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[~] Refactored, cleaned up, and consolidated the code of GPU examples (cascadeclassifier and cascadeclassifier_nvidia_api)

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This commit is contained in:
Anton Obukhov
2011-04-07 12:59:01 +00:00
parent daac469b83
commit 07d19c2c6f
4 changed files with 293 additions and 259 deletions
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260
samples/gpu/cascadeclassifier_nvidia_api.cpp
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@@ -1,50 +1,76 @@
#pragma warning( disable : 4201 4408 4127 4100)
#include <cstdio>
#include "cvconfig.h"
#if !defined(HAVE_CUDA)
int main( int argc, const char** argv ) { return printf("Please compile the library with CUDA support."), -1; }
#else
#include <cuda_runtime.h>
#include "opencv2/opencv.hpp"
#include <iostream>
#include <iomanip>
#include <opencv2/opencv.hpp>
#include <opencv2/gpu/gpu.hpp>
#include "NCVHaarObjectDetection.hpp"
using namespace std;
using namespace cv;
const Size2i preferredVideoFrameSize(640, 480);
std::string preferredClassifier = "haarcascade_frontalface_alt.xml";
std::string wndTitle = "NVIDIA Computer Vision SDK :: Face Detection in Video Feed";
void printSyntax(void)
#if !defined(HAVE_CUDA)
int main( int argc, const char** argv )
{
printf("Syntax: FaceDetectionFeed.exe [-c cameranum | -v filename] classifier.xml\n");
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 ostringstream &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.str(), org, fontFace, fontScale, CV_RGB(0,0,0), 5*fontThickness/2, 16);
putText(img, ss.str(), org, fontFace, fontScale, fontColor, fontThickness, 16);
}
void imagePrintf(Mat& img, int lineOffsY, Scalar color, const char *format, ...)
{
int fontFace = CV_FONT_HERSHEY_PLAIN;
double fontScale = 1;
int baseline;
Size textSize = cv::getTextSize("T", fontFace, fontScale, 1, &baseline);
va_list arg_ptr;
va_start(arg_ptr, format);
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);
char strBuf[4096];
vsprintf(&strBuf[0], format, arg_ptr);
ostringstream ss;
ss << "FPS = " << setprecision(1) << fixed << fps;
matPrint(canvas, 0, fontColorRed, ss);
ss.str("");
ss << "[" << canvas.cols << "x" << canvas.rows << "], " <<
(bGpu ? "GPU, " : "CPU, ") <<
(bLargestFace ? "OneFace, " : "MultiFace, ") <<
(bFilter ? "Filter:ON" : "Filter:OFF");
matPrint(canvas, 1, fontColorRed, ss);
Point org(1, 3 * textSize.height * (lineOffsY + 1) / 2);
putText(img, &strBuf[0], org, fontFace, fontScale, color);
va_end(arg_ptr);
if (bHelp)
{
matPrint(canvas, 2, fontColorNV, ostringstream("Space - switch GPU / CPU"));
matPrint(canvas, 3, fontColorNV, ostringstream("M - switch OneFace / MultiFace"));
matPrint(canvas, 4, fontColorNV, ostringstream("F - toggle rectangles Filter"));
matPrint(canvas, 5, fontColorNV, ostringstream("H - toggle hotkeys help"));
}
else
{
matPrint(canvas, 2, fontColorNV, ostringstream("H - toggle hotkeys help"));
}
}
NCVStatus process(Mat *srcdst,
Ncv32u width, Ncv32u height,
NcvBool bShowAllHypotheses, NcvBool bLargestFace,
NcvBool bFilterRects, NcvBool bLargestFace,
HaarClassifierCascadeDescriptor &haar,
NCVVector<HaarStage64> &d_haarStages, NCVVector<HaarClassifierNode128> &d_haarNodes,
NCVVector<HaarFeature64> &d_haarFeatures, NCVVector<HaarStage64> &h_haarStages,
@@ -87,7 +113,7 @@ NCVStatus process(Mat *srcdst,
d_src, roi, d_rects, numDetections, haar, h_haarStages,
d_haarStages, d_haarNodes, d_haarFeatures,
haar.ClassifierSize,
bShowAllHypotheses ? 0 : 4,
(bFilterRects || bLargestFace) ? 4 : 0,
1.2f, 1,
(bLargestFace ? NCVPipeObjDet_FindLargestObject : 0)
| NCVPipeObjDet_VisualizeInPlace,
@@ -111,80 +137,67 @@ NCVStatus process(Mat *srcdst,
return NCV_SUCCESS;
}
int main( int argc, const char** argv )
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);
string cascadeName = argv[1];
string inputName = argv[2];
NCVStatus ncvStat;
printf("NVIDIA Computer Vision SDK\n");
printf("Face Detection in video and live feed\n");
printf("=========================================\n");
printf(" Esc - Quit\n");
printf(" Space - Switch between NCV and OpenCV\n");
printf(" L - Switch between FullSearch and LargestFace modes\n");
printf(" U - Toggle unfiltered hypotheses visualization in FullSearch\n");
VideoCapture capture;
bool bQuit = false;
NcvBool bQuit = false;
VideoCapture capture;
Size2i frameSize;
if (argc != 4 && argc != 1)
//open content source
Mat image = imread(inputName);
Mat frame;
if (!image.empty())
{
printSyntax();
return -1;
}
if (argc == 1 || strcmp(argv[1], "-c") == 0)
{
// Camera input is specified
int camIdx = (argc == 3) ? atoi(argv[2]) : 0;
if(!capture.open(camIdx))
return printf("Error opening camera\n"), -1;
capture.set(CV_CAP_PROP_FRAME_WIDTH, preferredVideoFrameSize.width);
capture.set(CV_CAP_PROP_FRAME_HEIGHT, preferredVideoFrameSize.height);
capture.set(CV_CAP_PROP_FPS, 25);
frameSize = preferredVideoFrameSize;
}
else if (strcmp(argv[1], "-v") == 0)
{
// Video file input (avi)
if(!capture.open(argv[2]))
return printf("Error opening video file\n"), -1;
frameSize.width = (int)capture.get(CV_CAP_PROP_FRAME_WIDTH);
frameSize.height = (int)capture.get(CV_CAP_PROP_FRAME_HEIGHT);
frameSize.width = image.cols;
frameSize.height = image.rows;
}
else
return printSyntax(), -1;
{
if (!capture.open(inputName))
{
int camid = -1;
NcvBool bUseOpenCV = true;
NcvBool bLargestFace = false; //LargestFace=true is used usually during training
NcvBool bShowAllHypotheses = false;
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;
std::string classifierFile;
if (argc == 1)
{
classifierFile = preferredClassifier;
}
else
{
classifierFile.assign(argv[3]);
}
if (!classifierOpenCV.load(classifierFile))
{
printf("Error (in OpenCV) opening classifier\n");
printSyntax();
return -1;
}
ncvAssertPrintReturn(classifierOpenCV.load(cascadeName) != 0, "Error (in OpenCV) opening classifier", -1);
int devId;
ncvAssertCUDAReturn(cudaGetDevice(&devId), -1);
cudaDeviceProp devProp;
ncvAssertCUDAReturn(cudaGetDeviceProperties(&devProp, devId), -1);
printf("Using GPU %d %s, arch=%d.%d\n", devId, devProp.name, devProp.major, devProp.minor);
cout << "Using GPU: " << devId << "(" << devProp.name <<
"), arch=" << devProp.major << "." << devProp.minor << endl;
//==============================================================================
//
@@ -199,7 +212,7 @@ int main( int argc, const char** argv )
ncvAssertPrintReturn(cpuCascadeAllocator.isInitialized(), "Error creating cascade CPU allocator", -1);
Ncv32u haarNumStages, haarNumNodes, haarNumFeatures;
ncvStat = ncvHaarGetClassifierSize(classifierFile, 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);
@@ -210,7 +223,7 @@ int main( int argc, const char** argv )
ncvAssertPrintReturn(h_haarFeatures.isMemAllocated(), "Error in cascade CPU allocator", -1);
HaarClassifierCascadeDescriptor haar;
ncvStat = ncvHaarLoadFromFile_host(classifierFile, haar, h_haarStages, h_haarNodes, h_haarFeatures);
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);
@@ -258,30 +271,25 @@ int main( int argc, const char** argv )
//
//==============================================================================
namedWindow(wndTitle, 1);
Mat frame, gray, frameDisp;
namedWindow(wndTitle, 1);
Mat gray, frameDisp;
do
{
// For camera and video file, capture the next image
capture >> frame;
if (frame.empty())
break;
Mat gray;
cvtColor(frame, gray, CV_BGR2GRAY);
cvtColor((image.empty() ? frame : image), gray, CV_BGR2GRAY);
//
// process
//
NcvSize32u minSize = haar.ClassifierSize;
if (bLargestFace)
if (bLargestObject)
{
Ncv32u ratioX = preferredVideoFrameSize.width / minSize.width;
Ncv32u ratioY = preferredVideoFrameSize.height / minSize.height;
Ncv32u ratioSmallest = std::min(ratioX, ratioY);
ratioSmallest = std::max((Ncv32u)(ratioSmallest / 2.5f), (Ncv32u)1);
Ncv32u ratioSmallest = min(ratioX, ratioY);
ratioSmallest = max((Ncv32u)(ratioSmallest / 2.5f), (Ncv32u)1);
minSize.width *= ratioSmallest;
minSize.height *= ratioSmallest;
}
@@ -289,10 +297,10 @@ int main( int argc, const char** argv )
Ncv32f avgTime;
NcvTimer timer = ncvStartTimer();
if (!bUseOpenCV)
if (bUseGPU)
{
ncvStat = process(&gray, frameSize.width, frameSize.height,
bShowAllHypotheses, bLargestFace, haar,
bFilterRects, bLargestObject, haar,
d_haarStages, d_haarNodes,
d_haarFeatures, h_haarStages,
gpuAllocator, cpuAllocator, devProp);
@@ -306,8 +314,8 @@ int main( int argc, const char** argv )
gray,
rectsOpenCV,
1.2f,
bShowAllHypotheses && !bLargestFace ? 0 : 4,
(bLargestFace ? CV_HAAR_FIND_BIGGEST_OBJECT : 0)
bFilterRects ? 4 : 0,
(bLargestObject ? CV_HAAR_FIND_BIGGEST_OBJECT : 0)
| CV_HAAR_SCALE_IMAGE,
Size(minSize.width, minSize.height));
@@ -318,32 +326,41 @@ int main( int argc, const char** argv )
avgTime = (Ncv32f)ncvEndQueryTimerMs(timer);
cvtColor(gray, frameDisp, CV_GRAY2BGR);
displayState(frameDisp, bHelpScreen, bUseGPU, bLargestObject, bFilterRects, 1000.0f / avgTime);
imshow(wndTitle, frameDisp);
imagePrintf(frameDisp, 0, CV_RGB(255, 0,0), "Space - Switch NCV%s / OpenCV%s", bUseOpenCV?"":" (ON)", bUseOpenCV?" (ON)":"");
imagePrintf(frameDisp, 1, CV_RGB(255, 0,0), "L - Switch FullSearch%s / LargestFace%s modes", bLargestFace?"":" (ON)", bLargestFace?" (ON)":"");
imagePrintf(frameDisp, 2, CV_RGB(255, 0,0), "U - Toggle unfiltered hypotheses visualization in FullSearch %s", bShowAllHypotheses?"(ON)":"(OFF)");
imagePrintf(frameDisp, 3, CV_RGB(118,185,0), " Running at %f FPS on %s", 1000.0f / avgTime, bUseOpenCV?"CPU":"GPU");
cv::imshow(wndTitle, frameDisp);
//handle input
switch (cvWaitKey(3))
{
case ' ':
bUseOpenCV = !bUseOpenCV;
bUseGPU = !bUseGPU;
break;
case 'L':
case 'l':
bLargestFace = !bLargestFace;
case 'm':
case 'M':
bLargestObject = !bLargestObject;
break;
case 'U':
case 'u':
bShowAllHypotheses = !bShowAllHypotheses;
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());
@@ -351,5 +368,4 @@ int main( int argc, const char** argv )
return 0;
}
#endif
#endif //!defined(HAVE_CUDA)