int faceDetect_GPU(Mat &image, vector<Mat> &faces, int init)
{
static CascadeClassifier_GPU cascade_gpu;
static HANDLE init_mutex;
if(init == 1)
{
init_mutex = CreateMutex(NULL, FALSE, NULL);
cascade_gpu.load(string(CASCADE_PATH));
return 0;
}
Mat gray;
cvtColor(image, gray, CV_BGR2GRAY);
equalizeHist(gray, gray);
GpuMat image_gpu(gray);
GpuMat objbuf;
WaitForSingleObject(init_mutex, INFINITE);
double start = GetTickCount();
int detections_number = cascade_gpu.detectMultiScale(image_gpu, objbuf, 1.1, 3);
cout << "Face Detect GPU Time : " << GetTickCount()-start << "ms" << endl;
ReleaseMutex(init_mutex);
Mat obj_host;
objbuf.colRange(0, detections_number).download(obj_host);
Rect* det_faces = obj_host.ptr<Rect>();
for(int i = 0; i < detections_number; ++i)
{
Mat face = gray(det_faces[i]);
faces.push_back(face);
}
return 0;
}
void App::run(int argc, char **argv)
{
parseCmdArgs(argc, argv);
if (help_showed)
return;
if (getCudaEnabledDeviceCount() == 0)
throw runtime_error("No GPU found or the library is compiled without GPU support");
if (cascade_name.empty())
{
cout << "Using default cascade file...\n";
cascade_name = "data/face_detect/haarcascade_frontalface_alt.xml";
}
if (!cascade_gpu.load(cascade_name) || !cascade_cpu.load(cascade_name))
{
stringstream msg;
msg << "Could not load cascade classifier \"" << cascade_name << "\"";
throw runtime_error(msg.str());
}
if (sources.size() != 1)
{
cout << "Loading default frames source...\n";
sources.resize(1);
sources[0] = new VideoSource("data/face_detect/browser.flv");
}
Mat frame, frame_cpu, gray_cpu, resized_cpu, faces_downloaded, frameDisp;
vector<Rect> facesBuf_cpu;
GpuMat frame_gpu, gray_gpu, resized_gpu, facesBuf_gpu;
int detections_num;
while (!exited)
{
sources[0]->next(frame_cpu);
frame_gpu.upload(frame_cpu);
convertAndResize(frame_gpu, gray_gpu, resized_gpu, scaleFactor);
convertAndResize(frame_cpu, gray_cpu, resized_cpu, scaleFactor);
TickMeter tm;
tm.start();
if (useGPU)
{
cascade_gpu.visualizeInPlace = true;
cascade_gpu.findLargestObject = findLargestObject;
detections_num = cascade_gpu.detectMultiScale(resized_gpu, facesBuf_gpu, 1.2,
(filterRects || findLargestObject) ? 4 : 0);
facesBuf_gpu.colRange(0, detections_num).download(faces_downloaded);
}
else
{
Size minSize = cascade_gpu.getClassifierSize();
cascade_cpu.detectMultiScale(resized_cpu, facesBuf_cpu, 1.2,
(filterRects || findLargestObject) ? 4 : 0,
(findLargestObject ? CV_HAAR_FIND_BIGGEST_OBJECT : 0)
| CV_HAAR_SCALE_IMAGE,
minSize);
detections_num = (int)facesBuf_cpu.size();
}
if (!useGPU && detections_num)
{
for (int i = 0; i < detections_num; ++i)
{
rectangle(resized_cpu, facesBuf_cpu[i], Scalar(255));
}
}
if (useGPU)
{
resized_gpu.download(resized_cpu);
}
tm.stop();
double detectionTime = tm.getTimeMilli();
double fps = 1000 / detectionTime;
/*//print detections to console
cout << setfill(' ') << setprecision(2);
cout << setw(6) << fixed << fps << " FPS, " << detections_num << " det";
if ((filterRects || findLargestObject) && detections_num > 0)
{
Rect *faceRects = useGPU ? faces_downloaded.ptr<Rect>() : &facesBuf_cpu[0];
for (int i = 0; i < min(detections_num, 2); ++i)
{
cout << ", [" << setw(4) << faceRects[i].x
<< ", " << setw(4) << faceRects[i].y
<< ", " << setw(4) << faceRects[i].width
<< ", " << setw(4) << faceRects[i].height << "]";
}
}
cout << endl;*/
cvtColor(resized_cpu, frameDisp, CV_GRAY2BGR);
displayState(frameDisp, helpScreen, useGPU, findLargestObject, filterRects, fps);
imshow("face_detect_demo", frameDisp);
processKey(waitKey(3));
}
}
py::list face_detection(const cv::Mat& in,const bool useGPU=true,const bool filterRects=true,const bool findLargestObject=false,const double scaleFactor=1.0) {
/*
if (getCudaEnabledDeviceCount() == 0){
cerr << "No GPU found or the library is compiled without GPU support" << endl;
}
cv::gpu::printShortCudaDeviceInfo(cv::gpu::getDevice());
*/
/*
CascadeClassifier cascade_cpu;
if (!cascade_cpu.load(cascadeName)){
cerr << "ERROR: Could not load cascade classifier "" << cascadeName << """ << endl;
}
*/
Mat frame, frame_cpu, gray_cpu, resized_cpu, faces_downloaded, frameDisp;
//vector<Rect> facesBuf_cpu;
GpuMat frame_gpu, gray_gpu, resized_gpu, facesBuf_gpu;
//vector<Rect> face_rects ;
//in.copyTo(frame_cpu);
frame_gpu.upload(in);
//convertAndResize(frame_gpu, gray_gpu, resized_gpu, scaleFactor);
convertAndResize(frame_gpu, gray_gpu, resized_gpu, scaleFactor);
//TickMeter tm;
//tm.start();
if (useGPU){
//cascade_gpu.visualizeInPlace = true;
cascade_gpu.findLargestObject = findLargestObject;
detections_num = cascade_gpu.detectMultiScale(resized_gpu, facesBuf_gpu, 1.2,
(filterRects || findLargestObject) ? 4 : 0);
facesBuf_gpu.colRange(0, detections_num).download(faces_downloaded);
}
/*
else
{
Size minSize = cascade_gpu.getClassifierSize();
cascade_cpu.detectMultiScale(resized_cpu, facesBuf_cpu, 1.2,
(filterRects || findLargestObject) ? 4 : 0,
(findLargestObject ? CV_HAAR_FIND_BIGGEST_OBJECT : 0)
| CV_HAAR_SCALE_IMAGE,
minSize);
detections_num = (int)facesBuf_cpu.size();
}
*/
/*
tm.stop();
double detectionTime = tm.getTimeMilli();
double fps = 1000 / detectionTime;
//print detections to console
cout << setfill(' ') << setprecision(2);
cout << setw(6) << fixed << fps << " FPS, " << detections_num << " det";
*/
py::list facereturn;
//Rect *faces = useGPU ? faces_downloaded.ptr<Rect>() : &facesBuf_cpu[0];
Rect* faces = faces_downloaded.ptr<cv::Rect>();
for (int i = 0; i < detections_num; ++i)
{
py::list points;
points.append(faces[i].x);
points.append(faces[i].y);
points.append(faces[i].width);
points.append(faces[i].height);
facereturn.append(points);
//face_rects.push_back(faces[i]);
}
//std::cerr << "detections_num: " << detections_num << std::endl;
//std::cerr << "sz: " << in.size() << std::endl;
return facereturn;
}
void App::process()
{
if (cascade_name.empty())
{
cout << "Using default cascade file...\n";
cascade_name = "data/face_detect/haarcascade_frontalface_alt.xml";
}
if (!cascade_gpu.load(cascade_name) || !cascade_cpu.load(cascade_name))
{
ostringstream msg;
msg << "Could not load cascade classifier \"" << cascade_name << "\"";
throw runtime_error(msg.str());
}
if (sources.size() != 1)
{
cout << "Loading default frames source...\n";
sources.resize(1);
sources[0] = new VideoSource("data/face_detect/browser.flv");
}
cout << "\nControls:\n"
<< "\tESC - exit\n"
<< "\tSpace - change mode GPU <-> CPU\n"
<< "\t1/Q - increase/decrease scale\n"
<< "\tM - switch OneFace / MultiFace\n"
<< "\tF - toggle rectangles filter\n"
<< endl;
Mat frame, frame_cpu, gray_cpu, resized_cpu, img_to_show;
GpuMat frame_gpu, gray_gpu, resized_gpu, facesBuf_gpu;
vector<Rect> faces;
while (!exited)
{
int64 start = getTickCount();
sources[0]->next(frame_cpu);
double proc_fps;
if (useGPU)
{
frame_gpu.upload(frame_cpu);
resizeAndConvert(frame_gpu, resized_gpu, gray_gpu, scaleFactor);
cascade_gpu.visualizeInPlace = false;
cascade_gpu.findLargestObject = findLargestObject;
int64 proc_start = getTickCount();
int detections_num = cascade_gpu.detectMultiScale(gray_gpu, facesBuf_gpu, 1.2,
(filterRects || findLargestObject) ? 4 : 0);
if (detections_num == 0)
faces.clear();
else
{
faces.resize(detections_num);
cv::Mat facesMat(1, detections_num, DataType<Rect>::type, &faces[0]);
facesBuf_gpu.colRange(0, detections_num).download(facesMat);
}
proc_fps = getTickFrequency() / (getTickCount() - proc_start);
}
else
{
resizeAndConvert(frame_cpu, resized_cpu, gray_cpu, scaleFactor);
Size minSize = cascade_gpu.getClassifierSize();
int64 proc_start = getTickCount();
cascade_cpu.detectMultiScale(gray_cpu, faces, 1.2,
(filterRects || findLargestObject) ? 4 : 0,
(findLargestObject ? CV_HAAR_FIND_BIGGEST_OBJECT : 0)
| CV_HAAR_SCALE_IMAGE,
minSize);
proc_fps = getTickFrequency() / (getTickCount() - proc_start);
}
if (useGPU)
resized_gpu.download(img_to_show);
else
img_to_show = resized_cpu;
for (size_t i = 0; i < faces.size(); i++)
rectangle(img_to_show, faces[i], CV_RGB(0, 255, 0), 3);
double total_fps = getTickFrequency() / (getTickCount() - start);
displayState(img_to_show, proc_fps, total_fps);
imshow("face_detect_demo", img_to_show);
processKey(waitKey(3) & 0xff);
}
}