diff --git a/qurator/eynollah/cli.py b/qurator/eynollah/cli.py index ca938c4..18ea583 100644 --- a/qurator/eynollah/cli.py +++ b/qurator/eynollah/cli.py @@ -108,6 +108,12 @@ from qurator.eynollah.eynollah import Eynollah is_flag=True, help="if this parameter set to true, this tool would use lighter version", ) +@click.option( + "--ignore_page_extraction/--extract_page_included", + "-ipe/-epi", + is_flag=True, + help="if this parameter set to true, this tool would ignore page extraction", +) @click.option( "--log-level", "-l", @@ -132,6 +138,7 @@ def main( allow_scaling, headers_off, light_version, + ignore_page_extraction, log_level ): if log_level: @@ -161,6 +168,7 @@ def main( allow_scaling=allow_scaling, headers_off=headers_off, light_version=light_version, + ignore_page_extraction=ignore_page_extraction, ) eynollah.run() #pcgts = eynollah.run() diff --git a/qurator/eynollah/eynollah.py b/qurator/eynollah/eynollah.py index 48a640c..8957248 100644 --- a/qurator/eynollah/eynollah.py +++ b/qurator/eynollah/eynollah.py @@ -105,6 +105,7 @@ class Eynollah: allow_scaling=False, headers_off=False, light_version=False, + ignore_page_extraction=False, override_dpi=None, logger=None, pcgts=None, @@ -133,6 +134,7 @@ class Eynollah: self.allow_scaling = allow_scaling self.headers_off = headers_off self.light_version = light_version + self.ignore_page_extraction = ignore_page_extraction self.pcgts = pcgts if not dir_in: self.plotter = None if not enable_plotting else EynollahPlotter( @@ -886,169 +888,100 @@ class Eynollah: gc.collect() return prediction_true - def early_page_for_num_of_column_classification(self,img_bin): - self.logger.debug("enter early_page_for_num_of_column_classification") - if self.input_binary: - img =np.copy(img_bin) - 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) - _, thresh = cv2.threshold(imgray, 0, 255, 0) - thresh = cv2.dilate(thresh, KERNEL, iterations=3) - contours, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) - if len(contours)>0: - cnt_size = np.array([cv2.contourArea(contours[j]) for j in range(len(contours))]) - cnt = contours[np.argmax(cnt_size)] - x, y, w, h = cv2.boundingRect(cnt) - box = [x, y, w, h] - 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 - 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) - contours, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) - - if len(contours)>0: - cnt_size = np.array([cv2.contourArea(contours[j]) for j in range(len(contours))]) - cnt = contours[np.argmax(cnt_size)] - x, y, w, h = cv2.boundingRect(cnt) - if x <= 30: - w += x - x = 0 - if (self.image.shape[1] - (x + w)) <= 30: - w = w + (self.image.shape[1] - (x + w)) - if y <= 30: - h = h + y - y = 0 - if (self.image.shape[0] - (y + h)) <= 30: - h = h + (self.image.shape[0] - (y + h)) - - box = [x, y, w, h] + if not self.ignore_page_extraction: + 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) + contours, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) + + if len(contours)>0: + cnt_size = np.array([cv2.contourArea(contours[j]) for j in range(len(contours))]) + cnt = contours[np.argmax(cnt_size)] + x, y, w, h = cv2.boundingRect(cnt) + if x <= 30: + w += x + x = 0 + if (self.image.shape[1] - (x + w)) <= 30: + w = w + (self.image.shape[1] - (x + w)) + if y <= 30: + h = h + y + y = 0 + if (self.image.shape[0] - (y + h)) <= 30: + h = h + (self.image.shape[0] - (y + h)) + + box = [x, y, w, h] + else: + 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 + K.clear_session() + gc.collect() + self.logger.debug("exit extract_page") else: - 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 - K.clear_session() - gc.collect() - self.logger.debug("exit extract_page") + box = [0, 0, self.image.shape[1], self.image.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]]])) return croped_page, page_coord, cont_page def early_page_for_num_of_column_classification(self,img_bin): - self.logger.debug("enter early_page_for_num_of_column_classification") - if self.input_binary: - img =np.copy(img_bin) - img = img.astype(np.uint8) + if not self.ignore_page_extraction: + self.logger.debug("enter early_page_for_num_of_column_classification") + if self.input_binary: + img =np.copy(img_bin) + 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) + _, thresh = cv2.threshold(imgray, 0, 255, 0) + thresh = cv2.dilate(thresh, KERNEL, iterations=3) + contours, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) + if len(contours)>0: + cnt_size = np.array([cv2.contourArea(contours[j]) for j in range(len(contours))]) + cnt = contours[np.argmax(cnt_size)] + x, y, w, h = cv2.boundingRect(cnt) + box = [x, y, w, h] + 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 + K.clear_session() + + gc.collect() + + self.logger.debug("exit early_page_for_num_of_column_classification") 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) - _, thresh = cv2.threshold(imgray, 0, 255, 0) - thresh = cv2.dilate(thresh, KERNEL, iterations=3) - contours, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) - if len(contours)>0: - cnt_size = np.array([cv2.contourArea(contours[j]) for j in range(len(contours))]) - cnt = contours[np.argmax(cnt_size)] - x, y, w, h = cv2.boundingRect(cnt) - box = [x, y, w, h] - 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 - K.clear_session() - - gc.collect() - - self.logger.debug("exit early_page_for_num_of_column_classification") + croped_page, page_coord = crop_image_inside_box(box, img) 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) - contours, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) - - if len(contours)>0: - cnt_size = np.array([cv2.contourArea(contours[j]) for j in range(len(contours))]) - cnt = contours[np.argmax(cnt_size)] - x, y, w, h = cv2.boundingRect(cnt) - if x <= 30: - w += x - x = 0 - if (self.image.shape[1] - (x + w)) <= 30: - w = w + (self.image.shape[1] - (x + w)) - if y <= 30: - h = h + y - y = 0 - if (self.image.shape[0] - (y + h)) <= 30: - h = h + (self.image.shape[0] - (y + h)) - - box = [x, y, w, h] - else: - 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 - K.clear_session() - gc.collect() - self.logger.debug("exit extract_page") - return croped_page, page_coord, cont_page def extract_text_regions(self, img, patches, cols): self.logger.debug("enter extract_text_regions") @@ -2960,10 +2893,15 @@ class Eynollah: #self.logger.info('cont_page %s', cont_page) if not num_col: + print('buraya galir??') self.logger.info("No columns detected, outputting an empty PAGE-XML") pcgts = self.writer.build_pagexml_no_full_layout([], page_coord, [], [], [], [], [], [], [], [], [], [], cont_page, [], []) self.logger.info("Job done in %.1fs", time.time() - t1) - return pcgts + if self.dir_in: + self.writer.write_pagexml(pcgts) + continue + else: + return pcgts t1 = time.time() if not self.light_version: