OpenCv 019---图像直方图比较

1 前备知识

 图像直方图比较，就是比较两幅图像的直方图数据，比较两组数据的相似性，从而得到两幅图像之间的相似程度，直方图比较在早期的CBIR(基于内容的图像检索Content-based image retrieval)是较常应用的技术手段，通常结合边缘处理、词袋模型等技术一起使用。

词袋模型（英语：Bag-of-words model）是个在自然语言处理和信息检索(IR)下被简化的表达模型。此模型下，一段文本（比如一个句子或是一个文档）可以用一个装着这些词的袋子来表示，这种表示方式不考虑文法以及词的顺序。最近词袋模型也被应用在计算机视觉领域。

词袋模型被广泛应用在文件分类，词出现的频率可以用来当作训练分类器的特征。

2 所用到的主要OpenCv API

CV_EXPORTS void calcHist( const Mat* images, int nimages,
                          const int* channels, InputArray mask,
                          OutputArray hist, int dims, const int* histSize,
                          const float** ranges, bool uniform = true, bool accumulate = false );

/** @brief Calculates a histogram of a set of arrays.//计算一组数组的直方图

@param images Source arrays. They all should have the same depth, CV_8U, CV_16U or CV_32F , and the same
size. Each of them can have an arbitrary number of channels(任意的通道数).
@param nimages Number of source images. //原图数量
@param channels List of the dims channels used to compute the histogram. The first array channels
are numerated from 0 to images[0].channels()-1 , the second array channels are counted from
images[0].channels() to images[0].channels() + images[1].channels()-1, and so on.
@param mask Optional mask. If the matrix is not empty, it must be an 8-bit array of the same size
as images[i] . The non-zero mask elements mark the array elements counted in the histogram.
@param hist Output histogram, which is a dense or sparse dims -dimensional array.
@param dims Histogram dimensionality that must be positive and not greater than CV_MAX_DIMS
(equal to 32 in the current OpenCV version).
@param histSize Array of histogram sizes in each dimension.
@param 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 f$L_0f$ of the 0-th histogram bin and the upper (exclusive) boundary
f$U_{ exttt{histSize}[i]-1}f$ 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:
f$L_0, U_0=L_1, U_1=L_2, ..., U_{ exttt{histSize[i]}-2}=L_{ exttt{histSize[i]}-1}, U_{ exttt{histSize[i]}-1}f$
. The array elements, that are not between f$L_0f$ and f$U_{ exttt{histSize[i]}-1}f$ , are not
counted in the histogram.
@param uniform Flag indicating whether the histogram is uniform or not (see above).
@param 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.
*/

3 程序代码

#include<opencv2opencv.hpp>
#include <iostream>

using namespace std;
using namespace cv;

int main(int argc, char** argv)
{
    //load the image
    Mat src1 = imread("G:\CVworkstudy\program_wwx\Research society140\ZhaiZhigang140\m1.png");
    Mat src2 = imread("G:\CVworkstudy\program_wwx\Research society140\ZhaiZhigang140\m2.png");
    Mat src3 = imread("G:\CVworkstudy\program_wwx\Research society140\ZhaiZhigang140\flower.png");
    Mat src4 = imread("G:\CVworkstudy\program_wwx\Research society140\ZhaiZhigang140\wm.jpg");
    //show the image
    imshow("input1", src1);
    imshow("input2", src2);
    imshow("input3", src3);
    imshow("input4", src4);
    //convert hte HSV to BGR
    Mat hsv1, hsv2, hsv3, hsv4;
    cvtColor(src1, hsv1, COLOR_BGR2HSV);
    cvtColor(src2, hsv2, COLOR_BGR2HSV);
    cvtColor(src3, hsv3, COLOR_BGR2HSV);
    cvtColor(src4, hsv4, COLOR_BGR2HSV);
    //calculate the hist
    int h_bins = 60;
    int s_bins = 64;
    int histSize[] = { h_bins,s_bins };
    float h_ranges[] = { 0,180 };
    float s_ranges[] = { 0,255 };
    const float* ranges[] = { h_ranges,s_ranges };
    int channels[] = { 0,1 };
    Mat hist1, hist2, hist3, hist4;
    calcHist(&hsv1, 1, channels, Mat(), hist1, 2, histSize, ranges, true, false);
    calcHist(&hsv2, 1, channels, Mat(), hist2, 2, histSize, ranges, true, false);
    calcHist(&hsv3, 1, channels, Mat(), hist3, 2, histSize, ranges, true, false);
    calcHist(&hsv4, 1, channels, Mat(), hist4, 2, histSize, ranges, true, false);

    normalize(hist1, hist1, 0, 1, NORM_MINMAX, -1, Mat());
    normalize(hist2, hist2, 0, 1, NORM_MINMAX, -1, Mat());
    normalize(hist3, hist3, 0, 1, NORM_MINMAX, -1, Mat());
    normalize(hist4, hist4, 0, 1, NORM_MINMAX, -1, Mat());

    for (int i = 0; i < 4; i++)
    {
        int compare_method = i;
        double src1_src2 = compareHist(hist1, hist2, compare_method);
        double src3_src4 = compareHist(hist3, hist4, compare_method);
        printf(" Method [%d]: src1_src2: %f,src3_src4: %f,
", i, src1_src2, src3_src4);
    }

    waitKey(0);
    return 0;
}

4 运行结果

 

5 扩展及注意事项

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