Merge branch 'main' into 420-namespace-package

CHANGELOG.md

@@ -5,6 +5,17 @@ Versioned according to [Semantic Versioning](http://semver.org/).
 
 ## Unreleased
 
+## [0.2.0] - 2023-03-24
+
+Changed:
+
+  * Convert default model from HDFS to TF SavedModel, #91
+
+Added:
+
+  * parmeter `tables` to toggle table detectino, #91
+  * default model described in ocrd-tool.json, #91
+
 ## [0.1.0] - 2023-03-22
 
 Fixed:
@@ -85,6 +96,7 @@ Fixed:
 Initial release
 
 <!-- link-labels -->
+[0.2.0]: ../../compare/v0.2.0...v0.1.0
 [0.1.0]: ../../compare/v0.1.0...v0.0.11
 [0.0.11]: ../../compare/v0.0.11...v0.0.10
 [0.0.10]: ../../compare/v0.0.10...v0.0.9

qurator/eynollah/eynollah.py

@@ -146,6 +146,8 @@ class Eynollah:
         self.model_textline_dir = dir_models + "/model_textline_newspapers.h5"
         self.model_tables = dir_models + "/model_tables_ens_mixed_new_2.h5"
 
+        self.models = {}
+        
     def _cache_images(self, image_filename=None, image_pil=None):
         ret = {}
         if image_filename:
@@ -220,7 +222,8 @@ class Eynollah:
                     index_y_d = img_h - img_height_model
 
                 img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
-                label_p_pred = model_enhancement.predict(img_patch.reshape(1, img_patch.shape[0], img_patch.shape[1], img_patch.shape[2]))
+                label_p_pred = model_enhancement.predict(img_patch.reshape(1, img_patch.shape[0], img_patch.shape[1], img_patch.shape[2]),
+                                                         verbose=0)
 
                 seg = label_p_pred[0, :, :, :]
                 seg = seg * 255
@@ -254,11 +257,6 @@ class Eynollah:
                     prediction_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - margin, :] = seg
 
         prediction_true = prediction_true.astype(int)
-        session_enhancement.close()
-        del model_enhancement
-        del session_enhancement
-        gc.collect()
-
         return prediction_true
 
     def calculate_width_height_by_columns(self, img, num_col, width_early, label_p_pred):
@@ -355,21 +353,11 @@ class Eynollah:
             img_in[0, :, :, 1] = img_1ch[:, :]
             img_in[0, :, :, 2] = img_1ch[:, :]
 
-        label_p_pred = model_num_classifier.predict(img_in)
+        label_p_pred = model_num_classifier.predict(img_in, verbose=0)
         num_col = np.argmax(label_p_pred[0]) + 1
 
         self.logger.info("Found %s columns (%s)", num_col, label_p_pred)
 
-        session_col_classifier.close()
-        
-        del model_num_classifier
-        del session_col_classifier
-        
-        K.clear_session()
-        gc.collect()
-
-
-
         img_new, _ = self.calculate_width_height_by_columns(img, num_col, width_early, label_p_pred)
 
         if img_new.shape[1] > img.shape[1]:
@@ -393,11 +381,6 @@ class Eynollah:
             
             prediction_bin =np.repeat(prediction_bin[:, :, np.newaxis], 3, axis=2)
 
-            session_bin.close()
-            del model_bin
-            del session_bin
-            gc.collect()
-            
             prediction_bin = prediction_bin.astype(np.uint8)
             img= np.copy(prediction_bin)
             img_bin = np.copy(prediction_bin)
@@ -405,6 +388,7 @@ class Eynollah:
             img = self.imread()
             img_bin = None
 
+        t1 = time.time()
         _, page_coord = self.early_page_for_num_of_column_classification(img_bin)
         model_num_classifier, session_col_classifier = self.start_new_session_and_model(self.model_dir_of_col_classifier)
         
@@ -427,12 +411,10 @@ class Eynollah:
             img_in[0, :, :, 2] = img_1ch[:, :]
 
