第七章 学习OpenCV(7)
运行结果如下图：
在三种条件下建立两类肤色模板直方图。
1. 从室内、室外阴影和室外阳光下得到的第一类直方图作为模型，用其中每一个分别跟第二类图进行B距离测试，检验肤色匹配效果；
2. 利用（1）中设计的“场景检测器”确定要使用何种直方图模型：室内、室外阴影还是室外阳光；进行其他匹配方式，检验效果；
程序中三幅图像已经过处理，依次比前一幅亮度增加25，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[])
{
//源图像 HSV格式图像
IplImage* src1, *src2, *src3, *src4, *src5, *src6, *Imask, *hsv1, *hsv2, *hsv3, *hsv4,*hsv5, *hsv6;
//src1 src2 亮度相同 src3 src4亮度相同 src5 src6亮度相同 每级依次增加20
//模板 室内
if (!(src1 = cvLoadImage("D:\\Template\\OpenCV\\Template55_V_HS_Compare\\Debug\\handdd.jpg")))
return -1;
//测试 室内
if (!(src2 = cvLoadImage("D:\\Template\\OpenCV\\Template55_V_HS_Compare\\Debug\\handd.jpg")))
return -2;
//模板 室外阴影
if (!(src3 = cvLoadImage("D:\\Template\\OpenCV\\Template55_V_HS_Compare\\Debug\\handdd_out.jpg")))
return -3;
//测试 室外阴影
if (!(src4 = cvLoadImage("D:\\Template\\OpenCV\\Template55_V_HS_Compare\\Debug\\handd_out.jpg")))
return -4;
//模板 室外光照
if (!(src5 = cvLoadImage("D:\\Template\\OpenCV\\Template55_V_HS_Compare\\Debug\\handdd_out_sun.jpg")))
return -5;
if (!(src6 = cvLoadImage("D:\\Template\\OpenCV\\Template55_V_HS_Compare\\Debug\\handd_out_sun.jpg")))
return -6;
//Mask为手掌掩码 过滤掉其他背景 只分析手掌颜色直方图 可略
if (!(Imask = cvLoadImage("D:\\Template\\OpenCV\\Template54_value_Compare\\Debug\\Imask.jpg",
CV_LOAD_IMAGE_GRAYSCALE)))
return -7;
hsv1 = cvCreateImage(cvGetSize(src1), src1->depth, src1->nChannels);
hsv2 = cvCreateImage(cvGetSize(src2), src2->depth, src2->nChannels);
hsv3 = cvCreateImage(cvGetSize(src3), src3->depth, src3->nChannels);
hsv4 = cvCreateImage(cvGetSize(src4), src4->depth, src4->nChannels);
hsv5 = cvCreateImage(cvGetSize(src5), src5->depth, src5->nChannels);
hsv6 = cvCreateImage(cvGetSize(src6), src6->depth, src6->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
cvCvtColor(src3, hsv3, CV_BGR2HSV); //源图像->HSV格式图像
cvCvtColor(src4, hsv4, CV_BGR2HSV); //源图像->HSV格式图像
cvCvtColor(src5, hsv5, CV_BGR2HSV); //源图像->HSV格式图像
cvCvtColor(src6, hsv6, 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 *h_plane_3 = cvCreateImage(cvSize(hsv3->width, hsv3->height), IPL_DEPTH_8U, 1);
IplImage *s_plane_3 = cvCreateImage(cvSize(hsv3->width, hsv3->height), IPL_DEPTH_8U, 1);
IplImage *v_plane_3 = cvCreateImage(cvSize(hsv3->width, hsv3->height), IPL_DEPTH_8U, 1);
IplImage *h_plane_4 = cvCreateImage(cvSize(hsv4->width, hsv4->height), IPL_DEPTH_8U, 1);
IplImage *s_plane_4 = cvCreateImage(cvSize(hsv4->width, hsv4->height), IPL_DEPTH_8U, 1);
IplImage *v_plane_4 = cvCreateImage(cvSize(hsv4->width, hsv4->height), IPL_DEPTH_8U, 1);
IplImage *h_plane_5 = cvCreateImage(cvSize(hsv5->width, hsv5->height), IPL_DEPTH_8U, 1);
