mirror of
https://github.com/qurator-spk/eynollah.git
synced 2025-06-09 20:29:55 +02:00
🎨 remove trailing spaces
This commit is contained in:
Konstantin Baierer
parent
6ab7abdfcd
commit
a2341deab4
2 changed files with 280 additions and 280 deletions
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@ -1238,12 +1238,12 @@ class eynollah:
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crop_img, crop_coor = crop_image_inside_box(boxes_text[mv], image_page_rotated)
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# all_box_coord.append(crop_coor)
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mask_textline=np.zeros((textline_mask_tot_ea.shape))
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mask_textline=cv2.fillPoly(mask_textline,pts=[contours_per_process[mv]],color=(1,1,1))
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denoised = None
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all_text_region_raw=(textline_mask_tot_ea*mask_textline[:,:])[boxes_text[mv][1]:boxes_text[mv][1]+boxes_text[mv][3] , boxes_text[mv][0]:boxes_text[mv][0]+boxes_text[mv][2] ]
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@ -2758,258 +2758,258 @@ class eynollah:
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def get_regions_from_xy_2models(self,img,is_image_enhanced):
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img_org=np.copy(img)
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img_height_h=img_org.shape[0]
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img_width_h=img_org.shape[1]
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model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_ens)
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gaussian_filter=False
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patches=True
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binary=False
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ratio_y=1.3
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ratio_x=1
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median_blur=False
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img= self.resize_image(img_org, int(img_org.shape[0]*ratio_y), int(img_org.shape[1]*ratio_x))
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if binary:
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img = self.otsu_copy_binary(img)#self.otsu_copy(img)
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img = img.astype(np.uint16)
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if median_blur:
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img=cv2.medianBlur(img,5)
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if gaussian_filter:
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img= cv2.GaussianBlur(img,(5,5),0)
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img = img.astype(np.uint16)
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prediction_regions_org_y=self.do_prediction(patches,img,model_region)
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prediction_regions_org_y=self.resize_image(prediction_regions_org_y, img_height_h, img_width_h )
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#plt.imshow(prediction_regions_org_y[:,:,0])
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#plt.show()
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#sys.exit()
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prediction_regions_org_y=prediction_regions_org_y[:,:,0]
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mask_zeros_y=(prediction_regions_org_y[:,:]==0)*1
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if is_image_enhanced:
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ratio_x=1.2
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else:
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ratio_x=1
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ratio_y=1
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median_blur=False
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img= self.resize_image(img_org, int(img_org.shape[0]*ratio_y), int(img_org.shape[1]*ratio_x))
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if binary:
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img = self.otsu_copy_binary(img)#self.otsu_copy(img)
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img = img.astype(np.uint16)
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if median_blur:
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img=cv2.medianBlur(img,5)
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if gaussian_filter:
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img= cv2.GaussianBlur(img,(5,5),0)
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img = img.astype(np.uint16)
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prediction_regions_org=self.do_prediction(patches,img,model_region)
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prediction_regions_org=self.resize_image(prediction_regions_org, img_height_h, img_width_h )
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##plt.imshow(prediction_regions_org[:,:,0])
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##plt.show()
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##sys.exit()
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prediction_regions_org=prediction_regions_org[:,:,0]
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prediction_regions_org[(prediction_regions_org[:,:]==1) & (mask_zeros_y[:,:]==1)]=0
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session_region.close()
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del model_region
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del session_region
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gc.collect()
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###K.clear_session()
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model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p2)
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gaussian_filter=False
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patches=True
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binary=False
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ratio_x=1
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ratio_y=1
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median_blur=False
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img= self.resize_image(img_org, int(img_org.shape[0]*ratio_y), int(img_org.shape[1]*ratio_x))
