void *Gear_FaceTrack::loopThread()
{
_keepLooping = true;
while (_keepLooping)
{
// Tell the filter that we want a new frame
pthread_cond_wait(&_inputCond, &_inputMutex);
if ( !_keepLooping )
break;
_pCurrentFrame = (unsigned char*) _grayImage.data();
rgba2grayscale(_pCurrentFrame, (unsigned char*)_image->data(), _image->size());
computeIntegralImages();
findFaces();
pthread_mutex_lock(&_outputMutex);
// mergeFaces();
_drawingFoundFaces = _foundFaces;
pthread_mutex_unlock(&_outputMutex);
}
NOTICE("Exiting thread");
// pthread_mutex_unlock(&_exitMutex);
pthread_exit(NULL);
return NULL;
}
template <typename DataType, unsigned Dimension> void
pcl::IntegralImage2D<DataType, Dimension>::setInput (const DataType * data, unsigned width,unsigned height, unsigned element_stride, unsigned row_stride)
{
if ((width + 1) * (height + 1) > first_order_integral_image_.size () )
{
width_ = width;
height_ = height;
first_order_integral_image_.resize ( (width_ + 1) * (height_ + 1) );
finite_values_integral_image_.resize ( (width_ + 1) * (height_ + 1) );
if (compute_second_order_integral_images_)
second_order_integral_image_.resize ( (width_ + 1) * (height_ + 1) );
}
computeIntegralImages (data, row_stride, element_stride);
}
bool TLDDetector::ocl_detect(const Mat& img, const Mat& imgBlurred, Rect2d& res, std::vector<LabeledPatch>& patches, Size initSize)
{
patches.clear();
Mat_<uchar> standardPatch(STANDARD_PATCH_SIZE, STANDARD_PATCH_SIZE);
Mat tmp;
int dx = initSize.width / 10, dy = initSize.height / 10;
Size2d size = img.size();
double scale = 1.0;
int npos = 0, nneg = 0;
double maxSc = -5.0;
Rect2d maxScRect;
int scaleID;
std::vector <Mat> resized_imgs, blurred_imgs;
std::vector <Point> varBuffer, ensBuffer;
std::vector <int> varScaleIDs, ensScaleIDs;
//Detection part
//Generate windows and filter by variance
scaleID = 0;
resized_imgs.push_back(img);
blurred_imgs.push_back(imgBlurred);
do
{
Mat_<double> intImgP, intImgP2;
computeIntegralImages(resized_imgs[scaleID], intImgP, intImgP2);
for (int i = 0, imax = cvFloor((0.0 + resized_imgs[scaleID].cols - initSize.width) / dx); i < imax; i++)
{
for (int j = 0, jmax = cvFloor((0.0 + resized_imgs[scaleID].rows - initSize.height) / dy); j < jmax; j++)
{
if (!patchVariance(intImgP, intImgP2, originalVariancePtr, Point(dx * i, dy * j), initSize))
continue;
varBuffer.push_back(Point(dx * i, dy * j));
varScaleIDs.push_back(scaleID);
}
}
scaleID++;
size.width /= SCALE_STEP;
size.height /= SCALE_STEP;
scale *= SCALE_STEP;
resize(img, tmp, size, 0, 0, DOWNSCALE_MODE);
resized_imgs.push_back(tmp);
GaussianBlur(resized_imgs[scaleID], tmp, GaussBlurKernelSize, 0.0f);
blurred_imgs.push_back(tmp);
} while (size.width >= initSize.width && size.height >= initSize.height);
//Encsemble classification
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)
continue;
ensBuffer.push_back(varBuffer[i]);
ensScaleIDs.push_back(varScaleIDs[i]);
}
//NN classification
//Prepare batch of patches
int numOfPatches = (int)ensBuffer.size();
Mat_<uchar> stdPatches(numOfPatches, 225);
double *resultSr = new double[numOfPatches];
double *resultSc = new double[numOfPatches];
uchar *patchesData = stdPatches.data;
for (int i = 0; i < (int)ensBuffer.size(); i++)
{
resample(resized_imgs[ensScaleIDs[i]], Rect2d(ensBuffer[i], initSize), standardPatch);
uchar *stdPatchData = standardPatch.data;
for (int j = 0; j < 225; j++)
patchesData[225*i+j] = stdPatchData[j];
}
//Calculate Sr and Sc batches
ocl_batchSrSc(stdPatches, resultSr, resultSc, numOfPatches);
for (int i = 0; i < (int)ensBuffer.size(); i++)
{
LabeledPatch labPatch;
standardPatch.data = &stdPatches.data[225 * i];
double curScale = pow(SCALE_STEP, ensScaleIDs[i]);
labPatch.rect = Rect2d(ensBuffer[i].x*curScale, ensBuffer[i].y*curScale, initSize.width * curScale, initSize.height * curScale);
double srValue, scValue;
srValue = resultSr[i];
////To fix: Check the paper, probably this cause wrong learning
//
labPatch.isObject = srValue > THETA_NN;
labPatch.shouldBeIntegrated = abs(srValue - THETA_NN) < 0.1;
patches.push_back(labPatch);
//
if (!labPatch.isObject)
{
nneg++;
continue;
}
else
{
npos++;
}
scValue = resultSc[i];
if (scValue > maxSc)
{
maxSc = scValue;
maxScRect = labPatch.rect;
}
}
if (maxSc < 0)
return false;
res = maxScRect;
return true;
}
