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get_marginals(): improve contour assignment…

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- use undeskewed mask for contour comparisons
  instead of deskewed mask (less precise)
- rename `text_with_lines` → `text_mask_d`
- rename `mask_marginals` → `main_mask_d`
- rename `text_regions` → `early_layout`
- rename `...textline...` → `...text...`
This commit is contained in:
Robert Sachunsky 2026-04-25 03:06:34 +02:00
parent 6d55d0b87b
commit 68ceeec764
1 changed files with 77 additions and 89 deletions
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168
src/eynollah/utils/marginals.py
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@ -2,62 +2,63 @@ import numpy as np
import cv2 import cv2
from scipy.signal import find_peaks from scipy.signal import find_peaks
from scipy.ndimage import gaussian_filter1d from scipy.ndimage import gaussian_filter1d
from .contour import find_new_features_of_contours, return_contours_of_interested_region from .contour import find_center_of_contours, return_contours_of_interested_region
from .resize import resize_image from .resize import resize_image
from .rotate import rotate_image from .rotate import rotate_image
def get_marginals(text_with_lines, text_regions, num_col, slope_deskew, kernel=None): def get_marginals(text_mask_d, early_layout, num_col, slope_deskew, kernel=None,
# rs: text_with_lines should be called text_mask_d label_text=1,
# rs: text_regions should be called early_layout (contains other classes, too) label_marg=4,
# rs: text_with_lines is already deskewed, while text_regions is not... ):
mask_marginals = np.zeros_like(text_with_lines) # rs: text_mask_d is already deskewed, while early_layout is not...
height, width = mask_marginals.shape main_mask_d = np.zeros_like(text_mask_d)
height, width = main_mask_d.shape
##text_with_lines=cv2.erode(text_with_lines,self.kernel,iterations=3) ##text_mask_d=cv2.erode(text_mask_d,self.kernel,iterations=3)
text_with_lines_eroded = cv2.erode(text_with_lines,kernel,iterations=5) text_mask_d_eroded = cv2.erode(text_mask_d,kernel,iterations=5)
if height <= 1500: if height <= 1500:
pass pass
elif 1500 < height <= 1800: elif 1500 < height <= 1800:
# rs: why not / 1.5??? # rs: why not / 1.5???
text_with_lines = resize_image(text_with_lines, int(height * 1.5), width) text_mask_d = resize_image(text_mask_d, int(height * 1.5), width)
text_with_lines = cv2.erode(text_with_lines, kernel, iterations=5) text_mask_d = cv2.erode(text_mask_d, kernel, iterations=5)
# rs: and back to original size # rs: and back to original size
text_with_lines = resize_image(text_with_lines, height, width) text_mask_d = resize_image(text_mask_d, height, width)
else: else:
# rs: why not / 1.8??? # rs: why not / 1.8???
text_with_lines = resize_image(text_with_lines, int(height * 1.8), width) text_mask_d = resize_image(text_mask_d, int(height * 1.8), width)
text_with_lines = cv2.erode(text_with_lines, kernel, iterations=7) text_mask_d = cv2.erode(text_mask_d, kernel, iterations=7)
# rs: and back to original size # rs: and back to original size
text_with_lines = resize_image(text_with_lines, height, width) text_mask_d = resize_image(text_mask_d, height, width)
kernel_hor = np.ones((1, 5), dtype=np.uint8) kernel_hor = np.ones((1, 5), dtype=np.uint8)
text_with_lines = cv2.erode(text_with_lines, kernel_hor, iterations=6) text_mask_d = cv2.erode(text_mask_d, kernel_hor, iterations=6)
text_with_lines_y = text_with_lines.sum(axis=0) text_mask_d_y = text_mask_d.sum(axis=0)
text_with_lines_y_eroded = text_with_lines_eroded.sum(axis=0) text_mask_d_y_eroded = text_mask_d_eroded.sum(axis=0)
max_textline_thickness_percent = 100. * text_with_lines_y_eroded.max() / height max_text_thickness_percent = 100. * text_mask_d_y_eroded.max() / height
min_textline_thickness = max_textline_thickness_percent / 100. * height / 20. min_text_thickness = max_text_thickness_percent / 100. * height / 20.
