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@ -109,6 +109,7 @@ class Eynollah():
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):
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self.dirs = dirs
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self.logger = logger
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self.batch_processing_mode = bool(dirs.dir_in)
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if not dirs.dir_in:
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if image_pil:
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self._imgs = self._cache_images(image_pil=image_pil)
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@ -160,7 +161,7 @@ class Eynollah():
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self.models : dict[str, tf.keras.Model] = {}
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if self.dirs.dir_in and light_version:
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if self.batch_processing_mode and light_version:
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config = tf.compat.v1.ConfigProto()
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config.gpu_options.allow_growth = True
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session = tf.compat.v1.Session(config=config)
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@ -176,7 +177,7 @@ class Eynollah():
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self.ls_imgs = listdir(self.dirs.dir_in)
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if self.dirs.dir_in and not light_version:
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if self.batch_processing_mode and not light_version:
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config = tf.compat.v1.ConfigProto()
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config.gpu_options.allow_growth = True
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session = tf.compat.v1.Session(config=config)
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@ -392,7 +393,7 @@ class Eynollah():
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img = self.imread()
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_, page_coord = self.early_page_for_num_of_column_classification(img)
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if not self.dirs.dir_in:
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if not self.batch_processing_mode:
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model_num_classifier = self.load_model(self.model_dir_of_col_classifier)
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if self.input_binary:
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img_in = np.copy(img)
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@ -416,7 +417,7 @@ class Eynollah():
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img_in[0, :, :, 1] = img_1ch[:, :]
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img_in[0, :, :, 2] = img_1ch[:, :]
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if not self.dirs.dir_in:
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if not self.batch_processing_mode:
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label_p_pred = model_num_classifier.predict(img_in, verbose=0)
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else:
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label_p_pred = self.model_classifier.predict(img_in, verbose=0)
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@ -439,7 +440,7 @@ class Eynollah():
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self.logger.info("Detected %s DPI", dpi)
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if self.input_binary:
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img = self.imread()
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if self.dirs.dir_in:
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if self.batch_processing_mode:
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prediction_bin = self.do_prediction(True, img, self.model_bin)
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else:
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@ -461,7 +462,7 @@ class Eynollah():
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t1 = time.time()
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_, page_coord = self.early_page_for_num_of_column_classification(img_bin)
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if not self.dirs.dir_in:
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if not self.batch_processing_mode:
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model_num_classifier = self.load_model(self.model_dir_of_col_classifier)
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if self.input_binary:
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@ -483,7 +484,7 @@ class Eynollah():
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img_in[0, :, :, 2] = img_1ch[:, :]
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if self.dirs.dir_in:
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if self.batch_processing_mode:
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label_p_pred = self.model_classifier.predict(img_in, verbose=0)
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else:
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label_p_pred = model_num_classifier.predict(img_in, verbose=0)
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@ -873,10 +874,10 @@ class Eynollah():
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if not self.ignore_page_extraction:
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img = cv2.GaussianBlur(self.image, (5, 5), 0)
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if not self.dirs.dir_in:
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if not self.batch_processing_mode:
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model_page = self.load_model(self.model_page_dir)
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if not self.dirs.dir_in:
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if not self.batch_processing_mode:
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img_page_prediction = self.do_prediction(False, img, model_page)
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else:
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img_page_prediction = self.do_prediction(False, img, self.model_page)
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@ -921,11 +922,11 @@ class Eynollah():
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img = img.astype(np.uint8)
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else:
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img = self.imread()
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if not self.dirs.dir_in:
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if not self.batch_processing_mode:
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model_page = self.load_model(self.model_page_dir)
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img = cv2.GaussianBlur(img, (5, 5), 0)
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if self.dirs.dir_in:
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if self.batch_processing_mode:
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img_page_prediction = self.do_prediction(False, img, self.model_page)
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else:
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img_page_prediction = self.do_prediction(False, img, model_page)
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@ -954,7 +955,7 @@ class Eynollah():
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self.logger.debug("enter extract_text_regions")
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img_height_h = img.shape[0]
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img_width_h = img.shape[1]
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if not self.dirs.dir_in:
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if not self.batch_processing_mode:
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model_region = self.load_model(self.model_region_dir_fully if patches else self.model_region_dir_fully_np)
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else:
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model_region = self.model_region_fl if patches else self.model_region_fl_np
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@ -1421,19 +1422,19 @@ class Eynollah():
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def textline_contours(self, img, patches, scaler_h, scaler_w):
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self.logger.debug('enter textline_contours')
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if not self.dirs.dir_in:
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if not self.batch_processing_mode:
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model_textline = self.load_model(self.model_textline_dir if patches else self.model_textline_dir_np)
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img = img.astype(np.uint8)
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img_org = np.copy(img)
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img_h = img_org.shape[0]
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img_w = img_org.shape[1]
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img = resize_image(img_org, int(img_org.shape[0] * scaler_h), int(img_org.shape[1] * scaler_w))
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if not self.dirs.dir_in:
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if not self.batch_processing_mode:
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prediction_textline = self.do_prediction(patches, img, model_textline)
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else:
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prediction_textline = self.do_prediction(patches, img, self.model_textline)
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prediction_textline = resize_image(prediction_textline, img_h, img_w)
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if not self.dirs.dir_in:
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if not self.batch_processing_mode:
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prediction_textline_longshot = self.do_prediction(False, img, model_textline)
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else:
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prediction_textline_longshot = self.do_prediction(False, img, self.model_textline)
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@ -1514,7 +1515,7 @@ class Eynollah():
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img_h_new = int(img_org.shape[0] / float(img_org.shape[1]) * img_w_new)
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img_resized = resize_image(img,img_h_new, img_w_new )
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if not self.dirs.dir_in:
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if not self.batch_processing_mode:
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model_bin = self.load_model(self.model_dir_of_binarization)
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prediction_bin = self.do_prediction(True, img_resized, model_bin)
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else:
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@ -1533,7 +1534,7 @@ class Eynollah():
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textline_mask_tot_ea = self.run_textline(img_bin)
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if not self.dirs.dir_in:
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if not self.batch_processing_mode:
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model_region = self.load_model(self.model_region_dir_p_ens_light)
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prediction_regions_org = self.do_prediction_new_concept(True, img_bin, model_region)
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else:
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@ -1578,14 +1579,14 @@ class Eynollah():
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img_height_h = img_org.shape[0]
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img_width_h = img_org.shape[1]
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if not self.dirs.dir_in:
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if not self.batch_processing_mode:
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model_region = self.load_model(self.model_region_dir_p_ens)
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ratio_y=1.3
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ratio_x=1
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img = resize_image(img_org, int(img_org.shape[0]*ratio_y), int(img_org.shape[1]*ratio_x))
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if not self.dirs.dir_in:
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if not self.batch_processing_mode:
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prediction_regions_org_y = self.do_prediction(True, img, model_region)
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else:
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prediction_regions_org_y = self.do_prediction(True, img, self.model_region)
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@ -1607,7 +1608,7 @@ class Eynollah():
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img = resize_image(img_org, int(img_org.shape[0]), int(img_org.shape[1]*(1.2 if is_image_enhanced else 1)))
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if self.dirs.dir_in:
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if self.batch_processing_mode:
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prediction_regions_org = self.do_prediction(True, img, self.model_region)
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else:
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prediction_regions_org = self.do_prediction(True, img, model_region)
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@ -1617,12 +1618,12 @@ class Eynollah():
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prediction_regions_org[(prediction_regions_org[:,:]==1) & (mask_zeros_y[:,:]==1)]=0
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if not self.dirs.dir_in:
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if not self.batch_processing_mode:
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model_region = self.load_model(self.model_region_dir_p2)
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img = resize_image(img_org, int(img_org.shape[0]), int(img_org.shape[1]))
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if self.dirs.dir_in:
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if self.batch_processing_mode:
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prediction_regions_org2 = self.do_prediction(True, img, self.model_region_p2, 0.2)
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else:
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prediction_regions_org2 = self.do_prediction(True, img, model_region, 0.2)
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@ -1656,7 +1657,7 @@ class Eynollah():
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if self.input_binary:
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prediction_bin = np.copy(img_org)
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else:
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if not self.dirs.dir_in:
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if not self.batch_processing_mode:
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model_bin = self.load_model(self.model_dir_of_binarization)
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prediction_bin = self.do_prediction(True, img_org, model_bin)