 
-
+        label_p_pred = model_num_classifier.predict(img_in, verbose=0)
-        label_p_pred = model_num_classifier.predict(img_in)
         num_col = np.argmax(label_p_pred[0]) + 1
-        self.logger.info("Found %s columns (%s)", num_col, label_p_pred)
+        self.logger.info("Found %d columns (%s)", num_col, np.around(label_p_pred, decimals=5))
-        session_col_classifier.close()
+        self.logger.info("detecting columns took %.1fs", time.time() - t1)
-        K.clear_session()
 
         if dpi < DPI_THRESHOLD:
             img_new, num_column_is_classified = self.calculate_width_height_by_columns(img, num_col, width_early, label_p_pred)
@@ -443,9 +425,6 @@ class Eynollah:
             image_res = np.copy(img)
             is_image_enhanced = False
 
-        session_col_classifier.close()
-
-        
         self.logger.debug("exit resize_and_enhance_image_with_column_classifier")
         return is_image_enhanced, img, image_res, num_col, num_column_is_classified, img_bin
 
@@ -512,12 +491,24 @@ class Eynollah:
     
     def start_new_session_and_model(self, model_dir):
         self.logger.debug("enter start_new_session_and_model (model_dir=%s)", model_dir)
-        gpu_options = tf.compat.v1.GPUOptions(allow_growth=True)
+        #gpu_options = tf.compat.v1.GPUOptions(allow_growth=True)
         #gpu_options = tf.compat.v1.GPUOptions(per_process_gpu_memory_fraction=7.7, allow_growth=True)
-        session = tf.compat.v1.Session(config=tf.compat.v1.ConfigProto(gpu_options=gpu_options))
+        #session = tf.compat.v1.Session(config=tf.compat.v1.ConfigProto(gpu_options=gpu_options))
-        model = load_model(model_dir, compile=False)
+        physical_devices = tf.config.list_physical_devices('GPU')
+        try:
+            tf.config.experimental.set_memory_growth(physical_devices[0], True)
+        except:
+            self.logger.warning("no GPU device available")
+        if model_dir.endswith('.h5') and Path(model_dir[:-3]).exists():
+            # prefer SavedModel over HDF5 format if it exists
+            model_dir = model_dir[:-3]
+        if model_dir in self.models:
+            model = self.models[model_dir]
+        else:
+            model = load_model(model_dir, compile=False)
+            self.models[model_dir] = model
 
-        return model, session
+        return model, None
 
     def do_prediction(self, patches, img, model, marginal_of_patch_percent=0.1):
         self.logger.debug("enter do_prediction")
@@ -531,7 +522,8 @@ class Eynollah:
             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]))
+            label_p_pred = model.predict(img.reshape(1, img.shape[0], img.shape[1], img.shape[2]),
+                                         verbose=0)
 
             seg = np.argmax(label_p_pred, axis=3)[0]
             seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2)
@@ -583,7 +575,8 @@ class Eynollah:
                         index_y_d = img_h - img_height_model
 
                     img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
-                    label_p_pred = model.predict(img_patch.reshape(1, img_patch.shape[0], img_patch.shape[1], img_patch.shape[2]))
+                    label_p_pred = model.predict(img_patch.reshape(1, img_patch.shape[0], img_patch.shape[1], img_patch.shape[2]),
+                                                 verbose=0)
                     seg = np.argmax(label_p_pred, axis=3)[0]
                     seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2)
 
@@ -634,8 +627,6 @@ class Eynollah:
                         prediction_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - margin, :] = seg_color
 
             prediction_true = prediction_true.astype(np.uint8)
-        del model
-        gc.collect()
         return prediction_true
 
     def early_page_for_num_of_column_classification(self,img_bin):
@@ -662,19 +653,15 @@ class Eynollah:
         else:
             box = [0, 0, img.shape[1], img.shape[0]]
         croped_page, page_coord = crop_image_inside_box(box, img)
-        session_page.close()
-        del model_page
-        del session_page
-        gc.collect()
-        K.clear_session()
         self.logger.debug("exit early_page_for_num_of_column_classification")
         return croped_page, page_coord
 
