python - 如何使用Python CV2套件测量圆心角

Question

我们的团队使用摄像头，显微镜和可调透镜建立了视觉系统，以观察圆锥的内表面。

从视觉上讲，相机为一个视锥拍摄12张图像，每个图像覆盖30度。

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。
Python和cv2或其他软件包中有什么方法可以测量该中心角。谢谢。

Answer 1

这是在Python / OpenCV中执行此操作的一种方法。

阅读图片

转换为灰色

阈值

使用形态学打开和关闭以平滑并填充边界

应用Canny边缘提取

通过使每个边缘的相对侧变黑来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 Angular

计算两个 Angular 之间的差

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()

阈值图片:



形态处理图像:



边缘图像:



输入行:



锥角(度):

42.03975696357633