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eynollah
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only images extraction - update inference parameters

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pull/132/head
vahidrezanezhad 5 months ago
parent 7cbca79f16
commit 9170a9f21c
1 changed files with 11 additions and 4 deletions
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qurator/eynollah/eynollah.py
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@ -260,7 +260,7 @@ class Eynollah:
self.model_page = self.our_load_model(self.model_page_dir) self.model_page = self.our_load_model(self.model_page_dir)
self.model_classifier = self.our_load_model(self.model_dir_of_col_classifier) self.model_classifier = self.our_load_model(self.model_dir_of_col_classifier)
#self.model_bin = self.our_load_model(self.model_dir_of_binarization) self.model_bin = self.our_load_model(self.model_dir_of_binarization)
#self.model_textline = self.our_load_model(self.model_textline_dir) #self.model_textline = self.our_load_model(self.model_textline_dir)
self.model_region = self.our_load_model(self.model_region_dir_p_ens_light_only_images_extraction) self.model_region = self.our_load_model(self.model_region_dir_p_ens_light_only_images_extraction)
#self.model_region_fl_np = self.our_load_model(self.model_region_dir_fully_np) #self.model_region_fl_np = self.our_load_model(self.model_region_dir_fully_np)
@ -917,7 +917,8 @@ class Eynollah:
##seg2 = -label_p_pred[0,:,:,2] ##seg2 = -label_p_pred[0,:,:,2]
if self.extract_only_images: if self.extract_only_images:
seg_not_base[seg_not_base>0.3] =1 #seg_not_base[seg_not_base>0.3] =1
seg_not_base[seg_not_base>0.5] =1
seg_not_base[seg_not_base<1] =0 seg_not_base[seg_not_base<1] =0
else: else:
seg_not_base[seg_not_base>0.03] =1 seg_not_base[seg_not_base>0.03] =1
@ -955,7 +956,7 @@ class Eynollah:
##plt.show() ##plt.show()
#seg[seg==1]=0 #seg[seg==1]=0
#seg[seg_test==1]=1 #seg[seg_test==1]=1
seg[seg_not_base==1]=4 ###seg[seg_not_base==1]=4
if not self.extract_only_images: if not self.extract_only_images:
seg[seg_background==1]=0 seg[seg_background==1]=0
seg[(seg_line==1) & (seg==0)]=3 seg[(seg_line==1) & (seg==0)]=3
@ -1689,7 +1690,13 @@ class Eynollah:
text_regions_p_true = cv2.fillPoly(text_regions_p_true, pts = polygons_of_only_texts, color=(1,1,1)) text_regions_p_true = cv2.fillPoly(text_regions_p_true, pts = polygons_of_only_texts, color=(1,1,1))
polygons_of_images = return_contours_of_interested_region(text_regions_p_true, 2, 0.0001)
text_regions_p_true[text_regions_p_true.shape[0]-15:text_regions_p_true.shape[0], :] = 0
text_regions_p_true[:, text_regions_p_true.shape[1]-15:text_regions_p_true.shape[1]] = 0
##polygons_of_images = return_contours_of_interested_region(text_regions_p_true, 2, 0.0001)
polygons_of_images = return_contours_of_interested_region(text_regions_p_true, 2, 0.001)
image_boundary_of_doc = np.zeros((text_regions_p_true.shape[0], text_regions_p_true.shape[1])) image_boundary_of_doc = np.zeros((text_regions_p_true.shape[0], text_regions_p_true.shape[1]))

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