     def extract_page(self):
         self.logger.debug("enter extract_page")
         cont_page = []
-        model_page, session_page = self.start_new_session_and_model(self.model_page_dir)
         img = cv2.GaussianBlur(self.image, (5, 5), 0)
+        
+        model_page, session_page = self.start_new_session_and_model(self.model_page_dir)
         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)
@@ -701,11 +688,6 @@ class Eynollah:
             box = [0, 0, img.shape[1], img.shape[0]]
         croped_page, page_coord = crop_image_inside_box(box, self.image)
         cont_page.append(np.array([[page_coord[2], page_coord[0]], [page_coord[3], page_coord[0]], [page_coord[3], page_coord[1]], [page_coord[2], page_coord[1]]]))
-        session_page.close()
-        del model_page
-        del session_page
-        gc.collect()
-        K.clear_session()
         self.logger.debug("exit extract_page")
         return croped_page, page_coord, cont_page
 
@@ -801,11 +783,6 @@ class Eynollah:
         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)
 
-        session_region.close()
-        del model_region
-        del session_region
-        gc.collect()
-        
         self.logger.debug("exit extract_text_regions")
         return prediction_regions, prediction_regions2
 
@@ -1106,9 +1083,6 @@ class Eynollah:
         prediction_textline_longshot = self.do_prediction(False, img, model_textline)
         prediction_textline_longshot_true_size = resize_image(prediction_textline_longshot, img_h, img_w)
 
-        session_textline.close()
-
-
         return prediction_textline[:, :, 0], prediction_textline_longshot_true_size[:, :, 0]
 
     def do_work_of_slopes(self, q, poly, box_sub, boxes_per_process, textline_mask_tot, contours_per_process):
@@ -1186,22 +1160,12 @@ class Eynollah:
             prediction_regions_org=prediction_regions_org[:,:,0]
             prediction_regions_org[(prediction_regions_org[:,:]==1) & (mask_zeros_y[:,:]==1)]=0
 
-            session_region.close()
-            del model_region
-            del session_region
-            gc.collect()
-
             model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p2)
             img = resize_image(img_org, int(img_org.shape[0]), int(img_org.shape[1]))
             prediction_regions_org2 = self.do_prediction(True, img, model_region, 0.2)
             prediction_regions_org2=resize_image(prediction_regions_org2, img_height_h, img_width_h )
 
 
-            session_region.close()
-            del model_region
-            del session_region
-            gc.collect()
-
             mask_zeros2 = (prediction_regions_org2[:,:,0] == 0)
             mask_lines2 = (prediction_regions_org2[:,:,0] == 3)
             text_sume_early = (prediction_regions_org[:,:] == 1).sum()
@@ -1241,12 +1205,6 @@ class Eynollah:
                     
                     prediction_bin =np.repeat(prediction_bin[:, :, np.newaxis], 3, axis=2)
 
-                    session_bin.close()
-                    del model_bin
-                    del session_bin
-                    gc.collect()
-                
-                
                 
                 model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_ens)
                 ratio_y=1
@@ -1260,11 +1218,6 @@ class Eynollah:
                 prediction_regions_org=prediction_regions_org[:,:,0]
                 
                 mask_lines_only=(prediction_regions_org[:,:]==3)*1
-                session_region.close()
-                del model_region
-                del session_region
-                gc.collect()
-                
                 
             mask_texts_only=(prediction_regions_org[:,:]==1)*1
             mask_images_only=(prediction_regions_org[:,:]==2)*1
@@ -1283,20 +1236,12 @@ class Eynollah:
 
             text_regions_p_true=cv2.fillPoly(text_regions_p_true,pts=polygons_of_only_texts, color=(1,1,1))
 