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else:
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@ -1669,7 +1670,7 @@ class Eynollah():
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prediction_bin =np.repeat(prediction_bin[:, :, np.newaxis], 3, axis=2)
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if not self.dirs.dir_in:
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if not self.batch_processing_mode:
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model_region = self.load_model(self.model_region_dir_p_ens)
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ratio_y=1
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ratio_x=1
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@ -1677,7 +1678,7 @@ class Eynollah():
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img = resize_image(prediction_bin, int(img_org.shape[0]*ratio_y), int(img_org.shape[1]*ratio_x))
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if not self.dirs.dir_in:
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if not self.batch_processing_mode:
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prediction_regions_org = self.do_prediction(True, img, model_region)
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else:
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prediction_regions_org = self.do_prediction(True, img, self.model_region)
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@ -1709,7 +1710,7 @@ class Eynollah():
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if self.input_binary:
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prediction_bin = np.copy(img_org)
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if not self.dirs.dir_in:
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if not self.batch_processing_mode:
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model_bin = self.load_model(self.model_dir_of_binarization)
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prediction_bin = self.do_prediction(True, img_org, model_bin)
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else:
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@ -1724,7 +1725,7 @@ class Eynollah():
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prediction_bin =np.repeat(prediction_bin[:, :, np.newaxis], 3, axis=2)
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if not self.dirs.dir_in:
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if not self.batch_processing_mode:
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model_region = self.load_model(self.model_region_dir_p_ens)
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else:
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@ -1734,7 +1735,7 @@ class Eynollah():
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img = resize_image(prediction_bin, int(img_org.shape[0]*ratio_y), int(img_org.shape[1]*ratio_x))
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if not self.dirs.dir_in:
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if not self.batch_processing_mode:
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prediction_regions_org = self.do_prediction(True, img, model_region)
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else:
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prediction_regions_org = self.do_prediction(True, img, self.model_region)
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@ -2733,12 +2734,12 @@ class Eynollah():
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t0_tot = time.time()
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if not self.dirs.dir_in:
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if not self.batch_processing_mode:
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self.ls_imgs = [1]
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for img_name in self.ls_imgs:
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t0 = time.time()
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if self.dirs.dir_in:
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if self.batch_processing_mode:
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self.reset_file_name_dir(join(self.dirs.dir_in,img_name))
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img_res, is_image_enhanced, num_col_classifier, num_column_is_classified = self.run_enhancement(self.light_version)
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@ -2767,7 +2768,7 @@ class Eynollah():
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self.logger.info("No columns detected, outputting an empty PAGE-XML")
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pcgts = self.writer.build_pagexml_no_full_layout([], page_coord, [], [], [], [], [], [], [], [], [], [], cont_page, [], [])
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self.logger.info("Job done in %.1fs", time.time() - t1)
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if self.dirs.dir_in:
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if self.batch_processing_mode:
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self.writer.write_pagexml(pcgts)
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continue
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else:
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@ -2995,7 +2996,7 @@ class Eynollah():
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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_textline_polygons, all_found_textline_polygons_h, all_box_coord, all_box_coord_h, polygons_of_images, contours_tables, polygons_of_drop_capitals, polygons_of_marginals, all_found_textline_polygons_marginals, all_box_coord_marginals, slopes, slopes_h, slopes_marginals, cont_page, polygons_lines_xml)
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self.logger.info("Job done in %.1fs", time.time() - t0)
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if not self.dirs.dir_in:
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if not self.batch_processing_mode:
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return pcgts
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else:
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contours_only_text_parent_h = None
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@ -3006,11 +3007,11 @@ class Eynollah():
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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)
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pcgts = self.writer.build_pagexml_no_full_layout(txt_con_org, page_coord, order_text_new, id_of_texts_tot, all_found_textline_polygons, all_box_coord, polygons_of_images, polygons_of_marginals, all_found_textline_polygons_marginals, all_box_coord_marginals, slopes, slopes_marginals, cont_page, polygons_lines_xml, contours_tables)
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self.logger.info("Job done in %.1fs", time.time() - t0)
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if not self.dirs.dir_in:
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if not self.batch_processing_mode:
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return pcgts
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if self.dirs.dir_in:
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if self.batch_processing_mode:
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self.writer.write_pagexml(pcgts)
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#self.logger.info("Job done in %.1fs", time.time() - t0)
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if self.dirs.dir_in:
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if self.batch_processing_mode:
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self.logger.info("All jobs done in %.1fs", time.time() - t0_tot)
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kba
4 months ago