mirror of
https://github.com/qurator-spk/eynollah.git
synced 2025-11-17 01:44:14 +01:00
rename some more identifiers:
- `lines` → `seps` (to distinguish from textlines) - `text_regions_p_1_n` → `text_regions_p_d` (because all other deskewed variables are called like this) - `pixel` → `label`
This commit is contained in:
Robert Sachunsky
parent
1a76ce177d
commit
95f76081d1
1 changed files with 89 additions and 89 deletions
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@ -2091,19 +2091,19 @@ class Eynollah:
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prediction_regions_org = prediction_regions_org[page_coord[0] : page_coord[1], page_coord[2] : page_coord[3]]
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prediction_regions_org=prediction_regions_org[:,:,0]
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mask_lines_only = (prediction_regions_org[:,:] ==3)*1
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mask_seps_only = (prediction_regions_org[:,:] == 3)*1
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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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polygons_seplines, hir_seplines = return_contours_of_image(mask_lines_only)
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polygons_seplines, hir_seplines = return_contours_of_image(mask_seps_only)
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polygons_seplines = filter_contours_area_of_image(
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mask_lines_only, polygons_seplines, hir_seplines, max_area=1, min_area=0.00001, dilate=1)
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mask_seps_only, polygons_seplines, hir_seplines, max_area=1, min_area=0.00001, dilate=1)
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polygons_of_only_texts = return_contours_of_interested_region(mask_texts_only,1,0.00001)
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polygons_of_only_lines = return_contours_of_interested_region(mask_lines_only,1,0.00001)
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polygons_of_only_seps = return_contours_of_interested_region(mask_seps_only,1,0.00001)
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text_regions_p_true = np.zeros(prediction_regions_org.shape)
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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_seps, color=(3,3,3))
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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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@ -2282,7 +2282,7 @@ class Eynollah:
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img_bin = resize_image(img_bin, img_height_h, img_width_h )
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prediction_regions_org=prediction_regions_org[:,:,0]
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mask_lines_only = (prediction_regions_org[:,:] ==3)*1
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mask_seps_only = (prediction_regions_org[:,:] == 3)*1
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mask_texts_only = (prediction_regions_org[:,:] ==1)*1
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mask_texts_only = mask_texts_only.astype('uint8')
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@ -2293,7 +2293,7 @@ class Eynollah:
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mask_texts_only = cv2.dilate(mask_texts_only, kernel=np.ones((2,2), np.uint8), iterations=1)
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mask_images_only=(prediction_regions_org[:,:] ==2)*1
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polygons_seplines, hir_seplines = return_contours_of_image(mask_lines_only)
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polygons_seplines, hir_seplines = return_contours_of_image(mask_seps_only)
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test_khat = np.zeros(prediction_regions_org.shape)
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test_khat = cv2.fillPoly(test_khat, pts=polygons_seplines, color=(1,1,1))
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@ -2307,7 +2307,7 @@ class Eynollah:
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#plt.show()
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polygons_seplines = filter_contours_area_of_image(
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mask_lines_only, polygons_seplines, hir_seplines, max_area=1, min_area=0.00001, dilate=1)
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mask_seps_only, polygons_seplines, hir_seplines, max_area=1, min_area=0.00001, dilate=1)
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test_khat = np.zeros(prediction_regions_org.shape)
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test_khat = cv2.fillPoly(test_khat, pts = polygons_seplines, color=(1,1,1))
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@ -2318,10 +2318,10 @@ class Eynollah:
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polygons_of_only_texts = return_contours_of_interested_region(mask_texts_only,1,0.00001)
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##polygons_of_only_texts = dilate_textregion_contours(polygons_of_only_texts)
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polygons_of_only_lines = return_contours_of_interested_region(mask_lines_only,1,0.00001)
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polygons_of_only_seps = return_contours_of_interested_region(mask_seps_only,1,0.00001)
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text_regions_p_true = np.zeros(prediction_regions_org.shape)
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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_seps, color=(3,3,3))
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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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@ -2377,7 +2377,7 @@ class Eynollah:
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prediction_regions_org2=resize_image(prediction_regions_org2, img_height_h, img_width_h )
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mask_zeros2 = (prediction_regions_org2[:,:,0] == 0)
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mask_lines2 = (prediction_regions_org2[:,:,0] == 3)
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mask_seps2 = (prediction_regions_org2[:,:,0] == 3)
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text_sume_early = (prediction_regions_org[:,:] == 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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@ -2388,8 +2388,8 @@ class Eynollah:
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if not(is_image_enhanced and rate_two_models < RATIO_OF_TWO_MODEL_THRESHOLD):
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prediction_regions_org = np.copy(prediction_regions_org_copy)
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prediction_regions_org[(mask_lines2[:,:]==1) & (prediction_regions_org[:,:]==0)]=3
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mask_lines_only=(prediction_regions_org[:,:]==3)*1
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prediction_regions_org[(mask_seps2[:,:]==1) & (prediction_regions_org[:,:]==0)]=3
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mask_seps_only=(prediction_regions_org[:,:]==3)*1
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prediction_regions_org = cv2.erode(prediction_regions_org[:,:], KERNEL, iterations=2)
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prediction_regions_org = cv2.dilate(prediction_regions_org[:,:], KERNEL, iterations=2)
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@ -2411,20 +2411,20 @@ class Eynollah:
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prediction_regions_org = resize_image(prediction_regions_org, img_height_h, img_width_h )
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prediction_regions_org=prediction_regions_org[:,:,0]
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mask_lines_only=(prediction_regions_org[:,:]==3)*1
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mask_seps_only=(prediction_regions_org[:,:]==3)*1
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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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polygons_seplines, hir_seplines = return_contours_of_image(mask_lines_only)
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polygons_seplines, hir_seplines = return_contours_of_image(mask_seps_only)
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polygons_seplines = filter_contours_area_of_image(
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mask_lines_only, polygons_seplines, hir_seplines, max_area=1, min_area=0.00001, dilate=1)
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mask_seps_only, polygons_seplines, hir_seplines, max_area=1, min_area=0.00001, dilate=1)
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polygons_of_only_texts = return_contours_of_interested_region(mask_texts_only, 1, 0.00001)
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polygons_of_only_lines = return_contours_of_interested_region(mask_lines_only, 1, 0.00001)
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polygons_of_only_seps = return_contours_of_interested_region(mask_seps_only, 1, 0.00001)
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text_regions_p_true = np.zeros(prediction_regions_org.shape)
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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_seps, color=(3, 3, 3))
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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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@ -2449,7 +2449,7 @@ class Eynollah:
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prediction_regions_org = resize_image(prediction_regions_org, img_height_h, img_width_h )
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prediction_regions_org=prediction_regions_org[:,:,0]
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#mask_lines_only=(prediction_regions_org[:,:]==3)*1
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#mask_seps_only=(prediction_regions_org[:,:]==3)*1
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#img = resize_image(img_org, int(img_org.shape[0]*1), int(img_org.shape[1]*1))
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#prediction_regions_org = self.do_prediction(True, img, self.models["region"])
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@ -2457,19 +2457,19 @@ class Eynollah:
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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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mask_lines_only = (prediction_regions_org == 3)*1
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mask_seps_only = (prediction_regions_org == 3)*1
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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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polygons_seplines, hir_seplines = return_contours_of_image(mask_lines_only)
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polygons_seplines, hir_seplines = return_contours_of_image(mask_seps_only)
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polygons_seplines = filter_contours_area_of_image(
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mask_lines_only, polygons_seplines, hir_seplines, max_area=1, min_area=0.00001, dilate=1)
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mask_seps_only, polygons_seplines, hir_seplines, max_area=1, min_area=0.00001, dilate=1)
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polygons_of_only_texts = return_contours_of_interested_region(mask_texts_only,1,0.00001)
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polygons_of_only_lines = return_contours_of_interested_region(mask_lines_only,1,0.00001)
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polygons_of_only_seps = return_contours_of_interested_region(mask_seps_only,1,0.00001)
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text_regions_p_true = np.zeros(prediction_regions_org.shape)
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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_seps, color=(3,3,3))
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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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@ -2952,8 +2952,8 @@ class Eynollah:
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mask_images = (text_regions_p_1[:, :] == 2) * 1
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mask_images = mask_images.astype(np.uint8)
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mask_images = cv2.erode(mask_images[:, :], KERNEL, iterations=10)
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mask_lines = (text_regions_p_1[:, :] == 3) * 1
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mask_lines = mask_lines.astype(np.uint8)
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mask_seps = (text_regions_p_1[:, :] == 3) * 1
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mask_seps = mask_seps.astype(np.uint8)
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img_only_regions_with_sep = ((text_regions_p_1[:, :] != 3) & (text_regions_p_1[:, :] != 0)) * 1
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img_only_regions_with_sep = img_only_regions_with_sep.astype(np.uint8)
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@ -2979,7 +2979,7 @@ class Eynollah:
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self.logger.exception(why)
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num_col = None
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#print("inside graphics 3 ", time.time() - t_in_gr)
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return (num_col, num_col_classifier, img_only_regions, page_coord, image_page, mask_images, mask_lines,
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return (num_col, num_col_classifier, img_only_regions, page_coord, image_page, mask_images, mask_seps,
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text_regions_p_1, cont_page, table_prediction, textline_mask_tot_ea, img_bin_light)
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def run_graphics_and_columns_without_layout(self, textline_mask_tot_ea, img_bin_light):
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@ -3029,8 +3029,8 @@ class Eynollah:
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mask_images = (text_regions_p_1[:, :] == 2) * 1
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mask_images = mask_images.astype(np.uint8)
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mask_images = cv2.erode(mask_images[:, :], KERNEL, iterations=10)
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mask_lines = (text_regions_p_1[:, :] == 3) * 1
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mask_lines = mask_lines.astype(np.uint8)
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mask_seps = (text_regions_p_1[:, :] == 3) * 1
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mask_seps = mask_seps.astype(np.uint8)
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img_only_regions_with_sep = ((text_regions_p_1[:, :] != 3) & (text_regions_p_1[:, :] != 0)) * 1
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img_only_regions_with_sep = img_only_regions_with_sep.astype(np.uint8)
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@ -3046,7 +3046,7 @@ class Eynollah:
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except Exception as why:
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self.logger.exception(why)
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num_col = None
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return (num_col, num_col_classifier, img_only_regions, page_coord, image_page, mask_images, mask_lines,
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return (num_col, num_col_classifier, img_only_regions, page_coord, image_page, mask_images, mask_seps,
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text_regions_p_1, cont_page, table_prediction)
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def run_enhancement(self, light_version):
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@ -3101,13 +3101,13 @@ class Eynollah:
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return slope_deskew
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def run_marginals(
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self, textline_mask_tot_ea, mask_images, mask_lines,
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self, textline_mask_tot_ea, mask_images, mask_seps,
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num_col_classifier, slope_deskew, text_regions_p_1, table_prediction):
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textline_mask_tot = textline_mask_tot_ea[:, :]
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textline_mask_tot[mask_images[:, :] == 1] = 0
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text_regions_p_1[mask_lines[:, :] == 1] = 3
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text_regions_p_1[mask_seps[:, :] == 1] = 3
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text_regions_p = text_regions_p_1[:, :]
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text_regions_p = np.array(text_regions_p)
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if num_col_classifier in (1, 2):
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@ -3131,12 +3131,12 @@ class Eynollah:
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self.logger.debug('enter run_boxes_no_full_layout')
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t_0_box = time.time()
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if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
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_, textline_mask_tot_d, text_regions_p_1_n, table_prediction_n = rotation_not_90_func(
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_, textline_mask_tot_d, text_regions_p_d, table_prediction_n = rotation_not_90_func(
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image_page, textline_mask_tot, text_regions_p, table_prediction, slope_deskew)
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text_regions_p_1_n = resize_image(text_regions_p_1_n, text_regions_p.shape[0], text_regions_p.shape[1])
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text_regions_p_d = resize_image(text_regions_p_d, text_regions_p.shape[0], text_regions_p.shape[1])
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textline_mask_tot_d = resize_image(textline_mask_tot_d, text_regions_p.shape[0], text_regions_p.shape[1])
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table_prediction_n = resize_image(table_prediction_n, text_regions_p.shape[0], text_regions_p.shape[1])
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regions_without_separators_d = (text_regions_p_1_n[:, :] == 1) * 1
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regions_without_separators_d = (text_regions_p_d[:, :] == 1) * 1
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if self.tables:
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regions_without_separators_d[table_prediction_n[:,:] == 1] = 1
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regions_without_separators = (text_regions_p[:, :] == 1) * 1
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@ -3146,7 +3146,7 @@ class Eynollah:
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if self.tables:
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regions_without_separators[table_prediction ==1 ] = 1
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if np.abs(slope_deskew) < SLOPE_THRESHOLD:
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text_regions_p_1_n = None
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text_regions_p_d = None
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textline_mask_tot_d = None
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regions_without_separators_d = None
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label_seps = 3
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@ -3156,7 +3156,7 @@ class Eynollah:
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if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
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_, _, matrix_of_seps_ch_d, splitter_y_new_d, _ = find_number_of_columns_in_document(
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text_regions_p_1_n, num_col_classifier, self.tables, label_seps)
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text_regions_p_d, num_col_classifier, self.tables, label_seps)
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#print(time.time()-t_0_box,'time box in 2')
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self.logger.info("num_col_classifier: %s", num_col_classifier)
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@ -3171,7 +3171,7 @@ class Eynollah:
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t1 = time.time()
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if np.abs(slope_deskew) < SLOPE_THRESHOLD:
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boxes, peaks_neg_tot_tables = return_boxes_of_images_by_order_of_reading_new(
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splitter_y_new, regions_without_separators, matrix_of_seps_ch,
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splitter_y_new, regions_without_separators, text_regions_p, matrix_of_seps_ch,
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num_col_classifier, erosion_hurts, self.tables, self.right2left)
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boxes_d = None
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self.logger.debug("len(boxes): %s", len(boxes))
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@ -3193,7 +3193,7 @@ class Eynollah:
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#print(time.time()-t_0_box,'time box in 3.3')
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else:
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boxes_d, peaks_neg_tot_tables_d = return_boxes_of_images_by_order_of_reading_new(
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splitter_y_new_d, regions_without_separators_d, matrix_of_seps_ch_d,
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splitter_y_new_d, regions_without_separators_d, text_regions_p_d, matrix_of_seps_ch_d,
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num_col_classifier, erosion_hurts, self.tables, self.right2left)
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boxes = None
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self.logger.debug("len(boxes): %s", len(boxes_d))
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@ -3202,7 +3202,7 @@ class Eynollah:
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if self.light_version:
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pass
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else:
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text_regions_p_tables = np.copy(text_regions_p_1_n)
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text_regions_p_tables = np.copy(text_regions_p_d)
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text_regions_p_tables = np.round(text_regions_p_tables)
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text_regions_p_tables[(text_regions_p_tables != 3) & (table_prediction_n == 1)] = 10
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@ -3245,22 +3245,22 @@ class Eynollah:
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else:
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polygons_of_images = return_contours_of_interested_region(img_revised_tab, 2)
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pixel_img = 4
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label_marginalia = 4
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min_area_mar = 0.00001
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if self.light_version:
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marginal_mask = (text_regions_p[:,:]==pixel_img)*1
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marginal_mask = (text_regions_p[:,:]==label_marginalia)*1
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marginal_mask = marginal_mask.astype('uint8')
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marginal_mask = cv2.dilate(marginal_mask, KERNEL, iterations=2)
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|
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polygons_of_marginals = return_contours_of_interested_region(marginal_mask, 1, min_area_mar)
|
||||
else:
|
||||
polygons_of_marginals = return_contours_of_interested_region(text_regions_p, pixel_img, min_area_mar)
|
||||
polygons_of_marginals = return_contours_of_interested_region(text_regions_p, label_marginalia, min_area_mar)
|
||||
|
||||
pixel_img = 10
|
||||
contours_tables = return_contours_of_interested_region(text_regions_p, pixel_img, min_area_mar)
|
||||
label_tables = 10
|
||||
contours_tables = return_contours_of_interested_region(text_regions_p, label_tables, min_area_mar)
|
||||
#print(time.time()-t_0_box,'time box in 5')
|
||||
self.logger.debug('exit run_boxes_no_full_layout')
|
||||
return (polygons_of_images, img_revised_tab, text_regions_p_1_n, textline_mask_tot_d,
|
||||
return (polygons_of_images, img_revised_tab, text_regions_p_d, textline_mask_tot_d,
|
||||
regions_without_separators_d, boxes, boxes_d,
|
||||
polygons_of_marginals, contours_tables)
|
||||
|
||||
|
|
@ -3276,13 +3276,13 @@ class Eynollah:
|
|||
text_regions_p[:,:][table_prediction[:,:]==1] = 10
|
||||
img_revised_tab = text_regions_p[:,:]
|
||||
if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
|
||||
_, textline_mask_tot_d, text_regions_p_1_n, table_prediction_n = \
|
||||
_, textline_mask_tot_d, text_regions_p_d, table_prediction_n = \
|
||||
rotation_not_90_func(image_page, textline_mask_tot, text_regions_p,
|
||||
table_prediction, slope_deskew)
|
||||
|
||||
text_regions_p_1_n = resize_image(text_regions_p_1_n,
|
||||
text_regions_p.shape[0],
|
||||
text_regions_p.shape[1])
|
||||
text_regions_p_d = resize_image(text_regions_p_d,
|
||||
text_regions_p.shape[0],
|
||||
text_regions_p.shape[1])
|
||||
textline_mask_tot_d = resize_image(textline_mask_tot_d,
|
||||
text_regions_p.shape[0],
|
||||
text_regions_p.shape[1])
|
||||
|
|
@ -3290,10 +3290,10 @@ class Eynollah:
|
|||
text_regions_p.shape[0],
|
||||
text_regions_p.shape[1])
|
||||
|
||||
regions_without_separators_d = (text_regions_p_1_n[:,:] == 1)*1
|
||||
regions_without_separators_d = (text_regions_p_d[:,:] == 1)*1
|
||||
regions_without_separators_d[table_prediction_n[:,:] == 1] = 1
|
||||
else:
|
||||
text_regions_p_1_n = None
|
||||
text_regions_p_d = None
|
||||
textline_mask_tot_d = None
|
||||
regions_without_separators_d = None
|
||||
# regions_without_separators = ( text_regions_p[:,:]==1 | text_regions_p[:,:]==2 )*1
|
||||
|
|
@ -3303,13 +3303,13 @@ class Eynollah:
|
|||
|
||||
else:
|
||||
if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
|
||||
_, textline_mask_tot_d, text_regions_p_1_n, table_prediction_n = \
|
||||
_, textline_mask_tot_d, text_regions_p_d, table_prediction_n = \
|
||||
rotation_not_90_func(image_page, textline_mask_tot, text_regions_p,
|
||||
table_prediction, slope_deskew)
|
||||
|
||||
text_regions_p_1_n = resize_image(text_regions_p_1_n,
|
||||
text_regions_p.shape[0],
|
||||
text_regions_p.shape[1])
|
||||
text_regions_p_d = resize_image(text_regions_p_d,
|
||||
text_regions_p.shape[0],
|
||||
text_regions_p.shape[1])
|
||||
textline_mask_tot_d = resize_image(textline_mask_tot_d,
|
||||
text_regions_p.shape[0],
|
||||
text_regions_p.shape[1])
|
||||
|
|
@ -3317,10 +3317,10 @@ class Eynollah:
|
|||
text_regions_p.shape[0],
|
||||
text_regions_p.shape[1])
|
||||
|
||||
regions_without_separators_d = (text_regions_p_1_n[:,:] == 1)*1
|
||||
regions_without_separators_d = (text_regions_p_d[:,:] == 1)*1
|
||||
regions_without_separators_d[table_prediction_n[:,:] == 1] = 1
|
||||
else:
|
||||
text_regions_p_1_n = None
|
||||
text_regions_p_d = None
|
||||
textline_mask_tot_d = None
|
||||
regions_without_separators_d = None
|
||||
|
||||
|
|
@ -3331,12 +3331,12 @@ class Eynollah:
|
|||
|
||||
label_seps=3
|
||||
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
|
||||
num_col, _, matrix_of_lines_ch, splitter_y_new, _ = find_number_of_columns_in_document(
|
||||
num_col, _, matrix_of_seps_ch, splitter_y_new, _ = find_number_of_columns_in_document(
|
||||
text_regions_p, num_col_classifier, self.tables, label_seps)
|
||||
|
||||
if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
|
||||
num_col_d, _, matrix_of_lines_ch_d, splitter_y_new_d, _ = find_number_of_columns_in_document(
|
||||
text_regions_p_1_n, num_col_classifier, self.tables, label_seps)
|
||||
num_col_d, _, matrix_of_seps_ch_d, splitter_y_new_d, _ = find_number_of_columns_in_document(
|
||||
text_regions_p_d, num_col_classifier, self.tables, label_seps)
|
||||
|
||||
if num_col_classifier>=3:
|
||||
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
|
||||
|
|
@ -3351,7 +3351,7 @@ class Eynollah:
|
|||
|
||||
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
|
||||
boxes, peaks_neg_tot_tables = return_boxes_of_images_by_order_of_reading_new(
|
||||
splitter_y_new, regions_without_separators, matrix_of_lines_ch,
|
||||
splitter_y_new, regions_without_separators, text_regions_p, matrix_of_seps_ch,
|
||||
num_col_classifier, erosion_hurts, self.tables, self.right2left)
|
||||
text_regions_p_tables = np.copy(text_regions_p)
|
||||
text_regions_p_tables[:,:][(table_prediction[:,:]==1)] = 10
|
||||
|
|
@ -3364,9 +3364,9 @@ class Eynollah:
|
|||
img_revised_tab2, table_prediction, 10, num_col_classifier)
|
||||
else:
|
||||
boxes_d, peaks_neg_tot_tables_d = return_boxes_of_images_by_order_of_reading_new(
|
||||
splitter_y_new_d, regions_without_separators_d, matrix_of_lines_ch_d,
|
||||
splitter_y_new_d, regions_without_separators_d, text_regions_p_d, matrix_of_seps_ch_d,
|
||||
num_col_classifier, erosion_hurts, self.tables, self.right2left)
|
||||
text_regions_p_tables = np.copy(text_regions_p_1_n)
|
||||
text_regions_p_tables = np.copy(text_regions_p_d)
|
||||
text_regions_p_tables = np.round(text_regions_p_tables)
|
||||
text_regions_p_tables[(text_regions_p_tables != 3) & (table_prediction_n == 1)] = 10
|
||||
|
||||
|
|
@ -3399,20 +3399,20 @@ class Eynollah:
|
|||
text_regions_p[img_revised_tab == 10] = 10
|
||||
#img_revised_tab[img_revised_tab2 == 10] = 10
|
||||
|
||||
pixel_img = 4
|
||||
label_marginalia = 4
|
||||
min_area_mar = 0.00001
|
||||
|
||||
if self.light_version:
|
||||
marginal_mask = (text_regions_p[:,:]==pixel_img)*1
|
||||
marginal_mask = (text_regions_p[:,:]==label_marginalia)*1
|
||||
marginal_mask = marginal_mask.astype('uint8')
|
||||
marginal_mask = cv2.dilate(marginal_mask, KERNEL, iterations=2)
|
||||
|
||||
polygons_of_marginals = return_contours_of_interested_region(marginal_mask, 1, min_area_mar)
|
||||
else:
|
||||
polygons_of_marginals = return_contours_of_interested_region(text_regions_p, pixel_img, min_area_mar)
|
||||
polygons_of_marginals = return_contours_of_interested_region(text_regions_p, label_marginalia, min_area_mar)
|
||||
|
||||
pixel_img = 10
|
||||
contours_tables = return_contours_of_interested_region(text_regions_p, pixel_img, min_area_mar)
|
||||
label_tables = 10
|
||||
contours_tables = return_contours_of_interested_region(text_regions_p, label_tables, min_area_mar)
|
||||
|
||||
# set first model with second model
|
||||
text_regions_p[:, :][text_regions_p[:, :] == 2] = 5
|
||||
|
|
@ -3465,16 +3465,16 @@ class Eynollah:
|
|||
#plt.show()
|
||||
####if not self.tables:
|
||||
if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
|
||||
_, textline_mask_tot_d, text_regions_p_1_n, regions_fully_n = rotation_not_90_func_full_layout(
|
||||
_, textline_mask_tot_d, text_regions_p_d, regions_fully_n = rotation_not_90_func_full_layout(
|
||||
image_page, textline_mask_tot, text_regions_p, regions_fully, slope_deskew)
|
||||
|
||||
text_regions_p_1_n = resize_image(text_regions_p_1_n, text_regions_p.shape[0], text_regions_p.shape[1])
|
||||
text_regions_p_d = resize_image(text_regions_p_d, text_regions_p.shape[0], text_regions_p.shape[1])
|
||||
textline_mask_tot_d = resize_image(textline_mask_tot_d, text_regions_p.shape[0], text_regions_p.shape[1])
|
||||
regions_fully_n = resize_image(regions_fully_n, text_regions_p.shape[0], text_regions_p.shape[1])
|
||||
if not self.tables:
|
||||
regions_without_separators_d = (text_regions_p_1_n[:, :] == 1) * 1
|
||||
regions_without_separators_d = (text_regions_p_d[:, :] == 1) * 1
|
||||
else:
|
||||
text_regions_p_1_n = None
|
||||
text_regions_p_d = None
|
||||
textline_mask_tot_d = None
|
||||
regions_without_separators_d = None
|
||||
if not self.tables:
|
||||
|
|
@ -3484,7 +3484,7 @@ class Eynollah:
|
|||
|
||||
self.logger.debug('exit run_boxes_full_layout')
|
||||
#print("full inside 3", time.time()- t_full0)
|
||||
return (polygons_of_images, img_revised_tab, text_regions_p_1_n, textline_mask_tot_d,
|
||||
return (polygons_of_images, img_revised_tab, text_regions_p_d, textline_mask_tot_d,
|
||||
regions_without_separators_d, regions_fully, regions_without_separators,
|
||||
polygons_of_marginals, contours_tables)
|
||||
|
||||
|
|
@ -4301,7 +4301,7 @@ class Eynollah:
|
|||
slope_deskew = self.run_deskew(textline_mask_tot_ea)
|
||||
#print("text region early -2,5 in %.1fs", time.time() - t0)
|
||||
#self.logger.info("Textregion detection took %.1fs ", time.time() - t1t)
|
||||
num_col, num_col_classifier, img_only_regions, page_coord, image_page, mask_images, mask_lines, \
|
||||
num_col, num_col_classifier, img_only_regions, page_coord, image_page, mask_images, mask_seps, \
|
||||
text_regions_p_1, cont_page, table_prediction, textline_mask_tot_ea, img_bin_light = \
|
||||
self.run_graphics_and_columns_light(text_regions_p_1, textline_mask_tot_ea,
|
||||
num_col_classifier, num_column_is_classified,
|
||||
|
|
@ -4318,7 +4318,7 @@ class Eynollah:
|
|||
confidence_matrix = np.zeros((text_regions_p_1.shape[:2]))
|
||||
|
||||
t1 = time.time()
|
||||
num_col, num_col_classifier, img_only_regions, page_coord, image_page, mask_images, mask_lines, \
|
||||
num_col, num_col_classifier, img_only_regions, page_coord, image_page, mask_images, mask_seps, \
|
||||
text_regions_p_1, cont_page, table_prediction = \
|
||||
self.run_graphics_and_columns(text_regions_p_1, num_col_classifier, num_column_is_classified,
|
||||
erosion_hurts)
|
||||
|
|
@ -4356,12 +4356,12 @@ class Eynollah:
|
|||
image_page = resize_image(image_page,img_h_new, img_w_new )
|
||||
textline_mask_tot_ea = resize_image(textline_mask_tot_ea,img_h_new, img_w_new )
|
||||
mask_images = resize_image(mask_images,img_h_new, img_w_new )
|
||||
mask_lines = resize_image(mask_lines,img_h_new, img_w_new )
|
||||
mask_seps = resize_image(mask_seps, img_h_new, img_w_new)
|
||||
text_regions_p_1 = resize_image(text_regions_p_1,img_h_new, img_w_new )
|
||||
table_prediction = resize_image(table_prediction,img_h_new, img_w_new )
|
||||
|
||||
textline_mask_tot, text_regions_p = \
|
||||
self.run_marginals(textline_mask_tot_ea, mask_images, mask_lines,
|
||||
self.run_marginals(textline_mask_tot_ea, mask_images, mask_seps,
|
||||
num_col_classifier, slope_deskew, text_regions_p_1, table_prediction)
|
||||
if self.plotter:
|
||||
self.plotter.save_plot_of_layout_main_all(text_regions_p, image_page)
|
||||
|
|
@ -4398,14 +4398,14 @@ class Eynollah:
|
|||
## birdan sora chock chakir
|
||||
t1 = time.time()
|
||||
if not self.full_layout:
|
||||
polygons_of_images, img_revised_tab, text_regions_p_1_n, \
|
||||
polygons_of_images, img_revised_tab, text_regions_p_d, \
|
||||
textline_mask_tot_d, regions_without_separators_d, \
|
||||
boxes, boxes_d, polygons_of_marginals, contours_tables = \
|
||||
self.run_boxes_no_full_layout(image_page, textline_mask_tot, text_regions_p, slope_deskew,
|
||||
num_col_classifier, table_prediction, erosion_hurts)
|
||||
###polygons_of_marginals = dilate_textregion_contours(polygons_of_marginals)
|
||||
else:
|
||||
polygons_of_images, img_revised_tab, text_regions_p_1_n, \
|
||||
polygons_of_images, img_revised_tab, text_regions_p_d, \
|
||||
textline_mask_tot_d, regions_without_separators_d, \
|
||||
regions_fully, regions_without_separators, polygons_of_marginals, contours_tables = \
|
||||
self.run_boxes_full_layout(image_page, textline_mask_tot, text_regions_p, slope_deskew,
|
||||
|
|
@ -4419,7 +4419,7 @@ class Eynollah:
|
|||
|
||||
text_only = (img_revised_tab[:, :] == 1) * 1
|
||||
if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
|
||||
text_only_d = (text_regions_p_1_n[:, :] == 1) * 1
|
||||
text_only_d = ((text_regions_p_d[:, :] == 1)) * 1
|
||||
|
||||
#print("text region early 2 in %.1fs", time.time() - t0)
|
||||
###min_con_area = 0.000005
|
||||
|
|
@ -4695,18 +4695,18 @@ class Eynollah:
|
|||
label_seps = 6
|
||||
if not self.headers_off:
|
||||
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
|
||||
num_col, _, matrix_of_lines_ch, splitter_y_new, _ = find_number_of_columns_in_document(
|
||||
num_col, _, matrix_of_seps_ch, splitter_y_new, _ = find_number_of_columns_in_document(
|
||||
text_regions_p, num_col_classifier, self.tables, label_seps, contours_only_text_parent_h)
|
||||
else:
|
||||
_, _, matrix_of_lines_ch_d, splitter_y_new_d, _ = find_number_of_columns_in_document(
|
||||
text_regions_p_1_n, num_col_classifier, self.tables, label_seps, contours_only_text_parent_h_d_ordered)
|
||||
_, _, matrix_of_seps_ch_d, splitter_y_new_d, _ = find_number_of_columns_in_document(
|
||||
text_regions_p_d, num_col_classifier, self.tables, label_seps, contours_only_text_parent_h_d_ordered)
|
||||
elif self.headers_off:
|
||||
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
|
||||
num_col, _, matrix_of_lines_ch, splitter_y_new, _ = find_number_of_columns_in_document(
|
||||
num_col, _, matrix_of_seps_ch, splitter_y_new, _ = find_number_of_columns_in_document(
|
||||
text_regions_p, num_col_classifier, self.tables, label_seps)
|
||||
else:
|
||||
_, _, matrix_of_lines_ch_d, splitter_y_new_d, _ = find_number_of_columns_in_document(
|
||||
text_regions_p_1_n, num_col_classifier, self.tables, label_seps)
|
||||
_, _, matrix_of_seps_ch_d, splitter_y_new_d, _ = find_number_of_columns_in_document(
|
||||
text_regions_p_d, num_col_classifier, self.tables, label_seps)
|
||||
|
||||
if num_col_classifier >= 3:
|
||||
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
|
||||
|
|
@ -4718,12 +4718,12 @@ class Eynollah:
|
|||
|
||||
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
|
||||
boxes, _ = return_boxes_of_images_by_order_of_reading_new(
|
||||
splitter_y_new, regions_without_separators, matrix_of_lines_ch,
|
||||
splitter_y_new, regions_without_separators, text_regions_p, matrix_of_seps_ch,
|
||||
num_col_classifier, erosion_hurts, self.tables, self.right2left,
|
||||
logger=self.logger)
|
||||
else:
|
||||
boxes_d, _ = return_boxes_of_images_by_order_of_reading_new(
|
||||
splitter_y_new_d, regions_without_separators_d, matrix_of_lines_ch_d,
|
||||
splitter_y_new_d, regions_without_separators_d, text_regions_p_d, matrix_of_seps_ch_d,
|
||||
num_col_classifier, erosion_hurts, self.tables, self.right2left,
|
||||
logger=self.logger)
|
||||
else:
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue