flow from directory

qurator/eynollah/cli.py

@@ -10,7 +10,6 @@ from qurator.eynollah.eynollah import Eynollah
     "-i",
     help="image filename",
     type=click.Path(exists=True, dir_okay=False),
-    required=True,
 )
 @click.option(
     "--out",
@@ -19,6 +18,12 @@ from qurator.eynollah.eynollah import Eynollah
     type=click.Path(exists=True, file_okay=False),
     required=True,
 )
+@click.option(
+    "--dir_in",
+    "-di",
+    help="directory of images",
+    type=click.Path(exists=True, file_okay=False),
+)
 @click.option(
     "--model",
     "-m",
@@ -112,6 +117,7 @@ from qurator.eynollah.eynollah import Eynollah
 def main(
     image,
     out,
+    dir_in,
     model,
     save_images,
     save_layout,
@@ -140,6 +146,7 @@ def main(
     eynollah = Eynollah(
         image_filename=image,
         dir_out=out,
+        dir_in=dir_in,
         dir_models=model,
         dir_of_cropped_images=save_images,
         dir_of_layout=save_layout,
@@ -155,8 +162,9 @@ def main(
         headers_off=headers_off,
         light_version=light_version,
     )
-    pcgts = eynollah.run()
+    eynollah.run()
-    eynollah.writer.write_pagexml(pcgts)
+    #pcgts = eynollah.run()
+    ##eynollah.writer.write_pagexml(pcgts)
 
 if __name__ == "__main__":
     main()

qurator/eynollah/eynollah.py

@@ -29,6 +29,7 @@ warnings.filterwarnings("ignore")
 from scipy.signal import find_peaks
 import matplotlib.pyplot as plt
 from scipy.ndimage import gaussian_filter1d
+from keras.backend import set_session
 
 from .utils.contour import (
     filter_contours_area_of_image,
@@ -38,6 +39,7 @@ from .utils.contour import (
     find_features_of_contours,
     get_text_region_boxes_by_given_contours,
     get_textregion_contours_in_org_image,
+    get_textregion_contours_in_org_image_light,
     return_contours_of_image,
     return_contours_of_interested_region,
     return_contours_of_interested_region_by_min_size,
@@ -65,6 +67,7 @@ from .utils import (
     put_drop_out_from_only_drop_model,
     putt_bb_of_drop_capitals_of_model_in_patches_in_layout,
     check_any_text_region_in_model_one_is_main_or_header,
+    check_any_text_region_in_model_one_is_main_or_header_light,
     small_textlines_to_parent_adherence2,
     order_of_regions,
     find_number_of_columns_in_document,
@@ -84,10 +87,11 @@ class Eynollah:
     def __init__(
         self,
         dir_models,
-        image_filename,
+        image_filename=None,
         image_pil=None,
         image_filename_stem=None,
         dir_out=None,
+        dir_in=None,
         dir_of_cropped_images=None,
         dir_of_layout=None,
         dir_of_deskewed=None,
@@ -105,6 +109,7 @@ class Eynollah:
         logger=None,
         pcgts=None,
     ):
+        if not dir_in:
             if image_pil:
                 self._imgs = self._cache_images(image_pil=image_pil)
             else:
@@ -113,6 +118,7 @@ class Eynollah:
                 self.dpi = override_dpi
             self.image_filename = image_filename
         self.dir_out = dir_out
+        self.dir_in = dir_in
         self.allow_enhancement = allow_enhancement
         self.curved_line = curved_line
         self.full_layout = full_layout
@@ -121,6 +127,7 @@ class Eynollah:
         self.allow_scaling = allow_scaling
         self.headers_off = headers_off
         self.light_version = light_version
+        self.pcgts = pcgts
         self.plotter = None if not enable_plotting else EynollahPlotter(
             dir_out=self.dir_out,
             dir_of_all=dir_of_all,
@@ -128,6 +135,7 @@ class Eynollah:
             dir_of_cropped_images=dir_of_cropped_images,
             dir_of_layout=dir_of_layout,
             image_filename_stem=Path(Path(image_filename).name).stem)
+        if not dir_in:
             self.writer = EynollahXmlWriter(
                 dir_out=self.dir_out,
                 image_filename=self.image_filename,
@@ -149,6 +157,41 @@ class Eynollah:
         self.model_textline_dir = dir_models + "/model_textline_newspapers.h5"
         self.model_tables = dir_models + "/model_tables_ens_mixed_new_2.h5"
         
+        if dir_in and light_version:
+            config = tf.compat.v1.ConfigProto()
+            config.gpu_options.allow_growth = True
+            session = tf.compat.v1.Session(config=config)
+            set_session(session)
+            
+            self.model_page = self.our_load_model(self.model_page_dir)
+            self.model_classifier = self.our_load_model(self.model_dir_of_col_classifier)
+            self.model_bin = self.our_load_model(self.model_dir_of_binarization)
+            self.model_textline = self.our_load_model(self.model_textline_dir)
+            self.model_region = self.our_load_model(self.model_region_dir_p_ens_light)
+            self.model_region_fl_np = self.our_load_model(self.model_region_dir_fully_np)
+            self.model_region_fl = self.our_load_model(self.model_region_dir_fully)
+            
+            self.ls_imgs  = os.listdir(self.dir_in)
+            
+        if dir_in and not light_version:
+            config = tf.compat.v1.ConfigProto()
+            config.gpu_options.allow_growth = True
+            session = tf.compat.v1.Session(config=config)
+            set_session(session)
+            
+            self.model_page = self.our_load_model(self.model_page_dir)
+            self.model_classifier = self.our_load_model(self.model_dir_of_col_classifier)
+            self.model_bin = self.our_load_model(self.model_dir_of_binarization)
+            self.model_textline = self.our_load_model(self.model_textline_dir)
+            self.model_region = self.our_load_model(self.model_region_dir_p_ens)
+            self.model_region_p2 = self.our_load_model(self.model_region_dir_p2)
+            self.model_region_fl_np = self.our_load_model(self.model_region_dir_fully_np)
+            self.model_region_fl = self.our_load_model(self.model_region_dir_fully)
+            self.model_enhancement = self.our_load_model(self.model_dir_of_enhancement)
+            
+            self.ls_imgs  = os.listdir(self.dir_in)
+            
+        
     def _cache_images(self, image_filename=None, image_pil=None):
         ret = {}
         if image_filename:
@@ -161,7 +204,15 @@ class Eynollah:
         for prefix in ('',  '_grayscale'):
             ret[f'img{prefix}_uint8'] = ret[f'img{prefix}'].astype(np.uint8)
         return ret
+    def reset_file_name_dir(self, image_filename):
+        self._imgs = self._cache_images(image_filename=image_filename)
+        self.image_filename = image_filename
         