-            
-
-            K.clear_session()
             return text_regions_p_true, erosion_hurts, polygons_lines_xml
         except:
             
             if self.input_binary:
                 prediction_bin = np.copy(img_org)
             else:
-                session_region.close()
-                del model_region
-                del session_region
-                gc.collect()
-                
                 model_bin, session_bin = self.start_new_session_and_model(self.model_dir_of_binarization)
                 prediction_bin = self.do_prediction(True, img_org, model_bin)
                 prediction_bin = resize_image(prediction_bin, img_height_h, img_width_h )
@@ -1308,15 +1253,6 @@ class Eynollah:
                 
                 prediction_bin =np.repeat(prediction_bin[:, :, np.newaxis], 3, axis=2)
 
-                
-                
-                session_bin.close()
-                del model_bin
-                del session_bin
-                gc.collect()
-            
-            
-            
                 model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_ens)
             ratio_y=1
             ratio_x=1
@@ -1329,11 +1265,6 @@ class Eynollah:
             prediction_regions_org=prediction_regions_org[:,:,0]
             
             #mask_lines_only=(prediction_regions_org[:,:]==3)*1
-            session_region.close()
-            del model_region
-            del session_region
-            gc.collect()
-            
             #img = resize_image(img_org, int(img_org.shape[0]*1), int(img_org.shape[1]*1))
             
             #prediction_regions_org = self.do_prediction(True, img, model_region)
@@ -1343,11 +1274,6 @@ class Eynollah:
             #prediction_regions_org = prediction_regions_org[:,:,0]
             
             #prediction_regions_org[(prediction_regions_org[:,:] == 1) & (mask_zeros_y[:,:] == 1)]=0
-            #session_region.close()
-            #del model_region
-            #del session_region
-            #gc.collect()
-            
             
             
             
@@ -1375,7 +1301,7 @@ class Eynollah:
             text_regions_p_true = cv2.fillPoly(text_regions_p_true, pts = polygons_of_only_texts, color=(1,1,1))
             
             erosion_hurts = True
-            K.clear_session()
+
             return text_regions_p_true, erosion_hurts, polygons_lines_xml
 
     def do_order_of_regions_full_layout(self, contours_only_text_parent, contours_only_text_parent_h, boxes, textline_mask_tot):
@@ -1869,7 +1795,6 @@ class Eynollah:
             img_new[h_start:h_start+img.shape[0] ,w_start: w_start+img.shape[1], : ] =img[:,:,:]
 
             prediction_ext = self.do_prediction(patches, img_new, model_region)
-            
             pre_updown = self.do_prediction(patches, cv2.flip(img_new[:,:,:], -1), model_region)
             pre_updown = cv2.flip(pre_updown, -1)
             
@@ -1892,7 +1817,6 @@ class Eynollah:
             img_new[h_start:h_start+img.shape[0] ,w_start: w_start+img.shape[1], : ] =img[:,:,:]
 
             prediction_ext = self.do_prediction(patches, img_new, model_region)
-            
             pre_updown = self.do_prediction(patches, cv2.flip(img_new[:,:,:], -1), model_region)
             pre_updown = cv2.flip(pre_updown, -1)
             
@@ -1908,9 +1832,7 @@ class Eynollah:
 
             pre1 = self.do_prediction(patches, img[:,0:img_w_half,:], model_region)
             pre2 = self.do_prediction(patches, img[:,img_w_half:,:], model_region)
-            
             pre_full = self.do_prediction(patches, img[:,:,:], model_region)
-            
             pre_updown = self.do_prediction(patches, cv2.flip(img[:,:,:], -1), model_region)
             pre_updown = cv2.flip(pre_updown, -1)
             
@@ -1933,11 +1855,6 @@ class Eynollah:
         prediction_table_erode = cv2.erode(prediction_table[:,:,0], KERNEL, iterations=20)
         prediction_table_erode = cv2.dilate(prediction_table_erode, KERNEL, iterations=20)
 
-        del model_region
-        del session_region
-        gc.collect()
-        
-        
         return prediction_table_erode.astype(np.int16)
 
     def run_graphics_and_columns(self, text_regions_p_1, num_col_classifier, num_column_is_classified, erosion_hurts):
@@ -1989,7 +1906,7 @@ class Eynollah:
         self.logger.info("Resizing and enhancing image...")
         is_image_enhanced, img_org, img_res, num_col_classifier, num_column_is_classified, img_bin = self.resize_and_enhance_image_with_column_classifier()
         self.logger.info("Image was %senhanced.", '' if is_image_enhanced else 'not ')
-        K.clear_session()
+
         scale = 1
         if is_image_enhanced:
             if self.allow_enhancement:
@@ -2013,7 +1930,7 @@ class Eynollah:
         scaler_h_textline = 1  # 1.2#1.2
         scaler_w_textline = 1  # 0.9#1
         textline_mask_tot_ea, _ = self.textline_contours(image_page, True, scaler_h_textline, scaler_w_textline)
-        K.clear_session()
+
         if self.plotter:
             self.plotter.save_plot_of_textlines(textline_mask_tot_ea, image_page)
         return textline_mask_tot_ea
@@ -2026,7 +1943,7 @@ class Eynollah:
 
         if self.plotter:
             self.plotter.save_deskewed_image(slope_deskew)
-        self.logger.info("slope_deskew: %s", slope_deskew)
+        self.logger.info("slope_deskew: %.2f°", slope_deskew)
         return slope_deskew, slope_first
 
     def run_marginals(self, image_page, textline_mask_tot_ea, mask_images, mask_lines, num_col_classifier, slope_deskew, text_regions_p_1, table_prediction):
@@ -2075,7 +1992,6 @@ class Eynollah:
 
         if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
             _, _, matrix_of_lines_ch_d, splitter_y_new_d, _ = find_number_of_columns_in_document(np.repeat(text_regions_p_1_n[:, :, np.newaxis], 3, axis=2), num_col_classifier, self.tables, pixel_lines)
-        K.clear_session()
 
         self.logger.info("num_col_classifier: %s", num_col_classifier)
 
@@ -2141,7 +2057,6 @@ class Eynollah:
         contours_tables = return_contours_of_interested_region(text_regions_p, pixel_img, min_area_mar)
         
         
-        K.clear_session()
         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, regions_without_separators_d, boxes, boxes_d, polygons_of_marginals, contours_tables
 
@@ -2172,8 +2087,6 @@ class Eynollah:
             
             if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
                 num_col_d, peaks_neg_fin_d, matrix_of_lines_ch_d, splitter_y_new_d, seperators_closeup_n_d = find_number_of_columns_in_document(np.repeat(text_regions_p_1_n[:, :, np.newaxis], 3, axis=2),num_col_classifier, self.tables, pixel_lines)
-            K.clear_session()
-            gc.collect()
 
             if num_col_classifier>=3:
                 if np.abs(slope_deskew) < SLOPE_THRESHOLD:
@@ -2240,21 +2153,18 @@ class Eynollah:
         text_regions_p[:, :][text_regions_p[:, :] == 3] = 6
         text_regions_p[:, :][text_regions_p[:, :] == 4] = 8
 
-        K.clear_session()
         image_page = image_page.astype(np.uint8)
 
         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)
         regions_fully[:, :, 0][regions_fully_only_drop[:, :, 0] == 4] = 4
-        K.clear_session()
 
         # plt.imshow(regions_fully[:,:,0])
         # plt.show()
         regions_fully = putt_bb_of_drop_capitals_of_model_in_patches_in_layout(regions_fully)
         # plt.imshow(regions_fully[:,:,0])
         # plt.show()
-        K.clear_session()
         regions_fully_np, _ = self.extract_text_regions(image_page, False, cols=num_col_classifier)
         # plt.imshow(regions_fully_np[:,:,0])
         # plt.show()
@@ -2265,7 +2175,6 @@ class Eynollah:
 
         # plt.imshow(regions_fully_np[:,:,0])
         # plt.show()
-        K.clear_session()
         # plt.imshow(regions_fully[:,:,0])
         # plt.show()
         regions_fully = boosting_headers_by_longshot_region_segmentation(regions_fully, regions_fully_np, img_only_regions)
@@ -2291,7 +2200,6 @@ class Eynollah:
         if not self.tables:
             regions_without_separators = (text_regions_p[:, :] == 1) * 1
 
-        K.clear_session()
         img_revised_tab = np.copy(text_regions_p[:, :])
         polygons_of_images = return_contours_of_interested_region(img_revised_tab, 5)
         self.logger.debug('exit run_boxes_full_layout')
@@ -2353,15 +2261,15 @@ class Eynollah:
             contours_only_text_parent = return_parent_contours(contours_only_text, hir_on_text)
                         
             if len(contours_only_text_parent) > 0:
-                areas_cnt_text = np.array([cv2.contourArea(contours_only_text_parent[j]) for j in range(len(contours_only_text_parent))])
+                areas_cnt_text = np.array([cv2.contourArea(c) for c in contours_only_text_parent])
                 areas_cnt_text = areas_cnt_text / float(text_only.shape[0] * text_only.shape[1])
                 #self.logger.info('areas_cnt_text %s', areas_cnt_text)
                 contours_biggest = contours_only_text_parent[np.argmax(areas_cnt_text)]
-                contours_only_text_parent = [contours_only_text_parent[jz] for jz in range(len(contours_only_text_parent)) if areas_cnt_text[jz] > min_con_area]
+                contours_only_text_parent = [c for jz, c in enumerate(contours_only_text_parent) if areas_cnt_text[jz] > min_con_area]
-                areas_cnt_text_parent = [areas_cnt_text[jz] for jz in range(len(areas_cnt_text)) if areas_cnt_text[jz] > min_con_area]
+                areas_cnt_text_parent = [area for area in areas_cnt_text if area > min_con_area]
 
                 index_con_parents = np.argsort(areas_cnt_text_parent)
-                contours_only_text_parent = list(np.array(contours_only_text_parent)[index_con_parents])
+                contours_only_text_parent = list(np.array(contours_only_text_parent, dtype=object)[index_con_parents])
                 areas_cnt_text_parent = list(np.array(areas_cnt_text_parent)[index_con_parents])
 
                 cx_bigest_big, cy_biggest_big, _, _, _, _, _ = find_new_features_of_contours([contours_biggest])
@@ -2370,14 +2278,14 @@ class Eynollah:
                 contours_only_text_d, hir_on_text_d = return_contours_of_image(text_only_d)
                 contours_only_text_parent_d = return_parent_contours(contours_only_text_d, hir_on_text_d)
 
-                areas_cnt_text_d = np.array([cv2.contourArea(contours_only_text_parent_d[j]) for j in range(len(contours_only_text_parent_d))])
+                areas_cnt_text_d = np.array([cv2.contourArea(c) for c in contours_only_text_parent_d])
                 areas_cnt_text_d = areas_cnt_text_d / float(text_only_d.shape[0] * text_only_d.shape[1])
                 
                 if len(areas_cnt_text_d)>0:
                     contours_biggest_d = contours_only_text_parent_d[np.argmax(areas_cnt_text_d)]
-                    index_con_parents_d=np.argsort(areas_cnt_text_d)
+                    index_con_parents_d = np.argsort(areas_cnt_text_d)
-                    contours_only_text_parent_d=list(np.array(contours_only_text_parent_d)[index_con_parents_d] )
+                    contours_only_text_parent_d = list(np.array(contours_only_text_parent_d, dtype=object)[index_con_parents_d])
-                    areas_cnt_text_d=list(np.array(areas_cnt_text_d)[index_con_parents_d] )
+                    areas_cnt_text_d = list(np.array(areas_cnt_text_d)[index_con_parents_d])
 
                     cx_bigest_d_big, cy_biggest_d_big, _, _, _, _, _ = find_new_features_of_contours([contours_biggest_d])
                     cx_bigest_d, cy_biggest_d, _, _, _, _, _ = find_new_features_of_contours(contours_only_text_parent_d)
@@ -2432,15 +2340,15 @@ class Eynollah:
             contours_only_text_parent = return_parent_contours(contours_only_text, hir_on_text)
             
