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remove unused find_features_of_contours

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Konstantin Baierer 2021-02-23 16:39:33 +01:00
parent 823592126e
commit e2ae6dbd44
3 changed files with 13 additions and 14 deletions
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1
sbb_newspapers_org_image/eynollah.py
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@ -33,7 +33,6 @@ from .utils.contour import (
filter_contours_area_of_image_tables, filter_contours_area_of_image_tables,
filter_contours_area_of_image, filter_contours_area_of_image,
find_contours_mean_y_diff, find_contours_mean_y_diff,
find_features_of_contours,
find_new_features_of_contoures, find_new_features_of_contoures,
get_text_region_boxes_by_given_contours, get_text_region_boxes_by_given_contours,
get_textregion_contours_in_org_image, get_textregion_contours_in_org_image,

13
sbb_newspapers_org_image/unused.py
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@ -3057,3 +3057,16 @@ def return_bonding_box_of_contours(cnts):
boxes_tot.append(box) boxes_tot.append(box)
return boxes_tot return boxes_tot
def find_features_of_contours(contours_main):
areas_main = np.array([cv2.contourArea(contours_main[j]) for j in range(len(contours_main))])
M_main = [cv2.moments(contours_main[j]) for j in range(len(contours_main))]
cx_main = [(M_main[j]["m10"] / (M_main[j]["m00"] + 1e-32)) for j in range(len(M_main))]
cy_main = [(M_main[j]["m01"] / (M_main[j]["m00"] + 1e-32)) for j in range(len(M_main))]
x_min_main = np.array([np.min(contours_main[j][:, 0, 0]) for j in range(len(contours_main))])
x_max_main = np.array([np.max(contours_main[j][:, 0, 0]) for j in range(len(contours_main))])
y_min_main = np.array([np.min(contours_main[j][:, 0, 1]) for j in range(len(contours_main))])
y_max_main = np.array([np.max(contours_main[j][:, 0, 1]) for j in range(len(contours_main))])
return y_min_main, y_max_main, areas_main

13
sbb_newspapers_org_image/utils/contour.py
View file

@ -26,19 +26,6 @@ def find_contours_mean_y_diff(contours_main):
cy_main = [(M_main[j]["m01"] / (M_main[j]["m00"] + 1e-32)) for j in range(len(M_main))] cy_main = [(M_main[j]["m01"] / (M_main[j]["m00"] + 1e-32)) for j in range(len(M_main))]
return np.mean(np.diff(np.sort(np.array(cy_main)))) return np.mean(np.diff(np.sort(np.array(cy_main))))
def find_features_of_contours(contours_main):
areas_main = np.array([cv2.contourArea(contours_main[j]) for j in range(len(contours_main))])
M_main = [cv2.moments(contours_main[j]) for j in range(len(contours_main))]
cx_main = [(M_main[j]["m10"] / (M_main[j]["m00"] + 1e-32)) for j in range(len(M_main))]
cy_main = [(M_main[j]["m01"] / (M_main[j]["m00"] + 1e-32)) for j in range(len(M_main))]
x_min_main = np.array([np.min(contours_main[j][:, 0, 0]) for j in range(len(contours_main))])
x_max_main = np.array([np.max(contours_main[j][:, 0, 0]) for j in range(len(contours_main))])
y_min_main = np.array([np.min(contours_main[j][:, 0, 1]) for j in range(len(contours_main))])
y_max_main = np.array([np.max(contours_main[j][:, 0, 1]) for j in range(len(contours_main))])
return y_min_main, y_max_main, areas_main
def get_text_region_boxes_by_given_contours(contours): def get_text_region_boxes_by_given_contours(contours):