984eb99428
[~] Automatically tracked dependencies between modules [+] Support for optional module dependencies [+] Options to choose modules to build [~] Removed hardcoded modules lists from OpenCVConfig.cmake, opencv.pc and OpenCV.mk [+] Added COMPONENTS support for FIND_PACKAGE(OpenCV) [~] haartraining and traincascade are moved outside of modules folder since they aren't the modules
380 lines
14 KiB
C++
380 lines
14 KiB
C++
#if _MSC_VER >= 1400
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#pragma warning( disable : 4201 4408 4127 4100)
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#endif
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#include "cvconfig.h"
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#include <iostream>
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#include <iomanip>
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#include <cstdio>
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#include "opencv2/gpu/gpu.hpp"
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#ifdef HAVE_CUDA
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#include "NCVHaarObjectDetection.hpp"
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#endif
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using namespace std;
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using namespace cv;
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#if !defined(HAVE_CUDA)
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int main( int argc, const char** argv )
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{
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cout << "Please compile the library with CUDA support" << endl;
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return -1;
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}
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#else
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const Size2i preferredVideoFrameSize(640, 480);
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const string wndTitle = "NVIDIA Computer Vision :: Haar Classifiers Cascade";
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void matPrint(Mat &img, int lineOffsY, Scalar fontColor, const string &ss)
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{
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int fontFace = FONT_HERSHEY_DUPLEX;
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double fontScale = 0.8;
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int fontThickness = 2;
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Size fontSize = cv::getTextSize("T[]", fontFace, fontScale, fontThickness, 0);
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Point org;
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org.x = 1;
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org.y = 3 * fontSize.height * (lineOffsY + 1) / 2;
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putText(img, ss, org, fontFace, fontScale, CV_RGB(0,0,0), 5*fontThickness/2, 16);
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putText(img, ss, org, fontFace, fontScale, fontColor, fontThickness, 16);
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}
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void displayState(Mat &canvas, bool bHelp, bool bGpu, bool bLargestFace, bool bFilter, double fps)
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{
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Scalar fontColorRed = CV_RGB(255,0,0);
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Scalar fontColorNV = CV_RGB(118,185,0);
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ostringstream ss;
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ss << "FPS = " << setprecision(1) << fixed << fps;
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matPrint(canvas, 0, fontColorRed, ss.str());
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ss.str("");
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ss << "[" << canvas.cols << "x" << canvas.rows << "], " <<
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(bGpu ? "GPU, " : "CPU, ") <<
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(bLargestFace ? "OneFace, " : "MultiFace, ") <<
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(bFilter ? "Filter:ON" : "Filter:OFF");
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matPrint(canvas, 1, fontColorRed, ss.str());
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if (bHelp)
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{
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matPrint(canvas, 2, fontColorNV, "Space - switch GPU / CPU");
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matPrint(canvas, 3, fontColorNV, "M - switch OneFace / MultiFace");
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matPrint(canvas, 4, fontColorNV, "F - toggle rectangles Filter");
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matPrint(canvas, 5, fontColorNV, "H - toggle hotkeys help");
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}
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else
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{
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matPrint(canvas, 2, fontColorNV, "H - toggle hotkeys help");
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}
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}
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NCVStatus process(Mat *srcdst,
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Ncv32u width, Ncv32u height,
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NcvBool bFilterRects, NcvBool bLargestFace,
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HaarClassifierCascadeDescriptor &haar,
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NCVVector<HaarStage64> &d_haarStages, NCVVector<HaarClassifierNode128> &d_haarNodes,
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NCVVector<HaarFeature64> &d_haarFeatures, NCVVector<HaarStage64> &h_haarStages,
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INCVMemAllocator &gpuAllocator,
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INCVMemAllocator &cpuAllocator,
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cudaDeviceProp &devProp)
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{
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ncvAssertReturn(!((srcdst == NULL) ^ gpuAllocator.isCounting()), NCV_NULL_PTR);
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NCVStatus ncvStat;
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NCV_SET_SKIP_COND(gpuAllocator.isCounting());
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NCVMatrixAlloc<Ncv8u> d_src(gpuAllocator, width, height);
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ncvAssertReturn(d_src.isMemAllocated(), NCV_ALLOCATOR_BAD_ALLOC);
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NCVMatrixAlloc<Ncv8u> h_src(cpuAllocator, width, height);
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ncvAssertReturn(h_src.isMemAllocated(), NCV_ALLOCATOR_BAD_ALLOC);
