如何改善以下圆圈检测代码的性能
from matplotlib.pyplot import imshow, scatter, show
import cv2
image = cv2.imread('points.png', 0)
_, image = cv2.threshold(image, 254, 255, cv2.THRESH_BINARY)
image = cv2.Canny(image, 1, 1)
imshow(image, cmap='gray')
circles = cv2.HoughCircles(image, cv2.HOUGH_GRADIENT, 2, 32)
x = circles[0, :, 0]
y = circles[0, :, 1]
scatter(x, y)
show()
具有以下源图像:
我尝试过调整
HoughCircles
函数的参数,但是它们导致太多的误报或太多的误报。特别是,我无法在两个斑点之间的间隙中检测到虚假圆:最佳答案
@Carlos,在您所描述的情况下,我并不是Hough Circles的忠实粉丝。老实说,我发现此算法非常不直观。我建议您使用findContour()
函数,然后计算质心。因此,我对Hough的参数进行了一些调整以获得合理的结果。在Canny之前,我还使用了另一种预处理方法,因为我看不到该阈值在特定情况下如何在其他情况下起作用。
霍夫法:
寻找群众中心:
和代码:
from matplotlib.pyplot import imshow, scatter, show, savefig
import cv2
image = cv2.imread('circles.png', 0)
#_, image = cv2.threshold(image, 254, 255, cv2.THRESH_BINARY)
image = cv2.GaussianBlur(image.copy(), (27, 27), 0)
image = cv2.Canny(image, 0, 130)
cv2.imshow("canny", image)
cv2.waitKey(0)
imshow(image, cmap='gray')
circles = cv2.HoughCircles(image, cv2.HOUGH_GRADIENT, 22, minDist=1, maxRadius=50)
x = circles[0, :, 0]
y = circles[0, :, 1]
scatter(x, y)
show()
savefig('result1.png')
cv2.waitKey(0)
_, cnts, _ = cv2.findContours(image.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
# loop over the contours
for c in cnts:
# compute the center of the contour
M = cv2.moments(c)
cX = int(M["m10"] / M["m00"])
cY = int(M["m01"] / M["m00"])
#draw the contour and center of the shape on the image
cv2.drawContours(image, [c], -1, (125, 125, 125), 2)
cv2.circle(image, (cX, cY), 3, (255, 255, 255), -1)
cv2.imshow("Image", image)
cv2.imwrite("result2.png", image)
cv2.waitKey(0)
两种方法都需要进行一些微调,但我希望可以为您提供更多的帮助。
来源:this answer和pyimagesearch。