", ",", "ysb", "+", "1", ",", "zsb", "+", "0", ",", "wsb", "+", "0", ",", "dx5", ",", "dy5", ",", "dz5", ",", "dw5", ")", "# Contribution (1,0,1,0)", "dx6", "=", "dx0", "-", "1", "-", "2", "*", "SQUISH_CONSTANT_4D", "dy6", "=", "dy0", "-", "0", "-", "2", "*", "SQUISH_CONSTANT_4D", "dz6", "=", "dz0", "-", "1", "-", "2", "*", "SQUISH_CONSTANT_4D", "dw6", "=", "dw0", "-", "0", "-", "2", "*", "SQUISH_CONSTANT_4D", "attn6", "=", "2", "-", "dx6", "*", "dx6", "-", "dy6", "*", "dy6", "-", "dz6", "*", "dz6", "-", "dw6", "*", "dw6", "if", "attn6", ">", "0", ":", "attn6", "*=", "attn6", "value", "+=", "attn6", "*", "attn6", "*", "extrapolate", "(", "xsb", "+", "1", ",", "ysb", "+", "0", ",", "zsb", "+", "1", ",", "wsb", "+", "0", ",", "dx6", ",", "dy6", ",", "dz6", ",", "dw6", ")", "# Contribution (1,0,0,1)", "dx7", "=", "dx0", "-", "1", "-", "2", "*", "SQUISH_CONSTANT_4D", "dy7", "=", "dy0", "-", "0", "-", "2", "*", "SQUISH_CONSTANT_4D", "dz7", "=", "dz0", "-", "0", "-", "2", "*", "SQUISH_CONSTANT_4D", "dw7", "=", "dw0", "-", "1", "-", "2", "*", "SQUISH_CONSTANT_4D", "attn7", "=", "2", "-", "dx7", "*", "dx7", "-", "dy7", "*", "dy7", "-", "dz7", "*", "dz7", "-", "dw7", "*", "dw7", "if", "attn7", ">", "0", ":", "attn7", "*=", "attn7", "value", "+=", "attn7", "*", "attn7", "*", "extrapolate", "(", "xsb", "+", "1", ",", "ysb", "+", "0", ",", "zsb", "+", "0", ",", "wsb", "+", "1", ",", "dx7", ",", "dy7", ",", "dz7", ",", "dw7", ")", "# Contribution (0,1,1,0)", "dx8", "=", "dx0", "-", "0", "-", "2", "*", "SQUISH_CONSTANT_4D", "dy8", "=", "dy0", "-", "1", "-", "2", "*", "SQUISH_CONSTANT_4D", "dz8", "=", "dz0", "-", "1", "-", "2", "*", "SQUISH_CONSTANT_4D", "dw8", "=", "dw0", "-", "0", "-", "2", "*", "SQUISH_CONSTANT_4D", "attn8", "=", "2", "-", "dx8", "*", "dx8", "-", "dy8", "*", "dy8", "-", "dz8", "*", "dz8", "-", "dw8", "*", "dw8", "if", "attn8", ">", "0", ":", "attn8", "*=", "attn8", "value", "+=", "attn8", "*", "attn8", "*", "extrapolate", "(", "xsb", "+", "0", ",", "ysb", "+", "1", ",", "zsb", "+", "1", ",", "wsb", "+", "0", ",", "dx8", ",", "dy8", ",", "dz8", ",", "dw8", ")", "# Contribution (0,1,0,1)", "dx9", "=", "dx0", "-", "0", "-", "2", "*", "SQUISH_CONSTANT_4D", "dy9", "=", "dy0", "-", "1", "-", "2", "*", "SQUISH_CONSTANT_4D", "dz9", "=", "dz0", "-", "0", "-", "2", "*", "SQUISH_CONSTANT_4D", "dw9", "=", "dw0", "-", "1", "-", "2", "*", "SQUISH_CONSTANT_4D", "attn9", "=", "2", "-", "dx9", "*", "dx9", "-", "dy9", "*", "dy9", "-", "dz9", "*", "dz9", "-", "dw9", "*", "dw9", "if", "attn9", ">", "0", ":", "attn9", "*=", "attn9", "value", "+=", "attn9", "*", "attn9", "*", "extrapolate", "(", "xsb", "+", "0", ",", "ysb", "+", "1", ",", "zsb", "+", "0", ",", "wsb", "+", "1", ",", "dx9", ",", "dy9", ",", "dz9", ",", "dw9", ")", "# Contribution (0,0,1,1)", "dx10", "=", "dx0", "-", "0", "-", "2", "*", "SQUISH_CONSTANT_4D", "dy10", "=", "dy0", "-", "0", "-", "2", "*", "SQUISH_CONSTANT_4D", "dz10", "=", "dz0", "-", "1", "-", "2", "*", "SQUISH_CONSTANT_4D", "dw10", "=", "dw0", "-", "1", "-", "2", "*", "SQUISH_CONSTANT_4D", "attn10", "=", "2", "-", "dx10", "*", "dx10", "-", "dy10", "*", "dy10", "-", "dz10", "*", "dz10", "-", "dw10", "*", "dw10", "if", "attn10", ">", "0", ":", "attn10", "*=", "attn10", "value", "+=", "attn10", "*", "attn10", "*", "extrapolate", "(", "xsb", "+", "0", ",", "ysb", "+", "0", ",", "zsb", "+", "1", ",", "wsb", "+", "1", ",", "dx10", ",", "dy10", ",", "dz10", ",", "dw10", ")", "else", ":", "# We're inside the second dispentachoron (Rectified 4-Simplex)", "a_is_bigger_side", "=", "True", "b_is_bigger_side", "=", "True", "# Decide between (0,0,1,1) and (1,1,0,0)", "if", "xins", "+", "yins", "<", "zins", "+", "wins", ":", "a_score", "=", "xins", "+", "yins", "a_po", "=", "0x0C", "else", ":", "a_score", "=", "zins", "+", "wins", "a_po", "=", "0x03", "# Decide between (0,1,0,1) and (1,0,1,0)", "if", "xins", "+", "zins", "<", "yins", "+", "wins", ":", "b_score", "=", "xins", "+", "zins", "b_po", "=", "0x0A", "else", ":", "b_score", "=", "yins", "+", "wins", "b_po", "=", "0x05", "# Closer between (0,1,1,0) and (1,0,0,1) will replace the further of a and b, if closer.", "if", "xins", "+", "wins", "<", "yins", "+", "zins", ":", "score", "=", "xins", "+", "wins", "if", "a_score", "<=", "b_score", "and", "score", "<", "b_score", ":", "b_score", "=", "score", "b_po", "=", "0x06", "elif", "a_score", ">", "b_score", "and", "score", "<", "a_score", ":", "a_score", "=", "score", "a_po", "=", "0x06", "else", ":", "score", "=", "yins", "+", "zins", "if", "a_score", "<=", "b_score", "and", "score", "<", "b_score", ":", "b_score", "=", "score", "b_po", "=", "0x09", "elif", "a_score", ">", "b_score", "and", "score", "<", "a_score", ":", "a_score", "=", "score", "a_po", "=", "0x09", "# Decide if (0,1,1,1) is closer.", "p1", "=", "3", "-", "in_sum", "+", "xins", "if", "a_score", "<=", "b_score", "and", "p1", "<", "b_score", ":", "b_score", "=", "p1", "b_po", "=", "0x0E", "b_is_bigger_side", "=", "False", "elif", "a_score", ">", "b_score", "and", "p1", "<", "a_score", ":", "a_score", "=", "p1", "a_po", "=", "0x0E", "a_is_bigger_side", "=", "False", "# Decide if (1,0,1,1) is closer.", "p2", "=", "3", "-", "in_sum", "+", "yins", "if", "a_score", "<=", "b_score", "and", "p2", "<", "b_score", ":", "b_score", "=", "p2", "b_po", "=", "0x0D", "b_is_bigger_side", "=", "False", "elif", "a_score", ">", "b_score", "and", "p2", "<", "a_score", ":", "a_score", "=", "p2", "a_po", "=", "0x0D", "a_is_bigger_side", "=", "False", "# Decide if (1,1,0,1) is closer.", "p3", "=", "3", "-", "in_sum", "+", "zins", "if", "a_score", "<=", "b_score", "and", "p3", "<", "b_score", ":", "b_score", "=", "p3", "b_po", "=", "0x0B", "b_is_bigger_side", "=", "False", "elif", "a_score", ">", "b_score", "and", "p3", "<", "a_score", ":", "a_score", "=", "p3", "a_po", "=", "0x0B", "a_is_bigger_side", "=", "False", "# Decide if (1,1,1,0) is closer.", "p4", "=", "3", "-", "in_sum", "+", "wins", "if", "a_score", "<=", "b_score", "and", "p4", "<", "b_score", ":", "b_po", "=", "0x07", "b_is_bigger_side", "=", "False", "elif", "a_score", ">", "b_score", "and", "p4", "<", "a_score", ":", "a_po", "=", "0x07", "a_is_bigger_side", "=", "False", "# Where each of the two closest pos are determines how the extra three vertices are calculated.", "if", "a_is_bigger_side", "==", "b_is_bigger_side", ":", "if", "a_is_bigger_side", ":", "# Both closest pos on the bigger side", "c1", "=", "(", "a_po", "&", "b_po", ")", "c2", "=", "(", "a_po", "|", "b_po", ")", "# Two contributions are _permutations of (0,0,0,1) and (0,0,0,2) based on c1", "xsv_ext0", "=", "xsv_ext1", "=", "xsb", "ysv_ext0", "=", "ysv_ext1", "=", "ysb", "zsv_ext0", "=", "zsv_ext1", "=", "zsb", "wsv_ext0", "=", "wsv_ext1", "=", "wsb", "dx_ext0", "=", "dx0", "-", "SQUISH_CONSTANT_4D", "dy_ext0", "=", "dy0", "-", "SQUISH_CONSTANT_4D", "dz_ext0", "=", "dz0", "-", "SQUISH_CONSTANT_4D", "dw_ext0", "=", "dw0", "-", "SQUISH_CONSTANT_4D", "dx_ext1", "=", "dx0", "-", "2", "*", "SQUISH_CONSTANT_4D", "dy_ext1", "=", "dy0", "-", "2", "*", "SQUISH_CONSTANT_4D", "dz_ext1", "=", "dz0", "-", "2", "*", "SQUISH_CONSTANT_4D", "dw_ext1", "=", "dw0", "-", "2", "*", "SQUISH_CONSTANT_4D", "if", "(", "c1", "&", "0x01", ")", "!=", "0", ":", "xsv_ext0", "+=", "1", "dx_ext0", "-=", "1", "xsv_ext1", "+=", "2", "dx_ext1", "-=", "2", "elif", "(", "c1", "&", "0x02", ")", "!=", "0", ":", "ysv_ext0", "+=", "1", "dy_ext0", "-=", "1", "ysv_ext1", "+=", "2", "dy_ext1", "-=", "2", "elif", "(", "c1", "&", "0x04", ")", "!=", "0", ":", "zsv_ext0", "+=", "1", "dz_ext0", "-=", "1", "zsv_ext1", "+=", "2", "dz_ext1", "-=", "2", "else", ":", "wsv_ext0", "+=", "1", "dw_ext0", "-=", "1", "wsv_ext1", "+=", "2", "dw_ext1", "-=", "2", "# One contribution is a _permutation of (1,1,1,-1) based on c2", "xsv_ext2", "=", "xsb", "+", "1", "ysv_ext2", "=", "ysb", "+", "1", "zsv_ext2", "=", "zsb", "+", "1", "wsv_ext2", "=", "wsb", "+", "1", "dx_ext2", "=", "dx0", "-", "1", "-", "2", "*", "SQUISH_CONSTANT_4D", "dy_ext2", "=", "dy0", "-", "1", "-", "2", "*", "SQUISH_CONSTANT_4D", "dz_ext2", "=", "dz0", "-", "1", "-", "2", "*", "SQUISH_CONSTANT_4D", "dw_ext2", "=", "dw0", "-", "1", "-", "2", "*", "SQUISH_CONSTANT_4D", "if", "(", "c2", "&", "0x01", ")", "==", "0", ":", "xsv_ext2", "-=", "2", "dx_ext2", "+=", "2", "elif", "(", "c2", "&", "0x02", ")", "==", "0", ":", "ysv_ext2", "-=", "2", "dy_ext2", "+=", "2", "elif", "(", "c2", "&", "0x04", ")", "==", "0", ":", "zsv_ext2", "-=", "2", "dz_ext2", "+=", "2", "else", ":", "wsv_ext2", "-=", "2", "dw_ext2", "+=", "2", "else", ":", "# Both closest pos on the smaller side", "# One of the two extra pos is (1,1,1,1)", "xsv_ext2", "=", "xsb", "+", "1", "ysv_ext2", "=", "ysb", "+", "1", "zsv_ext2", "=", "zsb", "+", "1", "wsv_ext2", "=", "wsb", "+", "1", "dx_ext2", "=", "dx0", "-", "1", "-", "4", "*", "SQUISH_CONSTANT_4D", "dy_ext2", "=", "dy0", "-", "1", "-", "4", "*", "SQUISH_CONSTANT_4D", "dz_ext2", "=", "dz0", "-", "1", "-", "4", "*", "SQUISH_CONSTANT_4D", "dw_ext2", "=", "dw0", "-", "1", "-", "4", "*", "SQUISH_CONSTANT_4D", "# Other two pos are based on the shared axes.", "c", "=", "(", "a_po", "&", "b_po", ")", "if", "(", "c", "&", "0x01", ")", "!=", "0", ":", "xsv_ext0", "=", "xsb", "+", "2", "xsv_ext1", "=", "xsb", "+", "1", "dx_ext0", "=", "dx0", "-", "2", "-", "3", "*", "SQUISH_CONSTANT_4D", "dx_ext1", "=", "dx0", "-", "1", "-", "3", "*", "SQUISH_CONSTANT_4D", "else", ":", "xsv_ext0", "=", "xsv_ext1", "=", "xsb", "dx_ext0", "=", "dx_ext1", "=", "dx0", "-", "3", "*", "SQUISH_CONSTANT_4D", "if", "(", "c", "&", "0x02", ")", "!=", "0", ":", "ysv_ext0", "=", "ysv_ext1", "=", "ysb", "+", "1", "dy_ext0", "=", "dy_ext1", "=", "dy0", "-", "1", "-", "3", "*", "SQUISH_CONSTANT_4D", "if", "(", "c", "&", "0x01", ")", "==", "0", ":", "ysv_ext0", "+=", "1", "dy_ext0", "-=", "1", "else", ":", "ysv_ext1", "+=", "1", "dy_ext1", "-=", "1", "else", ":", "ysv_ext0", "=", "ysv_ext1", "=", "ysb", "dy_ext0", "=", "dy_ext1", "=", "dy0", "-", "3", "*", "SQUISH_CONSTANT_4D", "if", "(", "c", "&", "0x04", ")", "!=", "0", ":", "zsv_ext0", "=", "zsv_ext1", "=", "zsb", "+", "1", "dz_ext0", "=", "dz_ext1", "=", "dz0", "-", "1", "-", "3", "*", "SQUISH_CONSTANT_4D", "if", "(", "c", "&", "0x03", ")", "==", "0", ":", "zsv_ext0", "+=", "1", "dz_ext0", "-=", "1", "else", ":", "zsv_ext1", "+=", "1", "dz_ext1", "-=", "1", "else", ":", "zsv_ext0", "=", "zsv_ext1", "=", "zsb", "dz_ext0", "=", "dz_ext1", "=", "dz0", "-", "3", "*", "SQUISH_CONSTANT_4D", "if", "(", "c", "&", "0x08", ")", "!=", "0", ":", "wsv_ext0", "=", "wsb", "+", "1", "wsv_ext1", "=", "wsb", "+", "2", "dw_ext0", "=", "dw0", "-", "1", "-", "3", "*", "SQUISH_CONSTANT_4D", "dw_ext1", "=", "dw0", "-", "2", "-", "3", "*", "SQUISH_CONSTANT_4D", "else", ":", "wsv_ext0", "=", "wsv_ext1", "=", "wsb", "dw_ext0", "=", "dw_ext1", "=", "dw0", "-", "3", "*", "SQUISH_CONSTANT_4D", "else", ":", "# One po on each \"side\"", "if", "a_is_bigger_side", ":", "c1", "=", "a_po", "c2", "=", "b_po", "else", ":", "c1", "=", "b_po", "c2", "=", "a_po", "# Two contributions are the bigger-sided po with each 1 replaced with 2.", "if", "(", "c1", "&", "0x01", ")", "!=", "0", ":", "xsv_ext0", "=", "xsb", "+", "2", "xsv_ext1", "=", "xsb", "+", "1", "dx_ext0", "=", "dx0", "-", "2", "-", "3", "*", "SQUISH_CONSTANT_4D", "dx_ext1", "=", "dx0", "-", "1", "-", "3", "*", "SQUISH_CONSTANT_4D", "else", ":", "xsv_ext0", "=", "xsv_ext1", "=", "xsb", "dx_ext0", "=", "dx_ext1", "=", "dx0", "-", "3", "*", "SQUISH_CONSTANT_4D", "if", "(", "c1", "&", "0x02", ")", "!=", "0", ":", "ysv_ext0", "=", "ysv_ext1", "=", "ysb", "+", "1", "dy_ext0", "=", "dy_ext1", "=", "dy0", "-", "1", "-", "3", "*", "SQUISH_CONSTANT_4D", "if", "(", "c1", "&", "0x01", ")", "==", "0", ":", "ysv_ext0", "+=", "1", "dy_ext0", "-=", "1", "else", ":", "ysv_ext1", "+=", "1", "dy_ext1", "-=", "1", "else", ":", "ysv_ext0", "=", "ysv_ext1", "=", "ysb", "dy_ext0", "=", "dy_ext1", "=", "dy0", "-", "3", "*", "SQUISH_CONSTANT_4D", "if", "(", "c1", "&", "0x04", ")", "!=", "0", ":", "zsv_ext0", "=", "zsv_ext1", "=", "zsb", "+", "1", "dz_ext0", "=", "dz_ext1", "=", "dz0", "-", "1", "-", "3", "*", "SQUISH_CONSTANT_4D", "if", "(", "c1", "&", "0x03", ")", "==", "0", ":", "zsv_ext0", "+=", "1", "dz_ext0", "-=", "1", "else", ":", "zsv_ext1", "+=", "1", "dz_ext1", "-=", "1", "else", ":", "zsv_ext0", "=", "zsv_ext1", "=", "zsb", "dz_ext0", "=", "dz_ext1", "=", "dz0", "-", "3", "*", "SQUISH_CONSTANT_4D", "if", "(", "c1", "&", "0x08", ")", "!=", "0", ":", "wsv_ext0", "=", "wsb", "+", "1", "wsv_ext1", "=", "wsb", "+", "2", "dw_ext0", "=", "dw0", "-", "1", "-", "3", "*", "SQUISH_CONSTANT_4D", "dw_ext1", "=", "dw0", "-", "2", "-", "3", "*", "SQUISH_CONSTANT_4D", "else", ":", "wsv_ext0", "=", "wsv_ext1", "=", "wsb", "dw_ext0", "=", "dw_ext1", "=", "dw0", "-", "3", "*", "SQUISH_CONSTANT_4D", "# One contribution is a _permutation of (1,1,1,-1) based on the smaller-sided po", "xsv_ext2", "=", "xsb", "+", "1", "ysv_ext2", "=", "ysb", "+", "1", "zsv_ext2", "=", "zsb", "+", "1", "wsv_ext2", "=", "wsb", "+", "1", "dx_ext2", "=", "dx0", "-", "1", "-", "2", "*", "SQUISH_CONSTANT_4D", "dy_ext2", "=", "dy0", "-", "1", "-", "2", "*", "SQUISH_CONSTANT_4D", "dz_ext2", "=", "dz0", "-", "1", "-", "2", "*", "SQUISH_CONSTANT_4D", "dw_ext2", "=", "dw0", "-", "1", "-", "2", "*", "SQUISH_CONSTANT_4D", "if", "(", "c2", "&", "0x01", ")", "==", "0", ":", "xsv_ext2", "-=", "2", "dx_ext2", "+=", "2", "elif", "(", "c2", "&", "0x02", ")", "==", "0", ":", "ysv_ext2", "-=", "2", "dy_ext2", "+=", "2", "elif", "(", "c2", "&", "0x04", ")", "==", "0", ":", "zsv_ext2", "-=", "2", "dz_ext2", "+=", "2", "else", ":", "wsv_ext2", "-=", "2", "dw_ext2", "+=", "2", "# Contribution (1,1,1,0)", "dx4", "=", "dx0", "-", "1", "-", "3", "*", "SQUISH_CONSTANT_4D", "dy4", "=", "dy0", "-", "1", "-", "3", "*", "SQUISH_CONSTANT_4D", "dz4", "=", "dz0", "-", "1", "-", "3", "*", "SQUISH_CONSTANT_4D", "dw4", "=", "dw0", "-", "3", "*", "SQUISH_CONSTANT_4D", "attn4", "=", "2", "-", "dx4", "*", "dx4", "-", "dy4", "*", "dy4", "-", "dz4", "*", "dz4", "-", "dw4", "*", "dw4", "if", "attn4", ">", "0", ":", "attn4", "*=", "attn4", "value", "+=", "attn4", "*", "attn4", "*", "extrapolate", "(", "xsb", "+", "1", ",", "ysb", "+", "1", ",", "zsb", "+", "1", ",", "wsb", "+", "0", ",", "dx4", ",", "dy4", ",", "dz4", ",", "dw4", ")", "# Contribution (1,1,0,1)", "dx3", "=", "dx4", "dy3", "=", "dy4", "dz3", "=", "dz0", "-", "3", "*", "SQUISH_CONSTANT_4D", "dw3", "=", "dw0", "-", "1", "-", "3", "*", "SQUISH_CONSTANT_4D", "attn3", "=", "2", "-", "dx3", "*", "dx3", "-", "dy3", "*", "dy3", "-", "dz3", "*", "dz3", "-", "dw3", "*", "dw3", "if", "attn3", ">", "0", ":", "attn3", "*=", "attn3", "value", "+=", "attn3", "*", "attn3", "*", "extrapolate", "(", "xsb", "+", "1", ",", "ysb", "+", "1", ",", "zsb", "+", "0", ",", "wsb", "+", "1", ",", "dx3", ",", "dy3", ",", "dz3", ",", "dw3", ")", "# Contribution (1,0,1,1)", "dx2", "=", "dx4", "dy2", "=", "dy0", "-", "3", "*", "SQUISH_CONSTANT_4D", "dz2", "=", "dz4", "dw2", "=", "dw3", "attn2", "=", "2", "-", "dx2", "*", "dx2", "-", "dy2", "*", "dy2", "-", "dz2", "*", "dz2", "-", "dw2", "*", "dw2", "if", "attn2", ">", "0", ":", "attn2", "*=", "attn2", "value", "+=", "attn2", "*", "attn2", "*", "extrapolate", "(", "xsb", "+", "1", ",", "ysb", "+", "0", ",", "zsb", "+", "1", ",", "wsb", "+", "1", ",", "dx2", ",", "dy2", ",", "dz2", ",", "dw2", ")", "# Contribution (0,1,1,1)", "dx1", "=", "dx0", "-", "3", "*", "SQUISH_CONSTANT_4D", "dz1", "=", "dz4", "dy1", "=", "dy4", "dw1", "=", "dw3", "attn1", "=", "2", "-", "dx1", "*", "dx1", "-", "dy1", "*", "dy1", "-", "dz1", "*", "dz1", "-", "dw1", "*", "dw1", "if", "attn1", ">", "0", ":", "attn1", "*=", "attn1", "value", "+=", "attn1", "*", "attn1", "*", "extrapolate", "(", "xsb", "+", "0", ",", "ysb", "+", "1", ",", "zsb", "+", "1", ",", "wsb", "+", "1", ",", "dx1", ",", "dy1", ",", "dz1", ",", "dw1", ")", "# Contribution (1,1,0,0)", "dx5", "=", "dx0", "-", "1", "-", "2", "*", "SQUISH_CONSTANT_4D", "dy5", "=", "dy0", "-", "1", "-", "2", "*", "SQUISH_CONSTANT_4D", "dz5", "=", "dz0", "-", "0", "-", "2", "*", "SQUISH_CONSTANT_4D", "dw5", "=", "dw0", "-", "0", "-", "2", "*", "SQUISH_CONSTANT_4D", "attn5", "=", "2", "-", "dx5", "*", "dx5", "-", "dy5", "*", "dy5", "-", "dz5", "*", "dz5", "-", "dw5", "*", "dw5", "if", "attn5", ">", "0", ":", "attn5", "*=", "attn5", "value", "+=", "attn5", "*", "attn5", "*", "extrapolate", "(", "xsb", "+", "1", ",", "ysb", "+", "1", ",", "zsb", "+", "0", ",", "wsb", "+", "0", ",", "dx5", ",", "dy5", ",", "dz5", ",", "dw5", ")", "# Contribution (1,0,1,0)", "dx6", "=", "dx0", "-", "1", "-", "2", "*", "SQUISH_CONSTANT_4D", "dy6", "=", "dy0", "-", "0", "-", "2", "*", "SQUISH_CONSTANT_4D", "dz6", "=", "dz0", "-", "1", "-", "2", "*", "SQUISH_CONSTANT_4D", "dw6", "=", "dw0", "-", "0", "-", "2", "*", "SQUISH_CONSTANT_4D", "attn6", "=", "2", "-", "dx6", "*", "dx6", "-", "dy6", "*", "dy6", "-", "dz6", "*", "dz6", "-", "dw6", "*", "dw6", "if", "attn6", ">", "0", ":", "attn6", "*=", "attn6", "value", "+=", "attn6", "*", "attn6", "*", "extrapolate", "(", "xsb", "+", "1", ",", "ysb", "+", "0", ",", "zsb", "+", "1", ",", "wsb", "+", "0", ",", "dx6", ",", "dy6", ",", "dz6", ",", "dw6", ")", "# Contribution (1,0,0,1)", "dx7", "=", "dx0", "-", "1", "-", "2", "*", "SQUISH_CONSTANT_4D", "dy7", "=", "dy0", "-", "0", "-", "2", "*", "SQUISH_CONSTANT_4D", "dz7", "=", "dz0", "-", "0", "-", "2", "*", "SQUISH_CONSTANT_4D", "dw7", "=", "dw0", "-", "1", "-", "2", "*", "SQUISH_CONSTANT_4D", "attn7", "=", "2", "-", "dx7", "*", "dx7", "-", "dy7", "*", "dy7", "-", "dz7", "*", "dz7", "-", "dw7", "*", "dw7", "if", "attn7", ">", "0", ":", "attn7", "*=", "attn7", "value", "+=", "attn7", "*", "attn7", "*", "extrapolate", "(", "xsb", "+", "1", ",", "ysb", "+", "0", ",", "zsb", "+", "0", ",", "wsb", "+", "1", ",", "dx7", ",", "dy7", ",", "dz7", ",", "dw7", ")", "# Contribution (0,1,1,0)", "dx8", "=", "dx0", "-", "0", "-", "2", "*", "SQUISH_CONSTANT_4D", "dy8", "=", "dy0", "-", "1", "-", "2", "*", "SQUISH_CONSTANT_4D", "dz8", "=", "dz0", "-", "1", "-", "2", "*", "SQUISH_CONSTANT_4D", "dw8", "=", "dw0", "-", "0", "-", "2", "*", "SQUISH_CONSTANT_4D", "attn8", "=", "2", "-", "dx8", "*", "dx8", "-", "dy8", "*", "dy8", "-", "dz8", "*", "dz8", "-", "dw8", "*", "dw8", "if", "attn8", ">", "0", ":", "attn8", "*=", "attn8", "value", "+=", "attn8", "*", "attn8", "*", "extrapolate", "(", "xsb", "+", "0", ",", "ysb", "+", "1", ",", "zsb", "+", "1", ",", "wsb", "+", "0", ",", "dx8", ",", "dy8", ",", "dz8", ",", "dw8", ")", "# Contribution (0,1,0,1)", "dx9", "=", "dx0", "-", "0", "-", "2", "*", "SQUISH_CONSTANT_4D", "dy9", "=", "dy0", "-", "1", "-", "2", "*", "SQUISH_CONSTANT_4D", "dz9", "=", "dz0", "-", "0", "-", "2", "*", "SQUISH_CONSTANT_4D", "dw9", "=", "dw0", "-", "1", "-", "2", "*", "SQUISH_CONSTANT_4D", "attn9", "=", "2", "-", "dx9", "*", "dx9", "-", "dy9", "*", "dy9", "-", "dz9", "*", "dz9", "-", "dw9", "*", "dw9", "if", "attn9", ">", "0", ":", "attn9", "*=", "attn9", "value", "+=", "attn9", "*", "attn9", "*", "extrapolate", "(", "xsb", "+", "0", ",", "ysb", "+", "1", ",", "zsb", "+", "0", ",", "wsb", "+", "1", ",", "dx9", ",", "dy9", ",", "dz9", ",", "dw9", ")", "# Contribution (0,0,1,1)", "dx10", "=", "dx0", "-", "0", "-", "2", "*", "SQUISH_CONSTANT_4D", "dy10", "=", "dy0", "-", "0", "-", "2", "*", "SQUISH_CONSTANT_4D", "dz10", "=", "dz0", "-", "1", "-", "2", "*", "SQUISH_CONSTANT_4D", "dw10", "=", "dw0", "-", "1", "-", "2", "*", "SQUISH_CONSTANT_4D", "attn10", "=", "2", "-", "dx10", "*", "dx10", "-", "dy10", "*", "dy10", "-", "dz10", "*", "dz10", "-", "dw10", "*", "dw10", "if", "attn10", ">", "0", ":", "attn10", "*=", "attn10", "value", "+=", "attn10", "*", "attn10", "*", "extrapolate", "(", "xsb", "+", "0", ",", "ysb", "+", "0", ",", "zsb", "+", "1", ",", "wsb", "+", "1", ",", "dx10", ",", "dy10", ",", "dz10", ",", "dw10", ")", "# First extra vertex", "attn_ext0", "=", "2", "-", "dx_ext0", "*", "dx_ext0", "-", "dy_ext0", "*", "dy_ext0", "-", "dz_ext0", "*", "dz_ext0", "-", "dw_ext0", "*", "dw_ext0", "if", "attn_ext0", ">", "0", ":", "attn_ext0", "*=", "attn_ext0", "value", "+=", "attn_ext0", "*", "attn_ext0", "*", "extrapolate", "(", "xsv_ext0", ",", "ysv_ext0", ",", "zsv_ext0", ",", "wsv_ext0", ",", "dx_ext0", ",", "dy_ext0", ",", "dz_ext0", ",", "dw_ext0", ")", "# Second extra vertex", "attn_ext1", "=", "2", "-", "dx_ext1", "*", "dx_ext1", "-", "dy_ext1", "*", "dy_ext1", "-", "dz_ext1", "*", "dz_ext1", "-", "dw_ext1", "*", "dw_ext1", "if", "attn_ext1", ">", "0", ":", "attn_ext1", "*=", "attn_ext1", "value", "+=", "attn_ext1", "*", "attn_ext1", "*", "extrapolate", "(", "xsv_ext1", ",", "ysv_ext1", ",", "zsv_ext1", ",", "wsv_ext1", ",", "dx_ext1", ",", "dy_ext1", ",", "dz_ext1", ",", "dw_ext1", ")", "# Third extra vertex", "attn_ext2", "=", "2", "-", "dx_ext2", "*", "dx_ext2", "-", "dy_ext2", "*", "dy_ext2", "-", "dz_ext2", "*", "dz_ext2", "-", "dw_ext2", "*", "dw_ext2", "if", "attn_ext2", ">", "0", ":", "attn_ext2", "*=", "attn_ext2", "value", "+=", "attn_ext2", "*", "attn_ext2", "*", "extrapolate", "(", "xsv_ext2", ",", "ysv_ext2", ",", "zsv_ext2", ",", "wsv_ext2", ",", "dx_ext2", ",", "dy_ext2", ",", "dz_ext2", ",", "dw_ext2", ")", "return", "value", "/", "NORM_CONSTANT_4D" ]
Generate 4D OpenSimplex noise from X,Y,Z,W coordinates.
[ "Generate", "4D", "OpenSimplex", "noise", "from", "X", "Y", "Z", "W", "coordinates", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/external/opensimplex.py#L743-L1934
valid
aleju/imgaug
imgaug/augmenters/contrast.py
adjust_contrast_gamma
def adjust_contrast_gamma(arr, gamma): """ Adjust contrast by scaling each pixel value to ``255 * ((I_ij/255)**gamma)``. dtype support:: * ``uint8``: yes; fully tested (1) (2) (3) * ``uint16``: yes; tested (2) (3) * ``uint32``: yes; tested (2) (3) * ``uint64``: yes; tested (2) (3) (4) * ``int8``: limited; tested (2) (3) (5) * ``int16``: limited; tested (2) (3) (5) * ``int32``: limited; tested (2) (3) (5) * ``int64``: limited; tested (2) (3) (4) (5) * ``float16``: limited; tested (5) * ``float32``: limited; tested (5) * ``float64``: limited; tested (5) * ``float128``: no (6) * ``bool``: no (7) - (1) Handled by ``cv2``. Other dtypes are handled by ``skimage``. - (2) Normalization is done as ``I_ij/max``, where ``max`` is the maximum value of the dtype, e.g. 255 for ``uint8``. The normalization is reversed afterwards, e.g. ``result*255`` for ``uint8``. - (3) Integer-like values are not rounded after applying the contrast adjustment equation (before inverting the normalization to 0.0-1.0 space), i.e. projection from continuous space to discrete happens according to floor function. - (4) Note that scikit-image doc says that integers are converted to ``float64`` values before applying the contrast normalization method. This might lead to inaccuracies for large 64bit integer values. Tests showed no indication of that happening though. - (5) Must not contain negative values. Values >=0 are fully supported. - (6) Leads to error in scikit-image. - (7) Does not make sense for contrast adjustments. Parameters ---------- arr : numpy.ndarray Array for which to adjust the contrast. Dtype ``uint8`` is fastest. gamma : number Exponent for the contrast adjustment. Higher values darken the image. Returns ------- numpy.ndarray Array with adjusted contrast. """ # int8 is also possible according to docs # https://docs.opencv.org/3.0-beta/modules/core/doc/operations_on_arrays.html#cv2.LUT , but here it seemed # like `d` was 0 for CV_8S, causing that to fail if arr.dtype.name == "uint8": min_value, _center_value, max_value = iadt.get_value_range_of_dtype(arr.dtype) dynamic_range = max_value - min_value value_range = np.linspace(0, 1.0, num=dynamic_range+1, dtype=np.float32) # 255 * ((I_ij/255)**gamma) # using np.float32(.) here still works when the input is a numpy array of size 1 table = (min_value + (value_range ** np.float32(gamma)) * dynamic_range) arr_aug = cv2.LUT(arr, np.clip(table, min_value, max_value).astype(arr.dtype)) if arr.ndim == 3 and arr_aug.ndim == 2: return arr_aug[..., np.newaxis] return arr_aug else: return ski_exposure.adjust_gamma(arr, gamma)
python
def adjust_contrast_gamma(arr, gamma): """ Adjust contrast by scaling each pixel value to ``255 * ((I_ij/255)**gamma)``. dtype support:: * ``uint8``: yes; fully tested (1) (2) (3) * ``uint16``: yes; tested (2) (3) * ``uint32``: yes; tested (2) (3) * ``uint64``: yes; tested (2) (3) (4) * ``int8``: limited; tested (2) (3) (5) * ``int16``: limited; tested (2) (3) (5) * ``int32``: limited; tested (2) (3) (5) * ``int64``: limited; tested (2) (3) (4) (5) * ``float16``: limited; tested (5) * ``float32``: limited; tested (5) * ``float64``: limited; tested (5) * ``float128``: no (6) * ``bool``: no (7) - (1) Handled by ``cv2``. Other dtypes are handled by ``skimage``. - (2) Normalization is done as ``I_ij/max``, where ``max`` is the maximum value of the dtype, e.g. 255 for ``uint8``. The normalization is reversed afterwards, e.g. ``result*255`` for ``uint8``. - (3) Integer-like values are not rounded after applying the contrast adjustment equation (before inverting the normalization to 0.0-1.0 space), i.e. projection from continuous space to discrete happens according to floor function. - (4) Note that scikit-image doc says that integers are converted to ``float64`` values before applying the contrast normalization method. This might lead to inaccuracies for large 64bit integer values. Tests showed no indication of that happening though. - (5) Must not contain negative values. Values >=0 are fully supported. - (6) Leads to error in scikit-image. - (7) Does not make sense for contrast adjustments. Parameters ---------- arr : numpy.ndarray Array for which to adjust the contrast. Dtype ``uint8`` is fastest. gamma : number Exponent for the contrast adjustment. Higher values darken the image. Returns ------- numpy.ndarray Array with adjusted contrast. """ # int8 is also possible according to docs # https://docs.opencv.org/3.0-beta/modules/core/doc/operations_on_arrays.html#cv2.LUT , but here it seemed # like `d` was 0 for CV_8S, causing that to fail if arr.dtype.name == "uint8": min_value, _center_value, max_value = iadt.get_value_range_of_dtype(arr.dtype) dynamic_range = max_value - min_value value_range = np.linspace(0, 1.0, num=dynamic_range+1, dtype=np.float32) # 255 * ((I_ij/255)**gamma) # using np.float32(.) here still works when the input is a numpy array of size 1 table = (min_value + (value_range ** np.float32(gamma)) * dynamic_range) arr_aug = cv2.LUT(arr, np.clip(table, min_value, max_value).astype(arr.dtype)) if arr.ndim == 3 and arr_aug.ndim == 2: return arr_aug[..., np.newaxis] return arr_aug else: return ski_exposure.adjust_gamma(arr, gamma)
[ "def", "adjust_contrast_gamma", "(", "arr", ",", "gamma", ")", ":", "# int8 is also possible according to docs", "# https://docs.opencv.org/3.0-beta/modules/core/doc/operations_on_arrays.html#cv2.LUT , but here it seemed", "# like `d` was 0 for CV_8S, causing that to fail", "if", "arr", ".", "dtype", ".", "name", "==", "\"uint8\"", ":", "min_value", ",", "_center_value", ",", "max_value", "=", "iadt", ".", "get_value_range_of_dtype", "(", "arr", ".", "dtype", ")", "dynamic_range", "=", "max_value", "-", "min_value", "value_range", "=", "np", ".", "linspace", "(", "0", ",", "1.0", ",", "num", "=", "dynamic_range", "+", "1", ",", "dtype", "=", "np", ".", "float32", ")", "# 255 * ((I_ij/255)**gamma)", "# using np.float32(.) here still works when the input is a numpy array of size 1", "table", "=", "(", "min_value", "+", "(", "value_range", "**", "np", ".", "float32", "(", "gamma", ")", ")", "*", "dynamic_range", ")", "arr_aug", "=", "cv2", ".", "LUT", "(", "arr", ",", "np", ".", "clip", "(", "table", ",", "min_value", ",", "max_value", ")", ".", "astype", "(", "arr", ".", "dtype", ")", ")", "if", "arr", ".", "ndim", "==", "3", "and", "arr_aug", ".", "ndim", "==", "2", ":", "return", "arr_aug", "[", "...", ",", "np", ".", "newaxis", "]", "return", "arr_aug", "else", ":", "return", "ski_exposure", ".", "adjust_gamma", "(", "arr", ",", "gamma", ")" ]
Adjust contrast by scaling each pixel value to ``255 * ((I_ij/255)**gamma)``. dtype support:: * ``uint8``: yes; fully tested (1) (2) (3) * ``uint16``: yes; tested (2) (3) * ``uint32``: yes; tested (2) (3) * ``uint64``: yes; tested (2) (3) (4) * ``int8``: limited; tested (2) (3) (5) * ``int16``: limited; tested (2) (3) (5) * ``int32``: limited; tested (2) (3) (5) * ``int64``: limited; tested (2) (3) (4) (5) * ``float16``: limited; tested (5) * ``float32``: limited; tested (5) * ``float64``: limited; tested (5) * ``float128``: no (6) * ``bool``: no (7) - (1) Handled by ``cv2``. Other dtypes are handled by ``skimage``. - (2) Normalization is done as ``I_ij/max``, where ``max`` is the maximum value of the dtype, e.g. 255 for ``uint8``. The normalization is reversed afterwards, e.g. ``result*255`` for ``uint8``. - (3) Integer-like values are not rounded after applying the contrast adjustment equation (before inverting the normalization to 0.0-1.0 space), i.e. projection from continuous space to discrete happens according to floor function. - (4) Note that scikit-image doc says that integers are converted to ``float64`` values before applying the contrast normalization method. This might lead to inaccuracies for large 64bit integer values. Tests showed no indication of that happening though. - (5) Must not contain negative values. Values >=0 are fully supported. - (6) Leads to error in scikit-image. - (7) Does not make sense for contrast adjustments. Parameters ---------- arr : numpy.ndarray Array for which to adjust the contrast. Dtype ``uint8`` is fastest. gamma : number Exponent for the contrast adjustment. Higher values darken the image. Returns ------- numpy.ndarray Array with adjusted contrast.
[ "Adjust", "contrast", "by", "scaling", "each", "pixel", "value", "to", "255", "*", "((", "I_ij", "/", "255", ")", "**", "gamma", ")", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/augmenters/contrast.py#L41-L105
valid
aleju/imgaug
imgaug/augmenters/contrast.py
adjust_contrast_sigmoid
def adjust_contrast_sigmoid(arr, gain, cutoff): """ Adjust contrast by scaling each pixel value to ``255 * 1/(1 + exp(gain*(cutoff - I_ij/255)))``. dtype support:: * ``uint8``: yes; fully tested (1) (2) (3) * ``uint16``: yes; tested (2) (3) * ``uint32``: yes; tested (2) (3) * ``uint64``: yes; tested (2) (3) (4) * ``int8``: limited; tested (2) (3) (5) * ``int16``: limited; tested (2) (3) (5) * ``int32``: limited; tested (2) (3) (5) * ``int64``: limited; tested (2) (3) (4) (5) * ``float16``: limited; tested (5) * ``float32``: limited; tested (5) * ``float64``: limited; tested (5) * ``float128``: no (6) * ``bool``: no (7) - (1) Handled by ``cv2``. Other dtypes are handled by ``skimage``. - (2) Normalization is done as ``I_ij/max``, where ``max`` is the maximum value of the dtype, e.g. 255 for ``uint8``. The normalization is reversed afterwards, e.g. ``result*255`` for ``uint8``. - (3) Integer-like values are not rounded after applying the contrast adjustment equation (before inverting the normalization to 0.0-1.0 space), i.e. projection from continuous space to discrete happens according to floor function. - (4) Note that scikit-image doc says that integers are converted to ``float64`` values before applying the contrast normalization method. This might lead to inaccuracies for large 64bit integer values. Tests showed no indication of that happening though. - (5) Must not contain negative values. Values >=0 are fully supported. - (6) Leads to error in scikit-image. - (7) Does not make sense for contrast adjustments. Parameters ---------- arr : numpy.ndarray Array for which to adjust the contrast. Dtype ``uint8`` is fastest. gain : number Multiplier for the sigmoid function's output. Higher values lead to quicker changes from dark to light pixels. cutoff : number Cutoff that shifts the sigmoid function in horizontal direction. Higher values mean that the switch from dark to light pixels happens later, i.e. the pixels will remain darker. Returns ------- numpy.ndarray Array with adjusted contrast. """ # int8 is also possible according to docs # https://docs.opencv.org/3.0-beta/modules/core/doc/operations_on_arrays.html#cv2.LUT , but here it seemed # like `d` was 0 for CV_8S, causing that to fail if arr.dtype.name == "uint8": min_value, _center_value, max_value = iadt.get_value_range_of_dtype(arr.dtype) dynamic_range = max_value - min_value value_range = np.linspace(0, 1.0, num=dynamic_range+1, dtype=np.float32) # 255 * 1/(1 + exp(gain*(cutoff - I_ij/255))) # using np.float32(.) here still works when the input is a numpy array of size 1 gain = np.float32(gain) cutoff = np.float32(cutoff) table = min_value + dynamic_range * 1/(1 + np.exp(gain * (cutoff - value_range))) arr_aug = cv2.LUT(arr, np.clip(table, min_value, max_value).astype(arr.dtype)) if arr.ndim == 3 and arr_aug.ndim == 2: return arr_aug[..., np.newaxis] return arr_aug else: return ski_exposure.adjust_sigmoid(arr, cutoff=cutoff, gain=gain)
python
def adjust_contrast_sigmoid(arr, gain, cutoff): """ Adjust contrast by scaling each pixel value to ``255 * 1/(1 + exp(gain*(cutoff - I_ij/255)))``. dtype support:: * ``uint8``: yes; fully tested (1) (2) (3) * ``uint16``: yes; tested (2) (3) * ``uint32``: yes; tested (2) (3) * ``uint64``: yes; tested (2) (3) (4) * ``int8``: limited; tested (2) (3) (5) * ``int16``: limited; tested (2) (3) (5) * ``int32``: limited; tested (2) (3) (5) * ``int64``: limited; tested (2) (3) (4) (5) * ``float16``: limited; tested (5) * ``float32``: limited; tested (5) * ``float64``: limited; tested (5) * ``float128``: no (6) * ``bool``: no (7) - (1) Handled by ``cv2``. Other dtypes are handled by ``skimage``. - (2) Normalization is done as ``I_ij/max``, where ``max`` is the maximum value of the dtype, e.g. 255 for ``uint8``. The normalization is reversed afterwards, e.g. ``result*255`` for ``uint8``. - (3) Integer-like values are not rounded after applying the contrast adjustment equation (before inverting the normalization to 0.0-1.0 space), i.e. projection from continuous space to discrete happens according to floor function. - (4) Note that scikit-image doc says that integers are converted to ``float64`` values before applying the contrast normalization method. This might lead to inaccuracies for large 64bit integer values. Tests showed no indication of that happening though. - (5) Must not contain negative values. Values >=0 are fully supported. - (6) Leads to error in scikit-image. - (7) Does not make sense for contrast adjustments. Parameters ---------- arr : numpy.ndarray Array for which to adjust the contrast. Dtype ``uint8`` is fastest. gain : number Multiplier for the sigmoid function's output. Higher values lead to quicker changes from dark to light pixels. cutoff : number Cutoff that shifts the sigmoid function in horizontal direction. Higher values mean that the switch from dark to light pixels happens later, i.e. the pixels will remain darker. Returns ------- numpy.ndarray Array with adjusted contrast. """ # int8 is also possible according to docs # https://docs.opencv.org/3.0-beta/modules/core/doc/operations_on_arrays.html#cv2.LUT , but here it seemed # like `d` was 0 for CV_8S, causing that to fail if arr.dtype.name == "uint8": min_value, _center_value, max_value = iadt.get_value_range_of_dtype(arr.dtype) dynamic_range = max_value - min_value value_range = np.linspace(0, 1.0, num=dynamic_range+1, dtype=np.float32) # 255 * 1/(1 + exp(gain*(cutoff - I_ij/255))) # using np.float32(.) here still works when the input is a numpy array of size 1 gain = np.float32(gain) cutoff = np.float32(cutoff) table = min_value + dynamic_range * 1/(1 + np.exp(gain * (cutoff - value_range))) arr_aug = cv2.LUT(arr, np.clip(table, min_value, max_value).astype(arr.dtype)) if arr.ndim == 3 and arr_aug.ndim == 2: return arr_aug[..., np.newaxis] return arr_aug else: return ski_exposure.adjust_sigmoid(arr, cutoff=cutoff, gain=gain)
[ "def", "adjust_contrast_sigmoid", "(", "arr", ",", "gain", ",", "cutoff", ")", ":", "# int8 is also possible according to docs", "# https://docs.opencv.org/3.0-beta/modules/core/doc/operations_on_arrays.html#cv2.LUT , but here it seemed", "# like `d` was 0 for CV_8S, causing that to fail", "if", "arr", ".", "dtype", ".", "name", "==", "\"uint8\"", ":", "min_value", ",", "_center_value", ",", "max_value", "=", "iadt", ".", "get_value_range_of_dtype", "(", "arr", ".", "dtype", ")", "dynamic_range", "=", "max_value", "-", "min_value", "value_range", "=", "np", ".", "linspace", "(", "0", ",", "1.0", ",", "num", "=", "dynamic_range", "+", "1", ",", "dtype", "=", "np", ".", "float32", ")", "# 255 * 1/(1 + exp(gain*(cutoff - I_ij/255)))", "# using np.float32(.) here still works when the input is a numpy array of size 1", "gain", "=", "np", ".", "float32", "(", "gain", ")", "cutoff", "=", "np", ".", "float32", "(", "cutoff", ")", "table", "=", "min_value", "+", "dynamic_range", "*", "1", "/", "(", "1", "+", "np", ".", "exp", "(", "gain", "*", "(", "cutoff", "-", "value_range", ")", ")", ")", "arr_aug", "=", "cv2", ".", "LUT", "(", "arr", ",", "np", ".", "clip", "(", "table", ",", "min_value", ",", "max_value", ")", ".", "astype", "(", "arr", ".", "dtype", ")", ")", "if", "arr", ".", "ndim", "==", "3", "and", "arr_aug", ".", "ndim", "==", "2", ":", "return", "arr_aug", "[", "...", ",", "np", ".", "newaxis", "]", "return", "arr_aug", "else", ":", "return", "ski_exposure", ".", "adjust_sigmoid", "(", "arr", ",", "cutoff", "=", "cutoff", ",", "gain", "=", "gain", ")" ]
Adjust contrast by scaling each pixel value to ``255 * 1/(1 + exp(gain*(cutoff - I_ij/255)))``. dtype support:: * ``uint8``: yes; fully tested (1) (2) (3) * ``uint16``: yes; tested (2) (3) * ``uint32``: yes; tested (2) (3) * ``uint64``: yes; tested (2) (3) (4) * ``int8``: limited; tested (2) (3) (5) * ``int16``: limited; tested (2) (3) (5) * ``int32``: limited; tested (2) (3) (5) * ``int64``: limited; tested (2) (3) (4) (5) * ``float16``: limited; tested (5) * ``float32``: limited; tested (5) * ``float64``: limited; tested (5) * ``float128``: no (6) * ``bool``: no (7) - (1) Handled by ``cv2``. Other dtypes are handled by ``skimage``. - (2) Normalization is done as ``I_ij/max``, where ``max`` is the maximum value of the dtype, e.g. 255 for ``uint8``. The normalization is reversed afterwards, e.g. ``result*255`` for ``uint8``. - (3) Integer-like values are not rounded after applying the contrast adjustment equation (before inverting the normalization to 0.0-1.0 space), i.e. projection from continuous space to discrete happens according to floor function. - (4) Note that scikit-image doc says that integers are converted to ``float64`` values before applying the contrast normalization method. This might lead to inaccuracies for large 64bit integer values. Tests showed no indication of that happening though. - (5) Must not contain negative values. Values >=0 are fully supported. - (6) Leads to error in scikit-image. - (7) Does not make sense for contrast adjustments. Parameters ---------- arr : numpy.ndarray Array for which to adjust the contrast. Dtype ``uint8`` is fastest. gain : number Multiplier for the sigmoid function's output. Higher values lead to quicker changes from dark to light pixels. cutoff : number Cutoff that shifts the sigmoid function in horizontal direction. Higher values mean that the switch from dark to light pixels happens later, i.e. the pixels will remain darker. Returns ------- numpy.ndarray Array with adjusted contrast.
[ "Adjust", "contrast", "by", "scaling", "each", "pixel", "value", "to", "255", "*", "1", "/", "(", "1", "+", "exp", "(", "gain", "*", "(", "cutoff", "-", "I_ij", "/", "255", ")))", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/augmenters/contrast.py#L109-L181
valid
aleju/imgaug
imgaug/augmenters/contrast.py
adjust_contrast_log
def adjust_contrast_log(arr, gain): """ Adjust contrast by scaling each pixel value to ``255 * gain * log_2(1 + I_ij/255)``. dtype support:: * ``uint8``: yes; fully tested (1) (2) (3) * ``uint16``: yes; tested (2) (3) * ``uint32``: yes; tested (2) (3) * ``uint64``: yes; tested (2) (3) (4) * ``int8``: limited; tested (2) (3) (5) * ``int16``: limited; tested (2) (3) (5) * ``int32``: limited; tested (2) (3) (5) * ``int64``: limited; tested (2) (3) (4) (5) * ``float16``: limited; tested (5) * ``float32``: limited; tested (5) * ``float64``: limited; tested (5) * ``float128``: no (6) * ``bool``: no (7) - (1) Handled by ``cv2``. Other dtypes are handled by ``skimage``. - (2) Normalization is done as ``I_ij/max``, where ``max`` is the maximum value of the dtype, e.g. 255 for ``uint8``. The normalization is reversed afterwards, e.g. ``result*255`` for ``uint8``. - (3) Integer-like values are not rounded after applying the contrast adjustment equation (before inverting the normalization to 0.0-1.0 space), i.e. projection from continuous space to discrete happens according to floor function. - (4) Note that scikit-image doc says that integers are converted to ``float64`` values before applying the contrast normalization method. This might lead to inaccuracies for large 64bit integer values. Tests showed no indication of that happening though. - (5) Must not contain negative values. Values >=0 are fully supported. - (6) Leads to error in scikit-image. - (7) Does not make sense for contrast adjustments. Parameters ---------- arr : numpy.ndarray Array for which to adjust the contrast. Dtype ``uint8`` is fastest. gain : number Multiplier for the logarithm result. Values around 1.0 lead to a contrast-adjusted images. Values above 1.0 quickly lead to partially broken images due to exceeding the datatype's value range. Returns ------- numpy.ndarray Array with adjusted contrast. """ # int8 is also possible according to docs # https://docs.opencv.org/3.0-beta/modules/core/doc/operations_on_arrays.html#cv2.LUT , but here it seemed # like `d` was 0 for CV_8S, causing that to fail if arr.dtype.name == "uint8": min_value, _center_value, max_value = iadt.get_value_range_of_dtype(arr.dtype) dynamic_range = max_value - min_value value_range = np.linspace(0, 1.0, num=dynamic_range+1, dtype=np.float32) # 255 * 1/(1 + exp(gain*(cutoff - I_ij/255))) # using np.float32(.) here still works when the input is a numpy array of size 1 gain = np.float32(gain) table = min_value + dynamic_range * gain * np.log2(1 + value_range) arr_aug = cv2.LUT(arr, np.clip(table, min_value, max_value).astype(arr.dtype)) if arr.ndim == 3 and arr_aug.ndim == 2: return arr_aug[..., np.newaxis] return arr_aug else: return ski_exposure.adjust_log(arr, gain=gain)
python
def adjust_contrast_log(arr, gain): """ Adjust contrast by scaling each pixel value to ``255 * gain * log_2(1 + I_ij/255)``. dtype support:: * ``uint8``: yes; fully tested (1) (2) (3) * ``uint16``: yes; tested (2) (3) * ``uint32``: yes; tested (2) (3) * ``uint64``: yes; tested (2) (3) (4) * ``int8``: limited; tested (2) (3) (5) * ``int16``: limited; tested (2) (3) (5) * ``int32``: limited; tested (2) (3) (5) * ``int64``: limited; tested (2) (3) (4) (5) * ``float16``: limited; tested (5) * ``float32``: limited; tested (5) * ``float64``: limited; tested (5) * ``float128``: no (6) * ``bool``: no (7) - (1) Handled by ``cv2``. Other dtypes are handled by ``skimage``. - (2) Normalization is done as ``I_ij/max``, where ``max`` is the maximum value of the dtype, e.g. 255 for ``uint8``. The normalization is reversed afterwards, e.g. ``result*255`` for ``uint8``. - (3) Integer-like values are not rounded after applying the contrast adjustment equation (before inverting the normalization to 0.0-1.0 space), i.e. projection from continuous space to discrete happens according to floor function. - (4) Note that scikit-image doc says that integers are converted to ``float64`` values before applying the contrast normalization method. This might lead to inaccuracies for large 64bit integer values. Tests showed no indication of that happening though. - (5) Must not contain negative values. Values >=0 are fully supported. - (6) Leads to error in scikit-image. - (7) Does not make sense for contrast adjustments. Parameters ---------- arr : numpy.ndarray Array for which to adjust the contrast. Dtype ``uint8`` is fastest. gain : number Multiplier for the logarithm result. Values around 1.0 lead to a contrast-adjusted images. Values above 1.0 quickly lead to partially broken images due to exceeding the datatype's value range. Returns ------- numpy.ndarray Array with adjusted contrast. """ # int8 is also possible according to docs # https://docs.opencv.org/3.0-beta/modules/core/doc/operations_on_arrays.html#cv2.LUT , but here it seemed # like `d` was 0 for CV_8S, causing that to fail if arr.dtype.name == "uint8": min_value, _center_value, max_value = iadt.get_value_range_of_dtype(arr.dtype) dynamic_range = max_value - min_value value_range = np.linspace(0, 1.0, num=dynamic_range+1, dtype=np.float32) # 255 * 1/(1 + exp(gain*(cutoff - I_ij/255))) # using np.float32(.) here still works when the input is a numpy array of size 1 gain = np.float32(gain) table = min_value + dynamic_range * gain * np.log2(1 + value_range) arr_aug = cv2.LUT(arr, np.clip(table, min_value, max_value).astype(arr.dtype)) if arr.ndim == 3 and arr_aug.ndim == 2: return arr_aug[..., np.newaxis] return arr_aug else: return ski_exposure.adjust_log(arr, gain=gain)
[ "def", "adjust_contrast_log", "(", "arr", ",", "gain", ")", ":", "# int8 is also possible according to docs", "# https://docs.opencv.org/3.0-beta/modules/core/doc/operations_on_arrays.html#cv2.LUT , but here it seemed", "# like `d` was 0 for CV_8S, causing that to fail", "if", "arr", ".", "dtype", ".", "name", "==", "\"uint8\"", ":", "min_value", ",", "_center_value", ",", "max_value", "=", "iadt", ".", "get_value_range_of_dtype", "(", "arr", ".", "dtype", ")", "dynamic_range", "=", "max_value", "-", "min_value", "value_range", "=", "np", ".", "linspace", "(", "0", ",", "1.0", ",", "num", "=", "dynamic_range", "+", "1", ",", "dtype", "=", "np", ".", "float32", ")", "# 255 * 1/(1 + exp(gain*(cutoff - I_ij/255)))", "# using np.float32(.) here still works when the input is a numpy array of size 1", "gain", "=", "np", ".", "float32", "(", "gain", ")", "table", "=", "min_value", "+", "dynamic_range", "*", "gain", "*", "np", ".", "log2", "(", "1", "+", "value_range", ")", "arr_aug", "=", "cv2", ".", "LUT", "(", "arr", ",", "np", ".", "clip", "(", "table", ",", "min_value", ",", "max_value", ")", ".", "astype", "(", "arr", ".", "dtype", ")", ")", "if", "arr", ".", "ndim", "==", "3", "and", "arr_aug", ".", "ndim", "==", "2", ":", "return", "arr_aug", "[", "...", ",", "np", ".", "newaxis", "]", "return", "arr_aug", "else", ":", "return", "ski_exposure", ".", "adjust_log", "(", "arr", ",", "gain", "=", "gain", ")" ]
Adjust contrast by scaling each pixel value to ``255 * gain * log_2(1 + I_ij/255)``. dtype support:: * ``uint8``: yes; fully tested (1) (2) (3) * ``uint16``: yes; tested (2) (3) * ``uint32``: yes; tested (2) (3) * ``uint64``: yes; tested (2) (3) (4) * ``int8``: limited; tested (2) (3) (5) * ``int16``: limited; tested (2) (3) (5) * ``int32``: limited; tested (2) (3) (5) * ``int64``: limited; tested (2) (3) (4) (5) * ``float16``: limited; tested (5) * ``float32``: limited; tested (5) * ``float64``: limited; tested (5) * ``float128``: no (6) * ``bool``: no (7) - (1) Handled by ``cv2``. Other dtypes are handled by ``skimage``. - (2) Normalization is done as ``I_ij/max``, where ``max`` is the maximum value of the dtype, e.g. 255 for ``uint8``. The normalization is reversed afterwards, e.g. ``result*255`` for ``uint8``. - (3) Integer-like values are not rounded after applying the contrast adjustment equation (before inverting the normalization to 0.0-1.0 space), i.e. projection from continuous space to discrete happens according to floor function. - (4) Note that scikit-image doc says that integers are converted to ``float64`` values before applying the contrast normalization method. This might lead to inaccuracies for large 64bit integer values. Tests showed no indication of that happening though. - (5) Must not contain negative values. Values >=0 are fully supported. - (6) Leads to error in scikit-image. - (7) Does not make sense for contrast adjustments. Parameters ---------- arr : numpy.ndarray Array for which to adjust the contrast. Dtype ``uint8`` is fastest. gain : number Multiplier for the logarithm result. Values around 1.0 lead to a contrast-adjusted images. Values above 1.0 quickly lead to partially broken images due to exceeding the datatype's value range. Returns ------- numpy.ndarray Array with adjusted contrast.
