File size: 7,157 Bytes
54c9770 242f627 1a1d05a 242f627 1a1d05a e0f6bc4 1a1d05a e0f6bc4 1a1d05a e0f6bc4 1a1d05a e0f6bc4 1a1d05a 54c9770 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 |
import re
from typing import List
import cv2
import numpy as np
import supervision as sv
class Visualizer:
def __init__(
self,
line_thickness: int = 2,
mask_opacity: float = 0.1,
text_scale: float = 0.5
) -> None:
self.box_annotator = sv.BoundingBoxAnnotator(
color_lookup=sv.ColorLookup.INDEX,
thickness=line_thickness)
self.mask_annotator = sv.MaskAnnotator(
color_lookup=sv.ColorLookup.INDEX,
opacity=mask_opacity)
self.polygon_annotator = sv.PolygonAnnotator(
color_lookup=sv.ColorLookup.INDEX,
thickness=line_thickness)
self.label_annotator = sv.LabelAnnotator(
color_lookup=sv.ColorLookup.INDEX,
text_position=sv.Position.CENTER_OF_MASS,
text_scale=text_scale)
def visualize(
self,
image: np.ndarray,
detections: sv.Detections,
with_box: bool,
with_mask: bool,
with_polygon: bool,
with_label: bool
) -> np.ndarray:
annotated_image = image.copy()
if with_box:
annotated_image = self.box_annotator.annotate(
scene=annotated_image, detections=detections)
if with_mask:
annotated_image = self.mask_annotator.annotate(
scene=annotated_image, detections=detections)
if with_polygon:
annotated_image = self.polygon_annotator.annotate(
scene=annotated_image, detections=detections)
if with_label:
labels = list(map(str, range(len(detections))))
annotated_image = self.label_annotator.annotate(
scene=annotated_image, detections=detections, labels=labels)
return annotated_image
def refine_mask(
mask: np.ndarray,
area_threshold: float,
mode: str = 'islands'
) -> np.ndarray:
"""
Refines a mask by removing small islands or filling small holes based on area
threshold.
Parameters:
mask (np.ndarray): Input binary mask.
area_threshold (float): Threshold for relative area to remove or fill features.
mode (str): Operation mode ('islands' for removing islands, 'holes' for filling
holes).
Returns:
np.ndarray: Refined binary mask.
"""
mask = np.uint8(mask * 255)
operation = cv2.RETR_EXTERNAL if mode == 'islands' else cv2.RETR_CCOMP
contours, _ = cv2.findContours(
mask, operation, cv2.CHAIN_APPROX_SIMPLE
)
total_area = cv2.countNonZero(mask) if mode == 'islands' else mask.size
for contour in contours:
area = cv2.contourArea(contour)
relative_area = area / total_area
if relative_area < area_threshold:
cv2.drawContours(
mask, [contour], -1, (0 if mode == 'islands' else 255), -1
)
return np.where(mask > 0, 1, 0).astype(bool)
def filter_masks_by_relative_area(
masks: np.ndarray,
min_relative_area: float = 0.02,
max_relative_area: float = 1.0
) -> np.ndarray:
"""
Filters out masks based on their relative area.
Parameters:
masks (np.ndarray): A 3D numpy array where each slice along the third dimension
represents a mask.
min_relative_area (float): Minimum relative area threshold for keeping a mask.
max_relative_area (float): Maximum relative area threshold for keeping a mask.
Returns:
np.ndarray: A 3D numpy array of filtered masks.
"""
mask_areas = masks.sum(axis=(1, 2))
total_area = masks.shape[1] * masks.shape[2]
relative_areas = mask_areas / total_area
min_area_filter = relative_areas >= min_relative_area
max_area_filter = relative_areas <= max_relative_area
return masks[min_area_filter & max_area_filter]
def compute_iou(mask1: np.ndarray, mask2: np.ndarray) -> float:
"""
Computes the Intersection over Union (IoU) of two masks.
Parameters:
mask1, mask2 (np.ndarray): Two mask arrays.
Returns:
float: The IoU of the two masks.
"""
intersection = np.logical_and(mask1, mask2).sum()
union = np.logical_or(mask1, mask2).sum()
return intersection / union if union != 0 else 0
def filter_highly_overlapping_masks(
masks: np.ndarray,
iou_threshold: float
) -> np.ndarray:
"""
Removes masks with high overlap from a set of masks.
Parameters:
masks (np.ndarray): A 3D numpy array with shape (N, H, W), where N is the
number of masks, and H and W are the height and width of the masks.
iou_threshold (float): The IoU threshold above which masks will be considered as
overlapping.
Returns:
np.ndarray: A 3D numpy array of masks with highly overlapping masks removed.
"""
num_masks = masks.shape[0]
keep_mask = np.ones(num_masks, dtype=bool)
for i in range(num_masks):
for j in range(i + 1, num_masks):
if not keep_mask[i] or not keep_mask[j]:
continue
iou = compute_iou(masks[i, :, :], masks[j, :, :])
if iou > iou_threshold:
keep_mask[j] = False
return masks[keep_mask]
def postprocess_masks(
detections: sv.Detections,
area_threshold: float = 0.01,
min_relative_area: float = 0.01,
max_relative_area: float = 1.0,
iou_threshold: float = 0.9
) -> sv.Detections:
"""
Post-processes the masks of detection objects by removing small islands and filling
small holes.
Parameters:
detections (sv.Detections): Detection objects to be filtered.
area_threshold (float): Threshold for relative area to remove or fill features.
min_relative_area (float): Minimum relative area threshold for detections.
max_relative_area (float): Maximum relative area threshold for detections.
iou_threshold (float): The IoU threshold above which masks will be considered as
overlapping.
Returns:
np.ndarray: Post-processed masks.
"""
masks = detections.mask.copy()
for i in range(len(masks)):
masks[i] = refine_mask(
mask=masks[i],
area_threshold=area_threshold,
mode='islands'
)
masks[i] = refine_mask(
mask=masks[i],
area_threshold=area_threshold,
mode='holes'
)
masks = filter_masks_by_relative_area(
masks=masks,
min_relative_area=min_relative_area,
max_relative_area=max_relative_area)
masks = filter_highly_overlapping_masks(
masks=masks,
iou_threshold=iou_threshold)
return sv.Detections(
xyxy=sv.mask_to_xyxy(masks),
mask=masks
)
def extract_numbers_in_brackets(text: str) -> List[int]:
"""
Extracts all numbers enclosed in square brackets from a given string.
Args:
text (str): The string to be searched.
Returns:
List[int]: A list of integers found within square brackets.
"""
pattern = r'\[(\d+)\]'
numbers = [int(num) for num in re.findall(pattern, text)]
return numbers
|