如何使用Python CV2套件测量圆心角 [英] How to measure the central angle with Python cv2 package

问题描述

我们的团队通过摄像头，显微镜和可调透镜建立了视觉系统，以观察圆锥的内表面.

Our team set up a vision system with a camera, a microscope and a tunable lens to look at the internal surface of a cone.

从视觉上讲，相机拍摄一个圆锥体的12张图像，每个图像覆盖30度.

Visually speaking, the camera takes 12 image for one cone with each image covering 30 degrees.

现在，我们已经收集了许多示例图像，并希望确保每个风扇"(如下所示)至少为30度.

Now we've collected many sample images and want to make sure each "fan"(as shown below) is at least 30 degree.

在 Python 中，是否可以使用 cv2 或其他软件包来测量此中心角.谢谢.

Is there any way in Python, with cv2 or other packages, to measure this central angle. Thanks.

推荐答案

这是在Python/OpenCV中执行此操作的一种方法.

Here is one way to do that in Python/OpenCV.

• 阅读图像
• 转换为灰色
• 阈值
• 使用形态学进行开闭以平滑并填充边界
• 应用Canny边缘提取
• 通过涂黑每个边缘的相对侧，将图像分为顶部边缘和底部边缘
• 使线条适合顶部和底部边缘
• 计算每个边缘的角度
• 计算两个角度之差
• 在输入上画线
• 保存结果

输入:

import cv2
import numpy as np
import math

# read image
img = cv2.imread('cone_shape.jpg')

# convert to grayscale
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)

# threshold
thresh = cv2.threshold(gray,11,255,cv2.THRESH_BINARY)[1]

# apply open then close to smooth boundary
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (13,13))
morph = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel)
kernel = np.ones((33,33), np.uint8)
morph = cv2.morphologyEx(morph, cv2.MORPH_CLOSE, kernel)

# apply canny edge detection
edges = cv2.Canny(morph, 150, 200)
hh, ww = edges.shape
hh2 = hh // 2

# split edge image in half vertically and blacken opposite half
top_edge = edges.copy()
top_edge[hh2:hh, 0:ww] = 0
bottom_edge = edges.copy()
bottom_edge[0:hh2, 0:ww] = 0

# get coordinates of white pixels in top and bottom
# note: need to transpose y,x in numpy to x,y for opencv
top_white_pts = np.argwhere(top_edge.transpose()==255)
bottom_white_pts = np.argwhere(bottom_edge.transpose()==255)

# fit lines to white pixels
# (x,y) is point on line, (vx,vy) is unit vector along line
(vx1,vy1,x1,y1) = cv2.fitLine(top_white_pts, cv2.DIST_L2, 0, 0.01, 0.01)
(vx2,vy2,x2,y2) = cv2.fitLine(bottom_white_pts, cv2.DIST_L2, 0, 0.01, 0.01)

# compute angle for vectors vx,vy
top_angle = (180/math.pi)*math.atan(vy1/vx1)
bottom_angle = (180/math.pi)*math.atan(vy2/vx2)
print(top_angle, bottom_angle)

# cone angle is the difference
cone_angle = math.fabs(top_angle - bottom_angle)
print(cone_angle)

# draw lines on input
lines = img.copy()
p1x1 = int(x1-1000*vx1)
p1y1 = int(y1-1000*vy1)
p1x2 = int(x1+1000*vx1)
p1y2 = int(y1+1000*vy1)
cv2.line(lines, (p1x1,p1y1), (p1x2,p1y2), (0, 0, 255), 1)
p2x1 = int(x2-1000*vx2)
p2y1 = int(y2-1000*vy2)
p2x2 = int(x2+1000*vx2)
p2y2 = int(y2+1000*vy2)
cv2.line(lines, (p2x1,p2y1), (p2x2,p2y2), (0, 0, 255), 1)

# save resulting images
cv2.imwrite('cone_shape_thresh.jpg',thresh)
cv2.imwrite('cone_shape_morph.jpg',morph)
cv2.imwrite('cone_shape_edges.jpg',edges)
cv2.imwrite('cone_shape_lines.jpg',lines)

# show thresh and result    
cv2.imshow("thresh", thresh)
cv2.imshow("morph", morph)
cv2.imshow("edges", edges)
cv2.imshow("top edge", top_edge)
cv2.imshow("bottom edge", bottom_edge)
cv2.imshow("lines", lines)
cv2.waitKey(0)
cv2.destroyAllWindows()

阈值图像:

经形态处理的图像:

边缘图像:

输入行:

锥角(以度为单位):

42.03975696357633

42.03975696357633

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