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Warnings Fix #1

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pull/288/head
Vladimir 10 years ago
parent d4011aaea7
commit 17dbe91ac9
1 changed files with 28 additions and 30 deletions
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  1. 58
      modules/tracking/src/tldDetector.cpp

58
modules/tracking/src/tldDetector.cpp
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@ -81,9 +81,9 @@ namespace cv
return 0.0;
return splus / (sminus + splus);
*/
int64 e1, e2;
float t;
e1 = getTickCount();
//int64 e1, e2;
//float t;
//e1 = getTickCount();
double splus = 0.0, sminus = 0.0;
Mat_<uchar> modelSample(STANDARD_PATCH_SIZE, STANDARD_PATCH_SIZE);
for (int i = 0; i < *posNum; i++)
@ -96,8 +96,8 @@ namespace cv
modelSample.data = &(negExp->data[i * 225]);
sminus = std::max(sminus, 0.5 * (NCC(modelSample, patch) + 1.0));
}
e2 = getTickCount();
t = (e2 - e1) / getTickFrequency()*1000.0;
//e2 = getTickCount();
//t = (e2 - e1) / getTickFrequency()*1000.0;
//printf("Sr CPU: %f\n", t);
if (splus + sminus == 0.0)
return 0.0;
@ -107,7 +107,7 @@ namespace cv
double TLDDetector::ocl_Sr(const Mat_<uchar>& patch)
{
int64 e1, e2, e3, e4;
float t;
double t;
e1 = getTickCount();
e3 = getTickCount();
double splus = 0.0, sminus = 0.0;
@ -131,8 +131,8 @@ namespace cv
ocl::KernelArg::PtrReadOnly(devPositiveSamples),
ocl::KernelArg::PtrReadOnly(devNegativeSamples),
ocl::KernelArg::PtrWriteOnly(devNCC),
(int)posNum,
(int)negNum);
posNum,
negNum);
e4 = getTickCount();
t = (e4 - e3) / getTickFrequency()*1000.0;
@ -186,7 +186,7 @@ namespace cv
void TLDDetector::ocl_batchSrSc(const Mat_<uchar>& patches, double *resultSr, double *resultSc, int numOfPatches)
{
int64 e1, e2, e3, e4;
float t;
double t;
e1 = getTickCount();
e3 = getTickCount();
@ -235,28 +235,28 @@ namespace cv
//printf("Read Mem GPU: %f\n", t);
//Calculate Srs
for (int k = 0; k < numOfPatches; k++)
for (int id = 0; id < numOfPatches; id++)
{
double spr = 0.0, smr = 0.0, spc = 0.0, smc = 0;
int med = getMedian((*timeStampsPositive));
for (int i = 0; i < *posNum; i++)
{
spr = std::max(spr, 0.5 * (posNCC.at<float>(k * 500 + i) + 1.0));
spr = std::max(spr, 0.5 * (posNCC.at<float>(id * 500 + i) + 1.0));
if ((int)(*timeStampsPositive)[i] <= med)
spc = std::max(spr, 0.5 * (posNCC.at<float>(k * 500 + i) + 1.0));
spc = std::max(spr, 0.5 * (posNCC.at<float>(id * 500 + i) + 1.0));
}
for (int i = 0; i < *negNum; i++)
smc = smr = std::max(smr, 0.5 * (negNCC.at<float>(k * 500 + i) + 1.0));
smc = smr = std::max(smr, 0.5 * (negNCC.at<float>(id * 500 + i) + 1.0));
if (spr + smr == 0.0)
resultSr[k] = 0.0;
resultSr[id] = 0.0;
else
resultSr[k] = spr / (smr + spr);
resultSr[id] = spr / (smr + spr);
if (spc + smc == 0.0)
resultSc[k] = 0.0;
resultSc[id] = 0.0;
else
resultSc[k] = spc / (smc + spc);
resultSc[id] = spc / (smc + spc);
}
////Compare positive NCCs
@ -367,8 +367,8 @@ namespace cv
ocl::KernelArg::PtrReadOnly(devPositiveSamples),
ocl::KernelArg::PtrReadOnly(devNegativeSamples),
ocl::KernelArg::PtrWriteOnly(devNCC),
(int)posNum,
(int)negNum);
posNum,
negNum);
e4 = getTickCount();
t = (e4 - e3) / getTickFrequency()*1000.0;
@ -466,7 +466,6 @@ namespace cv
int dx = initSize.width / 10, dy = initSize.height / 10;
Size2d size = img.size();
double scale = 1.0;
int total = 0, pass = 0;
int npos = 0, nneg = 0;
double maxSc = -5.0;
Rect2d maxScRect;
@ -477,7 +476,7 @@ namespace cv
int64 e1, e2;
double t;
e1 = cvGetTickCount();
e1 = getTickCount();
//Detection part
//Generate windows and filter by variance
scaleID = 0;
@ -511,8 +510,8 @@ namespace cv
//printf("Variance: %d\t%f\n", varBuffer.size(), t);
//Encsemble classification
e1 = cvGetTickCount();
for (int i = 0; i < varBuffer.size(); i++)
e1 = getTickCount();
for (int i = 0; i < (int)varBuffer.size(); i++)
{
prepareClassifiers((int)blurred_imgs[varScaleIDs[i]].step[0]);
if (ensembleClassifierNum(&blurred_imgs[varScaleIDs[i]].at<uchar>(varBuffer[i].y, varBuffer[i].x)) <= ENSEMBLE_THRESHOLD)
@ -526,7 +525,7 @@ namespace cv
//NN classification
e1 = getTickCount();
for (int i = 0; i < ensBuffer.size(); i++)
for (int i = 0; i < (int)ensBuffer.size(); i++)
{
LabeledPatch labPatch;
double curScale = pow(SCALE_STEP, ensScaleIDs[i]);
@ -577,7 +576,6 @@ namespace cv
int dx = initSize.width / 10, dy = initSize.height / 10;
Size2d size = img.size();
double scale = 1.0;
int total = 0, pass = 0;
int npos = 0, nneg = 0;
double maxSc = -5.0;
Rect2d maxScRect;
@ -588,7 +586,7 @@ namespace cv
int64 e1, e2;
double t;
e1 = cvGetTickCount();
e1 = getTickCount();
//Detection part
//Generate windows and filter by variance
scaleID = 0;
@ -622,8 +620,8 @@ namespace cv
//printf("Variance: %d\t%f\n", varBuffer.size(), t);
//Encsemble classification
e1 = cvGetTickCount();
for (int i = 0; i < varBuffer.size(); i++)
e1 = getTickCount();
for (int i = 0; i < (int)varBuffer.size(); i++)
{
prepareClassifiers((int)blurred_imgs[varScaleIDs[i]].step[0]);
if (ensembleClassifierNum(&blurred_imgs[varScaleIDs[i]].at<uchar>(varBuffer[i].y, varBuffer[i].x)) <= ENSEMBLE_THRESHOLD)
@ -644,7 +642,7 @@ namespace cv
double *resultSc = new double[numOfPatches];
uchar *patchesData = stdPatches.data;
for (int i = 0; i < ensBuffer.size(); i++)
for (int i = 0; i < (int)ensBuffer.size(); i++)
{
resample(resized_imgs[ensScaleIDs[i]], Rect2d(ensBuffer[i], initSize), standardPatch);
uchar *stdPatchData = standardPatch.data;
@ -655,7 +653,7 @@ namespace cv
ocl_batchSrSc(stdPatches, resultSr, resultSc, numOfPatches);
for (int i = 0; i < ensBuffer.size(); i++)
for (int i = 0; i < (int)ensBuffer.size(); i++)
{
LabeledPatch labPatch;
standardPatch.data = &stdPatches.data[225 * i];

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