直方圖與通道分割

直方圖實在是這個世界上最有用的工具之一了，做做統計 做做均衡化，幹啥都要用到它。

下面給出自己用的一段簡單的程序，將圖像的H分量分離出來計算直方圖：H分量分成16個等級

int hsize = 16;
float hranges[] = {0,180};
const float* phranges = hranges;
int ch[] = {0, 0};
Mat hsv_src, hue_src,hist_src;
 cvtColor(img_src, hsv_src, CV_BGR2HSV); //OpenCV默認的圖片通道是BGR。IOS 是 RGBA
hue_src.create(hsv_src.size(), hsv_src.depth());
mixChannels(&hsv_src, 1, &hue_src, 1, ch, 1);
calcHist(&hue_src, 1, 0, Mat(), hist_src, 1, &hsize, &phranges);

 

上面分離h通道是怎麼做的呢，通過mixChannels 將hsv中的0號通道 放到hue_src中。那麼就被分割出來了。

普通的通道分割是怎麼做呢？

vector<Mat> img_plane; //或者如果知道是3通道 就std::vector<cv::Mat> img_plane(3);

//或者 std::vector<cv::Mat> img_plane(img.channels());
split(img, img_plane);
得到img_plane[channel_index]索引到圖像不同的通道
然後對通道做完操作之後,可以用merge函數將它們合成一個Mat
merge(img_planes, img_end)

如果我們只是對一個通道做某件事情 就不需要拆出那麼矩陣，而通過mixChannels 分離出一個通道，然後再通過mixChannels融合回去。

下面我是拆分了v通道

 int ch[] = {2, 0};  int ch1[] = {0,2};
 ……

 mixChannels(&hsv_src, 1, &v_src, 1, ch, 1);
 ……

 mixChannels(&v_src, 1, &hsv_src, 1,ch1 , 1);

 

 

網上有一個簡單的直方圖顯示函數 備份在這裏

使用的時候 直接用

Mat histImg = imHist(hist_src,5,5);

    namedWindow( "H-S Histogram", 1 );
    imshow( "H-S Histogram", histImg );
    waitKey();

就可以了。

Mat imHist(Mat hist, float scaleX=1, float scaleY=1){
  double maxVal=0;
  minMaxLoc(hist, 0, &maxVal, 0, 0);
  int rows = 64; //default height size
  int cols = hist.rows; //get the width size from the histogram
  Mat histImg = Mat::zeros(rows*scaleX, cols*scaleY, CV_8UC3);
  //for each bin
  for(int i=0;i<cols-1;i++) {
    float histValue = hist.at<float>(i,0);
    float nextValue = hist.at<float>(i+1,0);
    Point pt1 = Point(i*scaleX, rows*scaleY);
    Point pt2 = Point(i*scaleX+scaleX, rows*scaleY);
    Point pt3 = Point(i*scaleX+scaleX, (rows-nextValue*rows/maxVal)*scaleY);
    Point pt4 = Point(i*scaleX, (rows-nextValue*rows/maxVal)*scaleY);

    int numPts = 5;
    Point pts[] = {pt1, pt2, pt3, pt4, pt1};

    fillConvexPoly(histImg, pts, numPts, Scalar(255,255,255));
  }
  return histImg;
}

 

 

有人對直方圖函數做了詳盡的測試  可見 http://blog.csdn.net/ljbsdu/article/details/7420429

這是OpenCV給出的示例程序

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

using namespace cv;

int main( int argc, char** argv )
{
    Mat src, hsv;
    if( argc != 2 || !(src=imread(argv[1], 1)).data )
        return -1;

    cvtColor(src, hsv, CV_BGR2HSV);

    // Quantize the hue to 30 levels
    // and the saturation to 32 levels
    int hbins = 30, sbins = 32;
    int histSize[] = {hbins, sbins};
    // hue varies from 0 to 179, see cvtColor
    float hranges[] = { 0, 180 };
    // saturation varies from 0 (black-gray-white) to
    // 255 (pure spectrum color)
    float sranges[] = { 0, 256 };
    const float* ranges[] = { hranges, sranges };
    MatND hist;
    // we compute the histogram from the 0-th and 1-st channels
    int channels[] = {0, 1};

    calcHist( &hsv, 1, channels, Mat(), // do not use mask
             hist, 2, histSize, ranges,
             true, // the histogram is uniform
             false );
    double maxVal=0;
    minMaxLoc(hist, 0, &maxVal, 0, 0);

    int scale = 10;
    Mat histImg = Mat::zeros(sbins*scale, hbins*10, CV_8UC3);

    for( int h = 0; h < hbins; h++ )
        for( int s = 0; s < sbins; s++ )
        {
            float binVal = hist.at<float>(h, s);
            int intensity = cvRound(binVal*255/maxVal);
            rectangle( histImg, Point(h*scale, s*scale),
                        Point( (h+1)*scale - 1, (s+1)*scale - 1),
                        Scalar::all(intensity),
                        CV_FILLED );
        }

    namedWindow( "Source", 1 );
    imshow( "Source", src );

    namedWindow( "H-S Histogram", 1 );
    imshow( "H-S Histogram", histImg );
    waitKey();
}