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combine_hor_lines_and_delete_cross_points: improve

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- avoid unnecessary `fillPoly` (we already have the mask)
- do not merge hseps if vseps interfere
- remove old criterion (based on total length of hseps)
- create new criterion (no x overlap and x close to each other)
- rename identifiers:
  * `sum_dis` → `sum_xspan`
  * `diff_max_min_uniques` → `tot_xspan`
  * np.std / np.mean → `dev_xspan`
- remove rule cutting around the center of crossing seps
  (which is unnecessary and creates small isolated seps
  at the center, unrelated to the actual crossing points)
- create rule cutting hseps by vseps _prior_ to merging
This commit is contained in:
Robert Sachunsky 2025-11-28 17:34:11 +01:00
parent 5c12b6a851
commit a527d7a10d
1 changed files with 40 additions and 21 deletions
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61
src/eynollah/utils/__init__.py
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@ -1194,6 +1194,9 @@ def combine_hor_lines_and_delete_cross_points_and_get_lines_features_back_new(
* the y coordinates with horizontal separators spanning the full width * the y coordinates with horizontal separators spanning the full width
""" """
# cut horizontal seps by vertical seps
img_p_in_hor[img_p_in_ver > 0] = 0
#img_p_in_ver = cv2.erode(img_p_in_ver, self.kernel, iterations=2) #img_p_in_ver = cv2.erode(img_p_in_ver, self.kernel, iterations=2)
_, thresh = cv2.threshold(img_p_in_ver, 0, 255, 0) _, thresh = cv2.threshold(img_p_in_ver, 0, 255, 0)
contours_lines_ver, _ = cv2.findContours(thresh.astype(np.uint8), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) contours_lines_ver, _ = cv2.findContours(thresh.astype(np.uint8), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
@ -1237,24 +1240,34 @@ def combine_hor_lines_and_delete_cross_points_and_get_lines_features_back_new(
some_cy=cy_main_hor[pair] some_cy=cy_main_hor[pair]
some_x_min=x_min_main_hor[pair] some_x_min=x_min_main_hor[pair]
some_x_max=x_max_main_hor[pair] some_x_max=x_max_main_hor[pair]
some_y_min=y_min_main_hor[pair]
some_y_max=y_max_main_hor[pair]
if np.any(img_p_in_ver[some_y_min.min(): some_y_max.max(),
some_x_max.min(): some_x_min.max()]):
# print("horizontal pair cut by vertical sep", pair, some_args, some_cy,
# "%d:%d" % (some_x_min[0], some_x_max[0]),
# "%d:%d" % (some_x_min[1], some_x_max[1]))
continue
#img_in=np.zeros(separators_closeup_n[:,:,2].shape) #img_in=np.zeros(separators_closeup_n[:,:,2].shape)
#print(img_p_in_ver.shape[1],some_x_max-some_x_min,'xdiff') #print(img_p_in_ver.shape[1],some_x_max-some_x_min,'xdiff')
diff_x_some=some_x_max-some_x_min sum_xspan = dist_x_hor[some_args].sum()
for jv in range(len(some_args)): tot_xspan = np.max(x_max_main_hor[some_args]) - np.min(x_min_main_hor[some_args])
img_p_in=cv2.fillPoly(img_in_hor, pts=[contours_lines_hor[some_args[jv]]], color=(1,1,1)) dev_xspan = np.std(dist_x_hor[some_args]) / np.mean(dist_x_hor[some_args])
if any(i_diff>(img_p_in_ver.shape[1]/float(3.3)) for i_diff in diff_x_some): if (tot_xspan > sum_xspan and # no x overlap
img_p_in[int(np.mean(some_cy))-5: sum_xspan > 0.85 * tot_xspan): # x close to each other
int(np.mean(some_cy))+5, # print("merging horizontal pair", pair, some_args, some_cy,
int(np.min(some_x_min)): # "%d:%d" % (some_x_min[0], some_x_max[0]),
int(np.max(some_x_max)) ]=1 # "%d:%d" % (some_x_min[1], some_x_max[1]))
sum_dis=dist_x_hor[some_args].sum() img_p_in[int(np.mean(some_cy)) - 5:
diff_max_min_uniques=np.max(x_max_main_hor[some_args])-np.min(x_min_main_hor[some_args]) int(np.mean(some_cy)) + 5,
np.min(some_x_min):
np.max(some_x_max)] = 255
if (diff_max_min_uniques > sum_dis and if (tot_xspan > sum_xspan and # no x overlap
sum_dis / float(diff_max_min_uniques) > 0.85 and sum_xspan > 0.85 * tot_xspan and # x close to each other
diff_max_min_uniques / float(img_p_in_ver.shape[1]) > 0.85 and tot_xspan > 0.85 * width and # nearly full width
np.std(dist_x_hor[some_args]) < 0.55 * np.mean(dist_x_hor[some_args])): dev_xspan < 0.55): # similar x span
# print(dist_x_hor[some_args], # print(dist_x_hor[some_args],
# dist_x_hor[some_args].sum(), # dist_x_hor[some_args].sum(),
# np.min(x_min_main_hor[some_args]), # np.min(x_min_main_hor[some_args]),
@ -1263,17 +1276,23 @@ def combine_hor_lines_and_delete_cross_points_and_get_lines_features_back_new(
# np.std( dist_x_hor[some_args] ), # np.std( dist_x_hor[some_args] ),
# np.var( dist_x_hor[some_args] ),'jalibdiha') # np.var( dist_x_hor[some_args] ),'jalibdiha')
special_separators.append(np.mean(cy_main_hor[some_args])) special_separators.append(np.mean(cy_main_hor[some_args]))
# print("special separator for midline", special_separators[-1])
# plt.subplot(1, 2, 1, title='original horizontal (1) / vertical (2) seps')
# plt.imshow(1 * (img_p_in_hor > 0) + 2 * (img_p_in_ver > 0))
# plt.subplot(1, 2, 2, title='extended horizontal seps')
# plt.imshow(img_p_in)
# plt.show()
else: else:
img_p_in = img_p_in_hor img_p_in = img_p_in_hor
special_separators = [] special_separators = []
img_p_in_ver[img_p_in_ver == 255] = 1 #img_p_in_ver[img_p_in_ver == 255] = 1
sep_ver_hor_cross = 255 * ((img_p_in > 0) & (img_p_in_ver > 0)) # sep_ver_hor_cross = 255 * ((img_p_in > 0) & (img_p_in_ver > 0))
contours_cross, _ = cv2.findContours(thresh.astype(np.uint8), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) # contours_cross, _ = cv2.findContours(thresh.astype(np.uint8), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
center_cross = np.array(find_center_of_contours(contours_cross), dtype=int) # center_cross = np.array(find_center_of_contours(contours_cross), dtype=int)
for cx, cy in center_cross.T: # for cx, cy in center_cross.T:
img_p_in[cy - 30: cy + 30, cx + 5: cx + 40] = 0 # img_p_in[cy - 30: cy + 30, cx + 5: cx + 40] = 0
img_p_in[cy - 30: cy + 30, cx - 40: cx - 4] = 0 # img_p_in[cy - 30: cy + 30, cx - 40: cx - 4] = 0
else: else:
img_p_in = np.copy(img_p_in_hor) img_p_in = np.copy(img_p_in_hor)
special_separators = [] special_separators = []