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if binary:
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img = self.otsu_copy_binary(img)#self.otsu_copy(img)
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img = img.astype(np.uint16)
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if median_blur:
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img=cv2.medianBlur(img,5)
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if gaussian_filter:
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img= cv2.GaussianBlur(img,(5,5),0)
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img = img.astype(np.uint16)
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marginal_patch=0.2
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prediction_regions_org2=self.do_prediction(patches,img,model_region,marginal_patch)
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prediction_regions_org2=self.resize_image(prediction_regions_org2, img_height_h, img_width_h )
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#plt.imshow(prediction_regions_org2[:,:,0])
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#plt.show()
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#sys.exit()
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##prediction_regions_org=prediction_regions_org[:,:,0]
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session_region.close()
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del model_region
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del session_region
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gc.collect()
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###K.clear_session()
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mask_zeros2=(prediction_regions_org2[:,:,0]==0)*1
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mask_lines2=(prediction_regions_org2[:,:,0]==3)*1
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text_sume_early=( (prediction_regions_org[:,:]==1)*1 ).sum()
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prediction_regions_org_copy=np.copy(prediction_regions_org)
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prediction_regions_org_copy[(prediction_regions_org_copy[:,:]==1) & (mask_zeros2[:,:]==1)]=0
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text_sume_second=( (prediction_regions_org_copy[:,:]==1)*1 ).sum()
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rate_two_models=text_sume_second/float(text_sume_early)*100
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print(rate_two_models,'ratio_of_two_models')
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if is_image_enhanced and rate_two_models<95.50:#98.45:
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pass
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else:
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prediction_regions_org=np.copy(prediction_regions_org_copy)
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##prediction_regions_org[mask_lines2[:,:]==1]=3
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prediction_regions_org[(mask_lines2[:,:]==1) & (prediction_regions_org[:,:]==0)]=3
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del mask_lines2
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del mask_zeros2
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del prediction_regions_org2
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#if is_image_enhanced:
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#pass
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#else:
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#model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p2)
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#gaussian_filter=False
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#patches=True
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#binary=False
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#ratio_x=1
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#ratio_y=1
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#median_blur=False
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#img= self.resize_image(img_org, int(img_org.shape[0]*ratio_y), int(img_org.shape[1]*ratio_x))
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#if binary:
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#img = self.otsu_copy_binary(img)#self.otsu_copy(img)
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#img = img.astype(np.uint16)
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#if median_blur:
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#img=cv2.medianBlur(img,5)
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#if gaussian_filter:
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#img= cv2.GaussianBlur(img,(5,5),0)
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#img = img.astype(np.uint16)
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#prediction_regions_org2=self.do_prediction(patches,img,model_region)
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#prediction_regions_org2=self.resize_image(prediction_regions_org2, img_height_h, img_width_h )
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##plt.imshow(prediction_regions_org2[:,:,0])
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##plt.show()
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##sys.exit()
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###prediction_regions_org=prediction_regions_org[:,:,0]
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#session_region.close()
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#del model_region
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#del session_region
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#gc.collect()
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####K.clear_session()
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#mask_zeros2=(prediction_regions_org2[:,:,0]==0)*1
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#mask_lines2=(prediction_regions_org2[:,:,0]==3)*1
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#text_sume_early=( (prediction_regions_org[:,:]==1)*1 ).sum()
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#prediction_regions_org[(prediction_regions_org[:,:]==1) & (mask_zeros2[:,:]==1)]=0
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###prediction_regions_org[mask_lines2[:,:]==1]=3