# plt.figure() # plt.figure()
# ax1 = plt.subplot(2, 1, 1, title="text_with_lines_eroded") # ax1 = plt.subplot(2, 1, 1, title="text_mask_d_eroded")
# ax1.imshow(text_with_lines_eroded, aspect='auto') # ax1.imshow(text_mask_d_eroded, aspect='auto')
# ax2 = plt.subplot(2, 1, 2, title="text_with_lines_y_eroded", sharex=ax1) # ax2 = plt.subplot(2, 1, 2, title="text_mask_d_y_eroded", sharex=ax1)
# ax2.plot(list(range(width)), text_with_lines_y_eroded) # ax2.plot(list(range(width)), text_mask_d_y_eroded)
# ax2.hlines(int(0.14 * height), 0, width, # ax2.hlines(int(0.14 * height), 0, width,
# label='max_textline_thickness=14%', colors='r') # label='max_text_thickness=14%', colors='r')
# ax2.hlines([min_textline_thickness], 0, width, # ax2.hlines([min_text_thickness], 0, width,
# label='min_textline_thickness', colors='g') # label='min_text_thickness', colors='g')
# ax2.scatter([np.argmax(text_with_lines_y_eroded)], # ax2.scatter([np.argmax(text_mask_d_y_eroded)],
# [text_with_lines_y_eroded.max()], color='r', # [text_mask_d_y_eroded.max()], color='r',
# label='max = %d%%' % max_textline_thickness_percent) # label='max = %d%%' % max_text_thickness_percent)
# plt.legend() # plt.legend()
# plt.show() # plt.show()
if max_textline_thickness_percent >= 14: if max_text_thickness_percent >= 14:
text_with_lines_y_rev = np.max(text_with_lines_y) - text_with_lines_y text_mask_d_y_rev = np.max(text_mask_d_y) - text_mask_d_y
region_sum_0 = gaussian_filter1d(text_with_lines_y, 1) region_sum_0 = gaussian_filter1d(text_mask_d_y, 1)
first_nonzero = region_sum_0.nonzero()[0][0] # outer left first_nonzero = region_sum_0.nonzero()[0][0] # outer left
last_nonzero = region_sum_0.nonzero()[0][-1] # outer right last_nonzero = region_sum_0.nonzero()[0][-1] # outer right
mid_point = 0.5 * (last_nonzero + first_nonzero) mid_point = 0.5 * (last_nonzero + first_nonzero)
@ -65,14 +66,14 @@ def get_marginals(text_with_lines, text_regions, num_col, slope_deskew, kernel=N
one_third_left = (mid_point - first_nonzero) / 3.0 one_third_left = (mid_point - first_nonzero) / 3.0
# rs: constrain the distance at least 2 characters at 12pt, retrieve height and prominence # rs: constrain the distance at least 2 characters at 12pt, retrieve height and prominence
peaks, props = find_peaks(text_with_lines_y_rev, height=0, prominence=0, distance=30) peaks, props = find_peaks(text_mask_d_y_rev, height=0, prominence=0, distance=30)
peaks_orig = np.copy(peaks) peaks_orig = np.copy(peaks)
# rs: also calculate the product of prominence and height (for final selection) # rs: also calculate the product of prominence and height (for final selection)
scores = np.zeros(peaks.max() + 1) scores = np.zeros(peaks.max() + 1)
scores[peaks] = props['prominences'] * props['peak_heights'] scores[peaks] = props['prominences'] * props['peak_heights']
peaks = peaks[(peaks > first_nonzero) & (peaks < last_nonzero)] peaks = peaks[(peaks > first_nonzero) & (peaks < last_nonzero)]
peaks = peaks[region_sum_0[peaks] < min_textline_thickness] peaks = peaks[region_sum_0[peaks] < min_text_thickness]
if num_col == 1: if num_col == 1:
peaks_right = peaks[peaks > mid_point] peaks_right = peaks[peaks > mid_point]
@ -84,20 +85,20 @@ def get_marginals(text_with_lines, text_regions, num_col, slope_deskew, kernel=N
if len(peaks_left) == 0: if len(peaks_left) == 0:
if len(peaks_right) == 0: if len(peaks_right) == 0:
# plt.figure() # plt.figure()
# ax1 = plt.subplot(2, 1, 1, title='text_with_lines (deskewed text+sep mask)') # ax1 = plt.subplot(2, 1, 1, title='text_mask_d (deskewed text+sep mask)')
# ax1.imshow(text_with_lines, aspect='auto') # ax1.imshow(text_mask_d, aspect='auto')
# ax1.vlines([first_nonzero], 0, height, label='first_nonzero', colors='r') # ax1.vlines([first_nonzero], 0, height, label='first_nonzero', colors='r')
# ax1.vlines([last_nonzero], 0, height, label='last_nonzero', colors='r') # ax1.vlines([last_nonzero], 0, height, label='last_nonzero', colors='r')
# ax1.vlines(peaks_left, 0, height, label='peaks_left', colors='orange') # ax1.vlines(peaks_left, 0, height, label='peaks_left', colors='orange')
# ax1.vlines(peaks_right, 0, height, label='peaks_right', colors='orange') # ax1.vlines(peaks_right, 0, height, label='peaks_right', colors='orange')
# ax2 = plt.subplot(2, 1, 2, title='text_with_lines_y (smoothed)', sharex=ax1) # ax2 = plt.subplot(2, 1, 2, title='text_mask_d_y (smoothed)', sharex=ax1)
# ax2.plot(list(range(width)), region_sum_0) # ax2.plot(list(range(width)), region_sum_0)
# ax2.hlines(min_textline_thickness, 0, width, colors='g', # ax2.hlines(min_text_thickness, 0, width, colors='g',
# label='min_textline_thickness=%d' % min_textline_thickness) # label='min_text_thickness=%d' % min_text_thickness)
# ax2.scatter(peaks_orig, region_sum_0[peaks_orig], label='peaks') # ax2.scatter(peaks_orig, region_sum_0[peaks_orig], label='peaks')
# plt.legend() # plt.legend()
# plt.show() # plt.show()
return text_regions return early_layout
point_right = peaks_right[np.argmax(scores[peaks_right])] point_right = peaks_right[np.argmax(scores[peaks_right])]
#point_left = first_nonzero #point_left = first_nonzero
point_left = 0 point_left = 0
@ -113,75 +114,62 @@ def get_marginals(text_with_lines, text_regions, num_col, slope_deskew, kernel=N
point_left = peaks_left[np.argmax(scores[peaks_left])] point_left = peaks_left[np.argmax(scores[peaks_left])]
#point_right = last_nonzero #point_right = last_nonzero
# rs: should be called mask_main (i.e. inverted semantics here) main_mask_d[:, point_left: point_right] = 1
mask_marginals[:, point_left: point_right] = 1
# plt.figure() # plt.figure()
# ax1 = plt.subplot(2, 2, 1) # ax1 = plt.subplot(2, 2, 1)
# ax1.title.set_text('text_with_lines (deskewed text+sep mask)') # ax1.title.set_text('text_mask_d (deskewed text+table mask)')
# ax1.imshow(text_with_lines) # ax1.imshow(text_mask_d)
# ax1.vlines(peaks_left, 0, height, label='peaks_left', colors='b') # ax1.vlines(peaks_left, 0, height, label='peaks_left', colors='b')
# ax1.vlines(peaks_right, 0, height, label='peaks_right', colors='b') # ax1.vlines(peaks_right, 0, height, label='peaks_right', colors='b')
# ax1.vlines([first_nonzero], 0, height, label='first_nonzero', colors='g') # ax1.vlines([first_nonzero], 0, height, label='first_nonzero', colors='g')
# ax1.vlines([last_nonzero], 0, height, label='last_nonzero', colors='g') # ax1.vlines([last_nonzero], 0, height, label='last_nonzero', colors='g')
# ax1.vlines([point_left], 0, height, label='point_left', colors='r') # ax1.vlines([point_left], 0, height, label='point_left', colors='r')
# ax1.vlines([point_right], 0, height, label='point_right', colors='r') # ax1.vlines([point_right], 0, height, label='point_right', colors='r')
# ax2 = plt.subplot(2, 2, 2, title='mask_marginals (deskewed marginal mask)', sharey=ax1) # ax2 = plt.subplot(2, 2, 2, title='main_mask_d (deskewed main mask)', sharey=ax1)
# ax2.imshow(mask_marginals) # ax2.imshow(main_mask_d)
# ax3 = plt.subplot(2, 2, 3, title='text_with_lines_y (projection for minima)', sharex=ax1) # ax3 = plt.subplot(2, 2, 3, title='text_mask_d_y (projection for minima)', sharex=ax1)
# ax3.plot(list(range(width)), text_with_lines_y) # ax3.plot(list(range(width)), text_mask_d_y)
# ax3.set_aspect('auto') # ax3.set_aspect('auto')
# ax4 = plt.subplot(2, 2, 4, title='text_regions (undeskewed labels)') # ax4 = plt.subplot(2, 2, 4, title='early_layout (undeskewed labels)')
# ax4.imshow(text_regions) # ax4.imshow(early_layout)
# plt.legend() # plt.legend()
# plt.show() # plt.show()
# rs: rotate back (into undeskewed/original shape as text_regions input): # rs: rotate back (into undeskewed/original shape as early_layout input):
mask_marginals_rotated = rotate_image(mask_marginals, -slope_deskew) main_mask = rotate_image(main_mask_d, -slope_deskew)
mask_marginals_rotated_y = mask_marginals_rotated.sum(axis=0) main_mask_y = main_mask.sum(axis=0)
mask_marginals_rotated_y_nz = np.flatnonzero(mask_marginals_rotated_y) main_mask_y_nz = np.flatnonzero(main_mask_y)
min_point_of_left_marginal = max(0, np.min(mask_marginals_rotated_y_nz) - 16)
max_point_of_right_marginal = min(width - 1, np.max(mask_marginals_rotated_y_nz) + 16)
min_area_text = 0.00001 min_area_text = 0.00001
# rs: why not extract from mask_marginals_rotated??? main_contour = return_contours_of_interested_region(main_mask, 1, min_area_text)[0]
# rs: why not largest area instead of first? text_contours = return_contours_of_interested_region(early_layout, label_text, min_area_text)
polygon_mask_marginals_rotated = return_contours_of_interested_region(mask_marginals, 1, min_area_text)[0] cx_text, cy_text = find_center_of_contours(text_contours)
polygons_of_marginals = return_contours_of_interested_region(text_regions, 1, min_area_text)
(cx_text_only, marg_contours = []
cy_text_only, for i, contour in enumerate(text_contours):
x_min_text_only, if -1 == cv2.pointPolygonTest(main_contour,
x_max_text_only, (cx_text[i],
y_min_text_only, cy_text[i]),
y_max_text_only,
y_cor_x_min_main) = find_new_features_of_contours(polygons_of_marginals)
main_text_should_be_marginals = []
x_min_marginals_left=[]
x_min_marginals_right=[]
for i, polygon in enumerate(polygons_of_marginals):
if -1 == cv2.pointPolygonTest(polygon_mask_marginals_rotated,
(cx_text_only[i],
cy_text_only[i]),
False): False):
main_text_should_be_marginals.append(polygon) marg_contours.append(contour)
early_layout_orig = np.copy(early_layout)
early_layout = cv2.fillPoly(early_layout, pts=marg_contours, color=label_marg)
text_regions = cv2.fillPoly(text_regions, pts=main_text_should_be_marginals, color=4)
# plt.figure() # plt.figure()
# ax1 = plt.subplot(2, 2, 1, title='mask_marginals (deskewed marginal mask)') # ax1 = plt.subplot(2, 2, 1, title='main_mask_d (deskewed main mask)')
# plt.imshow(mask_marginals) # plt.imshow(main_mask_d)
# ax2 = plt.subplot(2, 2, 2, title='mask_marginals_rotated (undeskewed marginal mask)') # ax2 = plt.subplot(2, 2, 2, title='main_mask (undeskewed main mask)')
# plt.imshow(mask_marginals_rotated) # plt.imshow(main_mask)
# ax4 = plt.subplot(2, 2, 4, title='text_regions (undeskewed labels split)') # ax3 = plt.subplot(2, 2, 3, title='early_layout (undeskewed labels original)')
# plt.imshow(text_regions) # plt.imshow(early_layout_orig)
# ax4 = plt.subplot(2, 2, 4, title='early_layout (undeskewed labels split)')
# plt.imshow(early_layout)
# plt.show()
#plt.imshow(early_layout)
#plt.show() #plt.show()
#plt.imshow(text_regions)
#plt.show()
#sys.exit()
else: else:
pass pass
return text_regions return early_layout