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eynollah
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option to ignore page extraction

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pull/86/head
vahid 2 years ago
parent 3bbbeecfec
commit 735abc43f3
2 changed files with 98 additions and 152 deletions
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8
qurator/eynollah/cli.py
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@ -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()

242
qurator/eynollah/eynollah.py
Unescape Escape View File

@ -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:

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