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#prediction_regions_org[(mask_lines2[:,:]==1) & (prediction_regions_org[:,:]==0)]=3
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#text_sume_second=( (prediction_regions_org[:,:]==1)*1 ).sum()
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#print(text_sume_second/float(text_sume_early)*100,'twomodelsratio')
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#del mask_lines2
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#del mask_zeros2
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#del prediction_regions_org2
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mask_lines_only=(prediction_regions_org[:,:]==3)*1
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prediction_regions_org = cv2.erode(prediction_regions_org[:,:], self.kernel, iterations=2)
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#plt.imshow(text_region2_1st_channel)
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#plt.show()
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prediction_regions_org = cv2.dilate(prediction_regions_org[:,:], self.kernel, iterations=2)
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mask_texts_only=(prediction_regions_org[:,:]==1)*1
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mask_images_only=(prediction_regions_org[:,:]==2)*1
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pixel_img=1
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min_area_text=0.00001
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polygons_of_only_texts=self.return_contours_of_interested_region(mask_texts_only,pixel_img,min_area_text)
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polygons_of_only_images=self.return_contours_of_interested_region(mask_images_only,pixel_img)
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polygons_of_only_lines=self.return_contours_of_interested_region(mask_lines_only,pixel_img,min_area_text)
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text_regions_p_true=np.zeros(prediction_regions_org.shape)
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#text_regions_p_true[:,:]=text_regions_p_1[:,:]
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text_regions_p_true=cv2.fillPoly(text_regions_p_true,pts=polygons_of_only_lines, color=(3,3,3))
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##text_regions_p_true=cv2.fillPoly(text_regions_p_true,pts=polygons_of_only_images, color=(2,2,2))
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text_regions_p_true[:,:][mask_images_only[:,:]==1]=2
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text_regions_p_true=cv2.fillPoly(text_regions_p_true,pts=polygons_of_only_texts, color=(1,1,1))
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##print(np.unique(text_regions_p_true))
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#text_regions_p_true_3d=np.repeat(text_regions_p_1[:, :, np.newaxis], 3, axis=2)
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#text_regions_p_true_3d=text_regions_p_true_3d.astype(np.uint8)
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del polygons_of_only_texts
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del polygons_of_only_images
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del polygons_of_only_lines
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@ -3017,14 +3017,14 @@ class eynollah:
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del prediction_regions_org
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del img
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del mask_zeros_y
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del prediction_regions_org_y
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del img_org
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gc.collect()
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return text_regions_p_true
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def write_images_into_directory(self, img_contoures, dir_of_cropped_imgs, image_page):
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index = 0
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for cont_ind in img_contoures:
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@ -3044,13 +3044,13 @@ class eynollah:
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def get_marginals(self,text_with_lines,text_regions,num_col,slope_deskew):
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mask_marginals=np.zeros((text_with_lines.shape[0],text_with_lines.shape[1]))
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mask_marginals=mask_marginals.astype(np.uint8)
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text_with_lines=text_with_lines.astype(np.uint8)
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##text_with_lines=cv2.erode(text_with_lines,self.kernel,iterations=3)
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text_with_lines_eroded=cv2.erode(text_with_lines,self.kernel,iterations=5)
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if text_with_lines.shape[0]<=1500:
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pass
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elif text_with_lines.shape[0]>1500 and text_with_lines.shape[0]<=1800:
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@ -3061,46 +3061,46 @@ class eynollah:
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text_with_lines=self.resize_image(text_with_lines,int(text_with_lines.shape[0]*1.8),text_with_lines.shape[1])
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text_with_lines=cv2.erode(text_with_lines,self.kernel,iterations=7)
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text_with_lines=self.resize_image(text_with_lines,text_with_lines_eroded.shape[0],text_with_lines_eroded.shape[1])
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text_with_lines_y=text_with_lines.sum(axis=0)
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text_with_lines_y_eroded=text_with_lines_eroded.sum(axis=0)
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thickness_along_y_percent=text_with_lines_y_eroded.max()/(float(text_with_lines.shape[0]))*100
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thickness_along_y_percent=text_with_lines_y_eroded.max()/(float(text_with_lines.shape[0]))*100
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#print(thickness_along_y_percent,'thickness_along_y_percent')