[ "Adjust", "contrast", "by", "scaling", "each", "pixel", "value", "to", "255", "*", "gain", "*", "log_2", "(", "1", "+", "I_ij", "/", "255", ")", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/augmenters/contrast.py#L185-L252
valid
aleju/imgaug
imgaug/augmenters/contrast.py
adjust_contrast_linear
def adjust_contrast_linear(arr, alpha): """Adjust contrast by scaling each pixel value to ``127 + alpha*(I_ij-127)``. dtype support:: * ``uint8``: yes; fully tested (1) (2) * ``uint16``: yes; tested (2) * ``uint32``: yes; tested (2) * ``uint64``: no (3) * ``int8``: yes; tested (2) * ``int16``: yes; tested (2) * ``int32``: yes; tested (2) * ``int64``: no (2) * ``float16``: yes; tested (2) * ``float32``: yes; tested (2) * ``float64``: yes; tested (2) * ``float128``: no (2) * ``bool``: no (4) - (1) Handled by ``cv2``. Other dtypes are handled by raw ``numpy``. - (2) Only tested for reasonable alphas with up to a value of around 100. - (3) Conversion to ``float64`` is done during augmentation, hence ``uint64``, ``int64``, and ``float128`` support cannot be guaranteed. - (4) Does not make sense for contrast adjustments. Parameters ---------- arr : numpy.ndarray Array for which to adjust the contrast. Dtype ``uint8`` is fastest. alpha : number Multiplier to linearly pronounce (>1.0), dampen (0.0 to 1.0) or invert (<0.0) the difference between each pixel value and the center value, e.g. ``127`` for ``uint8``. Returns ------- numpy.ndarray Array with adjusted contrast. """ # int8 is also possible according to docs # https://docs.opencv.org/3.0-beta/modules/core/doc/operations_on_arrays.html#cv2.LUT , but here it seemed # like `d` was 0 for CV_8S, causing that to fail if arr.dtype.name == "uint8": min_value, center_value, max_value = iadt.get_value_range_of_dtype(arr.dtype) value_range = np.arange(0, 256, dtype=np.float32) # 127 + alpha*(I_ij-127) # using np.float32(.) here still works when the input is a numpy array of size 1 alpha = np.float32(alpha) table = center_value + alpha * (value_range - center_value) arr_aug = cv2.LUT(arr, np.clip(table, min_value, max_value).astype(arr.dtype)) if arr.ndim == 3 and arr_aug.ndim == 2: return arr_aug[..., np.newaxis] return arr_aug else: input_dtype = arr.dtype _min_value, center_value, _max_value = iadt.get_value_range_of_dtype(input_dtype) if input_dtype.kind in ["u", "i"]: center_value = int(center_value) image_aug = center_value + alpha * (arr.astype(np.float64)-center_value) image_aug = iadt.restore_dtypes_(image_aug, input_dtype) return image_aug
python
def adjust_contrast_linear(arr, alpha): """Adjust contrast by scaling each pixel value to ``127 + alpha*(I_ij-127)``. dtype support:: * ``uint8``: yes; fully tested (1) (2) * ``uint16``: yes; tested (2) * ``uint32``: yes; tested (2) * ``uint64``: no (3) * ``int8``: yes; tested (2) * ``int16``: yes; tested (2) * ``int32``: yes; tested (2) * ``int64``: no (2) * ``float16``: yes; tested (2) * ``float32``: yes; tested (2) * ``float64``: yes; tested (2) * ``float128``: no (2) * ``bool``: no (4) - (1) Handled by ``cv2``. Other dtypes are handled by raw ``numpy``. - (2) Only tested for reasonable alphas with up to a value of around 100. - (3) Conversion to ``float64`` is done during augmentation, hence ``uint64``, ``int64``, and ``float128`` support cannot be guaranteed. - (4) Does not make sense for contrast adjustments. Parameters ---------- arr : numpy.ndarray Array for which to adjust the contrast. Dtype ``uint8`` is fastest. alpha : number Multiplier to linearly pronounce (>1.0), dampen (0.0 to 1.0) or invert (<0.0) the difference between each pixel value and the center value, e.g. ``127`` for ``uint8``. Returns ------- numpy.ndarray Array with adjusted contrast. """ # int8 is also possible according to docs # https://docs.opencv.org/3.0-beta/modules/core/doc/operations_on_arrays.html#cv2.LUT , but here it seemed # like `d` was 0 for CV_8S, causing that to fail if arr.dtype.name == "uint8": min_value, center_value, max_value = iadt.get_value_range_of_dtype(arr.dtype) value_range = np.arange(0, 256, dtype=np.float32) # 127 + alpha*(I_ij-127) # using np.float32(.) here still works when the input is a numpy array of size 1 alpha = np.float32(alpha) table = center_value + alpha * (value_range - center_value) arr_aug = cv2.LUT(arr, np.clip(table, min_value, max_value).astype(arr.dtype)) if arr.ndim == 3 and arr_aug.ndim == 2: return arr_aug[..., np.newaxis] return arr_aug else: input_dtype = arr.dtype _min_value, center_value, _max_value = iadt.get_value_range_of_dtype(input_dtype) if input_dtype.kind in ["u", "i"]: center_value = int(center_value) image_aug = center_value + alpha * (arr.astype(np.float64)-center_value) image_aug = iadt.restore_dtypes_(image_aug, input_dtype) return image_aug
[ "def", "adjust_contrast_linear", "(", "arr", ",", "alpha", ")", ":", "# int8 is also possible according to docs", "# https://docs.opencv.org/3.0-beta/modules/core/doc/operations_on_arrays.html#cv2.LUT , but here it seemed", "# like `d` was 0 for CV_8S, causing that to fail", "if", "arr", ".", "dtype", ".", "name", "==", "\"uint8\"", ":", "min_value", ",", "center_value", ",", "max_value", "=", "iadt", ".", "get_value_range_of_dtype", "(", "arr", ".", "dtype", ")", "value_range", "=", "np", ".", "arange", "(", "0", ",", "256", ",", "dtype", "=", "np", ".", "float32", ")", "# 127 + alpha*(I_ij-127)", "# using np.float32(.) here still works when the input is a numpy array of size 1", "alpha", "=", "np", ".", "float32", "(", "alpha", ")", "table", "=", "center_value", "+", "alpha", "*", "(", "value_range", "-", "center_value", ")", "arr_aug", "=", "cv2", ".", "LUT", "(", "arr", ",", "np", ".", "clip", "(", "table", ",", "min_value", ",", "max_value", ")", ".", "astype", "(", "arr", ".", "dtype", ")", ")", "if", "arr", ".", "ndim", "==", "3", "and", "arr_aug", ".", "ndim", "==", "2", ":", "return", "arr_aug", "[", "...", ",", "np", ".", "newaxis", "]", "return", "arr_aug", "else", ":", "input_dtype", "=", "arr", ".", "dtype", "_min_value", ",", "center_value", ",", "_max_value", "=", "iadt", ".", "get_value_range_of_dtype", "(", "input_dtype", ")", "if", "input_dtype", ".", "kind", "in", "[", "\"u\"", ",", "\"i\"", "]", ":", "center_value", "=", "int", "(", "center_value", ")", "image_aug", "=", "center_value", "+", "alpha", "*", "(", "arr", ".", "astype", "(", "np", ".", "float64", ")", "-", "center_value", ")", "image_aug", "=", "iadt", ".", "restore_dtypes_", "(", "image_aug", ",", "input_dtype", ")", "return", "image_aug" ]
Adjust contrast by scaling each pixel value to ``127 + alpha*(I_ij-127)``. dtype support:: * ``uint8``: yes; fully tested (1) (2) * ``uint16``: yes; tested (2) * ``uint32``: yes; tested (2) * ``uint64``: no (3) * ``int8``: yes; tested (2) * ``int16``: yes; tested (2) * ``int32``: yes; tested (2) * ``int64``: no (2) * ``float16``: yes; tested (2) * ``float32``: yes; tested (2) * ``float64``: yes; tested (2) * ``float128``: no (2) * ``bool``: no (4) - (1) Handled by ``cv2``. Other dtypes are handled by raw ``numpy``. - (2) Only tested for reasonable alphas with up to a value of around 100. - (3) Conversion to ``float64`` is done during augmentation, hence ``uint64``, ``int64``, and ``float128`` support cannot be guaranteed. - (4) Does not make sense for contrast adjustments. Parameters ---------- arr : numpy.ndarray Array for which to adjust the contrast. Dtype ``uint8`` is fastest. alpha : number Multiplier to linearly pronounce (>1.0), dampen (0.0 to 1.0) or invert (<0.0) the difference between each pixel value and the center value, e.g. ``127`` for ``uint8``. Returns ------- numpy.ndarray Array with adjusted contrast.
[ "Adjust", "contrast", "by", "scaling", "each", "pixel", "value", "to", "127", "+", "alpha", "*", "(", "I_ij", "-", "127", ")", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/augmenters/contrast.py#L256-L318
valid
aleju/imgaug
imgaug/augmenters/contrast.py
GammaContrast
def GammaContrast(gamma=1, per_channel=False, name=None, deterministic=False, random_state=None): """ Adjust contrast by scaling each pixel value to ``255 * ((I_ij/255)**gamma)``. Values in the range ``gamma=(0.5, 2.0)`` seem to be sensible. dtype support:: See :func:`imgaug.augmenters.contrast.adjust_contrast_gamma`. Parameters ---------- gamma : number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional Exponent for the contrast adjustment. Higher values darken the image. * If a number, then that value will be used for all images. * If a tuple ``(a, b)``, then a value from the range ``[a, b]`` will be used per image. * If a list, then a random value will be sampled from that list per image. * If a StochasticParameter, then a value will be sampled per image from that parameter. per_channel : bool or float, optional Whether to use the same value for all channels (False) or to sample a new value for each channel (True). If this value is a float ``p``, then for ``p`` percent of all images `per_channel` will be treated as True, otherwise as False. name : None or str, optional See :func:`imgaug.augmenters.meta.Augmenter.__init__`. deterministic : bool, optional See :func:`imgaug.augmenters.meta.Augmenter.__init__`. random_state : None or int or numpy.random.RandomState, optional See :func:`imgaug.augmenters.meta.Augmenter.__init__`. Returns ------- _ContrastFuncWrapper Augmenter to perform gamma contrast adjustment. """ params1d = [iap.handle_continuous_param(gamma, "gamma", value_range=None, tuple_to_uniform=True, list_to_choice=True)] func = adjust_contrast_gamma return _ContrastFuncWrapper( func, params1d, per_channel, dtypes_allowed=["uint8", "uint16", "uint32", "uint64", "int8", "int16", "int32", "int64", "float16", "float32", "float64"], dtypes_disallowed=["float96", "float128", "float256", "bool"], name=name if name is not None else ia.caller_name(), deterministic=deterministic, random_state=random_state )
python
def GammaContrast(gamma=1, per_channel=False, name=None, deterministic=False, random_state=None): """ Adjust contrast by scaling each pixel value to ``255 * ((I_ij/255)**gamma)``. Values in the range ``gamma=(0.5, 2.0)`` seem to be sensible. dtype support:: See :func:`imgaug.augmenters.contrast.adjust_contrast_gamma`. Parameters ---------- gamma : number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional Exponent for the contrast adjustment. Higher values darken the image. * If a number, then that value will be used for all images. * If a tuple ``(a, b)``, then a value from the range ``[a, b]`` will be used per image. * If a list, then a random value will be sampled from that list per image. * If a StochasticParameter, then a value will be sampled per image from that parameter. per_channel : bool or float, optional Whether to use the same value for all channels (False) or to sample a new value for each channel (True). If this value is a float ``p``, then for ``p`` percent of all images `per_channel` will be treated as True, otherwise as False. name : None or str, optional See :func:`imgaug.augmenters.meta.Augmenter.__init__`. deterministic : bool, optional See :func:`imgaug.augmenters.meta.Augmenter.__init__`. random_state : None or int or numpy.random.RandomState, optional See :func:`imgaug.augmenters.meta.Augmenter.__init__`. Returns ------- _ContrastFuncWrapper Augmenter to perform gamma contrast adjustment. """ params1d = [iap.handle_continuous_param(gamma, "gamma", value_range=None, tuple_to_uniform=True, list_to_choice=True)] func = adjust_contrast_gamma return _ContrastFuncWrapper( func, params1d, per_channel, dtypes_allowed=["uint8", "uint16", "uint32", "uint64", "int8", "int16", "int32", "int64", "float16", "float32", "float64"], dtypes_disallowed=["float96", "float128", "float256", "bool"], name=name if name is not None else ia.caller_name(), deterministic=deterministic, random_state=random_state )
[ "def", "GammaContrast", "(", "gamma", "=", "1", ",", "per_channel", "=", "False", ",", "name", "=", "None", ",", "deterministic", "=", "False", ",", "random_state", "=", "None", ")", ":", "params1d", "=", "[", "iap", ".", "handle_continuous_param", "(", "gamma", ",", "\"gamma\"", ",", "value_range", "=", "None", ",", "tuple_to_uniform", "=", "True", ",", "list_to_choice", "=", "True", ")", "]", "func", "=", "adjust_contrast_gamma", "return", "_ContrastFuncWrapper", "(", "func", ",", "params1d", ",", "per_channel", ",", "dtypes_allowed", "=", "[", "\"uint8\"", ",", "\"uint16\"", ",", "\"uint32\"", ",", "\"uint64\"", ",", "\"int8\"", ",", "\"int16\"", ",", "\"int32\"", ",", "\"int64\"", ",", "\"float16\"", ",", "\"float32\"", ",", "\"float64\"", "]", ",", "dtypes_disallowed", "=", "[", "\"float96\"", ",", "\"float128\"", ",", "\"float256\"", ",", "\"bool\"", "]", ",", "name", "=", "name", "if", "name", "is", "not", "None", "else", "ia", ".", "caller_name", "(", ")", ",", "deterministic", "=", "deterministic", ",", "random_state", "=", "random_state", ")" ]
Adjust contrast by scaling each pixel value to ``255 * ((I_ij/255)**gamma)``. Values in the range ``gamma=(0.5, 2.0)`` seem to be sensible. dtype support:: See :func:`imgaug.augmenters.contrast.adjust_contrast_gamma`. Parameters ---------- gamma : number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional Exponent for the contrast adjustment. Higher values darken the image. * If a number, then that value will be used for all images. * If a tuple ``(a, b)``, then a value from the range ``[a, b]`` will be used per image. * If a list, then a random value will be sampled from that list per image. * If a StochasticParameter, then a value will be sampled per image from that parameter. per_channel : bool or float, optional Whether to use the same value for all channels (False) or to sample a new value for each channel (True). If this value is a float ``p``, then for ``p`` percent of all images `per_channel` will be treated as True, otherwise as False. name : None or str, optional See :func:`imgaug.augmenters.meta.Augmenter.__init__`. deterministic : bool, optional See :func:`imgaug.augmenters.meta.Augmenter.__init__`. random_state : None or int or numpy.random.RandomState, optional See :func:`imgaug.augmenters.meta.Augmenter.__init__`. Returns ------- _ContrastFuncWrapper Augmenter to perform gamma contrast adjustment.
[ "Adjust", "contrast", "by", "scaling", "each", "pixel", "value", "to", "255", "*", "((", "I_ij", "/", "255", ")", "**", "gamma", ")", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/augmenters/contrast.py#L321-L373
valid
aleju/imgaug
imgaug/augmenters/contrast.py
SigmoidContrast
def SigmoidContrast(gain=10, cutoff=0.5, per_channel=False, name=None, deterministic=False, random_state=None): """ Adjust contrast by scaling each pixel value to ``255 * 1/(1 + exp(gain*(cutoff - I_ij/255)))``. Values in the range ``gain=(5, 20)`` and ``cutoff=(0.25, 0.75)`` seem to be sensible. dtype support:: See :func:`imgaug.augmenters.contrast.adjust_contrast_sigmoid`. Parameters ---------- gain : number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional Multiplier for the sigmoid function's output. Higher values lead to quicker changes from dark to light pixels. * If a number, then that value will be used for all images. * If a tuple ``(a, b)``, then a value from the range ``[a, b]`` will be used per image. * If a list, then a random value will be sampled from that list per image. * If a StochasticParameter, then a value will be sampled per image from that parameter. cutoff : number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional Cutoff that shifts the sigmoid function in horizontal direction. Higher values mean that the switch from dark to light pixels happens later, i.e. the pixels will remain darker. * If a number, then that value will be used for all images. * If a tuple ``(a, b)``, then a value from the range ``[a, b]`` will be used per image. * If a list, then a random value will be sampled from that list per image. * If a StochasticParameter, then a value will be sampled per image from that parameter. per_channel : bool or float, optional Whether to use the same value for all channels (False) or to sample a new value for each channel (True). If this value is a float ``p``, then for ``p`` percent of all images `per_channel` will be treated as True, otherwise as False. name : None or str, optional See :func:`imgaug.augmenters.meta.Augmenter.__init__`. deterministic : bool, optional See :func:`imgaug.augmenters.meta.Augmenter.__init__`. random_state : None or int or numpy.random.RandomState, optional See :func:`imgaug.augmenters.meta.Augmenter.__init__`. Returns ------- _ContrastFuncWrapper Augmenter to perform sigmoid contrast adjustment. """ # TODO add inv parameter? params1d = [ iap.handle_continuous_param(gain, "gain", value_range=(0, None), tuple_to_uniform=True, list_to_choice=True), iap.handle_continuous_param(cutoff, "cutoff", value_range=(0, 1.0), tuple_to_uniform=True, list_to_choice=True) ] func = adjust_contrast_sigmoid return _ContrastFuncWrapper( func, params1d, per_channel, dtypes_allowed=["uint8", "uint16", "uint32", "uint64", "int8", "int16", "int32", "int64", "float16", "float32", "float64"], dtypes_disallowed=["float96", "float128", "float256", "bool"], name=name if name is not None else ia.caller_name(), deterministic=deterministic, random_state=random_state )
python
def SigmoidContrast(gain=10, cutoff=0.5, per_channel=False, name=None, deterministic=False, random_state=None): """ Adjust contrast by scaling each pixel value to ``255 * 1/(1 + exp(gain*(cutoff - I_ij/255)))``. Values in the range ``gain=(5, 20)`` and ``cutoff=(0.25, 0.75)`` seem to be sensible. dtype support:: See :func:`imgaug.augmenters.contrast.adjust_contrast_sigmoid`. Parameters ---------- gain : number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional Multiplier for the sigmoid function's output. Higher values lead to quicker changes from dark to light pixels. * If a number, then that value will be used for all images. * If a tuple ``(a, b)``, then a value from the range ``[a, b]`` will be used per image. * If a list, then a random value will be sampled from that list per image. * If a StochasticParameter, then a value will be sampled per image from that parameter. cutoff : number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional Cutoff that shifts the sigmoid function in horizontal direction. Higher values mean that the switch from dark to light pixels happens later, i.e. the pixels will remain darker. * If a number, then that value will be used for all images. * If a tuple ``(a, b)``, then a value from the range ``[a, b]`` will be used per image. * If a list, then a random value will be sampled from that list per image. * If a StochasticParameter, then a value will be sampled per image from that parameter. per_channel : bool or float, optional Whether to use the same value for all channels (False) or to sample a new value for each channel (True). If this value is a float ``p``, then for ``p`` percent of all images `per_channel` will be treated as True, otherwise as False. name : None or str, optional See :func:`imgaug.augmenters.meta.Augmenter.__init__`. deterministic : bool, optional See :func:`imgaug.augmenters.meta.Augmenter.__init__`. random_state : None or int or numpy.random.RandomState, optional See :func:`imgaug.augmenters.meta.Augmenter.__init__`. Returns ------- _ContrastFuncWrapper Augmenter to perform sigmoid contrast adjustment. """ # TODO add inv parameter? params1d = [ iap.handle_continuous_param(gain, "gain", value_range=(0, None), tuple_to_uniform=True, list_to_choice=True), iap.handle_continuous_param(cutoff, "cutoff", value_range=(0, 1.0), tuple_to_uniform=True, list_to_choice=True) ] func = adjust_contrast_sigmoid return _ContrastFuncWrapper( func, params1d, per_channel, dtypes_allowed=["uint8", "uint16", "uint32", "uint64", "int8", "int16", "int32", "int64", "float16", "float32", "float64"], dtypes_disallowed=["float96", "float128", "float256", "bool"], name=name if name is not None else ia.caller_name(), deterministic=deterministic, random_state=random_state )
[ "def", "SigmoidContrast", "(", "gain", "=", "10", ",", "cutoff", "=", "0.5", ",", "per_channel", "=", "False", ",", "name", "=", "None", ",", "deterministic", "=", "False", ",", "random_state", "=", "None", ")", ":", "# TODO add inv parameter?", "params1d", "=", "[", "iap", ".", "handle_continuous_param", "(", "gain", ",", "\"gain\"", ",", "value_range", "=", "(", "0", ",", "None", ")", ",", "tuple_to_uniform", "=", "True", ",", "list_to_choice", "=", "True", ")", ",", "iap", ".", "handle_continuous_param", "(", "cutoff", ",", "\"cutoff\"", ",", "value_range", "=", "(", "0", ",", "1.0", ")", ",", "tuple_to_uniform", "=", "True", ",", "list_to_choice", "=", "True", ")", "]", "func", "=", "adjust_contrast_sigmoid", "return", "_ContrastFuncWrapper", "(", "func", ",", "params1d", ",", "per_channel", ",", "dtypes_allowed", "=", "[", "\"uint8\"", ",", "\"uint16\"", ",", "\"uint32\"", ",", "\"uint64\"", ",", "\"int8\"", ",", "\"int16\"", ",", "\"int32\"", ",", "\"int64\"", ",", "\"float16\"", ",", "\"float32\"", ",", "\"float64\"", "]", ",", "dtypes_disallowed", "=", "[", "\"float96\"", ",", "\"float128\"", ",", "\"float256\"", ",", "\"bool\"", "]", ",", "name", "=", "name", "if", "name", "is", "not", "None", "else", "ia", ".", "caller_name", "(", ")", ",", "deterministic", "=", "deterministic", ",", "random_state", "=", "random_state", ")" ]
Adjust contrast by scaling each pixel value to ``255 * 1/(1 + exp(gain*(cutoff - I_ij/255)))``. Values in the range ``gain=(5, 20)`` and ``cutoff=(0.25, 0.75)`` seem to be sensible. dtype support:: See :func:`imgaug.augmenters.contrast.adjust_contrast_sigmoid`. Parameters ---------- gain : number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional Multiplier for the sigmoid function's output. Higher values lead to quicker changes from dark to light pixels. * If a number, then that value will be used for all images. * If a tuple ``(a, b)``, then a value from the range ``[a, b]`` will be used per image. * If a list, then a random value will be sampled from that list per image. * If a StochasticParameter, then a value will be sampled per image from that parameter. cutoff : number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional Cutoff that shifts the sigmoid function in horizontal direction. Higher values mean that the switch from dark to light pixels happens later, i.e. the pixels will remain darker. * If a number, then that value will be used for all images. * If a tuple ``(a, b)``, then a value from the range ``[a, b]`` will be used per image. * If a list, then a random value will be sampled from that list per image. * If a StochasticParameter, then a value will be sampled per image from that parameter. per_channel : bool or float, optional Whether to use the same value for all channels (False) or to sample a new value for each channel (True). If this value is a float ``p``, then for ``p`` percent of all images `per_channel` will be treated as True, otherwise as False. name : None or str, optional See :func:`imgaug.augmenters.meta.Augmenter.__init__`. deterministic : bool, optional See :func:`imgaug.augmenters.meta.Augmenter.__init__`. random_state : None or int or numpy.random.RandomState, optional See :func:`imgaug.augmenters.meta.Augmenter.__init__`. Returns ------- _ContrastFuncWrapper Augmenter to perform sigmoid contrast adjustment.
