OpenCV2 計算直方圖

我們仍然如前例一樣，構建一個類來處理得到直方圖。

我們來看看基本的類定義

#ifndef HISTOGRAM_H_
#define HISTOGRAM_H_
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <iostream>
#include <vector>

class Histogram {
private:
    int histSize[1];
    float hrangee[2];
    const float* ranges[1];
    int channels[1];

protected:
    cv::Mat getHistogram(const cv::Mat&);

public:
    Histogram();
    cv::Mat getHistogramImage(const cv::Mat&, int channel = 0);
};

#endif /* HISTOGRAM_H_ */

類的實現

#include "Histogram.h"

Histogram::Histogram() {
    histSize[0] = 256;
    hrangee[0] = 0.0;
    hrangee[1] = 255.0;
    ranges[0] = hrangee;
    channels[0] = 0;
}


cv::Mat Histogram::getHistogram(const cv::Mat& image){
    cv::MatND hist;
    cv::calcHist(&image, 1, channels, cv::Mat(), hist, 1, histSize, ranges);
    return hist;
}


cv::Mat Histogram::getHistogramImage(const cv::Mat& image, int channel){
    std::vector<cv::Mat> planes;
    cv::split(image,planes);
    cv::Scalar color;
    if(planes.size() == 1){
        channel = 0;
        color = cv::Scalar(0,0,0);
    }else{
        color = cv::Scalar(channel==0?255:0, channel==1?255:0, channel==2?255:0);
    }
    cv::MatND hist = getHistogram(planes[channel]);
    double maxVal = 0;
    double minVal = 0;
    cv::minMaxLoc(hist, &minVal, &maxVal, 0, 0);
    cv::Mat histImg(histSize[0], histSize[0], CV_8UC3, cv::Scalar(255,255,255));
    int hpt = static_cast<int>(0.9*histSize[0]);
    for(int h=0; h<histSize[0]-1; h++){
        float binVal = hist.at<float>(h);
        float binVal2 = hist.at<float>(h+1);
        int intensity = static_cast<int>(binVal*hpt/maxVal);
        int intensity2 = static_cast<int>(binVal2*hpt/maxVal);
        cv::line(histImg, cv::Point(h,histSize[0]-intensity),
                cv::Point(h,histSize[0]-intensity2), color);
    }
    return histImg;
}

這裡面要用的幾個函數在這裡列出：

C++: void minMaxLoc(const SparseMat& src, double* minVal, double* maxVal, int* minIdx=0, int* maxIdx=0)

其作用是尋找一個矩陣中最大和最小值，並得到它們的位置

C++: void split(const Mat& mtx, vector<Mat>& mv)

把一個多通道矩陣分成幾個單通道矩陣，在這裡主要用於計算各通道的直方圖

C++: void calcHist(const Mat* arrays, int narrays, const int* channels, InputArray mask, OutputArray hist, int dims, const int* histSize, const float** ranges, bool uniform=true, bool accumulate=false )

計算直方圖，各參數含義可在參考文檔中查到

Parameters:

• arrays – Source arrays. They all should have the same depth, CV_8U or CV_32F , and the same size. Each of them can have an arbitrary number of channels.
• narrays – Number of source arrays.
• channels – List of the dims channels used to compute the histogram. The first array channels are numerated from 0 to arrays[0].channels()-1 , the second array channels are counted from arrays[0].channels() to arrays[0].channels() + arrays[1].channels()-1, and so on.
• mask – Optional mask. If the matrix is not empty, it must be an 8-bit array of the same size as arrays[i] . The non-zero mask elements mark the array elements counted in the histogram.
• hist – Output histogram, which is a dense or sparse dims -dimensional array.
• dims – Histogram dimensionality that must be positive and not greater than CV_MAX_DIMS (equal to 32 in the current OpenCV version).
• histSize – Array of histogram sizes in each dimension.
• ranges – Array of the dims arrays of the histogram bin boundaries in each dimension. When the histogram is uniform ( uniform =true), then for each dimension i it is enough to specify the lower (inclusive) boundary of the 0-th histogram bin and the upper (exclusive) boundary for the last histogram bin histSize[i]-1 . That is, in case of a uniform histogram each of ranges[i] is an array of 2 elements. When the histogram is not uniform ( uniform=false ), then each of ranges[i] contains histSize[i]+1 elements: . The array elements, that are not between and , are not counted in the histogram.
• uniform – Flag indicatinfg whether the histogram is uniform or not (see above).
• accumulate – Accumulation flag. If it is set, the histogram is not cleared in the beginning when it is allocated. This feature enables you to compute a single histogram from several sets of arrays, or to update the histogram in time.

我們來看看主程序

#include "Histogram.h"

int main(){
    cv::Mat image = cv::imread("E:/Image/Lena.jpg");
    Histogram h;

    cv::namedWindow("Red");
    cv::namedWindow("Blue");
    cv::namedWindow("Green");
    cv::namedWindow("Original");
    cv::imshow("Original",image);
    cv::imshow("Red",h.getHistogramImage(image,2));
    cv::imshow("Green",h.getHistogramImage(image,1));
    cv::imshow("Blue",h.getHistogramImage(image));

    cv::waitKey(0);
    return 0;
}

運行效果如下：