opencv-055-二值图像分析(凸包检测)

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知识点

对二值图像进行轮廓分析之后，对获取到的每个轮廓数据，可以构建每个轮廓的凸包，构建完成之后会返回该凸包包含的点集。根据返回的凸包点集可以绘制该轮廓对应的凸包。OpenCV对轮廓提取凸包的API函数如下：

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void cv::convexHull(
    InputArray points,
    OutputArray hull,
    bool clockwise = false,
    bool returnPoints = true 
)
points参数是输入的轮廓点集
hull凸包检测的输出结果，当参数returnPoints为ture的时候返回凸包的顶点坐标是个点集、returnPoints为false的是返回的是一个integer的vector里面是凸包各个顶点在轮廓点集对应的index
clockwise 表示顺时针方向或者逆时针方向
returnPoints表示是否返回点集

OpenCV中的凸包寻找算法是基于Graham’s扫描法。
OpenCV中还提供了另外一个API函数用来判断一个轮廓是否为凸包，该方法如下：

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bool cv::isContourConvex(
	InputArray contour
)
该方法只有一个输入参数就是轮廓点集。

代码（c++,python）

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#include <iostream>
#include <opencv2/opencv.hpp>

using namespace std;
using namespace cv;

/*
 * 二值图像分析(凸包检测)
 */
int main() {
    Mat src = imread("../images/hand.jpg");
    if (src.empty()) {
        cout << "could not load image.." << endl;
    }
    imshow("input", src);

    // 二值化
    Mat gray, binary;
    cvtColor(src, gray, COLOR_BGR2GRAY);
    threshold(gray, binary, 0, 255, THRESH_BINARY | THRESH_OTSU);

    // 删除干扰块
    Mat k = getStructuringElement(MORPH_RECT, Size(3, 3), Point(-1, -1));
    morphologyEx(binary, binary, MORPH_OPEN, k);
    imshow("binary", binary);

    // 轮廓发现与绘制
    vector<vector<Point>> contours;
    vector<Vec4i> hierarchy;
    findContours(binary, contours, hierarchy, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE, Point());
    for (size_t t = 0; t < contours.size(); t++) {
        vector<Point> hull;
        convexHull(contours[t], hull);
        bool isHull = isContourConvex(contours[t]);
        printf("test convex of the contours %s", isHull?"y":"n");
        int len = hull.size();
        for (int i = 0; i < len; ++i) {
            circle(src, hull[i], 4, Scalar(255,0,0), 2);
            line(src, hull[i%len], hull[(i+1)%len], Scalar(0,0,255),2);
        }
    }

    imshow("contours", src);

    waitKey(0);
    return 0;
}
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import cv2 as cv
import numpy as np

src = cv.imread("D:/images/hand.jpg")
cv.namedWindow("input", cv.WINDOW_AUTOSIZE)
cv.imshow("input", src)
gray = cv.cvtColor(src, cv.COLOR_BGR2GRAY)
ret, binary = cv.threshold(gray, 0, 255, cv.THRESH_BINARY | cv.THRESH_OTSU)
k = cv.getStructuringElement(cv.MORPH_RECT, (3, 3))
binary = cv.morphologyEx(binary, cv.MORPH_OPEN, k)
cv.imshow("binary", binary)

# 轮廓发现
out, contours, hierarchy = cv.findContours(binary, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)
for c in range(len(contours)):
    ret = cv.isContourConvex(contours[c])
    points = cv.convexHull(contours[c])
    total = len(points)
    for i in range(len(points)):
        x1, y1 = points[i%total][0]
        x2, y2 = points[(i+1)%total][0]
        cv.circle(src, (x1, y1), 4, (255, 0, 0), 2, 8, 0)
        cv.line(src, (x1, y1), (x2, y2), (0, 0, 255), 2, 8, 0)
    print(points)
    print("convex : ", ret)


# 显示
cv.imshow("contours_analysis", src)
cv.imwrite("D:/contours_analysis.png", src)
cv.waitKey(0)
cv.destroyAllWindows()

结果

代码地址

github