File size: 11,401 Bytes
ebcc7d1 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 |
from skimage.io import imread
from skimage.color import rgb2gray
import matplotlib.pyplot as plt
import numpy as np
from skimage.filters import threshold_otsu
import os
from skimage.graph import route_through_array
from heapq import heappush, heappop
from loguru import logger
def heuristic(a, b):
"""Calculate the squared distance between two points."""
return (b[0] - a[0]) ** 2 + (b[1] - a[1]) ** 2
def get_binary(img):
"""Binarize the image using Otsu's threshold."""
mean = np.mean(img)
if mean == 0.0 or mean == 1.0:
return img
thresh = threshold_otsu(img)
binary = img <= thresh
binary = binary.astype(np.uint8)
return binary
def astar(array, start, goal):
"""Perform A* algorithm to find a path from start to goal in a binary array."""
neighbors = [(0,1),(0,-1),(1,0),(-1,0),(1,1),(1,-1),(-1,1),(-1,-1)]
close_set = set()
came_from = {}
gscore = {start:0}
fscore = {start:heuristic(start, goal)}
oheap = []
heappush(oheap, (fscore[start], start))
while oheap:
current = heappop(oheap)[1]
if current == goal:
data = []
while current in came_from:
data.append(current)
current = came_from[current]
return data
close_set.add(current)
for i, j in neighbors:
neighbor = current[0] + i, current[1] + j
tentative_g_score = gscore[current] + heuristic(current, neighbor)
if 0 <= neighbor[0] < array.shape[0]:
if 0 <= neighbor[1] < array.shape[1]:
if array[neighbor[0]][neighbor[1]] == 1:
continue
else:
# array bound y walls
continue
else:
# array bound x walls
continue
if neighbor in close_set and tentative_g_score >= gscore.get(neighbor, 0):
continue
if tentative_g_score < gscore.get(neighbor, 0) or neighbor not in [i[1] for i in oheap]:
came_from[neighbor] = current
gscore[neighbor] = tentative_g_score
fscore[neighbor] = tentative_g_score + heuristic(neighbor, goal)
heappush(oheap, (fscore[neighbor], neighbor))
return []
def preprocess_image(img, target_size):
"""Read and convert an image to grayscale."""
try:
if target_size is not None:
img = img[target_size[0]:target_size[1], target_size[2]:target_size[3],:]
if img.ndim == 3 and img.shape[2] == 4:
img = img[..., :3]
if img.ndim > 2:
img = rgb2gray(img)
return img
except Exception as e:
print(f"Error in preprocessing: {e}")
return None
def horizontal_projections(sobel_image):
"""Calculate horizontal projections of the binary image."""
return np.sum(sobel_image, axis=1)
def binarize_image(image):
"""Binarize an image using Otsu's threshold."""
threshold = threshold_otsu(image)
return image < threshold
def find_peak_regions(hpp, threshold):
"""Identify peak regions based on the horizontal projection profile."""
peaks = []
for i, hppv in enumerate(hpp):
if hppv < threshold:
peaks.append(i)
return peaks
def line_segmentation(image, threshold=None, min_peak_group_size=7, target_size=None,
ct=0, parent_line_num=None, recursive=False, recursive_count=1,
base_key="line"):
"""
Segment an image into lines using horizontal projections and A*.
Args:
image: Input image (numpy array)
threshold (float, optional): Threshold for peak detection
min_peak_group_size (int): Minimum size of peak groups to consider
target_size (tuple, optional): Target size for image preprocessing
ct (int): Counter for line numbering
parent_line_num (str, optional): Parent line number for recursive segmentation
recursive (bool): Whether this is a recursive call
recursive_count (int): Counter for recursive segmentation numbering
base_key (str): Base key for dictionary entries
Returns:
tuple: (segmented_images_dict, counter value, bool indicating if valid separations were found)
"""
segmented_images_dict = {}
img = preprocess_image(image, target_size)
if img is None:
print(f"Failed to preprocess image")
return segmented_images_dict, ct, False
# Binarize image and get projections
binarized_image = binarize_image(img)
hpp = horizontal_projections(binarized_image)
if threshold is None:
threshold = (np.max(hpp) - np.min(hpp)) / 2
# Find peaks
peaks = find_peak_regions(hpp, threshold)
if not peaks:
print(f"No peaks found in image")
return segmented_images_dict, ct, False
peaks_indexes = np.array(peaks).astype(int)
segmented_img = np.copy(img)
r, c = segmented_img.shape
for ri in range(r):
if ri in peaks_indexes:
segmented_img[ri, :] = 0
# Group peaks
diff_between_consec_numbers = np.diff(peaks_indexes)
indexes_with_larger_diff = np.where(diff_between_consec_numbers > 1)[0].flatten()
peak_groups = np.split(peaks_indexes, indexes_with_larger_diff + 1)
peak_groups = [item for item in peak_groups if len(item) > min_peak_group_size]
if not peak_groups:
print(f"No valid peak groups found in image")
return segmented_images_dict, ct, False
binary_image = get_binary(img)
segment_separating_lines = []
for sub_image_index in peak_groups:
try:
start_row = sub_image_index[0]
end_row = sub_image_index[-1]
start_row = max(0, start_row)
end_row = min(binary_image.shape[0], end_row)
if end_row <= start_row:
continue
nmap = binary_image[start_row:end_row, :]
if nmap.size == 0:
continue
start_point = (int(nmap.shape[0] / 2), 0)
end_point = (int(nmap.shape[0] / 2), nmap.shape[1] - 1)
path, _ = route_through_array(nmap, start_point, end_point)
path = np.array(path) + start_row
segment_separating_lines.append(path)
except Exception as e:
print(f"Failed to process sub-image: {e}")
continue
if not segment_separating_lines:
print(f"No valid segment separating lines found in image")
return segmented_images_dict, ct, False
# Separate images based on line segments
seperated_images = []
for index in range(len(segment_separating_lines) - 1):
try:
lower_line = np.min(segment_separating_lines[index][:, 0])
upper_line = np.max(segment_separating_lines[index + 1][:, 0])
if lower_line < upper_line and upper_line <= img.shape[0]:
line_image = img[lower_line:upper_line]
if line_image.size > 0:
seperated_images.append(line_image)
except Exception as e:
print(f"Failed to separate image at index {index}: {e}")
continue
if not seperated_images:
print(f"No valid separated images found in image")
return segmented_images_dict, ct, False
# Calculate height threshold
try:
image_heights = [line_image.shape[0] for line_image in seperated_images if line_image.size > 0]
if not image_heights:
print(f"No valid image heights found")
return segmented_images_dict, ct, False
height_threshold = np.percentile(image_heights, 90)
except Exception as e:
print(f"Failed to calculate height threshold: {e}")
return segmented_images_dict, ct, False
# Process each separated image
for index, line_image in enumerate(seperated_images):
try:
if line_image.size == 0 or line_image.shape[0] == 0 or line_image.shape[1] == 0:
continue
if parent_line_num is None:
dict_key = f'{base_key}_{ct + 1}'
else:
dict_key = f'{base_key}_{recursive_count}'
if index < len(seperated_images) - 1:
continue
segmented_images_dict[dict_key] = {
"image": line_image.copy(),
"transcription": f"{dict_key}"
}
# print(f"Added line image to dictionary with key: {dict_key}")
# Handle recursive segmentation
if line_image.shape[0] > height_threshold and not recursive:
try:
# Create recursive base key
recursive_base_key = f"{base_key}_{ct + 1}"
# Do recursive segmentation
recursive_dict, ct, found_valid_separations = line_segmentation(
line_image, threshold=threshold,
min_peak_group_size=3,
parent_line_num=str(ct + 1),
recursive=True,
ct=ct,
recursive_count=1,
base_key=recursive_base_key
)
if found_valid_separations:
del segmented_images_dict[dict_key]
segmented_images_dict.update(recursive_dict)
print(f"Replaced {dict_key} with recursive segmentation results")
else:
print(f"Keeping original image {dict_key} as no valid separations were found")
except Exception as e:
print(f"Failed during recursive segmentation of {dict_key}: {e}")
ct += 1
if recursive:
recursive_count += 1
except Exception as e:
print(f"Failed to process line image at index {index}: {e}")
continue
logger.info(f"Total lines segment found: {len(segmented_images_dict)}")
return segmented_images_dict, ct, len(seperated_images) > 0
def segment_image_to_lines(image_array, **kwargs):
"""
Convenience function to segment an image into lines.
Args:
image_array: Input image as numpy array
**kwargs: Additional arguments for line_segmentation
Returns:
dict: Dictionary with line keys and segmented image arrays as values
"""
try:
logger.info("Starting line segmentation...")
segmented_dict, _, success = line_segmentation(image_array, **kwargs)
if success:
logger.info(f"Line segmentation successful.....")
return segmented_dict
except Exception as e:
logger.error(f"Line segmentation failed: {e}")
return {}
# if __name__ == "__main__":
# # Example usage
# image_path = "./renAI-deploy/1.png"
# image = imread(image_path)
# segmented_lines = segment_image_to_lines(image, threshold=None, min_peak_group_size=10)
# print(len(segmented_lines.values()))
# for key, value in segmented_lines.items():
# print(f"{key}: {value['image'].shape}")
# print(f"{key}: {value['transcription']}")
# # plt.imshow(img, cmap='gray')
# # plt.title(key)
# # plt.show() |