[ "Adjust", "contrast", "by", "scaling", "each", "pixel", "value", "to", "255", "*", "1", "/", "(", "1", "+", "exp", "(", "gain", "*", "(", "cutoff", "-", "I_ij", "/", "255", ")))", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/augmenters/contrast.py#L376-L442
valid
aleju/imgaug
imgaug/augmenters/contrast.py
LogContrast
def LogContrast(gain=1, per_channel=False, name=None, deterministic=False, random_state=None): """ Adjust contrast by scaling each pixel value to ``255 * gain * log_2(1 + I_ij/255)``. dtype support:: See :func:`imgaug.augmenters.contrast.adjust_contrast_log`. Parameters ---------- gain : number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional Multiplier for the logarithm result. Values around 1.0 lead to a contrast-adjusted images. Values above 1.0 quickly lead to partially broken images due to exceeding the datatype's value range. * If a number, then that value will be used for all images. * If a tuple ``(a, b)``, then a value from the range ``[a, b]`` will be used per image. * If a list, then a random value will be sampled from that list per image. * If a StochasticParameter, then a value will be sampled per image from that parameter. per_channel : bool or float, optional Whether to use the same value for all channels (False) or to sample a new value for each channel (True). If this value is a float ``p``, then for ``p`` percent of all images `per_channel` will be treated as True, otherwise as False. name : None or str, optional See :func:`imgaug.augmenters.meta.Augmenter.__init__`. deterministic : bool, optional See :func:`imgaug.augmenters.meta.Augmenter.__init__`. random_state : None or int or numpy.random.RandomState, optional See :func:`imgaug.augmenters.meta.Augmenter.__init__`. Returns ------- _ContrastFuncWrapper Augmenter to perform logarithmic contrast adjustment. """ # TODO add inv parameter? params1d = [iap.handle_continuous_param(gain, "gain", value_range=(0, None), tuple_to_uniform=True, list_to_choice=True)] func = adjust_contrast_log return _ContrastFuncWrapper( func, params1d, per_channel, dtypes_allowed=["uint8", "uint16", "uint32", "uint64", "int8", "int16", "int32", "int64", "float16", "float32", "float64"], dtypes_disallowed=["float96", "float128", "float256", "bool"], name=name if name is not None else ia.caller_name(), deterministic=deterministic, random_state=random_state )
python
def LogContrast(gain=1, per_channel=False, name=None, deterministic=False, random_state=None): """ Adjust contrast by scaling each pixel value to ``255 * gain * log_2(1 + I_ij/255)``. dtype support:: See :func:`imgaug.augmenters.contrast.adjust_contrast_log`. Parameters ---------- gain : number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional Multiplier for the logarithm result. Values around 1.0 lead to a contrast-adjusted images. Values above 1.0 quickly lead to partially broken images due to exceeding the datatype's value range. * If a number, then that value will be used for all images. * If a tuple ``(a, b)``, then a value from the range ``[a, b]`` will be used per image. * If a list, then a random value will be sampled from that list per image. * If a StochasticParameter, then a value will be sampled per image from that parameter. per_channel : bool or float, optional Whether to use the same value for all channels (False) or to sample a new value for each channel (True). If this value is a float ``p``, then for ``p`` percent of all images `per_channel` will be treated as True, otherwise as False. name : None or str, optional See :func:`imgaug.augmenters.meta.Augmenter.__init__`. deterministic : bool, optional See :func:`imgaug.augmenters.meta.Augmenter.__init__`. random_state : None or int or numpy.random.RandomState, optional See :func:`imgaug.augmenters.meta.Augmenter.__init__`. Returns ------- _ContrastFuncWrapper Augmenter to perform logarithmic contrast adjustment. """ # TODO add inv parameter? params1d = [iap.handle_continuous_param(gain, "gain", value_range=(0, None), tuple_to_uniform=True, list_to_choice=True)] func = adjust_contrast_log return _ContrastFuncWrapper( func, params1d, per_channel, dtypes_allowed=["uint8", "uint16", "uint32", "uint64", "int8", "int16", "int32", "int64", "float16", "float32", "float64"], dtypes_disallowed=["float96", "float128", "float256", "bool"], name=name if name is not None else ia.caller_name(), deterministic=deterministic, random_state=random_state )
[ "def", "LogContrast", "(", "gain", "=", "1", ",", "per_channel", "=", "False", ",", "name", "=", "None", ",", "deterministic", "=", "False", ",", "random_state", "=", "None", ")", ":", "# TODO add inv parameter?", "params1d", "=", "[", "iap", ".", "handle_continuous_param", "(", "gain", ",", "\"gain\"", ",", "value_range", "=", "(", "0", ",", "None", ")", ",", "tuple_to_uniform", "=", "True", ",", "list_to_choice", "=", "True", ")", "]", "func", "=", "adjust_contrast_log", "return", "_ContrastFuncWrapper", "(", "func", ",", "params1d", ",", "per_channel", ",", "dtypes_allowed", "=", "[", "\"uint8\"", ",", "\"uint16\"", ",", "\"uint32\"", ",", "\"uint64\"", ",", "\"int8\"", ",", "\"int16\"", ",", "\"int32\"", ",", "\"int64\"", ",", "\"float16\"", ",", "\"float32\"", ",", "\"float64\"", "]", ",", "dtypes_disallowed", "=", "[", "\"float96\"", ",", "\"float128\"", ",", "\"float256\"", ",", "\"bool\"", "]", ",", "name", "=", "name", "if", "name", "is", "not", "None", "else", "ia", ".", "caller_name", "(", ")", ",", "deterministic", "=", "deterministic", ",", "random_state", "=", "random_state", ")" ]
Adjust contrast by scaling each pixel value to ``255 * gain * log_2(1 + I_ij/255)``. dtype support:: See :func:`imgaug.augmenters.contrast.adjust_contrast_log`. Parameters ---------- gain : number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional Multiplier for the logarithm result. Values around 1.0 lead to a contrast-adjusted images. Values above 1.0 quickly lead to partially broken images due to exceeding the datatype's value range. * If a number, then that value will be used for all images. * If a tuple ``(a, b)``, then a value from the range ``[a, b]`` will be used per image. * If a list, then a random value will be sampled from that list per image. * If a StochasticParameter, then a value will be sampled per image from that parameter. per_channel : bool or float, optional Whether to use the same value for all channels (False) or to sample a new value for each channel (True). If this value is a float ``p``, then for ``p`` percent of all images `per_channel` will be treated as True, otherwise as False. name : None or str, optional See :func:`imgaug.augmenters.meta.Augmenter.__init__`. deterministic : bool, optional See :func:`imgaug.augmenters.meta.Augmenter.__init__`. random_state : None or int or numpy.random.RandomState, optional See :func:`imgaug.augmenters.meta.Augmenter.__init__`. Returns ------- _ContrastFuncWrapper Augmenter to perform logarithmic contrast adjustment.
[ "Adjust", "contrast", "by", "scaling", "each", "pixel", "value", "to", "255", "*", "gain", "*", "log_2", "(", "1", "+", "I_ij", "/", "255", ")", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/augmenters/contrast.py#L445-L498
valid
aleju/imgaug
imgaug/augmenters/contrast.py
LinearContrast
def LinearContrast(alpha=1, per_channel=False, name=None, deterministic=False, random_state=None): """Adjust contrast by scaling each pixel value to ``127 + alpha*(I_ij-127)``. dtype support:: See :func:`imgaug.augmenters.contrast.adjust_contrast_linear`. Parameters ---------- alpha : number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional Multiplier to linearly pronounce (>1.0), dampen (0.0 to 1.0) or invert (<0.0) the difference between each pixel value and the center value, e.g. ``127`` for ``uint8``. * If a number, then that value will be used for all images. * If a tuple ``(a, b)``, then a value from the range ``[a, b]`` will be used per image. * If a list, then a random value will be sampled from that list per image. * If a StochasticParameter, then a value will be sampled per image from that parameter. per_channel : bool or float, optional Whether to use the same value for all channels (False) or to sample a new value for each channel (True). If this value is a float ``p``, then for ``p`` percent of all images `per_channel` will be treated as True, otherwise as False. name : None or str, optional See :func:`imgaug.augmenters.meta.Augmenter.__init__`. deterministic : bool, optional See :func:`imgaug.augmenters.meta.Augmenter.__init__`. random_state : None or int or numpy.random.RandomState, optional See :func:`imgaug.augmenters.meta.Augmenter.__init__`. Returns ------- _ContrastFuncWrapper Augmenter to perform contrast adjustment by linearly scaling the distance to 128. """ params1d = [ iap.handle_continuous_param(alpha, "alpha", value_range=None, tuple_to_uniform=True, list_to_choice=True) ] func = adjust_contrast_linear return _ContrastFuncWrapper( func, params1d, per_channel, dtypes_allowed=["uint8", "uint16", "uint32", "int8", "int16", "int32", "float16", "float32", "float64"], dtypes_disallowed=["uint64", "int64", "float96", "float128", "float256", "bool"], name=name if name is not None else ia.caller_name(), deterministic=deterministic, random_state=random_state )
python
def LinearContrast(alpha=1, per_channel=False, name=None, deterministic=False, random_state=None): """Adjust contrast by scaling each pixel value to ``127 + alpha*(I_ij-127)``. dtype support:: See :func:`imgaug.augmenters.contrast.adjust_contrast_linear`. Parameters ---------- alpha : number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional Multiplier to linearly pronounce (>1.0), dampen (0.0 to 1.0) or invert (<0.0) the difference between each pixel value and the center value, e.g. ``127`` for ``uint8``. * If a number, then that value will be used for all images. * If a tuple ``(a, b)``, then a value from the range ``[a, b]`` will be used per image. * If a list, then a random value will be sampled from that list per image. * If a StochasticParameter, then a value will be sampled per image from that parameter. per_channel : bool or float, optional Whether to use the same value for all channels (False) or to sample a new value for each channel (True). If this value is a float ``p``, then for ``p`` percent of all images `per_channel` will be treated as True, otherwise as False. name : None or str, optional See :func:`imgaug.augmenters.meta.Augmenter.__init__`. deterministic : bool, optional See :func:`imgaug.augmenters.meta.Augmenter.__init__`. random_state : None or int or numpy.random.RandomState, optional See :func:`imgaug.augmenters.meta.Augmenter.__init__`. Returns ------- _ContrastFuncWrapper Augmenter to perform contrast adjustment by linearly scaling the distance to 128. """ params1d = [ iap.handle_continuous_param(alpha, "alpha", value_range=None, tuple_to_uniform=True, list_to_choice=True) ] func = adjust_contrast_linear return _ContrastFuncWrapper( func, params1d, per_channel, dtypes_allowed=["uint8", "uint16", "uint32", "int8", "int16", "int32", "float16", "float32", "float64"], dtypes_disallowed=["uint64", "int64", "float96", "float128", "float256", "bool"], name=name if name is not None else ia.caller_name(), deterministic=deterministic, random_state=random_state )
[ "def", "LinearContrast", "(", "alpha", "=", "1", ",", "per_channel", "=", "False", ",", "name", "=", "None", ",", "deterministic", "=", "False", ",", "random_state", "=", "None", ")", ":", "params1d", "=", "[", "iap", ".", "handle_continuous_param", "(", "alpha", ",", "\"alpha\"", ",", "value_range", "=", "None", ",", "tuple_to_uniform", "=", "True", ",", "list_to_choice", "=", "True", ")", "]", "func", "=", "adjust_contrast_linear", "return", "_ContrastFuncWrapper", "(", "func", ",", "params1d", ",", "per_channel", ",", "dtypes_allowed", "=", "[", "\"uint8\"", ",", "\"uint16\"", ",", "\"uint32\"", ",", "\"int8\"", ",", "\"int16\"", ",", "\"int32\"", ",", "\"float16\"", ",", "\"float32\"", ",", "\"float64\"", "]", ",", "dtypes_disallowed", "=", "[", "\"uint64\"", ",", "\"int64\"", ",", "\"float96\"", ",", "\"float128\"", ",", "\"float256\"", ",", "\"bool\"", "]", ",", "name", "=", "name", "if", "name", "is", "not", "None", "else", "ia", ".", "caller_name", "(", ")", ",", "deterministic", "=", "deterministic", ",", "random_state", "=", "random_state", ")" ]
Adjust contrast by scaling each pixel value to ``127 + alpha*(I_ij-127)``. dtype support:: See :func:`imgaug.augmenters.contrast.adjust_contrast_linear`. Parameters ---------- alpha : number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional Multiplier to linearly pronounce (>1.0), dampen (0.0 to 1.0) or invert (<0.0) the difference between each pixel value and the center value, e.g. ``127`` for ``uint8``. * If a number, then that value will be used for all images. * If a tuple ``(a, b)``, then a value from the range ``[a, b]`` will be used per image. * If a list, then a random value will be sampled from that list per image. * If a StochasticParameter, then a value will be sampled per image from that parameter. per_channel : bool or float, optional Whether to use the same value for all channels (False) or to sample a new value for each channel (True). If this value is a float ``p``, then for ``p`` percent of all images `per_channel` will be treated as True, otherwise as False. name : None or str, optional See :func:`imgaug.augmenters.meta.Augmenter.__init__`. deterministic : bool, optional See :func:`imgaug.augmenters.meta.Augmenter.__init__`. random_state : None or int or numpy.random.RandomState, optional See :func:`imgaug.augmenters.meta.Augmenter.__init__`. Returns ------- _ContrastFuncWrapper Augmenter to perform contrast adjustment by linearly scaling the distance to 128.
[ "Adjust", "contrast", "by", "scaling", "each", "pixel", "value", "to", "127", "+", "alpha", "*", "(", "I_ij", "-", "127", ")", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/augmenters/contrast.py#L501-L552
valid
aleju/imgaug
imgaug/augmenters/color.py
InColorspace
def InColorspace(to_colorspace, from_colorspace="RGB", children=None, name=None, deterministic=False, random_state=None): """Convert images to another colorspace.""" return WithColorspace(to_colorspace, from_colorspace, children, name, deterministic, random_state)
python
def InColorspace(to_colorspace, from_colorspace="RGB", children=None, name=None, deterministic=False, random_state=None): """Convert images to another colorspace.""" return WithColorspace(to_colorspace, from_colorspace, children, name, deterministic, random_state)
[ "def", "InColorspace", "(", "to_colorspace", ",", "from_colorspace", "=", "\"RGB\"", ",", "children", "=", "None", ",", "name", "=", "None", ",", "deterministic", "=", "False", ",", "random_state", "=", "None", ")", ":", "return", "WithColorspace", "(", "to_colorspace", ",", "from_colorspace", ",", "children", ",", "name", ",", "deterministic", ",", "random_state", ")" ]
Convert images to another colorspace.
[ "Convert", "images", "to", "another", "colorspace", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/augmenters/color.py#L39-L42
valid
aleju/imgaug
imgaug/augmenters/color.py
Grayscale
def Grayscale(alpha=0, from_colorspace="RGB", name=None, deterministic=False, random_state=None): """ Augmenter to convert images to their grayscale versions. NOTE: Number of output channels is still 3, i.e. this augmenter just "removes" color. TODO check dtype support dtype support:: * ``uint8``: yes; fully tested * ``uint16``: ? * ``uint32``: ? * ``uint64``: ? * ``int8``: ? * ``int16``: ? * ``int32``: ? * ``int64``: ? * ``float16``: ? * ``float32``: ? * ``float64``: ? * ``float128``: ? * ``bool``: ? Parameters ---------- alpha : number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional The alpha value of the grayscale image when overlayed over the old image. A value close to 1.0 means, that mostly the new grayscale image is visible. A value close to 0.0 means, that mostly the old image is visible. * If a number, exactly that value will always be used. * If a tuple ``(a, b)``, a random value from the range ``a <= x <= b`` will be sampled per image. * If a list, then a random value will be sampled from that list per image. * If a StochasticParameter, a value will be sampled from the parameter per image. from_colorspace : str, optional The source colorspace (of the input images). Allowed strings are: ``RGB``, ``BGR``, ``GRAY``, ``CIE``, ``YCrCb``, ``HSV``, ``HLS``, ``Lab``, ``Luv``. See :func:`imgaug.augmenters.color.ChangeColorspace.__init__`. name : None or str, optional See :func:`imgaug.augmenters.meta.Augmenter.__init__`. deterministic : bool, optional See :func:`imgaug.augmenters.meta.Augmenter.__init__`. random_state : None or int or numpy.random.RandomState, optional See :func:`imgaug.augmenters.meta.Augmenter.__init__`. Examples -------- >>> aug = iaa.Grayscale(alpha=1.0) creates an augmenter that turns images to their grayscale versions. >>> aug = iaa.Grayscale(alpha=(0.0, 1.0)) creates an augmenter that turns images to their grayscale versions with an alpha value in the range ``0 <= alpha <= 1``. An alpha value of 0.5 would mean, that the output image is 50 percent of the input image and 50 percent of the grayscale image (i.e. 50 percent of color removed). """ if name is None: name = "Unnamed%s" % (ia.caller_name(),) return ChangeColorspace(to_colorspace=ChangeColorspace.GRAY, alpha=alpha, from_colorspace=from_colorspace, name=name, deterministic=deterministic, random_state=random_state)
python
def Grayscale(alpha=0, from_colorspace="RGB", name=None, deterministic=False, random_state=None): """ Augmenter to convert images to their grayscale versions. NOTE: Number of output channels is still 3, i.e. this augmenter just "removes" color. TODO check dtype support dtype support:: * ``uint8``: yes; fully tested * ``uint16``: ? * ``uint32``: ? * ``uint64``: ? * ``int8``: ? * ``int16``: ? * ``int32``: ? * ``int64``: ? * ``float16``: ? * ``float32``: ? * ``float64``: ? * ``float128``: ? * ``bool``: ? Parameters ---------- alpha : number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional The alpha value of the grayscale image when overlayed over the old image. A value close to 1.0 means, that mostly the new grayscale image is visible. A value close to 0.0 means, that mostly the old image is visible. * If a number, exactly that value will always be used. * If a tuple ``(a, b)``, a random value from the range ``a <= x <= b`` will be sampled per image. * If a list, then a random value will be sampled from that list per image. * If a StochasticParameter, a value will be sampled from the parameter per image. from_colorspace : str, optional The source colorspace (of the input images). Allowed strings are: ``RGB``, ``BGR``, ``GRAY``, ``CIE``, ``YCrCb``, ``HSV``, ``HLS``, ``Lab``, ``Luv``. See :func:`imgaug.augmenters.color.ChangeColorspace.__init__`. name : None or str, optional See :func:`imgaug.augmenters.meta.Augmenter.__init__`. deterministic : bool, optional See :func:`imgaug.augmenters.meta.Augmenter.__init__`. random_state : None or int or numpy.random.RandomState, optional See :func:`imgaug.augmenters.meta.Augmenter.__init__`. Examples -------- >>> aug = iaa.Grayscale(alpha=1.0) creates an augmenter that turns images to their grayscale versions. >>> aug = iaa.Grayscale(alpha=(0.0, 1.0)) creates an augmenter that turns images to their grayscale versions with an alpha value in the range ``0 <= alpha <= 1``. An alpha value of 0.5 would mean, that the output image is 50 percent of the input image and 50 percent of the grayscale image (i.e. 50 percent of color removed). """ if name is None: name = "Unnamed%s" % (ia.caller_name(),) return ChangeColorspace(to_colorspace=ChangeColorspace.GRAY, alpha=alpha, from_colorspace=from_colorspace, name=name, deterministic=deterministic, random_state=random_state)
[ "def", "Grayscale", "(", "alpha", "=", "0", ",", "from_colorspace", "=", "\"RGB\"", ",", "name", "=", "None", ",", "deterministic", "=", "False", ",", "random_state", "=", "None", ")", ":", "if", "name", "is", "None", ":", "name", "=", "\"Unnamed%s\"", "%", "(", "ia", ".", "caller_name", "(", ")", ",", ")", "return", "ChangeColorspace", "(", "to_colorspace", "=", "ChangeColorspace", ".", "GRAY", ",", "alpha", "=", "alpha", ",", "from_colorspace", "=", "from_colorspace", ",", "name", "=", "name", ",", "deterministic", "=", "deterministic", ",", "random_state", "=", "random_state", ")" ]
Augmenter to convert images to their grayscale versions. NOTE: Number of output channels is still 3, i.e. this augmenter just "removes" color. TODO check dtype support dtype support:: * ``uint8``: yes; fully tested * ``uint16``: ? * ``uint32``: ? * ``uint64``: ? * ``int8``: ? * ``int16``: ? * ``int32``: ? * ``int64``: ? * ``float16``: ? * ``float32``: ? * ``float64``: ? * ``float128``: ? * ``bool``: ? Parameters ---------- alpha : number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional The alpha value of the grayscale image when overlayed over the old image. A value close to 1.0 means, that mostly the new grayscale image is visible. A value close to 0.0 means, that mostly the old image is visible. * If a number, exactly that value will always be used. * If a tuple ``(a, b)``, a random value from the range ``a <= x <= b`` will be sampled per image. * If a list, then a random value will be sampled from that list per image. * If a StochasticParameter, a value will be sampled from the parameter per image. from_colorspace : str, optional The source colorspace (of the input images). Allowed strings are: ``RGB``, ``BGR``, ``GRAY``, ``CIE``, ``YCrCb``, ``HSV``, ``HLS``, ``Lab``, ``Luv``. See :func:`imgaug.augmenters.color.ChangeColorspace.__init__`. name : None or str, optional See :func:`imgaug.augmenters.meta.Augmenter.__init__`. deterministic : bool, optional See :func:`imgaug.augmenters.meta.Augmenter.__init__`. random_state : None or int or numpy.random.RandomState, optional See :func:`imgaug.augmenters.meta.Augmenter.__init__`. Examples -------- >>> aug = iaa.Grayscale(alpha=1.0) creates an augmenter that turns images to their grayscale versions. >>> aug = iaa.Grayscale(alpha=(0.0, 1.0)) creates an augmenter that turns images to their grayscale versions with an alpha value in the range ``0 <= alpha <= 1``. An alpha value of 0.5 would mean, that the output image is 50 percent of the input image and 50 percent of the grayscale image (i.e. 50 percent of color removed).
[ "Augmenter", "to", "convert", "images", "to", "their", "grayscale", "versions", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/augmenters/color.py#L596-L667
valid
aleju/imgaug
imgaug/augmentables/lines.py
LineString.height
def height(self): """Get the height of a bounding box encapsulating the line.""" if len(self.coords) <= 1: return 0 return np.max(self.yy) - np.min(self.yy)
python
def height(self): """Get the height of a bounding box encapsulating the line.""" if len(self.coords) <= 1: return 0 return np.max(self.yy) - np.min(self.yy)
[ "def", "height", "(", "self", ")", ":", "if", "len", "(", "self", ".", "coords", ")", "<=", "1", ":", "return", "0", "return", "np", ".", "max", "(", "self", ".", "yy", ")", "-", "np", ".", "min", "(", "self", ".", "yy", ")" ]
Get the height of a bounding box encapsulating the line.
[ "Get", "the", "height", "of", "a", "bounding", "box", "encapsulating", "the", "line", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/augmentables/lines.py#L100-L104
valid
aleju/imgaug
imgaug/augmentables/lines.py
LineString.width
def width(self): """Get the width of a bounding box encapsulating the line.""" if len(self.coords) <= 1: return 0 return np.max(self.xx) - np.min(self.xx)
python
def width(self): """Get the width of a bounding box encapsulating the line.""" if len(self.coords) <= 1: return 0 return np.max(self.xx) - np.min(self.xx)
[ "def", "width", "(", "self", ")", ":", "if", "len", "(", "self", ".", "coords", ")", "<=", "1", ":", "return", "0", "return", "np", ".", "max", "(", "self", ".", "xx", ")", "-", "np", ".", "min", "(", "self", ".", "xx", ")" ]
Get the width of a bounding box encapsulating the line.
[ "Get", "the", "width", "of", "a", "bounding", "box", "encapsulating", "the", "line", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/augmentables/lines.py#L107-L111
valid
aleju/imgaug
imgaug/augmentables/lines.py
LineString.get_pointwise_inside_image_mask
def get_pointwise_inside_image_mask(self, image): """ Get for each point whether it is inside of the given image plane. Parameters ---------- image : ndarray or tuple of int Either an image with shape ``(H,W,[C])`` or a tuple denoting such an image shape. Returns ------- ndarray Boolean array with one value per point indicating whether it is inside of the provided image plane (``True``) or not (``False``). """ if len(self.coords) == 0: return np.zeros((0,), dtype=bool) shape = normalize_shape(image) height, width = shape[0:2] x_within = np.logical_and(0 <= self.xx, self.xx < width) y_within = np.logical_and(0 <= self.yy, self.yy < height) return np.logical_and(x_within, y_within)
python
def get_pointwise_inside_image_mask(self, image): """ Get for each point whether it is inside of the given image plane. Parameters ---------- image : ndarray or tuple of int Either an image with shape ``(H,W,[C])`` or a tuple denoting such an image shape. Returns ------- ndarray Boolean array with one value per point indicating whether it is inside of the provided image plane (``True``) or not (``False``). """ if len(self.coords) == 0: return np.zeros((0,), dtype=bool) shape = normalize_shape(image) height, width = shape[0:2] x_within = np.logical_and(0 <= self.xx, self.xx < width) y_within = np.logical_and(0 <= self.yy, self.yy < height) return np.logical_and(x_within, y_within)
[ "def", "get_pointwise_inside_image_mask", "(", "self", ",", "image", ")", ":", "if", "len", "(", "self", ".", "coords", ")", "==", "0", ":", "return", "np", ".", "zeros", "(", "(", "0", ",", ")", ",", "dtype", "=", "bool", ")", "shape", "=", "normalize_shape", "(", "image", ")", "height", ",", "width", "=", "shape", "[", "0", ":", "2", "]", "x_within", "=", "np", ".", "logical_and", "(", "0", "<=", "self", ".", "xx", ",", "self", ".", "xx", "<", "width", ")", "y_within", "=", "np", ".", "logical_and", "(", "0", "<=", "self", ".", "yy", ",", "self", ".", "yy", "<", "height", ")", "return", "np", ".", "logical_and", "(", "x_within", ",", "y_within", ")" ]
Get for each point whether it is inside of the given image plane. Parameters ---------- image : ndarray or tuple of int Either an image with shape ``(H,W,[C])`` or a tuple denoting such an image shape. Returns ------- ndarray Boolean array with one value per point indicating whether it is inside of the provided image plane (``True``) or not (``False``).
[ "Get", "for", "each", "point", "whether", "it", "is", "inside", "of", "the", "given", "image", "plane", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/augmentables/lines.py#L113-L136
valid
aleju/imgaug
imgaug/augmentables/lines.py
LineString.compute_neighbour_distances
def compute_neighbour_distances(self): """ Get the euclidean distance between each two consecutive points. Returns ------- ndarray Euclidean distances between point pairs. Same order as in `coords`. For ``N`` points, ``N-1`` distances are returned. """ if len(self.coords) <= 1: return np.zeros((0,), dtype=np.float32) return np.sqrt( np.sum( (self.coords[:-1, :] - self.coords[1:, :]) ** 2, axis=1 ) )
python
def compute_neighbour_distances(self): """ Get the euclidean distance between each two consecutive points. Returns ------- ndarray Euclidean distances between point pairs. Same order as in `coords`. For ``N`` points, ``N-1`` distances are returned. """ if len(self.coords) <= 1: return np.zeros((0,), dtype=np.float32) return np.sqrt( np.sum( (self.coords[:-1, :] - self.coords[1:, :]) ** 2, axis=1 ) )
[ "def", "compute_neighbour_distances", "(", "self", ")", ":", "if", "len", "(", "self", ".", "coords", ")", "<=", "1", ":", "return", "np", ".", "zeros", "(", "(", "0", ",", ")", ",", "dtype", "=", "np", ".", "float32", ")", "return", "np", ".", "sqrt", "(", "np", ".", "sum", "(", "(", "self", ".", "coords", "[", ":", "-", "1", ",", ":", "]", "-", "self", ".", "coords", "[", "1", ":", ",", ":", "]", ")", "**", "2", ",", "axis", "=", "1", ")", ")" ]
Get the euclidean distance between each two consecutive points. Returns ------- ndarray Euclidean distances between point pairs. Same order as in `coords`. For ``N`` points, ``N-1`` distances are returned.
[ "Get", "the", "euclidean", "distance", "between", "each", "two", "consecutive", "points", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/augmentables/lines.py#L139-L158
valid
aleju/imgaug
imgaug/augmentables/lines.py
LineString.compute_pointwise_distances
def compute_pointwise_distances(self, other, default=None): """ Compute the minimal distance between each point on self and other. Parameters ---------- other : tuple of number \ or imgaug.augmentables.kps.Keypoint \ or imgaug.augmentables.LineString Other object to which to compute the distances. default Value to return if `other` contains no points. Returns ------- list of float Distances to `other` or `default` if not distance could be computed. """ import shapely.geometry from .kps import Keypoint if isinstance(other, Keypoint): other = shapely.geometry.Point((other.x, other.y)) elif isinstance(other, LineString): if len(other.coords) == 0: return default elif len(other.coords) == 1: other = shapely.geometry.Point(other.coords[0, :]) else: other = shapely.geometry.LineString(other.coords) elif isinstance(other, tuple): assert len(other) == 2 other = shapely.geometry.Point(other) else: raise ValueError( ("Expected Keypoint or LineString or tuple (x,y), " + "got type %s.") % (type(other),)) return [shapely.geometry.Point(point).distance(other) for point in self.coords]
python
def compute_pointwise_distances(self, other, default=None): """ Compute the minimal distance between each point on self and other. Parameters ---------- other : tuple of number \ or imgaug.augmentables.kps.Keypoint \ or imgaug.augmentables.LineString Other object to which to compute the distances. default Value to return if `other` contains no points. Returns ------- list of float Distances to `other` or `default` if not distance could be computed. """ import shapely.geometry from .kps import Keypoint if isinstance(other, Keypoint): other = shapely.geometry.Point((other.x, other.y)) elif isinstance(other, LineString): if len(other.coords) == 0: return default elif len(other.coords) == 1: other = shapely.geometry.Point(other.coords[0, :]) else: other = shapely.geometry.LineString(other.coords) elif isinstance(other, tuple): assert len(other) == 2 other = shapely.geometry.Point(other) else: raise ValueError( ("Expected Keypoint or LineString or tuple (x,y), " + "got type %s.") % (type(other),)) return [shapely.geometry.Point(point).distance(other) for point in self.coords]
[ "def", "compute_pointwise_distances", "(", "self", ",", "other", ",", "default", "=", "None", ")", ":", "import", "shapely", ".", "geometry", "from", ".", "kps", "import", "Keypoint", "if", "isinstance", "(", "other", ",", "Keypoint", ")", ":", "other", "=", "shapely", ".", "geometry", ".", "Point", "(", "(", "other", ".", "x", ",", "other", ".", "y", ")", ")", "elif", "isinstance", "(", "other", ",", "LineString", ")", ":", "if", "len", "(", "other", ".", "coords", ")", "==", "0", ":", "return", "default", "elif", "len", "(", "other", ".", "coords", ")", "==", "1", ":", "other", "=", "shapely", ".", "geometry", ".", "Point", "(", "other", ".", "coords", "[", "0", ",", ":", "]", ")", "else", ":", "other", "=", "shapely", ".", "geometry", ".", "LineString", "(", "other", ".", "coords", ")", "elif", "isinstance", "(", "other", ",", "tuple", ")", ":", "assert", "len", "(", "other", ")", "==", "2", "other", "=", "shapely", ".", "geometry", ".", "Point", "(", "other", ")", "else", ":", "raise", "ValueError", "(", "(", "\"Expected Keypoint or LineString or tuple (x,y), \"", "+", "\"got type %s.\"", ")", "%", "(", "type", "(", "other", ")", ",", ")", ")", "return", "[", "shapely", ".", "geometry", ".", "Point", "(", "point", ")", ".", "distance", "(", "other", ")", "for", "point", "in", "self", ".", "coords", "]" ]
Compute the minimal distance between each point on self and other. Parameters ---------- other : tuple of number \ or imgaug.augmentables.kps.Keypoint \ or imgaug.augmentables.LineString Other object to which to compute the distances. default Value to return if `other` contains no points. Returns ------- list of float Distances to `other` or `default` if not distance could be computed.
