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eynollah
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updating light version. Remove textlines or textregion contours inside a bigger one

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pull/138/head^2
vahidrezanezhad 3 months ago
parent ad32316217
commit 1774076f4a
1 changed files with 114 additions and 10 deletions
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qurator/eynollah/eynollah.py
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@ -252,7 +252,7 @@ class Eynollah:
self.model_region_dir_p_ens = dir_models + "/eynollah-main-regions-ensembled_20210425"
self.model_region_dir_p_ens_light = dir_models + "/eynollah-main-regions_20220314"
self.model_reading_order_machine_dir = dir_models + "/model_ens_reading_order_machine_based"
self.model_region_dir_p_1_2_sp_np = dir_models + "/modelens_earlylayout_12spaltige_2_3_5_6_7_8"#"/modelens_earlylayout_12spaltige_2_3_5_6_7_8"#"/modelens_early12_sp_2_3_5_6_7_8_9_10_12_14_15_16_18"#"/modelens_1_2_4_5_early_lay_1_2_spaltige"#"/model_3_eraly_layout_no_patches_1_2_spaltige"
self.model_region_dir_p_1_2_sp_np = dir_models + "/modelens_earlylay12sp_0_2"#"/modelens_earlylayout_12spaltige_2_3_5_6_7_8"#"/modelens_early12_sp_2_3_5_6_7_8_9_10_12_14_15_16_18"#"/modelens_1_2_4_5_early_lay_1_2_spaltige"#"/model_3_eraly_layout_no_patches_1_2_spaltige"
##self.model_region_dir_fully_new = dir_models + "/model_2_full_layout_new_trans"
self.model_region_dir_fully = dir_models + "/modelens_full_layout_24_till_28"#"/model_2_full_layout_new_trans"
if self.textline_light:
@ -1071,8 +1071,13 @@ class Eynollah:
seg = np.argmax(label_p_pred, axis=3)[0]
if thresholding_for_artificial_class_in_light_version:
#seg_text = label_p_pred[0,:,:,1]
#seg_text[seg_text<0.2] =0
#seg_text[seg_text>0] =1
#seg[seg_text==1]=1
seg_art = label_p_pred[0,:,:,4]
seg_art[seg_art<0.1] =0
seg_art[seg_art<0.2] =0
seg_art[seg_art>0] =1
seg[seg_art==1]=4
@ -2159,7 +2164,7 @@ class Eynollah:
img_h_new = int(img_org.shape[0] / float(img_org.shape[1]) * img_w_new)
elif num_col_classifier == 2:
img_w_new = 1300#1500
img_w_new = 1500#1500
img_h_new = int(img_org.shape[0] / float(img_org.shape[1]) * img_w_new)
elif num_col_classifier == 3:
@ -2222,7 +2227,7 @@ class Eynollah:
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)
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 = False)
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:
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_ens_light)
@ -2232,7 +2237,7 @@ class Eynollah:
else:
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))
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=False)
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
else:
prediction_regions_org = self.do_prediction_new_concept(True, img_bin, self.model_region, n_batch_inference=3)
@ -2249,16 +2254,19 @@ class Eynollah:
img_bin = resize_image(img_bin,img_height_h, img_width_h )
prediction_regions_org=prediction_regions_org[:,:,0]
mask_lines_only = (prediction_regions_org[:,:] ==3)*1
mask_texts_only = (prediction_regions_org[:,:] ==1)*1
mask_texts_only = mask_texts_only.astype('uint8')
##if num_col_classifier == 1 or num_col_classifier == 2:
###mask_texts_only = cv2.erode(mask_texts_only, KERNEL, iterations=1)
##mask_texts_only = cv2.dilate(mask_texts_only, KERNEL, iterations=1)
#if num_col_classifier == 1 or num_col_classifier == 2:
#mask_texts_only = cv2.erode(mask_texts_only, KERNEL, iterations=1)
#mask_texts_only = cv2.dilate(mask_texts_only, KERNEL, iterations=1)
mask_texts_only = cv2.dilate(mask_texts_only, kernel=np.ones((2,2), np.uint8), iterations=1)
@ -4110,6 +4118,7 @@ class Eynollah:
if dilation_m1<6:
dilation_m1 = 6
#print(dilation_m1, 'dilation_m1')
dilation_m1 = 5
dilation_m2 = int(dilation_m1/2.) +1
for i in range(len(x_differential)):
@ -4267,7 +4276,6 @@ class Eynollah:
for ij in range(len(all_found_textline_polygons[j])):
con_ind = all_found_textline_polygons[j][ij]
print(len(con_ind[:,0,0]),'con_ind[:,0,0]')
area = cv2.contourArea(con_ind)
con_ind = con_ind.astype(np.float)
@ -4303,7 +4311,7 @@ class Eynollah:
if dilation_m1<4:
dilation_m1 = 4
#print(dilation_m1, 'dilation_m1')
dilation_m2 = int(dilation_m1/2.) +1
dilation_m2 = int(dilation_m1/2.) +1
for i in range(len(x_differential)):
if abs_diff[i]==0:
@ -4339,6 +4347,100 @@ class Eynollah:
all_found_textline_polygons[j][ij][:,0,0] = con_scaled[:,0, 0]
return all_found_textline_polygons
def filter_contours_inside_a_bigger_one(self,contours, image, marginal_cnts=None, type_contour="textregion"):
if type_contour=="textregion":
areas = [cv2.contourArea(contours[j]) for j in range(len(contours))]
area_tot = image.shape[0]*image.shape[1]
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))]
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]
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], (cx_main[ind_small], cy_main[ind_small]), False) for ind in range(len(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)
for ind in indexes_to_be_removed:
contours.pop(ind)
return contours
else:
contours_txtline_of_all_textregions = []
for jj in range(len(contours)):
contours_txtline_of_all_textregions = contours_txtline_of_all_textregions + contours[jj]
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]
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))]
areas_ratio = np.array(areas)/ area_tot
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]
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 ]
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)
for ind in indexes_to_be_removed:
contours[jj].pop(ind)
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])):
@ -4725,6 +4827,7 @@ class Eynollah:
#print("text region early 3 in %.1fs", time.time() - t0)
if self.light_version:
contours_only_text_parent = self.dilate_textregions_contours(contours_only_text_parent)
contours_only_text_parent = self.filter_contours_inside_a_bigger_one(contours_only_text_parent, text_only, marginal_cnts=polygons_of_marginals)
txt_con_org = get_textregion_contours_in_org_image_light(contours_only_text_parent, self.image, slope_first)
#txt_con_org = self.dilate_textregions_contours(txt_con_org)
#contours_only_text_parent = self.dilate_textregions_contours(contours_only_text_parent)
@ -4742,6 +4845,7 @@ class Eynollah:
#all_found_textline_polygons = self.dilate_textlines(all_found_textline_polygons)
all_found_textline_polygons = self.dilate_textline_contours(all_found_textline_polygons)
all_found_textline_polygons = self.filter_contours_inside_a_bigger_one(all_found_textline_polygons, textline_mask_tot_ea_org, type_contour="textline")
all_found_textline_polygons_marginals = self.dilate_textline_contours(all_found_textline_polygons_marginals)
else:

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