def contourBtn(self):
"""
Obtain the contour of image
:return:
"""
print("Contour button clicked")
contour_ = getContourOfImage(self.image_gray)
# remove the break points
contour_ = removeBreakPointsOfContour(contour_)
self.contour_gray = contour_.copy()
qimg = QImage(contour_.data, contour_.shape[1], contour_.shape[0],
contour_.shape[1], QImage.Format_Indexed8)
self.contour_pix = QPixmap.fromImage(qimg)
self.temp_contour_pix = self.contour_pix.copy()
self.scene.addPixmap(self.contour_pix)
self.scene.update()
self.statusbar.showMessage("Contour successed!")
del contour_, qimg
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
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):
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 autoSmoothContoursOfComponent(component, eplison=10, max_error=200):
"""
Automatically smooth the contours of component.
:param component:
:return:
"""
if component is None:
return
# 5. Get contours of this component
component_contours = getContourOfImage(component)
contours = getConnectedComponents(component_contours, connectivity=8)
print("contours num: ", len(contours))
# 6. Process contours to get closed and 1-pixel width contours by removing break points
contours_processed = []
for cont in contours:
cont = removeBreakPointsOfContour(cont)
contours_processed.append(cont)
print("contours processed num: %d" % len(contours_processed))
# 7. Smooth contours with RDP and cubic bezeir fit curve
contours_smoothed = []
for cont in contours_processed:
cont_smoothed = []
# sorted points on contour
cont_sorted = sortPointsOnContourOfImage(cont)
# simplify contour with RDP
cont_simp = rdp(cont_sorted, eplison)
print("cont simp num: ", len(cont_simp))
# split contour into sub-contours
for i in range(len(cont_simp) - 1):
start_pt = cont_simp[i]
end_pt = cont_simp[i + 1]
start_index = cont_sorted.index(start_pt)
end_index = cont_sorted.index(end_pt)
sub_cont_points = np.array(cont_sorted[start_index:end_index + 1])
beziers = fitCurve(sub_cont_points, maxError=max_error)
for bez in beziers:
bezier_points = draw_cubic_bezier(bez[0], bez[1], bez[2],
bez[3])
cont_smoothed += bezier_points
contours_smoothed.append(cont_smoothed)
print("contours smoothed num: ", len(contours_smoothed))
# fill black color in contour area
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], "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], "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