OpenCV摄像头简单程序

无论是处理现有视频文件还是实时读取摄像头，其实都是读取连续的帧(frame)。每个frame都可以看做是一个图像(Mat)，图像处理的方式就可以用在video的处理上了。现在，动手试一试。

【代码1】用霍夫变换(Hough Transform)检测直线并且显示在实时video中

#include <cv.h>
#include <highgui.h>
#include <iostream>
#include <vector>

using namespace std;
using namespace cv;

#define PI 3.1415926

class LineFinder{
private:
    Mat img;
    vector<Vec4i>lines;
    double deltaRho;
    double deltaTheta;
    int minVote;
    double minLength;
    double maxGap;
public:
    LineFinder():deltaRho(1), deltaTheta(PI/180), minVote(10), minLength(0.), maxGap(0.){}
    
    void setAccResolution(double dRho, double dTheta){
        deltaRho=dRho;
        deltaTheta=dTheta;
    }
    void setMinVote(int minv){
        minVote=minv;
    }
    void setLineLengthAndGap(double length, double gap){
        minLength=length;
        maxGap=gap;
    }
    vector<Vec4i>findLines(Mat& binary){
        lines.clear();
        HoughLinesP(binary, lines, deltaRho, deltaTheta, minVote, minLength, maxGap);
        return lines;
    }
    void drawDetectedLines(Mat &image, Scalar color=Scalar(255, 255, 255)){
        vector<Vec4i>::const_iterator it2=lines.begin();
        while(it2!=lines.end()){
            Point pt1((*it2)[0], (*it2)[1]);
            Point pt2((*it2)[2], (*it2)[3]);
            line(image, pt1, pt2, color);
            ++it2;
        }
    }
};

int main(){
    
    
    string msg="press q , Q or ESC to close this program";
    cout << msg << endl;
    VideoCapture capture(0);
    if(!capture.isOpened()) return 1;
    Mat image; //frame of video
    string window_name="Extracted Frame";
    namedWindow(window_name);
    while(true){
        capture >> image;
        if(image.data){
            Mat contours;
            Canny(image, contours, 125, 350);
            LineFinder finder;
            finder.setLineLengthAndGap(100, 20);
            finder.setMinVote(80);
            vector<Vec4i>lines=finder.findLines(contours);
            finder.drawDetectedLines(image);
            namedWindow("Detected Lines with HoughP");
            imshow("Detected Lines with HoughP", image);
        }
        if(waitKey(30)>=0) break;
    }
    
    
    return 0;
}

【代码2】矩形检测

// The "Square Detector" program.
// It loads several images sequentially and tries to find squares in
// each image

#include "opencv2/core/core.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/highgui/highgui.hpp"

#include <iostream>
#include <math.h>
#include <string.h>

using namespace cv;
using namespace std;

static void help()
{
    cout <<
    "\nA program using pyramid scaling, Canny, contours, contour simpification and\n"
    "memory storage (it's got it all folks) to find\n"
    "squares in a list of images pic1-6.png\n"
    "Returns sequence of squares detected on the image.\n"
    "the sequence is stored in the specified memory storage\n"
    "Call:\n"
    "./squares\n"
    "Using OpenCV version %s\n" << CV_VERSION << "\n" << endl;
}


int thresh = 50, N = 11;
const char* wndname = "Square Detection Demo";

// helper function:
// finds a cosine of angle between vectors
// from pt0->pt1 and from pt0->pt2
static double angle( Point pt1, Point pt2, Point pt0 )
{
    double dx1 = pt1.x - pt0.x;
    double dy1 = pt1.y - pt0.y;
    double dx2 = pt2.x - pt0.x;
    double dy2 = pt2.y - pt0.y;
    return (dx1*dx2 + dy1*dy2)/sqrt((dx1*dx1 + dy1*dy1)*(dx2*dx2 + dy2*dy2) + 1e-10);
}

// returns sequence of squares detected on the image.
// the sequence is stored in the specified memory storage
static void findSquares( const Mat& image, vector<vector<Point> >& squares )
{
    squares.clear();

    Mat pyr, timg, gray0(image.size(), CV_8U), gray;

    // down-scale and upscale the image to filter out the noise
    pyrDown(image, pyr, Size(image.cols/2, image.rows/2));
    pyrUp(pyr, timg, image.size());
    vector<vector<Point> > contours;

    // find squares in every color plane of the image
    for( int c = 0; c < 3; c++ )
    {
        int ch[] = {c, 0};
        mixChannels(&timg, 1, &gray0, 1, ch, 1);

        // try several threshold levels
        for( int l = 0; l < N; l++ )
        {
            // hack: use Canny instead of zero threshold level.
            // Canny helps to catch squares with gradient shading
            if( l == 0 )
            {
                // apply Canny. Take the upper threshold from slider
                // and set the lower to 0 (which forces edges merging)
                Canny(gray0, gray, 0, thresh, 5);
                // dilate canny output to remove potential
                // holes between edge segments
                dilate(gray, gray, Mat(), Point(-1,-1));
            }
            else
            {
                // apply threshold if l!=0:
                //     tgray(x,y) = gray(x,y) < (l+1)*255/N ? 255 : 0
                gray = gray0 >= (l+1)*255/N;
            }

            // find contours and store them all as a list
            findContours(gray, contours, CV_RETR_LIST, CV_CHAIN_APPROX_SIMPLE);

            vector<Point> approx;

            // test each contour
            for( size_t i = 0; i < contours.size(); i++ )
            {
                // approximate contour with accuracy proportional
                // to the contour perimeter
                approxPolyDP(Mat(contours[i]), approx, arcLength(Mat(contours[i]), true)*0.02, true);

                // square contours should have 4 vertices after approximation
                // relatively large area (to filter out noisy contours)
                // and be convex.
                // Note: absolute value of an area is used because
                // area may be positive or negative - in accordance with the
                // contour orientation
                if( approx.size() == 4 &&
                    fabs(contourArea(Mat(approx))) > 1000 &&
                    isContourConvex(Mat(approx)) )
                {
                    double maxCosine = 0;

                    for( int j = 2; j < 5; j++ )
                    {
                        // find the maximum cosine of the angle between joint edges
                        double cosine = fabs(angle(approx[j%4], approx[j-2], approx[j-1]));
                        maxCosine = MAX(maxCosine, cosine);
                    }

                    // if cosines of all angles are small
                    // (all angles are ~90 degree) then write quandrange
                    // vertices to resultant sequence
                    if( maxCosine < 0.3 )
                        squares.push_back(approx);
                }
            }
        }
    }
}


// the function draws all the squares in the image
static void drawSquares( Mat& image, const vector<vector<Point> >& squares )
{
    for( size_t i = 0; i < squares.size(); i++ )
    {
        const Point* p = &squares[i][0];
        int n = (int)squares[i].size();
        polylines(image, &p, &n, 1, true, Scalar(0,255,0), 3, CV_AA);
    }

    imshow(wndname, image);
}

int main(){


    string msg="press q , Q or ESC to close this program";
    cout << msg << endl;
    VideoCapture capture(0);
    if(!capture.isOpened()) return 1;
    Mat image; //frame of video
    string window_name="Extracted Frame";
    namedWindow(window_name);
    while(true){
        capture >> image;
        vector<vector<Point> > squares;
        if(image.data){
            findSquares(image, squares);
            drawSquares(image, squares);
        }
        if(waitKey(30)>=0) break;
    }

    return 0;
}