[ "Compute", "the", "minimal", "distance", "between", "each", "point", "on", "self", "and", "other", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/augmentables/lines.py#L160-L201
valid
aleju/imgaug
imgaug/augmentables/lines.py
LineString.compute_distance
def compute_distance(self, other, default=None): """ Compute the minimal distance between the line string and `other`. Parameters ---------- other : tuple of number \ or imgaug.augmentables.kps.Keypoint \ or imgaug.augmentables.LineString Other object to which to compute the distance. default Value to return if this line string or `other` contain no points. Returns ------- float Distance to `other` or `default` if not distance could be computed. """ # FIXME this computes distance pointwise, does not have to be identical # with the actual min distance (e.g. edge center to other's point) distances = self.compute_pointwise_distances(other, default=[]) if len(distances) == 0: return default return min(distances)
python
def compute_distance(self, other, default=None): """ Compute the minimal distance between the line string and `other`. Parameters ---------- other : tuple of number \ or imgaug.augmentables.kps.Keypoint \ or imgaug.augmentables.LineString Other object to which to compute the distance. default Value to return if this line string or `other` contain no points. Returns ------- float Distance to `other` or `default` if not distance could be computed. """ # FIXME this computes distance pointwise, does not have to be identical # with the actual min distance (e.g. edge center to other's point) distances = self.compute_pointwise_distances(other, default=[]) if len(distances) == 0: return default return min(distances)
[ "def", "compute_distance", "(", "self", ",", "other", ",", "default", "=", "None", ")", ":", "# FIXME this computes distance pointwise, does not have to be identical", "# with the actual min distance (e.g. edge center to other's point)", "distances", "=", "self", ".", "compute_pointwise_distances", "(", "other", ",", "default", "=", "[", "]", ")", "if", "len", "(", "distances", ")", "==", "0", ":", "return", "default", "return", "min", "(", "distances", ")" ]
Compute the minimal distance between the line string and `other`. Parameters ---------- other : tuple of number \ or imgaug.augmentables.kps.Keypoint \ or imgaug.augmentables.LineString Other object to which to compute the distance. default Value to return if this line string or `other` contain no points. Returns ------- float Distance to `other` or `default` if not distance could be computed.
[ "Compute", "the", "minimal", "distance", "between", "the", "line", "string", "and", "other", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/augmentables/lines.py#L203-L228
valid
aleju/imgaug
imgaug/augmentables/lines.py
LineString.contains
def contains(self, other, max_distance=1e-4): """ Estimate whether the bounding box contains a point. Parameters ---------- other : tuple of number or imgaug.augmentables.kps.Keypoint Point to check for. max_distance : float Maximum allowed euclidean distance between the point and the closest point on the line. If the threshold is exceeded, the point is not considered to be contained in the line. Returns ------- bool True if the point is contained in the line string, False otherwise. It is contained if its distance to the line or any of its points is below a threshold. """ return self.compute_distance(other, default=np.inf) < max_distance
python
def contains(self, other, max_distance=1e-4): """ Estimate whether the bounding box contains a point. Parameters ---------- other : tuple of number or imgaug.augmentables.kps.Keypoint Point to check for. max_distance : float Maximum allowed euclidean distance between the point and the closest point on the line. If the threshold is exceeded, the point is not considered to be contained in the line. Returns ------- bool True if the point is contained in the line string, False otherwise. It is contained if its distance to the line or any of its points is below a threshold. """ return self.compute_distance(other, default=np.inf) < max_distance
[ "def", "contains", "(", "self", ",", "other", ",", "max_distance", "=", "1e-4", ")", ":", "return", "self", ".", "compute_distance", "(", "other", ",", "default", "=", "np", ".", "inf", ")", "<", "max_distance" ]
Estimate whether the bounding box contains a point. Parameters ---------- other : tuple of number or imgaug.augmentables.kps.Keypoint Point to check for. max_distance : float Maximum allowed euclidean distance between the point and the closest point on the line. If the threshold is exceeded, the point is not considered to be contained in the line. Returns ------- bool True if the point is contained in the line string, False otherwise. It is contained if its distance to the line or any of its points is below a threshold.
[ "Estimate", "whether", "the", "bounding", "box", "contains", "a", "point", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/augmentables/lines.py#L231-L253
valid
aleju/imgaug
imgaug/augmentables/lines.py
LineString.project
def project(self, from_shape, to_shape): """ Project the line string onto a differently shaped image. E.g. if a point of the line string is on its original image at ``x=(10 of 100 pixels)`` and ``y=(20 of 100 pixels)`` and is projected onto a new image with size ``(width=200, height=200)``, its new position will be ``(x=20, y=40)``. This is intended for cases where the original image is resized. It cannot be used for more complex changes (e.g. padding, cropping). Parameters ---------- from_shape : tuple of int or ndarray Shape of the original image. (Before resize.) to_shape : tuple of int or ndarray Shape of the new image. (After resize.) Returns ------- out : imgaug.augmentables.lines.LineString Line string with new coordinates. """ coords_proj = project_coords(self.coords, from_shape, to_shape) return self.copy(coords=coords_proj)
python
def project(self, from_shape, to_shape): """ Project the line string onto a differently shaped image. E.g. if a point of the line string is on its original image at ``x=(10 of 100 pixels)`` and ``y=(20 of 100 pixels)`` and is projected onto a new image with size ``(width=200, height=200)``, its new position will be ``(x=20, y=40)``. This is intended for cases where the original image is resized. It cannot be used for more complex changes (e.g. padding, cropping). Parameters ---------- from_shape : tuple of int or ndarray Shape of the original image. (Before resize.) to_shape : tuple of int or ndarray Shape of the new image. (After resize.) Returns ------- out : imgaug.augmentables.lines.LineString Line string with new coordinates. """ coords_proj = project_coords(self.coords, from_shape, to_shape) return self.copy(coords=coords_proj)
[ "def", "project", "(", "self", ",", "from_shape", ",", "to_shape", ")", ":", "coords_proj", "=", "project_coords", "(", "self", ".", "coords", ",", "from_shape", ",", "to_shape", ")", "return", "self", ".", "copy", "(", "coords", "=", "coords_proj", ")" ]
Project the line string onto a differently shaped image. E.g. if a point of the line string is on its original image at ``x=(10 of 100 pixels)`` and ``y=(20 of 100 pixels)`` and is projected onto a new image with size ``(width=200, height=200)``, its new position will be ``(x=20, y=40)``. This is intended for cases where the original image is resized. It cannot be used for more complex changes (e.g. padding, cropping). Parameters ---------- from_shape : tuple of int or ndarray Shape of the original image. (Before resize.) to_shape : tuple of int or ndarray Shape of the new image. (After resize.) Returns ------- out : imgaug.augmentables.lines.LineString Line string with new coordinates.
[ "Project", "the", "line", "string", "onto", "a", "differently", "shaped", "image", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/augmentables/lines.py#L255-L282
valid
aleju/imgaug
imgaug/augmentables/lines.py
LineString.is_fully_within_image
def is_fully_within_image(self, image, default=False): """ Estimate whether the line string is fully inside the image area. Parameters ---------- image : ndarray or tuple of int Either an image with shape ``(H,W,[C])`` or a tuple denoting such an image shape. default Default value to return if the line string contains no points. Returns ------- bool True if the line string is fully inside the image area. False otherwise. """ if len(self.coords) == 0: return default return np.all(self.get_pointwise_inside_image_mask(image))
python
def is_fully_within_image(self, image, default=False): """ Estimate whether the line string is fully inside the image area. Parameters ---------- image : ndarray or tuple of int Either an image with shape ``(H,W,[C])`` or a tuple denoting such an image shape. default Default value to return if the line string contains no points. Returns ------- bool True if the line string is fully inside the image area. False otherwise. """ if len(self.coords) == 0: return default return np.all(self.get_pointwise_inside_image_mask(image))
[ "def", "is_fully_within_image", "(", "self", ",", "image", ",", "default", "=", "False", ")", ":", "if", "len", "(", "self", ".", "coords", ")", "==", "0", ":", "return", "default", "return", "np", ".", "all", "(", "self", ".", "get_pointwise_inside_image_mask", "(", "image", ")", ")" ]
Estimate whether the line string is fully inside the image area. Parameters ---------- image : ndarray or tuple of int Either an image with shape ``(H,W,[C])`` or a tuple denoting such an image shape. default Default value to return if the line string contains no points. Returns ------- bool True if the line string is fully inside the image area. False otherwise.
[ "Estimate", "whether", "the", "line", "string", "is", "fully", "inside", "the", "image", "area", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/augmentables/lines.py#L284-L306
valid
aleju/imgaug
imgaug/augmentables/lines.py
LineString.is_partly_within_image
def is_partly_within_image(self, image, default=False): """ Estimate whether the line string is at least partially inside the image. Parameters ---------- image : ndarray or tuple of int Either an image with shape ``(H,W,[C])`` or a tuple denoting such an image shape. default Default value to return if the line string contains no points. Returns ------- bool True if the line string is at least partially inside the image area. False otherwise. """ if len(self.coords) == 0: return default # check mask first to avoid costly computation of intersection points # whenever possible mask = self.get_pointwise_inside_image_mask(image) if np.any(mask): return True return len(self.clip_out_of_image(image)) > 0
python
def is_partly_within_image(self, image, default=False): """ Estimate whether the line string is at least partially inside the image. Parameters ---------- image : ndarray or tuple of int Either an image with shape ``(H,W,[C])`` or a tuple denoting such an image shape. default Default value to return if the line string contains no points. Returns ------- bool True if the line string is at least partially inside the image area. False otherwise. """ if len(self.coords) == 0: return default # check mask first to avoid costly computation of intersection points # whenever possible mask = self.get_pointwise_inside_image_mask(image) if np.any(mask): return True return len(self.clip_out_of_image(image)) > 0
[ "def", "is_partly_within_image", "(", "self", ",", "image", ",", "default", "=", "False", ")", ":", "if", "len", "(", "self", ".", "coords", ")", "==", "0", ":", "return", "default", "# check mask first to avoid costly computation of intersection points", "# whenever possible", "mask", "=", "self", ".", "get_pointwise_inside_image_mask", "(", "image", ")", "if", "np", ".", "any", "(", "mask", ")", ":", "return", "True", "return", "len", "(", "self", ".", "clip_out_of_image", "(", "image", ")", ")", ">", "0" ]
Estimate whether the line string is at least partially inside the image. Parameters ---------- image : ndarray or tuple of int Either an image with shape ``(H,W,[C])`` or a tuple denoting such an image shape. default Default value to return if the line string contains no points. Returns ------- bool True if the line string is at least partially inside the image area. False otherwise.
[ "Estimate", "whether", "the", "line", "string", "is", "at", "least", "partially", "inside", "the", "image", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/augmentables/lines.py#L308-L335
valid
aleju/imgaug
imgaug/augmentables/lines.py
LineString.is_out_of_image
def is_out_of_image(self, image, fully=True, partly=False, default=True): """ Estimate whether the line is partially/fully outside of the image area. Parameters ---------- image : ndarray or tuple of int Either an image with shape ``(H,W,[C])`` or a tuple denoting such an image shape. fully : bool, optional Whether to return True if the bounding box is fully outside fo the image area. partly : bool, optional Whether to return True if the bounding box is at least partially outside fo the image area. default Default value to return if the line string contains no points. Returns ------- bool `default` if the line string has no points. True if the line string is partially/fully outside of the image area, depending on defined parameters. False otherwise. """ if len(self.coords) == 0: return default if self.is_fully_within_image(image): return False elif self.is_partly_within_image(image): return partly else: return fully
python
def is_out_of_image(self, image, fully=True, partly=False, default=True): """ Estimate whether the line is partially/fully outside of the image area. Parameters ---------- image : ndarray or tuple of int Either an image with shape ``(H,W,[C])`` or a tuple denoting such an image shape. fully : bool, optional Whether to return True if the bounding box is fully outside fo the image area. partly : bool, optional Whether to return True if the bounding box is at least partially outside fo the image area. default Default value to return if the line string contains no points. Returns ------- bool `default` if the line string has no points. True if the line string is partially/fully outside of the image area, depending on defined parameters. False otherwise. """ if len(self.coords) == 0: return default if self.is_fully_within_image(image): return False elif self.is_partly_within_image(image): return partly else: return fully
[ "def", "is_out_of_image", "(", "self", ",", "image", ",", "fully", "=", "True", ",", "partly", "=", "False", ",", "default", "=", "True", ")", ":", "if", "len", "(", "self", ".", "coords", ")", "==", "0", ":", "return", "default", "if", "self", ".", "is_fully_within_image", "(", "image", ")", ":", "return", "False", "elif", "self", ".", "is_partly_within_image", "(", "image", ")", ":", "return", "partly", "else", ":", "return", "fully" ]
Estimate whether the line is partially/fully outside of the image area. Parameters ---------- image : ndarray or tuple of int Either an image with shape ``(H,W,[C])`` or a tuple denoting such an image shape. fully : bool, optional Whether to return True if the bounding box is fully outside fo the image area. partly : bool, optional Whether to return True if the bounding box is at least partially outside fo the image area. default Default value to return if the line string contains no points. Returns ------- bool `default` if the line string has no points. True if the line string is partially/fully outside of the image area, depending on defined parameters. False otherwise.
[ "Estimate", "whether", "the", "line", "is", "partially", "/", "fully", "outside", "of", "the", "image", "area", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/augmentables/lines.py#L337-L375
valid
aleju/imgaug
imgaug/augmentables/lines.py
LineString.clip_out_of_image
def clip_out_of_image(self, image): """ Clip off all parts of the line_string that are outside of the image. Parameters ---------- image : ndarray or tuple of int Either an image with shape ``(H,W,[C])`` or a tuple denoting such an image shape. Returns ------- list of imgaug.augmentables.lines.LineString Line strings, clipped to the image shape. The result may contain any number of line strins, including zero. """ if len(self.coords) == 0: return [] inside_image_mask = self.get_pointwise_inside_image_mask(image) ooi_mask = ~inside_image_mask if len(self.coords) == 1: if not np.any(inside_image_mask): return [] return [self.copy()] if np.all(inside_image_mask): return [self.copy()] # top, right, bottom, left image edges # we subtract eps here, because intersection() works inclusively, # i.e. not subtracting eps would be equivalent to 0<=x<=C for C being # height or width # don't set the eps too low, otherwise points at height/width seem # to get rounded to height/width by shapely, which can cause problems # when first clipping and then calling is_fully_within_image() # returning false height, width = normalize_shape(image)[0:2] eps = 1e-3 edges = [ LineString([(0.0, 0.0), (width - eps, 0.0)]), LineString([(width - eps, 0.0), (width - eps, height - eps)]), LineString([(width - eps, height - eps), (0.0, height - eps)]), LineString([(0.0, height - eps), (0.0, 0.0)]) ] intersections = self.find_intersections_with(edges) points = [] gen = enumerate(zip(self.coords[:-1], self.coords[1:], ooi_mask[:-1], ooi_mask[1:], intersections)) for i, (line_start, line_end, ooi_start, ooi_end, inter_line) in gen: points.append((line_start, False, ooi_start)) for p_inter in inter_line: points.append((p_inter, True, False)) is_last = (i == len(self.coords) - 2) if is_last and not ooi_end: points.append((line_end, False, ooi_end)) lines = [] line = [] for i, (coord, was_added, ooi) in enumerate(points): # remove any point that is outside of the image, # also start a new line once such a point is detected if ooi: if len(line) > 0: lines.append(line) line = [] continue if not was_added: # add all points that were part of the original line string # AND that are inside the image plane line.append(coord) else: is_last_point = (i == len(points)-1) # ooi is a numpy.bool_, hence the bool(.) is_next_ooi = (not is_last_point and bool(points[i+1][2]) is True) # Add all points that were new (i.e. intersections), so # long that they aren't essentially identical to other point. # This prevents adding overlapping intersections multiple times. # (E.g. when a line intersects with a corner of the image plane # and also with one of its edges.) p_prev = line[-1] if len(line) > 0 else None # ignore next point if end reached or next point is out of image p_next = None if not is_last_point and not is_next_ooi: p_next = points[i+1][0] dist_prev = None dist_next = None if p_prev is not None: dist_prev = np.linalg.norm( np.float32(coord) - np.float32(p_prev)) if p_next is not None: dist_next = np.linalg.norm( np.float32(coord) - np.float32(p_next)) dist_prev_ok = (dist_prev is None or dist_prev > 1e-2) dist_next_ok = (dist_next is None or dist_next > 1e-2) if dist_prev_ok and dist_next_ok: line.append(coord) if len(line) > 0: lines.append(line) lines = [line for line in lines if len(line) > 0] return [self.deepcopy(coords=line) for line in lines]
python
def clip_out_of_image(self, image): """ Clip off all parts of the line_string that are outside of the image. Parameters ---------- image : ndarray or tuple of int Either an image with shape ``(H,W,[C])`` or a tuple denoting such an image shape. Returns ------- list of imgaug.augmentables.lines.LineString Line strings, clipped to the image shape. The result may contain any number of line strins, including zero. """ if len(self.coords) == 0: return [] inside_image_mask = self.get_pointwise_inside_image_mask(image) ooi_mask = ~inside_image_mask if len(self.coords) == 1: if not np.any(inside_image_mask): return [] return [self.copy()] if np.all(inside_image_mask): return [self.copy()] # top, right, bottom, left image edges # we subtract eps here, because intersection() works inclusively, # i.e. not subtracting eps would be equivalent to 0<=x<=C for C being # height or width # don't set the eps too low, otherwise points at height/width seem # to get rounded to height/width by shapely, which can cause problems # when first clipping and then calling is_fully_within_image() # returning false height, width = normalize_shape(image)[0:2] eps = 1e-3 edges = [ LineString([(0.0, 0.0), (width - eps, 0.0)]), LineString([(width - eps, 0.0), (width - eps, height - eps)]), LineString([(width - eps, height - eps), (0.0, height - eps)]), LineString([(0.0, height - eps), (0.0, 0.0)]) ] intersections = self.find_intersections_with(edges) points = [] gen = enumerate(zip(self.coords[:-1], self.coords[1:], ooi_mask[:-1], ooi_mask[1:], intersections)) for i, (line_start, line_end, ooi_start, ooi_end, inter_line) in gen: points.append((line_start, False, ooi_start)) for p_inter in inter_line: points.append((p_inter, True, False)) is_last = (i == len(self.coords) - 2) if is_last and not ooi_end: points.append((line_end, False, ooi_end)) lines = [] line = [] for i, (coord, was_added, ooi) in enumerate(points): # remove any point that is outside of the image, # also start a new line once such a point is detected if ooi: if len(line) > 0: lines.append(line) line = [] continue if not was_added: # add all points that were part of the original line string # AND that are inside the image plane line.append(coord) else: is_last_point = (i == len(points)-1) # ooi is a numpy.bool_, hence the bool(.) is_next_ooi = (not is_last_point and bool(points[i+1][2]) is True) # Add all points that were new (i.e. intersections), so # long that they aren't essentially identical to other point. # This prevents adding overlapping intersections multiple times. # (E.g. when a line intersects with a corner of the image plane # and also with one of its edges.) p_prev = line[-1] if len(line) > 0 else None # ignore next point if end reached or next point is out of image p_next = None if not is_last_point and not is_next_ooi: p_next = points[i+1][0] dist_prev = None dist_next = None if p_prev is not None: dist_prev = np.linalg.norm( np.float32(coord) - np.float32(p_prev)) if p_next is not None: dist_next = np.linalg.norm( np.float32(coord) - np.float32(p_next)) dist_prev_ok = (dist_prev is None or dist_prev > 1e-2) dist_next_ok = (dist_next is None or dist_next > 1e-2) if dist_prev_ok and dist_next_ok: line.append(coord) if len(line) > 0: lines.append(line) lines = [line for line in lines if len(line) > 0] return [self.deepcopy(coords=line) for line in lines]
[ "def", "clip_out_of_image", "(", "self", ",", "image", ")", ":", "if", "len", "(", "self", ".", "coords", ")", "==", "0", ":", "return", "[", "]", "inside_image_mask", "=", "self", ".", "get_pointwise_inside_image_mask", "(", "image", ")", "ooi_mask", "=", "~", "inside_image_mask", "if", "len", "(", "self", ".", "coords", ")", "==", "1", ":", "if", "not", "np", ".", "any", "(", "inside_image_mask", ")", ":", "return", "[", "]", "return", "[", "self", ".", "copy", "(", ")", "]", "if", "np", ".", "all", "(", "inside_image_mask", ")", ":", "return", "[", "self", ".", "copy", "(", ")", "]", "# top, right, bottom, left image edges", "# we subtract eps here, because intersection() works inclusively,", "# i.e. not subtracting eps would be equivalent to 0<=x<=C for C being", "# height or width", "# don't set the eps too low, otherwise points at height/width seem", "# to get rounded to height/width by shapely, which can cause problems", "# when first clipping and then calling is_fully_within_image()", "# returning false", "height", ",", "width", "=", "normalize_shape", "(", "image", ")", "[", "0", ":", "2", "]", "eps", "=", "1e-3", "edges", "=", "[", "LineString", "(", "[", "(", "0.0", ",", "0.0", ")", ",", "(", "width", "-", "eps", ",", "0.0", ")", "]", ")", ",", "LineString", "(", "[", "(", "width", "-", "eps", ",", "0.0", ")", ",", "(", "width", "-", "eps", ",", "height", "-", "eps", ")", "]", ")", ",", "LineString", "(", "[", "(", "width", "-", "eps", ",", "height", "-", "eps", ")", ",", "(", "0.0", ",", "height", "-", "eps", ")", "]", ")", ",", "LineString", "(", "[", "(", "0.0", ",", "height", "-", "eps", ")", ",", "(", "0.0", ",", "0.0", ")", "]", ")", "]", "intersections", "=", "self", ".", "find_intersections_with", "(", "edges", ")", "points", "=", "[", "]", "gen", "=", "enumerate", "(", "zip", "(", "self", ".", "coords", "[", ":", "-", "1", "]", ",", "self", ".", "coords", "[", "1", ":", "]", ",", "ooi_mask", "[", ":", "-", "1", "]", ",", "ooi_mask", "[", "1", ":", "]", ",", "intersections", ")", ")", "for", "i", ",", "(", "line_start", ",", "line_end", ",", "ooi_start", ",", "ooi_end", ",", "inter_line", ")", "in", "gen", ":", "points", ".", "append", "(", "(", "line_start", ",", "False", ",", "ooi_start", ")", ")", "for", "p_inter", "in", "inter_line", ":", "points", ".", "append", "(", "(", "p_inter", ",", "True", ",", "False", ")", ")", "is_last", "=", "(", "i", "==", "len", "(", "self", ".", "coords", ")", "-", "2", ")", "if", "is_last", "and", "not", "ooi_end", ":", "points", ".", "append", "(", "(", "line_end", ",", "False", ",", "ooi_end", ")", ")", "lines", "=", "[", "]", "line", "=", "[", "]", "for", "i", ",", "(", "coord", ",", "was_added", ",", "ooi", ")", "in", "enumerate", "(", "points", ")", ":", "# remove any point that is outside of the image,", "# also start a new line once such a point is detected", "if", "ooi", ":", "if", "len", "(", "line", ")", ">", "0", ":", "lines", ".", "append", "(", "line", ")", "line", "=", "[", "]", "continue", "if", "not", "was_added", ":", "# add all points that were part of the original line string", "# AND that are inside the image plane", "line", ".", "append", "(", "coord", ")", "else", ":", "is_last_point", "=", "(", "i", "==", "len", "(", "points", ")", "-", "1", ")", "# ooi is a numpy.bool_, hence the bool(.)", "is_next_ooi", "=", "(", "not", "is_last_point", "and", "bool", "(", "points", "[", "i", "+", "1", "]", "[", "2", "]", ")", "is", "True", ")", "# Add all points that were new (i.e. intersections), so", "# long that they aren't essentially identical to other point.", "# This prevents adding overlapping intersections multiple times.", "# (E.g. when a line intersects with a corner of the image plane", "# and also with one of its edges.)", "p_prev", "=", "line", "[", "-", "1", "]", "if", "len", "(", "line", ")", ">", "0", "else", "None", "# ignore next point if end reached or next point is out of image", "p_next", "=", "None", "if", "not", "is_last_point", "and", "not", "is_next_ooi", ":", "p_next", "=", "points", "[", "i", "+", "1", "]", "[", "0", "]", "dist_prev", "=", "None", "dist_next", "=", "None", "if", "p_prev", "is", "not", "None", ":", "dist_prev", "=", "np", ".", "linalg", ".", "norm", "(", "np", ".", "float32", "(", "coord", ")", "-", "np", ".", "float32", "(", "p_prev", ")", ")", "if", "p_next", "is", "not", "None", ":", "dist_next", "=", "np", ".", "linalg", ".", "norm", "(", "np", ".", "float32", "(", "coord", ")", "-", "np", ".", "float32", "(", "p_next", ")", ")", "dist_prev_ok", "=", "(", "dist_prev", "is", "None", "or", "dist_prev", ">", "1e-2", ")", "dist_next_ok", "=", "(", "dist_next", "is", "None", "or", "dist_next", ">", "1e-2", ")", "if", "dist_prev_ok", "and", "dist_next_ok", ":", "line", ".", "append", "(", "coord", ")", "if", "len", "(", "line", ")", ">", "0", ":", "lines", ".", "append", "(", "line", ")", "lines", "=", "[", "line", "for", "line", "in", "lines", "if", "len", "(", "line", ")", ">", "0", "]", "return", "[", "self", ".", "deepcopy", "(", "coords", "=", "line", ")", "for", "line", "in", "lines", "]" ]
Clip off all parts of the line_string that are outside of the image. Parameters ---------- image : ndarray or tuple of int Either an image with shape ``(H,W,[C])`` or a tuple denoting such an image shape. Returns ------- list of imgaug.augmentables.lines.LineString Line strings, clipped to the image shape. The result may contain any number of line strins, including zero.
[ "Clip", "off", "all", "parts", "of", "the", "line_string", "that", "are", "outside", "of", "the", "image", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/augmentables/lines.py#L377-L488
valid
aleju/imgaug
imgaug/augmentables/lines.py
LineString.find_intersections_with
def find_intersections_with(self, other): """ Find all intersection points between the line string and `other`. Parameters ---------- other : tuple of number or list of tuple of number or \ list of LineString or LineString The other geometry to use during intersection tests. Returns ------- list of list of tuple of number All intersection points. One list per pair of consecutive start and end point, i.e. `N-1` lists of `N` points. Each list may be empty or may contain multiple points. """ import shapely.geometry geom = _convert_var_to_shapely_geometry(other) result = [] for p_start, p_end in zip(self.coords[:-1], self.coords[1:]): ls = shapely.geometry.LineString([p_start, p_end]) intersections = ls.intersection(geom) intersections = list(_flatten_shapely_collection(intersections)) intersections_points = [] for inter in intersections: if isinstance(inter, shapely.geometry.linestring.LineString): inter_start = (inter.coords[0][0], inter.coords[0][1]) inter_end = (inter.coords[-1][0], inter.coords[-1][1]) intersections_points.extend([inter_start, inter_end]) else: assert isinstance(inter, shapely.geometry.point.Point), ( "Expected to find shapely.geometry.point.Point or " "shapely.geometry.linestring.LineString intersection, " "actually found %s." % (type(inter),)) intersections_points.append((inter.x, inter.y)) # sort by distance to start point, this makes it later on easier # to remove duplicate points inter_sorted = sorted( intersections_points, key=lambda p: np.linalg.norm(np.float32(p) - p_start) ) result.append(inter_sorted) return result
python
def find_intersections_with(self, other): """ Find all intersection points between the line string and `other`. Parameters ---------- other : tuple of number or list of tuple of number or \ list of LineString or LineString The other geometry to use during intersection tests. Returns ------- list of list of tuple of number All intersection points. One list per pair of consecutive start and end point, i.e. `N-1` lists of `N` points. Each list may be empty or may contain multiple points. """ import shapely.geometry geom = _convert_var_to_shapely_geometry(other) result = [] for p_start, p_end in zip(self.coords[:-1], self.coords[1:]): ls = shapely.geometry.LineString([p_start, p_end]) intersections = ls.intersection(geom) intersections = list(_flatten_shapely_collection(intersections)) intersections_points = [] for inter in intersections: if isinstance(inter, shapely.geometry.linestring.LineString): inter_start = (inter.coords[0][0], inter.coords[0][1]) inter_end = (inter.coords[-1][0], inter.coords[-1][1]) intersections_points.extend([inter_start, inter_end]) else: assert isinstance(inter, shapely.geometry.point.Point), ( "Expected to find shapely.geometry.point.Point or " "shapely.geometry.linestring.LineString intersection, " "actually found %s." % (type(inter),)) intersections_points.append((inter.x, inter.y)) # sort by distance to start point, this makes it later on easier # to remove duplicate points inter_sorted = sorted( intersections_points, key=lambda p: np.linalg.norm(np.float32(p) - p_start) ) result.append(inter_sorted) return result
[ "def", "find_intersections_with", "(", "self", ",", "other", ")", ":", "import", "shapely", ".", "geometry", "geom", "=", "_convert_var_to_shapely_geometry", "(", "other", ")", "result", "=", "[", "]", "for", "p_start", ",", "p_end", "in", "zip", "(", "self", ".", "coords", "[", ":", "-", "1", "]", ",", "self", ".", "coords", "[", "1", ":", "]", ")", ":", "ls", "=", "shapely", ".", "geometry", ".", "LineString", "(", "[", "p_start", ",", "p_end", "]", ")", "intersections", "=", "ls", ".", "intersection", "(", "geom", ")", "intersections", "=", "list", "(", "_flatten_shapely_collection", "(", "intersections", ")", ")", "intersections_points", "=", "[", "]", "for", "inter", "in", "intersections", ":", "if", "isinstance", "(", "inter", ",", "shapely", ".", "geometry", ".", "linestring", ".", "LineString", ")", ":", "inter_start", "=", "(", "inter", ".", "coords", "[", "0", "]", "[", "0", "]", ",", "inter", ".", "coords", "[", "0", "]", "[", "1", "]", ")", "inter_end", "=", "(", "inter", ".", "coords", "[", "-", "1", "]", "[", "0", "]", ",", "inter", ".", "coords", "[", "-", "1", "]", "[", "1", "]", ")", "intersections_points", ".", "extend", "(", "[", "inter_start", ",", "inter_end", "]", ")", "else", ":", "assert", "isinstance", "(", "inter", ",", "shapely", ".", "geometry", ".", "point", ".", "Point", ")", ",", "(", "\"Expected to find shapely.geometry.point.Point or \"", "\"shapely.geometry.linestring.LineString intersection, \"", "\"actually found %s.\"", "%", "(", "type", "(", "inter", ")", ",", ")", ")", "intersections_points", ".", "append", "(", "(", "inter", ".", "x", ",", "inter", ".", "y", ")", ")", "# sort by distance to start point, this makes it later on easier", "# to remove duplicate points", "inter_sorted", "=", "sorted", "(", "intersections_points", ",", "key", "=", "lambda", "p", ":", "np", ".", "linalg", ".", "norm", "(", "np", ".", "float32", "(", "p", ")", "-", "p_start", ")", ")", "result", ".", "append", "(", "inter_sorted", ")", "return", "result" ]
Find all intersection points between the line string and `other`. Parameters ---------- other : tuple of number or list of tuple of number or \ list of LineString or LineString The other geometry to use during intersection tests. Returns ------- list of list of tuple of number All intersection points. One list per pair of consecutive start and end point, i.e. `N-1` lists of `N` points. Each list may be empty or may contain multiple points.
