qurator-spk/eynollah
1
0
Fork 0
mirror of https://github.com/qurator-spk/eynollah.git synced 2025-10-27 07:44:12 +01:00
Code Issues Projects Releases Wiki Activity

avoid unnecessary 3-channel conversions: missing cases

Browse source
This commit is contained in:
Robert Sachunsky 2025-10-06 16:53:06 +02:00
parent 51995c9e46
commit 70344c137c
1 changed files with 8 additions and 11 deletions
Download patch file Download diff file Expand all files Collapse all files

19
src/eynollah/utils/separate_lines.py
View file

@ -142,13 +142,12 @@ def dedup_separate_lines(img_patch, contour_text_interest, thetha, axis):
rotation_matrix) rotation_matrix)
def separate_lines(img_patch, contour_text_interest, thetha, x_help, y_help): def separate_lines(img_patch, contour_text_interest, thetha, x_help, y_help):
(h, w) = img_patch.shape[:2] h, w = img_patch.shape[:2]
center = (w // 2, h // 2) center = (w // 2, h // 2)
M = cv2.getRotationMatrix2D(center, -thetha, 1.0) M = cv2.getRotationMatrix2D(center, -thetha, 1.0)
x_d = M[0, 2] x_d = M[0, 2]
y_d = M[1, 2] y_d = M[1, 2]
thetha = thetha / 180. * np.pi rotation_matrix = M[:2, :2]
rotation_matrix = np.array([[np.cos(thetha), -np.sin(thetha)], [np.sin(thetha), np.cos(thetha)]])
contour_text_interest_copy = contour_text_interest.copy() contour_text_interest_copy = contour_text_interest.copy()
x_cont = contour_text_interest[:, 0, 0] x_cont = contour_text_interest[:, 0, 0]
@ -1311,7 +1310,7 @@ def separate_lines_vertical_cont(img_patch, contour_text_interest, thetha, box_i
else: else:
cnts_images = (img_patch[:, :] == label) * 1 cnts_images = (img_patch[:, :] == label) * 1
_, thresh = cv2.threshold(cnts_images, 0, 255, 0) _, thresh = cv2.threshold(cnts_images, 0, 255, 0)
contours_imgs, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) contours_imgs, hierarchy = cv2.findContours(thresh.astype(np.uint8), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
contours_imgs = return_parent_contours(contours_imgs, hierarchy) contours_imgs = return_parent_contours(contours_imgs, hierarchy)
contours_imgs = filter_contours_area_of_image_tables(thresh, contours_imgs = filter_contours_area_of_image_tables(thresh,
@ -1319,14 +1318,12 @@ def separate_lines_vertical_cont(img_patch, contour_text_interest, thetha, box_i
max_area=max_area, min_area=min_area) max_area=max_area, min_area=min_area)
cont_final = [] cont_final = []
for i in range(len(contours_imgs)): for i in range(len(contours_imgs)):
img_contour = np.zeros((cnts_images.shape[0], cnts_images.shape[1], 3)) img_contour = np.zeros(cnts_images.shape[:2], dtype=np.uint8)
img_contour = cv2.fillPoly(img_contour, pts=[contours_imgs[i]], color=(255, 255, 255)) img_contour = cv2.fillPoly(img_contour, pts=[contours_imgs[i]], color=255)
img_contour = img_contour.astype(np.uint8)
img_contour = cv2.dilate(img_contour, kernel, iterations=4) img_contour = cv2.dilate(img_contour, kernel, iterations=4)
imgrayrot = cv2.cvtColor(img_contour, cv2.COLOR_BGR2GRAY) _, threshrot = cv2.threshold(img_contour, 0, 255, 0)
_, threshrot = cv2.threshold(imgrayrot, 0, 255, 0) contours_text_rot, _ = cv2.findContours(threshrot.astype(np.uint8), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
contours_text_rot, _ = cv2.findContours(threshrot.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
##contour_text_copy[:, 0, 0] = contour_text_copy[:, 0, 0] - box_ind[ ##contour_text_copy[:, 0, 0] = contour_text_copy[:, 0, 0] - box_ind[
##0] ##0]
@ -1378,7 +1375,7 @@ def textline_contours_postprocessing(textline_mask, slope,
img_contour_rot = rotate_image(img_contour_help, slope) img_contour_rot = rotate_image(img_contour_help, slope)
_, threshrot = cv2.threshold(img_contour_rot, 0, 255, 0) _, threshrot = cv2.threshold(img_contour_rot, 0, 255, 0)
contours_text_rot, _ = cv2.findContours(threshrot.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) contours_text_rot, _ = cv2.findContours(threshrot.astype(np.uint8), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
len_con_text_rot = [len(contours_text_rot[ib]) for ib in range(len(contours_text_rot))] len_con_text_rot = [len(contours_text_rot[ib]) for ib in range(len(contours_text_rot))]
ind_big_con = np.argmax(len_con_text_rot) ind_big_con = np.argmax(len_con_text_rot)