mirror of
https://github.com/qurator-spk/eynollah.git
synced 2025-06-09 04:09:54 +02:00
option to ignore page extraction
This commit is contained in:
vahid
parent
3bbbeecfec
commit
735abc43f3
2 changed files with 97 additions and 151 deletions
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@ -108,6 +108,12 @@ from qurator.eynollah.eynollah import Eynollah
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is_flag=True,
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help="if this parameter set to true, this tool would use lighter version",
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)
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@click.option(
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"--ignore_page_extraction/--extract_page_included",
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"-ipe/-epi",
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is_flag=True,
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help="if this parameter set to true, this tool would ignore page extraction",
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)
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@click.option(
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"--log-level",
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"-l",
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@ -132,6 +138,7 @@ def main(
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allow_scaling,
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headers_off,
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light_version,
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ignore_page_extraction,
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log_level
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):
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if log_level:
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@ -161,6 +168,7 @@ def main(
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allow_scaling=allow_scaling,
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headers_off=headers_off,
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light_version=light_version,
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ignore_page_extraction=ignore_page_extraction,
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)
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eynollah.run()
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#pcgts = eynollah.run()
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@ -105,6 +105,7 @@ class Eynollah:
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allow_scaling=False,
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headers_off=False,
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light_version=False,
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ignore_page_extraction=False,
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override_dpi=None,
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logger=None,
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pcgts=None,
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@ -133,6 +134,7 @@ class Eynollah:
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self.allow_scaling = allow_scaling
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self.headers_off = headers_off
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self.light_version = light_version
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self.ignore_page_extraction = ignore_page_extraction
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self.pcgts = pcgts
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if not dir_in:
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self.plotter = None if not enable_plotting else EynollahPlotter(
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@ -886,169 +888,100 @@ class Eynollah:
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gc.collect()
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return prediction_true
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def early_page_for_num_of_column_classification(self,img_bin):
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self.logger.debug("enter early_page_for_num_of_column_classification")
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if self.input_binary:
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img =np.copy(img_bin)
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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.dir_in:
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model_page, session_page = self.start_new_session_and_model(self.model_page_dir)
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img = cv2.GaussianBlur(img, (5, 5), 0)
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if self.dir_in:
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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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imgray = cv2.cvtColor(img_page_prediction, cv2.COLOR_BGR2GRAY)
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_, thresh = cv2.threshold(imgray, 0, 255, 0)
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thresh = cv2.dilate(thresh, KERNEL, iterations=3)
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contours, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
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if len(contours)>0:
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cnt_size = np.array([cv2.contourArea(contours[j]) for j in range(len(contours))])
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cnt = contours[np.argmax(cnt_size)]
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x, y, w, h = cv2.boundingRect(cnt)
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box = [x, y, w, h]
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else:
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box = [0, 0, img.shape[1], img.shape[0]]
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croped_page, page_coord = crop_image_inside_box(box, img)
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if not self.dir_in:
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session_page.close()
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del model_page
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del session_page
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K.clear_session()
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gc.collect()
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self.logger.debug("exit early_page_for_num_of_column_classification")
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return croped_page, page_coord
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def extract_page(self):
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self.logger.debug("enter extract_page")
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cont_page = []
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if not self.dir_in:
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model_page, session_page = self.start_new_session_and_model(self.model_page_dir)
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img = cv2.GaussianBlur(self.image, (5, 5), 0)
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if not self.dir_in:
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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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imgray = cv2.cvtColor(img_page_prediction, cv2.COLOR_BGR2GRAY)
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_, thresh = cv2.threshold(imgray, 0, 255, 0)
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thresh = cv2.dilate(thresh, KERNEL, iterations=3)
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contours, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
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if len(contours)>0:
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cnt_size = np.array([cv2.contourArea(contours[j]) for j in range(len(contours))])
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cnt = contours[np.argmax(cnt_size)]
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x, y, w, h = cv2.boundingRect(cnt)
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if x <= 30:
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w += x
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x = 0
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if (self.image.shape[1] - (x + w)) <= 30:
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w = w + (self.image.shape[1] - (x + w))
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if y <= 30:
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h = h + y
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y = 0
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if (self.image.shape[0] - (y + h)) <= 30:
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h = h + (self.image.shape[0] - (y + h))
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if not self.ignore_page_extraction:
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if not self.dir_in:
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model_page, session_page = self.start_new_session_and_model(self.model_page_dir)
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img = cv2.GaussianBlur(self.image, (5, 5), 0)
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if not self.dir_in:
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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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imgray = cv2.cvtColor(img_page_prediction, cv2.COLOR_BGR2GRAY)
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_, thresh = cv2.threshold(imgray, 0, 255, 0)
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thresh = cv2.dilate(thresh, KERNEL, iterations=3)
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contours, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
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if len(contours)>0:
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cnt_size = np.array([cv2.contourArea(contours[j]) for j in range(len(contours))])
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cnt = contours[np.argmax(cnt_size)]
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x, y, w, h = cv2.boundingRect(cnt)
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if x <= 30:
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w += x
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x = 0
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if (self.image.shape[1] - (x + w)) <= 30:
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w = w + (self.image.shape[1] - (x + w))
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if y <= 30:
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h = h + y
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y = 0
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if (self.image.shape[0] - (y + h)) <= 30:
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h = h + (self.image.shape[0] - (y + h))
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box = [x, y, w, h]
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box = [x, y, w, h]
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else:
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box = [0, 0, img.shape[1], img.shape[0]]
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croped_page, page_coord = crop_image_inside_box(box, self.image)
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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]]]))
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if not self.dir_in:
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session_page.close()
