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extract_page(): get external contours instead of indiscriminate tree

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Robert Sachunsky 2026-04-27 00:02:48 +02:00
parent 287bebde0d
commit b5bc161a4c
1 changed files with 5 additions and 7 deletions
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12
src/eynollah/eynollah.py
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@ -849,7 +849,6 @@ class Eynollah:
return segmentation, confidence
def extract_page(self, image):
self.logger.debug("enter extract_page")
cropped_page = img = image['img_res']
h, w = img.shape[:2]
page_coord = [0, h, 0, w]
@ -858,14 +857,13 @@ class Eynollah:
[[w, h]],
[[0, h]]])]
if not self.ignore_page_extraction:
self.logger.debug("enter extract_page")
#cv2.GaussianBlur(img, (5, 5), 0)
prediction = self.do_prediction(False, img, self.model_zoo.get("page"))
contours, _ = cv2.findContours(prediction, 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)]
contours, _ = cv2.findContours(prediction, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
if len(contours):
areas = np.array(list(map(cv2.contourArea, contours)))
cnt = contours[np.argmax(areas)]
cont_page = [cnt]
x, y, w, h = cv2.boundingRect(cnt)
#if x <= 30: