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eynollah
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updating light version features

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pull/138/head^2
vahidrezanezhad 3 months ago
parent 1774076f4a
commit ab63d5ba40
1 changed files with 63 additions and 42 deletions
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qurator/eynollah/eynollah.py
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@ -2225,8 +2225,11 @@ class Eynollah:
if not self.dir_in:
if num_col_classifier == 1 or num_col_classifier == 2:
prediction_regions_org = np.zeros((self.image_org.shape[0], self.image_org.shape[1], 3))
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_1_2_sp_np)
if self.image_org.shape[0]/self.image_org.shape[1] > 2.5:
prediction_regions_org = self.do_prediction_new_concept(True, img_resized, model_region, n_batch_inference=1, thresholding_for_artificial_class_in_light_version = True)
else:
prediction_regions_org = np.zeros((self.image_org.shape[0], self.image_org.shape[1], 3))
prediction_regions_page = self.do_prediction_new_concept(False, self.image_page_org_size, model_region, n_batch_inference=1, thresholding_for_artificial_class_in_light_version = True)
prediction_regions_org[self.page_coord[0] : self.page_coord[1], self.page_coord[2] : self.page_coord[3],:] = prediction_regions_page
else:
@ -2236,6 +2239,9 @@ class Eynollah:
##prediction_regions_org = self.do_prediction(True, img_bin, model_region, n_batch_inference=3, thresholding_for_some_classes_in_light_version=True)
else:
if num_col_classifier == 1 or num_col_classifier == 2:
if self.image_org.shape[0]/self.image_org.shape[1] > 2.5:
prediction_regions_org = self.do_prediction_new_concept(True, img_resized, self.model_region_1_2, n_batch_inference=1, thresholding_for_artificial_class_in_light_version=True)
else:
prediction_regions_org = np.zeros((self.image_org.shape[0], self.image_org.shape[1], 3))
prediction_regions_page = self.do_prediction_new_concept(False, self.image_page_org_size, self.model_region_1_2, n_batch_inference=1, thresholding_for_artificial_class_in_light_version=True)
prediction_regions_org[self.page_coord[0] : self.page_coord[1], self.page_coord[2] : self.page_coord[3],:] = prediction_regions_page
@ -4356,6 +4362,8 @@ class Eynollah:
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))]
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]
@ -4379,68 +4387,79 @@ class Eynollah:
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)
return contours
else:
contours_txtline_of_all_textregions = []
indexes_of_textline_tot = []
index_textline_inside_textregion = []
for jj in range(len(contours)):
contours_txtline_of_all_textregions = contours_txtline_of_all_textregions + contours[jj]
ind_ins = np.zeros( len(contours[jj]) ) + jj
list_ind_ins = list(ind_ins)
ind_textline_inside_tr = np.array (range(len(contours[jj])) )
list_ind_textline_inside_tr = list(ind_textline_inside_tr)
index_textline_inside_textregion = index_textline_inside_textregion + list_ind_textline_inside_tr
indexes_of_textline_tot = indexes_of_textline_tot + list_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]
area_tot_tot = image.shape[0]*image.shape[1]
areas_ratio_tot = np.array(areas_tot)/ area_tot_tot
contours_index_big_tot = [ind for ind in range(len(contours_txtline_of_all_textregions)) if areas_ratio_tot[ind] >= 1e-2]
textregion_index_to_del = []
textline_in_textregion_index_to_del = []
for ij in range(len(contours_txtline_of_all_textregions)):
for jj in range(len(contours)):
contours_in = contours[jj]
#print(len(contours_in))
areas = [cv2.contourArea(con_ind) for con_ind in contours_in]
area_tot = image.shape[0]*image.shape[1]
M_main = [cv2.moments(contours_in[j]) for j in range(len(contours_in))]
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))]
args_all = list(np.array(range(len(contours_txtline_of_all_textregions))))
areas_ratio = np.array(areas)/ area_tot
args_all.pop(ij)
if len(areas_ratio)>=1:
#print(np.max(areas_ratio), np.min(areas_ratio))
contours_index_small = [ind for ind in range(len(contours_in)) if areas_ratio[ind] < 1e-2]
#contours_index_big = [ind for ind in range(len(contours_in)) if areas_ratio[ind] >= 1e-3]
areas_without = np.array(areas_tot)[args_all]
area_of_con_interest = areas_tot[ij]
if len(contours_index_small)>0:
indexes_to_be_removed = []
for ind_small in contours_index_small:
results = [cv2.pointPolygonTest(contours_txtline_of_all_textregions[ind], (cx_main[ind_small], cy_main[ind_small]), False) for ind in contours_index_big_tot ]
args_with_bigger_area = np.array(args_all)[areas_without > area_of_con_interest]
if len(args_with_bigger_area)>0:
results = [cv2.pointPolygonTest(contours_txtline_of_all_textregions[ind], (cx_main_tot[ij], cy_main_tot[ij]), False) for ind in args_with_bigger_area ]
results = np.array(results)
if np.any(results==1):
indexes_to_be_removed.append(ind_small)
#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]))
uniqe_args_trs = np.unique(textregion_index_to_del)
if len(indexes_to_be_removed)>0:
indexes_to_be_removed = np.unique(indexes_to_be_removed)
for ind_u_a_trs in uniqe_args_trs:
textline_in_textregion_index_to_del_ind = np.array(textline_in_textregion_index_to_del)[np.array(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 ind in indexes_to_be_removed:
contours[jj].pop(ind)
for ittrd in textline_in_textregion_index_to_del_ind:
contours[ind_u_a_trs].pop(ittrd)
return contours
def dilate_textlines(self,all_found_textline_polygons):
for j in range(len(all_found_textline_polygons)):
for i in range(len(all_found_textline_polygons[j])):
@ -4852,6 +4871,8 @@ class Eynollah:
textline_mask_tot_ea = cv2.erode(textline_mask_tot_ea, kernel=KERNEL, iterations=1)
slopes, all_found_textline_polygons, boxes_text, txt_con_org, contours_only_text_parent, all_box_coord, index_by_text_par_con = self.get_slopes_and_deskew_new_light(txt_con_org, contours_only_text_parent, textline_mask_tot_ea, image_page_rotated, boxes_text, slope_deskew)
slopes_marginals, all_found_textline_polygons_marginals, boxes_marginals, _, polygons_of_marginals, all_box_coord_marginals, _ = self.get_slopes_and_deskew_new_light(polygons_of_marginals, polygons_of_marginals, textline_mask_tot_ea, image_page_rotated, boxes_marginals, slope_deskew)
#all_found_textline_polygons = self.filter_contours_inside_a_bigger_one(all_found_textline_polygons, textline_mask_tot_ea_org, type_contour="textline")
else:
textline_mask_tot_ea = cv2.erode(textline_mask_tot_ea, kernel=KERNEL, iterations=1)
slopes, all_found_textline_polygons, boxes_text, txt_con_org, contours_only_text_parent, all_box_coord, index_by_text_par_con = self.get_slopes_and_deskew_new(txt_con_org, contours_only_text_parent, textline_mask_tot_ea, image_page_rotated, boxes_text, slope_deskew)

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