opencv 2 computer vision application programming第四章翻译

有点晚了先开个头，明天翻译具体内容

第四章 用直方图统计像素
这一章包括:
计算图像的直方图
应用查表以修改图像外观
补偿图像直方图
幕后使用直方图以检测特定的图像内容
使用平均移动算法以找到物体
使用直方图比较以恢复相似图像

计算灰度图像的直方图，并用图显示出来：

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

using namespace std;
using namespace cv;

class Histogram1D{
private:
    int histSize[1];
    float hranges[2];
    const float* ranges[1];
    int channels[1];
public:
    Histogram1D(){
        histSize[0]=256;
        hranges[0]=0.0;
        hranges[1]=255.0;
        ranges[0]=hranges;
        channels[0]=0;
    }

    MatND getHistogram(const Mat& image){
        MatND hist;
        calcHist(&image, 1, channels, Mat(), hist, 1,
                 histSize, ranges);
        return hist;
    }
    Mat getHistogramImage(const Mat& image){
        MatND hist=getHistogram(image);
        double maxVal=0;
        double minVal=0;
        minMaxLoc(hist, &minVal, &maxVal, 0, 0);
        Mat histImg(histSize[0], histSize[0], CV_8U, Scalar(255));
        int hpt=static_cast<int>(0.9*histSize[0]);
        for(int h=0; h<histSize[0]; h++){
            float binVal=hist.at<float>(h);
            int intensity=static_cast<int>(binVal*hpt/maxVal);
            line(histImg, Point(h, histSize[0]), Point(h, histSize[0]-intensity),Scalar::all(0));
        }
        return histImg;
    }
};



int main(){
    Mat image=imread("C:/testdir/barcode.bmp");
    Histogram1D h;
    MatND histo=h.getHistogram(image);
/*    for(int i=0; i<256; i++){
        cout << "Value " << i << "=" <<
                histo.at<float>(i) << endl;
    }
    */
    namedWindow("Histogram");
    imshow("Histogram", h.getHistogramImage(image));
    waitKey(0);
}

 threshold是边缘检测函数，例如可以这样应用

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

using namespace std;
using namespace cv;

class Histogram1D{
private:
    int histSize[1];
    float hranges[2];
    const float* ranges[1];
    int channels[1];
public:
    Histogram1D(){
        histSize[0]=256;
        hranges[0]=0.0;
        hranges[1]=255.0;
        ranges[0]=hranges;
        channels[0]=0;
    }

    MatND getHistogram(const Mat& image){
        MatND hist;
        calcHist(&image, 1, channels, Mat(), hist, 1,
                 histSize, ranges);
        return hist;
    }
    Mat getHistogramImage(const Mat& image){
        MatND hist=getHistogram(image);
        double maxVal=0;
        double minVal=0;
        minMaxLoc(hist, &minVal, &maxVal, 0, 0);
        Mat histImg(histSize[0], histSize[0], CV_8U, Scalar(255));
        int hpt=static_cast<int>(0.9*histSize[0]);
        for(int h=0; h<histSize[0]; h++){
            float binVal=hist.at<float>(h);
            int intensity=static_cast<int>(binVal*hpt/maxVal);
            line(histImg, Point(h, histSize[0]), Point(h, histSize[0]-intensity),Scalar::all(0));
        }
        return histImg;
    }
};



int main(){
    Mat image=imread("C:/testdir/Koala.jpg");
//    Mat image=imread("C:/testdir/barcode.bmp");
    Histogram1D h;
    Mat thresholded;
    threshold(image, thresholded, 60, 255, THRESH_BINARY);
    MatND histo=h.getHistogram(image);
/*    for(int i=0; i<256; i++){
        cout << "Value " << i << "=" <<
                histo.at<float>(i) << endl;
    }
    */
    namedWindow("Histogram");
    imshow("Image", thresholded);
    imshow("Histogram", h.getHistogramImage(image));
    waitKey(0);
}

使用equalizeHist函数的时候报错，google之后发现要求图片格式应该为CV_8UC1才可以用的，所以平凡图像需要先转化咯：

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

using namespace std;
using namespace cv;

Mat equalize(const Mat& image){
    Mat result(image);
    equalizeHist(image, result);
    return result;
}

int main(){
    char* name="C:/testdir/Koala.jpg";
    IplImage *img = cvLoadImage( name, CV_LOAD_IMAGE_GRAYSCALE );
    Mat src( img, 0 );
    src = Mat_<float>( src );
    Mat dst = Mat::zeros( src.rows, src.cols, src.type() );
    src = Mat_<uchar>(src);
    equalizeHist( src, dst );
    imshow("dst", src);
    waitKey(0);
    return 0;
}