// 根据坐标和小图大小截取Bitmap.
public static Bitmap CutPic(String strFile, Location location, mySize size)
{
Bitmap bitRtn = new Bitmap(size.ROW, size.COL);
Image image = Image.FromFile(strFile);
// 抛出 内存不足错误
Bitmap bitmap = new Bitmap(image);
Color c = new Color();
for (int i = 0; i < size.ROW; i++) //整幅图像行(高)
{
for (int j = 0; j < size.COL; j++) //整幅图像列（宽）
{
// GetPixel 函数在读取元素时，行列是反着的
c = bitmap.GetPixel(j + location.column, i + location.row);//获取图片每个点灰度
// 新的图片里必须做偏移运算
bitRtn.SetPixel(j, i, c);
}
}
// new出来地方处理掉.
bitmap.Dispose();
return(bitRtn);
}
// 其中一个构造函数，用于在一个大矩阵中切割小矩阵
// 参数是起始位置
// 参数是一个原始大矩阵和需要切割的height和width
public Matrix(
double[,] D,
Location location,
mySize size
)
{
data = new double[size.ROW, size.COL];
// 根据开始位置开始读取值
int height = size.ROW;
int width = size.COL;
for (int i = 0; i < height; i++)
{
for (int j = 0; j < width; j++)
{
// 从开始点向右和向下 切割矩阵
// 在大矩阵中寻找需要切割的元素坐标
int SplitRow = location.row + i;
int SplitColumn = location.column + j;
data[i, j] = D[SplitRow, SplitColumn];
}
}
//return data;
}
// 获得小图在大图中的匹配数量
public static mySize GetPiPeiSize(Matrix MBig, Matrix MSmall)
{
int iNewRow = MBig.Row - MSmall.Row + 1;
int iNewCol = MBig.Column - MSmall.Column + 1;
mySize sizeRtn = new mySize(iNewRow, iNewCol);
return(sizeRtn);
}
// 2个矩阵匹配，输出一个匹配结果
public static Matrix PiPei(Matrix MSmall, Matrix MBig)
{
// 算出新的匹配矩阵的大小
// 小矩阵在大的矩阵上移动
int iNewRow = MBig.Row - MSmall.Row + 1;
int iNewCol = MBig.Column - MSmall.Column + 1;
Matrix Mrtn = new Matrix(iNewRow, iNewCol);
int iSmallRow = MSmall.Row;
int iSmallCol = MSmall.Column;
int offsetValue = 1;
// 偏移指针
Location baseL = new Location(0, 0);
mySize size = new mySize(iSmallRow, iSmallCol); // 小匹配图大小
for (int i = 0; i < iNewRow; i++)
{
// Big bug
// i没有做偏移.
baseL.row = i * offsetValue;
for (int j = 0; j < iNewCol; j++)
{
// 每次在大基准图切割一个和匹配小图一样大小的图
// 然后和匹配小图进行运算得到一个值
// 输出到矩阵的位置
// 切割.
// 在内循环中，首地址一直向右移动1个单元
// 在每一行中，一列一列的循环
// 偏移指针, 首地址向右根据当前小矩阵j来移动
// 比如 第一个地址为（0,0） 第二个为(0,1)
baseL.column = j * offsetValue; // 每次偏移一个像素位置
Matrix baseM = new Matrix(MBig.data, baseL, size);
// 比较.
double dRtn = CompareMatrix(baseM, MSmall);
// 放置值.
Mrtn.data[i, j] = dRtn;
}
}
return(Mrtn);
}
/// <summary>
/// 从文件获取Row和Col
/// </summary>
/// <param name="strFile"></param>
/// <returns></returns>
public static mySize GetSize(string strFile)
{
mySize size = new mySize(0, 0);
Image imageTemp = Image.FromFile(strFile);
// 根据事实设定
size.ROW = imageTemp.Height;
size.COL = imageTemp.Width;
imageTemp.Dispose();
return(size);
}
/// <summary>
/// 获得图片的Row和Colomn值
/// </summary>
/// <param name="strBase">文件名字</param>
/// <returns>返回mySize对象</returns>
private mySize GetPicSize(string strFile)
{
mySize sizeRtn = new mySize(0, 0);
// 用一个Image.
Image image = Image.FromFile(strFile);
// 这里会开辟新内存
Bitmap bitmap = new Bitmap(image);
// 如果是Tif 没有RGB分量 不用经过灰度图转换
sizeRtn.COL = image.Width;
sizeRtn.ROW = image.Height;
// 释放资源
bitmap.Dispose();
image.Dispose();
return(sizeRtn);
}
public static Location HandlePoint(Location curL, byte[,] bCsharp, mySize size)
{
// 只对Point里的 行和列值做越界判断，然后改变这个值
Location LRtn = new Location();
LRtn.row = curL.row;
LRtn.column = curL.column;
// 行
// 内框右边的值
int nSmallRow = curL.row + size.ROW - 1;
// 外框的坐标值
int nOutRow = bCsharp.GetLength(0) - 1;
if (nSmallRow > nOutRow)
{
// Row 越界 外框矩阵坐标 - 矩阵长度 + 1
int nReturnRow = nOutRow - size.ROW + 1;
LRtn.row = nReturnRow;
}
// 列
// 内框右边的值
int nSmallCol = curL.column + size.COL - 1;
// 外框的坐标值
int nOutCol = bCsharp.GetLength(1) - 1;
if (nSmallCol > nOutCol)
{
// Row 越界 外框矩阵坐标 - 矩阵长度 + 1
int nReturnCol = nOutCol - size.COL + 1;
LRtn.column = nReturnCol;
}
return(LRtn);
}
// 输入一个大矩阵，返回以8*8基本矩阵为基础的嵌套矩阵
public static Matrix[,] SplitMatrix(double[,] PicBase, mySize SmallBaseSize)
{
Matrix[,] mRtn = null;
// 判断需要开辟多大的矩阵
int iBaseSize = SmallBaseSize.ROW;
int iBigSize = PicBase.GetLength(0);
int nNeed = iBigSize / iBaseSize;
mRtn = new Matrix[nNeed, nNeed];
// 切割矩阵的控制代码，先写死 速度做出来
Location baseL = new Location(0, 0);
for (int i = 0; i < nNeed; i++)
{
// 在外循环中，首地址一直向下移动8个单元
// 便利循环下一列
baseL.row = i * iBaseSize;
for (int j = 0; j < nNeed; j++)
{
// 在内循环中，首地址一直向右移动8个单元
// 在每一行中，一列一列的循环
// 偏移指针, 首地址向右根据当前小矩阵j来移动
// 比如 第一个地址为（0,0） 第二个为(0,8)
baseL.column = j * 8;
Matrix eachM = new Matrix(PicBase, baseL, SmallBaseSize);
mRtn[i, j] = eachM;
}
}
return(mRtn);
}
public void Run()
{
// Console.WriteLine("Hello World!");
int nSetThread = 7;
// 先完成一个配置项
// 一个BasePic和一个目录=》 txt匹配结果
//String strBasePic = "";
//String StrDir = "";
// 先完成单个基础匹配策略.
// 一个BasePic和一个MatchPic => txt匹配结果
//String strBase = "../../../BasePics/ccd.tif";
//String strMatch = "../../../IR_Report/IR_256_256_20/Pic/ir_256_256_0_0.tif";
String strBase = "../../../BasePics/ccd3.tif";
String strMatch = "../../../IR3_Report/IR3_256_256_20/Pic/ir3_256_256_0_0.tif";
// 提取文件的Row和Col组合
String fileLocation = GetLocationForm(strMatch);
// 按照层次位置读取文件.
mySize BaseSize = GetPicSize(strBase);
double[,] PicBase = new double[BaseSize.ROW, BaseSize.COL];
mySize matchSize = GetPicSize(strMatch);
double[,] PicMatch = new double[matchSize.ROW, matchSize.COL];
// 把图片数字设置到分配的内存里
SetPicData(strBase, PicBase);
SetPicData(strMatch, PicMatch);
// 计算移动一个像素点时 需要匹配的次数.
MatrixCiCui nPiPeiSize = GetPiPeiTime(PicMatch, PicBase);
// 为每个线程对象分配 区域匹配任务.
// 计算是否单独多设置一个线程
int HandleUnitNUM = GetThreadNum(nSetThread, nPiPeiSize);
List <HandleUnit> HUnitList = new List <HandleUnit>();
for (int i = 0; i < HandleUnitNUM; i++)
{
HandleUnit hu = new HandleUnit();
HUnitList.Add(hu);
}
// 为每一个处理单元分配区域. 这里的值只是个数，从0计数
int TotalNum = nPiPeiSize.horSize * nPiPeiSize.verSize;
int baseTask = TotalNum / nSetThread;
for (int i = 0; i < HandleUnitNUM; i++)
{
HandleUnit each = HUnitList[i];
each.nStart = i * baseTask;
each.nEnd = each.nStart + baseTask - 1;
// 设置最后一个特殊值.
if (each.nEnd > (TotalNum - 1))
{
each.nEnd = TotalNum - 1;
}
each.mBasePic = PicBase;
each.mMatchPic = PicMatch;
each.mPiPeiSize = nPiPeiSize;
}
//设置线程.
List <Thread> threadList = new List <Thread>();
for (int i = 0; i < HandleUnitNUM; i++)
{
Thread HandleThread; // 发送数据的线程对象.
HandleUnit each = HUnitList[i];
HandleThread = new Thread(new ThreadStart(each.Matching));
threadList.Add(HandleThread);
}
// 同时启动线程
for (int i = 0; i < HandleUnitNUM; i++)
{
Thread each = threadList[i];
each.Start();
}
Console.WriteLine("Here");
bool bExit = false;
while (false == bExit)
{
bExit = CheckStop(threadList);
}
// 在分线程全部做完操作后 还可以整合资源
// 从HUnitList里找出最大的值 并输出文件结果
// 组合集合.
Hashtable TotalHT = GetTotalHT(HUnitList);
Location lResult = GetLocation(TotalHT);
// Console.WriteLine("{0},{1}",lResult.row,lResult.column);
// 写文件.
String strLog = "TestLog.txt";
//WriteLog(lResult, strMatch, strLog);
Console.ReadLine();
}
public String myRun(String strBase,
String strMatch,
int nSetThread)
{
String strRtn = string.Empty;
// 提取文件的Row和Col组合
String fileLocation = GetLocationForm(strMatch);
// 按照层次位置读取文件.
mySize BaseSize = GetPicSize(strBase);
double[,] PicBase = new double[BaseSize.ROW, BaseSize.COL];
mySize matchSize = GetPicSize(strMatch);
double[,] PicMatch = new double[matchSize.ROW, matchSize.COL];
// 把图片数字设置到分配的内存里
SetPicData(strBase, PicBase);
SetPicData(strMatch, PicMatch);
// 计算移动一个像素点时 需要匹配的次数.
MatrixCiCui nPiPeiSize = GetPiPeiTime(PicMatch, PicBase);
// 为每个线程对象分配 区域匹配任务.
// 计算是否单独多设置一个线程
int HandleUnitNUM = GetThreadNum(nSetThread, nPiPeiSize);
List <HandleUnit> HUnitList = new List <HandleUnit>();
for (int i = 0; i < HandleUnitNUM; i++)
{
HandleUnit hu = new HandleUnit();
HUnitList.Add(hu);
}
// 为每一个处理单元分配区域. 这里的值只是个数，从0计数
int TotalNum = nPiPeiSize.horSize * nPiPeiSize.verSize;
int baseTask = TotalNum / nSetThread;
for (int i = 0; i < HandleUnitNUM; i++)
{
HandleUnit each = HUnitList[i];
each.nStart = i * baseTask;
each.nEnd = each.nStart + baseTask - 1;
// 设置最后一个特殊值.
if (each.nEnd > (TotalNum - 1))
{
each.nEnd = TotalNum - 1;
}
each.mBasePic = PicBase;
each.mMatchPic = PicMatch;
each.mPiPeiSize = nPiPeiSize;
}
//设置线程.
List <Thread> threadList = new List <Thread>();
for (int i = 0; i < HandleUnitNUM; i++)
{
Thread HandleThread; // 发送数据的线程对象.
HandleUnit each = HUnitList[i];
HandleThread = new Thread(new ThreadStart(each.Matching));
threadList.Add(HandleThread);
}
// 同时启动线程
for (int i = 0; i < HandleUnitNUM; i++)
{
Thread each = threadList[i];
each.Start();
}
Console.WriteLine("Here");
bool bExit = false;
while (false == bExit)
{
bExit = CheckStop(threadList);
}
// 在分线程全部做完操作后 还可以整合资源
// 从HUnitList里找出最大的值 并输出文件结果
// 组合集合.
Hashtable TotalHT = GetTotalHT(HUnitList);
Location lResult = GetLocation(TotalHT);
String strLo = string.Format("{0},{1}", lResult.row, lResult.column);
// 写文件. 文件内容.
strRtn = string.Format("{0} {1}", fileLocation, strLo);
// 文件名字 大图+小图+小图位置.
String StrLogName = GetLogName(strBase, strMatch, fileLocation);
WriteLog(strRtn, StrLogName);
return(strRtn);
}
// Copy数据 从一部分拷贝
public static void CopyMatrixValue(byte[,] whiteIR, byte[,] ccd, Location TopLeft, mySize SmallSize)
{
// 循环遍历
int nRow = SmallSize.ROW;
int nCol = SmallSize.COL;
for (int i = 0; i < nRow; i++)
{
for (int j = 0; j < nCol; j++)
{
int nCurRow = i + TopLeft.row;
int nCurCol = j + TopLeft.column;
whiteIR[nCurRow, nCurCol] = ccd[nCurRow, nCurCol];
}
}
}
// 根据图片的i和j的值， 大图大小和小图大小， 获得左上角坐标值
public static Location GetTopLeftLocation(int nRowSmall,
int nColSmall,
mySize BigSize,
mySize SmallSize,
byte[,] bCsharp)
{
Location lRtn = new Location(0, 0);
//Math.Ceiling(3.1) = 4;
//Math.Floor(3.9) = 3;
// 行数为0 列数为1 Height为行数 Width为列数
int iBigRow = BigSize.ROW;
int iBigCol = BigSize.COL;
double iSmallRow = Convert.ToDouble(SmallSize.ROW);
double iSmallCol = Convert.ToDouble(SmallSize.COL);
int iSplitHeight = Convert.ToInt32(Math.Ceiling(iBigRow / iSmallRow));
int iSplitWidth = Convert.ToInt32(Math.Ceiling(iBigCol / iSmallCol));
// 基准点参考值 偏移指针
Location curL = new Location(0, 0);
mySize size = new mySize(SmallSize.ROW, SmallSize.COL);
// 循环遍历， 分割出矩阵后，直接输出小文件
// 需要分割的矩阵数量已经计算好
for (int i = 0; i < iSplitHeight; i++)
{
curL.row = i * SmallSize.ROW;
for (int j = 0; j < iSplitWidth; j++)
{
// 类似10*10的矩阵 [0,0] [0,10] [0,]
// [0,0] [0,10] [0,20]
// [10,0] [10,10] [10,20]
// [20,0] [20,10] [20,20]
curL.column = j * SmallSize.COL;
// 输入偏移指针，返回一个左上角坐标值
// 有异常情况 需要调整位置，都在这个函数里处理
Location handlePoint = HandlePoint(curL, bCsharp, size);
// 获得byte小矩阵
// byte[,] bSmall = GetSmallM(handlePoint, bCsharp, size);
if ((nRowSmall == i) && (nColSmall == j))
{
// 输出左上角坐标.
lRtn.row = handlePoint.row;
lRtn.column = handlePoint.column;
return(lRtn);
}
}
}
return(lRtn);
}
// 输入两个矩阵List, 输出一个匹配矩阵
public static Matrix PiPeiMatrix(List <Matrix> MBiglist, List <Matrix> MSmalist, Location[] Lvector)
{
// MBiglist * * 12
// MSmalist * * 12
// MFinallist * * 12
int nVector = Lvector.Length;
// 小图在大图上做移动匹配
List <Matrix> MFinallist = new List <Matrix>();
double dE = 0.0;
for (int z = 0; z < nVector; z++)
{
//mylist[i].name
Matrix MBig = MBiglist[z];
Matrix MSmall = MSmalist[z];
ABSMatrix(MBig);
ABSMatrix(MSmall);
// 输入2个矩阵，导出一个矩阵.
Matrix Mfinal = PiPei(MSmall, MBig);
ABSMatrix(Mfinal);
// 从Mfinal中找个最大值和第二大值
double dMax = FindMax(Mfinal);
double dSecondMax = FindSecondMax(Mfinal);
// 算出主峰与次峰比值
double dBiZhi = dMax / dSecondMax;
// 得出一个新矩阵
MatrixMulti(Mfinal, dBiZhi);
// 记录新矩阵
MFinallist.Add(Mfinal);
// 记录主次峰比值
dE = dE + dBiZhi;
}
// dE 之后会用到
nVector = MFinallist.Count;
// GetPiPeiSize
mySize PiPeiSize = GetPiPeiSize(MBiglist[0], MSmalist[0]);
Matrix MaxtrixSum = new Matrix(PiPeiSize.ROW, PiPeiSize.COL);
for (int i = 1; i < nVector; i++)
{
Matrix eachM = MFinallist[i];
// 求和
MaxtrixAdd(MaxtrixSum, eachM);
}
// 矩阵除法
Matrix MatrixG = MaxtrixDiv(MaxtrixSum, dE);
return(MatrixG);
}