             if len(contours_only_text_parent) > 0:
-                areas_cnt_text = np.array([cv2.contourArea(contours_only_text_parent[j]) for j in range(len(contours_only_text_parent))])
+                areas_cnt_text = np.array([cv2.contourArea(c) for c in contours_only_text_parent])
                 areas_cnt_text = areas_cnt_text / float(text_only.shape[0] * text_only.shape[1])
 
                 contours_biggest = contours_only_text_parent[np.argmax(areas_cnt_text)]
-                contours_only_text_parent = [contours_only_text_parent[jz] for jz in range(len(contours_only_text_parent)) if areas_cnt_text[jz] > min_con_area]
+                contours_only_text_parent = [c for jz, c in enumerate(contours_only_text_parent) if areas_cnt_text[jz] > min_con_area]
-                areas_cnt_text_parent = [areas_cnt_text[jz] for jz in range(len(areas_cnt_text)) if areas_cnt_text[jz] > min_con_area]
+                areas_cnt_text_parent = [area for area in areas_cnt_text if area > min_con_area]
 
                 index_con_parents = np.argsort(areas_cnt_text_parent)
-                contours_only_text_parent = list(np.array(contours_only_text_parent)[index_con_parents])
+                contours_only_text_parent = list(np.array(contours_only_text_parent, dtype=object)[index_con_parents])
                 areas_cnt_text_parent = list(np.array(areas_cnt_text_parent)[index_con_parents])
 
                 cx_bigest_big, cy_biggest_big, _, _, _, _, _ = find_new_features_of_contours([contours_biggest])
@@ -2450,6 +2358,14 @@ class Eynollah:
                 # self.logger.debug('len(contours_only_text_parent) %s', len(contours_only_text_parent_d))
             else:
                 pass
+
+        self.logger.info("Found %d text regions", len(contours_only_text_parent))
+        self.logger.info("Found %d margin regions", len(polygons_of_marginals))
+        self.logger.info("Found %d image regions", len(polygons_of_images))
+        self.logger.info("Found %d separator lines", len(polygons_lines_xml))
+        if self.tables:
+            self.logger.info("Found %d tables", len(contours_tables))
+
         txt_con_org = get_textregion_contours_in_org_image(contours_only_text_parent, self.image, slope_first)
         boxes_text, _ = get_text_region_boxes_by_given_contours(contours_only_text_parent)
         boxes_marginals, _ = get_text_region_boxes_by_given_contours(polygons_of_marginals)
@@ -2464,10 +2380,10 @@ class Eynollah:
             all_found_texline_polygons = small_textlines_to_parent_adherence2(all_found_texline_polygons, textline_mask_tot_ea, num_col_classifier)
             all_found_texline_polygons_marginals, boxes_marginals, _, polygons_of_marginals, all_box_coord_marginals, _, slopes_marginals = self.get_slopes_and_deskew_new_curved(polygons_of_marginals, polygons_of_marginals, cv2.erode(textline_mask_tot_ea, kernel=KERNEL, iterations=1), image_page_rotated, boxes_marginals, text_only, num_col_classifier, scale_param, slope_deskew)
             all_found_texline_polygons_marginals = small_textlines_to_parent_adherence2(all_found_texline_polygons_marginals, textline_mask_tot_ea, num_col_classifier)
-        K.clear_session()
+
         if self.full_layout:
             if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
-                contours_only_text_parent_d_ordered = list(np.array(contours_only_text_parent_d_ordered)[index_by_text_par_con])
+                contours_only_text_parent_d_ordered = list(np.array(contours_only_text_parent_d_ordered, dtype=object)[index_by_text_par_con])
                 text_regions_p, contours_only_text_parent, contours_only_text_parent_h, all_box_coord, all_box_coord_h, all_found_texline_polygons, all_found_texline_polygons_h, slopes, slopes_h, contours_only_text_parent_d_ordered, contours_only_text_parent_h_d_ordered = check_any_text_region_in_model_one_is_main_or_header(text_regions_p, regions_fully, contours_only_text_parent, all_box_coord, all_found_texline_polygons, slopes, contours_only_text_parent_d_ordered)
             else:
                 contours_only_text_parent_d_ordered = None
@@ -2477,8 +2393,6 @@ class Eynollah:
                 self.plotter.save_plot_of_layout(text_regions_p, image_page)
                 self.plotter.save_plot_of_layout_all(text_regions_p, image_page)
 
