Normalize line endings and whitespace
This commit is contained in:
OpenCV Buildbot
committed by
Andrey Kamaev
Andrey Kamaev
parent
0442bca235
commit
81f826db2b
@@ -1,380 +1,380 @@
|
||||
#if defined _MSC_VER && _MSC_VER >= 1400
|
||||
#pragma warning( disable : 4201 4408 4127 4100)
|
||||
#endif
|
||||
|
||||
#include "cvconfig.h"
|
||||
#include <iostream>
|
||||
#include <iomanip>
|
||||
#include <cstdio>
|
||||
#include "opencv2/gpu/gpu.hpp"
|
||||
#include "opencv2/highgui/highgui.hpp"
|
||||
|
||||
#ifdef HAVE_CUDA
|
||||
#include "NCVHaarObjectDetection.hpp"
|
||||
#endif
|
||||
|
||||
using namespace std;
|
||||
using namespace cv;
|
||||
|
||||
|
||||
#if !defined(HAVE_CUDA)
|
||||
int main( int, const char** )
|
||||
{
|
||||
cout << "Please compile the library with CUDA support" << endl;
|
||||
return -1;
|
||||
}
|
||||
#else
|
||||
|
||||
|
||||
const Size2i preferredVideoFrameSize(640, 480);
|
||||
const string wndTitle = "NVIDIA Computer Vision :: Haar Classifiers Cascade";
|
||||
|
||||
|
||||
void matPrint(Mat &img, int lineOffsY, Scalar fontColor, const string &ss)
|
||||
{
|
||||
int fontFace = FONT_HERSHEY_DUPLEX;
|
||||
double fontScale = 0.8;
|
||||
int fontThickness = 2;
|
||||
Size fontSize = cv::getTextSize("T[]", fontFace, fontScale, fontThickness, 0);
|
||||
|
||||
Point org;
|
||||
org.x = 1;
|
||||
org.y = 3 * fontSize.height * (lineOffsY + 1) / 2;
|
||||
putText(img, ss, org, fontFace, fontScale, CV_RGB(0,0,0), 5*fontThickness/2, 16);
|
||||
putText(img, ss, org, fontFace, fontScale, fontColor, fontThickness, 16);
|
||||
}
|
||||
|
||||
|
||||
void displayState(Mat &canvas, bool bHelp, bool bGpu, bool bLargestFace, bool bFilter, double fps)
|
||||
{
|
||||
Scalar fontColorRed = CV_RGB(255,0,0);
|
||||
Scalar fontColorNV = CV_RGB(118,185,0);
|
||||
|
||||
ostringstream ss;
|
||||
ss << "FPS = " << setprecision(1) << fixed << fps;
|
||||
matPrint(canvas, 0, fontColorRed, ss.str());
|
||||
ss.str("");
|
||||
ss << "[" << canvas.cols << "x" << canvas.rows << "], " <<
|
||||
(bGpu ? "GPU, " : "CPU, ") <<
|
||||
(bLargestFace ? "OneFace, " : "MultiFace, ") <<
|
||||
(bFilter ? "Filter:ON" : "Filter:OFF");
|
||||
matPrint(canvas, 1, fontColorRed, ss.str());
|
||||
|
||||
if (bHelp)
|
||||
{
|
||||
matPrint(canvas, 2, fontColorNV, "Space - switch GPU / CPU");
|
||||
matPrint(canvas, 3, fontColorNV, "M - switch OneFace / MultiFace");
|
||||
matPrint(canvas, 4, fontColorNV, "F - toggle rectangles Filter");
|
||||
matPrint(canvas, 5, fontColorNV, "H - toggle hotkeys help");
|
||||
}
|
||||
else
|
||||
{
|
||||
matPrint(canvas, 2, fontColorNV, "H - toggle hotkeys help");
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
NCVStatus process(Mat *srcdst,
|
||||
Ncv32u width, Ncv32u height,
|
||||
NcvBool bFilterRects, NcvBool bLargestFace,
|
||||
HaarClassifierCascadeDescriptor &haar,
|
||||
NCVVector<HaarStage64> &d_haarStages, NCVVector<HaarClassifierNode128> &d_haarNodes,
|
||||
NCVVector<HaarFeature64> &d_haarFeatures, NCVVector<HaarStage64> &h_haarStages,
|
||||
INCVMemAllocator &gpuAllocator,
|
||||
INCVMemAllocator &cpuAllocator,
|
||||
cudaDeviceProp &devProp)
|
||||
{
|
||||
ncvAssertReturn(!((srcdst == NULL) ^ gpuAllocator.isCounting()), NCV_NULL_PTR);
|
||||
|
||||
NCVStatus ncvStat;
|
||||
|
||||
NCV_SET_SKIP_COND(gpuAllocator.isCounting());
|
||||
|
||||
NCVMatrixAlloc<Ncv8u> d_src(gpuAllocator, width, height);
|
||||
ncvAssertReturn(d_src.isMemAllocated(), NCV_ALLOCATOR_BAD_ALLOC);
|
||||
NCVMatrixAlloc<Ncv8u> h_src(cpuAllocator, width, height);
|
||||
ncvAssertReturn(h_src.isMemAllocated(), NCV_ALLOCATOR_BAD_ALLOC);
|
||||
NCVVectorAlloc<NcvRect32u> d_rects(gpuAllocator, 100);
|
||||
ncvAssertReturn(d_rects.isMemAllocated(), NCV_ALLOCATOR_BAD_ALLOC);
|
||||
|
||||
NCV_SKIP_COND_BEGIN
|
||||
|
||||
for (Ncv32u i=0; i<(Ncv32u)srcdst->rows; i++)
|
||||
{
|
||||
memcpy(h_src.ptr() + i * h_src.stride(), srcdst->ptr(i), srcdst->cols);
|
||||
}
|
||||
|
||||
ncvStat = h_src.copySolid(d_src, 0);
|
||||
ncvAssertReturnNcvStat(ncvStat);
|
||||
ncvAssertCUDAReturn(cudaStreamSynchronize(0), NCV_CUDA_ERROR);
|
||||
|
||||
NCV_SKIP_COND_END
|
||||
|
||||
NcvSize32u roi;
|
||||
roi.width = d_src.width();
|
||||
roi.height = d_src.height();
|
||||
|
||||
Ncv32u numDetections;
|
||||
ncvStat = ncvDetectObjectsMultiScale_device(
|
||||
d_src, roi, d_rects, numDetections, haar, h_haarStages,
|
||||
d_haarStages, d_haarNodes, d_haarFeatures,
|
||||
haar.ClassifierSize,
|
||||
(bFilterRects || bLargestFace) ? 4 : 0,
|
||||
1.2f, 1,
|
||||
(bLargestFace ? NCVPipeObjDet_FindLargestObject : 0)
|
||||
| NCVPipeObjDet_VisualizeInPlace,
|
||||
gpuAllocator, cpuAllocator, devProp, 0);
|
||||
ncvAssertReturnNcvStat(ncvStat);
|
||||
ncvAssertCUDAReturn(cudaStreamSynchronize(0), NCV_CUDA_ERROR);
|
||||
|
||||
NCV_SKIP_COND_BEGIN
|
||||
|
||||
ncvStat = d_src.copySolid(h_src, 0);
|
||||
ncvAssertReturnNcvStat(ncvStat);
|
||||
ncvAssertCUDAReturn(cudaStreamSynchronize(0), NCV_CUDA_ERROR);
|
||||
|
||||
for (Ncv32u i=0; i<(Ncv32u)srcdst->rows; i++)
|
||||
{
|
||||
memcpy(srcdst->ptr(i), h_src.ptr() + i * h_src.stride(), srcdst->cols);
|
||||
}
|
||||
|
||||
NCV_SKIP_COND_END
|
||||
|
||||
return NCV_SUCCESS;
|
||||
}
|
||||
|
||||
|
||||
int main(int argc, const char** argv)
|
||||
{
|
||||
cout << "OpenCV / NVIDIA Computer Vision" << endl;
|
||||
cout << "Face Detection in video and live feed" << endl;
|
||||
cout << "Syntax: exename <cascade_file> <image_or_video_or_cameraid>" << endl;
|
||||
cout << "=========================================" << endl;
|
||||
|
||||
ncvAssertPrintReturn(cv::gpu::getCudaEnabledDeviceCount() != 0, "No GPU found or the library is compiled without GPU support", -1);
|
||||
ncvAssertPrintReturn(argc == 3, "Invalid number of arguments", -1);
|
||||
|
||||
cv::gpu::printShortCudaDeviceInfo(cv::gpu::getDevice());
|
||||
|
||||
string cascadeName = argv[1];
|
||||
string inputName = argv[2];
|
||||
|
||||
NCVStatus ncvStat;
|
||||
NcvBool bQuit = false;
|
||||
VideoCapture capture;
|
||||
Size2i frameSize;
|
||||
|
||||
//open content source
|
||||
Mat image = imread(inputName);
|
||||
Mat frame;
|
||||
if (!image.empty())
|
||||
{
|
||||
frameSize.width = image.cols;
|
||||
frameSize.height = image.rows;
|
||||
}
|
||||
else
|
||||
{
|
||||
if (!capture.open(inputName))
|
||||
{
|
||||
int camid = -1;
|
||||
|
||||
istringstream ss(inputName);
|
||||
int x = 0;
|
||||
ss >> x;
|
||||
|
||||
ncvAssertPrintReturn(capture.open(camid) != 0, "Can't open source", -1);
|
||||
}
|
||||
|
||||
capture >> frame;
|
||||
ncvAssertPrintReturn(!frame.empty(), "Empty video source", -1);
|
||||
|
||||
frameSize.width = frame.cols;
|
||||
frameSize.height = frame.rows;
|
||||
}
|
||||
|
||||
NcvBool bUseGPU = true;
|
||||
NcvBool bLargestObject = false;
|
||||
NcvBool bFilterRects = true;
|
||||
NcvBool bHelpScreen = false;
|
||||
|
||||
CascadeClassifier classifierOpenCV;
|
||||
ncvAssertPrintReturn(classifierOpenCV.load(cascadeName) != 0, "Error (in OpenCV) opening classifier", -1);
|
||||
|
||||
int devId;
|
||||
ncvAssertCUDAReturn(cudaGetDevice(&devId), -1);
|
||||
cudaDeviceProp devProp;
|
||||
ncvAssertCUDAReturn(cudaGetDeviceProperties(&devProp, devId), -1);
|
||||
cout << "Using GPU: " << devId << "(" << devProp.name <<
|
||||
"), arch=" << devProp.major << "." << devProp.minor << endl;
|
||||
|
||||
//==============================================================================
|
||||
//
|
||||
// Load the classifier from file (assuming its size is about 1 mb)
|
||||
// using a simple allocator
|
||||
//
|
||||
//==============================================================================
|
||||
|
||||
NCVMemNativeAllocator gpuCascadeAllocator(NCVMemoryTypeDevice, static_cast<Ncv32u>(devProp.textureAlignment));
|
||||
ncvAssertPrintReturn(gpuCascadeAllocator.isInitialized(), "Error creating cascade GPU allocator", -1);
|
||||
NCVMemNativeAllocator cpuCascadeAllocator(NCVMemoryTypeHostPinned, static_cast<Ncv32u>(devProp.textureAlignment));
|
||||
ncvAssertPrintReturn(cpuCascadeAllocator.isInitialized(), "Error creating cascade CPU allocator", -1);
|
||||
|
||||
Ncv32u haarNumStages, haarNumNodes, haarNumFeatures;
|
||||
ncvStat = ncvHaarGetClassifierSize(cascadeName, haarNumStages, haarNumNodes, haarNumFeatures);
|
||||
ncvAssertPrintReturn(ncvStat == NCV_SUCCESS, "Error reading classifier size (check the file)", -1);
|
||||
|
||||
NCVVectorAlloc<HaarStage64> h_haarStages(cpuCascadeAllocator, haarNumStages);
|
||||
ncvAssertPrintReturn(h_haarStages.isMemAllocated(), "Error in cascade CPU allocator", -1);
|
||||
NCVVectorAlloc<HaarClassifierNode128> h_haarNodes(cpuCascadeAllocator, haarNumNodes);
|
||||
ncvAssertPrintReturn(h_haarNodes.isMemAllocated(), "Error in cascade CPU allocator", -1);
|
||||
NCVVectorAlloc<HaarFeature64> h_haarFeatures(cpuCascadeAllocator, haarNumFeatures);
|
||||
|
||||
ncvAssertPrintReturn(h_haarFeatures.isMemAllocated(), "Error in cascade CPU allocator", -1);
|
||||
|
||||
HaarClassifierCascadeDescriptor haar;
|
||||
ncvStat = ncvHaarLoadFromFile_host(cascadeName, haar, h_haarStages, h_haarNodes, h_haarFeatures);
|
||||
ncvAssertPrintReturn(ncvStat == NCV_SUCCESS, "Error loading classifier", -1);
|
||||
|
||||
NCVVectorAlloc<HaarStage64> d_haarStages(gpuCascadeAllocator, haarNumStages);
|
||||
ncvAssertPrintReturn(d_haarStages.isMemAllocated(), "Error in cascade GPU allocator", -1);
|
||||
NCVVectorAlloc<HaarClassifierNode128> d_haarNodes(gpuCascadeAllocator, haarNumNodes);
|
||||
ncvAssertPrintReturn(d_haarNodes.isMemAllocated(), "Error in cascade GPU allocator", -1);
|
||||
NCVVectorAlloc<HaarFeature64> d_haarFeatures(gpuCascadeAllocator, haarNumFeatures);
|
||||
ncvAssertPrintReturn(d_haarFeatures.isMemAllocated(), "Error in cascade GPU allocator", -1);
|
||||
|
||||
ncvStat = h_haarStages.copySolid(d_haarStages, 0);
|
||||
ncvAssertPrintReturn(ncvStat == NCV_SUCCESS, "Error copying cascade to GPU", -1);
|
||||
ncvStat = h_haarNodes.copySolid(d_haarNodes, 0);
|
||||
ncvAssertPrintReturn(ncvStat == NCV_SUCCESS, "Error copying cascade to GPU", -1);
|
||||
ncvStat = h_haarFeatures.copySolid(d_haarFeatures, 0);
|
||||
ncvAssertPrintReturn(ncvStat == NCV_SUCCESS, "Error copying cascade to GPU", -1);
|
||||
|
||||
//==============================================================================
|
||||
//
|
||||
// Calculate memory requirements and create real allocators
|
||||
//
|
||||
//==============================================================================
|
||||
|
||||
NCVMemStackAllocator gpuCounter(static_cast<Ncv32u>(devProp.textureAlignment));
|
||||
ncvAssertPrintReturn(gpuCounter.isInitialized(), "Error creating GPU memory counter", -1);
|
||||
NCVMemStackAllocator cpuCounter(static_cast<Ncv32u>(devProp.textureAlignment));
|
||||
ncvAssertPrintReturn(cpuCounter.isInitialized(), "Error creating CPU memory counter", -1);
|
||||
|
||||
ncvStat = process(NULL, frameSize.width, frameSize.height,
|
||||
false, false, haar,
|
||||
d_haarStages, d_haarNodes,
|
||||
d_haarFeatures, h_haarStages,
|
||||
gpuCounter, cpuCounter, devProp);
|
||||
ncvAssertPrintReturn(ncvStat == NCV_SUCCESS, "Error in memory counting pass", -1);
|
||||
|
||||
NCVMemStackAllocator gpuAllocator(NCVMemoryTypeDevice, gpuCounter.maxSize(), static_cast<Ncv32u>(devProp.textureAlignment));
|
||||
ncvAssertPrintReturn(gpuAllocator.isInitialized(), "Error creating GPU memory allocator", -1);
|
||||
NCVMemStackAllocator cpuAllocator(NCVMemoryTypeHostPinned, cpuCounter.maxSize(), static_cast<Ncv32u>(devProp.textureAlignment));
|
||||
ncvAssertPrintReturn(cpuAllocator.isInitialized(), "Error creating CPU memory allocator", -1);
|
||||
|
||||
printf("Initialized for frame size [%dx%d]\n", frameSize.width, frameSize.height);
|
||||
|
||||
//==============================================================================
|
||||
//
|
||||
// Main processing loop
|
||||
//
|
||||
//==============================================================================
|
||||
|
||||
namedWindow(wndTitle, 1);
|
||||
Mat gray, frameDisp;
|
||||
|
||||
do
|
||||
{
|
||||
Mat gray;
|
||||
cvtColor((image.empty() ? frame : image), gray, CV_BGR2GRAY);
|
||||
|
||||
//
|
||||
// process
|
||||
//
|
||||
|
||||
NcvSize32u minSize = haar.ClassifierSize;
|
||||
if (bLargestObject)
|
||||
{
|
||||
Ncv32u ratioX = preferredVideoFrameSize.width / minSize.width;
|
||||
Ncv32u ratioY = preferredVideoFrameSize.height / minSize.height;
|
||||
Ncv32u ratioSmallest = min(ratioX, ratioY);
|
||||
ratioSmallest = max((Ncv32u)(ratioSmallest / 2.5f), (Ncv32u)1);
|
||||
minSize.width *= ratioSmallest;
|
||||
minSize.height *= ratioSmallest;
|
||||
}
|
||||
|
||||
Ncv32f avgTime;
|
||||
NcvTimer timer = ncvStartTimer();
|
||||
|
||||
if (bUseGPU)
|
||||
{
|
||||
ncvStat = process(&gray, frameSize.width, frameSize.height,
|
||||
bFilterRects, bLargestObject, haar,
|
||||
d_haarStages, d_haarNodes,
|
||||
d_haarFeatures, h_haarStages,
|
||||
gpuAllocator, cpuAllocator, devProp);
|
||||
ncvAssertPrintReturn(ncvStat == NCV_SUCCESS, "Error in memory counting pass", -1);
|
||||
}
|
||||
else
|
||||
{
|
||||
vector<Rect> rectsOpenCV;
|
||||
|
||||
classifierOpenCV.detectMultiScale(
|
||||
gray,
|
||||
rectsOpenCV,
|
||||
1.2f,
|
||||
bFilterRects ? 4 : 0,
|
||||
(bLargestObject ? CV_HAAR_FIND_BIGGEST_OBJECT : 0)
|
||||
| CV_HAAR_SCALE_IMAGE,
|
||||
Size(minSize.width, minSize.height));
|
||||
|
||||
for (size_t rt = 0; rt < rectsOpenCV.size(); ++rt)
|
||||
rectangle(gray, rectsOpenCV[rt], Scalar(255));
|
||||
}
|
||||
|
||||
avgTime = (Ncv32f)ncvEndQueryTimerMs(timer);
|
||||
|
||||
cvtColor(gray, frameDisp, CV_GRAY2BGR);
|
||||
displayState(frameDisp, bHelpScreen, bUseGPU, bLargestObject, bFilterRects, 1000.0f / avgTime);
|
||||
imshow(wndTitle, frameDisp);
|
||||
|
||||
//handle input
|
||||
switch (cvWaitKey(3))
|
||||
{
|
||||
case ' ':
|
||||
bUseGPU = !bUseGPU;
|
||||
break;
|
||||
case 'm':
|
||||
case 'M':
|
||||
bLargestObject = !bLargestObject;
|
||||
break;
|
||||
case 'f':
|
||||
case 'F':
|
||||
bFilterRects = !bFilterRects;
|
||||
break;
|
||||
case 'h':
|
||||
case 'H':
|
||||
bHelpScreen = !bHelpScreen;
|
||||
break;
|
||||
case 27:
|
||||
bQuit = true;
|
||||
break;
|
||||
}
|
||||
|
||||
// For camera and video file, capture the next image
|
||||
if (capture.isOpened())
|
||||
{
|
||||
capture >> frame;
|
||||
if (frame.empty())
|
||||
{
|
||||
break;
|
||||
}
|
||||
}
|
||||
} while (!bQuit);
|
||||
|
||||
cvDestroyWindow(wndTitle.c_str());
|
||||
|
||||
return 0;
|
||||
}
|
||||
|
||||
#endif //!defined(HAVE_CUDA)
|
||||
#if defined _MSC_VER && _MSC_VER >= 1400
|
||||
#pragma warning( disable : 4201 4408 4127 4100)
|
||||
#endif
|
||||
|
||||
#include "cvconfig.h"
|
||||
#include <iostream>
|
||||
#include <iomanip>
|
||||
#include <cstdio>
|
||||
#include "opencv2/gpu/gpu.hpp"
|
||||
#include "opencv2/highgui/highgui.hpp"
|
||||
|
||||
#ifdef HAVE_CUDA
|
||||
#include "NCVHaarObjectDetection.hpp"
|
||||
#endif
|
||||
|
||||
using namespace std;
|
||||
using namespace cv;
|
||||
|
||||
|
||||
#if !defined(HAVE_CUDA)
|
||||
int main( int, const char** )
|
||||
{
|
||||
cout << "Please compile the library with CUDA support" << endl;
|
||||
return -1;
|
||||
}
|
||||
#else
|
||||
|
||||
|
||||
const Size2i preferredVideoFrameSize(640, 480);
|
||||
const string wndTitle = "NVIDIA Computer Vision :: Haar Classifiers Cascade";
|
||||
|
||||
|
||||
void matPrint(Mat &img, int lineOffsY, Scalar fontColor, const string &ss)
|
||||
{
|
||||
int fontFace = FONT_HERSHEY_DUPLEX;
|
||||
double fontScale = 0.8;
|
||||
int fontThickness = 2;
|
||||
Size fontSize = cv::getTextSize("T[]", fontFace, fontScale, fontThickness, 0);
|
||||
|
||||
Point org;
|
||||
org.x = 1;
|
||||
org.y = 3 * fontSize.height * (lineOffsY + 1) / 2;
|
||||
putText(img, ss, org, fontFace, fontScale, CV_RGB(0,0,0), 5*fontThickness/2, 16);
|
||||
putText(img, ss, org, fontFace, fontScale, fontColor, fontThickness, 16);
|
||||
}
|
||||
|
||||
|
||||
void displayState(Mat &canvas, bool bHelp, bool bGpu, bool bLargestFace, bool bFilter, double fps)
|
||||
{
|
||||
Scalar fontColorRed = CV_RGB(255,0,0);
|
||||
Scalar fontColorNV = CV_RGB(118,185,0);
|
||||
|
||||
ostringstream ss;
|
||||
ss << "FPS = " << setprecision(1) << fixed << fps;
|
||||
matPrint(canvas, 0, fontColorRed, ss.str());
|
||||
ss.str("");
|
||||
ss << "[" << canvas.cols << "x" << canvas.rows << "], " <<
|
||||
(bGpu ? "GPU, " : "CPU, ") <<
|
||||
(bLargestFace ? "OneFace, " : "MultiFace, ") <<
|
||||
(bFilter ? "Filter:ON" : "Filter:OFF");
|
||||
matPrint(canvas, 1, fontColorRed, ss.str());
|
||||
|
||||
if (bHelp)
|
||||
{
|
||||
matPrint(canvas, 2, fontColorNV, "Space - switch GPU / CPU");
|
||||
matPrint(canvas, 3, fontColorNV, "M - switch OneFace / MultiFace");
|
||||
matPrint(canvas, 4, fontColorNV, "F - toggle rectangles Filter");
|
||||
matPrint(canvas, 5, fontColorNV, "H - toggle hotkeys help");
|
||||
}
|
||||
else
|
||||
{
|
||||
matPrint(canvas, 2, fontColorNV, "H - toggle hotkeys help");
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
NCVStatus process(Mat *srcdst,
|
||||
Ncv32u width, Ncv32u height,
|
||||
NcvBool bFilterRects, NcvBool bLargestFace,
|
||||
HaarClassifierCascadeDescriptor &haar,
|
||||
NCVVector<HaarStage64> &d_haarStages, NCVVector<HaarClassifierNode128> &d_haarNodes,
|
||||
NCVVector<HaarFeature64> &d_haarFeatures, NCVVector<HaarStage64> &h_haarStages,
|
||||
INCVMemAllocator &gpuAllocator,
|
||||
INCVMemAllocator &cpuAllocator,
|
||||
cudaDeviceProp &devProp)
|
||||
{
|
||||
ncvAssertReturn(!((srcdst == NULL) ^ gpuAllocator.isCounting()), NCV_NULL_PTR);
|
||||
|
||||
NCVStatus ncvStat;
|
||||
|
||||
NCV_SET_SKIP_COND(gpuAllocator.isCounting());
|
||||
|
||||
NCVMatrixAlloc<Ncv8u> d_src(gpuAllocator, width, height);
|
||||
ncvAssertReturn(d_src.isMemAllocated(), NCV_ALLOCATOR_BAD_ALLOC);
|
||||
NCVMatrixAlloc<Ncv8u> h_src(cpuAllocator, width, height);
|
||||
ncvAssertReturn(h_src.isMemAllocated(), NCV_ALLOCATOR_BAD_ALLOC);
|
||||
NCVVectorAlloc<NcvRect32u> d_rects(gpuAllocator, 100);
|
||||
ncvAssertReturn(d_rects.isMemAllocated(), NCV_ALLOCATOR_BAD_ALLOC);
|
||||
|
||||
NCV_SKIP_COND_BEGIN
|
||||
|
||||
for (Ncv32u i=0; i<(Ncv32u)srcdst->rows; i++)
|
||||
{
|
||||
memcpy(h_src.ptr() + i * h_src.stride(), srcdst->ptr(i), srcdst->cols);
|
||||
}
|
||||
|
||||
ncvStat = h_src.copySolid(d_src, 0);
|
||||
ncvAssertReturnNcvStat(ncvStat);
|
||||
ncvAssertCUDAReturn(cudaStreamSynchronize(0), NCV_CUDA_ERROR);
|
||||
|
||||
NCV_SKIP_COND_END
|
||||
|
||||
NcvSize32u roi;
|
||||
roi.width = d_src.width();
|
||||
roi.height = d_src.height();
|
||||
|
||||
Ncv32u numDetections;
|
||||
ncvStat = ncvDetectObjectsMultiScale_device(
|
||||
d_src, roi, d_rects, numDetections, haar, h_haarStages,
|
||||
d_haarStages, d_haarNodes, d_haarFeatures,
|
||||
haar.ClassifierSize,
|
||||
(bFilterRects || bLargestFace) ? 4 : 0,
|
||||
1.2f, 1,
|
||||
(bLargestFace ? NCVPipeObjDet_FindLargestObject : 0)
|
||||
| NCVPipeObjDet_VisualizeInPlace,
|
||||
gpuAllocator, cpuAllocator, devProp, 0);
|
||||
ncvAssertReturnNcvStat(ncvStat);
|
||||
ncvAssertCUDAReturn(cudaStreamSynchronize(0), NCV_CUDA_ERROR);
|
||||
|
||||
NCV_SKIP_COND_BEGIN
|
||||
|
||||
ncvStat = d_src.copySolid(h_src, 0);
|
||||
ncvAssertReturnNcvStat(ncvStat);
|
||||
ncvAssertCUDAReturn(cudaStreamSynchronize(0), NCV_CUDA_ERROR);
|
||||
|
||||
for (Ncv32u i=0; i<(Ncv32u)srcdst->rows; i++)
|
||||
{
|
||||
memcpy(srcdst->ptr(i), h_src.ptr() + i * h_src.stride(), srcdst->cols);
|
||||
}
|
||||
|
||||
NCV_SKIP_COND_END
|
||||
|
||||
return NCV_SUCCESS;
|
||||
}
|
||||
|
||||
|
||||
int main(int argc, const char** argv)
|
||||
{
|
||||
cout << "OpenCV / NVIDIA Computer Vision" << endl;
|
||||
cout << "Face Detection in video and live feed" << endl;
|
||||
cout << "Syntax: exename <cascade_file> <image_or_video_or_cameraid>" << endl;
|
||||
cout << "=========================================" << endl;
|
||||
|
||||
ncvAssertPrintReturn(cv::gpu::getCudaEnabledDeviceCount() != 0, "No GPU found or the library is compiled without GPU support", -1);
|
||||
ncvAssertPrintReturn(argc == 3, "Invalid number of arguments", -1);
|
||||
|
||||
cv::gpu::printShortCudaDeviceInfo(cv::gpu::getDevice());
|
||||
|
||||
string cascadeName = argv[1];
|
||||
string inputName = argv[2];
|
||||
|
||||
NCVStatus ncvStat;
|
||||
NcvBool bQuit = false;
|
||||
VideoCapture capture;
|
||||
Size2i frameSize;
|
||||
|
||||
//open content source
|
||||
Mat image = imread(inputName);
|
||||
Mat frame;
|
||||
if (!image.empty())
|
||||
{
|
||||
frameSize.width = image.cols;
|
||||
frameSize.height = image.rows;
|
||||
}
|
||||
else
|
||||
{
|
||||
if (!capture.open(inputName))
|
||||
{
|
||||
int camid = -1;
|
||||
|
||||
istringstream ss(inputName);
|
||||
int x = 0;
|
||||
ss >> x;
|
||||
|
||||
ncvAssertPrintReturn(capture.open(camid) != 0, "Can't open source", -1);
|
||||
}
|
||||
|
||||
capture >> frame;
|
||||
ncvAssertPrintReturn(!frame.empty(), "Empty video source", -1);
|
||||
|
||||
frameSize.width = frame.cols;
|
||||
frameSize.height = frame.rows;
|
||||
}
|
||||
|
||||
NcvBool bUseGPU = true;
|
||||
NcvBool bLargestObject = false;
|
||||
NcvBool bFilterRects = true;
|
||||
NcvBool bHelpScreen = false;
|
||||
|
||||
CascadeClassifier classifierOpenCV;
|
||||
ncvAssertPrintReturn(classifierOpenCV.load(cascadeName) != 0, "Error (in OpenCV) opening classifier", -1);
|
||||
|
||||
int devId;
|
||||
ncvAssertCUDAReturn(cudaGetDevice(&devId), -1);
|
||||
cudaDeviceProp devProp;
|
||||
ncvAssertCUDAReturn(cudaGetDeviceProperties(&devProp, devId), -1);
|
||||
cout << "Using GPU: " << devId << "(" << devProp.name <<
|
||||
"), arch=" << devProp.major << "." << devProp.minor << endl;
|
||||
|
||||
//==============================================================================
|
||||
//
|
||||
// Load the classifier from file (assuming its size is about 1 mb)
|
||||
// using a simple allocator
|
||||
//
|
||||
//==============================================================================
|
||||
|
||||
NCVMemNativeAllocator gpuCascadeAllocator(NCVMemoryTypeDevice, static_cast<Ncv32u>(devProp.textureAlignment));
|
||||
ncvAssertPrintReturn(gpuCascadeAllocator.isInitialized(), "Error creating cascade GPU allocator", -1);
|
||||
NCVMemNativeAllocator cpuCascadeAllocator(NCVMemoryTypeHostPinned, static_cast<Ncv32u>(devProp.textureAlignment));
|
||||
ncvAssertPrintReturn(cpuCascadeAllocator.isInitialized(), "Error creating cascade CPU allocator", -1);
|
||||
|
||||
Ncv32u haarNumStages, haarNumNodes, haarNumFeatures;
|
||||
ncvStat = ncvHaarGetClassifierSize(cascadeName, haarNumStages, haarNumNodes, haarNumFeatures);
|
||||
ncvAssertPrintReturn(ncvStat == NCV_SUCCESS, "Error reading classifier size (check the file)", -1);
|
||||
|
||||
NCVVectorAlloc<HaarStage64> h_haarStages(cpuCascadeAllocator, haarNumStages);
|
||||
ncvAssertPrintReturn(h_haarStages.isMemAllocated(), "Error in cascade CPU allocator", -1);
|
||||
NCVVectorAlloc<HaarClassifierNode128> h_haarNodes(cpuCascadeAllocator, haarNumNodes);
|
||||
ncvAssertPrintReturn(h_haarNodes.isMemAllocated(), "Error in cascade CPU allocator", -1);
|
||||
NCVVectorAlloc<HaarFeature64> h_haarFeatures(cpuCascadeAllocator, haarNumFeatures);
|
||||
|
||||
ncvAssertPrintReturn(h_haarFeatures.isMemAllocated(), "Error in cascade CPU allocator", -1);
|
||||
|
||||
HaarClassifierCascadeDescriptor haar;
|
||||
ncvStat = ncvHaarLoadFromFile_host(cascadeName, haar, h_haarStages, h_haarNodes, h_haarFeatures);
|
||||
ncvAssertPrintReturn(ncvStat == NCV_SUCCESS, "Error loading classifier", -1);
|
||||
|
||||
NCVVectorAlloc<HaarStage64> d_haarStages(gpuCascadeAllocator, haarNumStages);
|
||||
ncvAssertPrintReturn(d_haarStages.isMemAllocated(), "Error in cascade GPU allocator", -1);
|
||||
NCVVectorAlloc<HaarClassifierNode128> d_haarNodes(gpuCascadeAllocator, haarNumNodes);
|
||||
ncvAssertPrintReturn(d_haarNodes.isMemAllocated(), "Error in cascade GPU allocator", -1);
|
||||
NCVVectorAlloc<HaarFeature64> d_haarFeatures(gpuCascadeAllocator, haarNumFeatures);
|
||||
ncvAssertPrintReturn(d_haarFeatures.isMemAllocated(), "Error in cascade GPU allocator", -1);
|
||||
|
||||
ncvStat = h_haarStages.copySolid(d_haarStages, 0);
|
||||
ncvAssertPrintReturn(ncvStat == NCV_SUCCESS, "Error copying cascade to GPU", -1);
|
||||
ncvStat = h_haarNodes.copySolid(d_haarNodes, 0);
|
||||
ncvAssertPrintReturn(ncvStat == NCV_SUCCESS, "Error copying cascade to GPU", -1);
|
||||
ncvStat = h_haarFeatures.copySolid(d_haarFeatures, 0);
|
||||
ncvAssertPrintReturn(ncvStat == NCV_SUCCESS, "Error copying cascade to GPU", -1);
|
||||
|
||||
//==============================================================================
|
||||
//
|
||||
// Calculate memory requirements and create real allocators
|
||||
//
|
||||
//==============================================================================
|
||||
|
||||
NCVMemStackAllocator gpuCounter(static_cast<Ncv32u>(devProp.textureAlignment));
|
||||
ncvAssertPrintReturn(gpuCounter.isInitialized(), "Error creating GPU memory counter", -1);
|
||||
NCVMemStackAllocator cpuCounter(static_cast<Ncv32u>(devProp.textureAlignment));
|
||||
ncvAssertPrintReturn(cpuCounter.isInitialized(), "Error creating CPU memory counter", -1);
|
||||
|
||||
ncvStat = process(NULL, frameSize.width, frameSize.height,
|
||||
false, false, haar,
|
||||
d_haarStages, d_haarNodes,
|
||||
d_haarFeatures, h_haarStages,
|
||||
gpuCounter, cpuCounter, devProp);
|
||||
ncvAssertPrintReturn(ncvStat == NCV_SUCCESS, "Error in memory counting pass", -1);
|
||||
|
||||
NCVMemStackAllocator gpuAllocator(NCVMemoryTypeDevice, gpuCounter.maxSize(), static_cast<Ncv32u>(devProp.textureAlignment));
|
||||
ncvAssertPrintReturn(gpuAllocator.isInitialized(), "Error creating GPU memory allocator", -1);
|
||||
NCVMemStackAllocator cpuAllocator(NCVMemoryTypeHostPinned, cpuCounter.maxSize(), static_cast<Ncv32u>(devProp.textureAlignment));
|
||||
ncvAssertPrintReturn(cpuAllocator.isInitialized(), "Error creating CPU memory allocator", -1);
|
||||
|
||||
printf("Initialized for frame size [%dx%d]\n", frameSize.width, frameSize.height);
|
||||
|
||||
//==============================================================================
|
||||
//
|
||||
// Main processing loop
|
||||
//
|
||||
//==============================================================================
|
||||
|
||||
namedWindow(wndTitle, 1);
|
||||
Mat gray, frameDisp;
|
||||
|
||||
do
|
||||
{
|
||||
Mat gray;
|
||||
cvtColor((image.empty() ? frame : image), gray, CV_BGR2GRAY);
|
||||
|
||||
//
|
||||
// process
|
||||
//
|
||||
|
||||
NcvSize32u minSize = haar.ClassifierSize;
|
||||
if (bLargestObject)
|
||||
{
|
||||
Ncv32u ratioX = preferredVideoFrameSize.width / minSize.width;
|
||||
Ncv32u ratioY = preferredVideoFrameSize.height / minSize.height;
|
||||
Ncv32u ratioSmallest = min(ratioX, ratioY);
|
||||
ratioSmallest = max((Ncv32u)(ratioSmallest / 2.5f), (Ncv32u)1);
|
||||
minSize.width *= ratioSmallest;
|
||||
minSize.height *= ratioSmallest;
|
||||
}
|
||||
|
||||
Ncv32f avgTime;
|
||||
NcvTimer timer = ncvStartTimer();
|
||||
|
||||
if (bUseGPU)
|
||||
{
|
||||
ncvStat = process(&gray, frameSize.width, frameSize.height,
|
||||
bFilterRects, bLargestObject, haar,
|
||||
d_haarStages, d_haarNodes,
|
||||
d_haarFeatures, h_haarStages,
|
||||
gpuAllocator, cpuAllocator, devProp);
|
||||
ncvAssertPrintReturn(ncvStat == NCV_SUCCESS, "Error in memory counting pass", -1);
|
||||
}
|
||||
else
|
||||
{
|
||||
vector<Rect> rectsOpenCV;
|
||||
|
||||
classifierOpenCV.detectMultiScale(
|
||||
gray,
|
||||
rectsOpenCV,
|
||||
1.2f,
|
||||
bFilterRects ? 4 : 0,
|
||||
(bLargestObject ? CV_HAAR_FIND_BIGGEST_OBJECT : 0)
|
||||
| CV_HAAR_SCALE_IMAGE,
|
||||
Size(minSize.width, minSize.height));
|
||||
|
||||
for (size_t rt = 0; rt < rectsOpenCV.size(); ++rt)
|
||||
rectangle(gray, rectsOpenCV[rt], Scalar(255));
|
||||
}
|
||||
|
||||
avgTime = (Ncv32f)ncvEndQueryTimerMs(timer);
|
||||
|
||||
cvtColor(gray, frameDisp, CV_GRAY2BGR);
|
||||
displayState(frameDisp, bHelpScreen, bUseGPU, bLargestObject, bFilterRects, 1000.0f / avgTime);
|
||||
imshow(wndTitle, frameDisp);
|
||||
|
||||
//handle input
|
||||
switch (cvWaitKey(3))
|
||||
{
|
||||
case ' ':
|
||||
bUseGPU = !bUseGPU;
|
||||
break;
|
||||
case 'm':
|
||||
case 'M':
|
||||
bLargestObject = !bLargestObject;
|
||||
break;
|
||||
case 'f':
|
||||
case 'F':
|
||||
bFilterRects = !bFilterRects;
|
||||
break;
|
||||
case 'h':
|
||||
case 'H':
|
||||
bHelpScreen = !bHelpScreen;
|
||||
break;
|
||||
case 27:
|
||||
bQuit = true;
|
||||
break;
|
||||
}
|
||||
|
||||
// For camera and video file, capture the next image
|
||||
if (capture.isOpened())
|
||||
{
|
||||
capture >> frame;
|
||||
if (frame.empty())
|
||||
{
|
||||
break;
|
||||
}
|
||||
}
|
||||
} while (!bQuit);
|
||||
|
||||
cvDestroyWindow(wndTitle.c_str());
|
||||
|
||||
return 0;
|
||||
}
|
||||
|
||||
#endif //!defined(HAVE_CUDA)
|
||||
|
||||
Reference in New Issue
Block a user