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opencv/samples/gpu/cascadeclassifier_nvidia_api.cpp
Ishant Mrinal Haloi 52b4fe63c1 Update cascadeclassifier_nvidia_api.cpp
2015-11-02 15:07:45 +08:00

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#if defined _MSC_VER && _MSC_VER >= 1400
#pragma warning( disable : 4201 4408 4127 4100)
#endif
#include "opencv2/cvconfig.h"
#include <iostream>
#include <iomanip>
#include <cstdio>
#include "opencv2/core/cuda.hpp"
#include "opencv2/cudalegacy.hpp"
#include "opencv2/highgui.hpp"
#include "opencv2/imgproc.hpp"
#include "opencv2/objdetect.hpp"
#include "opencv2/objdetect/objdetect_c.h"
using namespace std;
using namespace cv;
#if !defined(HAVE_CUDA) || defined(__arm__)
int main( int, const char** )
{
#if !defined(HAVE_CUDA)
std::cout << "CUDA support is required (CMake key 'WITH_CUDA' must be true)." << std::endl;
#endif
#if defined(__arm__)
std::cout << "Unsupported for ARM CUDA library." << std::endl;
#endif
return 0;
}
#else
const Size2i preferredVideoFrameSize(640, 480);
const cv::String wndTitle = "NVIDIA Computer Vision :: Haar Classifiers Cascade";
static 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, Scalar(0,0,0), 5*fontThickness/2, 16);
putText(img, ss, org, fontFace, fontScale, fontColor, fontThickness, 16);
}
static void displayState(Mat &canvas, bool bHelp, bool bGpu, bool bLargestFace, bool bFilter, double fps)
{
Scalar fontColorRed(0,0,255);
Scalar fontColorNV(0,185,118);
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");
}
}
static 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::cuda::getCudaEnabledDeviceCount() != 0, "No GPU found or the library is compiled without CUDA support", -1);
ncvAssertPrintReturn(argc == 3, "Invalid number of arguments", -1);
cv::cuda::printShortCudaDeviceInfo(cv::cuda::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 frameDisp;
do
{
Mat gray;
cvtColor((image.empty() ? frame : image), gray, cv::COLOR_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::COLOR_GRAY2BGR);
displayState(frameDisp, bHelpScreen, bUseGPU, bLargestObject, bFilterRects, 1000.0f / avgTime);
imshow(wndTitle, frameDisp);
//handle input
switch (cv::waitKey(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);
cv::destroyWindow(wndTitle);
return 0;
}
#endif //!defined(HAVE_CUDA)
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