如何用Python+opencv检测此图像中的所有圆？

Question

我用了cv.HoughCircles函数，但不会选择合适的参数，效果不好。
原图：

结果：

{

```

```import cv2 as cv
import numpy as np
import matplotlib.pyplot as plt

def show(img):
if img.ndim == 2:
plt.imshow(img,cmap='gray')
else:
plt.imshow(cv.cvtColor(img,cv.COLOR_BGR2RGB))
plt.show()

img=cv.imread('C:/Users/1/Desktop/img/test.jpg')
gray=cv.cvtColor(img, cv.COLOR_BGR2GRAY)

dst = cv.equalizeHist(gray)#应用直方图均衡化
gaussian = cv.GaussianBlur(dst,(9,9),0)
#利用Canny进行边缘检测
GrayImage = cv.Canny(gaussian, 20,180, apertureSize=3)
ret, th1 = cv.threshold(GrayImage, 127, 255, cv.THRESH_TOZERO) # 固定阈值二值化
th2 = cv.adaptiveThreshold(th1, 255, cv.ADAPTIVE_THRESH_MEAN_C, cv.THRESH_BINARY, 3, 5)
th3 = cv.adaptiveThreshold(th2, 255, cv.ADAPTIVE_THRESH_GAUSSIAN_C, cv.THRESH_BINARY, 3, 5)
kernel = np.ones((6, 9), np.uint8)
erosion = cv.erode(th3, kernel, iterations=1) # 腐蚀处理
dilation = cv.dilate(erosion, kernel, iterations=1) # 膨胀处理
imgray = cv.Canny(erosion, 3, 8) # Canny算子边缘检测

circles = cv.HoughCircles(imgray, cv.HOUGH_GRADIENT, 1, 40, param1=100, param2=6, minRadius=8,maxRadius=10)

circles = np.uint16(np.around(circles))
P = circles[0] # 去掉circles数组一层外括号
for i in P:
cv.circle(img, (i[0], i[1]), i[2], (0, 255, 0), 5)
cv.circle(img, (i[0], i[1]), 5, (0, 0, 255), 3)

show(img)
}

我参考了一些博主的文章，链接：https://blog.csdn.net/SouthWooden/article/details/98741985

Answer 1

废话不多说，直接上代码

import cv2
import numpy as np


def cv_show(neme, img):
    cv2.imshow(neme, img)  
    cv2.waitKey(0)  
    cv2.destroyAllWindows()  


image = cv2.imread('1.jpg')
img = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# Canny 边缘检测（图像、min值、max值）
edges = cv2.Canny(img, 7, 12)
ret, thresh1 = cv2.threshold(edges, 127, 255, cv2.THRESH_BINARY)

# 膨胀操作
kernel1 = np.ones((5, 5), np.uint8)
# 换个封装函数，膨胀      当前载入 腐蚀后--膨胀
erosion = cv2.dilate(thresh1, kernel1, iterations=1)

kernel = np.ones((3, 3))
# 腐蚀操作
sb = cv2.erode(erosion, kernel, iterations=1)

cv_show("s", thresh1)
cv_show("name", erosion)
cv_show('sda', sb)

# 轮廓查询
contours, hierarchy = cv2.findContours(sb, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)

for i in range(len(contours)):
    # 轮廓外接圆
    (x, y), radius = cv2.minEnclosingCircle(contours[i])
    center = (int(x), int(y))
    radius = int(radius)

    # 轮廓区域
    # 轮廓面积
    area = cv2.contourArea(contours[i])
    if 300 <= area < 2000:
        image = cv2.circle(image, center, radius, (0, 0, 255), 1)

cv_show('sad', image)