+        self.writer = EynollahXmlWriter(
+            dir_out=self.dir_out,
+            image_filename=self.image_filename,
+            curved_line=self.curved_line,
+            pcgts=self.pcgts)
     def imread(self, grayscale=False, uint8=True):
         key = 'img'
         if grayscale:
@@ -335,6 +386,7 @@ class Eynollah:
             img = self.imread()
 
         _, page_coord = self.early_page_for_num_of_column_classification(img)
+        if not self.dir_in:
             model_num_classifier, session_col_classifier = self.start_new_session_and_model(self.model_dir_of_col_classifier)
         if self.input_binary:
             img_in = np.copy(img)
@@ -357,17 +409,18 @@ class Eynollah:
             img_in[0, :, :, 0] = img_1ch[:, :]
             img_in[0, :, :, 1] = img_1ch[:, :]
             img_in[0, :, :, 2] = img_1ch[:, :]
-
+        if not self.dir_in:
             label_p_pred = model_num_classifier.predict(img_in)
+        else:
+            label_p_pred = self.model_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)
-
+        if not self.dir_in:
             session_col_classifier.close()
             
             del model_num_classifier
             del session_col_classifier
-        
             K.clear_session()
         gc.collect()
 
@@ -383,13 +436,14 @@ class Eynollah:
 
     def resize_and_enhance_image_with_column_classifier(self,light_version):
         self.logger.debug("enter resize_and_enhance_image_with_column_classifier")
-        if light_version:
-            dpi = 300
-        else:
         dpi = self.dpi
         self.logger.info("Detected %s DPI", dpi)
         if self.input_binary:
             img = self.imread()
+            if self.dir_in:
+                prediction_bin = self.do_prediction(True, img, self.model_bin)
+            else:
+                
                 model_bin, session_bin = self.start_new_session_and_model(self.model_dir_of_binarization)
                 prediction_bin = self.do_prediction(True, img, model_bin)
             
@@ -399,6 +453,7 @@ class Eynollah:
             
             prediction_bin =np.repeat(prediction_bin[:, :, np.newaxis], 3, axis=2)
             
+            if not self.dir_in:
                 session_bin.close()
                 del model_bin
                 del session_bin
@@ -412,6 +467,7 @@ class Eynollah:
             img_bin = None
 
         _, page_coord = self.early_page_for_num_of_column_classification(img_bin)
+        if not self.dir_in:
             model_num_classifier, session_col_classifier = self.start_new_session_and_model(self.model_dir_of_col_classifier)
         
         if self.input_binary:
@@ -433,22 +489,28 @@ class Eynollah:
             img_in[0, :, :, 2] = img_1ch[:, :]
 
 
-
+        if self.dir_in:
+            label_p_pred = self.model_classifier.predict(img_in)
+        else:
             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)
+        if not self.dir_in:
             session_col_classifier.close()
             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)
+            if light_version:
+                image_res = np.copy(img_new)
+            else:
                 image_res = self.predict_enhancement(img_new)
             is_image_enhanced = True
         else:
             num_column_is_classified = True
             image_res = np.copy(img)
             is_image_enhanced = False
-
+        if not self.dir_in:
             session_col_classifier.close()
 
         
@@ -595,48 +657,48 @@ class Eynollah:
 
                     if i == 0 and j == 0:
                         seg_color = seg_color[0 : seg_color.shape[0] - margin, 0 : seg_color.shape[1] - margin, :]
-                        seg = seg[0 : seg.shape[0] - margin, 0 : seg.shape[1] - margin]
+                        #seg = seg[0 : seg.shape[0] - margin, 0 : seg.shape[1] - margin]
-                        mask_true[index_y_d + 0 : index_y_u - margin, index_x_d + 0 : index_x_u - margin] = seg
+                        #mask_true[index_y_d + 0 : index_y_u - margin, index_x_d + 0 : index_x_u - margin] = seg
                         prediction_true[index_y_d + 0 : index_y_u - margin, index_x_d + 0 : index_x_u - margin, :] = seg_color
                     elif i == nxf - 1 and j == nyf - 1:
                         seg_color = seg_color[margin : seg_color.shape[0] - 0, margin : seg_color.shape[1] - 0, :]
-                        seg = seg[margin : seg.shape[0] - 0, margin : seg.shape[1] - 0]
+                        #seg = seg[margin : seg.shape[0] - 0, margin : seg.shape[1] - 0]
-                        mask_true[index_y_d + margin : index_y_u - 0, index_x_d + margin : index_x_u - 0] = seg
+                        #mask_true[index_y_d + margin : index_y_u - 0, index_x_d + margin : index_x_u - 0] = seg
                         prediction_true[index_y_d + margin : index_y_u - 0, index_x_d + margin : index_x_u - 0, :] = seg_color
                     elif i == 0 and j == nyf - 1:
                         seg_color = seg_color[margin : seg_color.shape[0] - 0, 0 : seg_color.shape[1] - margin, :]
-                        seg = seg[margin : seg.shape[0] - 0, 0 : seg.shape[1] - margin]
+                        #seg = seg[margin : seg.shape[0] - 0, 0 : seg.shape[1] - margin]
-                        mask_true[index_y_d + margin : index_y_u - 0, index_x_d + 0 : index_x_u - margin] = seg
+                        #mask_true[index_y_d + margin : index_y_u - 0, index_x_d + 0 : index_x_u - margin] = seg
                         prediction_true[index_y_d + margin : index_y_u - 0, index_x_d + 0 : index_x_u - margin, :] = seg_color
                     elif i == nxf - 1 and j == 0:
                         seg_color = seg_color[0 : seg_color.shape[0] - margin, margin : seg_color.shape[1] - 0, :]
-                        seg = seg[0 : seg.shape[0] - margin, margin : seg.shape[1] - 0]
+                        #seg = seg[0 : seg.shape[0] - margin, margin : seg.shape[1] - 0]
-                        mask_true[index_y_d + 0 : index_y_u - margin, index_x_d + margin : index_x_u - 0] = seg
+                        #mask_true[index_y_d + 0 : index_y_u - margin, index_x_d + margin : index_x_u - 0] = seg
                         prediction_true[index_y_d + 0 : index_y_u - margin, index_x_d + margin : index_x_u - 0, :] = seg_color
                     elif i == 0 and j != 0 and j != nyf - 1:
                         seg_color = seg_color[margin : seg_color.shape[0] - margin, 0 : seg_color.shape[1] - margin, :]
-                        seg = seg[margin : seg.shape[0] - margin, 0 : seg.shape[1] - margin]
+                        #seg = seg[margin : seg.shape[0] - margin, 0 : seg.shape[1] - margin]
-                        mask_true[index_y_d + margin : index_y_u - margin, index_x_d + 0 : index_x_u - margin] = seg
+                        #mask_true[index_y_d + margin : index_y_u - margin, index_x_d + 0 : index_x_u - margin] = seg
                         prediction_true[index_y_d + margin : index_y_u - margin, index_x_d + 0 : index_x_u - margin, :] = seg_color
                     elif i == nxf - 1 and j != 0 and j != nyf - 1:
                         seg_color = seg_color[margin : seg_color.shape[0] - margin, margin : seg_color.shape[1] - 0, :]
-                        seg = seg[margin : seg.shape[0] - margin, margin : seg.shape[1] - 0]
+                        #seg = seg[margin : seg.shape[0] - margin, margin : seg.shape[1] - 0]
-                        mask_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - 0] = seg
+                        #mask_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - 0] = seg
                         prediction_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - 0, :] = seg_color
                     elif i != 0 and i != nxf - 1 and j == 0:
                         seg_color = seg_color[0 : seg_color.shape[0] - margin, margin : seg_color.shape[1] - margin, :]
-                        seg = seg[0 : seg.shape[0] - margin, margin : seg.shape[1] - margin]
+                        #seg = seg[0 : seg.shape[0] - margin, margin : seg.shape[1] - margin]
-                        mask_true[index_y_d + 0 : index_y_u - margin, index_x_d + margin : index_x_u - margin] = seg
+                        #mask_true[index_y_d + 0 : index_y_u - margin, index_x_d + margin : index_x_u - margin] = seg
                         prediction_true[index_y_d + 0 : index_y_u - margin, index_x_d + margin : index_x_u - margin, :] = seg_color
                     elif i != 0 and i != nxf - 1 and j == nyf - 1:
                         seg_color = seg_color[margin : seg_color.shape[0] - 0, margin : seg_color.shape[1] - margin, :]
-                        seg = seg[margin : seg.shape[0] - 0, margin : seg.shape[1] - margin]
+                        #seg = seg[margin : seg.shape[0] - 0, margin : seg.shape[1] - margin]
-                        mask_true[index_y_d + margin : index_y_u - 0, index_x_d + margin : index_x_u - margin] = seg
+                        #mask_true[index_y_d + margin : index_y_u - 0, index_x_d + margin : index_x_u - margin] = seg
                         prediction_true[index_y_d + margin : index_y_u - 0, index_x_d + margin : index_x_u - margin, :] = seg_color
                     else:
                         seg_color = seg_color[margin : seg_color.shape[0] - margin, margin : seg_color.shape[1] - margin, :]
-                        seg = seg[margin : seg.shape[0] - margin, margin : seg.shape[1] - margin]
+                        #seg = seg[margin : seg.shape[0] - margin, margin : seg.shape[1] - margin]
-                        mask_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - margin] = seg
+                        #mask_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - margin] = seg
                         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)
@@ -817,9 +879,12 @@ class Eynollah:
             img = img.astype(np.uint8)
         else:
             img = self.imread()
+        if not self.dir_in:
             model_page, session_page = self.start_new_session_and_model(self.model_page_dir)
         img = cv2.GaussianBlur(img, (5, 5), 0)
-
+        if self.dir_in:
+            img_page_prediction = self.do_prediction(False, img, self.model_page)
+        else:
             img_page_prediction = self.do_prediction(False, img, model_page)
 
