remove unnecessary patches assignment, simplify if-else

sbb_newspapers_org_image/eynollah.py

@@ -499,7 +499,26 @@ class eynollah:
         img_width_model = model.layers[len(model.layers) - 1].output_shape[2]
         n_classes = model.layers[len(model.layers) - 1].output_shape[3]
 
-        if patches:
+
+        if not patches:
+            img_h_page = img.shape[0]
+            img_w_page = img.shape[1]
+            img = img / float(255.0)
+            img = resize_image(img, img_height_model, img_width_model)
+
+            label_p_pred = model.predict(img.reshape(1, img.shape[0], img.shape[1], img.shape[2]))
+
+            seg = np.argmax(label_p_pred, axis=3)[0]
+            seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2)
+            prediction_true = resize_image(seg_color, img_h_page, img_w_page)
+            prediction_true = prediction_true.astype(np.uint8)
+
+            del img
+            del seg_color
+            del label_p_pred
+            del seg
+
+        else:
             if img.shape[0] < img_height_model:
                 img = resize_image(img, img_height_model, img.shape[1])
 
@@ -599,39 +618,18 @@ class eynollah:
             del seg_color
             del seg
             del img_patch
-
-        if not patches:
-            img_h_page = img.shape[0]
-            img_w_page = img.shape[1]
-            img = img / float(255.0)
-            img = resize_image(img, img_height_model, img_width_model)
-
-            label_p_pred = model.predict(img.reshape(1, img.shape[0], img.shape[1], img.shape[2]))
-
-            seg = np.argmax(label_p_pred, axis=3)[0]
-            seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2)
-            prediction_true = resize_image(seg_color, img_h_page, img_w_page)
-            prediction_true = prediction_true.astype(np.uint8)
-
-            del img
-            del seg_color
-            del label_p_pred
-            del seg
-        del model
         gc.collect()
-
         return prediction_true
 
     def early_page_for_num_of_column_classification(self):
         self.logger.debug("enter early_page_for_num_of_column_classification")
         img = cv2.imread(self.image_filename)
         img = img.astype(np.uint8)
-        patches = False
         model_page, session_page = self.start_new_session_and_model(self.model_page_dir)
         for ii in range(1):
             img = cv2.GaussianBlur(img, (5, 5), 0)
 
-        img_page_prediction = self.do_prediction(patches, img, model_page)
+        img_page_prediction = self.do_prediction(False, img, model_page)
 
         imgray = cv2.cvtColor(img_page_prediction, cv2.COLOR_BGR2GRAY)
         _, thresh = cv2.threshold(imgray, 0, 255, 0)
@@ -664,12 +662,11 @@ class eynollah:
 
     def extract_page(self):
         self.logger.debug("enter extract_page")
-        patches = False
         model_page, session_page = self.start_new_session_and_model(self.model_page_dir)
         for ii in range(1):
             img = cv2.GaussianBlur(self.image, (5, 5), 0)
 
-        img_page_prediction = self.do_prediction(patches, img, model_page)
+        img_page_prediction = self.do_prediction(False, img, model_page)
 
         imgray = cv2.cvtColor(img_page_prediction, cv2.COLOR_BGR2GRAY)
         _, thresh = cv2.threshold(imgray, 0, 255, 0)
@@ -715,12 +712,14 @@ class eynollah:
         img_height_h = img.shape[0]
         img_width_h = img.shape[1]
 
-        if patches:
+        model_region, session_region = self.start_new_session_and_model(self.model_region_dir_fully if patches else self.model_region_dir_fully_np)
-            model_region, session_region = self.start_new_session_and_model(self.model_region_dir_fully)
-        if not patches:
-            model_region, session_region = self.start_new_session_and_model(self.model_region_dir_fully_np)
 
-        if patches and cols == 1:
+        if not patches:
+            img = otsu_copy_binary(img)
+            img = img.astype(np.uint8)
+            prediction_regions2 = None
+        else:
+            if cols == 1:
                 img2 = otsu_copy_binary(img)
                 img2 = img2.astype(np.uint8)
                 img2 = resize_image(img2, int(img_height_h * 0.7), int(img_width_h * 0.7))
@@ -728,7 +727,7 @@ class eynollah:
                 prediction_regions2 = self.do_prediction(patches, img2, model_region, marginal_of_patch_percent)
                 prediction_regions2 = resize_image(prediction_regions2, img_height_h, img_width_h)
 
-        if patches and cols == 2:
+            if cols == 2:
                 img2 = otsu_copy_binary(img)
                 img2 = img2.astype(np.uint8)
                 img2 = resize_image(img2, int(img_height_h * 0.4), int(img_width_h * 0.4))
@@ -736,7 +735,7 @@ class eynollah:
                 prediction_regions2 = self.do_prediction(patches, img2, model_region, marginal_of_patch_percent)
                 prediction_regions2 = resize_image(prediction_regions2, img_height_h, img_width_h)
 
-        elif patches and cols > 2:
+            elif cols > 2:
                 img2 = otsu_copy_binary(img)
                 img2 = img2.astype(np.uint8)
                 img2 = resize_image(img2, int(img_height_h * 0.3), int(img_width_h * 0.3))
@@ -744,20 +743,20 @@ class eynollah:
                 prediction_regions2 = self.do_prediction(patches, img2, model_region, marginal_of_patch_percent)
                 prediction_regions2 = resize_image(prediction_regions2, img_height_h, img_width_h)
 
-        if patches and cols == 2:
+            if cols == 2:
                 img = otsu_copy_binary(img)
                 img = img.astype(np.uint8)
                 if img_width_h >= 2000:
                     img = resize_image(img, int(img_height_h * 0.9), int(img_width_h * 0.9))
                 img = img.astype(np.uint8)
 
-        if patches and cols == 1:
+            if cols == 1:
                 img = otsu_copy_binary(img)
                 img = img.astype(np.uint8)
                 img = resize_image(img, int(img_height_h * 0.5), int(img_width_h * 0.5))
                 img = img.astype(np.uint8)
 
-        if patches and cols == 3:
+            if cols == 3:
                 if (self.scale_x == 1 and img_width_h > 3000) or (self.scale_x != 1 and img_width_h > 2800):
                     img = otsu_copy_binary(img)
                     img = img.astype(np.uint8)
@@ -766,8 +765,7 @@ class eynollah:
                     img = otsu_copy_binary(img)
                     img = img.astype(np.uint8)
 
-        if patches and cols == 4:
+            if cols == 4:
-            #print(self.scale_x,img_width_h,'scale')
                 if (self.scale_x == 1 and img_width_h > 4000) or (self.scale_x != 1 and img_width_h > 3700):
                     img = otsu_copy_binary(img)
                     img = img.astype(np.uint8)
@@ -777,7 +775,7 @@ class eynollah:
                     img = img.astype(np.uint8)
                     img= resize_image(img, int(img_height_h * 0.9), int(img_width_h * 0.9))
 
-        if patches and cols==5:
+            if cols == 5:
                 if self.scale_x == 1 and img_width_h > 5000:
                     img = otsu_copy_binary(img)
                     img = img.astype(np.uint8)
@@ -787,7 +785,7 @@ class eynollah:
                     img = img.astype(np.uint8)
                     img= resize_image(img, int(img_height_h * 0.9), int(img_width_h * 0.9) )
 
-        if patches and cols>=6:
+            if cols >= 6:
                 if img_width_h > 5600:
                     img = otsu_copy_binary(img)
                     img = img.astype(np.uint8)
@@ -797,11 +795,6 @@ class eynollah:
                     img = img.astype(np.uint8)
                     img= resize_image(img, int(img_height_h * 0.9), int(img_width_h * 0.9))
 
-        if not patches:
-            img = otsu_copy_binary(img)
-            img = img.astype(np.uint8)
-            prediction_regions2 = None
-
         marginal_of_patch_percent = 0.1
         prediction_regions = self.do_prediction(patches, img, model_region, marginal_of_patch_percent)
         prediction_regions = resize_image(prediction_regions, img_height_h, img_width_h)
@@ -1105,10 +1098,7 @@ class eynollah:
     def textline_contours(self, img, patches, scaler_h, scaler_w):
         self.logger.debug('enter textline_contours')
 
-        if patches:
+        model_textline, session_textline = self.start_new_session_and_model(self.model_textline_dir if patches else self.model_textline_dir_np)
-            model_textline, session_textline = self.start_new_session_and_model(self.model_textline_dir)
-        if not patches:
-            model_textline, session_textline = self.start_new_session_and_model(self.model_textline_dir_np)
         img = img.astype(np.uint8)
         img_org = np.copy(img)
         img_h = img_org.shape[0]
@@ -1116,17 +1106,12 @@ class eynollah:
         img = resize_image(img_org, int(img_org.shape[0] * scaler_h), int(img_org.shape[1] * scaler_w))
         prediction_textline = self.do_prediction(patches, img, model_textline)
         prediction_textline = resize_image(prediction_textline, img_h, img_w)
-        patches = False
+        prediction_textline_longshot = self.do_prediction(False, img, model_textline)
-        prediction_textline_longshot = self.do_prediction(patches, img, model_textline)
         prediction_textline_longshot_true_size = resize_image(prediction_textline_longshot, img_h, img_w)
-
-        # prediction_textline_streched=self.do_prediction(patches,img,model_textline)
-        # prediction_textline_streched= resize_image(prediction_textline_streched, img_h, img_w)
         ##plt.imshow(prediction_textline_streched[:,:,0])
         ##plt.show()
 
         session_textline.close()
-
         del model_textline
         del session_textline
         del img
@@ -1697,7 +1682,6 @@ class eynollah:
         model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_ens)
 
         gaussian_filter=False
-        patches=True
         binary=False
         ratio_y=1.3
         ratio_x=1
@@ -1714,7 +1698,7 @@ class eynollah:
             img= cv2.GaussianBlur(img,(5,5),0)
             img = img.astype(np.uint16)
 
-        prediction_regions_org_y = self.do_prediction(patches,img,model_region)
+        prediction_regions_org_y = self.do_prediction(True, img, model_region)
         prediction_regions_org_y = resize_image(prediction_regions_org_y, img_height_h, img_width_h )
 
         #plt.imshow(prediction_regions_org_y[:,:,0])
@@ -1740,7 +1724,7 @@ class eynollah:
             img = cv2.GaussianBlur(img, (5,5 ), 0)
             img = img.astype(np.uint16)
 
-        prediction_regions_org = self.do_prediction(patches,img,model_region)
+        prediction_regions_org = self.do_prediction(True, img, model_region)
         prediction_regions_org = resize_image(prediction_regions_org, img_height_h, img_width_h )
 
         ##plt.imshow(prediction_regions_org[:,:,0])
@@ -1757,7 +1741,6 @@ class eynollah:
         model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p2)
 
         gaussian_filter=False
-        patches=True
         binary=False
         ratio_x=1
         ratio_y=1
@@ -1776,7 +1759,7 @@ class eynollah:
             img = img.astype(np.uint16)
 
         marginal_patch=0.2
-        prediction_regions_org2=self.do_prediction(patches,img,model_region,marginal_patch)
+        prediction_regions_org2=self.do_prediction(True, img, model_region, marginal_patch)
 
         prediction_regions_org2=resize_image(prediction_regions_org2, img_height_h, img_width_h )
 
@@ -2224,16 +2207,15 @@ class eynollah:
         num_col, num_col_classifier, img_only_regions, page_coord, image_page, mask_images, mask_lines = self.run_graphics_and_columns(text_regions_p_1, num_column_is_classified)
         self.logger.info("Graphics detection took %ss ", str(time.time() - t1))
 
-        #print(num_col, "num_colnum_col")
         if not num_col:
             self.logger.info("No columns detected, outputting an empty PAGE-XML")
             self.write_into_page_xml([], page_coord, self.dir_out, [], [], [], [], [], [], [], [], self.curved_line, [], [])
             self.logger.info("Job done in %ss", str(time.time() - t1))
             return
-        patches = True
+
         scaler_h_textline = 1  # 1.2#1.2
         scaler_w_textline = 1  # 0.9#1
-        textline_mask_tot_ea, textline_mask_tot_long_shot = self.textline_contours(image_page, patches, scaler_h_textline, scaler_w_textline)
+        textline_mask_tot_ea, textline_mask_tot_long_shot = self.textline_contours(image_page, True, scaler_h_textline, scaler_w_textline)
 
         K.clear_session()
         gc.collect()
@@ -2354,11 +2336,10 @@ class eynollah:
 
             K.clear_session()
             # gc.collect()
-            patches = True
             image_page = image_page.astype(np.uint8)
 
             # print(type(image_page))
-            regions_fully, regions_fully_only_drop = self.extract_text_regions(image_page, patches, cols=num_col_classifier)
+            regions_fully, regions_fully_only_drop = self.extract_text_regions(image_page, True, cols=num_col_classifier)
             text_regions_p[:,:][regions_fully[:,:,0]==6]=6
 
             regions_fully_only_drop = put_drop_out_from_only_drop_model(regions_fully_only_drop, text_regions_p)
@@ -2376,8 +2357,7 @@ class eynollah:
 
             K.clear_session()
             gc.collect()
-            patches = False
+            regions_fully_np, _ = self.extract_text_regions(image_page, False, cols=num_col_classifier)
-            regions_fully_np, _ = self.extract_text_regions(image_page, patches, cols=num_col_classifier)
 
             # plt.imshow(regions_fully_np[:,:,0])
             # plt.show()