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matching deskewed text region contours with predicted: improve

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- when matching undeskewed and new contours, do not just
  pick the closest centers, respectively, but also of similar
  size (by making the contour area the 3rd dimension of the
  vector norm in the distance calculation)
This commit is contained in:
Robert Sachunsky 2025-10-05 02:45:01 +02:00
parent 56f2d4131e
commit 29fcc75c0b
1 changed files with 5 additions and 1 deletions
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src/eynollah/eynollah.py
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@ -4608,7 +4608,11 @@ class Eynollah:
for i in range(len(contours_only_text_parent)): for i in range(len(contours_only_text_parent)):
p = np.dot(M_22, centers[:, i:i+1]) # [2, 1] p = np.dot(M_22, centers[:, i:i+1]) # [2, 1]
p -= offset p -= offset
dists = np.linalg.norm(p - centers_d, axis=0) # add dimension for area
#dists = np.linalg.norm(p - centers_d, axis=0)
diffs = (np.append(p, [[areas_cnt_text_parent[i]]], axis=0) -
np.append(centers_d, areas_cnt_text_d[np.newaxis], axis=0))
dists = np.linalg.norm(diffs, axis=0)
contours_only_text_parent_d_ordered.append( contours_only_text_parent_d_ordered.append(
contours_only_text_parent_d[np.argmin(dists)]) contours_only_text_parent_d[np.argmin(dists)])
# cv2.fillPoly(img2, pts=[contours_only_text_parent_d[np.argmin(dists)]], color=i + 1) # cv2.fillPoly(img2, pts=[contours_only_text_parent_d[np.argmin(dists)]], color=i + 1)