第七章 学习OpenCV(2)

运行结果如下图：

根据输入的图像计算色相饱和度（hue-saturation）直方图，然后利用该直方图创建EMD接口参数signature，最后利用EMD来度量两个分布之间的相似性，程序中src1与src2已经过处理，有40的亮度值偏移，具体代码如下：

#include <cv.h>
#include <highgui.h>  
#include <stdlib.h>  
#include <stdio.h> 
#include <math.h>
using namespace std;
int main(int argc, char* argv[])
{
    IplImage* src1,*src2,*Imask,*hsv1,*hsv2;                //源图像 HSV格式图像
    if (!(src1 = cvLoadImage("D:\\Template\\OpenCV\\Template47_hue-saturation_EMD\\Debug\\hand1.jpg")))
        return -1;
    if (!(src2 = cvLoadImage("D:\\Template\\OpenCV\\Template47_hue-saturation_EMD\\Debug\\hand3.jpg")))
        return -2;
    if (!(Imask = cvLoadImage("D:\\Template\\OpenCV\\Template47_hue-saturation_EMD\\Debug\\Imask.jpg", CV_LOAD_IMAGE_GRAYSCALE)))
        return -3;
    hsv1 = cvCreateImage(cvGetSize(src1), src1->depth, src1->nChannels);
    hsv2 = cvCreateImage(cvGetSize(src2), src2->depth, src2->nChannels);
    cvCvtColor(src1, hsv1, CV_BGR2HSV); //源图像->HSV格式图像
    cvCvtColor(src2, hsv2, CV_BGR2HSV); //源图像->HSV格式图像
    //色调(hue) 饱和度(saturation) 明度(value)
    IplImage *h_plane_1 = cvCreateImage(cvSize(hsv1->width, hsv1->height), IPL_DEPTH_8U, 1);
    IplImage *s_plane_1 = cvCreateImage(cvSize(hsv1->width, hsv1->height), IPL_DEPTH_8U, 1);
    IplImage *v_plane_1 = cvCreateImage(cvSize(hsv1->width, hsv1->height), IPL_DEPTH_8U, 1);
    IplImage *h_plane_2 = cvCreateImage(cvSize(hsv2->width, hsv2->height), IPL_DEPTH_8U, 1);
    IplImage *s_plane_2 = cvCreateImage(cvSize(hsv2->width, hsv2->height), IPL_DEPTH_8U, 1);
    IplImage *v_plane_2 = cvCreateImage(cvSize(hsv2->width, hsv2->height), IPL_DEPTH_8U, 1);
    IplImage *planes1[] = { h_plane_1, s_plane_1 };                     //色相饱和度数组
    IplImage *planes2[] = { h_plane_2, s_plane_2 };                     //色相饱和度数组
    cvCvtPixToPlane(hsv1, h_plane_1, s_plane_1, v_plane_1, NULL);       //图像分割
    cvCvtPixToPlane(hsv2, h_plane_2, s_plane_2, v_plane_2, NULL);       //图像分割
    //cvSplit(hsv, h_plane, s_plane, v_plane, NULL);
    int h_bins = 30, s_bins = 32;                               
    //建立直方图
    CvHistogram *hist1,*hist2;
    int hist_size[] = { h_bins, s_bins };       //对应维数包含bins个数的数组
    float h_ranges[] = { 0, 180 };              //H通道划分范围 饱和度0-180
    float s_ranges[] = { 0, 255 };              //S通道划分范围
    float* ranges[] = { h_ranges, s_ranges };   //划分范围数对, ****均匀bin，range只要最大最小边界
    //创建直方图 （维数，对应维数bins个数，密集矩阵方式存储，划分范围数对，均匀直方图）
    hist1 = cvCreateHist(2, hist_size, CV_HIST_ARRAY, ranges, 1);
    hist2 = cvCreateHist(2, hist_size, CV_HIST_ARRAY, ranges, 1);
    cvCalcHist(planes1, hist1, 0, 0);       //计算直方图（图像，直方图结构，不累加，掩码）
    cvCalcHist(planes2, hist2, 0, 0);       //计算直方图（图像，直方图结构，不累加，掩码）
    //cvCalcHist(planes1, hist1, 0, Imask);     //计算直方图（图像，直方图结构，不累加，掩码）
    //cvCalcHist(planes2, hist2, 0, Imask);     //计算直方图（图像，直方图结构，不累加，掩码）
    cvNormalizeHist(hist1, 1.0);            //直方图归一化
    cvNormalizeHist(hist2, 1.0);            //直方图归一化
    CvMat *sig1, *sig2;
    int numrows = h_bins*s_bins;
    sig1 = cvCreateMat(numrows, 3, CV_32FC1);   //numrows行 3列 矩阵
    sig2 = cvCreateMat(numrows, 3, CV_32FC1);
    for (int h = 0; h < h_bins; h++)
    {
        for (int s = 0; s < s_bins; s++)
        {
            float bin_val = cvQueryHistValue_2D(hist1, h, s);
            //h:行数 s_bins：总列数（行长度）s:列数  h*s_bins+s 当前bin对应的sig行数   
            cvSet2D(sig1, h*s_bins + s, 0, cvScalar(bin_val));  
            cvSet2D(sig1, h*s_bins + s, 1, cvScalar(h));
            cvSet2D(sig1, h*s_bins + s, 2, cvScalar(s));
            bin_val = cvQueryHistValue_2D(hist2, h, s);
            cvSet2D(sig2, h*s_bins + s, 0, cvScalar(bin_val));
            cvSet2D(sig2, h*s_bins + s, 1, cvScalar(h));
            cvSet2D(sig2, h*s_bins + s, 2, cvScalar(s));
        }
    }
    float emd = cvCalcEMD2(sig1, sig2, CV_DIST_L2);
    printf("EMD距离：%f; ", emd);
    cvNamedWindow("SRC1",1);
    cvNamedWindow("SRC2",2);
    cvShowImage("SRC1", src1);
    cvShowImage("SRC2", src2);
    cvWaitKey(0);
    //system("pause");
    cvReleaseMat(&sig1);
    cvReleaseMat(&sig2);
    cvReleaseHist(&hist1);
    cvReleaseHist(&hist2);
    cvReleaseImage(&src1);
    cvReleaseImage(&src2);
    cvReleaseImage(&Imask);
    cvReleaseImage(&hsv1);
    cvReleaseImage(&hsv2);
    cvReleaseImage(&h_plane_1);
    cvReleaseImage(&s_plane_1);
    cvReleaseImage(&v_plane_1);
    cvReleaseImage(&h_plane_2);
    cvReleaseImage(&s_plane_2);
    cvReleaseImage(&v_plane_2);
    cvDestroyAllWindows();
}

运行结果如下图：

根据输入的图像计算色相饱和度（hue-saturation）直方图，以网格形式显示，利用肤色模板直方图进行基于像素点的反向投影，在测试图像中找出该肤色模板直方图对应的区域，对应具体代码如下：

#include <cv.h>
#include <highgui.h>  
#include <stdlib.h>  
#include <stdio.h> 
#include <math.h>
using namespace std;
int main(int argc, char* argv[])
{
    IplImage* src1,*src2,*Imask,*hsv1,*hsv2;    //源图像 HSV格式图像
    if (!(src1 = cvLoadImage("D:\\Template\\OpenCV\\Template48_hue-saturation_BackProjection\\Debug\\hand1.jpg")))
        return -1;
    if (!(src2 = cvLoadImage("D:\\Template\\OpenCV\\Template48_hue-saturation_BackProjection\\Debug\\hand3.jpg")))
        return -2;
    //此处调入图像掩码应为单通道
    if (!(Imask = cvLoadImage("D:\\Template\\OpenCV\\Template48_hue-saturation_BackProjection\\Debug\\Imask.jpg", CV_LOAD_IMAGE_GRAYSCALE)))
        return -3;
    cvXorS(Imask, cvScalar(255), Imask);        //掩码图像按位异或，求反生成新的掩码处理模板
    cvSet(src1, cvScalarAll(0), Imask);
    hsv1 = cvCreateImage(cvGetSize(src1), src1->depth, src1->nChannels);
    hsv2 = cvCreateImage(cvGetSize(src2), src2->depth, src2->nChannels);
    cvCvtColor(src1, hsv1, CV_BGR2HSV); //源图像->HSV格式图像
    cvCvtColor(src2, hsv2, CV_BGR2HSV); //源图像->HSV格式图像
    //反向投影图像
    IplImage *back_projection = cvCreateImage(cvGetSize(src2), IPL_DEPTH_8U, 1);
    //色调(hue) 饱和度(saturation) 明度(value)
    IplImage *h_plane_1 = cvCreateImage(cvSize(hsv1->width, hsv1->height), IPL_DEPTH_8U, 1);
    IplImage *s_plane_1 = cvCreateImage(cvSize(hsv1->width, hsv1->height), IPL_DEPTH_8U, 1);
    IplImage *v_plane_1 = cvCreateImage(cvSize(hsv1->width, hsv1->height), IPL_DEPTH_8U, 1);
    IplImage *h_plane_2 = cvCreateImage(cvSize(hsv2->width, hsv2->height), IPL_DEPTH_8U, 1);
    IplImage *s_plane_2 = cvCreateImage(cvSize(hsv2->width, hsv2->height), IPL_DEPTH_8U, 1);
    IplImage *v_plane_2 = cvCreateImage(cvSize(hsv2->width, hsv2->height), IPL_DEPTH_8U, 1);
    IplImage *planes1[] = { h_plane_1, s_plane_1 };                     //色相饱和度数组
    IplImage *planes2[] = { h_plane_2, s_plane_2 };                     //色相饱和度数组
    cvCvtPixToPlane(hsv1, h_plane_1, s_plane_1, v_plane_1, NULL);       //图像分割
    cvCvtPixToPlane(hsv2, h_plane_2, s_plane_2, v_plane_2, NULL);       //图像分割
    //cvSplit(hsv, h_plane, s_plane, v_plane, NULL);
    int h_bins = 30, s_bins = 32;                               
    //建立直方图
    CvHistogram *hist_model,*hist_test;
    int hist_size[] = { h_bins, s_bins };       //对应维数包含bins个数的数组
    float h_ranges[] = { 0, 180 };              //H通道划分范围 饱和度0-180
    float s_ranges[] = { 0, 255 };              //S通道划分范围
    float* ranges[] = { h_ranges, s_ranges };   //划分范围数对, ****均匀bin，range只要最大最小边界
    //创建直方图 （维数，对应维数bins个数，密集矩阵方式存储，划分范围数对，均匀直方图）
    hist_model = cvCreateHist(2, hist_size, CV_HIST_ARRAY, ranges, 1);
    hist_test = cvCreateHist(2, hist_size, CV_HIST_ARRAY, ranges, 1);
    cvCalcHist(planes1, hist_model, 0, 0);      //计算直方图（图像，直方图结构，不累加，掩码）
    cvCalcHist(planes2, hist_test, 0, 0);       //计算直方图（图像，直方图结构，不累加，掩码）
    //cvNormalizeHist(hist_model, 1.0);         //直方图归一化
    //cvNormalizeHist(hist_test, 1.0);          //直方图归一化
    //绘制可视化直方图
    int scale = 10;
    IplImage* hist_img_model = cvCreateImage(cvSize(h_bins*scale, s_bins*scale), 8, 3); //300*320
    IplImage* hist_img_test = cvCreateImage(cvSize(h_bins*scale, s_bins*scale), 8, 3);  //300*320
    cvZero(hist_img_model);
    cvZero(hist_img_test);
    //以小灰度块填充图像
    float max_value_model = 0;
    float max_value_test = 0;
    cvGetMinMaxHistValue(hist_model, NULL, &max_value_model, NULL, NULL);   //获取直方图最大值
    cvGetMinMaxHistValue(hist_test, NULL, &max_value_test, NULL, NULL);     //获取直方图最大值
    for (int h = 0; h < h_bins; h++)
    {
        for (int s = 0; s < s_bins; s++)
        {
            float bin_val_model = cvQueryHistValue_2D(hist_model, h, s);    //获取直方图相应bin中的浮点数
            float bin_val_test = cvQueryHistValue_2D(hist_test, h, s);      //获取直方图相应bin中的浮点数
            int intensity1 = cvRound(bin_val_model * 255 / max_value_model);//映射到255空间
            int intensity2 = cvRound(bin_val_test * 255 / max_value_test);  //归一后太小
            cvRectangle(hist_img_model, cvPoint(h*scale, s*scale),
                cvPoint((h + 1)*scale - 1, (s + 1)*scale - 1),
                CV_RGB(intensity1, intensity1, intensity1), CV_FILLED);
            cvRectangle(hist_img_test, cvPoint(h*scale, s*scale),
                cvPoint((h + 1)*scale - 1, (s + 1)*scale - 1),
                CV_RGB(intensity2, intensity2, intensity2), CV_FILLED);
        }
    }
    cvCalcBackProject(planes2, back_projection, hist_model);        //像素点的反射投影
    cvNamedWindow("Mask", 1);
    cvNamedWindow("Model", 1);
    cvNamedWindow("Test", 1);
    cvNamedWindow("HIST_Model", 1);
    cvNamedWindow("HIST_Test", 1);
    cvNamedWindow("BACK_Projection", 1);
    cvShowImage("Mask", Imask);
    cvShowImage("Model", src1);
    cvShowImage("Test", src2);
    cvShowImage("HIST_Model", hist_img_model);
    cvShowImage("HIST_Test", hist_img_test);
    cvShowImage("BACK_Projection", back_projection);
    cvWaitKey(0);
    //system("pause");
    cvReleaseHist(&hist_model);
    cvReleaseHist(&hist_test);
    cvReleaseImage(&Imask);
    cvReleaseImage(&src1);
    cvReleaseImage(&src2);
    cvReleaseImage(&hist_img_model);
    cvReleaseImage(&hist_img_test);
    cvReleaseImage(&h_plane_1);
    cvReleaseImage(&s_plane_1);
    cvReleaseImage(&v_plane_1);
    cvReleaseImage(&h_plane_2);
    cvReleaseImage(&s_plane_2);
    cvReleaseImage(&v_plane_2);
    cvReleaseImage(&back_projection);
    cvDestroyAllWindows();
}