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if thickness_along_y_percent<30:
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min_textline_thickness=8
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elif thickness_along_y_percent>=30 and thickness_along_y_percent<50:
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min_textline_thickness=20
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else:
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min_textline_thickness=40
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if thickness_along_y_percent>=14:
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text_with_lines_y_rev=-1*text_with_lines_y[:]
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#print(text_with_lines_y)
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#print(text_with_lines_y_rev)
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#plt.plot(text_with_lines_y)
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#plt.show()
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text_with_lines_y_rev=text_with_lines_y_rev-np.min(text_with_lines_y_rev)
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#plt.plot(text_with_lines_y_rev)
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#plt.show()
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sigma_gaus=1
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region_sum_0= gaussian_filter1d(text_with_lines_y, sigma_gaus)
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region_sum_0_rev=gaussian_filter1d(text_with_lines_y_rev, sigma_gaus)
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#plt.plot(region_sum_0_rev)
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#plt.show()
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region_sum_0_updown=region_sum_0[len(region_sum_0)::-1]
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@ -3110,125 +3110,125 @@ class eynollah:
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last_nonzero=len(region_sum_0)-last_nonzero
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##img_sum_0_smooth_rev=-region_sum_0
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mid_point=(last_nonzero+first_nonzero)/2.
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one_third_right=(last_nonzero-mid_point)/3.0
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one_third_left=(mid_point-first_nonzero)/3.0
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#img_sum_0_smooth_rev=img_sum_0_smooth_rev-np.min(img_sum_0_smooth_rev)
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#img_sum_0_smooth_rev=img_sum_0_smooth_rev-np.min(img_sum_0_smooth_rev)
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peaks, _ = find_peaks(text_with_lines_y_rev, height=0)
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peaks=np.array(peaks)
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#print(region_sum_0[peaks])
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##plt.plot(region_sum_0)
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##plt.plot(peaks,region_sum_0[peaks],'*')
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##plt.show()
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#print(first_nonzero,last_nonzero,peaks)
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peaks=peaks[(peaks>first_nonzero) & ((peaks<last_nonzero))]
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#print(first_nonzero,last_nonzero,peaks)
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#print(region_sum_0[peaks]<10)
|
||||
####peaks=peaks[region_sum_0[peaks]<25 ]
|
||||
|
||||
|
||||
#print(region_sum_0[peaks])
|
||||
peaks=peaks[region_sum_0[peaks]<min_textline_thickness ]
|
||||
#print(peaks)
|
||||
#print(first_nonzero,last_nonzero,one_third_right,one_third_left)
|
||||
|
||||
|
||||
if num_col==1:
|
||||
peaks_right=peaks[peaks>mid_point]
|
||||
peaks_left=peaks[peaks<mid_point]
|
||||
if num_col==2:
|
||||
peaks_right=peaks[peaks>(mid_point+one_third_right)]
|
||||
peaks_left=peaks[peaks<(mid_point-one_third_left)]
|
||||
|
||||
|
||||
|
||||
|
||||
try:
|
||||
point_right=np.min(peaks_right)
|
||||
except:
|
||||
point_right=last_nonzero
|
||||
|
||||
|
||||
|
||||
|
||||
try:
|
||||
point_left=np.max(peaks_left)
|
||||
except:
|
||||
point_left=first_nonzero
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
#print(point_left,point_right)
|
||||
#print(text_regions.shape)
|
||||
if point_right>=mask_marginals.shape[1]:
|
||||
point_right=mask_marginals.shape[1]-1
|
||||
|
||||
|
||||
try:
|
||||
mask_marginals[:,point_left:point_right]=1
|
||||
except:
|
||||
mask_marginals[:,:]=1
|
||||
|
||||
|
||||
#print(mask_marginals.shape,point_left,point_right,'nadosh')
|
||||
mask_marginals_rotated=self.rotate_image(mask_marginals,-slope_deskew)
|
||||
|
||||
|
||||
#print(mask_marginals_rotated.shape,'nadosh')
|
||||
mask_marginals_rotated_sum=mask_marginals_rotated.sum(axis=0)
|
||||
|
||||
|
||||
mask_marginals_rotated_sum[mask_marginals_rotated_sum!=0]=1
|
||||
index_x=np.array(range(len(mask_marginals_rotated_sum)))+1
|
||||
|
||||
|
||||
index_x_interest=index_x[mask_marginals_rotated_sum==1]
|
||||
|
||||
|
||||
min_point_of_left_marginal=np.min(index_x_interest)-16
|
||||
max_point_of_right_marginal=np.max(index_x_interest)+16
|
||||
|
||||
|
||||
if min_point_of_left_marginal<0:
|
||||
min_point_of_left_marginal=0
|
||||
if max_point_of_right_marginal>=text_regions.shape[1]:
|
||||
max_point_of_right_marginal=text_regions.shape[1]-1
|
||||
|
||||
|
||||
|
||||
|
||||
#print(np.min(index_x_interest) ,np.max(index_x_interest),'minmaxnew')
|
||||
#print(mask_marginals_rotated.shape,text_regions.shape,'mask_marginals_rotated')
|
||||
#plt.imshow(mask_marginals)
|
||||
#plt.show()
|
||||
|
||||
|
||||
#plt.imshow(mask_marginals_rotated)
|
||||
#plt.show()
|
||||
|
||||
text_regions[(mask_marginals_rotated[:,:]!=1) & (text_regions[:,:]==1)]=4
|
||||
|
||||
|
||||
#plt.imshow(text_regions)
|
||||
#plt.show()
|
||||
|
||||
|
||||
pixel_img=4
|
||||
min_area_text=0.00001
|
||||
polygons_of_marginals=self.return_contours_of_interested_region(text_regions,pixel_img,min_area_text)
|
||||
|
||||
|
||||
cx_text_only,cy_text_only ,x_min_text_only,x_max_text_only, y_min_text_only ,y_max_text_only,y_cor_x_min_main=self.find_new_features_of_contoures(polygons_of_marginals)
|
||||
|
||||
|
||||
text_regions[(text_regions[:,:]==4)]=1
|
||||
|
||||
|
||||
marginlas_should_be_main_text=[]
|
||||
|
||||
|
||||
x_min_marginals_left=[]
|
||||
x_min_marginals_right=[]
|
||||
|
||||
|
||||
for i in range(len(cx_text_only)):
|
||||
|
||||
|
||||
x_width_mar=abs(x_min_text_only[i]-x_max_text_only[i])
|
||||
y_height_mar=abs(y_min_text_only[i]-y_max_text_only[i])
|
||||
#print(x_width_mar,y_height_mar,y_height_mar/x_width_mar,'y_height_mar')
|
||||
|
|
@ -3246,39 +3246,39 @@ class eynollah:
|
|||
x_min_marginals_right.append(x_min_marginals_right_new)
|
||||
else:
|
||||
x_min_marginals_right[0]=min(x_min_marginals_right[0],x_min_marginals_right_new)
|
||||
|
||||
if len(x_min_marginals_left)==0:
|
||||
|
||||
if len(x_min_marginals_left)==0:
|
||||
x_min_marginals_left=[0]
|
||||
if len(x_min_marginals_right)==0:
|
||||
x_min_marginals_right=[text_regions.shape[1]-1]
|
||||
|
||||
|
||||
|
||||
|
||||
#print(x_min_marginals_left[0],x_min_marginals_right[0],'margo')
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
#print(x_min_marginals_left[0],x_min_marginals_right[0],'margo')
|
||||
|
||||
#print(marginlas_should_be_main_text,'marginlas_should_be_main_text')
|
||||
text_regions=cv2.fillPoly(text_regions, pts =marginlas_should_be_main_text, color=(4,4))
|
||||
|
||||
text_regions=cv2.fillPoly(text_regions, pts =marginlas_should_be_main_text, color=(4,4))
|
||||
|
||||
#print(np.unique(text_regions))
|
||||
|
||||
|
||||
#text_regions[:,:int(x_min_marginals_left[0])][text_regions[:,:int(x_min_marginals_left[0])]==1]=0
|
||||
#text_regions[:,int(x_min_marginals_right[0]):][text_regions[:,int(x_min_marginals_right[0]):]==1]=0
|
||||
|
||||
|
||||
text_regions[:,:int(min_point_of_left_marginal)][text_regions[:,:int(min_point_of_left_marginal)]==1]=0
|
||||
text_regions[:,int(max_point_of_right_marginal):][text_regions[:,int(max_point_of_right_marginal):]==1]=0
|
||||
|
||||
|
||||
###text_regions[:,0:point_left][text_regions[:,0:point_left]==1]=4
|
||||
|
||||
|
||||
###text_regions[:,point_right:][ text_regions[:,point_right:]==1]=4
|
||||
#plt.plot(region_sum_0)
|
||||
#plt.plot(peaks,region_sum_0[peaks],'*')
|
||||
#plt.show()
|
||||
|
||||
|
||||
|
||||
|
||||
#plt.imshow(text_regions)
|
||||
#plt.show()
|
||||
|
||||
|
||||
#sys.exit()
|
||||
else:
|
||||
pass
|
||||
|
|
|
|||
|
|
@ -1526,12 +1526,12 @@ def find_num_col_deskew(regions_without_seperators, sigma_, multiplier=3.8):
|
|||
###peaks, _ = find_peaks(z, height=0)
|
||||
|
||||
###peaks_neg=peaks_neg-10-10
|
||||
|
||||
|
||||
####print(np.std(z),'np.std(z)np.std(z)np.std(z)')
|
||||
|
||||
|
||||
#####plt.plot(z)
|
||||
#####plt.show()
|
||||
|
||||
|
||||
#####plt.imshow(regions_without_seperators)
|
||||
#####plt.show()
|
||||
###"""
|
||||
|
|
@ -1539,18 +1539,18 @@ def find_num_col_deskew(regions_without_seperators, sigma_, multiplier=3.8):
|
|||
###first_nonzero=first_nonzero+0#+100
|
||||
|
||||
###peaks_neg=peaks_neg[(peaks_neg>first_nonzero) & (peaks_neg<last_nonzero)]
|
||||
|
||||
|
||||
###peaks=peaks[(peaks>.06*regions_without_seperators.shape[1]) & (peaks<0.94*regions_without_seperators.shape[1])]
|
||||
###"""
|
||||
###interest_pos=z[peaks]
|
||||
|
||||
|
||||
###interest_pos=interest_pos[interest_pos>10]
|
||||
|
||||
|
||||
###interest_neg=z[peaks_neg]
|
||||
|
||||
|
||||
###min_peaks_pos=np.mean(interest_pos)
|
||||
###min_peaks_neg=0#np.min(interest_neg)
|
||||
|
||||
|
||||
###dis_talaei=(min_peaks_pos-min_peaks_neg)/multiplier
|
||||
####print(interest_pos)
|
||||
###grenze=min_peaks_pos-dis_talaei#np.mean(y[peaks_neg[0]:peaks_neg[len(peaks_neg)-1]])-np.std(y[peaks_neg[0]:peaks_neg[len(peaks_neg)-1]])/2.0
|
||||
|
|
@ -1558,18 +1558,18 @@ def find_num_col_deskew(regions_without_seperators, sigma_, multiplier=3.8):
|
|||
###interest_neg_fin=interest_neg[(interest_neg<grenze)]
|
||||
###peaks_neg_fin=peaks_neg[(interest_neg<grenze)]
|
||||
###interest_neg_fin=interest_neg[(interest_neg<grenze)]
|
||||
|
||||
|
||||
###"""
|
||||
###if interest_neg[0]<0.1:
|
||||
###interest_neg=interest_neg[1:]
|
||||
###if interest_neg[len(interest_neg)-1]<0.1:
|
||||
###interest_neg=interest_neg[:len(interest_neg)-1]
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
###min_peaks_pos=np.min(interest_pos)
|
||||
###min_peaks_neg=0#np.min(interest_neg)
|
||||
|
||||
|
||||
|
||||
###dis_talaei=(min_peaks_pos-min_peaks_neg)/multiplier
|
||||
###grenze=min_peaks_pos-dis_talaei#np.mean(y[peaks_neg[0]:peaks_neg[len(peaks_neg)-1]])-np.std(y[peaks_neg[0]:peaks_neg[len(peaks_neg)-1]])/2.0
|
||||
|
|
@ -1586,11 +1586,11 @@ def find_num_col_deskew(regions_without_seperators, sigma_, multiplier=3.8):
|
|||
###p_m=int(len(y)/2.)
|
||||
###p_g_l=int(len(y)/3.)
|
||||
###p_g_u=len(y)-int(len(y)/3.)
|
||||
|
||||
|
||||
|
||||
|
||||
###diff_peaks=np.abs( np.diff(peaks_neg_fin) )
|
||||
###diff_peaks_annormal=diff_peaks[diff_peaks<30]
|
||||
|
||||
|
||||
#print(len(interest_neg_fin),np.mean(interest_neg_fin))
|
||||
return np.std(z)#interest_neg_fin,np.std(z)
|
||||
|
||||
|
|
@ -2349,11 +2349,11 @@ def return_regions_without_seperators(regions_pre):
|
|||
|
||||
|
||||
def return_deskew_slop(img_patch_org, sigma_des, main_page=False, dir_of_all=None, f_name=None):
|
||||
|
||||
|
||||
|
||||
|
||||
if main_page and dir_of_all is not None:
|
||||
|
||||
|
||||
|
||||
|
||||
plt.figure(figsize=(70,40))
|
||||
plt.rcParams['font.size']='50'
|
||||
plt.subplot(1,2,1)
|
||||
|
|
@ -2364,49 +2364,49 @@ def return_deskew_slop(img_patch_org, sigma_des, main_page=False, dir_of_all=Non
|
|||
plt.ylabel('Height',fontsize=60)
|
||||
plt.yticks([0,len(gaussian_filter1d(img_patch_org.sum(axis=1), 3))])
|
||||
plt.gca().invert_yaxis()
|
||||
|
||||
|
||||
plt.savefig(os.path.join(dir_of_all, f_name+'_density_of_textline.png'))
|
||||
#print(np.max(img_patch_org.sum(axis=0)) ,np.max(img_patch_org.sum(axis=1)),'axislar')
|
||||
|
||||
|
||||
#img_patch_org=resize_image(img_patch_org,int(img_patch_org.shape[0]*2.5),int(img_patch_org.shape[1]/2.5))
|
||||
|
||||
|
||||
#print(np.max(img_patch_org.sum(axis=0)) ,np.max(img_patch_org.sum(axis=1)),'axislar2')
|
||||
|
||||
|
||||
img_int=np.zeros((img_patch_org.shape[0],img_patch_org.shape[1]))
|
||||
img_int[:,:]=img_patch_org[:,:]#img_patch_org[:,:,0]
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
max_shape=np.max(img_int.shape)
|
||||
img_resized=np.zeros((int( max_shape*(1.1) ) , int( max_shape*(1.1) ) ))
|
||||
|
||||
|
||||
|
||||
|
||||
onset_x=int((img_resized.shape[1]-img_int.shape[1])/2.)
|
||||
onset_y=int((img_resized.shape[0]-img_int.shape[0])/2.)
|
||||
|
||||
|
||||
#img_resized=np.zeros((int( img_int.shape[0]*(1.8) ) , int( img_int.shape[1]*(2.6) ) ))
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
#img_resized[ int( img_int.shape[0]*(.4)):int( img_int.shape[0]*(.4))+img_int.shape[0] , int( img_int.shape[1]*(.8)):int( img_int.shape[1]*(.8))+img_int.shape[1] ]=img_int[:,:]
|
||||
img_resized[ onset_y:onset_y+img_int.shape[0] , onset_x:onset_x+img_int.shape[1] ]=img_int[:,:]
|
||||
|
||||
|
||||
#print(img_resized.shape,'img_resizedshape')
|
||||
#plt.imshow(img_resized)
|
||||
#plt.show()
|
||||
|
||||
|
||||
if main_page and img_patch_org.shape[1]>img_patch_org.shape[0]:
|
||||
|
||||
|
||||
#plt.imshow(img_resized)
|
||||
#plt.show()
|
||||
angels=np.array([-45, 0 , 45 , 90 , ])#np.linspace(-12,12,100)#np.array([0 , 45 , 90 , -45])
|
||||
|
||||
|
||||
#res=[]
|
||||
#num_of_peaks=[]
|
||||
#index_cor=[]
|
||||
var_res=[]
|
||||
|
||||
|
||||
#indexer=0
|
||||
for rot in angels:
|
||||
img_rot=self.rotate_image(img_resized,rot)
|
||||
|
|
@ -2414,8 +2414,8 @@ def return_deskew_slop(img_patch_org, sigma_des, main_page=False, dir_of_all=Non
|
|||
#plt.show()
|
||||
img_rot[img_rot!=0]=1
|
||||
#res_me=np.mean(self.find_num_col_deskew(img_rot,sigma_des,2.0 ))
|
||||
|
||||
|
||||
|
||||
|
||||
#neg_peaks,var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3 )
|
||||
#print(var_spectrum,'var_spectrum')
|
||||
try:
|
||||
|
|
@ -2426,7 +2426,7 @@ def return_deskew_slop(img_patch_org, sigma_des, main_page=False, dir_of_all=Non
|
|||
#res_me=1000000000000000000000
|
||||
#else:
|
||||
#pass
|
||||
|
||||
|
||||
#res_num=len(neg_peaks)
|
||||
except:
|
||||
#res_me=1000000000000000000000
|
||||
|
|
@ -2440,7 +2440,7 @@ def return_deskew_slop(img_patch_org, sigma_des, main_page=False, dir_of_all=Non
|
|||
#num_of_peaks.append( res_num )
|
||||
#index_cor.append(indexer)
|
||||
#indexer=indexer+1
|
||||
|
||||
|
||||
var_res.append(var_spectrum)
|
||||
#index_cor.append(indexer)
|
||||
#indexer=indexer+1
|
||||
|
|
@ -2448,19 +2448,19 @@ def return_deskew_slop(img_patch_org, sigma_des, main_page=False, dir_of_all=Non
|
|||
|
||||
try:
|
||||
var_res=np.array(var_res)
|
||||
|
||||
|
||||
ang_int=angels[np.argmax(var_res)]#angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
|
||||
except:
|
||||
ang_int=0
|
||||
|
||||
|
||||
|
||||
|
||||
angels=np.linspace(ang_int-22.5,ang_int+22.5,100)
|
||||
|
||||
#res=[]
|
||||
#num_of_peaks=[]
|
||||
#index_cor=[]
|
||||
var_res=[]
|
||||
|
||||
|
||||
|
||||
for rot in angels:
|
||||
img_rot=self.rotate_image(img_resized,rot)
|
||||
|
|
@ -2476,33 +2476,33 @@ def return_deskew_slop(img_patch_org, sigma_des, main_page=False, dir_of_all=Non
|
|||
|
||||
var_res.append(var_spectrum)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
try:
|
||||
var_res=np.array(var_res)
|
||||
|
||||
|
||||
ang_int=angels[np.argmax(var_res)]#angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
|
||||
except:
|
||||
ang_int=0
|
||||
|
||||
|
||||
elif main_page and img_patch_org.shape[1]<=img_patch_org.shape[0]:
|
||||
|
||||
|
||||
#plt.imshow(img_resized)
|
||||
#plt.show()
|
||||
angels=np.linspace(-12,12,100)#np.array([0 , 45 , 90 , -45])
|
||||
|
||||
|
||||
|
||||
var_res=[]
|
||||
|
||||
|
||||
for rot in angels:
|
||||
img_rot=self.rotate_image(img_resized,rot)
|
||||
#plt.imshow(img_rot)
|
||||
#plt.show()
|
||||
img_rot[img_rot!=0]=1
|
||||
#res_me=np.mean(self.find_num_col_deskew(img_rot,sigma_des,2.0 ))
|
||||
|
||||
|
||||
|
||||
|
||||
#neg_peaks,var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3 )
|
||||
#print(var_spectrum,'var_spectrum')
|
||||
try:
|
||||
|
|
@ -2513,7 +2513,7 @@ def return_deskew_slop(img_patch_org, sigma_des, main_page=False, dir_of_all=Non
|
|||
|
||||
var_res.append(var_spectrum)
|
||||
|
||||
|
||||
|
||||
if self.dir_of_all is not None:
|
||||
#print('galdi?')
