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| import random | |
| from PIL import Image, ImageOps, ImageFilter | |
| import torch | |
| from torchvision import transforms | |
| import torch.nn.functional as F | |
| import numpy as np | |
| import cv2 | |
| import math | |
| def apply_min_size(sample, size, image_interpolation_method=cv2.INTER_AREA): | |
| """Rezise the sample to ensure the given size. Keeps aspect ratio. | |
| Args: | |
| sample (dict): sample | |
| size (tuple): image size | |
| Returns: | |
| tuple: new size | |
| """ | |
| shape = list(sample["disparity"].shape) | |
| if shape[0] >= size[0] and shape[1] >= size[1]: | |
| return sample | |
| scale = [0, 0] | |
| scale[0] = size[0] / shape[0] | |
| scale[1] = size[1] / shape[1] | |
| scale = max(scale) | |
| shape[0] = math.ceil(scale * shape[0]) | |
| shape[1] = math.ceil(scale * shape[1]) | |
| # resize | |
| sample["image"] = cv2.resize( | |
| sample["image"], tuple(shape[::-1]), interpolation=image_interpolation_method | |
| ) | |
| sample["disparity"] = cv2.resize( | |
| sample["disparity"], tuple(shape[::-1]), interpolation=cv2.INTER_NEAREST | |
| ) | |
| sample["mask"] = cv2.resize( | |
| sample["mask"].astype(np.float32), | |
| tuple(shape[::-1]), | |
| interpolation=cv2.INTER_NEAREST, | |
| ) | |
| sample["mask"] = sample["mask"].astype(bool) | |
| return tuple(shape) | |
| class Resize(object): | |
| """Resize sample to given size (width, height). | |
| """ | |
| def __init__( | |
| self, | |
| width, | |
| height, | |
| resize_target=True, | |
| keep_aspect_ratio=False, | |
| ensure_multiple_of=1, | |
| resize_method="lower_bound", | |
| image_interpolation_method=cv2.INTER_AREA, | |
| ): | |
| """Init. | |
| Args: | |
| width (int): desired output width | |
| height (int): desired output height | |
| resize_target (bool, optional): | |
| True: Resize the full sample (image, mask, target). | |
| False: Resize image only. | |
| Defaults to True. | |
| keep_aspect_ratio (bool, optional): | |
| True: Keep the aspect ratio of the input sample. | |
| Output sample might not have the given width and height, and | |
| resize behaviour depends on the parameter 'resize_method'. | |
| Defaults to False. | |
| ensure_multiple_of (int, optional): | |
| Output width and height is constrained to be multiple of this parameter. | |
| Defaults to 1. | |
| resize_method (str, optional): | |
| "lower_bound": Output will be at least as large as the given size. | |
| "upper_bound": Output will be at max as large as the given size. (Output size might be smaller than given size.) | |
| "minimal": Scale as least as possible. (Output size might be smaller than given size.) | |
| Defaults to "lower_bound". | |
| """ | |
| self.__width = width | |
| self.__height = height | |
| self.__resize_target = resize_target | |
| self.__keep_aspect_ratio = keep_aspect_ratio | |
| self.__multiple_of = ensure_multiple_of | |
| self.__resize_method = resize_method | |
| self.__image_interpolation_method = image_interpolation_method | |
| def constrain_to_multiple_of(self, x, min_val=0, max_val=None): | |
| y = (np.round(x / self.__multiple_of) * self.__multiple_of).astype(int) | |
| if max_val is not None and y > max_val: | |
| y = (np.floor(x / self.__multiple_of) * self.__multiple_of).astype(int) | |
| if y < min_val: | |
| y = (np.ceil(x / self.__multiple_of) * self.__multiple_of).astype(int) | |
| return y | |
| def get_size(self, width, height): | |
| # determine new height and width | |
| scale_height = self.__height / height | |
| scale_width = self.__width / width | |
| if self.__keep_aspect_ratio: | |
| if self.__resize_method == "lower_bound": | |
| # scale such that output size is lower bound | |
| if scale_width > scale_height: | |
| # fit width | |
| scale_height = scale_width | |
| else: | |
| # fit height | |
| scale_width = scale_height | |
| elif self.__resize_method == "upper_bound": | |
| # scale such that output size is upper bound | |
| if scale_width < scale_height: | |
| # fit width | |
| scale_height = scale_width | |
| else: | |
| # fit height | |
| scale_width = scale_height | |
| elif self.__resize_method == "minimal": | |
| # scale as least as possbile | |
| if abs(1 - scale_width) < abs(1 - scale_height): | |
| # fit width | |
| scale_height = scale_width | |
| else: | |
| # fit height | |
| scale_width = scale_height | |
| else: | |
| raise ValueError( | |
| f"resize_method {self.__resize_method} not implemented" | |
| ) | |
| if self.__resize_method == "lower_bound": | |
| new_height = self.constrain_to_multiple_of( | |
| scale_height * height, min_val=self.__height | |
| ) | |
| new_width = self.constrain_to_multiple_of( | |
| scale_width * width, min_val=self.__width | |
| ) | |
| elif self.__resize_method == "upper_bound": | |
| new_height = self.constrain_to_multiple_of( | |
| scale_height * height, max_val=self.__height | |
| ) | |
| new_width = self.constrain_to_multiple_of( | |
| scale_width * width, max_val=self.__width | |
| ) | |
| elif self.__resize_method == "minimal": | |
| new_height = self.constrain_to_multiple_of(scale_height * height) | |
| new_width = self.constrain_to_multiple_of(scale_width * width) | |
| else: | |
| raise ValueError(f"resize_method {self.__resize_method} not implemented") | |
| return (new_width, new_height) | |
| def __call__(self, sample): | |
| width, height = self.get_size( | |
| sample["image"].shape[1], sample["image"].shape[0] | |
| ) | |
| # resize sample | |
| sample["image"] = cv2.resize( | |
| sample["image"], | |
| (width, height), | |
| interpolation=self.__image_interpolation_method, | |
| ) | |
| if self.__resize_target: | |
| if "disparity" in sample: | |
| sample["disparity"] = cv2.resize( | |
| sample["disparity"], | |
| (width, height), | |
| interpolation=cv2.INTER_NEAREST, | |
| ) | |
| if "depth" in sample: | |
| sample["depth"] = cv2.resize( | |
| sample["depth"], (width, height), interpolation=cv2.INTER_NEAREST | |
| ) | |
| if "semseg_mask" in sample: | |
| # sample["semseg_mask"] = cv2.resize( | |
| # sample["semseg_mask"], (width, height), interpolation=cv2.INTER_NEAREST | |
| # ) | |
| sample["semseg_mask"] = F.interpolate(torch.from_numpy(sample["semseg_mask"]).float()[None, None, ...], (height, width), mode='nearest').numpy()[0, 0] | |
| if "mask" in sample: | |
| sample["mask"] = cv2.resize( | |
| sample["mask"].astype(np.float32), | |
| (width, height), | |
| interpolation=cv2.INTER_NEAREST, | |
| ) | |
| # sample["mask"] = sample["mask"].astype(bool) | |
| # print(sample['image'].shape, sample['depth'].shape) | |
| return sample | |
| class NormalizeImage(object): | |
| """Normlize image by given mean and std. | |
| """ | |
| def __init__(self, mean, std): | |
| self.__mean = mean | |
| self.__std = std | |
| def __call__(self, sample): | |
| sample["image"] = (sample["image"] - self.__mean) / self.__std | |
| return sample | |
| class PrepareForNet(object): | |
| """Prepare sample for usage as network input. | |
| """ | |
| def __init__(self): | |
| pass | |
| def __call__(self, sample): | |
| image = np.transpose(sample["image"], (2, 0, 1)) | |
| sample["image"] = np.ascontiguousarray(image).astype(np.float32) | |
| if "mask" in sample: | |
| sample["mask"] = sample["mask"].astype(np.float32) | |
| sample["mask"] = np.ascontiguousarray(sample["mask"]) | |
| if "depth" in sample: | |
| depth = sample["depth"].astype(np.float32) | |
| sample["depth"] = np.ascontiguousarray(depth) | |
| if "semseg_mask" in sample: | |
| sample["semseg_mask"] = sample["semseg_mask"].astype(np.float32) | |
| sample["semseg_mask"] = np.ascontiguousarray(sample["semseg_mask"]) | |
| return sample | |