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NCVVectorAlloc<NcvRect32u> d_rects(gpuAllocator, 100);
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ncvAssertReturn(d_rects.isMemAllocated(), NCV_ALLOCATOR_BAD_ALLOC);
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NCV_SKIP_COND_BEGIN
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for (Ncv32u i=0; i<(Ncv32u)srcdst->rows; i++)
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{
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memcpy(h_src.ptr() + i * h_src.stride(), srcdst->ptr(i), srcdst->cols);
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}
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ncvStat = h_src.copySolid(d_src, 0);
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ncvAssertReturnNcvStat(ncvStat);
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ncvAssertCUDAReturn(cudaStreamSynchronize(0), NCV_CUDA_ERROR);
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NCV_SKIP_COND_END
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NcvSize32u roi;
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roi.width = d_src.width();
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roi.height = d_src.height();
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Ncv32u numDetections;
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ncvStat = ncvDetectObjectsMultiScale_device(
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d_src, roi, d_rects, numDetections, haar, h_haarStages,
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d_haarStages, d_haarNodes, d_haarFeatures,
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haar.ClassifierSize,
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(bFilterRects || bLargestFace) ? 4 : 0,
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1.2f, 1,
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(bLargestFace ? NCVPipeObjDet_FindLargestObject : 0)
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| NCVPipeObjDet_VisualizeInPlace,
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gpuAllocator, cpuAllocator, devProp, 0);
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ncvAssertReturnNcvStat(ncvStat);
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ncvAssertCUDAReturn(cudaStreamSynchronize(0), NCV_CUDA_ERROR);
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NCV_SKIP_COND_BEGIN
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ncvStat = d_src.copySolid(h_src, 0);
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ncvAssertReturnNcvStat(ncvStat);
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ncvAssertCUDAReturn(cudaStreamSynchronize(0), NCV_CUDA_ERROR);
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for (Ncv32u i=0; i<(Ncv32u)srcdst->rows; i++)
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{
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memcpy(srcdst->ptr(i), h_src.ptr() + i * h_src.stride(), srcdst->cols);
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}
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NCV_SKIP_COND_END
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return NCV_SUCCESS;
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}
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int main(int argc, const char** argv)
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{
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cout << "OpenCV / NVIDIA Computer Vision" << endl;
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cout << "Face Detection in video and live feed" << endl;
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cout << "Syntax: exename <cascade_file> <image_or_video_or_cameraid>" << endl;
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cout << "=========================================" << endl;
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ncvAssertPrintReturn(cv::gpu::getCudaEnabledDeviceCount() != 0, "No GPU found or the library is compiled without GPU support", -1);
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ncvAssertPrintReturn(argc == 3, "Invalid number of arguments", -1);
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cv::gpu::printShortCudaDeviceInfo(cv::gpu::getDevice());
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string cascadeName = argv[1];
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string inputName = argv[2];
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NCVStatus ncvStat;
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NcvBool bQuit = false;
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VideoCapture capture;
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Size2i frameSize;
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//open content source
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Mat image = imread(inputName);
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Mat frame;
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if (!image.empty())
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{
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frameSize.width = image.cols;
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frameSize.height = image.rows;
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}
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else
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{
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if (!capture.open(inputName))
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{
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int camid = -1;
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istringstream ss(inputName);
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int x = 0;
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ss >> x;
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ncvAssertPrintReturn(capture.open(camid) != 0, "Can't open source", -1);
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}
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capture >> frame;
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ncvAssertPrintReturn(!frame.empty(), "Empty video source", -1);
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frameSize.width = frame.cols;
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frameSize.height = frame.rows;
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}
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NcvBool bUseGPU = true;
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NcvBool bLargestObject = false;
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NcvBool bFilterRects = true;
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NcvBool bHelpScreen = false;
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CascadeClassifier classifierOpenCV;
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ncvAssertPrintReturn(classifierOpenCV.load(cascadeName) != 0, "Error (in OpenCV) opening classifier", -1);
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int devId;
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ncvAssertCUDAReturn(cudaGetDevice(&devId), -1);
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cudaDeviceProp devProp;
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ncvAssertCUDAReturn(cudaGetDeviceProperties(&devProp, devId), -1);
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cout << "Using GPU: " << devId << "(" << devProp.name <<
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"), arch=" << devProp.major << "." << devProp.minor << endl;
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//==============================================================================
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//
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// Load the classifier from file (assuming its size is about 1 mb)
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// using a simple allocator
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//
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//==============================================================================
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NCVMemNativeAllocator gpuCascadeAllocator(NCVMemoryTypeDevice, static_cast<Ncv32u>(devProp.textureAlignment));
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ncvAssertPrintReturn(gpuCascadeAllocator.isInitialized(), "Error creating cascade GPU allocator", -1);
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NCVMemNativeAllocator cpuCascadeAllocator(NCVMemoryTypeHostPinned, static_cast<Ncv32u>(devProp.textureAlignment));
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ncvAssertPrintReturn(cpuCascadeAllocator.isInitialized(), "Error creating cascade CPU allocator", -1);
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Ncv32u haarNumStages, haarNumNodes, haarNumFeatures;
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ncvStat = ncvHaarGetClassifierSize(cascadeName, haarNumStages, haarNumNodes, haarNumFeatures);
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ncvAssertPrintReturn(ncvStat == NCV_SUCCESS, "Error reading classifier size (check the file)", -1);
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NCVVectorAlloc<HaarStage64> h_haarStages(cpuCascadeAllocator, haarNumStages);
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ncvAssertPrintReturn(h_haarStages.isMemAllocated(), "Error in cascade CPU allocator", -1);
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NCVVectorAlloc<HaarClassifierNode128> h_haarNodes(cpuCascadeAllocator, haarNumNodes);
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ncvAssertPrintReturn(h_haarNodes.isMemAllocated(), "Error in cascade CPU allocator", -1);
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NCVVectorAlloc<HaarFeature64> h_haarFeatures(cpuCascadeAllocator, haarNumFeatures);
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ncvAssertPrintReturn(h_haarFeatures.isMemAllocated(), "Error in cascade CPU allocator", -1);
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HaarClassifierCascadeDescriptor haar;
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ncvStat = ncvHaarLoadFromFile_host(cascadeName, haar, h_haarStages, h_haarNodes, h_haarFeatures);
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ncvAssertPrintReturn(ncvStat == NCV_SUCCESS, "Error loading classifier", -1);
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NCVVectorAlloc<HaarStage64> d_haarStages(gpuCascadeAllocator, haarNumStages);
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ncvAssertPrintReturn(d_haarStages.isMemAllocated(), "Error in cascade GPU allocator", -1);
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NCVVectorAlloc<HaarClassifierNode128> d_haarNodes(gpuCascadeAllocator, haarNumNodes);
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ncvAssertPrintReturn(d_haarNodes.isMemAllocated(), "Error in cascade GPU allocator", -1);
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NCVVectorAlloc<HaarFeature64> d_haarFeatures(gpuCascadeAllocator, haarNumFeatures);
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ncvAssertPrintReturn(d_haarFeatures.isMemAllocated(), "Error in cascade GPU allocator", -1);
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ncvStat = h_haarStages.copySolid(d_haarStages, 0);
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ncvAssertPrintReturn(ncvStat == NCV_SUCCESS, "Error copying cascade to GPU", -1);
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ncvStat = h_haarNodes.copySolid(d_haarNodes, 0);
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ncvAssertPrintReturn(ncvStat == NCV_SUCCESS, "Error copying cascade to GPU", -1);
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ncvStat = h_haarFeatures.copySolid(d_haarFeatures, 0);
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ncvAssertPrintReturn(ncvStat == NCV_SUCCESS, "Error copying cascade to GPU", -1);
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//==============================================================================
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//
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// Calculate memory requirements and create real allocators
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//
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//==============================================================================
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NCVMemStackAllocator gpuCounter(static_cast<Ncv32u>(devProp.textureAlignment));
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ncvAssertPrintReturn(gpuCounter.isInitialized(), "Error creating GPU memory counter", -1);
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NCVMemStackAllocator cpuCounter(static_cast<Ncv32u>(devProp.textureAlignment));
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ncvAssertPrintReturn(cpuCounter.isInitialized(), "Error creating CPU memory counter", -1);
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ncvStat = process(NULL, frameSize.width, frameSize.height,
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false, false, haar,
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d_haarStages, d_haarNodes,
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d_haarFeatures, h_haarStages,
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gpuCounter, cpuCounter, devProp);
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ncvAssertPrintReturn(ncvStat == NCV_SUCCESS, "Error in memory counting pass", -1);
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NCVMemStackAllocator gpuAllocator(NCVMemoryTypeDevice, gpuCounter.maxSize(), static_cast<Ncv32u>(devProp.textureAlignment));
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ncvAssertPrintReturn(gpuAllocator.isInitialized(), "Error creating GPU memory allocator", -1);
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NCVMemStackAllocator cpuAllocator(NCVMemoryTypeHostPinned, cpuCounter.maxSize(), static_cast<Ncv32u>(devProp.textureAlignment));
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ncvAssertPrintReturn(cpuAllocator.isInitialized(), "Error creating CPU memory allocator", -1);
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printf("Initialized for frame size [%dx%d]\n", frameSize.width, frameSize.height);
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//==============================================================================
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//
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// Main processing loop
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//
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//==============================================================================
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namedWindow(wndTitle, 1);
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Mat gray, frameDisp;
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do
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{
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Mat gray;
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cvtColor((image.empty() ? frame : image), gray, CV_BGR2GRAY);
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//
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// process
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//
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NcvSize32u minSize = haar.ClassifierSize;
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if (bLargestObject)
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{
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Ncv32u ratioX = preferredVideoFrameSize.width / minSize.width;
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Ncv32u ratioY = preferredVideoFrameSize.height / minSize.height;
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Ncv32u ratioSmallest = min(ratioX, ratioY);
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ratioSmallest = max((Ncv32u)(ratioSmallest / 2.5f), (Ncv32u)1);
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minSize.width *= ratioSmallest;
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minSize.height *= ratioSmallest;
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}
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Ncv32f avgTime;
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NcvTimer timer = ncvStartTimer();
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if (bUseGPU)
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{
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ncvStat = process(&gray, frameSize.width, frameSize.height,
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bFilterRects, bLargestObject, haar,
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d_haarStages, d_haarNodes,
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d_haarFeatures, h_haarStages,
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gpuAllocator, cpuAllocator, devProp);
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ncvAssertPrintReturn(ncvStat == NCV_SUCCESS, "Error in memory counting pass", -1);
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}
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else
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{
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vector<Rect> rectsOpenCV;
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classifierOpenCV.detectMultiScale(
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gray,
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rectsOpenCV,
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1.2f,
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bFilterRects ? 4 : 0,
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(bLargestObject ? CV_HAAR_FIND_BIGGEST_OBJECT : 0)
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| CV_HAAR_SCALE_IMAGE,
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Size(minSize.width, minSize.height));
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for (size_t rt = 0; rt < rectsOpenCV.size(); ++rt)
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rectangle(gray, rectsOpenCV[rt], Scalar(255));
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}
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avgTime = (Ncv32f)ncvEndQueryTimerMs(timer);
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cvtColor(gray, frameDisp, CV_GRAY2BGR);
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displayState(frameDisp, bHelpScreen, bUseGPU, bLargestObject, bFilterRects, 1000.0f / avgTime);
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imshow(wndTitle, frameDisp);
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//handle input
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switch (cvWaitKey(3))
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{
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case ' ':
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bUseGPU = !bUseGPU;
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break;
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case 'm':
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case 'M':
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bLargestObject = !bLargestObject;
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break;
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case 'f':
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case 'F':
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bFilterRects = !bFilterRects;
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break;
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case 'h':
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case 'H':
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bHelpScreen = !bHelpScreen;
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break;
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case 27:
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bQuit = true;
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break;
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}
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// For camera and video file, capture the next image
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if (capture.isOpened())
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{
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capture >> frame;
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if (frame.empty())
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{
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break;
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}
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}
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} while (!bQuit);
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cvDestroyWindow(wndTitle.c_str());
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return 0;
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}
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#endif //!defined(HAVE_CUDA)
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