         imgray = cv2.cvtColor(img_page_prediction, cv2.COLOR_BGR2GRAY)
@@ -834,20 +899,25 @@ class Eynollah:
         else:
             box = [0, 0, img.shape[1], img.shape[0]]
         croped_page, page_coord = crop_image_inside_box(box, img)
+        if not self.dir_in:
             session_page.close()
             del model_page
             del session_page
-        gc.collect()
             K.clear_session()
+        gc.collect()
         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 = []
+        if not self.dir_in:
             model_page, session_page = self.start_new_session_and_model(self.model_page_dir)
         img = cv2.GaussianBlur(self.image, (5, 5), 0)
+        if not self.dir_in:
             img_page_prediction = self.do_prediction(False, img, model_page)
+        else:
+            img_page_prediction = self.do_prediction(False, img, self.model_page)
         imgray = cv2.cvtColor(img_page_prediction, cv2.COLOR_BGR2GRAY)
         _, thresh = cv2.threshold(imgray, 0, 255, 0)
         thresh = cv2.dilate(thresh, KERNEL, iterations=3)
@@ -873,11 +943,12 @@ 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]]]))
+        if not self.dir_in:
             session_page.close()
             del model_page
             del session_page
-        gc.collect()
             K.clear_session()
+        gc.collect()
         self.logger.debug("exit extract_page")
         return croped_page, page_coord, cont_page
 
@@ -888,9 +959,13 @@ class Eynollah:
             img = img.astype(np.uint8)
         else:
             img = self.imread()
+        if not self.dir_in:
             model_page, session_page = self.start_new_session_and_model(self.model_page_dir)
         img = cv2.GaussianBlur(img, (5, 5), 0)
         
+        if self.dir_in:
+            img_page_prediction = self.do_prediction(False, img, self.model_page)
+        else:
             img_page_prediction = self.do_prediction(False, img, model_page)
 
         imgray = cv2.cvtColor(img_page_prediction, cv2.COLOR_BGR2GRAY)
@@ -905,20 +980,28 @@ class Eynollah:
         else:
             box = [0, 0, img.shape[1], img.shape[0]]
         croped_page, page_coord = crop_image_inside_box(box, img)
+        
+        if not self.dir_in:
             session_page.close()
             del model_page
             del session_page
-        gc.collect()
             K.clear_session()
+        
+        gc.collect()
+        
         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 = []
+        if not self.dir_in:
             model_page, session_page = self.start_new_session_and_model(self.model_page_dir)
         img = cv2.GaussianBlur(self.image, (5, 5), 0)
+        if not self.dir_in:
             img_page_prediction = self.do_prediction(False, img, model_page)
+        else:
+            img_page_prediction = self.do_prediction(False, img, self.model_page)
         imgray = cv2.cvtColor(img_page_prediction, cv2.COLOR_BGR2GRAY)
         _, thresh = cv2.threshold(imgray, 0, 255, 0)
         thresh = cv2.dilate(thresh, KERNEL, iterations=3)
@@ -944,11 +1027,12 @@ 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]]]))
+        if not self.dir_in:
             session_page.close()
             del model_page
             del session_page
-        gc.collect()
             K.clear_session()
+        gc.collect()
         self.logger.debug("exit extract_page")
         return croped_page, page_coord, cont_page
 
@@ -956,8 +1040,10 @@ class Eynollah:
         self.logger.debug("enter extract_text_regions")
         img_height_h = img.shape[0]
         img_width_h = img.shape[1]
-
+        if not self.dir_in:
             model_region, session_region = self.start_new_session_and_model(self.model_region_dir_fully if patches else self.model_region_dir_fully_np)
+        else:
+            model_region = self.model_region_fl if patches else self.model_region_fl_np
 
         if not patches:
             img = otsu_copy_binary(img)
@@ -1044,6 +1130,7 @@ 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)
         
+        if not self.dir_in:
             session_region.close()
             del model_region
             del session_region
@@ -1422,18 +1509,25 @@ class Eynollah:
 
     def textline_contours(self, img, patches, scaler_h, scaler_w):
         self.logger.debug('enter textline_contours')
-
+        if not self.dir_in:
             model_textline, session_textline = self.start_new_session_and_model(self.model_textline_dir if patches else self.model_textline_dir_np)
         img = img.astype(np.uint8)
         img_org = np.copy(img)
         img_h = img_org.shape[0]
         img_w = img_org.shape[1]
         img = resize_image(img_org, int(img_org.shape[0] * scaler_h), int(img_org.shape[1] * scaler_w))
+        if not self.dir_in:
             prediction_textline = self.do_prediction(patches, img, model_textline)
+        else:
+            prediction_textline = self.do_prediction(patches, img, self.model_textline)
         prediction_textline = resize_image(prediction_textline, img_h, img_w)
+        if not self.dir_in:
             prediction_textline_longshot = self.do_prediction(False, img, model_textline)
+        else:
+            prediction_textline_longshot = self.do_prediction(False, img, self.model_textline)
         prediction_textline_longshot_true_size = resize_image(prediction_textline_longshot, img_h, img_w)
         
+        if not self.dir_in:
             session_textline.close()
 
 
@@ -1508,17 +1602,17 @@ class Eynollah:
         ##img_resized = resize_image(img_bin,img_height_h, img_width_h )
         img_resized = resize_image(img,img_h_new, img_w_new )
         
-        tbin = time.time()
+        if not self.dir_in:
             model_bin, session_bin = self.start_new_session_and_model(self.model_dir_of_binarization)
-        print("time bin session", time.time()-tbin)
             prediction_bin = self.do_prediction(True, img_resized, model_bin)
-        print("time bin all ", time.time()-tbin)
+        else:
+            prediction_bin = self.do_prediction(True, img_resized, self.model_bin)
         prediction_bin=prediction_bin[:,:,0]
         prediction_bin = (prediction_bin[:,:]==0)*1
         prediction_bin = prediction_bin*255
         
         prediction_bin =np.repeat(prediction_bin[:, :, np.newaxis], 3, axis=2)
-
+        if not self.dir_in:
             session_bin.close()
             del model_bin
             del session_bin
@@ -1530,17 +1624,13 @@ class Eynollah:
         
         
         
-
-        tline = time.time()
         textline_mask_tot_ea = self.run_textline(img_bin)
-        print("time line all ", time.time()-tline)
-        model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_ens_light)
-        
-        
-        #plt.imshow(img_bin)
-        #plt.show()
 
+        if not self.dir_in:
+            model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_ens_light)
             prediction_regions_org = self.do_prediction_new_concept(True, img_bin, model_region)
+        else:
+            prediction_regions_org = self.do_prediction_new_concept(True, img_bin, self.model_region)
             
         #plt.imshow(prediction_regions_org[:,:,0])
         #plt.show()
@@ -1564,7 +1654,6 @@ class Eynollah:
         
         polygons_of_only_lines = return_contours_of_interested_region(mask_lines_only,1,0.00001)
         
-        
         text_regions_p_true = np.zeros(prediction_regions_org.shape)
         
         text_regions_p_true = cv2.fillPoly(text_regions_p_true, pts = polygons_of_only_lines, color=(3,3,3))
@@ -1574,6 +1663,7 @@ class Eynollah:
         text_regions_p_true = cv2.fillPoly(text_regions_p_true, pts = polygons_of_only_texts, color=(1,1,1))
         
         #erosion_hurts = True
+        if not self.dir_in:
             K.clear_session()
         return text_regions_p_true, erosion_hurts, polygons_lines_xml, textline_mask_tot_ea
 
@@ -1584,14 +1674,17 @@ class Eynollah:
         img_height_h = img_org.shape[0]
         img_width_h = img_org.shape[1]
         
+        if not self.dir_in:
             model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_ens)
 
         ratio_y=1.3
         ratio_x=1
 
         img = resize_image(img_org, int(img_org.shape[0]*ratio_y), int(img_org.shape[1]*ratio_x))
-
+        if not self.dir_in:
             prediction_regions_org_y = self.do_prediction(True, img, model_region)
+        else:
+            prediction_regions_org_y = self.do_prediction(True, img, self.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])
@@ -1610,6 +1703,9 @@ class Eynollah:
             
             img = resize_image(img_org, int(img_org.shape[0]), int(img_org.shape[1]*(1.2 if is_image_enhanced else 1)))
             
+            if self.dir_in:
+                prediction_regions_org = self.do_prediction(True, img, self.model_region)
+            else:
                 prediction_regions_org = self.do_prediction(True, img, model_region)
             prediction_regions_org = resize_image(prediction_regions_org, img_height_h, img_width_h )
 
@@ -1618,17 +1714,23 @@ class Eynollah:
             prediction_regions_org=prediction_regions_org[:,:,0]
             prediction_regions_org[(prediction_regions_org[:,:]==1) & (mask_zeros_y[:,:]==1)]=0
             
+            if not self.dir_in:
                 session_region.close()
                 del model_region
                 del session_region
             gc.collect()
             
+            if not self.dir_in:
                 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]))
+            
+            if self.dir_in:
+                prediction_regions_org2 = self.do_prediction(True, img, self.model_region_p2, 0.2)
+            else:
                 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 )
 
-
+            if not self.dir_in:
                 session_region.close()
                 del model_region
                 del session_region
@@ -1663,8 +1765,11 @@ class Eynollah:
                 if self.input_binary:
                     prediction_bin = np.copy(img_org)
                 else:
+                    if not self.dir_in:
                         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)
+                    else:
+                        prediction_bin = self.do_prediction(True, img_org, self.model_bin)
                     prediction_bin = resize_image(prediction_bin, img_height_h, img_width_h )
                     
                     prediction_bin=prediction_bin[:,:,0]
@@ -1673,13 +1778,14 @@ class Eynollah:
                     
                     prediction_bin =np.repeat(prediction_bin[:, :, np.newaxis], 3, axis=2)
                     
+                    if not self.dir_in:
                         session_bin.close()
                         del model_bin
                         del session_bin
                     gc.collect()
                 
                 
-                
+                if not self.dir_in:
                     model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_ens)
                 ratio_y=1
                 ratio_x=1
@@ -1687,11 +1793,15 @@ class Eynollah:
 
                 img = resize_image(prediction_bin, int(img_org.shape[0]*ratio_y), int(img_org.shape[1]*ratio_x))
                 
+                if not self.dir_in:
                     prediction_regions_org = self.do_prediction(True, img, model_region)
+                else:
+                    prediction_regions_org = self.do_prediction(True, img, self.model_region)
                 prediction_regions_org = resize_image(prediction_regions_org, img_height_h, img_width_h )
                 prediction_regions_org=prediction_regions_org[:,:,0]
                 
                 mask_lines_only=(prediction_regions_org[:,:]==3)*1
+                if not self.dir_in:
                     session_region.close()
                     del model_region
                     del session_region
@@ -1716,7 +1826,7 @@ class Eynollah:
             text_regions_p_true=cv2.fillPoly(text_regions_p_true,pts=polygons_of_only_texts, color=(1,1,1))
 
             
-
+            if not self.dir_in:
                 K.clear_session()
             return text_regions_p_true, erosion_hurts, polygons_lines_xml
         except:
@@ -1724,13 +1834,17 @@ class Eynollah:
             if self.input_binary:
                 prediction_bin = np.copy(img_org)
             else:
+                if not self.dir_in:
                     session_region.close()
                     del model_region
                     del session_region
                 gc.collect()
                 
+                if not self.dir_in:
                     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)
+                else:
+                    prediction_bin = self.do_prediction(True, img_org, self.model_bin)
                 prediction_bin = resize_image(prediction_bin, img_height_h, img_width_h )
                 prediction_bin=prediction_bin[:,:,0]
                 
@@ -1741,26 +1855,29 @@ class Eynollah:
                 prediction_bin =np.repeat(prediction_bin[:, :, np.newaxis], 3, axis=2)
 
                 
-                
+                if not self.dir_in:
                     session_bin.close()
                     del model_bin
                     del session_bin
                 gc.collect()
             
             
-            
+                if not self.dir_in:
                     model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_ens)
             ratio_y=1
             ratio_x=1
 
 
             img = resize_image(prediction_bin, int(img_org.shape[0]*ratio_y), int(img_org.shape[1]*ratio_x))
-
+            if not self.dir_in:
                 prediction_regions_org = self.do_prediction(True, img, model_region)
+            else:
+                prediction_regions_org = self.do_prediction(True, img, self.model_region)
             prediction_regions_org = resize_image(prediction_regions_org, img_height_h, img_width_h )
             prediction_regions_org=prediction_regions_org[:,:,0]
             
             #mask_lines_only=(prediction_regions_org[:,:]==3)*1
+            if not self.dir_in:
                 session_region.close()
                 del model_region
                 del session_region
@@ -1807,6 +1924,7 @@ class Eynollah:
             text_regions_p_true = cv2.fillPoly(text_regions_p_true, pts = polygons_of_only_texts, color=(1,1,1))
             
             erosion_hurts = True
+            if not self.dir_in:
                 K.clear_session()
             return text_regions_p_true, erosion_hurts, polygons_lines_xml
 
@@ -2409,7 +2527,7 @@ class Eynollah:
         ##print(img_only_regions.shape,'img_only_regions')
         ##plt.imshow(img_only_regions[:,:])
         ##plt.show()
-        num_col, _ = find_num_col(img_only_regions, num_col_classifier, self.tables, multiplier=6.0)
+        ##num_col, _ = find_num_col(img_only_regions, num_col_classifier, self.tables, multiplier=6.0)
         try:
             num_col, _ = find_num_col(img_only_regions, num_col_classifier, self.tables, multiplier=6.0)
             num_col = num_col + 1
@@ -2468,6 +2586,7 @@ 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(light_version)
         self.logger.info("Image was %senhanced.", '' if is_image_enhanced else 'not ')
+        if not self.dir_in:
             K.clear_session()
         scale = 1
         if is_image_enhanced:
@@ -2492,6 +2611,7 @@ 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)
+        if not self.dir_in:
             K.clear_session()
         if self.plotter:
             self.plotter.save_plot_of_textlines(textline_mask_tot_ea, image_page)
@@ -2554,6 +2674,7 @@ 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)
+        if not self.dir_in:
             K.clear_session()
 
         self.logger.info("num_col_classifier: %s", num_col_classifier)
@@ -2619,7 +2740,7 @@ class Eynollah:
         pixel_img = 10
         contours_tables = return_contours_of_interested_region(text_regions_p, pixel_img, min_area_mar)
         
-        
+        if not self.dir_in:
             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
@@ -2651,6 +2772,7 @@ 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)
+            if not self.dir_in:
                 K.clear_session()
             gc.collect()
 
@@ -2718,7 +2840,7 @@ class Eynollah:
         text_regions_p[:, :][text_regions_p[:, :] == 2] = 5
         text_regions_p[:, :][text_regions_p[:, :] == 3] = 6
         text_regions_p[:, :][text_regions_p[:, :] == 4] = 8
-
+        if not self.dir_in:
             K.clear_session()
         image_page = image_page.astype(np.uint8)
 
@@ -2726,6 +2848,7 @@ class Eynollah:
         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
+        if not self.dir_in:
             K.clear_session()
 
         # plt.imshow(regions_fully[:,:,0])
@@ -2733,6 +2856,7 @@ class Eynollah:
         regions_fully = putt_bb_of_drop_capitals_of_model_in_patches_in_layout(regions_fully)
         # plt.imshow(regions_fully[:,:,0])
         # plt.show()
+        if not self.dir_in:
             K.clear_session()
         regions_fully_np, _ = self.extract_text_regions(image_page, False, cols=num_col_classifier)
         # plt.imshow(regions_fully_np[:,:,0])
@@ -2744,6 +2868,7 @@ class Eynollah:
 
         # plt.imshow(regions_fully_np[:,:,0])
         # plt.show()
+        if not self.dir_in:
             K.clear_session()
         # plt.imshow(regions_fully[:,:,0])
         # plt.show()
@@ -2769,20 +2894,35 @@ class Eynollah:
             regions_without_separators_d = None
         if not self.tables:
             regions_without_separators = (text_regions_p[:, :] == 1) * 1
-
+        if not self.dir_in:
             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')
         return polygons_of_images, img_revised_tab, text_regions_p_1_n, textline_mask_tot_d, regions_without_separators_d, regions_fully, regions_without_separators, polygons_of_marginals, contours_tables
     
+    def our_load_model(self, model_file):
+
+        model = load_model(model_file, compile=False)
+
+        return model
+
     def run(self):
         """
         Get image and scales, then extract the page of scanned image
         """
         self.logger.debug("enter run")
 
+        t0_tot = time.time()
+
+        if not self.dir_in:
+            self.ls_imgs = [1]
+        
+        for img_name in self.ls_imgs:
             t0 = time.time()
+            if self.dir_in:
+                self.reset_file_name_dir(os.path.join(self.dir_in,img_name))
+            
             img_res, is_image_enhanced, num_col_classifier, num_column_is_classified = self.run_enhancement(self.light_version)
             
             self.logger.info("Enhancing took %.1fs ", time.time() - t0)
@@ -2790,11 +2930,11 @@ class Eynollah:
             t1 = time.time()
             if self.light_version:
                 text_regions_p_1 ,erosion_hurts, polygons_lines_xml, textline_mask_tot_ea = self.get_regions_from_xy_2models_light(img_res, is_image_enhanced, num_col_classifier)
-            
                 slope_deskew, slope_first = self.run_deskew(textline_mask_tot_ea)
-            
+                #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, text_regions_p_1, cont_page, table_prediction, textline_mask_tot_ea = \
                         self.run_graphics_and_columns_light(text_regions_p_1, textline_mask_tot_ea, num_col_classifier, num_column_is_classified, erosion_hurts)
+                #self.logger.info("run graphics %.1fs ", time.time() - t1t)
             else:
                 text_regions_p_1 ,erosion_hurts, polygons_lines_xml = self.get_regions_from_xy_2models(img_res, is_image_enhanced, num_col_classifier)
                 self.logger.info("Textregion detection took %.1fs ", time.time() - t1)
@@ -2803,7 +2943,7 @@ class Eynollah:
                 num_col, num_col_classifier, img_only_regions, page_coord, image_page, mask_images, mask_lines, 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)
                 self.logger.info("Graphics detection took %.1fs ", time.time() - t1)
-            self.logger.info('cont_page %s', cont_page)
+                #self.logger.info('cont_page %s', cont_page)
             
             if not num_col:
                 self.logger.info("No columns detected, outputting an empty PAGE-XML")
@@ -2835,6 +2975,7 @@ class Eynollah:
             if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
                 text_only_d = ((text_regions_p_1_n[:, :] == 1)) * 1
             
+            
             min_con_area = 0.000005
             if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
                 contours_only_text, hir_on_text = return_contours_of_image(text_only)
@@ -2843,7 +2984,7 @@ class Eynollah:
                 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 = areas_cnt_text / float(text_only.shape[0] * text_only.shape[1])
-                self.logger.info('areas_cnt_text %s', areas_cnt_text)
+                    #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]
                     areas_cnt_text_parent = [areas_cnt_text[jz] for jz in range(len(areas_cnt_text)) if areas_cnt_text[jz] > min_con_area]
@@ -2914,7 +3055,6 @@ class Eynollah:
                     contours_only_text_parent_d_ordered = []
                     contours_only_text_parent_d = []
                     contours_only_text_parent = []
-                
             else:
                 contours_only_text, hir_on_text = return_contours_of_image(text_only)
                 contours_only_text_parent = return_parent_contours(contours_only_text, hir_on_text)
@@ -2933,11 +3073,14 @@ class Eynollah:
 
                     cx_bigest_big, cy_biggest_big, _, _, _, _, _ = find_new_features_of_contours([contours_biggest])
                     cx_bigest, cy_biggest, _, _, _, _, _ = find_new_features_of_contours(contours_only_text_parent)
-                self.logger.debug('areas_cnt_text_parent %s', areas_cnt_text_parent)
+                    #self.logger.debug('areas_cnt_text_parent %s', areas_cnt_text_parent)
                     # self.logger.debug('areas_cnt_text_parent_d %s', areas_cnt_text_parent_d)
                     # self.logger.debug('len(contours_only_text_parent) %s', len(contours_only_text_parent_d))
                 else:
                     pass
+            if self.light_version:
+                txt_con_org = get_textregion_contours_in_org_image_light(contours_only_text_parent, self.image, slope_first)
+            else:
                 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)
@@ -2956,26 +3099,33 @@ 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)
+            if not self.dir_in:
                 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])
+                    if self.light_version:
+                        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_light(text_regions_p, regions_fully, contours_only_text_parent, all_box_coord, all_found_texline_polygons, slopes, contours_only_text_parent_d_ordered)
+                    else:
                         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:
+                    #takes long timee
                     contours_only_text_parent_d_ordered = None
+                    if self.light_version:
+                        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_light(text_regions_p, regions_fully, contours_only_text_parent, all_box_coord, all_found_texline_polygons, slopes, contours_only_text_parent_d_ordered)
+                    else:
                         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)
 
                 if self.plotter:
                     self.plotter.save_plot_of_layout(text_regions_p, image_page)
                     self.plotter.save_plot_of_layout_all(text_regions_p, image_page)
-
+                if not self.dir_in:
                     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)
-
-            # print(len(contours_only_text_parent_h),len(contours_only_text_parent_h_d_ordered),'contours_only_text_parent_h')
                 pixel_lines = 6
                 
 
@@ -2990,45 +3140,15 @@ class Eynollah:
                     else:
                         _, _, 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)
 
-            # print(peaks_neg_fin,peaks_neg_fin_d,'num_col2')
-            # print(splitter_y_new,splitter_y_new_d,'num_col_classifier')
-            # print(matrix_of_lines_ch.shape,matrix_of_lines_ch_d.shape,'matrix_of_lines_ch')
-
                 if num_col_classifier >= 3:
                     if np.abs(slope_deskew) < SLOPE_THRESHOLD:
                         regions_without_separators = regions_without_separators.astype(np.uint8)
                         regions_without_separators = cv2.erode(regions_without_separators[:, :], KERNEL, iterations=6)
 
-                    #regions_without_separators_0 = regions_without_separators[:, :].sum(axis=0)
-                    #meda_n_updown = regions_without_separators_0[len(regions_without_separators_0) :: -1]
-                    #first_nonzero = next((i for i, x in enumerate(regions_without_separators_0) if x), 0)
-                    #last_nonzero = next((i for i, x in enumerate(meda_n_updown) if x), 0)
-                    #last_nonzero = len(regions_without_separators_0) - last_nonzero
-                    
-                    #random_pixels_for_image = np.random.randn(regions_without_separators.shape[0], regions_without_separators.shape[1])
-                    #random_pixels_for_image[random_pixels_for_image < -0.5] = 0
-                    #random_pixels_for_image[random_pixels_for_image != 0] = 1
-                    #regions_without_separators[(random_pixels_for_image[:, :] == 1) & (text_regions_p[:, :] == 5)] = 1
-                    
-                    #regions_without_separators[:, 0:first_nonzero] = 0
-                    #regions_without_separators[:, last_nonzero:] = 0
                     else:
                         regions_without_separators_d = regions_without_separators_d.astype(np.uint8)
                         regions_without_separators_d = cv2.erode(regions_without_separators_d[:, :], KERNEL, iterations=6)
                         
-                    #regions_without_separators_0 = regions_without_separators_d[:, :].sum(axis=0)
-                    #meda_n_updown = regions_without_separators_0[len(regions_without_separators_0) :: -1]
-                    #first_nonzero = next((i for i, x in enumerate(regions_without_separators_0) if x), 0)
-                    #last_nonzero = next((i for i, x in enumerate(meda_n_updown) if x), 0)
-                    #last_nonzero = len(regions_without_separators_0) - last_nonzero
-                    
-                    #random_pixels_for_image = np.random.randn(regions_without_separators_d.shape[0], regions_without_separators_d.shape[1])
-                    #random_pixels_for_image[random_pixels_for_image < -0.5] = 0
-                    #random_pixels_for_image[random_pixels_for_image != 0] = 1
-                    ##regions_without_separators_d[(random_pixels_for_image[:, :] == 1) & (text_regions_p_1_n[:, :] == 5)] = 1
-                    
-                    #regions_without_separators_d[:, 0:first_nonzero] = 0
-                    #regions_without_separators_d[:, last_nonzero:] = 0
 
                 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, num_col_classifier, erosion_hurts, self.tables)
@@ -3037,7 +3157,7 @@ class Eynollah:
             
             if self.plotter:
                 self.plotter.write_images_into_directory(polygons_of_images, image_page)
-
+            t_order = time.time()
             if self.full_layout:
                 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)
@@ -3046,7 +3166,7 @@ class Eynollah:
 
                 pcgts = self.writer.build_pagexml_full_layout(contours_only_text_parent, contours_only_text_parent_h, page_coord, order_text_new, id_of_texts_tot, all_found_texline_polygons, all_found_texline_polygons_h, all_box_coord, all_box_coord_h, polygons_of_images, contours_tables, polygons_of_drop_capitals, polygons_of_marginals, all_found_texline_polygons_marginals, all_box_coord_marginals, slopes, slopes_h, slopes_marginals, cont_page, polygons_lines_xml)
                 self.logger.info("Job done in %.1fs", time.time() - t0)
-            return pcgts
+                ##return pcgts
             else:
                 contours_only_text_parent_h = None
                 if np.abs(slope_deskew) < SLOPE_THRESHOLD:
@@ -3056,4 +3176,7 @@ class Eynollah:
                     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)
-            return pcgts
+                ##return pcgts
+            self.writer.write_pagexml(pcgts)
+            #self.logger.info("Job done in %.1fs", time.time() - t0)
+        self.logger.info("All jobs done in %.1fs", time.time() - t0_tot)

qurator/eynollah/utils/__init__.py

@@ -797,6 +797,7 @@ def putt_bb_of_drop_capitals_of_model_in_patches_in_layout(layout_in_patch):
     return 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):
+    
     cx_main,cy_main ,x_min_main , x_max_main, y_min_main ,y_max_main,y_corr_x_min_from_argmin=find_new_features_of_contours(contours_only_text_parent)
 
     length_con=x_max_main-x_min_main
@@ -851,10 +852,85 @@ def check_any_text_region_in_model_one_is_main_or_header(regions_model_1,regions
 
         #print(all_pixels,pixels_main,pixels_header)
 
+    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
 
 
-        #plt.imshow(img[:,:,0])
+def check_any_text_region_in_model_one_is_main_or_header_light(regions_model_1,regions_model_full,contours_only_text_parent,all_box_coord,all_found_texline_polygons,slopes,contours_only_text_parent_d_ordered):
-        #plt.show()
+    
+    ### to make it faster
+    h_o = regions_model_1.shape[0]
+    w_o = regions_model_1.shape[1]
+    
+    regions_model_1 = cv2.resize(regions_model_1, (int(regions_model_1.shape[1]/3.), int(regions_model_1.shape[0]/3.)), interpolation=cv2.INTER_NEAREST)
+    regions_model_full = cv2.resize(regions_model_full, (int(regions_model_full.shape[1]/3.), int(regions_model_full.shape[0]/3.)), interpolation=cv2.INTER_NEAREST)
+    contours_only_text_parent = [ (i/3.).astype(np.int32) for i in  contours_only_text_parent]
+
+    ###
+    
+    cx_main,cy_main ,x_min_main , x_max_main, y_min_main ,y_max_main,y_corr_x_min_from_argmin=find_new_features_of_contours(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)
+            if contours_only_text_parent_d_ordered is not None:
+                contours_only_text_parent_head_d.append(contours_only_text_parent_d_ordered[ii])
+            all_box_coord_head.append(all_box_coord[ii])
+            slopes_head.append(slopes[ii])
+            all_found_texline_polygons_head.append(all_found_texline_polygons[ii])
+        else:
+            regions_model_1[:,:][(regions_model_1[:,:]==1) & (img[:,:,0]==255) ]=1
+            contours_only_text_parent_main.append(con)
+            if contours_only_text_parent_d_ordered is not None:
+                contours_only_text_parent_main_d.append(contours_only_text_parent_d_ordered[ii])
+            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)
+
+
+
+    ### to make it faster
+    
+    regions_model_1 = cv2.resize(regions_model_1, (w_o, h_o), interpolation=cv2.INTER_NEAREST)
+    #regions_model_full = cv2.resize(img, (int(regions_model_full.shape[1]/3.), int(regions_model_full.shape[0]/3.)), interpolation=cv2.INTER_NEAREST)
+    contours_only_text_parent_head = [ (i*3.).astype(np.int32) for i in  contours_only_text_parent_head]
+    contours_only_text_parent_main = [ (i*3.).astype(np.int32) for i in  contours_only_text_parent_main]
+    ###
+    
     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
 
 def small_textlines_to_parent_adherence2(textlines_con, textline_iamge, num_col):

qurator/eynollah/utils/contour.py

@@ -3,7 +3,8 @@ import numpy as np
 from shapely import geometry
 
 from .rotate import rotate_image, rotation_image_new
-
+from multiprocessing import Process, Queue, cpu_count
+from multiprocessing import Pool
 def contours_in_same_horizon(cy_main_hor):
     X1 = np.zeros((len(cy_main_hor), len(cy_main_hor)))
     X2 = np.zeros((len(cy_main_hor), len(cy_main_hor)))
@@ -147,6 +148,96 @@ def return_contours_of_interested_region(region_pre_p, pixel, min_area=0.0002):
 
