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skeleton.py
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skeleton.py
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import cv2
import yaml
from skimage import morphology
import numpy as np
META_PATH = "/home/jason/turtlebot_custom_maps/test0.yaml"
def skeleton():
image_path, resolution, origin, negate, occupied_thresh, free_thresh = readconfig()
image = cv2.imread(image_path)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
retval, bin_image = cv2.threshold(gray, 220, 1, cv2.THRESH_BINARY)
cv2.imshow("oringin",gray)
dilate = dilate_image(bin_image)
skeleton = thin2_image(dilate)
skeleton_rgb = cv2.cvtColor(skeleton, cv2.COLOR_GRAY2BGR)
harris_result, point_list = harris_corner_point(skeleton_rgb)
robot1_route = []
robot2_route = []
if len(point_list) is not 0:
#binary division of the points
for i in range(len(point_list)):
if i < len(point_list) / 2:
robot1_route.append(point_list[i])
else:
robot2_route.append(point_list[i])
print ("Robot1 route: {}".format(robot1_route))
print ("Robot2 route: {}".format(robot2_route))
cv2.imshow("harris_skeleton", harris_result)
cv2.waitKey()
# show_image(gray,skeleton=skeleton)
#
# def show_image(grey,skeleton):
# fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(8, 4))
# ax1.imshow(grey, cmap=plt.cm.gray)
# ax1.axis('off')
# ax1.set_title('original', fontsize=20)
# ax2.imshow(skeleton, cmap=plt.cm.gray)
# ax2.axis('off')
# ax2.set_title('skeleton', fontsize=20)
# fig.tight_layout()
# plt.show()
# cv2.waitKey()
# erode method
def erode_image(grey):
kernel = np.uint8(np.zeros((3, 3)))
for x in range(3):
kernel[x, 1] = 1;
kernel[1, x] = 1;
eroded = cv2.erode(grey, kernel)
return eroded
# p9 p2 p3
# p8 p1 p4
# p7 p6 p5
# A fast parallel algorithm for thinning digital patterns
def thin1_image(image, max_iterations=-1):
height = image.shape[0]
width = image.shape[1]
# record the iterate time
count = 0;
while (1):
count = count + 1
if (max_iterations != -1 and count > max_iterations):
break
# the first subiternation
for i in range(height):
p = []
for j in range(width):
# print image[i,j]
p1 = image[i, j]
if p1 == 1:
p4 = 0 if j == width - 1 else image[i, j + 1]
p8 = 0 if j == 0 else image[i, j - 1]
p2 = 0 if i == 0 else image[i - 1, j]
p3 = 0 if (i == 0 or j == width - 1) else image[i - 1, j + 1]
p9 = 0 if (i == 0 or j == 0) else image[i - 1, j - 1]
p6 = 0 if (i == height - 1) else image[i + 1, j]
p5 = 0 if (i == height - 1 or j == width - 1) else image[i + 1, j + 1]
p7 = 0 if (i == height - 1 or j == 0) else image[i + 1, j - 1]
if ((p2 + p3 + p4 + p5 + p6 + p7 + p8 + p9) >= 2
and (p2 + p3 + p4 + p5 + p6 + p7 + p8 + p9) <= 6):
ap = 0
if (p2 == 0 and p3 == 1):
ap = ap + 1
if (p3 == 0 and p4 == 1):
ap = ap + 1
if (p4 == 0 and p5 == 1):
ap = ap + 1
if (p5 == 0 and p6 == 1):
ap = ap + 1
if (p6 == 0 and p7 == 1):
ap = ap + 1
if (p7 == 0 and p8 == 1):
ap = ap + 1
if (p8 == 0 and p9 == 1):
ap = ap + 1
if (p9 == 0 and p2 == 1):
ap = ap + 1
print ap
if (ap == 1 and p2 * p4 * p6 == 0 and p4 * p6 * p8 == 0):
print i, j
p.append((i, j))
image[i, j] = 0
if len(p) == 0:
break
else:
p = []
for i in range(height):
for j in range(width):
p1 = image[i, j]
if p1 == 1:
p4 = 0 if j == width - 1 else image[i, j + 1]
p8 = 0 if j == 0 else image[i, j - 1]
p2 = 0 if i == 0 else image[i - 1, j]
p3 = 0 if (i == 0 or j == width - 1) else image[i - 1, j + 1]
p9 = 0 if (i == 0 or j == 0) else image[i - 1, j - 1]
p6 = 0 if (i == height - 1) else image[i + 1, j]
p5 = 0 if (i == height - 1 or j == width - 1) else image[i + 1, j + 1]
p7 = 0 if (i == height - 1 or j == 0) else image[i + 1, j - 1]
if ((p2 + p3 + p4 + p5 + p6 + p7 + p8 + p9) >= 2
and (p2 + p3 + p4 + p5 + p6 + p7 + p8 + p9) <= 6):
ap = 0
if (p2 == 0 and p3 == 1):
ap = ap + 1
if (p3 == 0 and p4 == 1):
ap = ap + 1
if (p4 == 0 and p5 == 1):
ap = ap + 1
if (p5 == 0 and p6 == 1):
ap = ap + 1
if (p6 == 0 and p7 == 1):
ap = ap + 1
if (p7 == 0 and p8 == 1):
ap = ap + 1
if (p8 == 0 and p9 == 1):
ap = ap + 1
if (p9 == 0 and p2 == 1):
ap = ap + 1
if (ap == 1 and p2 * p4 * p8 == 0 and p2 * p6 * p8 == 0):
p.append((i, j))
image[i, j] = 0
if len(p) == 0:
break
else:
p = []
return image
# open operation
def open_operation(grey):
erode = erode_image(grey)
result = dilate_image(erode)
return result
# close operation
def close_operation(grey):
dilate = dilate_image(grey)
result = erode_image(dilate)
return result
# dilate the image
def dilate_image(grey):
kernel = np.uint8(np.zeros((3, 3)))
for x in range(3):
kernel[x, 1] = 1;
kernel[1, x] = 1;
dilate = cv2.dilate(grey, kernel)
return dilate
def thin2_image(grey):
skeleton = morphology.skeletonize(grey)
height = skeleton.shape[0]
width = skeleton.shape[1]
print ("height {}".format(skeleton.shape[0]))
print ("width {}".format(skeleton.shape[1]))
result = np.uint8(np.zeros((height, width)))
for i in range(skeleton.shape[0]):
for j in range(skeleton.shape[1]):
if skeleton[i, j]:
result[i, j] = 255
return result
def harris_corner_point(image):
image_temp = image
grey = cv2.cvtColor(image_temp, cv2.COLOR_BGR2GRAY)
grimage_tempey = np.float32(grey)
dst = cv2.cornerHarris(grey, 2, 15, 0.18)
dst = cv2.dilate(dst, None)
count = 0
keypoint = (0, 0)
point_list = []
for i in range(dst.shape[0]):
for j in range(dst.shape[1]):
if dst[i, j] > 0.1 * dst.max():
# delete the points to make the route more concise
euclidean = map(lambda x, y: (x - y) ** 2, keypoint, (i, j))
distance = euclidean[0] + euclidean[1]
if distance > 800:
print distance
keypoint = (i, j)
point_list.append((i, j))
image_temp[i, j] = [0, 0, 255]
count = count + 1
print ("point x :{}, point y:{}".format(i, j))
else:
continue
print count
return image_temp, point_list
def readconfig():
with open(META_PATH, 'r') as f:
attr = yaml.load(f)
image_path = attr["image"]
resolution = attr["resolution"]
origin = attr["origin"]
negate = attr["negate"]
occupied_thresh = attr["occupied_thresh"]
free_thresh = attr["free_thresh"]
return image_path, resolution, origin, negate, occupied_thresh, free_thresh
if __name__ == "__main__":
skeleton()