import cv2
import numpy as np
import math
from utils.Functions import getSkeletonOfImage, getCrossPointsOfSkeletonLine, getEndPointsOfSkeletonLine, \
removeShortBranchesOfSkeleton
path = "test_images/1133壬.jpg"
img = cv2.imread(path, 0)
_, img_bit = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY)
# get skeleton
skeleton = getSkeletonOfImage(img_bit)
end_points = getEndPointsOfSkeletonLine(skeleton)
cross_points = getCrossPointsOfSkeletonLine(skeleton)
print("end points num: %d" % len(end_points))
print("cross points num: %d" % len(cross_points))
cv2.imshow("skeleton original", skeleton)
# length threshold
skeleton = removeShortBranchesOfSkeleton(skeleton, length_threshold=30)
cv2.imshow("skeleton smoothed", skeleton)
cv2.waitKey(0)
cv2.destroyAllWindows()
def autoSmoothContoursOfComponent(component, blockSize=3, ksize=3, k=0.04):
"""
:param component:
:return:
"""
if component is None:
return
# 5. Using corner detection to get corner regions
corner_component = np.float32(component)
dst = cv2.cornerHarris(corner_component,
blockSize=blockSize,
ksize=ksize,
k=k)
dst = cv2.dilate(dst, None)
corners_area_points = []
for y in range(dst.shape[0]):
for x in range(dst.shape[1]):
if dst[y][x] > 0.1 * dst.max():
corners_area_points.append((x, y))
print("corner area points num: %d" % len(corners_area_points))
# 6. Determine center points of corner areas
blank_gray = createBlankGrayscaleImage(component)
for pt in corners_area_points:
blank_gray[pt[1]][pt[0]] = 0.0
rectangles = getAllMiniBoundingBoxesOfImage(blank_gray)
corners_area_center_points = []
for rect in rectangles:
corners_area_center_points.append(
(rect[0] + int(rect[2] / 2.), rect[1] + int(rect[3] / 2.)))
print("corner area center points num: %d" %
len(corners_area_center_points))
# based the distance to end points and cross points, remove extra corners area center points
component_skeleton = getSkeletonOfImage(component)
end_points = getEndPointsOfSkeletonLine(component_skeleton)
cross_points = getCrossPointsOfSkeletonLine(component_skeleton)
# remove extra branches
# img_skeleton = removeBranchOfSkeletonLine(img_skeleton, end_points, cross_points)
# end_points = getEndPointsOfSkeletonLine(img_skeleton)
# cross_points = getEndPointsOfSkeletonLine(img_skeleton)
# detect valid corner region center points closed to end points and cross points
valid_corners_area_center_points = []
dist_threshold = 40
for pt in corners_area_center_points:
is_valid = False
for ept in end_points:
dist = math.sqrt((pt[0] - ept[0])**2 + (pt[1] + ept[1])**2)
if dist <= dist_threshold:
is_valid = True
break
if is_valid:
valid_corners_area_center_points.append(pt)
continue
for cpt in cross_points:
dist = math.sqrt((pt[0] - cpt[0])**2 + (pt[1] - cpt[1])**2)
if dist <= dist_threshold:
is_valid = True
break
if is_valid:
valid_corners_area_center_points.append(pt)
print("valid corner area center points num: %d" %
len(valid_corners_area_center_points))
del blank_gray
# 7. Get all contours of component
component_contours = getContourOfImage(component)
contours = getConnectedComponents(component_contours, connectivity=8)
print("contours num: %d" % len(contours))
# 8. Process contours to get closed and 1-pixel width contours
contours_processed = []
for cont in contours:
cont = removeBreakPointsOfContour(cont)
contours_processed.append(cont)
print("contours processed num: %d" % len(contours_processed))
# 9. Find corner points of conthours closed to corner region center points. For each contour, there is a coner points list.
contours_corner_points = []
for i in range(len(contours_processed)):
corner_points = []
contour = contours_processed[i]
for pt in valid_corners_area_center_points:
x0 = target_x = pt[0]
y0 = target_y = pt[1]
min_dist = 10000
# search target point in region: 20 * 20 of center is (x0, y0)
for y in range(y0 - 10, y0 + 10):
for x in range(x0 - 10, x0 + 10):
if contour[y][x] == 255:
continue
dist = math.sqrt((x - x0)**2 + (y - y0)**2)
if dist < min_dist:
min_dist = dist
target_x = x
target_y = y
if min_dist < 5:
corner_points.append((target_x, target_y))
contours_corner_points.append(corner_points)
total_num = 0
for cont in contours_corner_points:
total_num += len(cont)
if total_num == len(valid_corners_area_center_points):
print("corner points not ignored")
else:
print("corner points be ignored")
# 10. Separate contours into sub-contours based on the corner points on different contours
sub_contours = []
for i in range(len(contours_processed)):
contour = contours_processed[i]
corner_points = contours_corner_points[i]
# sorted the contour
contour_points_sorted = sortPointsOnContourOfImage(contour)
# sorted the corner points
corner_points_sorted = []
for pt in contour_points_sorted:
if pt in corner_points:
corner_points_sorted.append(pt)
# sepate the contour into sub-contour
for j in range(len(corner_points_sorted)):
start_pt = corner_points_sorted[j]
end_pt = None
if j == len(corner_points_sorted) - 1:
end_pt = corner_points_sorted[0]
else:
end_pt = corner_points_sorted[j + 1]
# find indexes of start point and end point in contour_points_sorted
start_index = contour_points_sorted.index(start_pt)
end_index = contour_points_sorted.index(end_pt)
# separate
sub_contour = None
if start_index <= end_index:
if end_index == len(contour_points_sorted) - 1:
sub_contour = contour_points_sorted[
start_index:len(contour_points_sorted)]
sub_contour.append(contour_points_sorted[0])
else:
sub_contour = contour_points_sorted[start_index:end_index +
1]
else:
sub_contour = contour_points_sorted[
start_index:len(contour_points_sorted
)] + contour_points_sorted[0:end_index + 1]
sub_contours.append(sub_contour)
print("sub contours num: %d" % len(sub_contours))
# 11. Beizer curve fit all sub-contours under maximal error
max_error = 100
sub_contours_smoothed = []
for id in range(len(sub_contours)):
# single sub-contour
sub_contour = np.array(sub_contours[id])
if len(sub_contour) < 2:
continue
beziers = fitCurve(sub_contour, maxError=max_error)
sub_contour_smoothed = []
for bez in beziers:
bezier_points = draw_cubic_bezier(bez[0], bez[1], bez[2], bez[3])
sub_contour_smoothed += bezier_points
sub_contours_smoothed.append(sub_contour_smoothed)
# 12. Merge sub-contours together
img_smoothed_gray = createBlankGrayscaleImage(component)
# merge all smoothed sub-contours
for sub in sub_contours_smoothed:
for pt in sub:
img_smoothed_gray[pt[1]][pt[0]] = 0.0
# process smoothed contours to get closed and 1-pixel width
img_smoothed_gray = getSkeletonOfImage(img_smoothed_gray)
# remove single points that 8
cv2.imshow("img_smoothed_gray", img_smoothed_gray)
contours_smoothed = getConnectedComponents(img_smoothed_gray)
if len(contours_smoothed) == 1:
# no hole exist, directly fill black in the contour
cont = contours_smoothed[0]
cont_points = sortPointsOnContourOfImage(cont)
cont_points = np.array([cont_points], "int32")
fill_contour_smooth = np.ones_like(component) * 255
fill_contour_smooth = np.array(fill_contour_smooth, dtype=np.uint8)
fill_contour_smooth = cv2.fillPoly(fill_contour_smooth, cont_points, 0)
return fill_contour_smooth
else:
# exist hole, should processed
print("there are holes!")
fill_img_list = []
hole_points = []
for cont in contours_smoothed:
cont_points = sortPointsOnContourOfImage(cont)
cont_points = np.array([cont_points], "int32")
fill_contour_smooth = np.ones_like(component) * 255
fill_contour_smooth = np.array(fill_contour_smooth, dtype=np.uint8)
fill_contour_smooth = cv2.fillPoly(fill_contour_smooth,
cont_points, 0)
valid_num = same_num = 0
for y in range(component.shape[0]):
for x in range(component.shape[1]):
if component[y][x] == 0.0:
valid_num += 1
if fill_contour_smooth[y][x] == 0.0:
same_num += 1
if 1.0 * same_num / valid_num > 0.8:
fill_img_list.append(fill_contour_smooth)
print("ratio: %f" % (1.0 * same_num / valid_num))
else:
print("ratio: %f" % (1.0 * same_num / valid_num))
for y in range(fill_contour_smooth.shape[0]):
for x in range(fill_contour_smooth.shape[1]):
if fill_contour_smooth[y][x] == 0.0:
hole_points.append((x, y))
# merge filled images
blank_temp = np.ones_like(component) * 255
for fl in fill_img_list:
for y in range(fl.shape[0]):
for x in range(fl.shape[1]):
if fl[y][x] == 0.0:
blank_temp[y][x] = fl[y][x]
# hole points
for pt in hole_points:
blank_temp[pt[1]][pt[0]] = 255
return blank_temp
def autoStrokeExtracting(index, image, threshold_value=200):
"""
Automatic strokes extracting
:param image: grayscale image
:return: strokes images with same size
"""
strokes = []
if image is None:
return strokes
# get connected components from the grayscale image, not for the binary image.
contour_img = getContourImage(image)
contours = getConnectedComponents(contour_img) # no holes, num=1, holes exist, num >= 2
print("contours num: %d" % len(contours))
corners_points_sorted = []
for ct in contours:
points = sortPointsOnContourOfImage(ct)
corners_points_sorted.append(points)
if len(corners_points_sorted) == 1:
print("No holes exist!")
elif len(corners_points_sorted) >= 2:
print("Holes exist!")
# grayscale image to binary image
_, img_bit = cv2.threshold(image, threshold_value, 255, cv2.THRESH_BINARY)
# skeleton image of width 1 pixel of binray image
skeleton_img = getSkeletonOfImage(img_bit)
skeleton_img = removeExtraBranchesOfSkeleton(skeleton_img)
end_points = getEndPointsOfSkeletonLine(skeleton_img) # end points
cross_points = getCrossPointsOfSkeletonLine(skeleton_img) # croiss points
print("end points num: %d" % len(end_points))
print("cross points num: %d" % len(cross_points))
if len(cross_points) == 0:
print("no cross points!")
strokes.append(image)
return strokes
# corner area points
corners_all_points = getCornersPoints(image.copy(), contour_img, blockSize=3, ksize=3, k=0.04)
corners_points = getValidCornersPoints(corners_all_points, cross_points, end_points, distance_threshold=30)
print("corners points num: %d" % len(corners_points))
if len(corners_points) == 0:
print("no corner point")
strokes.append(image)
return strokes
contour_rgb = cv2.cvtColor(contour_img, cv2.COLOR_GRAY2RGB)
contour_gray = cv2.cvtColor(contour_rgb, cv2.COLOR_RGB2GRAY)
_, contour_gray = cv2.threshold(contour_gray, 200, 255, cv2.THRESH_BINARY)
for pt in corners_points:
contour_rgb[pt[1]][pt[0]] = (0, 0, 255)
# cluster corners points based on the cross point
corner_points_cluster = getClusterOfCornerPoints(corners_points, cross_points)
# cropping lines based on the corner points
crop_lines = getCropLines(corner_points_cluster, None)
for line in crop_lines:
cv2.line(contour_rgb, line[0], line[1], (0, 255, 0), 1)
cv2.line(contour_gray, line[0], line[1], 0, 1)
# split contour to components
ret, labels = cv2.connectedComponents(contour_gray, connectivity=4)
components = []
for r in range(1, ret):
img_ = createBlankGrayscaleImage(contour_gray)
for y in range(contour_gray.shape[0]):
for x in range(contour_gray.shape[1]):
if labels[y][x] == r:
img_[y][x] = 0.0
if img_[0][0] != 0.0 and isValidComponent(img_, img_bit):
components.append(img_)
print("components num : %d" % len(components))
used_components = []
component_line_relation = {} # {component_id: [line_id1, line_id2]}
# merge contour to components
for i in range(len(components)):
merge_points = []
for y in range(1, contour_gray.shape[0]-1):
for x in range(1, contour_gray.shape[1]-1):
if contour_gray[y][x] == 0.0:
# 4 nearby pixels should be black in components
if components[i][y-1][x] == 0.0 or components[i][y][x+1] == 0.0 or components[i][y+1][x] == 0.0 or \
components[i][y][x-1] == 0.0:
merge_points.append((x, y))
for pt in merge_points:
components[i][pt[1]][pt[0]] = 0.0
# merge cropping lines on the components
for i in range(len(crop_lines)):
components_id = []
line = crop_lines[i]
for j in range(len(components)):
dist_startpt = getDistanceBetweenPointAndComponent(line[0], components[j])
# print("dist startpt:%f" % dist_startpt)
dist_endpt = getDistanceBetweenPointAndComponent(line[1], components[j])
# print("dist end pt: %f" % dist_endpt)
if dist_startpt < 3 and dist_endpt < 3:
cv2.line(components[j], line[0], line[1], 0, 1)
components_id.append(j)
if len(components_id) >= 2:
break
# find overlap region components
overlap_components = []
for i in range(len(components)):
part = components[i]
part_lines = []
part_lines_id = []
for j in range(len(crop_lines)):
line = crop_lines[j]
if part[line[0][1]][line[0][0]] == 0.0 and part[line[1][1]][line[1][0]] == 0.0:
part_lines.append(line)
part_lines_id.append(j)
# check number of lines == 4 and cross each other
if len(part_lines) == 4:
points_set = set()
for line in part_lines:
points_set.add(line[0])
points_set.add(line[1])
if len(points_set) == 4:
# only 4 points
overlap_components.append(part)
used_components.append(i)
component_line_relation[i] = part_lines_id
print("overlap components num: %d" % len(overlap_components))
print(component_line_relation)
# cluster components based on the cropping lines
for i in range(len(components)):
part = components[i]
part_lines = [] # used to detect overlap region components.
# find single part is stroke
is_single = True
for line in crop_lines:
x1 = line[0][0]; y1 = line[0][1]
x2 = line[1][0]; y2 = line[1][1]
if part[y1][x1] == 0.0 and part[y2][x2] != 0.0 or part[y1][x1] != 0.0 and part[y2][x2] == 0.0:
is_single = False
break
if part[y1][x1] == 0.0 and part[y2][x2] == 0.0:
part_lines.append(line)
if is_single and isIndependentCropLines(part_lines):
strokes.append(part)
used_components.append(i)
print("single stroke num: %d" % len(strokes))
print("used components num: %d" % len(used_components))
# cluster components based on the cropping lines
for i in range(len(components)):
if i in used_components:
continue
# find corresponding crop lines
lines_id = []
for j in range(len(crop_lines)):
line = crop_lines[j]
if components[i][line[0][1]][line[0][0]] == 0.0 and components[i][line[1][1]][line[1][0]] != 0.0:
lines_id.append(j)
if components[i][line[0][1]][line[0][0]] != 0.0 and components[i][line[1][1]][line[1][0]] == 0.0:
lines_id.append(j)
if len(lines_id) == 0:
continue
component_line_relation[i] = lines_id
used_components.append(i)
# cluster components based on the relations and merge those related components
clusters = []
for k1, v1 in component_line_relation.items():
cluster = [k1]; value_sets = [set(v1)]
for k2, v2 in component_line_relation.items():
is_related = True
for value in value_sets:
if not value.intersection(set(v2)):
is_related = False
break
if is_related and k2 not in cluster:
cluster.append(k2)
value_sets.append(set(v2))
cluster = sorted(cluster)
if cluster not in clusters:
clusters.append(cluster)
print(clusters)
# merge components based on the cluster
for i in range(len(clusters)):
cluster = clusters[i]
bk = createBlankGrayscaleImage(image)
for clt in cluster:
bk = mergeBkAndComponent(bk, components[clt])
# add to strokes
strokes.append(bk)
# check the stroke is valid
for i in range(len(strokes)):
stroke = strokes[i]
cv2.imshow("radical_%d" % index, contour_rgb)
cv2.imshow("radical_gray_%d" % index, contour_gray)
return strokes
def main():
src_path = "../strokes/src_strokes7.png"
tag_path = "../strokes/tag_strokes7.png"
src_img = cv2.imread(src_path, 0)
tag_img = cv2.imread(tag_path, 0)
# threshold
_, src_img = cv2.threshold(src_img, 127, 255, cv2.THRESH_BINARY)
_, tag_img = cv2.threshold(tag_img, 127, 255, cv2.THRESH_BINARY)
# get the minimum bounding boxs
src_box = getSingleMaxBoundingBoxOfImage(src_img)
tag_box = getSingleMaxBoundingBoxOfImage(tag_img)
# get the region of strokes
src_region = src_img[src_box[1] - 5:src_box[1] + src_box[3] + 5,
src_box[0] - 5:src_box[0] + src_box[2] + 5]
tag_region = tag_img[tag_box[1] - 5:tag_box[1] + tag_box[3] + 5,
tag_box[0] - 5:tag_box[0] + tag_box[2] + 5]
# get the contour of storkes based on the Canny algorithm
src_edge = getContourOfImage(src_region)
tag_edge = getContourOfImage(tag_region)
cv2.imshow("src edge", src_edge)
cv2.imshow("tag edge", tag_edge)
# get the skeletons of strokes based on the thinning algorithm
src_img_ = src_region != 255
tag_img_ = tag_region != 255
src_skel = skeletonize(src_img_)
tag_skel = skeletonize(tag_img_)
src_skel = (1 - src_skel) * 255
tag_skel = (1 - tag_skel) * 255
src_skel = np.array(src_skel, dtype=np.uint8)
tag_skel = np.array(tag_skel, dtype=np.uint8)
src_end_points = getEndPointsOfSkeletonLine(src_skel)
tag_end_points = getEndPointsOfSkeletonLine(tag_skel)
src_cross_points = getCrossPointsOfSkeletonLine(src_skel)
tag_cross_points = getCrossPointsOfSkeletonLine(tag_skel)
# if len(src_cross_points) > 0:
# # exist branches
# src_skel = removeBranchOfSkeletonLine(src_skel, src_end_points, src_cross_points)
#
# if len(tag_cross_points) > 0:
# # exist branches
# tag_skel = removeBranchOfSkeletonLine(tag_skel, tag_end_points, tag_cross_points)
cv2.imshow("src skel", src_skel)
cv2.imshow("tag skel", tag_skel)
# split the strokes based on the rule: begin, middle and the end parts.
src_regions = splitStrokes(src_region, type="LongHeng")
tag_regions = splitStrokes(tag_region, type="LongHeng")
print('len src regions: %d' % len(src_regions))
print('len tag regions: %d' % len(tag_regions))
cv2.imshow('src begin', src_regions[0])
cv2.imshow('src middle', src_regions[1])
cv2.imshow('src end', src_regions[2])
cv2.imshow("src", src_region)
cv2.imshow("tag", tag_region)
cv2.waitKey(0)
cv2.destroyAllWindows()
def autoStrokeExtractFromComponent(component):
"""
Automatically strokes extract from the component.
:param component:
:return:
"""
strokes = []
if component is None:
return strokes
# 6. Get skeletons of component.
comp_skeleton = getSkeletonOfImage(component.copy())
# cv2.imshow("skeleton_original", comp_skeleton)
# 7. Process the skeleton by remove extra branches.
comp_skeleton = removeShortBranchesOfSkeleton(comp_skeleton, length_threshold=30)
# cv2.imshow("skeleton_smoothed", comp_skeleton)
# 8. Get the end points and cross points after skeleton processed
end_points = getEndPointsOfSkeletonLine(comp_skeleton)
cross_points = getCrossPointsOfSkeletonLine(comp_skeleton)
print("end points num: %d ,and cross points num: %d" % (len(end_points), len(cross_points)))
# 9. Get contour image of component
comp_contours_img = getContourImage(component.copy())
# 10. Detect the number of contours and return all contours
comp_contours = getConnectedComponents(comp_contours_img, connectivity=8)
print("contours num: %d" % len(comp_contours))
# 11. Get points on contours
corners_points = []
for cont in comp_contours:
cont = removeBreakPointsOfContour(cont)
cont_sorted = sortPointsOnContourOfImage(cont)
cont_points = rdp(cont_sorted, 5)
corners_points += cont_points
print("corner points num:", len(corners_points))
CORNER_CROSS_DIST_THRESHOLD = 30
corners_points_merged = []
for pt in corners_points:
for cpt in cross_points:
dist = math.sqrt((pt[0] - cpt[0]) ** 2 + (pt[1] - cpt[1]) ** 2)
if dist < CORNER_CROSS_DIST_THRESHOLD:
corners_points_merged.append(pt)
break
corners_points = corners_points_merged
print("merged corner points num:", len(corners_points))
# # 11. Detect the corner regions of component
# # Harris corner detector
# corner_region_img = np.float32(component.copy())
# dst = cv2.cornerHarris(corner_region_img, blockSize=3, ksize=3, k=0.04)
# dst = cv2.dilate(dst, None)
#
# # get all points in corners area
# corners_area_points = []
# for y in range(dst.shape[0]):
# for x in range(dst.shape[1]):
# if dst[y][x] > 0.1 * dst.max():
# corners_area_points.append((x, y))
#
# # get all center points of corner area
# corners_img = createBlankGrayscaleImage(component)
# for pt in corners_area_points:
# corners_img[pt[1]][pt[0]] = 0.0
#
# rectangles = getAllMiniBoundingBoxesOfImage(corners_img)
#
# corners_area_center_points = []
# for rect in rectangles:
# corners_area_center_points.append((rect[0] + int(rect[2] / 2.), rect[1] + int(rect[3] / 2.)))
#
# # get all corner points in coutour image.
# corners_points = []
# for pt in corners_area_center_points:
# if comp_contours_img[pt[1]][pt[0]] == 0.0:
# corners_points.append(pt)
# else:
# min_dist = 100000
# min_x = min_y = 0
# for y in range(comp_contours_img.shape[0]):
# for x in range(comp_contours_img.shape[1]):
# cpt = comp_contours_img[y][x]
# if cpt == 0.0:
# dist = math.sqrt((x - pt[0]) ** 2 + (y - pt[1]) ** 2)
# if dist < min_dist:
# min_dist = dist
# min_x = x
# min_y = y
# # points on contour
# corners_points.append((min_x, min_y))
print("corners points num: %d" % len(corners_points))
# 12. Get valid corner points based on the end points and cross points
corners_points = getValidCornersPoints(corners_points, cross_points, end_points, distance_threshold=30)
print("corners points num: %d" % len(corners_points))
if len(corners_points) == 0:
print("no corner points")
strokes.append(component)
return strokes
# 13. Cluster these corner points based on the distance between them and cross points
corners_points_cluster = getClusterOfCornerPoints(corners_points, cross_points, threshold_distance=70)
print("corner points cluster num: %d" % len(corners_points_cluster))
print(corners_points_cluster)
# 14. Generate cropping lines between two corner points
crop_lines = getCropLines(corners_points_cluster, comp_contours)
print("cropping lines num: %d" % len(crop_lines))
# 15. Separate the components based on the cropping lines
component_ = component.copy()
# add white and 1-pixel width line in component to separate it.
for line in crop_lines:
cv2.line(component_, line[0], line[1], 255, 1)
# 16. Get parts of component.
comp_parts = []
# invert color !!!
component_ = 255 - component_
ret, labels = cv2.connectedComponents(component_, connectivity=4)
print(ret)
for r in range(1, ret):
img_ = createBlankGrayscaleImage(component_)
for y in range(component_.shape[0]):
for x in range(component_.shape[1]):
if labels[y][x] == r:
img_[y][x] = 0.0
if img_[0][0] != 0.0 and isValidComponent(img_, component):
comp_parts.append(img_)
print("parts of component num: %d" % len(comp_parts))
# 17. Add cropping lines to corresponding parts of component
# add lines to parts of component.
for i in range(len(comp_parts)):
part = comp_parts[i]
for line in crop_lines:
start_dist = getDistanceBetweenPointAndComponent(line[0], part)
end_dist = getDistanceBetweenPointAndComponent(line[1], part)
if start_dist <= 3 and end_dist <= 3:
cv2.line(part, line[0], line[1], 0, 1)
# 18. Find intersection parts of component
used_index = []
intersect_parts_index = []
for i in range(len(comp_parts)):
part = comp_parts[i]
num = 0 # number of lines in part
for line in crop_lines:
if part[line[0][1]][line[0][0]] == 0.0 and part[line[1][1]][line[1][0]] == 0.0:
num += 1
if num == 4: # 4 lines in one part, this part is the intersection part
intersect_parts_index.append(i)
used_index.append(i)
print("intersection parts num: %d" % len(intersect_parts_index))
# 19. Find the relation part and crop lines - one line -> one part or three part (part1 + intersect_part + part2)
intersect_parts_crop_lines_index = []
for i in range(len(crop_lines)):
line = crop_lines[i]
for index in intersect_parts_index:
intersect_part = comp_parts[index]
if intersect_part[line[0][1]][line[0][0]] == 0.0 and intersect_part[line[1][1]][line[1][0]] == 0.0:
# this line in intersection part
intersect_parts_crop_lines_index.append(i)
print("crop lines in intersection part num: %d" % len(intersect_parts_crop_lines_index))
# Cropping lines are divided into two types: in intersect part and not in this part.
line_parts_relation = []
for index in intersect_parts_crop_lines_index:
line = crop_lines[index]
# line and parts that are connected by this crop line: A - intersect_part - B
line_connected_parts = []
# find intersection part contains this line
for i in intersect_parts_index:
intersect_part = comp_parts[i]
if intersect_part[line[0][1]][line[0][0]] == 0.0 and intersect_part[line[1][1]][line[1][0]] == 0.0:
# line in this intersect part
line_connected_parts.append(i)
# find two parts connectd by this crop line
for i in range(len(comp_parts)):
# part should not be the intersect part
if i in intersect_parts_index:
continue
# check only end point of line in part.
part = comp_parts[i]
if part[line[0][1]][line[0][0]] == 0.0 and part[line[1][1]][line[1][0]] != 0.0 or \
part[line[0][1]][line[0][0]] != 0.0 and part[line[1][1]][line[1][0]] == 0.0:
line_connected_parts.append(i)
# add line connected parts to relation list.
if line_connected_parts not in line_parts_relation:
line_parts_relation.append(line_connected_parts)
# add independent parts to relation of line and parts
for i in range(len(comp_parts)):
line_connected_parts = []
is_independent = True
for relation in line_parts_relation:
if i in relation:
is_independent = False
# check this part is independent or not
if is_independent:
line_connected_parts.append(i)
if line_connected_parts != []:
line_parts_relation.append(line_connected_parts)
# 20. Merge parts based on the line parts relation
for i in range(len(line_parts_relation)):
# blank image
blank_ = createBlankGrayscaleImage(component)
# parts relation list
relation = line_parts_relation[i]
for rel in relation:
part = comp_parts[rel]
if part is None:
continue
# merge part and blank image
for y in range(part.shape[0]):
for x in range(part.shape[1]):
if part[y][x] == 0.0:
blank_[y][x] = 0.0
# add to strokes list
strokes.append(blank_)
return strokes
def main():
# 0107亻 1133壬 0554十 0427凹
path = "0554十.jpg"
# open image
img = cv2.imread(path, 0)
_, img = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY)
img_rgb = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
# contour without break points
contour = getContourOfImage(img.copy())
contour = removeBreakPointsOfContour(contour)
contour_rgb = cv2.cvtColor(contour, cv2.COLOR_GRAY2RGB)
contours = splitConnectedComponents(contour)
print("contours num: %d" % len(contours))
contours_sorted = []
for cont in contours:
points = sortPointsOnContourOfImage(cont)
print("points num: %d" % len(points))
contours_sorted.append(points)
contour_points = []
for y in range(contour.shape[0]):
for x in range(contour.shape[1]):
if contour[y][x] == 0.0:
# black points
contour_points.append((x, y))
print("contour points num:%d" % len(contour_points))
# skeleton without extra branches
skeleton = getSkeletonOfImage(img.copy())
# remove extra branches
end_points = getEndPointsOfSkeletonLine(skeleton)
cross_points = getCrossPointsOfSkeletonLine(skeleton)
print("originale end: %d and cross: %d" %
(len(end_points), len(cross_points)))
skeleton_nobranches = removeBranchOfSkeletonLine(skeleton.copy(),
end_points, cross_points)
skeleton = skeleton_nobranches
# new end points and cross points
end_points = getEndPointsOfSkeletonLine(skeleton)
cross_points = getCrossPointsOfSkeletonLine(skeleton)
cross_points_bk = cross_points.copy()
# merge the close points
cross_points_merged = []
cross_distance_threshold = 10
used_index = []
for i in range(len(cross_points)):
if i in used_index:
continue
pt1 = cross_points[i]
midd_pt = None
used_index.append(i)
for j in range(len(cross_points)):
if i == j or j in used_index:
continue
pt2 = cross_points[j]
dist = math.sqrt((pt2[0] - pt1[0])**2 + (pt2[1] - pt1[1])**2)
if dist < cross_distance_threshold:
used_index.append(j)
offset = (pt1[0] - pt2[0], pt1[1] - pt2[1])
print(offset)
midd_pt = (pt2[0] + int(offset[0] / 2.),
pt2[1] + int(offset[1] / 2.0))
if skeleton[midd_pt[1]][midd_pt[0]] == 0.0:
cross_points_merged.append(midd_pt)
else:
min_distance = 100000000
current_pt = None
for y in range(skeleton.shape[0]):
for x in range(skeleton.shape[1]):
if skeleton[y][x] == 0:
dist = math.sqrt((midd_pt[0] - x)**2 +
(midd_pt[1] - y)**2)
if dist < min_distance:
min_distance = dist
current_pt = (x, y)
if current_pt:
cross_points_merged.append(current_pt)
print("After merge cross points num: %d" % len(cross_points_merged))
cross_points = cross_points_merged
print("After end: %d and cross: %d" % (len(end_points), len(cross_points)))
skeleton_rgb = cv2.cvtColor(skeleton, cv2.COLOR_GRAY2RGB)
# display all end points
for pt in end_points:
skeleton_rgb[pt[1]][pt[0]] = (0, 0, 255)
for pt in cross_points:
skeleton_rgb[pt[1]][pt[0]] = (0, 255, 0)
for pt in cross_points_bk:
skeleton_rgb[pt[1]][pt[0]] = (0, 0, 255)
# all corner points on contour
img = np.float32(img.copy())
dst = cv2.cornerHarris(img, 3, 3, 0.03)
dst = cv2.dilate(dst, None)
corners_area_points = []
for y in range(dst.shape[0]):
for x in range(dst.shape[1]):
if dst[y][x] > 0.1 * dst.max():
corners_area_points.append((x, y))
# show the corner points
for pt in corners_area_points:
if img[pt[1]][pt[0]] == 0:
img_rgb[pt[1]][pt[0]] = (0, 255, 0)
else:
img_rgb[pt[1]][pt[0]] = (0, 0, 255)
# all corner area points on the contour
corners_lines_points = []
for pt in corners_area_points:
if pt in contour_points:
corners_lines_points.append(pt)
for pt in corners_lines_points:
contour_rgb[pt[1]][pt[0]] = (0, 255, 0)
# merge points of corner points
corners_merged_points = []
for contour_sorted in contours_sorted:
i = 0
while True:
midd_index = -1
pt = contour_sorted[i]
if pt in corners_lines_points:
# red point
start = i
end = start
while True:
end += 1
if end >= len(contour_sorted):
break
# next point
next_pt = contour_sorted[end]
if next_pt in corners_lines_points:
# red point
continue
else:
# black point
break
end -= 1
midd_index = start + int((end - start) / 2.0)
i = end
i += 1
if i >= len(contour_sorted):
break
if midd_index != -1:
corners_merged_points.append(contour_sorted[midd_index])
print("After merged, corner points num: %d" % len(corners_merged_points))
for pt in corners_merged_points:
contour_rgb[pt[1]][pt[0]] = (0, 0, 255)
# remove the no-corner points
corners_points = []
threshold_distance = 30
for pt in corners_merged_points:
dist_cross = min_distance_point2pointlist(pt, cross_points)
dist_end = min_distance_point2pointlist(pt, end_points)
if dist_cross < threshold_distance and dist_end > threshold_distance / 3.:
corners_points.append(pt)
print("corner pints num: %d" % len(corners_points))
for pt in corners_points:
contour_rgb[pt[1]][pt[0]] = (255, 0, 0)
# segment contour to sub-contours based on the corner points
def segmentContourBasedOnCornerPoints(contour_sorted, corner_points):
"""
Segment contour to sub-contours based on the corner points
:param contour_sorted:
:param corner_points:
:return:
"""
if contour_sorted is None or corner_points is None:
return
# sub conotour index
sub_contour_index = []
for pt in corner_points:
index = contour_sorted.index(pt)
sub_contour_index.append(index)
print("sub contour index num: %d" % len(sub_contour_index))
sub_contours = []
for i in range(len(sub_contour_index)):
if i == len(sub_contour_index) - 1:
sub_contour = contour_sorted[sub_contour_index[i]:len(
contour_sorted)] + contour_sorted[0:sub_contour_index[0] +
1]
else:
sub_contour = contour_sorted[
sub_contour_index[i]:sub_contour_index[i + 1] + 1]
sub_contours.append(sub_contour)
print("sub contours num: %d" % len(sub_contours))
return sub_contours
# segment contour to sub-contours
for contour in contours:
cont_sorted = sortPointsOnContourOfImage(contour)
sub_contours = segmentContourBasedOnCornerPoints(
cont_sorted, corners_points)
# cluster corner points
corner_points_cluster = []
used_index = []
colinear_couple = []
for i in range(len(corners_points)):
if i in used_index:
continue
for j in range(len(corners_points)):
if i == j or j in used_index:
continue
min_offset = min(abs(corners_points[i][0] - corners_points[j][0]),
abs(corners_points[i][1] - corners_points[j][1]))
if min_offset < 20:
couple = [corners_points[i], corners_points[j]]
colinear_couple.append(couple)
used_index.append(j)
print("co linear num: %d" % len(colinear_couple))
print("sub contours num: %d" % len(sub_contours))
stroke1_img = np.ones_like(contour) * 255
stroke1_img = np.array(stroke1_img, dtype=np.uint8)
stroke1_img_rgb = cv2.cvtColor(stroke1_img, cv2.COLOR_GRAY2RGB)
for pt in sub_contours[0]:
stroke1_img_rgb[pt[1]][pt[0]] = (0, 0, 0)
stroke1_img[pt[1]][pt[0]] = 0
for pt in sub_contours[2]:
stroke1_img_rgb[pt[1]][pt[0]] = (0, 0, 0)
stroke1_img[pt[1]][pt[0]] = 0
cv2.line(stroke1_img_rgb, sub_contours[0][0], sub_contours[2][-1],
(0, 0, 255), 1)
cv2.line(stroke1_img_rgb, sub_contours[0][-1], sub_contours[2][0],
(0, 0, 255), 1)
cv2.line(stroke1_img, sub_contours[0][0], sub_contours[2][-1], 0, 1)
cv2.line(stroke1_img, sub_contours[0][-1], sub_contours[2][0], 0, 1)
stroke2_img = np.ones_like(contour) * 255
stroke2_img = np.array(stroke2_img, dtype=np.uint8)
stroke2_img_rgb = cv2.cvtColor(stroke2_img, cv2.COLOR_GRAY2RGB)
for pt in sub_contours[1]:
stroke2_img_rgb[pt[1]][pt[0]] = (0, 0, 0)
stroke2_img[pt[1]][pt[0]] = 0
for pt in sub_contours[3]:
stroke2_img_rgb[pt[1]][pt[0]] = (0, 0, 0)
stroke2_img[pt[1]][pt[0]] = 0
cv2.line(stroke2_img_rgb, sub_contours[1][0], sub_contours[3][-1],
(0, 0, 255), 1)
cv2.line(stroke2_img_rgb, sub_contours[1][-1], sub_contours[3][0],
(0, 0, 255), 1)
cv2.line(stroke2_img, sub_contours[1][0], sub_contours[3][-1], 0, 1)
cv2.line(stroke2_img, sub_contours[1][-1], sub_contours[3][0], 0, 1)
storke1_points = sortPointsOnContourOfImage(stroke1_img)
stroke2_points = sortPointsOnContourOfImage(stroke2_img)
stroke1_img = np.ones_like(stroke1_img) * 255
stroke1_img = np.array(stroke1_img, dtype=np.uint8)
storke1_points = np.array([storke1_points], "int32")
cv2.fillPoly(stroke1_img, storke1_points, 0)
stroke2_img = np.ones_like(stroke2_img) * 255
stroke2_img = np.array(stroke2_img, dtype=np.uint8)
storke2_points = np.array([stroke2_points], "int32")
cv2.fillPoly(stroke2_img, storke2_points, 0)
# find corresponding sub-contours based on the co-linear couple
# for sub in sub_contours:
# pt1 = sub[0]
# pt2 = sub[-1]
#
# couples = []
# for coup in colinear_couple:
# if pt1 in coup or pt2 in coup:
# # if 4 points, 2 points should be in same sub-contour
# if pt1 in coup and pt2 in coup:
# continue
# couples.append(coup)
# print("sub couples num: %d" % len(couples))
# cv2.imshow("img rgb", img_rgb)
# cv2.imshow("skeleton", skeleton)
# cv2.imshow("skeleton no branches", skeleton_nobranches )
cv2.imshow("skeleton rgb", skeleton_rgb)
cv2.imshow("contour rgb", contour_rgb)
cv2.imshow("stroke 1", stroke1_img)
cv2.imshow("stroke 2", stroke2_img)
cv2.imshow("stroke1rgb", stroke1_img_rgb)
cv2.imshow("stroke2rgb", stroke2_img_rgb)
# for i in range(len(contours)):
# cv2.imshow("contour %d" % i, contours[i])
cv2.waitKey(0)
cv2.destroyAllWindows()
def main():
# Point
Point = namedtuple("Point", ["x", "y"])
# target image
target_path = "../templates/ben.png"
target_img = cv2.imread(target_path, 0)
_, target_img = cv2.threshold(target_img, 127, 255, cv2.THRESH_BINARY)
print(target_img.shape)
target_img_rgb = cv2.cvtColor(target_img, cv2.COLOR_GRAY2RGB)
# connected components with labeling algorithm
partial_parts = splitConnectedComponents(target_img)
print("number of parts: %d" % len(partial_parts))
# part 1
part_1 = partial_parts[0]
# skeleton line
part_1_ = part_1 != 255
part_skel = skeletonize(part_1_)
part_skel = (1 - part_skel) * 255
part_skel = np.array(part_skel, dtype=np.uint8)
for y in range(part_skel.shape[0]):
for x in range(part_skel.shape[1]):
if part_skel[y][x] == 0.0:
target_img_rgb[y][x] = (0, 255, 0)
# remove extra branch
end_points = getEndPointsOfSkeletonLine(part_skel)
cross_points = getCrossPointsOfSkeletonLine(part_skel)
part_skel = removeBranchOfSkeletonLine(
part_skel,
end_points,
cross_points,
)
part_1_rgb_no_branch = cv2.cvtColor(part_1, cv2.COLOR_GRAY2RGB)
for y in range(part_skel.shape[0]):
for x in range(part_skel.shape[1]):
if part_skel[y][x] == 0.0:
part_1_rgb_no_branch[y][x] = (0, 255, 0)
# new end points and cross points without extra branches
end_points = getEndPointsOfSkeletonLine(part_skel)
print("number of end points: %d" % len(end_points))
cross_points = getCrossPointsOfSkeletonLine(part_skel)
print("number of cross points: %d" % len(cross_points))
# add end points to image with blue color
for (x, y) in end_points:
part_1_rgb_no_branch[y][x] = (255, 0, 0)
# add cross points to image with red color
for (x, y) in cross_points:
part_1_rgb_no_branch[y][x] = (0, 0, 255)
# Contour of character
part_1_edges = cv2.Canny(part_1, 100, 200)
part_1_edges = 255 - part_1_edges
# Order the points on contours of character
print(part_1_edges.shape)
begin_point = None
# find the begin point
for y in range(part_1_edges.shape[0]):
for x in range(part_1_edges.shape[1]):
if part_1_edges[y][x] == 0.0:
# first black point
begin_point = (x, y)
break
if begin_point:
break
print("begin point: (%d, %d)" % (begin_point[0], begin_point[1]))
edge_order_lables = np.zeros_like(part_1_edges)
edge_order_lables[begin_point[1]][begin_point[0]] = 1.
curr_point = begin_point
# find the second point
if part_1_edges[y][x + 1] == 0.0:
print("Second point is 4 position")
curr_point = (x + 1, y)
elif part_1_edges[y + 1][x + 1] == 0.0:
print("Second point is 5 position")
curr_point = (x + 1, y + 1)
print(curr_point)
edge_order_lables[curr_point[1]][curr_point[0]] = 1.
edge_points = []
edge_points.append(begin_point)
edge_points.append(curr_point)
next_point = curr_point
edge_id = 0
while True:
x = curr_point[0]
y = curr_point[1]
# 2,4,6,8 position firstly and then 3,5,7,9 position
# point in 2 position
if part_1_edges[y - 1][x] == 0.0 and edge_order_lables[y -
1][x] == 0.0:
print("%d po" % 2)
next_point = (x, y - 1)
edge_order_lables[y - 1][x] = 1.
# point in 4 position
elif part_1_edges[y][x + 1] == 0.0 and edge_order_lables[y][x +
1] == 0.0:
print("%d po" % 4)
next_point = (x + 1, y)
edge_order_lables[y][x + 1] = 1.
# point in 6 position
elif part_1_edges[y + 1][x] == 0.0 and edge_order_lables[y +
1][x] == 0.0:
print("%d po" % 6)
next_point = (x, y + 1)
edge_order_lables[y + 1][x] = 1.
# point in 8 position
elif part_1_edges[y][x - 1] == 0.0 and edge_order_lables[y][x -
1] == 0.0:
print("%d po" % 8)
next_point = (x - 1, y)
edge_order_lables[y][x - 1] = 1.
# point in 3 position
elif part_1_edges[y - 1][x + 1] == 0.0 and edge_order_lables[y - 1][
x + 1] == 0.0:
print("%d po" % 3)
next_point = (x + 1, y - 1)
edge_order_lables[y - 1][x + 1] = 1.
# point in 5 position
elif part_1_edges[y + 1][x + 1] == 0.0 and edge_order_lables[y + 1][
x + 1] == 0.0:
print("%d po" % 5)
next_point = (x + 1, y + 1)
edge_order_lables[y + 1][x + 1] = 1.
# point in 7 position
elif part_1_edges[y + 1][x - 1] == 0.0 and edge_order_lables[y + 1][
x - 1] == 0.0:
print("%d po" % 7)
next_point = (x - 1, y + 1)
edge_order_lables[y + 1][x - 1] = 1.
# point in 9 position
elif part_1_edges[y - 1][x - 1] == 0.0 and edge_order_lables[y - 1][
x - 1] == 0.0:
print("%d po" % 9)
next_point = (x - 1, y - 1)
edge_order_lables[y - 1][x - 1] = 1.
if next_point == curr_point:
print(next_point)
print(curr_point)
edge_points.append(curr_point)
break
else:
edge_points.append(curr_point)
edge_id += 1
print("edge id: %d" % edge_id)
curr_point = next_point
print("edge points len: %d" % len(edge_points))
for pt in edge_points:
print(pt)
part_1_rgb_no_branch[pt[1]][pt[0]] = (0, 0, 255)
# # houngh lines
# rho_resolution = 1
# theta_resolution = np.pi / 180
# threshold = 155
# hough_lines = cv2.HoughLines(part_1_edges, rho_resolution, theta_resolution, threshold)
#
# print("number of hough lines: %d " % len(hough_lines))
#
# hough_lines_img = np.zeros_like(part_1_edges)
# draw_lines(hough_lines_img, hough_lines)
# original_image_with_hough_lines = weighted_img(hough_lines_img, part_1_edges)
#
# cv2.imshow("hough line image", hough_lines_img)
# cv2.imshow("original hough", original_image_with_hough_lines)
# # Corner detection
# corners = cv2.goodFeaturesToTrack(part_1_edges, 100, 0.01, 10)
# corners = np.int0(corners)
#
# for i in corners:
# x, y = i.ravel()
# cv2.circle(part_1_rgb_no_branch, (x, y), 3, 255, -1)
cv2.imshow("img", target_img)
# cv2.imshow("skel", part_skel)
# cv2.imshow("img_rgb", target_img_rgb)
cv2.imshow("img_rgb_no_branch", part_1_rgb_no_branch)
cv2.imshow("edges", part_1_edges)
cv2.imwrite("../templates/ben_skeleton.png", target_img_rgb)
cv2.imwrite("../templates/ben_skeleton_no_branch.png",
part_1_rgb_no_branch)
cv2.waitKey(0)
cv2.destroyAllWindows()
def main():
# src_path = "../strokes/src_strokes4.png"
src_path = "../chars/src_dan_svg_simple_resized.png"
tag_path = "../strokes/tag_strokes4.png"
src_img = cv2.imread(src_path, 0)
tag_img = cv2.imread(tag_path, 0)
ret, src_img = cv2.threshold(src_img, 127, 255, cv2.THRESH_BINARY)
ret, tag_img = cv2.threshold(tag_img, 127, 255, cv2.THRESH_BINARY)
# resize
src_img, tag_img = resizeImages(src_img, tag_img)
ret, src_img = cv2.threshold(src_img, 127, 255, cv2.THRESH_BINARY)
ret, tag_img = cv2.threshold(tag_img, 127, 255, cv2.THRESH_BINARY)
# obtain the skeleton of strokes
src_img_ = src_img != 255
tag_img_ = tag_img != 255
src_skel = skeletonize(src_img_)
tag_skel = skeletonize(tag_img_)
src_skel = (1 - src_skel) * 255
tag_skel = (1 - tag_skel) * 255
src_skel = np.array(src_skel, dtype=np.uint8)
tag_skel = np.array(tag_skel, dtype=np.uint8)
src_skel_rgb = cv2.cvtColor(src_skel, cv2.COLOR_GRAY2BGR)
tag_skel_rgb = cv2.cvtColor(tag_skel, cv2.COLOR_GRAY2BGR)
src_end_points = getEndPointsOfSkeletonLine(src_skel)
tag_end_points = getEndPointsOfSkeletonLine(tag_skel)
for (x, y) in src_end_points:
src_skel_rgb[y][x] = (0, 0, 255)
for (x, y) in tag_end_points:
tag_skel_rgb[y][x] = (0, 0, 255)
print("src end points len: %d" % len(src_end_points))
print("tag end points len: %d" % len(tag_end_points))
if len(src_end_points) > 2:
print("src skeleton line has branch")
if len(tag_end_points) > 2:
print("tag skeleton line has branch")
src_cross_points = getCrossPointsOfSkeletonLine(src_skel)
tag_cross_points = getCrossPointsOfSkeletonLine(tag_skel)
for (x, y) in src_cross_points:
src_skel_rgb[y][x] = (255, 0, 0)
for (x, y) in tag_cross_points:
tag_skel_rgb[y][x] = (255, 0, 0)
print("src cross len: %d" % len(src_cross_points))
print("tag cross len: %d" % len(tag_cross_points))
if len(src_cross_points) > 0:
# exist branches
src_skel = removeBranchOfSkeletonLine(src_skel, src_end_points, src_cross_points, )
if len(tag_cross_points) > 0:
# exist branches
tag_skel = removeBranchOfSkeletonLine(tag_skel, tag_end_points, tag_cross_points, )
# src_skel_no_branch = removeBranchOfSkeletonLine(src_skel, src_end_points, src_cross_points)
# tag_skel_no_btranch = removeBranchOfSkeletonLine(tag_skel, tag_end_points, tag_cross_points)
cv2.imshow("coverage img", src_img)
cv2.imshow("new coverage img", tag_img)
cv2.imshow("src rgb", src_skel_rgb)
cv2.imshow("tag rgb", tag_skel_rgb)
cv2.imshow("src skeleton img", src_skel)
cv2.imshow("tag skeleton img", tag_skel)
# cv2.imshow("src skeleton img no branch", src_skel_no_branch)
# cv2.imshow("tag skeleton img no branch", tag_skel_no_branch)
cv2.waitKey(0)
cv2.destroyAllWindows()
components = getConnectedComponents(img, connectivity=8)
print("components num: %d" % len(components))
# 6. Get skeletons of component.
comp_skeleton = getSkeletonOfImage(components[2])
comp_skeleton = getSkeletonOfImage(comp_skeleton)
cv2.imshow("skeleton_original", comp_skeleton)
# 7. Process the skeleton by remove extra branches.
# comp_skeleton = removeShortBranchesOfSkeleton(comp_skeleton, length_threshold=30)
comp_skeleton_rgb = cv2.cvtColor(comp_skeleton, cv2.COLOR_GRAY2RGB)
cv2.imshow("skeleton_smoothed", comp_skeleton)
# 8. Get the end points and cross points after skeleton processed
end_points = getEndPointsOfSkeletonLine(comp_skeleton)
cross_points = getCrossPointsOfSkeletonLine(comp_skeleton)
print("end points num: %d ,and cross points num: %d" %
(len(end_points), len(cross_points)))
# 9. Get contour image of component
comp_contours_img = getContourImage(components[2])
comp_contours_img_rbg = cv2.cvtColor(comp_contours_img, cv2.COLOR_GRAY2RGB)
# 10. Detect the number of contours and return all contours
comp_contours = getConnectedComponents(comp_contours_img, connectivity=8)
print("contours num: %d" % len(comp_contours))
# 11. Get points on contours
corners_points = []
for cont in comp_contours: