【图像轮廓与分割二】

一：内容介绍
本节主要介绍OpenCV的imgproc模块的图像轮廓与分割部分：
1. 查找并绘制轮廓
2. 寻找物体的凸包
3. 使用多边形将轮廓包围
4. 图像的矩
5. 分水岭算法
6. 图像修补
二：学习笔记
1. findContours()函数查找图像轮廓和canny检测边缘、hough检测直线，这些都非常使用。但是关于opencv中findContours()的具体原理我也没看，想深入研究的话可以看What is the algorithm that opencv uses for finding contours?
2. 寻找凸包和使用多边形将轮廓包围
3. 图像矩作为图像的一种统计特征，满足平移、伸缩、旋转的不变性。同时，矩本身也有一定的物理含义，特殊地，轮廓的m00矩代表轮廓的面积。
4. 分水岭算法可以将图像的边缘转换为“山脉”，将均匀区域转化为“山谷”，有助于图像分割。例程里边用的还有点复杂，得稍微理解一下。
5. 图像修补，不怎么用感觉。
三：相关源码及解析
本章示例较多，示例列表：
1.查找并绘制轮廓
2.寻找和绘制物体的凸包
3.使用多边形将包围轮廓
4.查找并绘制图像轮廓矩
5.分水岭算法
6.图像修补
1. 查找并绘制轮廓

#include<opencv2\opencv.hpp>
#include<vector>
#include<iostream>
using namespace std;
using namespace cv;
 
#define WINDOW_NAME1 "【原始图窗口】"
#define WINDOW_NAME2 "【轮廓图】"
 
Mat g_srcImage, g_grayImage;
int g_nThresh = 80;
int g_nThresh_max = 255;
RNG g_rng;
Mat g_cannyMat_output;
vector<vector<point> > g_vContours;
vector<vec4i> g_vHierarchy;
 
void on_ThreshChange(int, void*);
 
int main()
{
    g_srcImage = imread("poster_cola.jpg");  //加载源图像
    cvtColor(g_srcImage, g_grayImage, COLOR_BGR2GRAY);  //转换灰度图
    blur(g_grayImage, g_grayImage, Size(3, 3) ); //降噪
    namedWindow(WINDOW_NAME1);
    imshow(WINDOW_NAME1, g_srcImage);
    createTrackbar("canny阈值", WINDOW_NAME1, &g_nThresh, g_nThresh_max, on_ThreshChange);
    on_ThreshChange(0, 0);
    while(waitKey(9)!=27);
    return 0;
}
 
void on_ThreshChange(int, void*)
{
    Canny(g_grayImage, g_cannyMat_output, g_nThresh, g_nThresh*2); //用canny算子检测边缘
    Mat temp = g_cannyMat_output.clone();
    findContours(g_cannyMat_output, g_vContours, g_vHierarchy, RETR_TREE, CHAIN_APPROX_SIMPLE);  
    //此程序中对二值图像寻找轮廓是有点问题的
    Mat drawing = Mat::zeros(g_cannyMat_output.size(), CV_8UC3);
    for (int i = 0; i < g_vContours.size(); i++)
    {
        Scalar color = Scalar(g_rng.uniform(0, 255), g_rng.uniform(0, 255), g_rng.uniform(0, 255));  //任意值
        drawContours(drawing, g_vContours, i, color);
    }
    imshow(WINDOW_NAME2, drawing);
}

官方samples里带的寻找轮廓的例子，更容易理解一点：

#include<opencv2\opencv.hpp>
#include <math.h>
#include <iostream>
 
using namespace cv;
using namespace std;
 
const int w = 500;  //图像的长和宽
int levels = 3;
 
vector<vector<point> > contours;
vector<vec4i> hierarchy;
 
static void on_trackbar(int, void*)
{
    Mat cnt_img = Mat::zeros(w, w, CV_8UC3);
    int _levels = levels - 3;
    drawContours(cnt_img, contours, _levels <= 0 ? 3 : -1, Scalar(128, 255, 255),  //可尝试更换此处的3试一下
        3, LINE_AA, hierarchy, std::abs(_levels));
 
    imshow("contours", cnt_img);
}
 
int main(int argc, char** argv)
{
    Mat img = Mat::zeros(w, w, CV_8UC1);
    //Draw 6 faces
    for (int i = 0; i < 6; i++)
    {
        int dx = (i % 2) * 250 - 30;
        int dy = (i / 2) * 150;
        const Scalar white = Scalar(255);
        const Scalar black = Scalar(0);
 
        if (i == 0)
        {
            for (int j = 0; j <= 10; j++)
            {
                double angle = (j + 5)*CV_PI / 21;
                line(img, Point(cvRound(dx + 100 + j * 10 - 80 * cos(angle)),
                    cvRound(dy + 100 - 90 * sin(angle))),
                    Point(cvRound(dx + 100 + j * 10 - 30 * cos(angle)),
                        cvRound(dy + 100 - 30 * sin(angle))), white, 1, 8, 0);
            }
        }
        ellipse(img, Point(dx + 150, dy + 100), Size(100, 70), 0, 0, 360, white, -1, 8, 0);
        ellipse(img, Point(dx + 115, dy + 70), Size(30, 20), 0, 0, 360, black, -1, 8, 0);
        ellipse(img, Point(dx + 185, dy + 70), Size(30, 20), 0, 0, 360, black, -1, 8, 0);
        ellipse(img, Point(dx + 115, dy + 70), Size(15, 15), 0, 0, 360, white, -1, 8, 0);
        ellipse(img, Point(dx + 185, dy + 70), Size(15, 15), 0, 0, 360, white, -1, 8, 0);
        ellipse(img, Point(dx + 115, dy + 70), Size(5, 5), 0, 0, 360, black, -1, 8, 0);
        ellipse(img, Point(dx + 185, dy + 70), Size(5, 5), 0, 0, 360, black, -1, 8, 0);
        ellipse(img, Point(dx + 150, dy + 100), Size(10, 5), 0, 0, 360, black, -1, 8, 0);
        ellipse(img, Point(dx + 150, dy + 150), Size(40, 10), 0, 0, 360, black, -1, 8, 0);
        ellipse(img, Point(dx + 27, dy + 100), Size(20, 35), 0, 0, 360, white, -1, 8, 0);
        ellipse(img, Point(dx + 273, dy + 100), Size(20, 35), 0, 0, 360, white, -1, 8, 0);
    }
    //show the faces
    namedWindow("image", 1);
    imshow("image", img);
    //Extract the contours so that
    vector<vector<point> > contours0;
    findContours(img, contours0, hierarchy, RETR_TREE, CHAIN_APPROX_SIMPLE);
 
    contours.resize(contours0.size());
    for (size_t k = 0; k < contours0.size(); k++)
        approxPolyDP(Mat(contours0[k]), contours[k], 3, true);
 
    namedWindow("contours", 1);
    createTrackbar("levels+3", "contours", &levels, 7, on_trackbar);
 
    on_trackbar(0, 0);
    waitKey();
 
    return 0;
}

2 . 寻找和绘制物体的凸包

#include<opencv2\opencv.hpp>
#include<iostream>
using namespace cv;
using namespace std;
 
#define WINDOW_NAME1 "【原始图窗口】"
#define WINDOW_NAME2 "【效果图窗口】"
Mat g_srcImage, g_grayImage;
int g_nThresh = 50;
int g_maxThresh = 255;
RNG g_rng;
Mat srcImage_copy = g_srcImage.clone();
Mat g_thresholdImage_output;
vector<vector<point> > g_vContours;
vector<vec4i> g_vHierarchy;
void on_ThreshChange(int, void*);
 
int main()
{
    g_srcImage = imread("poster_cartoon_1.jpg");
    cvtColor(g_srcImage, g_grayImage, COLOR_BGR2GRAY);
    blur(g_grayImage, g_grayImage, Size(3, 3));
    namedWindow(WINDOW_NAME1);
    imshow(WINDOW_NAME1, g_srcImage);
    createTrackbar("阈值：", WINDOW_NAME1, &g_nThresh, g_maxThresh, on_ThreshChange);
    on_ThreshChange(0, 0); //调用一次进行初始化
    while (waitKey(2) != 27);
    return 0;
}
 
void on_ThreshChange(int, void*)
{
    threshold(g_grayImage, g_thresholdImage_output, g_nThresh, 255, THRESH_BINARY);  //二值化
    findContours(g_thresholdImage_output, g_vContours, g_vHierarchy, RETR_TREE, CHAIN_APPROX_SIMPLE);
    //遍历每个轮廓，寻找其凸包
    vector<vector<point> > hull(g_vContours.size());
    for (unsigned int i = 0; i < g_vContours.size(); i++)
    {
        convexHull(Mat(g_vContours[i]), hull[i]);
    }
    //绘出轮廓及凸包
    Mat drawing = Mat::zeros(g_thresholdImage_output.size(), CV_8UC3);
    for (unsigned int i = 0; i < g_vContours.size(); i++)
    {
        Scalar color = Scalar(g_rng.uniform(0, 255), g_rng.uniform(0, 255), g_rng.uniform(0, 255) );
        drawContours(drawing, g_vContours, i, color); //画轮廓
        color = Scalar(g_rng.uniform(0, 255), g_rng.uniform(0, 255), g_rng.uniform(0, 255));
        drawContours(drawing, hull, i, color);  //画凸包图
    }
    imshow(WINDOW_NAME2, drawing);
}

3 . 使用多边形将包围轮廓

#include<opencv2\opencv.hpp>
#include<iostream>
using namespace cv;
using namespace std;
 
#define WINDOW_NAME1 "【原始图窗口】"
#define WINDOW_NAME2 "【效果图窗口】"
Mat g_srcImage;
Mat g_grayImage;
int g_nThresh = 50; //阈值
int g_nMaxThresh = 255; //最大阈值
RNG g_rng;
void on_ContoursChange(int, void*);
 
int main()
{
    g_srcImage = imread("poster_landscape_4.jpg");
    cvtColor(g_srcImage, g_grayImage, COLOR_BGR2GRAY);  //转化为灰度图
    blur(g_grayImage, g_grayImage, Size(3, 3));  //平滑处理
    namedWindow(WINDOW_NAME1);
    imshow(WINDOW_NAME1, g_srcImage);
    createTrackbar("阈值：", WINDOW_NAME1, &g_nThresh, g_nMaxThresh, on_ContoursChange);
    on_ContoursChange(0, 0);
 
    while (waitKey(3) != 27);
    return 0;
}
void on_ContoursChange(int, void*)
{
    Mat threshold_output;
    vector<vector<point> > contours;
    vector<vec4i> hierarchy;
    threshold(g_grayImage, threshold_output, g_nThresh, 255, THRESH_BINARY);  //Threshold检测边缘
    findContours(threshold_output, contours, hierarchy, RETR_TREE, CHAIN_APPROX_SIMPLE);
    //多边形逼近轮廓+获取矩形和圆形边界框
    vector<vector<point> > contours_poly(contours.size());
    vector<rect> boundRect(contours.size());
    vector<point2f> center(contours.size());
    vector<float> radius(contours.size());
//  Mat tmp(contours[3]);
    //一个循环，遍历所有部分，进行本程序最核心的操作
    for (unsigned int i = 0; i < contours.size(); i++)
    {
        approxPolyDP(Mat(contours[i]), contours_poly[i], 3, true);  //指定精度逼近多边形曲线
        boundRect[i] = boundingRect(Mat(contours_poly[i]));  //计算点集的最外面矩形边框
        minEnclosingCircle(contours_poly[i], center[i], radius[i]);  //对给定的2D点集，寻找最小面积的包围圆形
    }
    //绘制多边形轮廓+包围的矩形框+圆形框
    Mat drawing = Mat::zeros(threshold_output.size(), CV_8UC3);
    for (unsigned int i = 0; i < contours.size(); i++)
    {
        Scalar color = Scalar(g_rng.uniform(0, 255), g_rng.uniform(0, 255), g_rng.uniform(0, 255)); //随机设置颜色
        drawContours(drawing, contours_poly, i, color);
        rectangle(drawing, boundRect[i].tl(), boundRect[i].br(), color);
        circle(drawing, center[i], (int)radius[i], color);
    }
    namedWindow(WINDOW_NAME2);
    imshow(WINDOW_NAME2, drawing);
}

4 . 查找并绘制图像轮廓矩

#include<opencv2\opencv.hpp>
#include<iostream>
using namespace cv;
using namespace std;
 
#define WINDOW_NAME1 "【原始图】"
#define WINDOW_NAME2 "【图像轮廓】"
Mat g_srcImage;
Mat g_grayImage;
int g_nThresh = 100;
int g_nMaxThresh = 255;
RNG g_rng;
Mat g_cannyMat_output;
vector<vector<point> > g_vContours;
vector<vec4i> g_vHierarchy;
void on_ThreshChange(int, void*);
 
int main()
{
    g_srcImage = imread("poster_landscape_5.jpg");
    cvtColor(g_srcImage, g_grayImage, COLOR_BGR2GRAY);
    blur(g_grayImage, g_grayImage, Size(3,3));
    namedWindow(WINDOW_NAME1);
    imshow(WINDOW_NAME1, g_srcImage);
    createTrackbar("阈值", WINDOW_NAME1, &g_nThresh, g_nMaxThresh, on_ThreshChange);
    on_ThreshChange(0, 0);
 
