• 使用OpenCV函数 convexHull

步骤

1. 滤波——消除噪声
2. 增强——二值化，使轮廓更明显
3. 检测——选出边缘点
4. 计算凸包

例程

#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include <opencv2/highgui/highgui_c.h>
#include <iostream>
#include <stdio.h>
#include <stdlib.h>

using namespace cv;
using namespace std;

Mat src; Mat src_gray;
int thresh = 100;
int max_thresh = 255;
RNG rng(12345);

/// Function header
void thresh_callback(int, void*);

/** @function main */
int main(int argc, char** argv)
{
    /// 加载源图像
    src = imread("../images/hands2.jpg");

    /// 转成灰度图并进行模糊降噪
    cvtColor(src, src_gray, CV_BGR2GRAY);
    blur(src_gray, src_gray, Size(3, 3));

    /// 创建窗体
    const char* source_window = "Source";
    namedWindow(source_window, CV_WINDOW_AUTOSIZE);
    imshow(source_window, src);

    createTrackbar(" Threshold:", "Source", &thresh, max_thresh, thresh_callback);
    thresh_callback(0, 0);

    waitKey(0);
    return(0);
}

/** @function thresh_callback */
void thresh_callback(int, void*)
{
    Mat src_copy = src.clone();
    Mat threshold_output;
    vector<vector<Point> > contours;
    vector<Vec4i> hierarchy;

    /// 对图像进行二值化
    threshold(src_gray, threshold_output, thresh, 255, THRESH_BINARY);

    /// 寻找轮廓
    findContours(threshold_output, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, Point(0, 0));

    /// 对每个轮廓计算其凸包
    vector<vector<Point> >hull(contours.size());
    for (int i = 0; i < contours.size(); i++)
    {
        convexHull(Mat(contours[i]), hull[i], false);
    }

    /// 绘出轮廓及其凸包
    Mat drawing = Mat::zeros(threshold_output.size(), CV_8UC3);
    for (int i = 0; i < contours.size(); i++)
    {
        Scalar color = Scalar(rng.uniform(0, 255), rng.uniform(0, 255), rng.uniform(0, 255));
        drawContours(drawing, contours, i, color, 1, 8, vector<Vec4i>(), 0, Point());
        drawContours(drawing, hull, i, color, 1, 8, vector<Vec4i>(), 0, Point());
    }

    /// 把结果显示在窗体
    namedWindow("Hull demo", CV_WINDOW_AUTOSIZE);
    imshow("Hull demo", drawing);
}

这个手实在是太花哨了，必须得把阈值调得很大
这张颜色就很单一，阈值相对低很多