IplImage *s_plane_5 = cvCreateImage(cvSize(hsv5->width, hsv5->height), IPL_DEPTH_8U, 1);
IplImage *v_plane_5 = cvCreateImage(cvSize(hsv5->width, hsv5->height), IPL_DEPTH_8U, 1);
IplImage *h_plane_6 = cvCreateImage(cvSize(hsv6->width, hsv6->height), IPL_DEPTH_8U, 1);
IplImage *s_plane_6 = cvCreateImage(cvSize(hsv6->width, hsv6->height), IPL_DEPTH_8U, 1);
IplImage *v_plane_6 = cvCreateImage(cvSize(hsv6->width, hsv6->height), IPL_DEPTH_8U, 1);
IplImage *planes1[] = { v_plane_1 }; //亮度数组
IplImage *planes2[] = { v_plane_2 };
IplImage *planes3[] = { v_plane_3 };
IplImage *planes4[] = { v_plane_4 };
IplImage *planes5[] = { v_plane_5 };
IplImage *planes6[] = { v_plane_6 };
IplImage *planes7[] = { h_plane_1, s_plane_1 }; //色相饱和度数组
IplImage *planes8[] = { h_plane_2, s_plane_2 };
IplImage *planes9[] = { h_plane_3, s_plane_3 };
IplImage *planes10[] = { h_plane_4, s_plane_4 };
IplImage *planes11[] = { h_plane_5, s_plane_5 };
IplImage *planes12[] = { h_plane_6, s_plane_6 };
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
cvCvtPixToPlane(hsv3, h_plane_3, s_plane_3, v_plane_3, NULL); //图像分割
cvCvtPixToPlane(hsv4, h_plane_4, s_plane_4, v_plane_4, NULL); //图像分割
cvCvtPixToPlane(hsv5, h_plane_5, s_plane_5, v_plane_5, NULL); //图像分割
cvCvtPixToPlane(hsv6, h_plane_6, s_plane_6, v_plane_6, NULL); //图像分割
//cvSplit(hsv, h_plane, s_plane, v_plane, NULL);
//建立直方图 v直方图：筛选合适的光照条件 hs直方图：肤色匹配
CvHistogram *hist_v[6], *hist_h_s[6];
int v_bins = 32;
int hist_v_size[] = { v_bins }; //对应维数包含bins个数的数组
int h_bins = 30, s_bins = 32;
int hist_h_s_size[] = { h_bins, s_bins }; //对应维数包含bins个数的数组
float v_range[] = { 0, 255 }; //V通道划分范围
float* v_ranges[] = { v_range }; //划分范围数对, ****均匀bin，range只要最大最小边界
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* h_s_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_v[0] = cvCreateHist(1, hist_v_size, CV_HIST_ARRAY, v_ranges, 1);
hist_v[1] = cvCreateHist(1, hist_v_size, CV_HIST_ARRAY, v_ranges, 1);
hist_v[2] = cvCreateHist(1, hist_v_size, CV_HIST_ARRAY, v_ranges, 1);
hist_v[3] = cvCreateHist(1, hist_v_size, CV_HIST_ARRAY, v_ranges, 1);
hist_v[4] = cvCreateHist(1, hist_v_size, CV_HIST_ARRAY, v_ranges, 1);
hist_v[5] = cvCreateHist(1, hist_v_size, CV_HIST_ARRAY, v_ranges, 1);
hist_h_s[0] = cvCreateHist(1, hist_h_s_size, CV_HIST_ARRAY, h_s_ranges, 1);
hist_h_s[1] = cvCreateHist(1, hist_h_s_size, CV_HIST_ARRAY, h_s_ranges, 1);
hist_h_s[2] = cvCreateHist(1, hist_h_s_size, CV_HIST_ARRAY, h_s_ranges, 1);
hist_h_s[3] = cvCreateHist(1, hist_h_s_size, CV_HIST_ARRAY, h_s_ranges, 1);
hist_h_s[4] = cvCreateHist(1, hist_h_s_size, CV_HIST_ARRAY, h_s_ranges, 1);
hist_h_s[5] = cvCreateHist(1, hist_h_s_size, CV_HIST_ARRAY, h_s_ranges, 1);
//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）
cvCalcHist(planes1, hist_v[0], 0, Imask); //计算直方图（图像，直方图结构，不累加，mask仅采集手掌）
cvCalcHist(planes2, hist_v[1], 0, 0); //计算直方图（图像，直方图结构，不累加，掩码）
cvCalcHist(planes3, hist_v[2], 0, Imask); //计算直方图（图像，直方图结构，不累加，掩码）
cvCalcHist(planes4, hist_v[3], 0, 0); //计算直方图（图像，直方图结构，不累加，掩码）
cvCalcHist(planes5, hist_v[4], 0, Imask); //计算直方图（图像，直方图结构，不累加，掩码）
cvCalcHist(planes6, hist_v[5], 0, 0); //计算直方图（图像，直方图结构，不累加，掩码）
cvCalcHist(planes7 , hist_h_s[0], 0, 0);//计算直方图（图像，直方图结构，不累加，mask仅采集手掌）
cvCalcHist(planes8 , hist_h_s[1], 0, 0); //计算直方图（图像，直方图结构，不累加，掩码）
cvCalcHist(planes9 , hist_h_s[2], 0, 0);//计算直方图（图像，直方图结构，不累加，掩码）
cvCalcHist(planes10, hist_h_s[3], 0, 0); //计算直方图（图像，直方图结构，不累加，掩码）
cvCalcHist(planes11, hist_h_s[4], 0, 0);//计算直方图（图像，直方图结构，不累加，掩码）
cvCalcHist(planes12, hist_h_s[5], 0, 0); //计算直方图（图像，直方图结构，不累加，掩码）
//直方图归一化
cvNormalizeHist(hist_v[0], 1.0);
cvNormalizeHist(hist_v[1], 1.0);
cvNormalizeHist(hist_v[2], 1.0);
cvNormalizeHist(hist_v[3], 1.0);
cvNormalizeHist(hist_v[4], 1.0);
cvNormalizeHist(hist_v[5], 1.0);
cvNormalizeHist(hist_h_s[0], 1.0);
cvNormalizeHist(hist_h_s[1], 1.0);
cvNormalizeHist(hist_h_s[2], 1.0);
cvNormalizeHist(hist_h_s[3], 1.0);
cvNormalizeHist(hist_h_s[4], 1.0);
cvNormalizeHist(hist_h_s[5], 1.0);
for (int i = 1; i < 4; i++)
{
double min = 0,max=0;
CvPoint point;
CvMat* mat = cvCreateMat(1, 3, CV_64FC1);
double value1 = cvCompareHist(hist_v[0], hist_v[2 * i - 1], CV_COMP_BHATTACHARYYA); //B距离比较
double value2 = cvCompareHist(hist_v[2], hist_v[2 * i - 1], CV_COMP_BHATTACHARYYA); //B距离比较
double value3 = cvCompareHist(hist_v[4], hist_v[2 * i - 1], CV_COMP_BHATTACHARYYA); //B距离比较
printf("第 %d 幅测试图像与亮度模板比较值:\n",i);
printf(" 室内:%lf, 室外:%lf, 室外阳光:%lf\n", value1, value2, value3);
cvSet1D(mat, 0, cvScalar(value1));
cvSet1D(mat, 1, cvScalar(value2));
cvSet1D(mat, 2, cvScalar(value3));
cvMinMaxLoc(mat, &min, &max, &point, NULL, NULL);
switch (point.x)
{
case 0:
printf("第 %d 幅测试图像与亮度模板比较值最小值:%lf\n判定在室内环境下拍摄.\n", i, min);
printf("应当选择室内环境肤色模板进行肤色匹配：\n");
//不同比较方式的结果
for (int j = 0; j < 4; j++)
{
double h_s_value = cvCompareHist(hist_h_s[0], hist_h_s[2 * i - 1], j);
if (j == 0)
{
printf(" 相关CV_COMP_CORREL: %lf;\n",h_s_value);
}
if (j == 1)
{
printf(" 卡方CV_COMP_CHISQR: %lf;\n",h_s_value);
}
if (j == 2)
{
printf(" 相交CV_COMP_INTERSECT: %lf;\n",h_s_value);
}
if (j == 3)
{
printf(" B距离CV_CCOMP_BHATTACHARYYA: %lf;\n",h_s_value);
}
}
break;
case 1:
printf("第 %d 幅测试图像与亮度模板比较值最小值:%lf\n判定在室外环境下拍摄.\n", i, min);
printf("应当选择室外环境肤色模板进行肤色匹配：\n");
//不同比较方式的结果
for (int j = 0; j < 4; j++)
{
double h_s_value = cvCompareHist(hist_h_s[2], hist_h_s[2 * i - 1], j);
if (j == 0)
{
printf(" 相关CV_COMP_CORREL: %lf;\n", h_s_value);
}
if (j == 1)
{
printf(" 卡方CV_COMP_CHISQR: %lf;\n", h_s_value);
}
if (j == 2)
{
printf(" 相交CV_COMP_INTERSECT: %lf;\n", h_s_value);
}
if (j == 3)
{
printf(" B距离CV_CCOMP_BHATTACHARYYA: %lf;\n", h_s_value);
}
}
break;
case 2:
printf("第 %d 幅测试图像与亮度模板比较值最小值:%lf\n判定在室外阳光下拍摄.\n", i, min);
printf("应当选择室外阳光肤色模板进行肤色匹配：\n");
//不同比较方式的结果
for (int j = 0; j < 4; j++)
{
double h_s_value = cvCompareHist(hist_h_s[4], hist_h_s[2 * i - 1], j);
if (j == 0)
{
printf(" 相关CV_COMP_CORREL: %lf;\n", h_s_value);
}
if (j == 1)
{
printf(" 卡方CV_COMP_CHISQR: %lf;\n", h_s_value);
}
if (j == 2)
{
printf(" 相交CV_COMP_INTERSECT: %lf;\n", h_s_value);
}
if (j == 3)
{
printf(" B距离CV_CCOMP_BHATTACHARYYA: %lf;\n", h_s_value);
}
}
break;
}
cvReleaseMat(&mat);
cout << endl;
}
cvNamedWindow("Room_model", 1);
cvNamedWindow("Out_model", 1);
cvNamedWindow("Out_Sun_model", 1);
cvNamedWindow("IMASK", 1);
cvNamedWindow("Room_test_第1幅", 1);
cvNamedWindow("Out_test_第2幅", 1);
cvNamedWindow("Out_Sun_test_第3幅", 1);
cvShowImage("Room_model", src1);
cvShowImage("Out_model", src3);
cvShowImage("Out_Sun_model", src5);
cvShowImage("IMASK", Imask);
cvShowImage("Room_test_第1幅", src2);
cvShowImage("Out_test_第2幅", src4);
cvShowImage("Out_Sun_test_第3幅", src6);
cvWaitKey(0);
//system("pause");
cvReleaseHist(&hist_v[0]);
cvReleaseHist(&hist_v[1]);
cvReleaseHist(&hist_v[2]);
cvReleaseHist(&hist_v[3]);
cvReleaseHist(&hist_v[4]);
cvReleaseHist(&hist_v[5]);
cvReleaseHist(&hist_h_s[0]);
cvReleaseHist(&hist_h_s[1]);
cvReleaseHist(&hist_h_s[2]);
cvReleaseHist(&hist_h_s[3]);
cvReleaseHist(&hist_h_s[4]);
cvReleaseHist(&hist_h_s[5]);
cvReleaseImage(&src1);
cvReleaseImage(&src2);
cvReleaseImage(&src3);
cvReleaseImage(&src4);
cvReleaseImage(&src5);
cvReleaseImage(&src6);
cvReleaseImage(&Imask);
cvReleaseImage(&hsv1);
cvReleaseImage(&hsv2);
cvReleaseImage(&hsv3);
cvReleaseImage(&hsv4);
cvReleaseImage(&hsv5);
cvReleaseImage(&hsv6);
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(&h_plane_3);
cvReleaseImage(&s_plane_3);
cvReleaseImage(&v_plane_3);
cvReleaseImage(&h_plane_4);
cvReleaseImage(&s_plane_4);
cvReleaseImage(&v_plane_4);
cvReleaseImage(&h_plane_5);
cvReleaseImage(&s_plane_5);
cvReleaseImage(&v_plane_5);
cvReleaseImage(&h_plane_6);
cvReleaseImage(&s_plane_6);
cvReleaseImage(&v_plane_6);
cvDestroyAllWindows();
}
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运行结果如下图： 在三种条件下建立两类肤色模板直方图。 1. 从室内、室外阴影和室外阳光下得到的第一类直方图作为模型，用其中每一个分别跟第二类
-
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苏岗岗
特意造了几个合格品去送检
勾践