圆形检测--轮廓检测法

本文编写了一种轮廓对比方法检测圆形，比霍夫检测更准确一些。
输入为路径，如果有圆，则返回圆的轮廓点，还可以画出来。

import numpy as np
import cv2

def img_read(path):  #读图片，返回二值图
    image = cv2.imread(path)
    img_gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)   
    ret,th1 = cv2.threshold(img_gray,200,255,cv2.THRESH_BINARY)
    return th1
def img_erosion(image,k):   #图像腐蚀 增强线条 k为核大小
    kernel = np.ones((k,k),np.uint8)  
    erosion = cv2.erode(image,kernel,iterations = 1)
#    dilation = cv2.dilate(th1,kernel,iterations = 1)
    return erosion
def drawContours(image,counts): #根据点画出点图像
    color = cv2.cvtColor(image, cv2.COLOR_GRAY2BGR)
    dst = cv2.drawContours(color, counts, -1, (0,255,0), 2) 
    cv2.imshow("dst", dst)
    cv2.waitKey(0)

path="test_images/26.jpg"
#path2="test_images/20.jpg"
def circle(path): #检测圆，返回圆的列表
    img1=img_read(path)
    img_er=img_erosion(img1,3)    
    img1,contours1,hierarchy1=cv2.findContours(img_er,cv2.RETR_CCOMP, cv2.CHAIN_APPROX_NONE)
    ret_list=[] #相似率
    cnt_list=[] #圆的点
    for contour in contours1:   
        cnt1 = contour     
        cnt2 = np.load("counts2.npy")
        ret = cv2.matchShapes(cnt1,cnt2,1,0.0)
        if ret<0.005: #阀值设置
            ret_list.append(ret)
            cnt_list.append(cnt1)
#    print(ret_list)
    drawContours(img1,cnt_list)
    return cnt_list