本文整理汇总了C#中CvMat.SetReal1D方法的典型用法代码示例。如果您正苦于以下问题：C# CvMat.SetReal1D方法的具体用法？C# CvMat.SetReal1D怎么用？C# CvMat.SetReal1D使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类CvMat的用法示例。
在下文中一共展示了CvMat.SetReal1D方法的3个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的C#代码示例。
示例1: BuildRtreesClassifier
/// <summary>
/// RTrees
/// </summary>
/// <param name="dataFilename"></param>
/// <param name="filenameToSave"></param>
/// <param name="filenameToLoad"></param>
private void BuildRtreesClassifier(string dataFilename, string filenameToSave, string filenameToLoad)
{
CvMat data = null;
CvMat responses = null;
CvMat varType = null;
CvMat sampleIdx = null;
int nsamplesAll = 0, ntrainSamples = 0;
double trainHr = 0, testHr = 0;
CvRTrees forest = new CvRTrees();
try
{
ReadNumClassData(dataFilename, 16, out data, out responses);
}
catch
{
Console.WriteLine("Could not read the database {0}", dataFilename);
return;
}
Console.WriteLine("The database {0} is loaded.", dataFilename);
nsamplesAll = data.Rows;
ntrainSamples = (int)(nsamplesAll * 0.8);
// Create or load Random Trees classifier
if (filenameToLoad != null)
{
// load classifier from the specified file
forest.Load(filenameToLoad);
ntrainSamples = 0;
if (forest.GetTreeCount() == 0)
{
Console.WriteLine("Could not read the classifier {0}", filenameToLoad);
return;
}
Console.WriteLine("The classifier {0} is loaded.", filenameToLoad);
}
else
{
// create classifier by using <data> and <responses>
Console.Write("Training the classifier ...");
// 1. create type mask
varType = new CvMat(data.Cols + 1, 1, MatrixType.U8C1);
varType.Set(CvScalar.ScalarAll(CvStatModel.CV_VAR_ORDERED));
varType.SetReal1D(data.Cols, CvStatModel.CV_VAR_CATEGORICAL);
// 2. create sample_idx
sampleIdx = new CvMat(1, nsamplesAll, MatrixType.U8C1);
{
CvMat mat;
Cv.GetCols(sampleIdx, out mat, 0, ntrainSamples);
mat.Set(CvScalar.RealScalar(1));
Cv.GetCols(sampleIdx, out mat, ntrainSamples, nsamplesAll);
mat.SetZero();
}
// 3. train classifier
forest.Train(
data, DTreeDataLayout.RowSample, responses, null, sampleIdx, varType, null,
new CvRTParams(10, 10, 0, false, 15, null, true, 4, new CvTermCriteria(100, 0.01f))
);
Console.WriteLine();
}
// compute prediction error on train and test data
for (int i = 0; i < nsamplesAll; i++)
{
double r;
CvMat sample;
Cv.GetRow(data, out sample, i);
r = forest.Predict(sample);
r = Math.Abs((double)r - responses.DataArraySingle[i]) <= float.Epsilon ? 1 : 0;
if (i < ntrainSamples)
trainHr += r;
else
testHr += r;
}
testHr /= (double)(nsamplesAll - ntrainSamples);
trainHr /= (double)ntrainSamples;
Console.WriteLine("Recognition rate: train = {0:F1}%, test = {1:F1}%", trainHr * 100.0, testHr * 100.0);
Console.WriteLine("Number of trees: {0}", forest.GetTreeCount());
// Print variable importance
Mat varImportance0 = forest.GetVarImportance();
CvMat varImportance = varImportance0.ToCvMat();
if (varImportance != null)
//.........这里部分代码省略.........
示例2: BuildBoostClassifier
/// <summary>
///
/// </summary>
/// <param name="dataFilename"></param>
/// <param name="filenameToSave"></param>
/// <param name="filenameToLoad"></param>
private void BuildBoostClassifier(string dataFilename, string filenameToSave, string filenameToLoad)
{
const int ClassCount = 26;
CvMat data = null;
CvMat responses = null;
CvMat varType = null;
CvMat tempSample = null;
CvMat weakResponses = null;
int nsamplesAall = 0, ntrainSamples = 0;
int varCount;
double trainHr = 0, testHr = 0;
CvBoost boost = new CvBoost();
try
{
ReadNumClassData(dataFilename, 16, out data, out responses);
}
catch
{
Console.WriteLine("Could not read the database {0}", dataFilename);
return;
}
Console.WriteLine("The database {0} is loaded.", dataFilename);
nsamplesAall = data.Rows;
ntrainSamples = (int)(nsamplesAall * 0.5);
varCount = data.Cols;
// Create or load Boosted Tree classifier
if (filenameToLoad != null)
{
// load classifier from the specified file
boost.Load(filenameToLoad);
ntrainSamples = 0;
if (boost.GetWeakPredictors() == null)
{
Console.WriteLine("Could not read the classifier {0}", filenameToLoad);
return;
}
Console.WriteLine("The classifier {0} is loaded.", filenameToLoad);
}
else
{
// !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
//
// As currently boosted tree classifier in MLL can only be trained
// for 2-class problems, we transform the training database by
// "unrolling" each training sample as many times as the number of
// classes (26) that we have.
//
// !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
using (CvMat newData = new CvMat(ntrainSamples * ClassCount, varCount + 1, MatrixType.F32C1))
using (CvMat newResponses = new CvMat(ntrainSamples * ClassCount, 1, MatrixType.S32C1))
{
// 1. unroll the database type mask
Console.WriteLine("Unrolling the database...");
for (int i = 0; i < ntrainSamples; i++)
{
unsafe
{
float* dataRow = (float*)(data.DataByte + data.Step * i);
for (int j = 0; j < ClassCount; j++)
{
float* newDataRow = (float*)(newData.DataByte + newData.Step * (i * ClassCount + j));
for (int k = 0; k < varCount; k++)
{
newDataRow[k] = dataRow[k];
}
newDataRow[varCount] = (float)j;
newResponses.DataInt32[i * ClassCount + j] = (responses.DataSingle[i] == j + 'A') ? 1 : 0;
}
}
}
// 2. create type mask
varType = new CvMat(varCount + 2, 1, MatrixType.U8C1);
varType.Set(CvScalar.ScalarAll(CvStatModel.CV_VAR_ORDERED));
// the last indicator variable, as well
// as the new (binary) response are categorical
varType.SetReal1D(varCount, CvStatModel.CV_VAR_CATEGORICAL);
varType.SetReal1D(varCount + 1, CvStatModel.CV_VAR_CATEGORICAL);
// 3. train classifier
Console.Write("Training the classifier (may take a few minutes)...");
boost.Train(
newData, DTreeDataLayout.RowSample, newResponses, null, null, varType, null,
new CvBoostParams(CvBoost.REAL, 100, 0.95, 5, false, null)
);
}
Console.WriteLine();
//.........这里部分代码省略.........
示例3: FindExtrinsicCameraParams2Cs
//.........这里部分代码省略.........
CvMat _LV = new CvMat(12, 12, MatrixType.F64C1, LV);
CvMat _RR, _tt;
CvPoint3D64f* M = (CvPoint3D64f*)matM.DataDouble;
CvPoint2D64f* mn = (CvPoint2D64f*)_mn.DataDouble;
CvMat matL = new CvMat(2 * count, 12, MatrixType.F64C1);
double* L = matL.DataDouble;
for (int i = 0; i < count; i++, L += 24)
{
double x = -mn[i].X, y = -mn[i].Y;
L[0] = L[16] = M[i].X;
L[1] = L[17] = M[i].Y;
L[2] = L[18] = M[i].Z;
L[3] = L[19] = 1.0;
L[4] = L[5] = L[6] = L[7] = 0.0;
L[12] = L[13] = L[14] = L[15] = 0.0;
L[8] = x * M[i].X;
L[9] = x * M[i].Y;
L[10] = x * M[i].Z;
L[11] = x;
L[20] = y * M[i].X;
L[21] = y * M[i].Y;
L[22] = y * M[i].Z;
L[23] = y;
}
MulTransposed(matL, _LL, true);
SVD(_LL, _LW, null, _LV, SVDFlag.ModifyA | SVDFlag.V_T);
double[] LV12 = new double[12];
Array.Copy(LV, 11 * 12, LV12, 0, 12);
CvMat _RRt = new CvMat(3, 4, MatrixType.F64C1, LV12);
GetCols(_RRt, out _RR, 0, 3);
GetCol(_RRt, out _tt, 3);
if (Det(_RR) < 0)
Scale(_RRt, _RRt, -1);
double sc = Norm(_RR);
SVD(_RR, matW, matU, matV, SVDFlag.ModifyA | SVDFlag.U_T | SVDFlag.V_T);
GEMM(matU, matV, 1, null, 0, matR, GemmOperation.A_T);
Scale(_tt, _t, Norm(matR) / sc);
Rodrigues2_(matR, _r);
}
}
Cv.Reshape(matM, matM, 3, 1);
Cv.Reshape(_mn, _mn, 2, 1);
// refine extrinsic parameters using iterative algorithm
CvLevMarq solver = new CvLevMarq(6, count * 2, new CvTermCriteria(maxIter, float.Epsilon), true);
Copy(_param, solver.Param);
/*
Console.WriteLine("matM-----");
for (int i = 0; i < matM.Rows * matM.Cols; i++)
{
Console.WriteLine("{0}\t", matM[i].Val0);
}
Console.WriteLine("_mn-----");
for (int i = 0; i < _mn.Rows * _mn.Cols; i++)
{
Console.WriteLine(_mn[i].Val0);
}
Console.WriteLine("_param-----");
for (int i = 0; i < _param.Rows * _param.Cols; i++)
{
Console.WriteLine(_param[i].Val0);
}*/
for (; ; )
{
CvMat matJ, _err, __param;
bool proceed = solver.Update(out __param, out matJ, out _err);
Copy(__param, _param);
if (!proceed || _err == null)
break;
Reshape(_err, _err, 2, 1);
if (matJ != null)
{
GetCols(matJ, out _dpdr, 0, 3);
GetCols(matJ, out _dpdt, 3, 6);
ProjectPoints2(matM, _r, _t, matA, distCoeffs,
_err, _dpdr, _dpdt, null, null, null);
}
else
{
ProjectPoints2(matM, _r, _t, matA, distCoeffs, _err, null, null, null, null, null);
}
Sub(_err, _m, _err);
Reshape(_err, _err, 1, 2 * count);
}
Copy(solver.Param, _param);
for (int i = 0; i < 3; i++)
{
rvec.SetReal1D(i, param[i]);
tvec.SetReal1D(i, param[i + 3]);
}
}
}