mirror of
https://github.com/qurator-spk/eynollah.git
synced 2025-10-15 19:09:58 +02:00
filter_contours_inside_a_bigger_one: simplify
- use new `find_center_of_contours` - avoid loops in favour of array processing - use sets instead of `np.unique` and `np.delete` instead of list.pop
This commit is contained in:
Robert Sachunsky
parent
0b9d4901a6
commit
81827c2942
1 changed files with 39 additions and 63 deletions
|
|
@ -4208,7 +4208,7 @@ class Eynollah:
|
|||
return generated_text
|
||||
|
||||
def return_list_of_contours_with_desired_order(self, ls_cons, sorted_indexes):
|
||||
return [ls_cons[sorted_indexes[index]] for index in range(len(sorted_indexes))]
|
||||
return list(np.array(ls_cons)[np.array(sorted_indexes)])
|
||||
|
||||
def return_it_in_two_groups(self, x_differential):
|
||||
split = [ind if x_differential[ind]!=x_differential[ind+1] else -1
|
||||
|
|
@ -4238,46 +4238,37 @@ class Eynollah:
|
|||
def filter_contours_inside_a_bigger_one(self, contours, contours_d_ordered, image,
|
||||
marginal_cnts=None, type_contour="textregion"):
|
||||
if type_contour == "textregion":
|
||||
areas = [cv2.contourArea(contours[j]) for j in range(len(contours))]
|
||||
areas = np.array(list(map(cv2.contourArea, contours)))
|
||||
area_tot = image.shape[0]*image.shape[1]
|
||||
areas_ratio = areas / area_tot
|
||||
cx_main, cy_main = find_center_of_contours(contours)
|
||||
|
||||
M_main = [cv2.moments(contours[j])
|
||||
for j in range(len(contours))]
|
||||
cx_main = [(M_main[j]["m10"] / (M_main[j]["m00"] + 1e-32)) for j in range(len(M_main))]
|
||||
cy_main = [(M_main[j]["m01"] / (M_main[j]["m00"] + 1e-32)) for j in range(len(M_main))]
|
||||
contours_index_small = np.flatnonzero(areas_ratio < 1e-3)
|
||||
contours_index_large = np.flatnonzero(areas_ratio >= 1e-3)
|
||||
|
||||
areas_ratio = np.array(areas)/ area_tot
|
||||
contours_index_small = [ind for ind in range(len(contours)) if areas_ratio[ind] < 1e-3]
|
||||
contours_index_big = [ind for ind in range(len(contours)) if areas_ratio[ind] >= 1e-3]
|
||||
|
||||
#contours_> = [contours[ind] for ind in contours_index_big]
|
||||
#contours_> = [contours[ind] for ind in contours_index_large]
|
||||
indexes_to_be_removed = []
|
||||
for ind_small in contours_index_small:
|
||||
results = [cv2.pointPolygonTest(contours[ind], (cx_main[ind_small], cy_main[ind_small]), False)
|
||||
for ind in contours_index_big]
|
||||
if marginal_cnts:
|
||||
results_marginal = [cv2.pointPolygonTest(marginal_cnts[ind],
|
||||
results = [cv2.pointPolygonTest(contours[ind_large], (cx_main[ind_small],
|
||||
cy_main[ind_small]),
|
||||
False)
|
||||
for ind_large in contours_index_large]
|
||||
results = np.array(results)
|
||||
if np.any(results==1):
|
||||
indexes_to_be_removed.append(ind_small)
|
||||
elif marginal_cnts:
|
||||
results_marginal = [cv2.pointPolygonTest(marginal_cnt,
|
||||
(cx_main[ind_small],
|
||||
cy_main[ind_small]),
|
||||
False)
|
||||
for ind in range(len(marginal_cnts))]
|
||||
for marginal_cnt in marginal_cnts]
|
||||
results_marginal = np.array(results_marginal)
|
||||
|
||||
if np.any(results_marginal==1):
|
||||
indexes_to_be_removed.append(ind_small)
|
||||
|
||||
results = np.array(results)
|
||||
|
||||
if np.any(results==1):
|
||||
indexes_to_be_removed.append(ind_small)
|
||||
|
||||
if len(indexes_to_be_removed)>0:
|
||||
indexes_to_be_removed = np.unique(indexes_to_be_removed)
|
||||
indexes_to_be_removed = np.sort(indexes_to_be_removed)[::-1]
|
||||
for ind in indexes_to_be_removed:
|
||||
contours.pop(ind)
|
||||
if len(contours_d_ordered)>0:
|
||||
contours_d_ordered.pop(ind)
|
||||
contours = np.delete(contours, indexes_to_be_removed, axis=0)
|
||||
if len(contours_d_ordered):
|
||||
contours_d_ordered = np.delete(contours_d_ordered, indexes_to_be_removed, axis=0)
|
||||
|
||||
return contours, contours_d_ordered
|
||||
|
||||
|
|
@ -4285,33 +4276,21 @@ class Eynollah:
|
|||
contours_txtline_of_all_textregions = []
|
||||
indexes_of_textline_tot = []
|
||||
index_textline_inside_textregion = []
|
||||
for ind_region, textlines in enumerate(contours):
|
||||
contours_txtline_of_all_textregions.extend(textlines)
|
||||
index_textline_inside_textregion.extend(list(range(len(textlines))))
|
||||
indexes_of_textline_tot.extend([ind_region] * len(textlines))
|
||||
|
||||
for jj in range(len(contours)):
|
||||
contours_txtline_of_all_textregions = contours_txtline_of_all_textregions + contours[jj]
|
||||
|
||||
ind_textline_inside_tr = list(range(len(contours[jj])))
|
||||
index_textline_inside_textregion = index_textline_inside_textregion + ind_textline_inside_tr
|
||||
ind_ins = [jj] * len(contours[jj])
|
||||
indexes_of_textline_tot = indexes_of_textline_tot + ind_ins
|
||||
|
||||
M_main_tot = [cv2.moments(contours_txtline_of_all_textregions[j])
|
||||
for j in range(len(contours_txtline_of_all_textregions))]
|
||||
cx_main_tot = [(M_main_tot[j]["m10"] / (M_main_tot[j]["m00"] + 1e-32)) for j in range(len(M_main_tot))]
|
||||
cy_main_tot = [(M_main_tot[j]["m01"] / (M_main_tot[j]["m00"] + 1e-32)) for j in range(len(M_main_tot))]
|
||||
|
||||
areas_tot = [cv2.contourArea(con_ind) for con_ind in contours_txtline_of_all_textregions]
|
||||
areas_tot = np.array(list(map(cv2.contourArea, contours_txtline_of_all_textregions)))
|
||||
area_tot_tot = image.shape[0]*image.shape[1]
|
||||
cx_main_tot, cy_main_tot = find_center_of_contours(contours_txtline_of_all_textregions)
|
||||
|
||||
textregion_index_to_del = []
|
||||
textline_in_textregion_index_to_del = []
|
||||
textline_in_textregion_index_to_del = {}
|
||||
for ij in range(len(contours_txtline_of_all_textregions)):
|
||||
args_all = list(np.array(range(len(contours_txtline_of_all_textregions))))
|
||||
args_all.pop(ij)
|
||||
|
||||
areas_without = np.array(areas_tot)[args_all]
|
||||
area_of_con_interest = areas_tot[ij]
|
||||
|
||||
args_with_bigger_area = np.array(args_all)[areas_without > 1.5*area_of_con_interest]
|
||||
args_without = np.delete(np.arange(len(contours_txtline_of_all_textregions)), ij)
|
||||
areas_without = areas_tot[args_without]
|
||||
args_with_bigger_area = args_without[areas_without > 1.5*area_of_con_interest]
|
||||
|
||||
if len(args_with_bigger_area)>0:
|
||||
results = [cv2.pointPolygonTest(contours_txtline_of_all_textregions[ind],
|
||||
|
|
@ -4322,18 +4301,15 @@ class Eynollah:
|
|||
results = np.array(results)
|
||||
if np.any(results==1):
|
||||
#print(indexes_of_textline_tot[ij], index_textline_inside_textregion[ij])
|
||||
textregion_index_to_del.append(int(indexes_of_textline_tot[ij]))
|
||||
textline_in_textregion_index_to_del.append(int(index_textline_inside_textregion[ij]))
|
||||
#contours[int(indexes_of_textline_tot[ij])].pop(int(index_textline_inside_textregion[ij]))
|
||||
textline_in_textregion_index_to_del.setdefault(
|
||||
indexes_of_textline_tot[ij], list()).append(
|
||||
index_textline_inside_textregion[ij])
|
||||
#contours[indexes_of_textline_tot[ij]].pop(index_textline_inside_textregion[ij])
|
||||
|
||||
textregion_index_to_del = np.array(textregion_index_to_del)
|
||||
textline_in_textregion_index_to_del = np.array(textline_in_textregion_index_to_del)
|
||||
for ind_u_a_trs in np.unique(textregion_index_to_del):
|
||||
textline_in_textregion_index_to_del_ind = \
|
||||
textline_in_textregion_index_to_del[textregion_index_to_del==ind_u_a_trs]
|
||||
textline_in_textregion_index_to_del_ind = np.sort(textline_in_textregion_index_to_del_ind)[::-1]
|
||||
for ittrd in textline_in_textregion_index_to_del_ind:
|
||||
contours[ind_u_a_trs].pop(ittrd)
|
||||
for textregion_index_to_del in textline_in_textregion_index_to_del:
|
||||
contours[textregion_index_to_del] = list(np.delete(
|
||||
contours[textregion_index_to_del],
|
||||
textline_in_textregion_index_to_del[textregion_index_to_del]))
|
||||
|
||||
return contours
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue