第七章 学习OpenCV(4)
运行结果如下图：
读入一个模板和要匹配的图像，然后分别利用6种方法进行匹配，规范化后将匹配结果显示出来，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 匹配模板 不同匹配方法结果
IplImage* src, *temp1, *result[6];
//杯子源图像
if (!(src = cvLoadImage("D:\\Template\\OpenCV\\Template50_Match_Template\\Debug\\cup1.jpg")))
return -1;
//用于匹配的杯子模板图像
if (!(temp1 = cvLoadImage("D:\\Template\\OpenCV\\Template50_Match_Template\\Debug\\cup2.jpg")))
return -2;
//结果图像尺寸
int result_width = src->width - temp1->width + 1;
int result_height = src->height - temp1->height + 1;;
CvSize result_size = cvSize(result_width, result_height);
//创建结果图像
for (int i = 0; i < 6; ++i)
{
printf("i=%d\n", i);
result[i] = cvCreateImage(result_size, IPL_DEPTH_32F, 1);
}
//均衡化图像
for (int i = 0; i < 6; i++)
{
printf("i=%d\n", i);
cvMatchTemplate(src, temp1, result[i], i); //模板匹配
cvNormalize(result[i], result[i], 1, 0, CV_MINMAX); //元素规范化 平移缩放返回值[0,1]
}
cvNamedWindow("Src", 1);
cvNamedWindow("Template", 1);
cvNamedWindow("SQDIFF", 1);
cvNamedWindow("CCORR", 1);
cvNamedWindow("CCOEFF", 1);
cvNamedWindow("SQDIFF_NORMED", 1);
cvNamedWindow("CCORR_NORMED", 1);
cvNamedWindow("CCOEFF_NORMED", 1);
cvShowImage("Src", src);
cvShowImage("Template", temp1);
cvShowImage("SQDIFF", result[0]);
cvShowImage("SQDIFF_NORMED", result[1]);
cvShowImage("CCORR", result[2]);
cvShowImage("CCORR_NORMED", result[3]);
cvShowImage("CCOEFF", result[4]);
cvShowImage("CCOEFF_NORMED", result[5]);
cvWaitKey(0);
//system("pause");
cvReleaseImage(&src);
cvReleaseImage(&temp1);
cvReleaseImage(&result[0]);
cvReleaseImage(&result[1]);
cvReleaseImage(&result[2]);
cvReleaseImage(&result[3]);
cvReleaseImage(&result[4]);
cvReleaseImage(&result[5]);
cvDestroyAllWindows();
}运行结果如下图：
注意：本程序中，打印了两次i的值，分别对应for循环中的“++i”“i++”,打印结果相同，并不代表“++i”“i++”没有区别，而是因为for循环中表达式是作为一个语句来执行，因此此处i均是其最终的值。
在0~1之间生成1000个随机值ri，定义一个bin的大小，并且建立一个直方图1/ri,，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[])
{
//产生1000个随机数
CvRNG rng;
IplImage *Img = cvCreateImage(cvSize(1000,1),32,1); //数据图像
cvSetZero(Img); //清零
rng = cvRNG(cvGetTickCount()); //64位长整数的时间数据作为种子
for (int i = 0; i<1000; i++)
{
double value; //获取的随机值
cvSetReal1D(Img, i, cvRandReal(&rng)); //返回均匀分布，0~1之间的随机小数
value = cvGetReal1D(Img, i); //返回图像中小数值
//printf("%d\n", cvRandInt(&rng) % 6); //返回均匀分布32位的随机数，%6将会是0~255的正整数
printf("%.2lf\n", value); //打印
}
printf("Tick Frequency= %f\n", cvGetTickFrequency()); //系统时钟频率
system("pause");
//建立直方图
CvHistogram *hist;
int dims = 1; //维数
int bins = 1000; //bins个数
int hist_size[] = { bins }; //对应维数包含bins个数的数组
float range[] = { 0, 1 }; //划分范围[0,1]
float* ranges[] = { range }; //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 = cvCreateHist(dims, hist_size, CV_HIST_ARRAY, ranges, 1);
IplImage *img[] = { Img }; //计算直方图的图像数组
cvCalcHist(img, hist, 0, 0); //计算直方图
for (int j = 0; j < bins; j++)
{
float bin_val = cvQueryHistValue_1D(hist,j); //获取直方图相应bin中的浮点数
cout << "the bins of " << j << ":" << bin_val << endl;
}
system("pause");
cvWaitKey(0);
cvReleaseHist(&hist);
cvReleaseImage(&Img);
}运行结果如下图：
对于同一幅场景，我们按波长从小到大依次用每一个区间波长的光去拍摄图像，将得到2500~400/n幅图像，这组图像作为整体被称作高光谱图像。当空间三维场景被投影为二维图像时，同一景物在不同视点下的图像会有很大不同，而且场景中的诸多因素，如光照条件、景物几何形状和物理特性、噪声干扰和畸变以及摄像机特性等，都被综合成单一的图像灰度值。0都难以胜任室内抓拍(夜晚室内60w光照条件，布键不错。
1. 依次尝试用少量的bin（如每维有2个），中等数目的bin（每维有16个）和很多bin（每维256个），然后对各种光线下的图像运行匹配程序（使用所有的直方图匹配方法）；
2. 现在加上每维为8个和32个bin，在各种光线条件下进行匹配；
程序中三幅图像已经过处理，依次比前一幅亮度增加40，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, *src3,*Imask, *hsv1, *hsv2,*hsv3; //源图像 HSV格式图像
//src1 src2 亮度较前一张增加了10 src2 src3 亮度较前一张增加了40
if (!(src1 = cvLoadImage("D:\\Template\\OpenCV\\Template52_hue-saturation_Compare\\Debug\\hand1.jpg")))
return -1;
if (!(src2 = cvLoadImage("D:\\Template\\OpenCV\\Template52_hue-saturation_Compare\\Debug\\hand3.jpg")))
return -2;
if (!(src3 = cvLoadImage("D:\\Template\\OpenCV\\Template52_hue-saturation_Compare\\Debug\\hand5.jpg")))
return -3;
//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\\Template52_hue-saturation_Compare\\Debug\\Imask.jpg",
CV_LOAD_IMAGE_GRAYSCALE)))
return -4;
hsv1 = cvCreateImage(cvGetSize(src1), src1->depth, src1->nChannels);
hsv2 = cvCreateImage(cvGetSize(src2), src2->depth, src2->nChannels);
hsv3 = cvCreateImage(cvGetSize(src3), src3->depth, src3->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
//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 *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
IplImage *planes3[] = { h_plane_3, s_plane_3 }; //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
cvCvtPixToPlane(hsv3, h_plane_3, s_plane_3, v_plane_3, NULL); //图像分割
//cvSplit(hsv, h_plane, s_plane, v_plane, NULL);
for (int i = 0; i < 5; i++)
{
//建立直方图
CvHistogram *hist1, *hist2, *hist3;
int bins=0;
int h_bins_1 = 2, s_bins_1 = 2;
int h_bins_2 = 8, s_bins_2 = 8;
int h_bins_3 = 16, s_bins_3 = 16;
int h_bins_4 = 32, s_bins_4 = 32;
int h_bins_5 = 256, s_bins_5 = 256;
int hist_size_1[] = { h_bins_1, s_bins_1 }; //对应维数包含bins个数的数组
int hist_size_2[] = { h_bins_2, s_bins_2 }; //对应维数包含bins个数的数组
int hist_size_3[] = { h_bins_3, s_bins_3 }; //对应维数包含bins个数的数组
int hist_size_4[] = { h_bins_4, s_bins_4 }; //对应维数包含bins个数的数组
int hist_size_5[] = { h_bins_5, s_bins_5 }; //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
//bins 2*2
if (i == 0)
{
hist1 = cvCreateHist(2, hist_size_1, CV_HIST_ARRAY, ranges, 1);
hist2 = cvCreateHist(2, hist_size_1, CV_HIST_ARRAY, ranges, 1);
hist3 = cvCreateHist(2, hist_size_1, CV_HIST_ARRAY, ranges, 1);
bins = h_bins_1;
}
//bins 8*8
if (i == 1)
{
hist1 = cvCreateHist(2, hist_size_2, CV_HIST_ARRAY, ranges, 1);
hist2 = cvCreateHist(2, hist_size_2, CV_HIST_ARRAY, ranges, 1);
hist3 = cvCreateHist(2, hist_size_2, CV_HIST_ARRAY, ranges, 1);
bins = h_bins_2;
}
//bins 16*16
if (i == 2)
{
hist1 = cvCreateHist(2, hist_size_3, CV_HIST_ARRAY, ranges, 1);
hist2 = cvCreateHist(2, hist_size_3, CV_HIST_ARRAY, ranges, 1);
hist3 = cvCreateHist(2, hist_size_3, CV_HIST_ARRAY, ranges, 1);
bins = h_bins_3;
}
//bins 32*32
if (i == 3)
{
hist1 = cvCreateHist(2, hist_size_4, CV_HIST_ARRAY, ranges, 1);
hist2 = cvCreateHist(2, hist_size_4, CV_HIST_ARRAY, ranges, 1);
hist3 = cvCreateHist(2, hist_size_4, CV_HIST_ARRAY, ranges, 1);
bins = h_bins_4;
}
//bins 256*256
if (i == 4)
{
hist1 = cvCreateHist(2, hist_size_5, CV_HIST_ARRAY, ranges, 1);
hist2 = cvCreateHist(2, hist_size_5, CV_HIST_ARRAY, ranges, 1);
hist3 = cvCreateHist(2, hist_size_5, CV_HIST_ARRAY, ranges, 1);
bins = h_bins_5;
}
//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, 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(planes3, hist3, 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）
//cvCalcHist(planes3, hist3, 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
cvNormalizeHist(hist3, 1.0); //直方图归一化
//比较直方图
for (int j = 0; j < 4; j++)
{
double value1 = cvCompareHist(hist1, hist2, j); //相关方式比较
double value2 = cvCompareHist(hist1, hist3, j); //相关方式比较
if (j == 0)
{
printf("Bins:%d*%d ,Hist1 & Hist2 ,CORREL: %lf;\n", bins, bins, value1);
printf("Bins:%d*%d ,Hist1 & Hist3 ,CORREL: %lf;\n", bins, bins, value2);
}
if (j == 1)
{
printf("Bins:%d*%d ,Hist1 & Hist2 ,CHISQR: %lf;\n", bins, bins, value1);
printf("Bins:%d*%d ,Hist1 & Hist3 ,CHISQR: %lf;\n", bins, bins, value2);
}
if (j == 2)
{
printf("Bins:%d*%d ,Hist1 & Hist2 ,INTERSECT: %lf;\n", bins, bins, value1);
printf("Bins:%d*%d ,Hist1 & Hist3 ,INTERSECT: %lf;\n", bins, bins, value2);
}
if (j == 3)
{
printf("Bins:%d*%d ,Hist1 & Hist2 ,BHATTACHARYYA: %lf;\n", bins, bins, value1);
printf("Bins:%d*%d ,Hist1 & Hist3 ,BHATTACHARYYA: %lf;\n", bins, bins, value2);
}
}
cvReleaseHist(&hist1);
cvReleaseHist(&hist2);
cvReleaseHist(&hist3);
cout << endl;
}
cvNamedWindow("SRC1", 1);
cvNamedWindow("SRC2", 1);
cvNamedWindow("SRC3", 1);
cvNamedWindow("IMASK", 1);
cvShowImage("SRC1", src1);
cvShowImage("SRC2", src2);
cvShowImage("SRC3", src3);
cvShowImage("IMASK", Imask);
cvWaitKey(0);
system("pause");
cvReleaseImage(&src1);
cvReleaseImage(&src2);
cvReleaseImage(&src3);
cvReleaseImage(&Imask);
cvReleaseImage(&hsv1);
cvReleaseImage(&hsv2);
cvReleaseImage(&hsv3);
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);
cvDestroyAllWindows();
}
http://www.jiaoanw.com/%E6%95%99%E6%A1%88%E6%A0%BC%E5%BC%8F/article-43870-4.html
http://www.jiaoanw.com/
true
教案网
http://www.jiaoanw.com/%E6%95%99%E6%A1%88%E6%A0%BC%E5%BC%8F/article-43870-4.html
report
25306
运行结果如下图： 读入一个模板和要匹配的图像，然后分别利用6种方法进行匹配，规范化后将匹配结果显示出来，具体代码如下： #include cv.h #include hig
-
-
宋昭公杵臼
第一
-
钱勇超
你们就靠着混日子了
我也是很支持进一步巩固领先优势的