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added different win_stride values feature into gpu HOG, refactored gpu HOG sample

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pull/13383/head
Alexey Spizhevoy 15 years ago
parent 27542529a5
commit 2d01558479
3 changed files with 48 additions and 31 deletions
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  1. 17
      modules/gpu/src/cuda/hog.cu
  2. 14
      modules/gpu/src/hog.cpp
  3. 34
      samples/gpu/gpu_hog.cpp

17
modules/gpu/src/cuda/hog.cu
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@ -336,6 +336,7 @@ void normalize_hists(int nbins, int block_stride_x, int block_stride_y,
template <int nthreads, // Number of threads per one histogram block
int nblocks> // Number of histogram block processed by single GPU thread block
__global__ void classify_hists_kernel_many_blocks(const int img_win_width, const int img_block_width,
const int win_block_stride_x, const int win_block_stride_y,
const float* block_hists, const float* coefs,
float free_coef, float threshold, unsigned char* labels)
{
@ -343,8 +344,8 @@ __global__ void classify_hists_kernel_many_blocks(const int img_win_width, const
if (blockIdx.x * blockDim.z + win_x >= img_win_width)
return;
const float* hist = block_hists + (blockIdx.y * img_block_width +
blockIdx.x * blockDim.z + win_x) *
const float* hist = block_hists + (blockIdx.y * win_block_stride_y * img_block_width +
blockIdx.x * win_block_stride_x * blockDim.z + win_x) *
cblock_hist_size;
float product = 0.f;
@ -397,15 +398,18 @@ __global__ void classify_hists_kernel_many_blocks(const int img_win_width, const
// We only support win_stride_x == block_stride_x, win_stride_y == block_stride_y
void classify_hists(int win_height, int win_width, int block_stride_x, int block_stride_y,
void classify_hists(int win_height, int win_width, int block_stride_y, int block_stride_x,
int win_stride_y, int win_stride_x,
int height, int width, float* block_hists, float* coefs,
float free_coef, float threshold, unsigned char* labels)
{
const int nthreads = 256;
const int nblocks = 1;
int img_win_width = (width - win_width + block_stride_x) / block_stride_x;
int img_win_height = (height - win_height + block_stride_y) / block_stride_y;
int win_block_stride_x = win_stride_x / block_stride_x;
int win_block_stride_y = win_stride_y / block_stride_y;
int img_win_width = (width - win_width + win_stride_x) / win_stride_x;
int img_win_height = (height - win_height + win_stride_y) / win_stride_y;
dim3 threads(nthreads, 1, nblocks);
dim3 grid(div_up(img_win_width, nblocks), img_win_height);
@ -416,7 +420,8 @@ void classify_hists(int win_height, int win_width, int block_stride_x, int block
int img_block_width = (width - CELLS_PER_BLOCK_X * CELL_WIDTH + block_stride_x) /
block_stride_x;
classify_hists_kernel_many_blocks<nthreads, nblocks><<<grid, threads>>>(
img_win_width, img_block_width, block_hists, coefs, free_coef, threshold, labels);
img_win_width, img_block_width, win_block_stride_x, win_block_stride_y,
block_hists, coefs, free_coef, threshold, labels);
cudaSafeCall(cudaThreadSynchronize());
}

14
modules/gpu/src/hog.cpp
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@ -73,9 +73,10 @@ void compute_hists(int nbins, int block_stride_x, int blovck_stride_y,
void normalize_hists(int nbins, int block_stride_x, int block_stride_y,
int height, int width, float* block_hists, float threshold);
void classify_hists(int win_height, int win_width, int block_stride_x,
int block_stride_y, int height, int width, float* block_hists,
float* coefs, float free_coef, float threshold, unsigned char* labels);
void classify_hists(int win_height, int win_width, int block_stride_y,
int block_stride_x, int win_stride_y, int win_stride_x, int height,
int width, float* block_hists, float* coefs, float free_coef,
float threshold, unsigned char* labels);
void compute_gradients_8UC1(int nbins, int height, int width, const cv::gpu::DevMem2D& img,
float angle_scale, cv::gpu::DevMem2Df grad, cv::gpu::DevMem2D qangle);
@ -209,7 +210,8 @@ void cv::gpu::HOGDescriptor::detect(const GpuMat& img, vector<Point>& hits, doub
if (win_stride == Size())
win_stride = block_stride;
else
CV_Assert(win_stride == block_stride);
CV_Assert(win_stride.width % block_stride.width == 0 &&
win_stride.height % block_stride.height == 0);
CV_Assert(padding == Size(0, 0));
@ -229,8 +231,8 @@ void cv::gpu::HOGDescriptor::detect(const GpuMat& img, vector<Point>& hits, doub
block_hists.ptr<float>(), (float)threshold_L2hys);
hog::classify_hists(win_size.height, win_size.width, block_stride.height, block_stride.width,
img.rows, img.cols, block_hists.ptr<float>(), detector.ptr<float>(),
(float)free_coef, (float)hit_threshold, labels.ptr());
win_stride.height, win_stride.width, img.rows, img.cols, block_hists.ptr<float>(),
detector.ptr<float>(), (float)free_coef, (float)hit_threshold, labels.ptr());
labels.download(labels_host);
unsigned char* vec = labels_host.ptr();

34
samples/gpu/gpu_hog.cpp
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@ -31,6 +31,8 @@ public:
int gr_threshold;
double hit_threshold;
int win_width;
int win_stride_width;
int win_stride_height;
};
@ -94,6 +96,8 @@ int main(int argc, char** argv)
<< " [-scale <double>] # HOG window scale factor\n"
<< " [-nlevels <int>] # max number of HOG window scales\n"
<< " [-win_width <int>] # width of the window (48 or 64)\n"
<< " [-win_stride_width <int>] # distance by OX axis between neighbour wins\n"
<< " [-win_stride_height <int>] # distance by OY axis between neighbour wins\n"
<< " [-gr_threshold <int>] # merging similar rects constant\n";
return 1;
}
@ -118,6 +122,8 @@ Settings::Settings()
gr_threshold = 8;
hit_threshold = 1.4;
win_width = 48;
win_stride_width = 8;
win_stride_height = 8;
}
@ -139,6 +145,8 @@ Settings Settings::Read(int argc, char** argv)
else if (key == "-scale") settings.scale = atof(val.c_str());
else if (key == "-nlevels") settings.nlevels = atoi(val.c_str());
else if (key == "-win_width") settings.win_width = atoi(val.c_str());
else if (key == "-win_stride_width") settings.win_stride_width = atoi(val.c_str());
else if (key == "-win_stride_height") settings.win_stride_height = atoi(val.c_str());
else if (key == "-gr_threshold") settings.gr_threshold = atoi(val.c_str());
else throw exception((string("Unknown key: ") + key).c_str());
}
@ -152,13 +160,13 @@ App::App(const Settings &s)
{
settings = s;
cout << "\nControls:\n"
<< "ESC - exit\n"
<< "m - change mode GPU <-> CPU\n"
<< "g - convert image to gray or not\n"
<< "1/q - increase/decrease HOG scale\n"
<< "2/w - increase/decrease levels count\n"
<< "3/e - increase/decrease HOG group threshold\n"
<< "4/r - increase/decrease hit threshold\n"
<< "\tESC - exit\n"
<< "\tm - change mode GPU <-> CPU\n"
<< "\tg - convert image to gray or not\n"
<< "\t1/q - increase/decrease HOG scale\n"
<< "\t2/w - increase/decrease levels count\n"
<< "\t3/e - increase/decrease HOG group threshold\n"
<< "\t4/r - increase/decrease hit threshold\n"
<< endl;
use_gpu = true;
@ -171,10 +179,11 @@ App::App(const Settings &s)
if (settings.win_width != 64 && settings.win_width != 48)
settings.win_width = 64;
cout << endl << "Scale: " << scale << endl;
cout << "Scale: " << scale << endl;
cout << "Group threshold: " << gr_threshold << endl;
cout << "Levels number: " << nlevels << endl;
cout << "Win width: " << settings.win_width << endl;
cout << "Win stride: (" << settings.win_stride_width << ", " << settings.win_stride_height << ")\n";
cout << "Hit threshold: " << hit_threshold << endl;
cout << endl;
}
@ -185,10 +194,11 @@ void App::RunOpencvGui()
running = true;
Size win_size(settings.win_width, settings.win_width * 2); //(64, 128) or (48, 96)
Size win_stride(settings.win_stride_width, settings.win_stride_height);
vector<float> detector;
if (win_size == Size(64,128))
if (win_size == Size(64, 128))
detector = cv::gpu::HOGDescriptor::getPeopleDetector_64x128();
else
detector = cv::gpu::HOGDescriptor::getPeopleDetector_48x96();
@ -198,7 +208,7 @@ void App::RunOpencvGui()
gpu_hog.setSVMDetector(detector);
// CPU's HOG classifier
cv::HOGDescriptor cpu_hog(win_size, Size(16,16), Size(8,8), Size(8,8), 9, 1, -1, HOGDescriptor::L2Hys, 0.2, true, 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, true, HOGDescriptor::DEFAULT_NLEVELS);
cpu_hog.setSVMDetector(detector);
// Make endless cycle from video (if src is video)
@ -250,10 +260,10 @@ void App::RunOpencvGui()
if (use_gpu)
{
gpu_img = img;
gpu_hog.detectMultiScale(gpu_img, found, hit_threshold, Size(8, 8), Size(0, 0), scale, gr_threshold);
gpu_hog.detectMultiScale(gpu_img, found, hit_threshold, win_stride, Size(0, 0), scale, gr_threshold);
}
else
cpu_hog.detectMultiScale(img, found, hit_threshold, Size(8, 8), Size(0, 0), scale, gr_threshold);
cpu_hog.detectMultiScale(img, found, hit_threshold, win_stride, Size(0, 0), scale, gr_threshold);
HogWorkEnd();
// Draw positive classified windows

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