[ "Find", "all", "intersection", "points", "between", "the", "line", "string", "and", "other", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/augmentables/lines.py#L491-L540
valid
aleju/imgaug
imgaug/augmentables/lines.py
LineString.shift
def shift(self, top=None, right=None, bottom=None, left=None): """ Shift/move the line string from one or more image sides. Parameters ---------- top : None or int, optional Amount of pixels by which to shift the bounding box from the top. right : None or int, optional Amount of pixels by which to shift the bounding box from the right. bottom : None or int, optional Amount of pixels by which to shift the bounding box from the bottom. left : None or int, optional Amount of pixels by which to shift the bounding box from the left. Returns ------- result : imgaug.augmentables.lines.LineString Shifted line string. """ top = top if top is not None else 0 right = right if right is not None else 0 bottom = bottom if bottom is not None else 0 left = left if left is not None else 0 coords = np.copy(self.coords) coords[:, 0] += left - right coords[:, 1] += top - bottom return self.copy(coords=coords)
python
def shift(self, top=None, right=None, bottom=None, left=None): """ Shift/move the line string from one or more image sides. Parameters ---------- top : None or int, optional Amount of pixels by which to shift the bounding box from the top. right : None or int, optional Amount of pixels by which to shift the bounding box from the right. bottom : None or int, optional Amount of pixels by which to shift the bounding box from the bottom. left : None or int, optional Amount of pixels by which to shift the bounding box from the left. Returns ------- result : imgaug.augmentables.lines.LineString Shifted line string. """ top = top if top is not None else 0 right = right if right is not None else 0 bottom = bottom if bottom is not None else 0 left = left if left is not None else 0 coords = np.copy(self.coords) coords[:, 0] += left - right coords[:, 1] += top - bottom return self.copy(coords=coords)
[ "def", "shift", "(", "self", ",", "top", "=", "None", ",", "right", "=", "None", ",", "bottom", "=", "None", ",", "left", "=", "None", ")", ":", "top", "=", "top", "if", "top", "is", "not", "None", "else", "0", "right", "=", "right", "if", "right", "is", "not", "None", "else", "0", "bottom", "=", "bottom", "if", "bottom", "is", "not", "None", "else", "0", "left", "=", "left", "if", "left", "is", "not", "None", "else", "0", "coords", "=", "np", ".", "copy", "(", "self", ".", "coords", ")", "coords", "[", ":", ",", "0", "]", "+=", "left", "-", "right", "coords", "[", ":", ",", "1", "]", "+=", "top", "-", "bottom", "return", "self", ".", "copy", "(", "coords", "=", "coords", ")" ]
Shift/move the line string from one or more image sides. Parameters ---------- top : None or int, optional Amount of pixels by which to shift the bounding box from the top. right : None or int, optional Amount of pixels by which to shift the bounding box from the right. bottom : None or int, optional Amount of pixels by which to shift the bounding box from the bottom. left : None or int, optional Amount of pixels by which to shift the bounding box from the left. Returns ------- result : imgaug.augmentables.lines.LineString Shifted line string.
[ "Shift", "/", "move", "the", "line", "string", "from", "one", "or", "more", "image", "sides", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/augmentables/lines.py#L543-L578
valid
aleju/imgaug
imgaug/augmentables/lines.py
LineString.draw_mask
def draw_mask(self, image_shape, size_lines=1, size_points=0, raise_if_out_of_image=False): """ Draw this line segment as a binary image mask. Parameters ---------- image_shape : tuple of int The shape of the image onto which to draw the line mask. size_lines : int, optional Thickness of the line segments. size_points : int, optional Size of the points in pixels. raise_if_out_of_image : bool, optional Whether to raise an error if the line string is fully outside of the image. If set to False, no error will be raised and only the parts inside the image will be drawn. Returns ------- ndarray Boolean line mask of shape `image_shape` (no channel axis). """ heatmap = self.draw_heatmap_array( image_shape, alpha_lines=1.0, alpha_points=1.0, size_lines=size_lines, size_points=size_points, antialiased=False, raise_if_out_of_image=raise_if_out_of_image) return heatmap > 0.5
python
def draw_mask(self, image_shape, size_lines=1, size_points=0, raise_if_out_of_image=False): """ Draw this line segment as a binary image mask. Parameters ---------- image_shape : tuple of int The shape of the image onto which to draw the line mask. size_lines : int, optional Thickness of the line segments. size_points : int, optional Size of the points in pixels. raise_if_out_of_image : bool, optional Whether to raise an error if the line string is fully outside of the image. If set to False, no error will be raised and only the parts inside the image will be drawn. Returns ------- ndarray Boolean line mask of shape `image_shape` (no channel axis). """ heatmap = self.draw_heatmap_array( image_shape, alpha_lines=1.0, alpha_points=1.0, size_lines=size_lines, size_points=size_points, antialiased=False, raise_if_out_of_image=raise_if_out_of_image) return heatmap > 0.5
[ "def", "draw_mask", "(", "self", ",", "image_shape", ",", "size_lines", "=", "1", ",", "size_points", "=", "0", ",", "raise_if_out_of_image", "=", "False", ")", ":", "heatmap", "=", "self", ".", "draw_heatmap_array", "(", "image_shape", ",", "alpha_lines", "=", "1.0", ",", "alpha_points", "=", "1.0", ",", "size_lines", "=", "size_lines", ",", "size_points", "=", "size_points", ",", "antialiased", "=", "False", ",", "raise_if_out_of_image", "=", "raise_if_out_of_image", ")", "return", "heatmap", ">", "0.5" ]
Draw this line segment as a binary image mask. Parameters ---------- image_shape : tuple of int The shape of the image onto which to draw the line mask. size_lines : int, optional Thickness of the line segments. size_points : int, optional Size of the points in pixels. raise_if_out_of_image : bool, optional Whether to raise an error if the line string is fully outside of the image. If set to False, no error will be raised and only the parts inside the image will be drawn. Returns ------- ndarray Boolean line mask of shape `image_shape` (no channel axis).
[ "Draw", "this", "line", "segment", "as", "a", "binary", "image", "mask", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/augmentables/lines.py#L580-L613
valid
aleju/imgaug
imgaug/augmentables/lines.py
LineString.draw_lines_heatmap_array
def draw_lines_heatmap_array(self, image_shape, alpha=1.0, size=1, antialiased=True, raise_if_out_of_image=False): """ Draw the line segments of the line string as a heatmap array. Parameters ---------- image_shape : tuple of int The shape of the image onto which to draw the line mask. alpha : float, optional Opacity of the line string. Higher values denote a more visible line string. size : int, optional Thickness of the line segments. antialiased : bool, optional Whether to draw the line with anti-aliasing activated. raise_if_out_of_image : bool, optional Whether to raise an error if the line string is fully outside of the image. If set to False, no error will be raised and only the parts inside the image will be drawn. Returns ------- ndarray Float array of shape `image_shape` (no channel axis) with drawn line string. All values are in the interval ``[0.0, 1.0]``. """ assert len(image_shape) == 2 or ( len(image_shape) == 3 and image_shape[-1] == 1), ( "Expected (H,W) or (H,W,1) as image_shape, got %s." % ( image_shape,)) arr = self.draw_lines_on_image( np.zeros(image_shape, dtype=np.uint8), color=255, alpha=alpha, size=size, antialiased=antialiased, raise_if_out_of_image=raise_if_out_of_image ) return arr.astype(np.float32) / 255.0
python
def draw_lines_heatmap_array(self, image_shape, alpha=1.0, size=1, antialiased=True, raise_if_out_of_image=False): """ Draw the line segments of the line string as a heatmap array. Parameters ---------- image_shape : tuple of int The shape of the image onto which to draw the line mask. alpha : float, optional Opacity of the line string. Higher values denote a more visible line string. size : int, optional Thickness of the line segments. antialiased : bool, optional Whether to draw the line with anti-aliasing activated. raise_if_out_of_image : bool, optional Whether to raise an error if the line string is fully outside of the image. If set to False, no error will be raised and only the parts inside the image will be drawn. Returns ------- ndarray Float array of shape `image_shape` (no channel axis) with drawn line string. All values are in the interval ``[0.0, 1.0]``. """ assert len(image_shape) == 2 or ( len(image_shape) == 3 and image_shape[-1] == 1), ( "Expected (H,W) or (H,W,1) as image_shape, got %s." % ( image_shape,)) arr = self.draw_lines_on_image( np.zeros(image_shape, dtype=np.uint8), color=255, alpha=alpha, size=size, antialiased=antialiased, raise_if_out_of_image=raise_if_out_of_image ) return arr.astype(np.float32) / 255.0
[ "def", "draw_lines_heatmap_array", "(", "self", ",", "image_shape", ",", "alpha", "=", "1.0", ",", "size", "=", "1", ",", "antialiased", "=", "True", ",", "raise_if_out_of_image", "=", "False", ")", ":", "assert", "len", "(", "image_shape", ")", "==", "2", "or", "(", "len", "(", "image_shape", ")", "==", "3", "and", "image_shape", "[", "-", "1", "]", "==", "1", ")", ",", "(", "\"Expected (H,W) or (H,W,1) as image_shape, got %s.\"", "%", "(", "image_shape", ",", ")", ")", "arr", "=", "self", ".", "draw_lines_on_image", "(", "np", ".", "zeros", "(", "image_shape", ",", "dtype", "=", "np", ".", "uint8", ")", ",", "color", "=", "255", ",", "alpha", "=", "alpha", ",", "size", "=", "size", ",", "antialiased", "=", "antialiased", ",", "raise_if_out_of_image", "=", "raise_if_out_of_image", ")", "return", "arr", ".", "astype", "(", "np", ".", "float32", ")", "/", "255.0" ]
Draw the line segments of the line string as a heatmap array. Parameters ---------- image_shape : tuple of int The shape of the image onto which to draw the line mask. alpha : float, optional Opacity of the line string. Higher values denote a more visible line string. size : int, optional Thickness of the line segments. antialiased : bool, optional Whether to draw the line with anti-aliasing activated. raise_if_out_of_image : bool, optional Whether to raise an error if the line string is fully outside of the image. If set to False, no error will be raised and only the parts inside the image will be drawn. Returns ------- ndarray Float array of shape `image_shape` (no channel axis) with drawn line string. All values are in the interval ``[0.0, 1.0]``.
[ "Draw", "the", "line", "segments", "of", "the", "line", "string", "as", "a", "heatmap", "array", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/augmentables/lines.py#L615-L659
valid
aleju/imgaug
imgaug/augmentables/lines.py
LineString.draw_points_heatmap_array
def draw_points_heatmap_array(self, image_shape, alpha=1.0, size=1, raise_if_out_of_image=False): """ Draw the points of the line string as a heatmap array. Parameters ---------- image_shape : tuple of int The shape of the image onto which to draw the point mask. alpha : float, optional Opacity of the line string points. Higher values denote a more visible points. size : int, optional Size of the points in pixels. raise_if_out_of_image : bool, optional Whether to raise an error if the line string is fully outside of the image. If set to False, no error will be raised and only the parts inside the image will be drawn. Returns ------- ndarray Float array of shape `image_shape` (no channel axis) with drawn line string points. All values are in the interval ``[0.0, 1.0]``. """ assert len(image_shape) == 2 or ( len(image_shape) == 3 and image_shape[-1] == 1), ( "Expected (H,W) or (H,W,1) as image_shape, got %s." % ( image_shape,)) arr = self.draw_points_on_image( np.zeros(image_shape, dtype=np.uint8), color=255, alpha=alpha, size=size, raise_if_out_of_image=raise_if_out_of_image ) return arr.astype(np.float32) / 255.0
python
def draw_points_heatmap_array(self, image_shape, alpha=1.0, size=1, raise_if_out_of_image=False): """ Draw the points of the line string as a heatmap array. Parameters ---------- image_shape : tuple of int The shape of the image onto which to draw the point mask. alpha : float, optional Opacity of the line string points. Higher values denote a more visible points. size : int, optional Size of the points in pixels. raise_if_out_of_image : bool, optional Whether to raise an error if the line string is fully outside of the image. If set to False, no error will be raised and only the parts inside the image will be drawn. Returns ------- ndarray Float array of shape `image_shape` (no channel axis) with drawn line string points. All values are in the interval ``[0.0, 1.0]``. """ assert len(image_shape) == 2 or ( len(image_shape) == 3 and image_shape[-1] == 1), ( "Expected (H,W) or (H,W,1) as image_shape, got %s." % ( image_shape,)) arr = self.draw_points_on_image( np.zeros(image_shape, dtype=np.uint8), color=255, alpha=alpha, size=size, raise_if_out_of_image=raise_if_out_of_image ) return arr.astype(np.float32) / 255.0
[ "def", "draw_points_heatmap_array", "(", "self", ",", "image_shape", ",", "alpha", "=", "1.0", ",", "size", "=", "1", ",", "raise_if_out_of_image", "=", "False", ")", ":", "assert", "len", "(", "image_shape", ")", "==", "2", "or", "(", "len", "(", "image_shape", ")", "==", "3", "and", "image_shape", "[", "-", "1", "]", "==", "1", ")", ",", "(", "\"Expected (H,W) or (H,W,1) as image_shape, got %s.\"", "%", "(", "image_shape", ",", ")", ")", "arr", "=", "self", ".", "draw_points_on_image", "(", "np", ".", "zeros", "(", "image_shape", ",", "dtype", "=", "np", ".", "uint8", ")", ",", "color", "=", "255", ",", "alpha", "=", "alpha", ",", "size", "=", "size", ",", "raise_if_out_of_image", "=", "raise_if_out_of_image", ")", "return", "arr", ".", "astype", "(", "np", ".", "float32", ")", "/", "255.0" ]
Draw the points of the line string as a heatmap array. Parameters ---------- image_shape : tuple of int The shape of the image onto which to draw the point mask. alpha : float, optional Opacity of the line string points. Higher values denote a more visible points. size : int, optional Size of the points in pixels. raise_if_out_of_image : bool, optional Whether to raise an error if the line string is fully outside of the image. If set to False, no error will be raised and only the parts inside the image will be drawn. Returns ------- ndarray Float array of shape `image_shape` (no channel axis) with drawn line string points. All values are in the interval ``[0.0, 1.0]``.
[ "Draw", "the", "points", "of", "the", "line", "string", "as", "a", "heatmap", "array", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/augmentables/lines.py#L661-L700
valid
aleju/imgaug
imgaug/augmentables/lines.py
LineString.draw_heatmap_array
def draw_heatmap_array(self, image_shape, alpha_lines=1.0, alpha_points=1.0, size_lines=1, size_points=0, antialiased=True, raise_if_out_of_image=False): """ Draw the line segments and points of the line string as a heatmap array. Parameters ---------- image_shape : tuple of int The shape of the image onto which to draw the line mask. alpha_lines : float, optional Opacity of the line string. Higher values denote a more visible line string. alpha_points : float, optional Opacity of the line string points. Higher values denote a more visible points. size_lines : int, optional Thickness of the line segments. size_points : int, optional Size of the points in pixels. antialiased : bool, optional Whether to draw the line with anti-aliasing activated. raise_if_out_of_image : bool, optional Whether to raise an error if the line string is fully outside of the image. If set to False, no error will be raised and only the parts inside the image will be drawn. Returns ------- ndarray Float array of shape `image_shape` (no channel axis) with drawn line segments and points. All values are in the interval ``[0.0, 1.0]``. """ heatmap_lines = self.draw_lines_heatmap_array( image_shape, alpha=alpha_lines, size=size_lines, antialiased=antialiased, raise_if_out_of_image=raise_if_out_of_image) if size_points <= 0: return heatmap_lines heatmap_points = self.draw_points_heatmap_array( image_shape, alpha=alpha_points, size=size_points, raise_if_out_of_image=raise_if_out_of_image) heatmap = np.dstack([heatmap_lines, heatmap_points]) return np.max(heatmap, axis=2)
python
def draw_heatmap_array(self, image_shape, alpha_lines=1.0, alpha_points=1.0, size_lines=1, size_points=0, antialiased=True, raise_if_out_of_image=False): """ Draw the line segments and points of the line string as a heatmap array. Parameters ---------- image_shape : tuple of int The shape of the image onto which to draw the line mask. alpha_lines : float, optional Opacity of the line string. Higher values denote a more visible line string. alpha_points : float, optional Opacity of the line string points. Higher values denote a more visible points. size_lines : int, optional Thickness of the line segments. size_points : int, optional Size of the points in pixels. antialiased : bool, optional Whether to draw the line with anti-aliasing activated. raise_if_out_of_image : bool, optional Whether to raise an error if the line string is fully outside of the image. If set to False, no error will be raised and only the parts inside the image will be drawn. Returns ------- ndarray Float array of shape `image_shape` (no channel axis) with drawn line segments and points. All values are in the interval ``[0.0, 1.0]``. """ heatmap_lines = self.draw_lines_heatmap_array( image_shape, alpha=alpha_lines, size=size_lines, antialiased=antialiased, raise_if_out_of_image=raise_if_out_of_image) if size_points <= 0: return heatmap_lines heatmap_points = self.draw_points_heatmap_array( image_shape, alpha=alpha_points, size=size_points, raise_if_out_of_image=raise_if_out_of_image) heatmap = np.dstack([heatmap_lines, heatmap_points]) return np.max(heatmap, axis=2)
[ "def", "draw_heatmap_array", "(", "self", ",", "image_shape", ",", "alpha_lines", "=", "1.0", ",", "alpha_points", "=", "1.0", ",", "size_lines", "=", "1", ",", "size_points", "=", "0", ",", "antialiased", "=", "True", ",", "raise_if_out_of_image", "=", "False", ")", ":", "heatmap_lines", "=", "self", ".", "draw_lines_heatmap_array", "(", "image_shape", ",", "alpha", "=", "alpha_lines", ",", "size", "=", "size_lines", ",", "antialiased", "=", "antialiased", ",", "raise_if_out_of_image", "=", "raise_if_out_of_image", ")", "if", "size_points", "<=", "0", ":", "return", "heatmap_lines", "heatmap_points", "=", "self", ".", "draw_points_heatmap_array", "(", "image_shape", ",", "alpha", "=", "alpha_points", ",", "size", "=", "size_points", ",", "raise_if_out_of_image", "=", "raise_if_out_of_image", ")", "heatmap", "=", "np", ".", "dstack", "(", "[", "heatmap_lines", ",", "heatmap_points", "]", ")", "return", "np", ".", "max", "(", "heatmap", ",", "axis", "=", "2", ")" ]
Draw the line segments and points of the line string as a heatmap array. Parameters ---------- image_shape : tuple of int The shape of the image onto which to draw the line mask. alpha_lines : float, optional Opacity of the line string. Higher values denote a more visible line string. alpha_points : float, optional Opacity of the line string points. Higher values denote a more visible points. size_lines : int, optional Thickness of the line segments. size_points : int, optional Size of the points in pixels. antialiased : bool, optional Whether to draw the line with anti-aliasing activated. raise_if_out_of_image : bool, optional Whether to raise an error if the line string is fully outside of the image. If set to False, no error will be raised and only the parts inside the image will be drawn. Returns ------- ndarray Float array of shape `image_shape` (no channel axis) with drawn line segments and points. All values are in the interval ``[0.0, 1.0]``.
[ "Draw", "the", "line", "segments", "and", "points", "of", "the", "line", "string", "as", "a", "heatmap", "array", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/augmentables/lines.py#L702-L759
valid
aleju/imgaug
imgaug/augmentables/lines.py
LineString.draw_lines_on_image
def draw_lines_on_image(self, image, color=(0, 255, 0), alpha=1.0, size=3, antialiased=True, raise_if_out_of_image=False): """ Draw the line segments of the line string on a given image. Parameters ---------- image : ndarray or tuple of int The image onto which to draw. Expected to be ``uint8`` and of shape ``(H, W, C)`` with ``C`` usually being ``3`` (other values are not tested). If a tuple, expected to be ``(H, W, C)`` and will lead to a new ``uint8`` array of zeros being created. color : int or iterable of int Color to use as RGB, i.e. three values. alpha : float, optional Opacity of the line string. Higher values denote a more visible line string. size : int, optional Thickness of the line segments. antialiased : bool, optional Whether to draw the line with anti-aliasing activated. raise_if_out_of_image : bool, optional Whether to raise an error if the line string is fully outside of the image. If set to False, no error will be raised and only the parts inside the image will be drawn. Returns ------- ndarray `image` with line drawn on it. """ from .. import dtypes as iadt from ..augmenters import blend as blendlib image_was_empty = False if isinstance(image, tuple): image_was_empty = True image = np.zeros(image, dtype=np.uint8) assert image.ndim in [2, 3], ( ("Expected image or shape of form (H,W) or (H,W,C), " + "got shape %s.") % (image.shape,)) if len(self.coords) <= 1 or alpha < 0 + 1e-4 or size < 1: return np.copy(image) if raise_if_out_of_image \ and self.is_out_of_image(image, partly=False, fully=True): raise Exception( "Cannot draw line string '%s' on image with shape %s, because " "it would be out of bounds." % ( self.__str__(), image.shape)) if image.ndim == 2: assert ia.is_single_number(color), ( "Got a 2D image. Expected then 'color' to be a single number, " "but got %s." % (str(color),)) color = [color] elif image.ndim == 3 and ia.is_single_number(color): color = [color] * image.shape[-1] image = image.astype(np.float32) height, width = image.shape[0:2] # We can't trivially exclude lines outside of the image here, because # even if start and end point are outside, there can still be parts of # the line inside the image. # TODO Do this with edge-wise intersection tests lines = [] for line_start, line_end in zip(self.coords[:-1], self.coords[1:]): # note that line() expects order (y1, x1, y2, x2), hence ([1], [0]) lines.append((line_start[1], line_start[0], line_end[1], line_end[0])) # skimage.draw.line can only handle integers lines = np.round(np.float32(lines)).astype(np.int32) # size == 0 is already covered above # Note here that we have to be careful not to draw lines two times # at their intersection points, e.g. for (p0, p1), (p1, 2) we could # end up drawing at p1 twice, leading to higher values if alpha is used. color = np.float32(color) heatmap = np.zeros(image.shape[0:2], dtype=np.float32) for line in lines: if antialiased: rr, cc, val = skimage.draw.line_aa(*line) else: rr, cc = skimage.draw.line(*line) val = 1.0 # mask check here, because line() can generate coordinates # outside of the image plane rr_mask = np.logical_and(0 <= rr, rr < height) cc_mask = np.logical_and(0 <= cc, cc < width) mask = np.logical_and(rr_mask, cc_mask) if np.any(mask): rr = rr[mask] cc = cc[mask] val = val[mask] if not ia.is_single_number(val) else val heatmap[rr, cc] = val * alpha if size > 1: kernel = np.ones((size, size), dtype=np.uint8) heatmap = cv2.dilate(heatmap, kernel) if image_was_empty: image_blend = image + heatmap * color else: image_color_shape = image.shape[0:2] if image.ndim == 3: image_color_shape = image_color_shape + (1,) image_color = np.tile(color, image_color_shape) image_blend = blendlib.blend_alpha(image_color, image, heatmap) image_blend = iadt.restore_dtypes_(image_blend, np.uint8) return image_blend
python
def draw_lines_on_image(self, image, color=(0, 255, 0), alpha=1.0, size=3, antialiased=True, raise_if_out_of_image=False): """ Draw the line segments of the line string on a given image. Parameters ---------- image : ndarray or tuple of int The image onto which to draw. Expected to be ``uint8`` and of shape ``(H, W, C)`` with ``C`` usually being ``3`` (other values are not tested). If a tuple, expected to be ``(H, W, C)`` and will lead to a new ``uint8`` array of zeros being created. color : int or iterable of int Color to use as RGB, i.e. three values. alpha : float, optional Opacity of the line string. Higher values denote a more visible line string. size : int, optional Thickness of the line segments. antialiased : bool, optional Whether to draw the line with anti-aliasing activated. raise_if_out_of_image : bool, optional Whether to raise an error if the line string is fully outside of the image. If set to False, no error will be raised and only the parts inside the image will be drawn. Returns ------- ndarray `image` with line drawn on it. """ from .. import dtypes as iadt from ..augmenters import blend as blendlib image_was_empty = False if isinstance(image, tuple): image_was_empty = True image = np.zeros(image, dtype=np.uint8) assert image.ndim in [2, 3], ( ("Expected image or shape of form (H,W) or (H,W,C), " + "got shape %s.") % (image.shape,)) if len(self.coords) <= 1 or alpha < 0 + 1e-4 or size < 1: return np.copy(image) if raise_if_out_of_image \ and self.is_out_of_image(image, partly=False, fully=True): raise Exception( "Cannot draw line string '%s' on image with shape %s, because " "it would be out of bounds." % ( self.__str__(), image.shape)) if image.ndim == 2: assert ia.is_single_number(color), ( "Got a 2D image. Expected then 'color' to be a single number, " "but got %s." % (str(color),)) color = [color] elif image.ndim == 3 and ia.is_single_number(color): color = [color] * image.shape[-1] image = image.astype(np.float32) height, width = image.shape[0:2] # We can't trivially exclude lines outside of the image here, because # even if start and end point are outside, there can still be parts of # the line inside the image. # TODO Do this with edge-wise intersection tests lines = [] for line_start, line_end in zip(self.coords[:-1], self.coords[1:]): # note that line() expects order (y1, x1, y2, x2), hence ([1], [0]) lines.append((line_start[1], line_start[0], line_end[1], line_end[0])) # skimage.draw.line can only handle integers lines = np.round(np.float32(lines)).astype(np.int32) # size == 0 is already covered above # Note here that we have to be careful not to draw lines two times # at their intersection points, e.g. for (p0, p1), (p1, 2) we could # end up drawing at p1 twice, leading to higher values if alpha is used. color = np.float32(color) heatmap = np.zeros(image.shape[0:2], dtype=np.float32) for line in lines: if antialiased: rr, cc, val = skimage.draw.line_aa(*line) else: rr, cc = skimage.draw.line(*line) val = 1.0 # mask check here, because line() can generate coordinates # outside of the image plane rr_mask = np.logical_and(0 <= rr, rr < height) cc_mask = np.logical_and(0 <= cc, cc < width) mask = np.logical_and(rr_mask, cc_mask) if np.any(mask): rr = rr[mask] cc = cc[mask] val = val[mask] if not ia.is_single_number(val) else val heatmap[rr, cc] = val * alpha if size > 1: kernel = np.ones((size, size), dtype=np.uint8) heatmap = cv2.dilate(heatmap, kernel) if image_was_empty: image_blend = image + heatmap * color else: image_color_shape = image.shape[0:2] if image.ndim == 3: image_color_shape = image_color_shape + (1,) image_color = np.tile(color, image_color_shape) image_blend = blendlib.blend_alpha(image_color, image, heatmap) image_blend = iadt.restore_dtypes_(image_blend, np.uint8) return image_blend
[ "def", "draw_lines_on_image", "(", "self", ",", "image", ",", "color", "=", "(", "0", ",", "255", ",", "0", ")", ",", "alpha", "=", "1.0", ",", "size", "=", "3", ",", "antialiased", "=", "True", ",", "raise_if_out_of_image", "=", "False", ")", ":", "from", ".", ".", "import", "dtypes", "as", "iadt", "from", ".", ".", "augmenters", "import", "blend", "as", "blendlib", "image_was_empty", "=", "False", "if", "isinstance", "(", "image", ",", "tuple", ")", ":", "image_was_empty", "=", "True", "image", "=", "np", ".", "zeros", "(", "image", ",", "dtype", "=", "np", ".", "uint8", ")", "assert", "image", ".", "ndim", "in", "[", "2", ",", "3", "]", ",", "(", "(", "\"Expected image or shape of form (H,W) or (H,W,C), \"", "+", "\"got shape %s.\"", ")", "%", "(", "image", ".", "shape", ",", ")", ")", "if", "len", "(", "self", ".", "coords", ")", "<=", "1", "or", "alpha", "<", "0", "+", "1e-4", "or", "size", "<", "1", ":", "return", "np", ".", "copy", "(", "image", ")", "if", "raise_if_out_of_image", "and", "self", ".", "is_out_of_image", "(", "image", ",", "partly", "=", "False", ",", "fully", "=", "True", ")", ":", "raise", "Exception", "(", "\"Cannot draw line string '%s' on image with shape %s, because \"", "\"it would be out of bounds.\"", "%", "(", "self", ".", "__str__", "(", ")", ",", "image", ".", "shape", ")", ")", "if", "image", ".", "ndim", "==", "2", ":", "assert", "ia", ".", "is_single_number", "(", "color", ")", ",", "(", "\"Got a 2D image. Expected then 'color' to be a single number, \"", "\"but got %s.\"", "%", "(", "str", "(", "color", ")", ",", ")", ")", "color", "=", "[", "color", "]", "elif", "image", ".", "ndim", "==", "3", "and", "ia", ".", "is_single_number", "(", "color", ")", ":", "color", "=", "[", "color", "]", "*", "image", ".", "shape", "[", "-", "1", "]", "image", "=", "image", ".", "astype", "(", "np", ".", "float32", ")", "height", ",", "width", "=", "image", ".", "shape", "[", "0", ":", "2", "]", "# We can't trivially exclude lines outside of the image here, because", "# even if start and end point are outside, there can still be parts of", "# the line inside the image.", "# TODO Do this with edge-wise intersection tests", "lines", "=", "[", "]", "for", "line_start", ",", "line_end", "in", "zip", "(", "self", ".", "coords", "[", ":", "-", "1", "]", ",", "self", ".", "coords", "[", "1", ":", "]", ")", ":", "# note that line() expects order (y1, x1, y2, x2), hence ([1], [0])", "lines", ".", "append", "(", "(", "line_start", "[", "1", "]", ",", "line_start", "[", "0", "]", ",", "line_end", "[", "1", "]", ",", "line_end", "[", "0", "]", ")", ")", "# skimage.draw.line can only handle integers", "lines", "=", "np", ".", "round", "(", "np", ".", "float32", "(", "lines", ")", ")", ".", "astype", "(", "np", ".", "int32", ")", "# size == 0 is already covered above", "# Note here that we have to be careful not to draw lines two times", "# at their intersection points, e.g. for (p0, p1), (p1, 2) we could", "# end up drawing at p1 twice, leading to higher values if alpha is used.", "color", "=", "np", ".", "float32", "(", "color", ")", "heatmap", "=", "np", ".", "zeros", "(", "image", ".", "shape", "[", "0", ":", "2", "]", ",", "dtype", "=", "np", ".", "float32", ")", "for", "line", "in", "lines", ":", "if", "antialiased", ":", "rr", ",", "cc", ",", "val", "=", "skimage", ".", "draw", ".", "line_aa", "(", "*", "line", ")", "else", ":", "rr", ",", "cc", "=", "skimage", ".", "draw", ".", "line", "(", "*", "line", ")", "val", "=", "1.0", "# mask check here, because line() can generate coordinates", "# outside of the image plane", "rr_mask", "=", "np", ".", "logical_and", "(", "0", "<=", "rr", ",", "rr", "<", "height", ")", "cc_mask", "=", "np", ".", "logical_and", "(", "0", "<=", "cc", ",", "cc", "<", "width", ")", "mask", "=", "np", ".", "logical_and", "(", "rr_mask", ",", "cc_mask", ")", "if", "np", ".", "any", "(", "mask", ")", ":", "rr", "=", "rr", "[", "mask", "]", "cc", "=", "cc", "[", "mask", "]", "val", "=", "val", "[", "mask", "]", "if", "not", "ia", ".", "is_single_number", "(", "val", ")", "else", "val", "heatmap", "[", "rr", ",", "cc", "]", "=", "val", "*", "alpha", "if", "size", ">", "1", ":", "kernel", "=", "np", ".", "ones", "(", "(", "size", ",", "size", ")", ",", "dtype", "=", "np", ".", "uint8", ")", "heatmap", "=", "cv2", ".", "dilate", "(", "heatmap", ",", "kernel", ")", "if", "image_was_empty", ":", "image_blend", "=", "image", "+", "heatmap", "*", "color", "else", ":", "image_color_shape", "=", "image", ".", "shape", "[", "0", ":", "2", "]", "if", "image", ".", "ndim", "==", "3", ":", "image_color_shape", "=", "image_color_shape", "+", "(", "1", ",", ")", "image_color", "=", "np", ".", "tile", "(", "color", ",", "image_color_shape", ")", "image_blend", "=", "blendlib", ".", "blend_alpha", "(", "image_color", ",", "image", ",", "heatmap", ")", "image_blend", "=", "iadt", ".", "restore_dtypes_", "(", "image_blend", ",", "np", ".", "uint8", ")", "return", "image_blend" ]
Draw the line segments of the line string on a given image. Parameters ---------- image : ndarray or tuple of int The image onto which to draw. Expected to be ``uint8`` and of shape ``(H, W, C)`` with ``C`` usually being ``3`` (other values are not tested). If a tuple, expected to be ``(H, W, C)`` and will lead to a new ``uint8`` array of zeros being created. color : int or iterable of int Color to use as RGB, i.e. three values. alpha : float, optional Opacity of the line string. Higher values denote a more visible line string. size : int, optional Thickness of the line segments. antialiased : bool, optional Whether to draw the line with anti-aliasing activated. raise_if_out_of_image : bool, optional Whether to raise an error if the line string is fully outside of the image. If set to False, no error will be raised and only the parts inside the image will be drawn. Returns ------- ndarray `image` with line drawn on it.
