def smoothBtn(self):
"""
Smooth button clicked!
:return:
"""
print("Smooth button clicked")
if self.scene.points is None or len(self.scene.points) == 0:
return
# max Error
max_error = int(self.maxerror_ledit.text())
# new contour image
contour_img = np.array(np.ones_like(self.contour_gray) * 255,
dtype=np.uint8)
# smooth the contour segmentations.
contour_sorted = sortPointsOnContourOfImage(self.contour_gray.copy())
feature_points = []
for pt in self.scene.points:
nearest_pt = None
max_dist = 1000000
for cpt in contour_sorted:
dist_ = math.sqrt((pt[0] - cpt[0])**2 + (pt[1] - cpt[1])**2)
if dist_ < max_dist:
max_dist = dist_
nearest_pt = cpt
# select feature points
feature_points.append(nearest_pt)
# add first point as the last end point
feature_points.append(feature_points[0])
print(self.scene.points)
print(feature_points)
self.feature_points = feature_points.copy()
# extract segmentations based on the feature points
contour_segmentations = []
for id in range(len(feature_points) - 1):
start_pt = feature_points[id]
end_pt = feature_points[id + 1]
start_index = contour_sorted.index(start_pt)
end_index = contour_sorted.index(end_pt)
if start_index < end_index:
segmentation = contour_sorted[start_index:end_index]
if end_index == len(contour_sorted) - 1:
segmentation.append(contour_sorted[0])
else:
segmentation.append(contour_sorted[end_index + 1])
elif start_index >= end_index:
segmentation = contour_sorted[start_index:len(
contour_sorted)] + contour_sorted[0:end_index + 1]
contour_segmentations.append(segmentation)
print("contour segmentation len: %d" % len(contour_segmentations))
self.contour_segmentations = contour_segmentations.copy()
# smooth contour segmentations
smoothed_contour_points = []
for id in range(len(self.contour_segmentations)):
print("Line index: %d" % id)
# smooth contour segmentation
li_seg = np.array(self.contour_segmentations[id])
beziers = fitCurve(li_seg, maxError=max_error)
for bez in beziers:
bezier_points = draw_cubic_bezier(bez[0], bez[1], bez[2],
bez[3])
for id in range(len(bezier_points) - 1):
start_pt = bezier_points[id]
end_pt = bezier_points[id + 1]
cv2.line(contour_img,
start_pt,
end_pt,
color=0,
thickness=1)
smoothed_contour_points += bezier_points
qimg = QImage(contour_img.data, contour_img.shape[1], contour_img.shape[0], contour_img.shape[1], \
QImage.Format_Indexed8)
contour_pix = QPixmap.fromImage(qimg)
self.scene.addPixmap(contour_pix)
self.scene.update()
# clean data
self.scene.points = []
self.contour_gray = contour_img.copy()
self.smoothed_contour_points = smoothed_contour_points.copy()
# update status bar
self.statusbar.showMessage("Smooth successed!")
del contour_img, contour_pix, contour_segmentations, contour_sorted, smoothed_contour_points, \
qimg, feature_points
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 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 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():
# 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()
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:
cont = removeBreakPointsOfContour(cont)
cont_sorted = sortPointsOnContourOfImage(cont)
cont_points = rdp(cont_sorted, 5)
corners_points += cont_points
CORNER_CROSS_DIST_THRESHOLD = 20
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
# for pt in cross_points_:
# cv2.circle(comp_skeleton_rgb, pt, 2, (0,255,0), 3)
for pt in cross_points:
def main():
# load image
img_path = "../templates/stroke_dan.png"
img = cv2.imread(img_path, 0)
_, img = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY)
img_rgb = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
# get contour of image
contour = getContourOfImage(img)
contour_rgb = cv2.cvtColor(contour, cv2.COLOR_GRAY2RGB)
# fix breaking points on the contour
break_points = []
for y in range(1, contour.shape[0] - 1):
for x in range(1, contour.shape[1] - 1):
if contour[y][x] == 0.0:
num_ = getNumberOfValidPixels(contour, x, y)
if num_ == 1:
print((x, y))
break_points.append((x, y))
if len(break_points) != 0:
contour = cv2.line(contour,
break_points[0],
break_points[1],
color=0,
thickness=1)
cv2.imshow("c", contour)
# order the contour points
contour_points_ordered = sortPointsOnContourOfImage(contour)
# contour_points_counter_clockwise = order_points(contour, isClockwise=False)
print("number of points in ordered contour: %d" %
len(contour_points_ordered))
# print("counter clock: %d" % len(contour_points_counter_clockwise))
contour_rgb_clock = contour_rgb.copy()
contour_smooth_rgb_clock = contour_rgb.copy()
# contour_rgb_counter_clock = contour_rgb.copy()
# get key points on contour
corners = cv2.goodFeaturesToTrack(contour, 6, 0.01, 10)
corners = np.int0(corners)
print("number of key points on contour: %d" % len(corners))
index = 0
corner_points_ = []
for i in corners:
MAX_DIST = 10000
x, y = i.ravel()
pt_ = None
if (x, y - 1) in contour_points_ordered:
pt_ = (x, y - 1)
elif (x + 1, y - 1) in contour_points_ordered:
pt_ = (x + 1, y - 1)
elif (x + 1, y) in contour_points_ordered:
pt_ = (x + 1, y)
elif (x + 1, y + 1) in contour_points_ordered:
pt_ = (x + 1, y + 1)
elif (x, y + 1) in contour_points_ordered:
pt_ = (x, y + 1)
elif (x - 1, y + 1) in contour_points_ordered:
pt_ = (x - 1, y + 1)
elif (x - 1, y) in contour_points_ordered:
pt_ = (x - 1, y)
elif (x - 1, y - 1) in contour_points_ordered:
pt_ = (x - 1, y - 1)
else:
# find the nearest point on the contour
minx = 0
miny = 0
for cp in contour_points_ordered:
dist = math.sqrt((x - cp[0])**2 + (y - cp[1])**2)
if dist < MAX_DIST:
MAX_DIST = dist
minx = cp[0]
miny = cp[1]
pt_ = (minx, miny)
corner_points_.append(pt_)
cv2.circle(contour_rgb, (pt_[0], pt_[1]), 1, (0, 0, 255), -1)
cv2.putText(contour_rgb, str(index), (pt_[0], pt_[1]),
cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 0), 2, cv2.LINE_AA)
index += 1
print("orignal corner points number: %d" % len(corner_points_))
# order the corner points in the clockwise direction
corner_points = []
index = 0
for pt in contour_points_ordered:
if pt in corner_points_:
corner_points.append(pt)
cv2.circle(contour_rgb_clock, (pt[0], pt[1]), 3, (255, 0, 0), -1)
cv2.putText(contour_rgb_clock, str(index), (pt[0], pt[1]),
cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 0), 2, cv2.LINE_AA)
index += 1
print("corner points len: %d" % len(corner_points))
# contour segmentation based on the corner points
contour_lines = []
for id in range(len(corner_points)):
start_point = corner_points[id]
end_point = start_point
if id == len(corner_points) - 1:
end_point = corner_points[0]
else:
end_point = corner_points[id + 1]
# contour segmentation
contour_segmentation = []
start_index = contour_points_ordered.index(start_point)
end_index = contour_points_ordered.index(end_point)
if start_index <= end_index:
# normal index
contour_segmentation = contour_points_ordered[
start_index:end_index + 1]
else:
# end is at
contour_segmentation = contour_points_ordered[start_index: len(contour_points_ordered)] + \
contour_points_ordered[0: end_index+1]
contour_lines.append(contour_segmentation)
print("number of contour segmentation: %d" % len(contour_lines))
# use different color to show the contour segmentation
for id in range(len(contour_lines)):
if id % 3 == 0:
# red lines
for pt in contour_lines[id]:
contour_rgb_clock[pt[1]][pt[0]] = (0, 0, 255)
elif id % 3 == 1:
# blue line
for pt in contour_lines[id]:
contour_rgb_clock[pt[1]][pt[0]] = (255, 0, 0)
elif id % 3 == 2:
# green line
for pt in contour_lines[id]:
contour_rgb_clock[pt[1]][pt[0]] = (0, 255, 0)
# original and smooth contour
smoothed_contour_points = []
for id in range(len(contour_lines)):
print("line index: %d" % id)
# original contour
for pt in contour_lines[id]:
contour_smooth_rgb_clock[pt[1]][pt[0]] = (0, 0, 255)
# smooth contour
li_points = np.array(contour_lines[id])
beziers = fitCurve(li_points, maxError=30)
print("len bezier: %d" % len(beziers))
# # print(beziers)
for bez in beziers:
print(len(bez))
bezier_points = draw_cubic_bezier(bez[0], bez[1], bez[2], bez[3])
for id in range(len(bezier_points) - 1):
start_pt = bezier_points[id]
end_pt = bezier_points[id + 1]
cv2.line(contour_smooth_rgb_clock, start_pt, end_pt,
(255, 0, 0))
smoothed_contour_points += bezier_points
# fill color in contour with sorted smooth contour points
print(len(smoothed_contour_points))
smoothed_contour_points = np.array([smoothed_contour_points], "int32")
fill_contour_smooth = np.ones(img.shape) * 255
fill_contour_smooth = np.array(fill_contour_smooth, dtype=np.uint8)
fill_contour_smooth = cv2.fillPoly(fill_contour_smooth,
smoothed_contour_points, 0)
cv2.imshow("src", img)
cv2.imshow("contour", contour)
# cv2.imshow("corners", contour_rgb)
cv2.imshow("contour clock", contour_rgb_clock)
cv2.imshow("smooth contour clock", contour_smooth_rgb_clock)
# cv2.imshow("contour counter clock", contour_rgb_counter_clock)
cv2.imshow("fill contour", fill_contour_smooth)
cv2.waitKey(0)
cv2.destroyAllWindows()
from utils.Functions import getContourOfImage, sortPointsOnContourOfImage, removeBreakPointsOfContour, \
getSkeletonOfImage, removeBranchOfSkeletonLine, getEndPointsOfSkeletonLine, \
getCrossPointsOfSkeletonLine, getNumberOfValidPixels
# 0107亻 1133壬 0554十
path = "0107亻.jpg"
img = cv2.imread(path, 0)
_, img = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY)
img_copy = img.copy()
contour = getContourOfImage(img)
contour = removeBreakPointsOfContour(contour)
contour_sorted = sortPointsOnContourOfImage(contour)
print(len(contour_sorted))
print(contour_sorted[0])
print(contour_sorted[-1])
img = np.float32(img)
dst = cv2.cornerHarris(img, 3, 3, 0.05)
dst = cv2.dilate(dst, None)
img_rgb = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
contour_rgb = cv2.cvtColor(contour, cv2.COLOR_GRAY2RGB)
contour_rgb1 = contour_rgb.copy()
print(dst.shape)
corner_points = []
for y in range(dst.shape[0]):
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
