qurator-spk/eynollah
1
0
Fork 0
mirror of https://github.com/qurator-spk/eynollah.git synced 2025-10-15 19:09:58 +02:00
Code Issues Projects Releases Wiki Activity

matching deskewed text region contours with predicted: improve

Browse source 
- apply same min-area filter to deskewed contours as to original ones
This commit is contained in:
Robert Sachunsky 2025-10-06 12:55:10 +02:00
parent 0f33c21eb3
commit 0e00d7868b
1 changed files with 5 additions and 15 deletions
Download patch file Download diff file Expand all files Collapse all files

20
src/eynollah/eynollah.py
View file

@ -4568,7 +4568,6 @@ class Eynollah:
centers = np.stack(find_center_of_contours(contours_only_text_parent)) # [2, N] centers = np.stack(find_center_of_contours(contours_only_text_parent)) # [2, N]
contour0 = contours_only_text_parent[-1]
center0 = centers[:, -1:] # [2, 1] center0 = centers[:, -1:] # [2, 1]
if np.abs(slope_deskew) >= SLOPE_THRESHOLD: if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
@ -4578,6 +4577,9 @@ class Eynollah:
areas_cnt_text_d = np.array([cv2.contourArea(c) for c in contours_only_text_parent_d]) areas_cnt_text_d = np.array([cv2.contourArea(c) for c in contours_only_text_parent_d])
areas_cnt_text_d = areas_cnt_text_d / float(text_only_d.shape[0] * text_only_d.shape[1]) areas_cnt_text_d = areas_cnt_text_d / float(text_only_d.shape[0] * text_only_d.shape[1])
contours_only_text_parent_d = np.array(contours_only_text_parent_d)[areas_cnt_text_d > MIN_AREA_REGION]
areas_cnt_text_d = areas_cnt_text_d[areas_cnt_text_d > MIN_AREA_REGION]
if len(contours_only_text_parent_d): if len(contours_only_text_parent_d):
index_con_parents_d = np.argsort(areas_cnt_text_d) index_con_parents_d = np.argsort(areas_cnt_text_d)
contours_only_text_parent_d = np.array(contours_only_text_parent_d)[index_con_parents_d] contours_only_text_parent_d = np.array(contours_only_text_parent_d)[index_con_parents_d]
@ -4585,9 +4587,10 @@ class Eynollah:
centers_d = np.stack(find_center_of_contours(contours_only_text_parent_d)) # [2, N] centers_d = np.stack(find_center_of_contours(contours_only_text_parent_d)) # [2, N]
contour0_d = contours_only_text_parent_d[-1]
center0_d = centers_d[:, -1:] # [2, 1] center0_d = centers_d[:, -1:] # [2, 1]
# find the largest among the largest 5 deskewed contours
# that is also closest to the largest original contour
last5_centers_d = centers_d[:, -5:] last5_centers_d = centers_d[:, -5:]
dists_d = np.linalg.norm(center0 - last5_centers_d, axis=0) dists_d = np.linalg.norm(center0 - last5_centers_d, axis=0)
ind_largest = len(contours_only_text_parent_d) - last5_centers_d.shape[1] + np.argmin(dists_d) ind_largest = len(contours_only_text_parent_d) - last5_centers_d.shape[1] + np.argmin(dists_d)
@ -4762,14 +4765,7 @@ class Eynollah:
if np.abs(slope_deskew) >= SLOPE_THRESHOLD: if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
contours_only_text_parent_d_ordered = self.return_list_of_contours_with_desired_order( contours_only_text_parent_d_ordered = self.return_list_of_contours_with_desired_order(
contours_only_text_parent_d_ordered, index_by_text_par_con) contours_only_text_parent_d_ordered, index_by_text_par_con)
#try:
#contours_only_text_parent_d_ordered = \
#list(np.array(contours_only_text_parent_d_ordered, dtype=np.int32)[index_by_text_par_con])
#except:
#contours_only_text_parent_d_ordered = \
#list(np.array(contours_only_text_parent_d_ordered, dtype=object)[index_by_text_par_con])
else: else:
#takes long timee
contours_only_text_parent_d_ordered = None contours_only_text_parent_d_ordered = None
if self.light_version: if self.light_version:
fun = check_any_text_region_in_model_one_is_main_or_header_light fun = check_any_text_region_in_model_one_is_main_or_header_light
@ -4949,12 +4945,6 @@ class Eynollah:
else: else:
contours_only_text_parent_d_ordered = self.return_list_of_contours_with_desired_order( contours_only_text_parent_d_ordered = self.return_list_of_contours_with_desired_order(
contours_only_text_parent_d_ordered, index_by_text_par_con) contours_only_text_parent_d_ordered, index_by_text_par_con)
#try:
#contours_only_text_parent_d_ordered = \
#list(np.array(contours_only_text_parent_d_ordered, dtype=object)[index_by_text_par_con])
#except:
#contours_only_text_parent_d_ordered = \
#list(np.array(contours_only_text_parent_d_ordered, dtype=np.int32)[index_by_text_par_con])
order_text_new, id_of_texts_tot = self.do_order_of_regions( order_text_new, id_of_texts_tot = self.do_order_of_regions(
contours_only_text_parent_d_ordered, contours_only_text_parent_h, boxes_d, textline_mask_tot_d) contours_only_text_parent_d_ordered, contours_only_text_parent_h, boxes_d, textline_mask_tot_d)