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del model_page
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del session_page
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K.clear_session()
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gc.collect()
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self.logger.debug("exit extract_page")
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else:
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box = [0, 0, img.shape[1], img.shape[0]]
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croped_page, page_coord = crop_image_inside_box(box, self.image)
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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]]]))
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if not self.dir_in:
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session_page.close()
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del model_page
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del session_page
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K.clear_session()
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gc.collect()
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self.logger.debug("exit extract_page")
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box = [0, 0, self.image.shape[1], self.image.shape[0]]
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croped_page, page_coord = crop_image_inside_box(box, self.image)
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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]]]))
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return croped_page, page_coord, cont_page
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def early_page_for_num_of_column_classification(self,img_bin):
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self.logger.debug("enter early_page_for_num_of_column_classification")
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if self.input_binary:
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img =np.copy(img_bin)
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img = img.astype(np.uint8)
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if not self.ignore_page_extraction:
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self.logger.debug("enter early_page_for_num_of_column_classification")
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if self.input_binary:
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img =np.copy(img_bin)
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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.dir_in:
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model_page, session_page = self.start_new_session_and_model(self.model_page_dir)
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img = cv2.GaussianBlur(img, (5, 5), 0)
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if self.dir_in:
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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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imgray = cv2.cvtColor(img_page_prediction, cv2.COLOR_BGR2GRAY)
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_, thresh = cv2.threshold(imgray, 0, 255, 0)
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thresh = cv2.dilate(thresh, KERNEL, iterations=3)
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contours, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
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if len(contours)>0:
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cnt_size = np.array([cv2.contourArea(contours[j]) for j in range(len(contours))])
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cnt = contours[np.argmax(cnt_size)]
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x, y, w, h = cv2.boundingRect(cnt)
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box = [x, y, w, h]
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else:
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box = [0, 0, img.shape[1], img.shape[0]]
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croped_page, page_coord = crop_image_inside_box(box, img)
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if not self.dir_in:
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session_page.close()
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del model_page
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del session_page
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K.clear_session()
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gc.collect()
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self.logger.debug("exit early_page_for_num_of_column_classification")
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else:
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img = self.imread()
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if not self.dir_in:
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model_page, session_page = self.start_new_session_and_model(self.model_page_dir)
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img = cv2.GaussianBlur(img, (5, 5), 0)
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if self.dir_in:
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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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imgray = cv2.cvtColor(img_page_prediction, cv2.COLOR_BGR2GRAY)
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_, thresh = cv2.threshold(imgray, 0, 255, 0)
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thresh = cv2.dilate(thresh, KERNEL, iterations=3)
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contours, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
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if len(contours)>0:
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cnt_size = np.array([cv2.contourArea(contours[j]) for j in range(len(contours))])
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cnt = contours[np.argmax(cnt_size)]
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x, y, w, h = cv2.boundingRect(cnt)
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box = [x, y, w, h]
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else:
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box = [0, 0, img.shape[1], img.shape[0]]
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croped_page, page_coord = crop_image_inside_box(box, img)
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if not self.dir_in:
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session_page.close()
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del model_page
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del session_page
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K.clear_session()
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gc.collect()
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self.logger.debug("exit early_page_for_num_of_column_classification")
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croped_page, page_coord = crop_image_inside_box(box, img)
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return croped_page, page_coord
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def extract_page(self):
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self.logger.debug("enter extract_page")
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cont_page = []
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if not self.dir_in:
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model_page, session_page = self.start_new_session_and_model(self.model_page_dir)
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img = cv2.GaussianBlur(self.image, (5, 5), 0)
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if not self.dir_in:
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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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imgray = cv2.cvtColor(img_page_prediction, cv2.COLOR_BGR2GRAY)
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_, thresh = cv2.threshold(imgray, 0, 255, 0)
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thresh = cv2.dilate(thresh, KERNEL, iterations=3)
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contours, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
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if len(contours)>0:
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cnt_size = np.array([cv2.contourArea(contours[j]) for j in range(len(contours))])
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cnt = contours[np.argmax(cnt_size)]
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x, y, w, h = cv2.boundingRect(cnt)
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if x <= 30:
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w += x
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x = 0
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if (self.image.shape[1] - (x + w)) <= 30:
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w = w + (self.image.shape[1] - (x + w))
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if y <= 30:
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h = h + y
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y = 0
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if (self.image.shape[0] - (y + h)) <= 30:
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h = h + (self.image.shape[0] - (y + h))
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box = [x, y, w, h]
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else:
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box = [0, 0, img.shape[1], img.shape[0]]
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croped_page, page_coord = crop_image_inside_box(box, self.image)
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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]]]))
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if not self.dir_in:
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session_page.close()
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del model_page
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del session_page
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K.clear_session()
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gc.collect()
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self.logger.debug("exit extract_page")
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return croped_page, page_coord, cont_page
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def extract_text_regions(self, img, patches, cols):
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self.logger.debug("enter extract_text_regions")
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@ -2960,10 +2893,15 @@ class Eynollah:
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#self.logger.info('cont_page %s', cont_page)
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if not num_col:
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print('buraya galir??')
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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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return pcgts
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if self.dir_in:
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self.writer.write_pagexml(pcgts)
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continue
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else:
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return pcgts
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t1 = time.time()
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if not self.light_version:
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