[ "Draw", "the", "line", "segments", "of", "the", "line", "string", "on", "a", "given", "image", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/augmentables/lines.py#L763-L887
valid
aleju/imgaug
imgaug/augmentables/lines.py
LineString.draw_points_on_image
def draw_points_on_image(self, image, color=(0, 128, 0), alpha=1.0, size=3, copy=True, raise_if_out_of_image=False): """ Draw the points of the line string on a given image. Parameters ---------- image : ndarray or tuple of int The image onto which to draw. Expected to be ``uint8`` and of shape ``(H, W, C)`` with ``C`` usually being ``3`` (other values are not tested). If a tuple, expected to be ``(H, W, C)`` and will lead to a new ``uint8`` array of zeros being created. color : iterable of int Color to use as RGB, i.e. three values. alpha : float, optional Opacity of the line string points. Higher values denote a more visible points. size : int, optional Size of the points in pixels. copy : bool, optional Whether it is allowed to draw directly in the input array (``False``) or it has to be copied (``True``). The routine may still have to copy, even if ``copy=False`` was used. Always use the return value. raise_if_out_of_image : bool, optional Whether to raise an error if the line string is fully outside of the image. If set to False, no error will be raised and only the parts inside the image will be drawn. Returns ------- ndarray Float array of shape `image_shape` (no channel axis) with drawn line string points. All values are in the interval ``[0.0, 1.0]``. """ from .kps import KeypointsOnImage kpsoi = KeypointsOnImage.from_xy_array(self.coords, shape=image.shape) image = kpsoi.draw_on_image( image, color=color, alpha=alpha, size=size, copy=copy, raise_if_out_of_image=raise_if_out_of_image) return image
python
def draw_points_on_image(self, image, color=(0, 128, 0), alpha=1.0, size=3, copy=True, raise_if_out_of_image=False): """ Draw the points of the line string on a given image. Parameters ---------- image : ndarray or tuple of int The image onto which to draw. Expected to be ``uint8`` and of shape ``(H, W, C)`` with ``C`` usually being ``3`` (other values are not tested). If a tuple, expected to be ``(H, W, C)`` and will lead to a new ``uint8`` array of zeros being created. color : iterable of int Color to use as RGB, i.e. three values. alpha : float, optional Opacity of the line string points. Higher values denote a more visible points. size : int, optional Size of the points in pixels. copy : bool, optional Whether it is allowed to draw directly in the input array (``False``) or it has to be copied (``True``). The routine may still have to copy, even if ``copy=False`` was used. Always use the return value. raise_if_out_of_image : bool, optional Whether to raise an error if the line string is fully outside of the image. If set to False, no error will be raised and only the parts inside the image will be drawn. Returns ------- ndarray Float array of shape `image_shape` (no channel axis) with drawn line string points. All values are in the interval ``[0.0, 1.0]``. """ from .kps import KeypointsOnImage kpsoi = KeypointsOnImage.from_xy_array(self.coords, shape=image.shape) image = kpsoi.draw_on_image( image, color=color, alpha=alpha, size=size, copy=copy, raise_if_out_of_image=raise_if_out_of_image) return image
[ "def", "draw_points_on_image", "(", "self", ",", "image", ",", "color", "=", "(", "0", ",", "128", ",", "0", ")", ",", "alpha", "=", "1.0", ",", "size", "=", "3", ",", "copy", "=", "True", ",", "raise_if_out_of_image", "=", "False", ")", ":", "from", ".", "kps", "import", "KeypointsOnImage", "kpsoi", "=", "KeypointsOnImage", ".", "from_xy_array", "(", "self", ".", "coords", ",", "shape", "=", "image", ".", "shape", ")", "image", "=", "kpsoi", ".", "draw_on_image", "(", "image", ",", "color", "=", "color", ",", "alpha", "=", "alpha", ",", "size", "=", "size", ",", "copy", "=", "copy", ",", "raise_if_out_of_image", "=", "raise_if_out_of_image", ")", "return", "image" ]
Draw the points of the line string on a given image. Parameters ---------- image : ndarray or tuple of int The image onto which to draw. Expected to be ``uint8`` and of shape ``(H, W, C)`` with ``C`` usually being ``3`` (other values are not tested). If a tuple, expected to be ``(H, W, C)`` and will lead to a new ``uint8`` array of zeros being created. color : iterable of int Color to use as RGB, i.e. three values. alpha : float, optional Opacity of the line string points. Higher values denote a more visible points. size : int, optional Size of the points in pixels. copy : bool, optional Whether it is allowed to draw directly in the input array (``False``) or it has to be copied (``True``). The routine may still have to copy, even if ``copy=False`` was used. Always use the return value. raise_if_out_of_image : bool, optional Whether to raise an error if the line string is fully outside of the image. If set to False, no error will be raised and only the parts inside the image will be drawn. Returns ------- ndarray Float array of shape `image_shape` (no channel axis) with drawn line string points. All values are in the interval ``[0.0, 1.0]``.
[ "Draw", "the", "points", "of", "the", "line", "string", "on", "a", "given", "image", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/augmentables/lines.py#L889-L939
valid
aleju/imgaug
imgaug/augmentables/lines.py
LineString.draw_on_image
def draw_on_image(self, image, color=(0, 255, 0), color_lines=None, color_points=None, alpha=1.0, alpha_lines=None, alpha_points=None, size=1, size_lines=None, size_points=None, antialiased=True, raise_if_out_of_image=False): """ Draw the line string on an image. Parameters ---------- image : ndarray The `(H,W,C)` `uint8` image onto which to draw the line string. color : iterable of int, optional Color to use as RGB, i.e. three values. The color of the line and points are derived from this value, unless they are set. color_lines : None or iterable of int Color to use for the line segments as RGB, i.e. three values. If ``None``, this value is derived from `color`. color_points : None or iterable of int Color to use for the points as RGB, i.e. three values. If ``None``, this value is derived from ``0.5 * color``. alpha : float, optional Opacity of the line string. Higher values denote more visible points. The alphas of the line and points are derived from this value, unless they are set. alpha_lines : None or float, optional Opacity of the line string. Higher values denote more visible line string. If ``None``, this value is derived from `alpha`. alpha_points : None or float, optional Opacity of the line string points. Higher values denote more visible points. If ``None``, this value is derived from `alpha`. size : int, optional Size of the line string. The sizes of the line and points are derived from this value, unless they are set. size_lines : None or int, optional Thickness of the line segments. If ``None``, this value is derived from `size`. size_points : None or int, optional Size of the points in pixels. If ``None``, this value is derived from ``3 * size``. antialiased : bool, optional Whether to draw the line with anti-aliasing activated. This does currently not affect the point drawing. raise_if_out_of_image : bool, optional Whether to raise an error if the line string is fully outside of the image. If set to False, no error will be raised and only the parts inside the image will be drawn. Returns ------- ndarray Image with line string drawn on it. """ assert color is not None assert alpha is not None assert size is not None color_lines = color_lines if color_lines is not None \ else np.float32(color) color_points = color_points if color_points is not None \ else np.float32(color) * 0.5 alpha_lines = alpha_lines if alpha_lines is not None \ else np.float32(alpha) alpha_points = alpha_points if alpha_points is not None \ else np.float32(alpha) size_lines = size_lines if size_lines is not None else size size_points = size_points if size_points is not None else size * 3 image = self.draw_lines_on_image( image, color=np.array(color_lines).astype(np.uint8), alpha=alpha_lines, size=size_lines, antialiased=antialiased, raise_if_out_of_image=raise_if_out_of_image) image = self.draw_points_on_image( image, color=np.array(color_points).astype(np.uint8), alpha=alpha_points, size=size_points, copy=False, raise_if_out_of_image=raise_if_out_of_image) return image
python
def draw_on_image(self, image, color=(0, 255, 0), color_lines=None, color_points=None, alpha=1.0, alpha_lines=None, alpha_points=None, size=1, size_lines=None, size_points=None, antialiased=True, raise_if_out_of_image=False): """ Draw the line string on an image. Parameters ---------- image : ndarray The `(H,W,C)` `uint8` image onto which to draw the line string. color : iterable of int, optional Color to use as RGB, i.e. three values. The color of the line and points are derived from this value, unless they are set. color_lines : None or iterable of int Color to use for the line segments as RGB, i.e. three values. If ``None``, this value is derived from `color`. color_points : None or iterable of int Color to use for the points as RGB, i.e. three values. If ``None``, this value is derived from ``0.5 * color``. alpha : float, optional Opacity of the line string. Higher values denote more visible points. The alphas of the line and points are derived from this value, unless they are set. alpha_lines : None or float, optional Opacity of the line string. Higher values denote more visible line string. If ``None``, this value is derived from `alpha`. alpha_points : None or float, optional Opacity of the line string points. Higher values denote more visible points. If ``None``, this value is derived from `alpha`. size : int, optional Size of the line string. The sizes of the line and points are derived from this value, unless they are set. size_lines : None or int, optional Thickness of the line segments. If ``None``, this value is derived from `size`. size_points : None or int, optional Size of the points in pixels. If ``None``, this value is derived from ``3 * size``. antialiased : bool, optional Whether to draw the line with anti-aliasing activated. This does currently not affect the point drawing. raise_if_out_of_image : bool, optional Whether to raise an error if the line string is fully outside of the image. If set to False, no error will be raised and only the parts inside the image will be drawn. Returns ------- ndarray Image with line string drawn on it. """ assert color is not None assert alpha is not None assert size is not None color_lines = color_lines if color_lines is not None \ else np.float32(color) color_points = color_points if color_points is not None \ else np.float32(color) * 0.5 alpha_lines = alpha_lines if alpha_lines is not None \ else np.float32(alpha) alpha_points = alpha_points if alpha_points is not None \ else np.float32(alpha) size_lines = size_lines if size_lines is not None else size size_points = size_points if size_points is not None else size * 3 image = self.draw_lines_on_image( image, color=np.array(color_lines).astype(np.uint8), alpha=alpha_lines, size=size_lines, antialiased=antialiased, raise_if_out_of_image=raise_if_out_of_image) image = self.draw_points_on_image( image, color=np.array(color_points).astype(np.uint8), alpha=alpha_points, size=size_points, copy=False, raise_if_out_of_image=raise_if_out_of_image) return image
[ "def", "draw_on_image", "(", "self", ",", "image", ",", "color", "=", "(", "0", ",", "255", ",", "0", ")", ",", "color_lines", "=", "None", ",", "color_points", "=", "None", ",", "alpha", "=", "1.0", ",", "alpha_lines", "=", "None", ",", "alpha_points", "=", "None", ",", "size", "=", "1", ",", "size_lines", "=", "None", ",", "size_points", "=", "None", ",", "antialiased", "=", "True", ",", "raise_if_out_of_image", "=", "False", ")", ":", "assert", "color", "is", "not", "None", "assert", "alpha", "is", "not", "None", "assert", "size", "is", "not", "None", "color_lines", "=", "color_lines", "if", "color_lines", "is", "not", "None", "else", "np", ".", "float32", "(", "color", ")", "color_points", "=", "color_points", "if", "color_points", "is", "not", "None", "else", "np", ".", "float32", "(", "color", ")", "*", "0.5", "alpha_lines", "=", "alpha_lines", "if", "alpha_lines", "is", "not", "None", "else", "np", ".", "float32", "(", "alpha", ")", "alpha_points", "=", "alpha_points", "if", "alpha_points", "is", "not", "None", "else", "np", ".", "float32", "(", "alpha", ")", "size_lines", "=", "size_lines", "if", "size_lines", "is", "not", "None", "else", "size", "size_points", "=", "size_points", "if", "size_points", "is", "not", "None", "else", "size", "*", "3", "image", "=", "self", ".", "draw_lines_on_image", "(", "image", ",", "color", "=", "np", ".", "array", "(", "color_lines", ")", ".", "astype", "(", "np", ".", "uint8", ")", ",", "alpha", "=", "alpha_lines", ",", "size", "=", "size_lines", ",", "antialiased", "=", "antialiased", ",", "raise_if_out_of_image", "=", "raise_if_out_of_image", ")", "image", "=", "self", ".", "draw_points_on_image", "(", "image", ",", "color", "=", "np", ".", "array", "(", "color_points", ")", ".", "astype", "(", "np", ".", "uint8", ")", ",", "alpha", "=", "alpha_points", ",", "size", "=", "size_points", ",", "copy", "=", "False", ",", "raise_if_out_of_image", "=", "raise_if_out_of_image", ")", "return", "image" ]
Draw the line string on an image. Parameters ---------- image : ndarray The `(H,W,C)` `uint8` image onto which to draw the line string. color : iterable of int, optional Color to use as RGB, i.e. three values. The color of the line and points are derived from this value, unless they are set. color_lines : None or iterable of int Color to use for the line segments as RGB, i.e. three values. If ``None``, this value is derived from `color`. color_points : None or iterable of int Color to use for the points as RGB, i.e. three values. If ``None``, this value is derived from ``0.5 * color``. alpha : float, optional Opacity of the line string. Higher values denote more visible points. The alphas of the line and points are derived from this value, unless they are set. alpha_lines : None or float, optional Opacity of the line string. Higher values denote more visible line string. If ``None``, this value is derived from `alpha`. alpha_points : None or float, optional Opacity of the line string points. Higher values denote more visible points. If ``None``, this value is derived from `alpha`. size : int, optional Size of the line string. The sizes of the line and points are derived from this value, unless they are set. size_lines : None or int, optional Thickness of the line segments. If ``None``, this value is derived from `size`. size_points : None or int, optional Size of the points in pixels. If ``None``, this value is derived from ``3 * size``. antialiased : bool, optional Whether to draw the line with anti-aliasing activated. This does currently not affect the point drawing. raise_if_out_of_image : bool, optional Whether to raise an error if the line string is fully outside of the image. If set to False, no error will be raised and only the parts inside the image will be drawn. Returns ------- ndarray Image with line string drawn on it.
[ "Draw", "the", "line", "string", "on", "an", "image", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/augmentables/lines.py#L941-L1041
valid
aleju/imgaug
imgaug/augmentables/lines.py
LineString.extract_from_image
def extract_from_image(self, image, size=1, pad=True, pad_max=None, antialiased=True, prevent_zero_size=True): """ Extract the image pixels covered by the line string. It will only extract pixels overlapped by the line string. This function will by default zero-pad the image if the line string is partially/fully outside of the image. This is for consistency with the same implementations for bounding boxes and polygons. Parameters ---------- image : ndarray The image of shape `(H,W,[C])` from which to extract the pixels within the line string. size : int, optional Thickness of the line. pad : bool, optional Whether to zero-pad the image if the object is partially/fully outside of it. pad_max : None or int, optional The maximum number of pixels that may be zero-paded on any side, i.e. if this has value ``N`` the total maximum of added pixels is ``4*N``. This option exists to prevent extremely large images as a result of single points being moved very far away during augmentation. antialiased : bool, optional Whether to apply anti-aliasing to the line string. prevent_zero_size : bool, optional Whether to prevent height or width of the extracted image from becoming zero. If this is set to True and height or width of the line string is below 1, the height/width will be increased to 1. This can be useful to prevent problems, e.g. with image saving or plotting. If it is set to False, images will be returned as ``(H', W')`` or ``(H', W', 3)`` with ``H`` or ``W`` potentially being 0. Returns ------- image : (H',W') ndarray or (H',W',C) ndarray Pixels overlapping with the line string. Zero-padded if the line string is partially/fully outside of the image and ``pad=True``. If `prevent_zero_size` is activated, it is guarantueed that ``H'>0`` and ``W'>0``, otherwise only ``H'>=0`` and ``W'>=0``. """ from .bbs import BoundingBox assert image.ndim in [2, 3], ( "Expected image of shape (H,W,[C]), " "got shape %s." % (image.shape,)) if len(self.coords) == 0 or size <= 0: if prevent_zero_size: return np.zeros((1, 1) + image.shape[2:], dtype=image.dtype) return np.zeros((0, 0) + image.shape[2:], dtype=image.dtype) xx = self.xx_int yy = self.yy_int # this would probably work if drawing was subpixel-accurate # x1 = np.min(self.coords[:, 0]) - (size / 2) # y1 = np.min(self.coords[:, 1]) - (size / 2) # x2 = np.max(self.coords[:, 0]) + (size / 2) # y2 = np.max(self.coords[:, 1]) + (size / 2) # this works currently with non-subpixel-accurate drawing sizeh = (size - 1) / 2 x1 = np.min(xx) - sizeh y1 = np.min(yy) - sizeh x2 = np.max(xx) + 1 + sizeh y2 = np.max(yy) + 1 + sizeh bb = BoundingBox(x1=x1, y1=y1, x2=x2, y2=y2) if len(self.coords) == 1: return bb.extract_from_image(image, pad=pad, pad_max=pad_max, prevent_zero_size=prevent_zero_size) heatmap = self.draw_lines_heatmap_array( image.shape[0:2], alpha=1.0, size=size, antialiased=antialiased) if image.ndim == 3: heatmap = np.atleast_3d(heatmap) image_masked = image.astype(np.float32) * heatmap extract = bb.extract_from_image(image_masked, pad=pad, pad_max=pad_max, prevent_zero_size=prevent_zero_size) return np.clip(np.round(extract), 0, 255).astype(np.uint8)
python
def extract_from_image(self, image, size=1, pad=True, pad_max=None, antialiased=True, prevent_zero_size=True): """ Extract the image pixels covered by the line string. It will only extract pixels overlapped by the line string. This function will by default zero-pad the image if the line string is partially/fully outside of the image. This is for consistency with the same implementations for bounding boxes and polygons. Parameters ---------- image : ndarray The image of shape `(H,W,[C])` from which to extract the pixels within the line string. size : int, optional Thickness of the line. pad : bool, optional Whether to zero-pad the image if the object is partially/fully outside of it. pad_max : None or int, optional The maximum number of pixels that may be zero-paded on any side, i.e. if this has value ``N`` the total maximum of added pixels is ``4*N``. This option exists to prevent extremely large images as a result of single points being moved very far away during augmentation. antialiased : bool, optional Whether to apply anti-aliasing to the line string. prevent_zero_size : bool, optional Whether to prevent height or width of the extracted image from becoming zero. If this is set to True and height or width of the line string is below 1, the height/width will be increased to 1. This can be useful to prevent problems, e.g. with image saving or plotting. If it is set to False, images will be returned as ``(H', W')`` or ``(H', W', 3)`` with ``H`` or ``W`` potentially being 0. Returns ------- image : (H',W') ndarray or (H',W',C) ndarray Pixels overlapping with the line string. Zero-padded if the line string is partially/fully outside of the image and ``pad=True``. If `prevent_zero_size` is activated, it is guarantueed that ``H'>0`` and ``W'>0``, otherwise only ``H'>=0`` and ``W'>=0``. """ from .bbs import BoundingBox assert image.ndim in [2, 3], ( "Expected image of shape (H,W,[C]), " "got shape %s." % (image.shape,)) if len(self.coords) == 0 or size <= 0: if prevent_zero_size: return np.zeros((1, 1) + image.shape[2:], dtype=image.dtype) return np.zeros((0, 0) + image.shape[2:], dtype=image.dtype) xx = self.xx_int yy = self.yy_int # this would probably work if drawing was subpixel-accurate # x1 = np.min(self.coords[:, 0]) - (size / 2) # y1 = np.min(self.coords[:, 1]) - (size / 2) # x2 = np.max(self.coords[:, 0]) + (size / 2) # y2 = np.max(self.coords[:, 1]) + (size / 2) # this works currently with non-subpixel-accurate drawing sizeh = (size - 1) / 2 x1 = np.min(xx) - sizeh y1 = np.min(yy) - sizeh x2 = np.max(xx) + 1 + sizeh y2 = np.max(yy) + 1 + sizeh bb = BoundingBox(x1=x1, y1=y1, x2=x2, y2=y2) if len(self.coords) == 1: return bb.extract_from_image(image, pad=pad, pad_max=pad_max, prevent_zero_size=prevent_zero_size) heatmap = self.draw_lines_heatmap_array( image.shape[0:2], alpha=1.0, size=size, antialiased=antialiased) if image.ndim == 3: heatmap = np.atleast_3d(heatmap) image_masked = image.astype(np.float32) * heatmap extract = bb.extract_from_image(image_masked, pad=pad, pad_max=pad_max, prevent_zero_size=prevent_zero_size) return np.clip(np.round(extract), 0, 255).astype(np.uint8)
[ "def", "extract_from_image", "(", "self", ",", "image", ",", "size", "=", "1", ",", "pad", "=", "True", ",", "pad_max", "=", "None", ",", "antialiased", "=", "True", ",", "prevent_zero_size", "=", "True", ")", ":", "from", ".", "bbs", "import", "BoundingBox", "assert", "image", ".", "ndim", "in", "[", "2", ",", "3", "]", ",", "(", "\"Expected image of shape (H,W,[C]), \"", "\"got shape %s.\"", "%", "(", "image", ".", "shape", ",", ")", ")", "if", "len", "(", "self", ".", "coords", ")", "==", "0", "or", "size", "<=", "0", ":", "if", "prevent_zero_size", ":", "return", "np", ".", "zeros", "(", "(", "1", ",", "1", ")", "+", "image", ".", "shape", "[", "2", ":", "]", ",", "dtype", "=", "image", ".", "dtype", ")", "return", "np", ".", "zeros", "(", "(", "0", ",", "0", ")", "+", "image", ".", "shape", "[", "2", ":", "]", ",", "dtype", "=", "image", ".", "dtype", ")", "xx", "=", "self", ".", "xx_int", "yy", "=", "self", ".", "yy_int", "# this would probably work if drawing was subpixel-accurate", "# x1 = np.min(self.coords[:, 0]) - (size / 2)", "# y1 = np.min(self.coords[:, 1]) - (size / 2)", "# x2 = np.max(self.coords[:, 0]) + (size / 2)", "# y2 = np.max(self.coords[:, 1]) + (size / 2)", "# this works currently with non-subpixel-accurate drawing", "sizeh", "=", "(", "size", "-", "1", ")", "/", "2", "x1", "=", "np", ".", "min", "(", "xx", ")", "-", "sizeh", "y1", "=", "np", ".", "min", "(", "yy", ")", "-", "sizeh", "x2", "=", "np", ".", "max", "(", "xx", ")", "+", "1", "+", "sizeh", "y2", "=", "np", ".", "max", "(", "yy", ")", "+", "1", "+", "sizeh", "bb", "=", "BoundingBox", "(", "x1", "=", "x1", ",", "y1", "=", "y1", ",", "x2", "=", "x2", ",", "y2", "=", "y2", ")", "if", "len", "(", "self", ".", "coords", ")", "==", "1", ":", "return", "bb", ".", "extract_from_image", "(", "image", ",", "pad", "=", "pad", ",", "pad_max", "=", "pad_max", ",", "prevent_zero_size", "=", "prevent_zero_size", ")", "heatmap", "=", "self", ".", "draw_lines_heatmap_array", "(", "image", ".", "shape", "[", "0", ":", "2", "]", ",", "alpha", "=", "1.0", ",", "size", "=", "size", ",", "antialiased", "=", "antialiased", ")", "if", "image", ".", "ndim", "==", "3", ":", "heatmap", "=", "np", ".", "atleast_3d", "(", "heatmap", ")", "image_masked", "=", "image", ".", "astype", "(", "np", ".", "float32", ")", "*", "heatmap", "extract", "=", "bb", ".", "extract_from_image", "(", "image_masked", ",", "pad", "=", "pad", ",", "pad_max", "=", "pad_max", ",", "prevent_zero_size", "=", "prevent_zero_size", ")", "return", "np", ".", "clip", "(", "np", ".", "round", "(", "extract", ")", ",", "0", ",", "255", ")", ".", "astype", "(", "np", ".", "uint8", ")" ]
Extract the image pixels covered by the line string. It will only extract pixels overlapped by the line string. This function will by default zero-pad the image if the line string is partially/fully outside of the image. This is for consistency with the same implementations for bounding boxes and polygons. Parameters ---------- image : ndarray The image of shape `(H,W,[C])` from which to extract the pixels within the line string. size : int, optional Thickness of the line. pad : bool, optional Whether to zero-pad the image if the object is partially/fully outside of it. pad_max : None or int, optional The maximum number of pixels that may be zero-paded on any side, i.e. if this has value ``N`` the total maximum of added pixels is ``4*N``. This option exists to prevent extremely large images as a result of single points being moved very far away during augmentation. antialiased : bool, optional Whether to apply anti-aliasing to the line string. prevent_zero_size : bool, optional Whether to prevent height or width of the extracted image from becoming zero. If this is set to True and height or width of the line string is below 1, the height/width will be increased to 1. This can be useful to prevent problems, e.g. with image saving or plotting. If it is set to False, images will be returned as ``(H', W')`` or ``(H', W', 3)`` with ``H`` or ``W`` potentially being 0. Returns ------- image : (H',W') ndarray or (H',W',C) ndarray Pixels overlapping with the line string. Zero-padded if the line string is partially/fully outside of the image and ``pad=True``. If `prevent_zero_size` is activated, it is guarantueed that ``H'>0`` and ``W'>0``, otherwise only ``H'>=0`` and ``W'>=0``.
[ "Extract", "the", "image", "pixels", "covered", "by", "the", "line", "string", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/augmentables/lines.py#L1043-L1135
valid
aleju/imgaug
imgaug/augmentables/lines.py
LineString.concatenate
def concatenate(self, other): """ Concatenate this line string with another one. This will add a line segment between the end point of this line string and the start point of `other`. Parameters ---------- other : imgaug.augmentables.lines.LineString or ndarray \ or iterable of tuple of number The points to add to this line string. Returns ------- imgaug.augmentables.lines.LineString New line string with concatenated points. The `label` of this line string will be kept. """ if not isinstance(other, LineString): other = LineString(other) return self.deepcopy( coords=np.concatenate([self.coords, other.coords], axis=0))
python
def concatenate(self, other): """ Concatenate this line string with another one. This will add a line segment between the end point of this line string and the start point of `other`. Parameters ---------- other : imgaug.augmentables.lines.LineString or ndarray \ or iterable of tuple of number The points to add to this line string. Returns ------- imgaug.augmentables.lines.LineString New line string with concatenated points. The `label` of this line string will be kept. """ if not isinstance(other, LineString): other = LineString(other) return self.deepcopy( coords=np.concatenate([self.coords, other.coords], axis=0))
[ "def", "concatenate", "(", "self", ",", "other", ")", ":", "if", "not", "isinstance", "(", "other", ",", "LineString", ")", ":", "other", "=", "LineString", "(", "other", ")", "return", "self", ".", "deepcopy", "(", "coords", "=", "np", ".", "concatenate", "(", "[", "self", ".", "coords", ",", "other", ".", "coords", "]", ",", "axis", "=", "0", ")", ")" ]
Concatenate this line string with another one. This will add a line segment between the end point of this line string and the start point of `other`. Parameters ---------- other : imgaug.augmentables.lines.LineString or ndarray \ or iterable of tuple of number The points to add to this line string. Returns ------- imgaug.augmentables.lines.LineString New line string with concatenated points. The `label` of this line string will be kept.
[ "Concatenate", "this", "line", "string", "with", "another", "one", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/augmentables/lines.py#L1137-L1160
valid
aleju/imgaug
imgaug/augmentables/lines.py
LineString.subdivide
def subdivide(self, points_per_edge): """ Adds ``N`` interpolated points with uniform spacing to each edge. For each edge between points ``A`` and ``B`` this adds points at ``A + (i/(1+N)) * (B - A)``, where ``i`` is the index of the added point and ``N`` is the number of points to add per edge. Calling this method two times will split each edge at its center and then again split each newly created edge at their center. It is equivalent to calling `subdivide(3)`. Parameters ---------- points_per_edge : int Number of points to interpolate on each edge. Returns ------- LineString Line string with subdivided edges. """ if len(self.coords) <= 1 or points_per_edge < 1: return self.deepcopy() coords = interpolate_points(self.coords, nb_steps=points_per_edge, closed=False) return self.deepcopy(coords=coords)
python
def subdivide(self, points_per_edge): """ Adds ``N`` interpolated points with uniform spacing to each edge. For each edge between points ``A`` and ``B`` this adds points at ``A + (i/(1+N)) * (B - A)``, where ``i`` is the index of the added point and ``N`` is the number of points to add per edge. Calling this method two times will split each edge at its center and then again split each newly created edge at their center. It is equivalent to calling `subdivide(3)`. Parameters ---------- points_per_edge : int Number of points to interpolate on each edge. Returns ------- LineString Line string with subdivided edges. """ if len(self.coords) <= 1 or points_per_edge < 1: return self.deepcopy() coords = interpolate_points(self.coords, nb_steps=points_per_edge, closed=False) return self.deepcopy(coords=coords)
[ "def", "subdivide", "(", "self", ",", "points_per_edge", ")", ":", "if", "len", "(", "self", ".", "coords", ")", "<=", "1", "or", "points_per_edge", "<", "1", ":", "return", "self", ".", "deepcopy", "(", ")", "coords", "=", "interpolate_points", "(", "self", ".", "coords", ",", "nb_steps", "=", "points_per_edge", ",", "closed", "=", "False", ")", "return", "self", ".", "deepcopy", "(", "coords", "=", "coords", ")" ]
Adds ``N`` interpolated points with uniform spacing to each edge. For each edge between points ``A`` and ``B`` this adds points at ``A + (i/(1+N)) * (B - A)``, where ``i`` is the index of the added point and ``N`` is the number of points to add per edge. Calling this method two times will split each edge at its center and then again split each newly created edge at their center. It is equivalent to calling `subdivide(3)`. Parameters ---------- points_per_edge : int Number of points to interpolate on each edge. Returns ------- LineString Line string with subdivided edges.
[ "Adds", "N", "interpolated", "points", "with", "uniform", "spacing", "to", "each", "edge", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/augmentables/lines.py#L1163-L1190
valid
aleju/imgaug
imgaug/augmentables/lines.py
LineString.to_keypoints
def to_keypoints(self): """ Convert the line string points to keypoints. Returns ------- list of imgaug.augmentables.kps.Keypoint Points of the line string as keypoints. """ # TODO get rid of this deferred import from imgaug.augmentables.kps import Keypoint return [Keypoint(x=x, y=y) for (x, y) in self.coords]
python
def to_keypoints(self): """ Convert the line string points to keypoints. Returns ------- list of imgaug.augmentables.kps.Keypoint Points of the line string as keypoints. """ # TODO get rid of this deferred import from imgaug.augmentables.kps import Keypoint return [Keypoint(x=x, y=y) for (x, y) in self.coords]
[ "def", "to_keypoints", "(", "self", ")", ":", "# TODO get rid of this deferred import", "from", "imgaug", ".", "augmentables", ".", "kps", "import", "Keypoint", "return", "[", "Keypoint", "(", "x", "=", "x", ",", "y", "=", "y", ")", "for", "(", "x", ",", "y", ")", "in", "self", ".", "coords", "]" ]
Convert the line string points to keypoints. Returns ------- list of imgaug.augmentables.kps.Keypoint Points of the line string as keypoints.
[ "Convert", "the", "line", "string", "points", "to", "keypoints", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/augmentables/lines.py#L1192-L1204
valid
aleju/imgaug
imgaug/augmentables/lines.py
LineString.to_bounding_box
def to_bounding_box(self): """ Generate a bounding box encapsulating the line string. Returns ------- None or imgaug.augmentables.bbs.BoundingBox Bounding box encapsulating the line string. ``None`` if the line string contained no points. """ from .bbs import BoundingBox # we don't have to mind the case of len(.) == 1 here, because # zero-sized BBs are considered valid if len(self.coords) == 0: return None return BoundingBox(x1=np.min(self.xx), y1=np.min(self.yy), x2=np.max(self.xx), y2=np.max(self.yy), label=self.label)
python
def to_bounding_box(self): """ Generate a bounding box encapsulating the line string. Returns ------- None or imgaug.augmentables.bbs.BoundingBox Bounding box encapsulating the line string. ``None`` if the line string contained no points. """ from .bbs import BoundingBox # we don't have to mind the case of len(.) == 1 here, because # zero-sized BBs are considered valid if len(self.coords) == 0: return None return BoundingBox(x1=np.min(self.xx), y1=np.min(self.yy), x2=np.max(self.xx), y2=np.max(self.yy), label=self.label)
[ "def", "to_bounding_box", "(", "self", ")", ":", "from", ".", "bbs", "import", "BoundingBox", "# we don't have to mind the case of len(.) == 1 here, because", "# zero-sized BBs are considered valid", "if", "len", "(", "self", ".", "coords", ")", "==", "0", ":", "return", "None", "return", "BoundingBox", "(", "x1", "=", "np", ".", "min", "(", "self", ".", "xx", ")", ",", "y1", "=", "np", ".", "min", "(", "self", ".", "yy", ")", ",", "x2", "=", "np", ".", "max", "(", "self", ".", "xx", ")", ",", "y2", "=", "np", ".", "max", "(", "self", ".", "yy", ")", ",", "label", "=", "self", ".", "label", ")" ]
Generate a bounding box encapsulating the line string. Returns ------- None or imgaug.augmentables.bbs.BoundingBox Bounding box encapsulating the line string. ``None`` if the line string contained no points.
[ "Generate", "a", "bounding", "box", "encapsulating", "the", "line", "string", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/augmentables/lines.py#L1206-L1224
valid
aleju/imgaug
imgaug/augmentables/lines.py
LineString.to_polygon
def to_polygon(self): """ Generate a polygon from the line string points. Returns ------- imgaug.augmentables.polys.Polygon Polygon with the same corner points as the line string. Note that the polygon might be invalid, e.g. contain less than 3 points or have self-intersections. """ from .polys import Polygon return Polygon(self.coords, label=self.label)
python
def to_polygon(self): """ Generate a polygon from the line string points. Returns ------- imgaug.augmentables.polys.Polygon Polygon with the same corner points as the line string. Note that the polygon might be invalid, e.g. contain less than 3 points or have self-intersections. """ from .polys import Polygon return Polygon(self.coords, label=self.label)
[ "def", "to_polygon", "(", "self", ")", ":", "from", ".", "polys", "import", "Polygon", "return", "Polygon", "(", "self", ".", "coords", ",", "label", "=", "self", ".", "label", ")" ]
Generate a polygon from the line string points. Returns ------- imgaug.augmentables.polys.Polygon Polygon with the same corner points as the line string. Note that the polygon might be invalid, e.g. contain less than 3 points or have self-intersections.
[ "Generate", "a", "polygon", "from", "the", "line", "string", "points", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/augmentables/lines.py#L1226-L1239
valid
aleju/imgaug
imgaug/augmentables/lines.py
LineString.to_heatmap
def to_heatmap(self, image_shape, size_lines=1, size_points=0, antialiased=True, raise_if_out_of_image=False): """ Generate a heatmap object from the line string. This is similar to :func:`imgaug.augmentables.lines.LineString.draw_lines_heatmap_array` executed with ``alpha=1.0``. The result is wrapped in a ``HeatmapsOnImage`` object instead of just an array. No points are drawn. Parameters ---------- image_shape : tuple of int The shape of the image onto which to draw the line mask. size_lines : int, optional Thickness of the line. size_points : int, optional Size of the points in pixels. antialiased : bool, optional Whether to draw the line with anti-aliasing activated. raise_if_out_of_image : bool, optional Whether to raise an error if the line string is fully outside of the image. If set to False, no error will be raised and only the parts inside the image will be drawn. Returns ------- imgaug.augmentables.heatmaps.HeatmapOnImage Heatmap object containing drawn line string. """ from .heatmaps import HeatmapsOnImage return HeatmapsOnImage( self.draw_heatmap_array( image_shape, size_lines=size_lines, size_points=size_points, antialiased=antialiased, raise_if_out_of_image=raise_if_out_of_image), shape=image_shape )
python
def to_heatmap(self, image_shape, size_lines=1, size_points=0, antialiased=True, raise_if_out_of_image=False): """ Generate a heatmap object from the line string. This is similar to :func:`imgaug.augmentables.lines.LineString.draw_lines_heatmap_array` executed with ``alpha=1.0``. The result is wrapped in a ``HeatmapsOnImage`` object instead of just an array. No points are drawn. Parameters ---------- image_shape : tuple of int The shape of the image onto which to draw the line mask. size_lines : int, optional Thickness of the line. size_points : int, optional Size of the points in pixels. antialiased : bool, optional Whether to draw the line with anti-aliasing activated. raise_if_out_of_image : bool, optional Whether to raise an error if the line string is fully outside of the image. If set to False, no error will be raised and only the parts inside the image will be drawn. Returns ------- imgaug.augmentables.heatmaps.HeatmapOnImage Heatmap object containing drawn line string. """ from .heatmaps import HeatmapsOnImage return HeatmapsOnImage( self.draw_heatmap_array( image_shape, size_lines=size_lines, size_points=size_points, antialiased=antialiased, raise_if_out_of_image=raise_if_out_of_image), shape=image_shape )
[ "def", "to_heatmap", "(", "self", ",", "image_shape", ",", "size_lines", "=", "1", ",", "size_points", "=", "0", ",", "antialiased", "=", "True", ",", "raise_if_out_of_image", "=", "False", ")", ":", "from", ".", "heatmaps", "import", "HeatmapsOnImage", "return", "HeatmapsOnImage", "(", "self", ".", "draw_heatmap_array", "(", "image_shape", ",", "size_lines", "=", "size_lines", ",", "size_points", "=", "size_points", ",", "antialiased", "=", "antialiased", ",", "raise_if_out_of_image", "=", "raise_if_out_of_image", ")", ",", "shape", "=", "image_shape", ")" ]
Generate a heatmap object from the line string. This is similar to :func:`imgaug.augmentables.lines.LineString.draw_lines_heatmap_array` executed with ``alpha=1.0``. The result is wrapped in a ``HeatmapsOnImage`` object instead of just an array. No points are drawn. Parameters ---------- image_shape : tuple of int The shape of the image onto which to draw the line mask. size_lines : int, optional Thickness of the line. size_points : int, optional Size of the points in pixels. antialiased : bool, optional Whether to draw the line with anti-aliasing activated. raise_if_out_of_image : bool, optional Whether to raise an error if the line string is fully outside of the image. If set to False, no error will be raised and only the parts inside the image will be drawn. Returns ------- imgaug.augmentables.heatmaps.HeatmapOnImage Heatmap object containing drawn line string.
[ "Generate", "a", "heatmap", "object", "from", "the", "line", "string", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/augmentables/lines.py#L1241-L1284
valid
aleju/imgaug
imgaug/augmentables/lines.py
LineString.to_segmentation_map
def to_segmentation_map(self, image_shape, size_lines=1, size_points=0, raise_if_out_of_image=False): """ Generate a segmentation map object from the line string. This is similar to :func:`imgaug.augmentables.lines.LineString.draw_mask`. The result is wrapped in a ``SegmentationMapOnImage`` object instead of just an array. Parameters ---------- image_shape : tuple of int The shape of the image onto which to draw the line mask. size_lines : int, optional Thickness of the line. size_points : int, optional Size of the points in pixels. raise_if_out_of_image : bool, optional Whether to raise an error if the line string is fully outside of the image. If set to False, no error will be raised and only the parts inside the image will be drawn. Returns ------- imgaug.augmentables.segmaps.SegmentationMapOnImage Segmentation map object containing drawn line string. """ from .segmaps import SegmentationMapOnImage return SegmentationMapOnImage( self.draw_mask( image_shape, size_lines=size_lines, size_points=size_points, raise_if_out_of_image=raise_if_out_of_image), shape=image_shape )
python
def to_segmentation_map(self, image_shape, size_lines=1, size_points=0, raise_if_out_of_image=False): """ Generate a segmentation map object from the line string. This is similar to :func:`imgaug.augmentables.lines.LineString.draw_mask`. The result is wrapped in a ``SegmentationMapOnImage`` object instead of just an array. Parameters ---------- image_shape : tuple of int The shape of the image onto which to draw the line mask. size_lines : int, optional Thickness of the line. size_points : int, optional Size of the points in pixels. raise_if_out_of_image : bool, optional Whether to raise an error if the line string is fully outside of the image. If set to False, no error will be raised and only the parts inside the image will be drawn. Returns ------- imgaug.augmentables.segmaps.SegmentationMapOnImage Segmentation map object containing drawn line string. """ from .segmaps import SegmentationMapOnImage return SegmentationMapOnImage( self.draw_mask( image_shape, size_lines=size_lines, size_points=size_points, raise_if_out_of_image=raise_if_out_of_image), shape=image_shape )
[ "def", "to_segmentation_map", "(", "self", ",", "image_shape", ",", "size_lines", "=", "1", ",", "size_points", "=", "0", ",", "raise_if_out_of_image", "=", "False", ")", ":", "from", ".", "segmaps", "import", "SegmentationMapOnImage", "return", "SegmentationMapOnImage", "(", "self", ".", "draw_mask", "(", "image_shape", ",", "size_lines", "=", "size_lines", ",", "size_points", "=", "size_points", ",", "raise_if_out_of_image", "=", "raise_if_out_of_image", ")", ",", "shape", "=", "image_shape", ")" ]
Generate a segmentation map object from the line string. This is similar to :func:`imgaug.augmentables.lines.LineString.draw_mask`. The result is wrapped in a ``SegmentationMapOnImage`` object instead of just an array. Parameters ---------- image_shape : tuple of int The shape of the image onto which to draw the line mask. size_lines : int, optional Thickness of the line. size_points : int, optional Size of the points in pixels. raise_if_out_of_image : bool, optional Whether to raise an error if the line string is fully outside of the image. If set to False, no error will be raised and only the parts inside the image will be drawn. Returns ------- imgaug.augmentables.segmaps.SegmentationMapOnImage Segmentation map object containing drawn line string.
[ "Generate", "a", "segmentation", "map", "object", "from", "the", "line", "string", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/augmentables/lines.py#L1286-L1324
valid
aleju/imgaug
imgaug/augmentables/lines.py
LineString.coords_almost_equals
def coords_almost_equals(self, other, max_distance=1e-6, points_per_edge=8): """ Compare this and another LineString's coordinates. This is an approximate method based on pointwise distances and can in rare corner cases produce wrong outputs. Parameters ---------- other : imgaug.augmentables.lines.LineString \ or tuple of number \ or ndarray \ or list of ndarray \ or list of tuple of number The other line string or its coordinates. max_distance : float Max distance of any point from the other line string before the two line strings are evaluated to be unequal. points_per_edge : int, optional How many points to interpolate on each edge. Returns ------- bool Whether the two LineString's coordinates are almost identical, i.e. the max distance is below the threshold. If both have no coordinates, ``True`` is returned. If only one has no coordinates, ``False`` is returned. Beyond that, the number of points is not evaluated. """ if isinstance(other, LineString): pass elif isinstance(other, tuple): other = LineString([other]) else: other = LineString(other) if len(self.coords) == 0 and len(other.coords) == 0: return True elif 0 in [len(self.coords), len(other.coords)]: # only one of the two line strings has no coords return False self_subd = self.subdivide(points_per_edge) other_subd = other.subdivide(points_per_edge) dist_self2other = self_subd.compute_pointwise_distances(other_subd) dist_other2self = other_subd.compute_pointwise_distances(self_subd) dist = max(np.max(dist_self2other), np.max(dist_other2self)) return dist < max_distance
python
def coords_almost_equals(self, other, max_distance=1e-6, points_per_edge=8): """ Compare this and another LineString's coordinates. This is an approximate method based on pointwise distances and can in rare corner cases produce wrong outputs. Parameters ---------- other : imgaug.augmentables.lines.LineString \ or tuple of number \ or ndarray \ or list of ndarray \ or list of tuple of number The other line string or its coordinates. max_distance : float Max distance of any point from the other line string before the two line strings are evaluated to be unequal. points_per_edge : int, optional How many points to interpolate on each edge. Returns ------- bool Whether the two LineString's coordinates are almost identical, i.e. the max distance is below the threshold. If both have no coordinates, ``True`` is returned. If only one has no coordinates, ``False`` is returned. Beyond that, the number of points is not evaluated. """ if isinstance(other, LineString): pass elif isinstance(other, tuple): other = LineString([other]) else: other = LineString(other) if len(self.coords) == 0 and len(other.coords) == 0: return True elif 0 in [len(self.coords), len(other.coords)]: # only one of the two line strings has no coords return False self_subd = self.subdivide(points_per_edge) other_subd = other.subdivide(points_per_edge) dist_self2other = self_subd.compute_pointwise_distances(other_subd) dist_other2self = other_subd.compute_pointwise_distances(self_subd) dist = max(np.max(dist_self2other), np.max(dist_other2self)) return dist < max_distance
[ "def", "coords_almost_equals", "(", "self", ",", "other", ",", "max_distance", "=", "1e-6", ",", "points_per_edge", "=", "8", ")", ":", "if", "isinstance", "(", "other", ",", "LineString", ")", ":", "pass", "elif", "isinstance", "(", "other", ",", "tuple", ")", ":", "other", "=", "LineString", "(", "[", "other", "]", ")", "else", ":", "other", "=", "LineString", "(", "other", ")", "if", "len", "(", "self", ".", "coords", ")", "==", "0", "and", "len", "(", "other", ".", "coords", ")", "==", "0", ":", "return", "True", "elif", "0", "in", "[", "len", "(", "self", ".", "coords", ")", ",", "len", "(", "other", ".", "coords", ")", "]", ":", "# only one of the two line strings has no coords", "return", "False", "self_subd", "=", "self", ".", "subdivide", "(", "points_per_edge", ")", "other_subd", "=", "other", ".", "subdivide", "(", "points_per_edge", ")", "dist_self2other", "=", "self_subd", ".", "compute_pointwise_distances", "(", "other_subd", ")", "dist_other2self", "=", "other_subd", ".", "compute_pointwise_distances", "(", "self_subd", ")", "dist", "=", "max", "(", "np", ".", "max", "(", "dist_self2other", ")", ",", "np", ".", "max", "(", "dist_other2self", ")", ")", "return", "dist", "<", "max_distance" ]
Compare this and another LineString's coordinates. This is an approximate method based on pointwise distances and can in rare corner cases produce wrong outputs. Parameters ---------- other : imgaug.augmentables.lines.LineString \ or tuple of number \ or ndarray \ or list of ndarray \ or list of tuple of number The other line string or its coordinates. max_distance : float Max distance of any point from the other line string before the two line strings are evaluated to be unequal. points_per_edge : int, optional How many points to interpolate on each edge. Returns ------- bool Whether the two LineString's coordinates are almost identical, i.e. the max distance is below the threshold. If both have no coordinates, ``True`` is returned. If only one has no coordinates, ``False`` is returned. Beyond that, the number of points is not evaluated.
[ "Compare", "this", "and", "another", "LineString", "s", "coordinates", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/augmentables/lines.py#L1327-L1379
valid
aleju/imgaug
imgaug/augmentables/lines.py
LineString.almost_equals
def almost_equals(self, other, max_distance=1e-4, points_per_edge=8): """ Compare this and another LineString. Parameters ---------- other: imgaug.augmentables.lines.LineString The other line string. Must be a LineString instance, not just its coordinates. max_distance : float, optional See :func:`imgaug.augmentables.lines.LineString.coords_almost_equals`. points_per_edge : int, optional See :func:`imgaug.augmentables.lines.LineString.coords_almost_equals`. Returns ------- bool ``True`` if the coordinates are almost equal according to :func:`imgaug.augmentables.lines.LineString.coords_almost_equals` and additionally the labels are identical. Otherwise ``False``. """ if self.label != other.label: return False return self.coords_almost_equals( other, max_distance=max_distance, points_per_edge=points_per_edge)
python
def almost_equals(self, other, max_distance=1e-4, points_per_edge=8): """ Compare this and another LineString. Parameters ---------- other: imgaug.augmentables.lines.LineString The other line string. Must be a LineString instance, not just its coordinates. max_distance : float, optional See :func:`imgaug.augmentables.lines.LineString.coords_almost_equals`. points_per_edge : int, optional See :func:`imgaug.augmentables.lines.LineString.coords_almost_equals`. Returns ------- bool ``True`` if the coordinates are almost equal according to :func:`imgaug.augmentables.lines.LineString.coords_almost_equals` and additionally the labels are identical. Otherwise ``False``. """ if self.label != other.label: return False return self.coords_almost_equals( other, max_distance=max_distance, points_per_edge=points_per_edge)
[ "def", "almost_equals", "(", "self", ",", "other", ",", "max_distance", "=", "1e-4", ",", "points_per_edge", "=", "8", ")", ":", "if", "self", ".", "label", "!=", "other", ".", "label", ":", "return", "False", "return", "self", ".", "coords_almost_equals", "(", "other", ",", "max_distance", "=", "max_distance", ",", "points_per_edge", "=", "points_per_edge", ")" ]
Compare this and another LineString. Parameters ---------- other: imgaug.augmentables.lines.LineString The other line string. Must be a LineString instance, not just its coordinates. max_distance : float, optional See :func:`imgaug.augmentables.lines.LineString.coords_almost_equals`. points_per_edge : int, optional See :func:`imgaug.augmentables.lines.LineString.coords_almost_equals`. Returns ------- bool ``True`` if the coordinates are almost equal according to :func:`imgaug.augmentables.lines.LineString.coords_almost_equals` and additionally the labels are identical. Otherwise ``False``.
[ "Compare", "this", "and", "another", "LineString", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/augmentables/lines.py#L1381-L1408
valid
aleju/imgaug
imgaug/augmentables/lines.py
LineString.copy
def copy(self, coords=None, label=None): """ Create a shallow copy of the LineString object. Parameters ---------- coords : None or iterable of tuple of number or ndarray If not ``None``, then the coords of the copied object will be set to this value. label : None or str If not ``None``, then the label of the copied object will be set to this value. Returns ------- imgaug.augmentables.lines.LineString Shallow copy. """ return LineString(coords=self.coords if coords is None else coords, label=self.label if label is None else label)
python
def copy(self, coords=None, label=None): """ Create a shallow copy of the LineString object. Parameters ---------- coords : None or iterable of tuple of number or ndarray If not ``None``, then the coords of the copied object will be set to this value. label : None or str If not ``None``, then the label of the copied object will be set to this value. Returns ------- imgaug.augmentables.lines.LineString Shallow copy. """ return LineString(coords=self.coords if coords is None else coords, label=self.label if label is None else label)
[ "def", "copy", "(", "self", ",", "coords", "=", "None", ",", "label", "=", "None", ")", ":", "return", "LineString", "(", "coords", "=", "self", ".", "coords", "if", "coords", "is", "None", "else", "coords", ",", "label", "=", "self", ".", "label", "if", "label", "is", "None", "else", "label", ")" ]
Create a shallow copy of the LineString object. Parameters ---------- coords : None or iterable of tuple of number or ndarray If not ``None``, then the coords of the copied object will be set to this value. label : None or str If not ``None``, then the label of the copied object will be set to this value. Returns ------- imgaug.augmentables.lines.LineString Shallow copy.
[ "Create", "a", "shallow", "copy", "of", "the", "LineString", "object", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/augmentables/lines.py#L1410-L1431
valid
aleju/imgaug
imgaug/augmentables/lines.py
LineString.deepcopy
def deepcopy(self, coords=None, label=None): """ Create a deep copy of the BoundingBox object. Parameters ---------- coords : None or iterable of tuple of number or ndarray If not ``None``, then the coords of the copied object will be set to this value. label : None or str If not ``None``, then the label of the copied object will be set to this value. Returns ------- imgaug.augmentables.lines.LineString Deep copy. """ return LineString( coords=np.copy(self.coords) if coords is None else coords, label=copylib.deepcopy(self.label) if label is None else label)
python
def deepcopy(self, coords=None, label=None): """ Create a deep copy of the BoundingBox object. Parameters ---------- coords : None or iterable of tuple of number or ndarray If not ``None``, then the coords of the copied object will be set to this value. label : None or str If not ``None``, then the label of the copied object will be set to this value. Returns ------- imgaug.augmentables.lines.LineString Deep copy. """ return LineString( coords=np.copy(self.coords) if coords is None else coords, label=copylib.deepcopy(self.label) if label is None else label)
[ "def", "deepcopy", "(", "self", ",", "coords", "=", "None", ",", "label", "=", "None", ")", ":", "return", "LineString", "(", "coords", "=", "np", ".", "copy", "(", "self", ".", "coords", ")", "if", "coords", "is", "None", "else", "coords", ",", "label", "=", "copylib", ".", "deepcopy", "(", "self", ".", "label", ")", "if", "label", "is", "None", "else", "label", ")" ]
Create a deep copy of the BoundingBox object. Parameters ---------- coords : None or iterable of tuple of number or ndarray If not ``None``, then the coords of the copied object will be set to this value. label : None or str If not ``None``, then the label of the copied object will be set to this value. Returns ------- imgaug.augmentables.lines.LineString Deep copy.
[ "Create", "a", "deep", "copy", "of", "the", "BoundingBox", "object", "." ]
786be74aa855513840113ea523c5df495dc6a8af
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/augmentables/lines.py#L1433-L1455
valid