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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
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@ -4208,7 +4208,7 @@ class Eynollah:
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return generated_text
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return generated_text
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def return_list_of_contours_with_desired_order(self, ls_cons, sorted_indexes):
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def return_list_of_contours_with_desired_order(self, ls_cons, sorted_indexes):
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return [ls_cons[sorted_indexes[index]] for index in range(len(sorted_indexes))]
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return list(np.array(ls_cons)[np.array(sorted_indexes)])
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def return_it_in_two_groups(self, x_differential):
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def return_it_in_two_groups(self, x_differential):
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split = [ind if x_differential[ind]!=x_differential[ind+1] else -1
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split = [ind if x_differential[ind]!=x_differential[ind+1] else -1
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@ -4237,47 +4237,38 @@ class Eynollah:
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def filter_contours_inside_a_bigger_one(self, contours, contours_d_ordered, image,
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def filter_contours_inside_a_bigger_one(self, contours, contours_d_ordered, image,
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marginal_cnts=None, type_contour="textregion"):
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marginal_cnts=None, type_contour="textregion"):
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if type_contour=="textregion":
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if type_contour == "textregion":
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areas = [cv2.contourArea(contours[j]) for j in range(len(contours))]
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areas = np.array(list(map(cv2.contourArea, contours)))
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area_tot = image.shape[0]*image.shape[1]
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area_tot = image.shape[0]*image.shape[1]
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areas_ratio = areas / area_tot
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cx_main, cy_main = find_center_of_contours(contours)
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M_main = [cv2.moments(contours[j])
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contours_index_small = np.flatnonzero(areas_ratio < 1e-3)
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for j in range(len(contours))]
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contours_index_large = np.flatnonzero(areas_ratio >= 1e-3)
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cx_main = [(M_main[j]["m10"] / (M_main[j]["m00"] + 1e-32)) for j in range(len(M_main))]
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cy_main = [(M_main[j]["m01"] / (M_main[j]["m00"] + 1e-32)) for j in range(len(M_main))]
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areas_ratio = np.array(areas)/ area_tot
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#contours_> = [contours[ind] for ind in contours_index_large]
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contours_index_small = [ind for ind in range(len(contours)) if areas_ratio[ind] < 1e-3]
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contours_index_big = [ind for ind in range(len(contours)) if areas_ratio[ind] >= 1e-3]
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#contours_> = [contours[ind] for ind in contours_index_big]
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indexes_to_be_removed = []
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indexes_to_be_removed = []
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for ind_small in contours_index_small:
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for ind_small in contours_index_small:
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results = [cv2.pointPolygonTest(contours[ind], (cx_main[ind_small], cy_main[ind_small]), False)
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results = [cv2.pointPolygonTest(contours[ind_large], (cx_main[ind_small],
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for ind in contours_index_big]
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cy_main[ind_small]),
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if marginal_cnts:
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False)
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results_marginal = [cv2.pointPolygonTest(marginal_cnts[ind],
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for ind_large in contours_index_large]
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results = np.array(results)
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if np.any(results==1):
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indexes_to_be_removed.append(ind_small)
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elif marginal_cnts:
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results_marginal = [cv2.pointPolygonTest(marginal_cnt,
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(cx_main[ind_small],
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(cx_main[ind_small],
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cy_main[ind_small]),
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cy_main[ind_small]),
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False)
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False)
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for ind in range(len(marginal_cnts))]
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for marginal_cnt in marginal_cnts]
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results_marginal = np.array(results_marginal)
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results_marginal = np.array(results_marginal)
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if np.any(results_marginal==1):
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if np.any(results_marginal==1):
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indexes_to_be_removed.append(ind_small)
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indexes_to_be_removed.append(ind_small)
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results = np.array(results)
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contours = np.delete(contours, indexes_to_be_removed, axis=0)
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if len(contours_d_ordered):
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if np.any(results==1):
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contours_d_ordered = np.delete(contours_d_ordered, indexes_to_be_removed, axis=0)
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indexes_to_be_removed.append(ind_small)
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if len(indexes_to_be_removed)>0:
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indexes_to_be_removed = np.unique(indexes_to_be_removed)
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indexes_to_be_removed = np.sort(indexes_to_be_removed)[::-1]
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for ind in indexes_to_be_removed:
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contours.pop(ind)
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if len(contours_d_ordered)>0:
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contours_d_ordered.pop(ind)
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return contours, contours_d_ordered
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return contours, contours_d_ordered
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@ -4285,33 +4276,21 @@ class Eynollah:
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contours_txtline_of_all_textregions = []
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contours_txtline_of_all_textregions = []
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indexes_of_textline_tot = []
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indexes_of_textline_tot = []
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index_textline_inside_textregion = []
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index_textline_inside_textregion = []
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for ind_region, textlines in enumerate(contours):
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contours_txtline_of_all_textregions.extend(textlines)
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index_textline_inside_textregion.extend(list(range(len(textlines))))
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indexes_of_textline_tot.extend([ind_region] * len(textlines))
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for jj in range(len(contours)):
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areas_tot = np.array(list(map(cv2.contourArea, contours_txtline_of_all_textregions)))
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contours_txtline_of_all_textregions = contours_txtline_of_all_textregions + contours[jj]
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ind_textline_inside_tr = list(range(len(contours[jj])))
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index_textline_inside_textregion = index_textline_inside_textregion + ind_textline_inside_tr
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ind_ins = [jj] * len(contours[jj])
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indexes_of_textline_tot = indexes_of_textline_tot + ind_ins
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M_main_tot = [cv2.moments(contours_txtline_of_all_textregions[j])
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for j in range(len(contours_txtline_of_all_textregions))]
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cx_main_tot = [(M_main_tot[j]["m10"] / (M_main_tot[j]["m00"] + 1e-32)) for j in range(len(M_main_tot))]
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cy_main_tot = [(M_main_tot[j]["m01"] / (M_main_tot[j]["m00"] + 1e-32)) for j in range(len(M_main_tot))]
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areas_tot = [cv2.contourArea(con_ind) for con_ind in contours_txtline_of_all_textregions]
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area_tot_tot = image.shape[0]*image.shape[1]
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area_tot_tot = image.shape[0]*image.shape[1]
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cx_main_tot, cy_main_tot = find_center_of_contours(contours_txtline_of_all_textregions)
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textregion_index_to_del = []
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textline_in_textregion_index_to_del = {}
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textline_in_textregion_index_to_del = []
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for ij in range(len(contours_txtline_of_all_textregions)):
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for ij in range(len(contours_txtline_of_all_textregions)):
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args_all = list(np.array(range(len(contours_txtline_of_all_textregions))))
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args_all.pop(ij)
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areas_without = np.array(areas_tot)[args_all]
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area_of_con_interest = areas_tot[ij]
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area_of_con_interest = areas_tot[ij]
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args_without = np.delete(np.arange(len(contours_txtline_of_all_textregions)), ij)
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args_with_bigger_area = np.array(args_all)[areas_without > 1.5*area_of_con_interest]
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areas_without = areas_tot[args_without]
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args_with_bigger_area = args_without[areas_without > 1.5*area_of_con_interest]
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if len(args_with_bigger_area)>0:
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if len(args_with_bigger_area)>0:
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results = [cv2.pointPolygonTest(contours_txtline_of_all_textregions[ind],
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results = [cv2.pointPolygonTest(contours_txtline_of_all_textregions[ind],
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@ -4322,18 +4301,15 @@ class Eynollah:
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results = np.array(results)
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results = np.array(results)
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if np.any(results==1):
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if np.any(results==1):
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#print(indexes_of_textline_tot[ij], index_textline_inside_textregion[ij])
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#print(indexes_of_textline_tot[ij], index_textline_inside_textregion[ij])
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textregion_index_to_del.append(int(indexes_of_textline_tot[ij]))
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textline_in_textregion_index_to_del.setdefault(
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textline_in_textregion_index_to_del.append(int(index_textline_inside_textregion[ij]))
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indexes_of_textline_tot[ij], list()).append(
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#contours[int(indexes_of_textline_tot[ij])].pop(int(index_textline_inside_textregion[ij]))
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index_textline_inside_textregion[ij])
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#contours[indexes_of_textline_tot[ij]].pop(index_textline_inside_textregion[ij])
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textregion_index_to_del = np.array(textregion_index_to_del)
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for textregion_index_to_del in textline_in_textregion_index_to_del:
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textline_in_textregion_index_to_del = np.array(textline_in_textregion_index_to_del)
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contours[textregion_index_to_del] = list(np.delete(
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for ind_u_a_trs in np.unique(textregion_index_to_del):
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contours[textregion_index_to_del],
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textline_in_textregion_index_to_del_ind = \
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textline_in_textregion_index_to_del[textregion_index_to_del]))
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textline_in_textregion_index_to_del[textregion_index_to_del==ind_u_a_trs]
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textline_in_textregion_index_to_del_ind = np.sort(textline_in_textregion_index_to_del_ind)[::-1]
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for ittrd in textline_in_textregion_index_to_del_ind:
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contours[ind_u_a_trs].pop(ittrd)
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return contours
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return contours
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