    while (waitKey(5)!=27);
    return 0;
}
 
void on_ThreshChange(int, void*)
{
    Canny(g_grayImage, g_cannyMat_output, g_nThresh, g_nThresh*2);  //使用canny检测边缘
    findContours(g_cannyMat_output, g_vContours, g_vHierarchy, RETR_TREE, CHAIN_APPROX_SIMPLE);  //找到轮廓
    vector<moments> mu(g_vContours.size());   //计算矩
    for (unsigned int i = 0; i < g_vContours.size(); i++)
        mu[i] = moments(g_vContours[i], false);
    vector<point2f> mc(g_vContours.size());  //计算中心矩
    for (unsigned int i = 0; i < g_vContours.size(); i++)
        mc[i] = Point2f(static_cast<float>(mu[i].m10/mu[i].m00), static_cast<float>(mu[i].m01 / mu[i].m00));    
    cout << "输出内容：面积和轮廓长度" << endl;
    Mat drawing = Mat::zeros(g_cannyMat_output.size(), CV_8UC3);  //绘制轮廓
    for (unsigned int i = 0; i < g_vContours.size(); i++)  //通过m00计算轮廓面积并且和OpenCV函数比较
    {
        cout << "通过m00计算出轮廓" << i << "的面积，（M_00）=" << mu[i].m00 << endl
            << " OpenCV函数计算出的面积=" << contourArea(g_vContours[i]) << ", 长度: " << arcLength(g_vContours[i], true) << endl << endl;
        Scalar color = Scalar(g_rng.uniform(0, 255), g_rng.uniform(0, 255), g_rng.uniform(0, 255));
        drawContours(drawing, g_vContours, i, color, 2, 8, g_vHierarchy, 0, Point());  //绘制外层和内层轮廓
        circle(drawing, mc[i], 4, color, -1);
    }
    namedWindow(WINDOW_NAME2);  //显示到窗口
    imshow(WINDOW_NAME2, drawing);
}

5 . 分水岭算法

#include<opencv2\opencv.hpp>
#include<iostream>
using namespace std;
using namespace cv;
 
#define WINDOW_NAME "【程序窗口1】"
Mat g_maskImage, g_srcImage;
Point prevPt(-1, -1);
static void on_Mouse(int event, int x, int y, int flags, void*);
 
int main()
{
    //载入原图，初始化掩膜和灰度图
    g_srcImage = imread("poster_landscape_6.jpg");
    imshow(WINDOW_NAME, g_srcImage);
    Mat srcImage, grayImage;
    g_srcImage.copyTo(srcImage);
    cvtColor(g_srcImage, g_maskImage, COLOR_BGR2GRAY);
    cvtColor(g_maskImage, grayImage, COLOR_GRAY2BGR);
    g_maskImage = Scalar::all(0);
    //设置鼠标回调函数
    setMouseCallback(WINDOW_NAME, on_Mouse);
    //轮询按键
    while (1)
    {
        int c = waitKey(0);
        if ((char)c == 27) break;
        if ((char)c == '2') {   //按键‘2’， 恢复源图
            g_maskImage = Scalar::all(0);
            srcImage.copyTo(g_srcImage);
            imshow("image", g_srcImage);
        }
        if ((char)c=='1' || (char)c==' ' ) {
            //定义一些参数            
            vector<vector<point> > contours;
            vector<vec4i> hierarchy;
            //寻找轮廓
            findContours(g_maskImage, contours, hierarchy, CV_RETR_CCOMP, CV_CHAIN_APPROX_SIMPLE);
            //轮廓为空时的处理
            if (contours.empty()) continue;
            //复制掩膜
            Mat maskImage(g_maskImage.size(), CV_32S);
            maskImage = Scalar::all(0);
            //循环绘制出轮廓
            int compCount = 0;
            for (int index = 0; index >= 0; index = hierarchy[index][0], compCount++)
                drawContours(maskImage, contours, index, Scalar::all(compCount+1), -1, LINE_8, hierarchy);
            //compCount为零时的处理
            if (compCount == 0)
                continue;
            //生成随机颜色
            vector<vec3b> colorTab;
            for (unsigned int i = 0; i < compCount; i++) {
                int b = theRNG().uniform(0, 255);
                int g = theRNG().uniform(0, 255);
                int r = theRNG().uniform(0, 255);
                colorTab.push_back(Vec3b((uchar)b, (uchar)g, (uchar)r));
            }
            //计算处理时间并输出到窗口中
            double dTime = (double)getTickCount();
            watershed(srcImage, maskImage);
            dTime = (double)getTickCount() - dTime;
            printf("处理时间=%gms\n", dTime*1000./getTickFrequency());
            //双层循环，将分水岭图像遍历存入watershedImage中
            Mat watershedImage(maskImage.size(), CV_8UC3);
            for (unsigned int i = 0; i < maskImage.rows; i++)
                for (unsigned int j = 0; j < maskImage.cols; j++)
                {
                    int index = maskImage.at<int>(i, j);
                    if (index == -1)
                        watershedImage.at<vec3b>(i, j) = Vec3b(255, 255, 255);
                    else if (index <= 0 || index > compCount)
                        watershedImage.at<vec3b>(i, j) = Vec3b(0, 0, 0);
                    else
                        watershedImage.at<vec3b>(i, j) = colorTab[index - 1];
                }
            //混合灰度图和分水岭效果图并显示最终的窗口
            watershedImage = watershedImage*0.5 + grayImage*0.5;
            imshow("watershed transform", watershedImage);
        }
    }
    return 0;
}
 
static void on_Mouse(int event, int x, int y, int flags, void*)
{
    //处理鼠标不在窗口中的情况
    if (x < 0 || x >= g_srcImage.cols || y < 0 || y >= g_srcImage.rows) return;
    //处理鼠标左键相关消息
    if (event == EVENT_LBUTTONUP || !(flags & EVENT_FLAG_LBUTTON))   //左键抬起动作或处于没有按下状态
        prevPt = Point(-1, -1);
    else if (event == EVENT_LBUTTONDOWN)  //左键按下动作
        prevPt = Point(x, y);
    //鼠标左键按下并移动，绘制出白色线条
    else if (event == EVENT_MOUSEMOVE && (flags & EVENT_FLAG_LBUTTON))
    {
        Point pt(x, y);
        if (prevPt.x < 0) prevPt = pt;
        line(g_maskImage, prevPt, pt, Scalar::all(255), 5);
        line(g_srcImage, prevPt, pt, Scalar::all(255), 5);
        prevPt = pt;
        imshow(WINDOW_NAME, g_srcImage);
    }
 
}

6 . 图像修补

#include<opencv2\opencv.hpp>
#include<iostream>
using namespace cv;
using namespace std;
 
#define WINDOW_NAME1 "【原始图】"
#define WINDOW_NAME2 "【修补后的效果图】"
Mat srcImage1, inpaintMask;
Point previousPoint(-1, -1); //原来的点坐标
 
static void on_Mouse(int event, int x, int y, int flags, void*)
{
    if (event == EVENT_LBUTTONUP || !(flags & EVENT_FLAG_LBUTTON))  //鼠标左键抬起或没有按键按下
        previousPoint = Point(-1, -1);
    else if (event == EVENT_LBUTTONDOWN)  //鼠标左键按下消息
        previousPoint = Point(x, y);
    else if (event==EVENT_MOUSEMOVE && (flags & EVENT_FLAG_LBUTTON))  //鼠标左键按下并移动，进行绘制
    {
        Point pt(x, y);
        if (previousPoint.x < 0) previousPoint = pt;
        //绘制白色线条
        line(inpaintMask, previousPoint, pt, Scalar::all(255), 5);
        line(srcImage1, previousPoint, pt, Scalar::all(255), 5);
        previousPoint = pt;
        imshow(WINDOW_NAME1, srcImage1);
    }
}
 
int main()
{
    Mat srcImage = imread("poster_landscape_7.jpg");
    srcImage1 = srcImage.clone();
    inpaintMask = Mat::zeros(srcImage1.size(), CV_8U);
    imshow(WINDOW_NAME1, srcImage1);  //显示原始图
    cvSetMouseCallback(WINDOW_NAME1, on_Mouse);  //设置鼠标回调消息
    while (1)  //轮询按键，根据不同的按键进行处理
    {
        char c = (char)waitKey();
        if (c == 27) break;
        if (c == '2') {  //键值为2， 恢复成原始图像
            inpaintMask = Scalar::all(0);
            srcImage.copyTo(srcImage1);
            imshow(WINDOW_NAME1, srcImage1);
        }
        if (c=='1' || c==' ')  //键值为1或者空格，进行图像修补操作
        {
            Mat inpaintedImage;
            inpaint(srcImage1, inpaintMask, inpaintedImage, 3, INPAINT_TELEA);
            imshow(WINDOW_NAME2, inpaintedImage);
        }
    }
    return 0;
}