     return contours_imgs
 
+def do_work_of_contours_in_image(queue_of_all_params, contours_per_process, indexes_r_con_per_pro, img, slope_first):
+    cnts_org_per_each_subprocess = []
+    index_by_text_region_contours = []
+    for mv in range(len(contours_per_process)):
+        index_by_text_region_contours.append(indexes_r_con_per_pro[mv])
+        
+        img_copy = np.zeros(img.shape)
+        img_copy = cv2.fillPoly(img_copy, pts=[contours_per_process[mv]], color=(1, 1, 1))
+
+        img_copy = rotation_image_new(img_copy, -slope_first)
+
+        img_copy = img_copy.astype(np.uint8)
+        imgray = cv2.cvtColor(img_copy, cv2.COLOR_BGR2GRAY)
+        ret, thresh = cv2.threshold(imgray, 0, 255, 0)
+
+        cont_int, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
+
+        cont_int[0][:, 0, 0] = cont_int[0][:, 0, 0] + np.abs(img_copy.shape[1] - img.shape[1])
+        cont_int[0][:, 0, 1] = cont_int[0][:, 0, 1] + np.abs(img_copy.shape[0] - img.shape[0])
+
+
+        cnts_org_per_each_subprocess.append(cont_int[0])
+
+    queue_of_all_params.put([ cnts_org_per_each_subprocess, index_by_text_region_contours])
+
+
+def get_textregion_contours_in_org_image_multi(cnts, img, slope_first):
+    
+    num_cores = cpu_count()
+    queue_of_all_params = Queue()
+
+    processes = []
+    nh = np.linspace(0, len(cnts), num_cores + 1)
+    indexes_by_text_con = np.array(range(len(cnts)))
+    for i in range(num_cores):
+        contours_per_process = cnts[int(nh[i]) : int(nh[i + 1])]
+        indexes_text_con_per_process = indexes_by_text_con[int(nh[i]) : int(nh[i + 1])]
+
+        processes.append(Process(target=do_work_of_contours_in_image, args=(queue_of_all_params, contours_per_process, indexes_text_con_per_process, img,slope_first )))
+    for i in range(num_cores):
+        processes[i].start()
+    cnts_org = []
+    all_index_text_con = []
+    for i in range(num_cores):
+        list_all_par = queue_of_all_params.get(True)
+        contours_for_sub_process = list_all_par[0]
+        indexes_for_sub_process = list_all_par[1]
+        for j in range(len(contours_for_sub_process)):
+            cnts_org.append(contours_for_sub_process[j])
+            all_index_text_con.append(indexes_for_sub_process[j])
+    for i in range(num_cores):
+        processes[i].join()
+
+    print(all_index_text_con)
+    return cnts_org
+def loop_contour_image(index_l, cnts,img, slope_first):
+    img_copy = np.zeros(img.shape)
+    img_copy = cv2.fillPoly(img_copy, pts=[cnts[index_l]], color=(1, 1, 1))
+
+    # plt.imshow(img_copy)
+    # plt.show()
+
+    # print(img.shape,'img')
+    img_copy = rotation_image_new(img_copy, -slope_first)
+    ##print(img_copy.shape,'img_copy')
+    # plt.imshow(img_copy)
+    # plt.show()
+
+    img_copy = img_copy.astype(np.uint8)
+    imgray = cv2.cvtColor(img_copy, cv2.COLOR_BGR2GRAY)
+    ret, thresh = cv2.threshold(imgray, 0, 255, 0)
+
+    cont_int, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
+
+    cont_int[0][:, 0, 0] = cont_int[0][:, 0, 0] + np.abs(img_copy.shape[1] - img.shape[1])
+    cont_int[0][:, 0, 1] = cont_int[0][:, 0, 1] + np.abs(img_copy.shape[0] - img.shape[0])
+    # print(np.shape(cont_int[0]))
+    return cont_int[0]
+
+def get_textregion_contours_in_org_image_multi2(cnts, img, slope_first):
+
+    cnts_org = []
+    # print(cnts,'cnts')
+    with Pool(cpu_count()) as p:
+        cnts_org = p.starmap(loop_contour_image, [(index_l,cnts, img,slope_first) for index_l in range(len(cnts))])
+        
+    print(len(cnts_org),'lendiha')
+
+    return cnts_org
+
 def get_textregion_contours_in_org_image(cnts, img, slope_first):
 
     cnts_org = []
@@ -175,11 +266,43 @@ def get_textregion_contours_in_org_image(cnts, img, slope_first):
         # print(np.shape(cont_int[0]))
         cnts_org.append(cont_int[0])
 
-    # print(cnts_org,'cnts_org')
+    return cnts_org
+
+def get_textregion_contours_in_org_image_light(cnts, img, slope_first):
+    
+    h_o = img.shape[0]
+    w_o = img.shape[1]
+    
+    img = cv2.resize(img, (int(img.shape[1]/3.), int(img.shape[0]/3.)), interpolation=cv2.INTER_NEAREST)
+    ##cnts = list( (np.array(cnts)/2).astype(np.int16) )
+    #cnts = cnts/2
+    cnts = [(i/ 3).astype(np.int32) for i in cnts]
+    cnts_org = []
+    #print(cnts,'cnts')
+    for i in range(len(cnts)):
+        img_copy = np.zeros(img.shape)
+        img_copy = cv2.fillPoly(img_copy, pts=[cnts[i]], color=(1, 1, 1))
+
+        # plt.imshow(img_copy)
+        # plt.show()
+
+        # print(img.shape,'img')
+        img_copy = rotation_image_new(img_copy, -slope_first)
+        ##print(img_copy.shape,'img_copy')
+        # plt.imshow(img_copy)
+        # plt.show()
+
+        img_copy = img_copy.astype(np.uint8)
+        imgray = cv2.cvtColor(img_copy, cv2.COLOR_BGR2GRAY)
+        ret, thresh = cv2.threshold(imgray, 0, 255, 0)
+
+        cont_int, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
+
+        cont_int[0][:, 0, 0] = cont_int[0][:, 0, 0] + np.abs(img_copy.shape[1] - img.shape[1])
+        cont_int[0][:, 0, 1] = cont_int[0][:, 0, 1] + np.abs(img_copy.shape[0] - img.shape[0])
+        # print(np.shape(cont_int[0]))
+        cnts_org.append(cont_int[0]*3)
 
-    # sys.exit()
-    # self.y_shift = np.abs(img_copy.shape[0] - img.shape[0])
-    # self.x_shift = np.abs(img_copy.shape[1] - img.shape[1])
     return cnts_org
 
 def return_contours_of_interested_textline(region_pre_p, pixel):