本文整理汇总了C++中CStopWatch::CheckTime方法的典型用法代码示例。如果您正苦于以下问题：C++ CStopWatch::CheckTime方法的具体用法？C++ CStopWatch::CheckTime怎么用？C++ CStopWatch::CheckTime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CStopWatch的用法示例。
在下文中一共展示了CStopWatch::CheckTime方法的2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: ClassifySTL
// ------------------------------------------------------------------------------------------
// Classify STL Test Image Set and save each file separated class into Image/class no. folder
//
void ClassifySTL()
{
CImageObject debugImage;
//debugImage.CreateReshape(serialized, rt.Width(), rt.Height(), 3);
//m_ImageDebugDlg.DrawImage(debugImage);
#if 1
CMatLoader inputImages("trainImages2000");
CMatLoader inputLabels("trainLabels2000");
CMatLoader pooledFeatures("pooledFeaturesTrain2000");
CMatLoader softmaxOptTheta("softmaxOptTheta2000");
#else
CMatLoader inputImages("testImages");
CMatLoader inputLabels("testLabels");
// 3600 x m
CMatLoader pooledFeatures("pooledFeaturesTest");
CMatLoader softmaxOptTheta("softmaxOptTheta2000");
#endif
// 4 x 3600
vector <BYTE *> vecImages;
SerializeSTLImage(inputImages, vecImages);
// CMatLoader testImages("testImages");
CStopWatch w;
vector <short> vecLabel;
vector <float> vecConfidence;
ClassifySoftmaxRegression(pooledFeatures, softmaxOptTheta, vecLabel, vecConfidence);
int count = 0;
int i;
//CLog log("result2.csv", true);
for (i=0;i<(int)vecLabel.size();i++)
{
if (vecLabel[i] == (int)inputLabels.data[i])
count++;
//log.WriteLog("%d, %d, %d\n", vecLabel[i] , (int)inputLabels.data[i], vecLabel[i] - (int)inputLabels.data[i]);
}
printf("Classification finished accuracy : %f %%\n", (float)count / (float)vecLabel.size() * 100.0f);
printf("Elapsed time %.0f msec\n", w.CheckTime());
mkdir("ResultImage");
mkdir("ResultImage\\1");
mkdir("ResultImage\\2");
mkdir("ResultImage\\3");
mkdir("ResultImage\\4");
mkdir("ResultImage\\5");
if (1)
{
for (i=0;i<(int)vecImages.size();i++)
//for (i=0;i<100;i++)
{
debugImage.CreateReshape(vecImages[i], 64, 64, 3);
CString str;
str.Format("ResultImage\\%d\\%d.bmp", vecLabel[i],i);
debugImage.SaveToBMP(str.GetBuffer(0));
}
// Classification(pooledFeaturesTest, softmaxOptTheta, vecLabel);
}
printf("%d classified images are saved into ResultImage/classNo directory\n",(int)vecImages.size());
for (i = 0;i<(int)vecImages.size();i++)
delete [] vecImages[i];
return;
} 示例2: ClassifyIntegralFeature
//.........这里部分代码省略.........
{
meanFeature.data[featureIndex++] = (features[i].GetBlockMeanByIntegralImage(col + (col2 * k), row + (row2 * k), k, k));
//if (col == 95 && row == 0)
// log.WriteLog("%f\n", meanFeature.data[i]);
}
}
}
//log.WriteLog("\n");
//ClassifySoftmaxRegressionSingle(meanFeature, softmaxOptTheta, vecLabel, vecConfidence);
//if (vecLabel[0] == 5 ) continue;
CRectEx rt;
rt.SetRect(0,0,k*3+8,k*3+8);
rt.Offset(col,row);
//m_OvrDisp.DrawRect(rt,color[vecLabel[0]], 10,"%s(%d)",className[vecLabel[0]], vecLabel[0]);
//log.WriteLog("%s,%d, %.1f, %.1f, %.1f, %.1f,%.1f,",className[vecLabel[0]], vecLabel[0], vecConfidence[0],vecConfidence[1],vecConfidence[2],vecConfidence[3], vecConfidence[4]);
ClassifySoftmaxRegressionSingle(meanFeature, softmaxOptTheta5320, vecLabel, vecConfidence);
if (k==start)
vecCandidateLabel.push_back(vecLabel[0]-1);
else if (vecLabel[0] == candidate)
{
//m_OvrDisp.DrawRect(rt,color[vecLabel[0]], 10,"%s(%d) %.1f %.1f %.1f %.1f",className[vecLabel[0]], vecLabel[0], vecConfidence[0]);
//vecRect.push_back(rt);
rtFinal = rt;
found = TRUE;
}
log.WriteLog(",%s,%d,%.1f, %.1f, %.1f, %.1f\n",className[vecLabel[0]], vecLabel[0], vecConfidence[0],vecConfidence[1],vecConfidence[2],vecConfidence[3]);
if (vecLabel[0] != 5 && vecConfidence[0] > 0.99)
{
//ClassifySoftmaxRegression(meanFeature, softmaxOptTheta100percentTraining, vecLabel, vecConfidence);
// m_OvrDisp.DrawRect(rt,color[vecLabel[0]], 10,"%s(%d) %.1f %.1f %.1f %.1f",className[vecLabel[0]], vecLabel[0], vecConfidence[0]);
// goto FINISH;
}
else
{
//m_OvrDisp.DrawRect(rt,color[vecLabel[0]], 10,"%s(%d) %f",className[vecLabel[0]], vecLabel[0], vecConfidence[0]);
}
//m_OvrDisp.DrawRect(rt,MCYAN);
//log.WriteLog("%d,%f\n",vecLabel[0],vecConfidence[0]);
}
}
if (k == start)
{
vector <int> histo;
histo.resize(4);
for (int l=0;l<(int)vecCandidateLabel.size();l++)
histo[vecCandidateLabel[l]]++;
candidate = distance(histo.begin(), max_element(histo.begin(), histo.end())) + 1;
rtCadidate.SetRect(0,0,k*3+8,k*3+8);
//rtCadidate.Offset(col,row);
}
}
if (found == FALSE)
printf("%d, %d, %d %d = %s\n", rtCadidate.left,rtCadidate.top,rtCadidate.right,rtCadidate.bottom, className[candidate]);
else
printf("%d, %d, %d %d = %s\n", rtCadidate.left,rtFinal.top,rtFinal.right,rtFinal.bottom, className[candidate]);
printf("Elapsed time in %.0f msec (%d x %d image)\n", w.CheckTime(),m_Image.Width(), m_Image.Height());
/*for (row = 0;row <= rowSize-3;row+=step)
//for (row = 0;row < 1;row++)
{
for (col = 0;col <= colSize-3;col+=step)
//for (col = 0;col < 1;col++)
{
//w.StartTime();
//double error = ClassifyMeans(pooledFeaturesLarge,row, col,3,3, meanFeature, vecLabel);
//if (vecLabel[0] == 0 ) continue;
ClassifySoftmaxRegression(convolvedFeatures,row, col,3,3, softmaxOptTheta, vecLabel, vecConfidence);
//if (vecLabel[0] == 5) continue;
//if (vecConfidence[0] < thr) continue;
//m_OvrDisp.DrawText(10 + (row *10),10, MGREEN, 20, "%.0f msec", w.CheckTime());
CRectEx rt;
rt.SetRect(0,0,63,63);
rt.Offset(int((float)col / 3.0f * 64.0f),int((float)row / 3.0f * 64.0f));
m_OvrDisp.DrawRect(rt,color[vecLabel[0]], 10,"%s(%d) %f",className[vecLabel[0]], vecLabel[0], vecConfidence[0]);
//m_OvrDisp.DrawText(rt.CenterPoint().x,rt.CenterPoint().y,MGREEN, 10,"%s",className[vecLabel[0]]);
//m_OvrDisp.DrawRect(rt,MRED, 10,"%d,%d,%d,%d",vecLabel[0], vecLabel[1],vecLabel[2],vecLabel[3]);
//m_OvrDisp.DrawRect(rt,MRED);
}
}*/
//m_OvrDisp.DrawText(10,10, MCYAN, 15, "%d x %d %.0f msec", m_Image.Width(), m_Image.Height(),w.CheckTime());
delete [] features;
return;
}