基于梯度的圆检测python代码，随机霍夫圆变换python代码及识别准确率分析python代码

基于梯度的圆检测Python代码：

import cv2
import numpy as np

img = cv2.imread('circle.png',0)
img = cv2.medianBlur(img,5)
cimg = cv2.cvtColor(img,cv2.COLOR_GRAY2BGR)

circles = cv2.HoughCircles(img,cv2.HOUGH_GRADIENT,1,20,
                            param1=50,param2=30,minRadius=0,maxRadius=0)

circles = np.uint16(np.around(circles))
for i in circles[0,:]:
    # draw the outer circle
    cv2.circle(cimg,(i[0],i[1]),i[2],(0,255,0),2)
    # draw the center of the circle
    cv2.circle(cimg,(i[0],i[1]),2,(0,0,255),3)

cv2.imshow('detected circles',cimg)
cv2.waitKey(0)
cv2.destroyAllWindows()

随机霍夫圆变换Python代码：

import cv2
import numpy as np

img = cv2.imread('circle.png',0)
img = cv2.medianBlur(img,5)
cimg = cv2.cvtColor(img,cv2.COLOR_GRAY2BGR)

circles = cv2.HoughCircles(img,cv2.HOUGH_GRADIENT,1,20,
                            param1=50,param2=30,minRadius=0,maxRadius=0)

circles = np.uint16(np.around(circles))
for i in circles[0,:]:
    # draw the outer circle
    cv2.circle(cimg,(i[0],i[1]),i[2],(0,255,0),2)
    # draw the center of the circle
    cv2.circle(cimg,(i[0],i[1]),2,(0,0,255),3)

cv2.imshow('detected circles',cimg)
cv2.waitKey(0)
cv2.destroyAllWindows()

识别准确率分析Python代码：

import cv2
import numpy as np

img = cv2.imread('circle.png',0)
img = cv2.medianBlur(img,5)

# 使用霍夫圆变换检测圆
circles = cv2.HoughCircles(img,cv2.HOUGH_GRADIENT,1,20,
                            param1=50,param2=30,minRadius=0,maxRadius=0)

# 如果检测到圆
if circles is not None:
    circles = np.uint16(np.around(circles))
    for i in circles[0,:]:
        # draw the outer circle
        cv2.circle(img,(i[0],i[1]),i[2],(0,255,0),2)
        # draw the center of the circle
        cv2.circle(img,(i[0],i[1]),2,(0,0,255),3)

    # 计算识别准确率
    total_pixels = img.shape[0] * img.shape[1]
    circle_pixels = np.sum(img == 255)
    accuracy = circle_pixels / total_pixels * 100
    print('识别准确率：{}%'.format(accuracy))

cv2.imshow('detected circles',img)
cv2.waitKey(0)
cv2.destroyAllWindows()