|
||||
plt.figure(figsize=(60,30))
|
||||
|
|
@ -2521,7 +2521,7 @@ def return_deskew_slop(img_patch_org, sigma_des, main_page=False, dir_of_all=Non
|
|||
plt.plot(angels,np.array(var_res),'-o',markersize=25,linewidth=4)
|
||||
plt.xlabel('angle',fontsize=50)
|
||||
plt.ylabel('variance of sum of rotated textline in direction of x axis',fontsize=50)
|
||||
|
||||
|
||||
plt.plot(angels[np.argmax(var_res)],var_res[np.argmax(np.array(var_res))] ,'*',markersize=50,label='Angle of deskewing=' +str("{:.2f}".format(angels[np.argmax(var_res)]))+r'$\degree$')
|
||||
plt.legend(loc='best')
|
||||
plt.savefig(os.path.join(self.dir_of_all,self.f_name+'_rotation_angle.png'))
|
||||
|
|
@ -2529,19 +2529,19 @@ def return_deskew_slop(img_patch_org, sigma_des, main_page=False, dir_of_all=Non
|
|||
|
||||
try:
|
||||
var_res=np.array(var_res)
|
||||
|
||||
|
||||
ang_int=angels[np.argmax(var_res)]#angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
|
||||
except:
|
||||
ang_int=0
|
||||
|
||||
|
||||
|
||||
|
||||
early_slope_edge=11
|
||||
if abs(ang_int)>early_slope_edge and ang_int<0:
|
||||
|
||||
|
||||
angels=np.linspace(-90,-12,100)
|
||||
|
||||
var_res=[]
|
||||
|
||||
|
||||
for rot in angels:
|
||||
img_rot=self.rotate_image(img_resized,rot)
|
||||
##plt.imshow(img_rot)
|
||||
|
|
@ -2558,18 +2558,18 @@ def return_deskew_slop(img_patch_org, sigma_des, main_page=False, dir_of_all=Non
|
|||
|
||||
try:
|
||||
var_res=np.array(var_res)
|
||||
|
||||
|
||||
ang_int=angels[np.argmax(var_res)]#angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
|
||||
except:
|
||||
ang_int=0
|
||||
|
||||
|
||||
elif abs(ang_int)>early_slope_edge and ang_int>0:
|
||||
|
||||
|
||||
angels=np.linspace(90,12,100)
|
||||
|
||||
|
||||
var_res=[]
|
||||
|
||||
|
||||
for rot in angels:
|
||||
img_rot=self.rotate_image(img_resized,rot)
|
||||
##plt.imshow(img_rot)
|
||||
|
|
@ -2587,17 +2587,17 @@ def return_deskew_slop(img_patch_org, sigma_des, main_page=False, dir_of_all=Non
|
|||
|
||||
try:
|
||||
var_res=np.array(var_res)
|
||||
|
||||
|
||||
ang_int=angels[np.argmax(var_res)]#angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
|
||||
except:
|
||||
ang_int=0
|
||||
else:
|
||||
|
||||
|
||||
|
||||
angels=np.linspace(-25,25,60)
|
||||
|
||||
var_res=[]
|
||||
|
||||
|
||||
indexer=0
|
||||
for rot in angels:
|
||||
img_rot=self.rotate_image(img_resized,rot)
|
||||
|
|
@ -2605,39 +2605,39 @@ def return_deskew_slop(img_patch_org, sigma_des, main_page=False, dir_of_all=Non
|
|||
#plt.show()
|
||||
img_rot[img_rot!=0]=1
|
||||
#res_me=np.mean(self.find_num_col_deskew(img_rot,sigma_des,2.0 ))
|
||||
|
||||
|
||||
|
||||
|
||||
#neg_peaks,var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3 )
|
||||
#print(var_spectrum,'var_spectrum')
|
||||
try:
|
||||
var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3 )
|
||||
except:
|
||||
var_spectrum=0
|
||||
|
||||
|
||||
var_res.append(var_spectrum)
|
||||
|
||||
|
||||
try:
|
||||
var_res=np.array(var_res)
|
||||
|
||||
|
||||
ang_int=angels[np.argmax(var_res)]#angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
|
||||
except:
|
||||
ang_int=0
|
||||
|
||||
|
||||
#plt.plot(var_res)
|
||||
#plt.show()
|
||||
|
||||
|
||||
##plt.plot(mom3_res)
|
||||
##plt.show()
|
||||
#print(ang_int,'ang_int111')
|
||||
|
||||
early_slope_edge=22
|
||||
if abs(ang_int)>early_slope_edge and ang_int<0:
|
||||
|
||||
|
||||
angels=np.linspace(-90,-25,60)
|
||||
|
||||
var_res=[]
|
||||
|
||||
|
||||
for rot in angels:
|
||||
img_rot=self.rotate_image(img_resized,rot)
|
||||
##plt.imshow(img_rot)
|
||||
|
|
@ -2649,24 +2649,24 @@ def return_deskew_slop(img_patch_org, sigma_des, main_page=False, dir_of_all=Non
|
|||
|
||||
except:
|
||||
var_spectrum=0
|
||||
|
||||
|
||||
var_res.append(var_spectrum)
|
||||
|
||||
|
||||
|
||||
try:
|
||||
var_res=np.array(var_res)
|
||||
|
||||
|
||||
ang_int=angels[np.argmax(var_res)]#angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
|
||||
except:
|
||||
ang_int=0
|
||||
|
||||
|
||||
elif abs(ang_int)>early_slope_edge and ang_int>0:
|
||||
|
||||
|
||||
angels=np.linspace(90,25,60)