-            K.clear_session()
-
             pixel_img = 4
             polygons_of_drop_capitals = return_contours_of_interested_region_by_min_size(text_regions_p, pixel_img)
             all_found_texline_polygons = adhere_drop_capital_region_into_corresponding_textline(text_regions_p, polygons_of_drop_capitals, contours_only_text_parent, contours_only_text_parent_h, all_box_coord, all_box_coord_h, all_found_texline_polygons, all_found_texline_polygons_h, kernel=KERNEL, curved_line=self.curved_line)
@@ -2560,7 +2474,7 @@ class Eynollah:
             if np.abs(slope_deskew) < SLOPE_THRESHOLD:
                 order_text_new, id_of_texts_tot = self.do_order_of_regions(contours_only_text_parent, contours_only_text_parent_h, boxes, textline_mask_tot)
             else:
-                contours_only_text_parent_d_ordered = list(np.array(contours_only_text_parent_d_ordered)[index_by_text_par_con])
+                contours_only_text_parent_d_ordered = list(np.array(contours_only_text_parent_d_ordered, dtype=object)[index_by_text_par_con])
                 order_text_new, id_of_texts_tot = self.do_order_of_regions(contours_only_text_parent_d_ordered, contours_only_text_parent_h, boxes_d, textline_mask_tot_d)
             pcgts = self.writer.build_pagexml_no_full_layout(txt_con_org, page_coord, order_text_new, id_of_texts_tot, all_found_texline_polygons, all_box_coord, polygons_of_images, polygons_of_marginals, all_found_texline_polygons_marginals, all_box_coord_marginals, slopes, slopes_marginals, cont_page, polygons_lines_xml, contours_tables)
             self.logger.info("Job done in %.1fs", time.time() - t0)

qurator/eynollah/ocrd-tool.json

@@ -1,5 +1,5 @@
 {
-  "version": "0.1.0",
+  "version": "0.2.0",
   "git_url": "https://github.com/qurator-spk/eynollah",
   "tools": {
     "ocrd-eynollah-segment": {
@@ -29,6 +29,11 @@
           "default": true,
           "description": "Try to detect all element subtypes, including drop-caps and headings"
         },
+	"tables": {
+	  "type": "boolean",
+	  "default": false,
+	  "description": "Try to detect table regions"
+	},
         "curved_line": {
           "type": "boolean",
           "default": false,
@@ -44,7 +49,17 @@
           "default": false,
           "description": "ignore the special role of headings during reading order detection"
         }
-      }
+      },
+      "resources": [
+	{
+	  "description": "models for eynollah (TensorFlow format)",
+	  "url": "https://qurator-data.de/eynollah/2021-04-25/SavedModel.tar.gz",
+	  "name": "default",
+	  "size": 1483106598,
+	  "type": "archive",
+	  "path_in_archive": "default"
+	}
+      ]
     }
   }
 }

qurator/eynollah/processor.py

@@ -50,6 +50,7 @@ class EynollahProcessor(Processor):
                 'full_layout': self.parameter['full_layout'],
                 'allow_scaling': self.parameter['allow_scaling'],
                 'headers_off': self.parameter['headers_off'],
+                'tables': self.parameter['tables'],
                 'override_dpi': self.parameter['dpi'],
                 'logger': LOG,
                 'pcgts': pcgts,

qurator/eynollah/utils/contour.py

@@ -19,7 +19,7 @@ def contours_in_same_horizon(cy_main_hor):
         list_h.append(i)
         if len(list_h) > 1:
             all_args.append(list(set(list_h)))
-    return np.unique(all_args)
+    return np.unique(np.array(all_args, dtype=object))
 
 def find_contours_mean_y_diff(contours_main):
     M_main = [cv2.moments(contours_main[j]) for j in range(len(contours_main))]