OpenCV-目标轮廓提取

原理：目标物体的轮廓实质是指一系列像素点构成，这些点构成了一个有序的点集。我们可以通过findContours函数将二值图像的边缘像素点分成多个轮廓，从而逐个提取目标外部轮廓，内部轮廓有待研究。

Python：

import cv2 as cv
import numpy as np


if __name__=="__main__":
    img=cv.imread("D:/testimage/number.jpg")
    #img1=cv.imread("D:/testimage/sample.jpg")
    #灰度化+高斯滤波
    gray_dst = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
    blur_dst=cv.GaussianBlur(gray_dst,(3,3),0)
    #OTSU阈值分割
    ret,otsu_dst=cv.threshold(blur_dst,0,255,cv.THRESH_OTSU)
    cv.imshow("s", otsu_dst)
    #Canny算子提取边缘轮廓
    canny_dst=cv.Canny(otsu_dst,10,250)
    cv.imshow("s", canny_dst)
    #寻找二值图像轮廓点
    edge_points,h=cv.findContours(canny_dst,cv.RETR_EXTERNAL,
                                cv.CHAIN_APPROX_SIMPLE)
    contours=edge_points
    #轮廓的数量
    k=len(edge_points)
    src=np.zeros(gray_dst.shape,np.uint8)
    for i in range(k):
        #画出轮廓
        cv.drawContours(src,contours,i,250,2)
        #最小外包圆
        circle=cv.minEnclosingCircle(contours[i])
        cv.circle(img,(int(circle[0][0]),int(circle[0][1])),int(circle[1]),(0,255,0),2)
        #绘制多边形
        approxCurve=cv.approxPolyDP(contours[i],0.3,True)
        t=approxCurve.shape[0]
        for i in range(t-1):
            cv.line(img,(approxCurve[i,0,0],approxCurve[i,0,1]),
                    (approxCurve[i+1,0,0],approxCurve[i+1,0,1]),(255,0,0),2)
            cv.line(img, (approxCurve[t-1, 0, 0], approxCurve[t-1, 0, 1]),
                    (approxCurve[0, 0, 0], approxCurve[0, 0, 1]),(255,0,0),2)
    cv.imshow("1",src)
    cv.imshow("2",img)
    cv.imwrite("D:/testimage/result-1.jpg", src)
    cv.imwrite("D:/testimage/result-2.jpg", img)
    cv.waitKey(0)
    cv.destroyAllWindows()

原图：

边缘提取：

目标轮廓：