OpenCV2马拉松第8圈——绘制直方图

C++: void calcHist(const Mat* images, int nimages, const int* channels, InputArray mask, SparseMat& hist, int dims, const int* histSize, const float** ranges, bool uniform=true, bool accumulate=false )

	images – 是一个图像数组，简单起见，我们都只传一个图像.  nimages – 图片数组大小，我们固定为1 channels – 是一个数组，灰度图像为0就可以，彩色图像要0，1，2 mask – 这里我们不需要，传Mat()就可以 hist – 输出的直方图 dims – 计算彩色RGB要3，gray传1 histSize – 是一个数组，一般来说内容就是256 ranges – 是一个二维数组，每个数组包括的都是一个范围，[0,255]最常用，下面会看到 uniform – 在这里用默认就足够. accumulate – 在这里用默认就足够.

class Histogram1D {
private:
	int histSize[1]; // number of bins
	float hranges[2]; // min and max pixel value
	const float* ranges[1];
	int channels[1]; // only 1 channel used here
public:
	Histogram1D() {
		histSize[0]= 256;
		hranges[0]= 0.0;
		hranges[1]= 255.0;
		ranges[0]= hranges;
		channels[0]= 0; // by default, we look at channel 0
	}
	
	// Computes the 1D histogram.
	Mat getHistogram(const cv::Mat &image) {
		Mat hist;
		// Compute histogram
		calcHist(&image,1,channels,Mat(),hist,1,histSize,ranges);
		return hist;
	}
	
	Mat getHistogramImage(const cv::Mat &image){
		// Compute histogram first
		Mat hist= getHistogram(image);
		// Get min and max bin values
		double maxVal=0;
		double minVal=0;
		minMaxLoc(hist, &minVal, &maxVal, 0, 0);
		// Image on which to display histogram
		Mat histImg(histSize[0], histSize[0],CV_8U,Scalar(255));
		// set highest point at 90% of nbins
		int hpt = static_cast<int>(0.9*histSize[0]);
		// Draw a vertical line for each bin
		for( int h = 0; h < histSize[0]; h++ ) {
			float binVal = hist.at<float>(h);
			int intensity = static_cast<int>(binVal*hpt/maxVal);
			// This function draws a line between 2 points
			line(histImg,Point(h,histSize[0]),
			Point(h,histSize[0]-intensity),
			Scalar::all(0));
		}
		return histImg;
	}
};

Mat image,gray;
image = imread( argv[1], 1 );
if( !image.data )
	return -1;
 cvtColor(image, gray, CV_BGR2GRAY);
    
Histogram1D h;
namedWindow("Histogram");
imshow("Histogram",h.getHistogramImage(gray));

/// Separate the image in 3 places ( B, G and R )
vector<Mat> bgr_planes;
split( src, bgr_planes );

#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);
    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();
}