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contour features: avoid unused calculations, simplify, add shortcuts

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- new function: `find_center_of_contours`
- simplified: `find_(new_)features_of_contours`
This commit is contained in:
Robert Sachunsky 2025-10-02 20:51:03 +02:00
parent 3aa7ad04fa
commit 0b9d4901a6
1 changed files with 27 additions and 51 deletions
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78
src/eynollah/utils/contour.py
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@ -79,61 +79,37 @@ def filter_contours_area_of_image_tables(image, contours, hierarchy, max_area=1.
found_polygons_early.append(polygon2contour(polygon))
return found_polygons_early
def find_new_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))]
try:
x_min_main = np.array([np.min(contours_main[j][:, 0, 0])
for j in range(len(contours_main))])
argmin_x_main = np.array([np.argmin(contours_main[j][:, 0, 0])
for j in range(len(contours_main))])
x_min_from_argmin = np.array([contours_main[j][argmin_x_main[j], 0, 0]
for j in range(len(contours_main))])
y_corr_x_min_from_argmin = np.array([contours_main[j][argmin_x_main[j], 0, 1]
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))])
except:
x_min_main = np.array([np.min(contours_main[j][:, 0])
for j in range(len(contours_main))])
argmin_x_main = np.array([np.argmin(contours_main[j][:, 0])
for j in range(len(contours_main))])
x_min_from_argmin = np.array([contours_main[j][argmin_x_main[j], 0]
for j in range(len(contours_main))])
y_corr_x_min_from_argmin = np.array([contours_main[j][argmin_x_main[j], 1]
for j in range(len(contours_main))])
x_max_main = np.array([np.max(contours_main[j][:, 0])
for j in range(len(contours_main))])
y_min_main = np.array([np.min(contours_main[j][:, 1])
for j in range(len(contours_main))])
y_max_main = np.array([np.max(contours_main[j][:, 1])
for j in range(len(contours_main))])
# dis_x=np.abs(x_max_main-x_min_main)
def find_center_of_contours(contours):
moments = [cv2.moments(contour) for contour in contours]
cx = [feat["m10"] / (feat["m00"] + 1e-32)
for feat in moments]
cy = [feat["m01"] / (feat["m00"] + 1e-32)
for feat in moments]
return cx, cy
return cx_main, cy_main, x_min_main, x_max_main, y_min_main, y_max_main, y_corr_x_min_from_argmin
def find_new_features_of_contours(contours):
# areas = np.array([cv2.contourArea(contour) for contour in contours])
cx, cy = find_center_of_contours(contours)
slice_x = np.index_exp[:, 0, 0]
slice_y = np.index_exp[:, 0, 1]
if any(contour.ndim < 3 for contour in contours):
slice_x = np.index_exp[:, 0]
slice_y = np.index_exp[:, 1]
x_min = np.array([np.min(contour[slice_x]) for contour in contours])
x_max = np.array([np.max(contour[slice_x]) for contour in contours])
y_min = np.array([np.min(contour[slice_y]) for contour in contours])
y_max = np.array([np.max(contour[slice_y]) for contour in contours])
# dis_x=np.abs(x_max-x_min)
y_corr_x_min = np.array([contour[np.argmin(contour[slice_x])][slice_y[1:]]
for contour in contours])
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))])
return cx, cy, x_min, x_max, y_min, y_max, y_corr_x_min
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))])
def find_features_of_contours(contours):
y_min = np.array([np.min(contour[:,0,1]) for contour in contours])
y_max = np.array([np.max(contour[:,0,1]) for contour in contours])
return y_min_main, y_max_main
return y_min, y_max
def return_parent_contours(contours, hierarchy):
contours_parent = [contours[i]