移动侦测算法

1、思路：1、 YUV数据输入，YUV4:2:2，Y为逐点采样的亮点，提取每点的Y得到一帧图像的灰阶图。2、 根据灰阶图，可以得到一个直方图统计。3、 通过比较两帧的直方图得到两帧的匹配度。4、 当匹配度低于阈值判断两帧为不匹配，为有物体移动。难点：1、 直方图比较两帧得到匹配度。2、 如何选取两帧，两帧的比较选取。3、 由灰阶得到直方图#include"params.h"/*histgram- image_in: hist:*/void histgram(unsigned char image_inxziseysize,long hist256)int i,j,n;for(n

2、=0;n<256;n+) histn=0;for(i=0;i<ysize;i+) for(j=0;j<xsize;j+) n=image_inij; histn+; 图像相似度检测之C+代码（8位-256色）本代码属于原创，如需引用，请声明来源下面是直方图相交的代码，同种图片的识别率达90%以上，性能非常稳定。程序的例子是8位（256色）位图，其他位图类似。view plaincopy to clipboardprint?#include <fstream>#include <map>#include <string>#include &l

3、t;iostream> using namespace std; typedef unsigned char uvar8; typedef unsigned int uvar32; typedef unsigned short int uvar16; typedef struct uvar8 Blue; /*蓝色所占比重*/ uvar8 Green; /*绿色所占比重*/ uvar8 Red; /*红色所占比重*/ uvar8 Reserved; Palette; /定义bmp结构 uvar8 bfType2; /* 类型标志，总是BM */ typedef struct uvar32

4、bfSize; /* 文件大小 */ uvar32 bfReserved1; uvar32 bfOffBits; /* 位图点阵偏移量*/ HEAD; typedef struct uvar32 biSize; /* 结构体字节总数 */ uvar32 biWidth; /* 图像宽度 */ uvar32 biHeight; /*图像高度 */ uvar16 biPlanes; /* 必须为1 */ uvar16 biBitCount; /* 每个像素所占二进制位数，可能是1,4,8或 24 */ uvar32 biCompress; /*压缩方式*/ uvar32 biSizeImage;

5、/*像素点阵大小 */ uvar32 biXPelsPerMeter; /* 水平像素数*/ uvar32 biYPelsPerMeter; /* 垂直像素数 */ uvar32 biClrUsed; /*使用的颜色数 */ uvar32 biClrImportant; /*重要颜色数 */ INFO; bool ReadBMP(string strFileUrl,uvar8* & pImgData,int& iSum) HEAD strHead; INFO strInfo; uvar8 type2; /uvar32 imdata256256; Palette Rgbquad;

6、 ifstream fin; fin.open(strFileUrl.c_str(),ios_base:in | ios_base:binary); /*以二进制读方式打开该文件，一定要二进制的!*/ if(!fin) cout<<"No this file!n" return false; fin.read(char*)&bfType,2); fin.read(char*)&strHead,sizeof(strHead); fin.read(char*)&strInfo,sizeof(strInfo); fin.seekg(strHea

7、d.bfOffBits,ios:beg); if (strInfo.biBitCount = 8) /pImgData = (uvar8*)pImgData; pImgData = new uvar8strInfo.biWidth*strInfo.biHeight; fin.read(char*)(pImgData),strInfo.biWidth * strInfo.biHeight* sizeof uvar8); fin.close(); iSum = strInfo.biWidth * strInfo.biHeight; return true; int main() uvar8 *pI

8、mgData1 =NULL ,*pImgData2 = NULL; int i,iSum1,iSum2; while(1) string url1; string url2; cout<<"cin bmp file name"<<endl; cin>> url1 >> url2; ReadBMP(url1,pImgData1,iSum1); ReadBMP(url2,pImgData2,iSum2); map<int,int> Map1,Map2; for (i=0;i<iSum1;i+) if (Map1.

9、find(pImgData1i)!=Map1.end() Map1(uvar8)pImgData1i+; else Map1(uvar8)pImgData1i = 0; for (i=0;i<iSum2;i+) if (Map2.find(pImgData2i)!=Map2.end() Map2(uvar8)pImgData2i+; else Map2(uvar8)pImgData2i = 0; int iSum = 0; for (map<int,int>:iterator it = Map1.begin(); it!= Map1.end(); it+) iSum += M

10、ap1(uvar8)it->first < Map2(uvar8)it->first ? Map1(uvar8)it->first : Map2(uvar8)it->first; double dResult = (double) iSum / iSum1 > (double) iSum / iSum2 ? (double) iSum / iSum1:(double) iSum / iSum2; /- cout<<endl; cout<<"special dots the sum of pic1 the sum of pic

11、2"<<endl; for (map<int,int>:iterator it1 = Map1.begin(); it1!= Map1.end(); it1+) cout<<it1->first<<" "<<it1->second<<" "<<Map2it1->first<<endl; cout<<"*"<<endl; cout<<"conculsion："

12、<<endl; cout<< "The sum of dots in Pic1: "<< iSum1<<endl; cout<< "The sum of dots in Pic2: "<< iSum2<<endl; cout<< "The similar dots betwwen pic1 and pic2 : "<<iSum<<endl; cout<<"The rate of simliar

13、betwwen the above two pictures: " << dResult <<endl<<endl; delete pImgData1; delete pImgData2; getchar(); return 0; /函数名称: histeq()/传入参数: / BYTE*dstData 要匹配的灰度图像内存空间/ double *srcArray 模版的直方图累积，并进行归一化，大小为256/ int m_Width 匹配内存空间的宽度/ int m_Height 匹配内存空间的高度/ int m_pitch 匹配内存空间的每行所

14、在内存大小/void histeq(BYTE*dstData,double *srcArray,int m_Width,int m_Height,int m_pitch) double dstHist256; memset(dstHist,0,256 * sizeof(double); int i,j; double dstArray256; memset(dstArray,0,256 * sizeof(double); /统计直方图 for (i = 0;i < m_Height;i+) for (j = 0;j < m_Width;j+) dstHist(int)dstData

15、i * m_pitch + j+; /计算直方图累积 double m_Bytes = m_Width * m_Height; dstArray0 = dstHist0; for (i = 1;i < 256;i+) dstArrayi = dstArrayi - 1 + dstHisti; /直方图累积归一化 for (i = 0;i < 256;i+) dstArrayi /= m_Bytes; /直方图匹配 double m_diffA,m_diffB; int k = 0; BYTE mapPixel256; memset(mapPixel,0,256 * sizeof(B

16、YTE); for (i = 0;i < 256;i+) m_diffB = 1; for (j = k; j < 256;j+) m_diffA = abs(dstArrayi - srcArrayj); if (m_diffA - m_diffB < 1.0E-5) m_diffB = m_diffA; k = j; else k = j - 1; break; if (k = 255) for (int l = i;l < 256;l+) mapPixell = (BYTE) k; break; mapPixeli = (BYTE) k; /目标图像查找索引表 f

17、or (i = 0;i < m_Height;i+) for (j = 0;j < m_Width;j+) dstDatai * m_pitch + j = mapPixeldstDatai * m_pitch + j; / 计算两个直方图的相似度/ </summary>/ <param name="actualHisogram">待测图片的直方图</param>/ <param name="expectedHisogram">参考图片的直方图</param>/ <returns>得到直方图的相似度</returns> private float GetFinalResult(int actualHisogram, int expectedHisogram)if (actualHisogram.Length != expectedHisogram.Length)return 0;float result = 0;int j = actualHisogram.Length;for (int i = 0; i < j; i+)result += 1 - GetAbs(a