|
||||
|
||||
var_res=[]
|
||||
|
||||
|
||||
indexer=0
|
||||
for rot in angels:
|
||||
img_rot=self.rotate_image(img_resized,rot)
|
||||
|
|
@ -2686,11 +2686,11 @@ def return_deskew_slop(img_patch_org, sigma_des, main_page=False, dir_of_all=Non
|
|||
|
||||
try:
|
||||
var_res=np.array(var_res)
|
||||
|
||||
|
||||
ang_int=angels[np.argmax(var_res)]#angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
|
||||
except:
|
||||
ang_int=0
|
||||
|
||||
|
||||
return ang_int
|
||||
|
||||
|
||||
|
|
@ -2749,55 +2749,55 @@ def putt_bb_of_drop_capitals_of_model_in_patches_in_layout(layout_in_patch):
|
|||
def check_any_text_region_in_model_one_is_main_or_header(regions_model_1,regions_model_full,contours_only_text_parent,all_box_coord,all_found_texline_polygons,slopes,contours_only_text_parent_d_ordered):
|
||||
#text_only=(regions_model_1[:,:]==1)*1
|
||||
#contours_only_text,hir_on_text=self.return_contours_of_image(text_only)
|
||||
|
||||
|
||||
"""
|
||||
contours_only_text_parent=self.return_parent_contours( contours_only_text,hir_on_text)
|
||||
|
||||
|
||||
areas_cnt_text=np.array([cv2.contourArea(contours_only_text_parent[j]) for j in range(len(contours_only_text_parent))])
|
||||
areas_cnt_text=areas_cnt_text/float(text_only.shape[0]*text_only.shape[1])
|
||||
|
||||
|
||||
###areas_cnt_text_h=np.array([cv2.contourArea(contours_only_text_parent_h[j]) for j in range(len(contours_only_text_parent_h))])
|
||||
###areas_cnt_text_h=areas_cnt_text_h/float(text_only_h.shape[0]*text_only_h.shape[1])
|
||||
|
||||
###contours_only_text_parent=[contours_only_text_parent[jz] for jz in range(len(contours_only_text_parent)) if areas_cnt_text[jz]>0.0002]
|
||||
contours_only_text_parent=[contours_only_text_parent[jz] for jz in range(len(contours_only_text_parent)) if areas_cnt_text[jz]>0.00001]
|
||||
"""
|
||||
|
||||
|
||||
cx_main,cy_main ,x_min_main , x_max_main, y_min_main ,y_max_main,y_corr_x_min_from_argmin=self.find_new_features_of_contoures(contours_only_text_parent)
|
||||
|
||||
|
||||
length_con=x_max_main-x_min_main
|
||||
height_con=y_max_main-y_min_main
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
all_found_texline_polygons_main=[]
|
||||
all_found_texline_polygons_head=[]
|
||||
|
||||
|
||||
all_box_coord_main=[]
|
||||
all_box_coord_head=[]
|
||||
|
||||
|
||||
slopes_main=[]
|
||||
slopes_head=[]
|
||||
|
||||
|
||||
contours_only_text_parent_main=[]
|
||||
contours_only_text_parent_head=[]
|
||||
|
||||
|
||||
contours_only_text_parent_main_d=[]
|
||||
contours_only_text_parent_head_d=[]
|
||||
|
||||
|
||||
for ii in range(len(contours_only_text_parent)):
|
||||
con=contours_only_text_parent[ii]
|
||||
img=np.zeros((regions_model_1.shape[0],regions_model_1.shape[1],3))
|
||||
img = cv2.fillPoly(img, pts=[con], color=(255, 255, 255))
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
all_pixels=((img[:,:,0]==255)*1).sum()
|
||||
|
||||
|
||||
pixels_header=( ( (img[:,:,0]==255) & (regions_model_full[:,:,0]==2) )*1 ).sum()
|
||||
pixels_main=all_pixels-pixels_header
|
||||
|
||||
|
||||
|
||||
|
||||
if (pixels_header>=pixels_main) and ( (length_con[ii]/float(height_con[ii]) )>=1.3 ):
|
||||
regions_model_1[:,:][(regions_model_1[:,:]==1) & (img[:,:,0]==255) ]=2
|
||||
contours_only_text_parent_head.append(con)
|
||||
|
|
@ -2814,11 +2814,11 @@ def check_any_text_region_in_model_one_is_main_or_header(regions_model_1,regions
|
|||
all_box_coord_main.append(all_box_coord[ii])
|
||||
slopes_main.append(slopes[ii])
|
||||
all_found_texline_polygons_main.append(all_found_texline_polygons[ii])
|
||||
|
||||
#print(all_pixels,pixels_main,pixels_header)
|
||||
|
||||
|
||||
|
||||
#print(all_pixels,pixels_main,pixels_header)
|
||||
|
||||
|
||||
|
||||
#plt.imshow(img[:,:,0])
|
||||
#plt.show()
|
||||
return regions_model_1,contours_only_text_parent_main,contours_only_text_parent_head,all_box_coord_main,all_box_coord_head,all_found_texline_polygons_main,all_found_texline_polygons_head,slopes_main,slopes_head,contours_only_text_parent_main_d,contours_only_text_parent_head_d
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue