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Merge pull request #3600 from jet47:cuda-objdetect-module

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This commit is contained in:
Vadim Pisarevsky
2015-01-20 13:29:32 +00:00
parent 579ce93885 dccdadc38a
commit 95ecdc3af9
23 changed files with 2809 additions and 2397 deletions
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2
samples/gpu/CMakeLists.txt
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@@ -3,7 +3,7 @@ SET(OPENCV_CUDA_SAMPLES_REQUIRED_DEPS opencv_core opencv_flann opencv_imgproc op
opencv_calib3d opencv_cuda opencv_superres
opencv_cudaarithm opencv_cudafilters opencv_cudawarping opencv_cudaimgproc
opencv_cudafeatures2d opencv_cudaoptflow opencv_cudabgsegm
opencv_cudastereo opencv_cudalegacy)
opencv_cudastereo opencv_cudalegacy opencv_cudaobjdetect)
ocv_check_dependencies(${OPENCV_CUDA_SAMPLES_REQUIRED_DEPS})

62
samples/gpu/cascadeclassifier.cpp
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@@ -9,7 +9,7 @@
#include "opencv2/objdetect/objdetect.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/cuda.hpp"
#include "opencv2/cudaobjdetect.hpp"
#include "opencv2/cudaimgproc.hpp"
#include "opencv2/cudawarping.hpp"
@@ -173,13 +173,9 @@ int main(int argc, const char *argv[])
}
}
CascadeClassifier_CUDA cascade_gpu;
if (!cascade_gpu.load(cascadeName))
{
return cerr << "ERROR: Could not load cascade classifier \"" << cascadeName << "\"" << endl, help(), -1;
}
Ptr<cuda::CascadeClassifier> cascade_gpu = cuda::CascadeClassifier::create(cascadeName);
CascadeClassifier cascade_cpu;
cv::CascadeClassifier cascade_cpu;
if (!cascade_cpu.load(cascadeName))
{
return cerr << "ERROR: Could not load cascade classifier \"" << cascadeName << "\"" << endl, help(), -1;
@@ -206,8 +202,8 @@ int main(int argc, const char *argv[])
namedWindow("result", 1);
Mat frame, frame_cpu, gray_cpu, resized_cpu, faces_downloaded, frameDisp;
vector<Rect> facesBuf_cpu;
Mat frame, frame_cpu, gray_cpu, resized_cpu, frameDisp;
vector<Rect> faces;
GpuMat frame_gpu, gray_gpu, resized_gpu, facesBuf_gpu;
@@ -218,7 +214,6 @@ int main(int argc, const char *argv[])
bool filterRects = true;
bool helpScreen = false;
int detections_num;
for (;;)
{
if (isInputCamera || isInputVideo)
@@ -241,40 +236,26 @@ int main(int argc, const char *argv[])
if (useGPU)
{
//cascade_gpu.visualizeInPlace = true;
cascade_gpu.findLargestObject = findLargestObject;
cascade_gpu->setFindLargestObject(findLargestObject);
cascade_gpu->setScaleFactor(1.2);
cascade_gpu->setMinNeighbors((filterRects || findLargestObject) ? 4 : 0);
detections_num = cascade_gpu.detectMultiScale(resized_gpu, facesBuf_gpu, 1.2,
(filterRects || findLargestObject) ? 4 : 0);
facesBuf_gpu.colRange(0, detections_num).download(faces_downloaded);
cascade_gpu->detectMultiScale(resized_gpu, facesBuf_gpu);
cascade_gpu->convert(facesBuf_gpu, faces);
}
else
{
Size minSize = cascade_gpu.getClassifierSize();
cascade_cpu.detectMultiScale(resized_cpu, facesBuf_cpu, 1.2,
Size minSize = cascade_gpu->getClassifierSize();
cascade_cpu.detectMultiScale(resized_cpu, faces, 1.2,
(filterRects || findLargestObject) ? 4 : 0,
(findLargestObject ? CASCADE_FIND_BIGGEST_OBJECT : 0)
| CASCADE_SCALE_IMAGE,
minSize);
detections_num = (int)facesBuf_cpu.size();
}
if (!useGPU && detections_num)
for (size_t i = 0; i < faces.size(); ++i)
{
for (int i = 0; i < detections_num; ++i)
{
rectangle(resized_cpu, facesBuf_cpu[i], Scalar(255));
}
}
if (useGPU)
{
resized_gpu.download(resized_cpu);
for (int i = 0; i < detections_num; ++i)
{
rectangle(resized_cpu, faces_downloaded.ptr<cv::Rect>()[i], Scalar(255));
}
rectangle(resized_cpu, faces[i], Scalar(255));
}
tm.stop();
@@ -283,16 +264,15 @@ int main(int argc, const char *argv[])
//print detections to console
cout << setfill(' ') << setprecision(2);
cout << setw(6) << fixed << fps << " FPS, " << detections_num << " det";
if ((filterRects || findLargestObject) && detections_num > 0)
cout << setw(6) << fixed << fps << " FPS, " << faces.size() << " det";
if ((filterRects || findLargestObject) && !faces.empty())
{
Rect *faceRects = useGPU ? faces_downloaded.ptr<Rect>() : &facesBuf_cpu[0];
for (int i = 0; i < min(detections_num, 2); ++i)
for (size_t i = 0; i < faces.size(); ++i)
{
cout << ", [" << setw(4) << faceRects[i].x
<< ", " << setw(4) << faceRects[i].y
<< ", " << setw(4) << faceRects[i].width
<< ", " << setw(4) << faceRects[i].height << "]";
cout << ", [" << setw(4) << faces[i].x
<< ", " << setw(4) << faces[i].y
<< ", " << setw(4) << faces[i].width
<< ", " << setw(4) << faces[i].height << "]";
}
}
cout << endl;

37
samples/gpu/hog.cpp
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@@ -5,7 +5,7 @@
#include <iomanip>
#include <stdexcept>
#include <opencv2/core/utility.hpp>
#include "opencv2/cuda.hpp"
#include "opencv2/cudaobjdetect.hpp"
#include "opencv2/highgui.hpp"
#include "opencv2/objdetect.hpp"
#include "opencv2/imgproc.hpp"
@@ -252,19 +252,13 @@ void App::run()
Size win_size(args.win_width, args.win_width * 2); //(64, 128) or (48, 96)
Size win_stride(args.win_stride_width, args.win_stride_height);
// Create HOG descriptors and detectors here
vector<float> detector;
if (win_size == Size(64, 128))
detector = cv::cuda::HOGDescriptor::getPeopleDetector64x128();
else
detector = cv::cuda::HOGDescriptor::getPeopleDetector48x96();
cv::Ptr<cv::cuda::HOG> gpu_hog = cv::cuda::HOG::create(win_size);
cv::HOGDescriptor cpu_hog(win_size, Size(16, 16), Size(8, 8), Size(8, 8), 9);
cv::cuda::HOGDescriptor gpu_hog(win_size, Size(16, 16), Size(8, 8), Size(8, 8), 9,
cv::cuda::HOGDescriptor::DEFAULT_WIN_SIGMA, 0.2, gamma_corr,
cv::cuda::HOGDescriptor::DEFAULT_NLEVELS);
cv::HOGDescriptor cpu_hog(win_size, Size(16, 16), Size(8, 8), Size(8, 8), 9, 1, -1,
HOGDescriptor::L2Hys, 0.2, gamma_corr, cv::HOGDescriptor::DEFAULT_NLEVELS);
gpu_hog.setSVMDetector(detector);
// Create HOG descriptors and detectors here
Mat detector = gpu_hog->getDefaultPeopleDetector();
gpu_hog->setSVMDetector(detector);
cpu_hog.setSVMDetector(detector);
while (running)
@@ -315,9 +309,6 @@ void App::run()
else img = img_aux;
img_to_show = img;
gpu_hog.nlevels = nlevels;
cpu_hog.nlevels = nlevels;
vector<Rect> found;
// Perform HOG classification
@@ -325,11 +316,19 @@ void App::run()
if (use_gpu)
{
gpu_img.upload(img);
gpu_hog.detectMultiScale(gpu_img, found, hit_threshold, win_stride,
Size(0, 0), scale, gr_threshold);
gpu_hog->setNumLevels(nlevels);
gpu_hog->setHitThreshold(hit_threshold);
gpu_hog->setWinStride(win_stride);
gpu_hog->setScaleFactor(scale);
gpu_hog->setGroupThreshold(gr_threshold);
gpu_hog->detectMultiScale(gpu_img, found);
}
else cpu_hog.detectMultiScale(img, found, hit_threshold, win_stride,
else
{
cpu_hog.nlevels = nlevels;
cpu_hog.detectMultiScale(img, found, hit_threshold, win_stride,
Size(0, 0), scale, gr_threshold);
}
hogWorkEnd();
// Draw positive classified windows