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changed StereoBeliefPropagation_GPU output disparity default type to CV_32S

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This commit is contained in:
Vladislav Vinogradov 2010-07-29 08:47:06 +00:00
parent 7083f0f815
commit 63fed0f831
3 changed files with 63 additions and 47 deletions
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8
modules/gpu/include/opencv2/gpu/gpu.hpp
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@ -387,8 +387,8 @@ namespace cv
//! number of levels, truncation of discontinuity cost, truncation of data cost and weighting of data cost.
StereoBeliefPropagation_GPU(int ndisp, int iters, int levels, float disc_cost, float data_cost, float lambda);
//! the stereo correspondence operator. Finds the disparity for the specified rectified stereo pair
//! Output disparity has CV_8U type.
//! the stereo correspondence operator. Finds the disparity for the specified rectified stereo pair,
//! if disparity is empty output type will be CV_32S else output type will be disparity.type().
void operator()(const GpuMat& left, const GpuMat& right, GpuMat& disparity);
//! Acync version
@ -409,8 +409,8 @@ namespace cv
float lambda;
private:
GpuMat u, d, l, r, u2, d2, l2, r2;
std::vector<GpuMat> datas;
std::vector<GpuMat> datas;
GpuMat out;
};
}
}

53
modules/gpu/src/beliefpropagation_gpu.cpp
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@ -63,6 +63,7 @@ static const float DEFAULT_DATA_COST = 10.0f;
static const float DEFAULT_LAMBDA_COST = 0.07f;
typedef DevMem2D_<float> DevMem2Df;
typedef DevMem2D_<int> DevMem2Di;
namespace cv { namespace gpu { namespace impl {
extern "C" void load_constants(int ndisp, float disc_cost, float data_cost, float lambda);
@ -70,31 +71,27 @@ namespace cv { namespace gpu { namespace impl {
extern "C" void data_down_kernel_caller(int dst_cols, int dst_rows, int src_rows, const DevMem2Df& src, DevMem2Df dst, const cudaStream_t& stream);
extern "C" void level_up(int dst_idx, int dst_cols, int dst_rows, int src_rows, DevMem2Df* mu, DevMem2Df* md, DevMem2Df* ml, DevMem2Df* mr, const cudaStream_t& stream);
extern "C" void call_all_iterations(int cols, int rows, int iters, DevMem2Df& u, DevMem2Df& d, DevMem2Df& l, DevMem2Df& r, const DevMem2Df& data, const cudaStream_t& stream);
extern "C" void output_caller(const DevMem2Df& u, const DevMem2Df& d, const DevMem2Df& l, const DevMem2Df& r, const DevMem2Df& data, DevMem2D disp, const cudaStream_t& stream);
extern "C" void output_caller(const DevMem2Df& u, const DevMem2Df& d, const DevMem2Df& l, const DevMem2Df& r, const DevMem2Df& data, DevMem2Di disp, const cudaStream_t& stream);
}}}
cv::gpu::StereoBeliefPropagation_GPU::StereoBeliefPropagation_GPU(int ndisp_, int iters_, int levels_)
: ndisp(ndisp_), iters(iters_), levels(levels_), disc_cost(DEFAULT_DISC_COST), data_cost(DEFAULT_DATA_COST), lambda(DEFAULT_LAMBDA_COST), datas(levels_)
{
const int max_supported_ndisp = 1 << (sizeof(unsigned char) * 8);
CV_Assert(0 < ndisp && ndisp <= max_supported_ndisp);
CV_Assert(0 < ndisp);
CV_Assert(ndisp % 8 == 0);
}
cv::gpu::StereoBeliefPropagation_GPU::StereoBeliefPropagation_GPU(int ndisp_, int iters_, int levels_, float disc_cost_, float data_cost_, float lambda_)
: ndisp(ndisp_), iters(iters_), levels(levels_), disc_cost(disc_cost_), data_cost(data_cost_), lambda(lambda_), datas(levels_)
{
const int max_supported_ndisp = 1 << (sizeof(unsigned char) * 8);
CV_Assert(0 < ndisp && ndisp <= max_supported_ndisp);
CV_Assert(0 < ndisp);
CV_Assert(ndisp % 8 == 0);
}
static void stereo_bp_gpu_operator(int ndisp, int iters, int levels, float disc_cost, float data_cost, float lambda,
GpuMat& u, GpuMat& d, GpuMat& l, GpuMat& r,
GpuMat& u2, GpuMat& d2, GpuMat& l2, GpuMat& r2,
vector<GpuMat>& datas,
GpuMat& u2, GpuMat& d2, GpuMat& l2, GpuMat& r2,
vector<GpuMat>& datas, GpuMat& out,
const GpuMat& left, const GpuMat& right, GpuMat& disp,
const cudaStream_t& stream)
{
@ -111,8 +108,6 @@ static void stereo_bp_gpu_operator(int ndisp, int iters, int levels, float disc_
const int min_image_dim_size = 20;
CV_Assert(min(lowest_cols, lowest_rows) > min_image_dim_size);
disp.create(rows, cols, CV_8U);
u.create(rows * ndisp, cols, CV_32F);
d.create(rows * ndisp, cols, CV_32F);
l.create(rows * ndisp, cols, CV_32F);
@ -146,10 +141,16 @@ static void stereo_bp_gpu_operator(int ndisp, int iters, int levels, float disc_
}
impl::load_constants(ndisp, disc_cost, data_cost, lambda);
vector<int> cols_all(levels);
vector<int> rows_all(levels);
vector<int> iters_all(levels);
datas.resize(levels);
AutoBuffer<int> cols_all_buf(levels);
AutoBuffer<int> rows_all_buf(levels);
AutoBuffer<int> iters_all_buf(levels);
int *cols_all = cols_all_buf;
int *rows_all = rows_all_buf;
int *iters_all = iters_all_buf;
cols_all[0] = cols;
rows_all[0] = rows;
@ -190,18 +191,34 @@ static void stereo_bp_gpu_operator(int ndisp, int iters, int levels, float disc_
mem_idx = (mem_idx + 1) & 1;
}
if (disp.empty())
disp.create(rows, cols, CV_32S);
impl::output_caller(u, d, l, r, datas.front(), disp, stream);
if (disp.type() == CV_32S)
{
disp = zero;
impl::output_caller(u, d, l, r, datas.front(), disp, stream);
}
else
{
out.create(rows, cols, CV_32S);
out = zero;
impl::output_caller(u, d, l, r, datas.front(), out, stream);
out.convertTo(disp, disp.type());
}
}
void cv::gpu::StereoBeliefPropagation_GPU::operator()(const GpuMat& left, const GpuMat& right, GpuMat& disp)
{
::stereo_bp_gpu_operator(ndisp, iters, levels, disc_cost, data_cost, lambda, u, d, l, r, u2, d2, l2, r2, datas, left, right, disp, 0);
::stereo_bp_gpu_operator(ndisp, iters, levels, disc_cost, data_cost, lambda, u, d, l, r, u2, d2, l2, r2, datas, out, left, right, disp, 0);
}
void cv::gpu::StereoBeliefPropagation_GPU::operator()(const GpuMat& left, const GpuMat& right, GpuMat& disp, const CudaStream& stream)
{
::stereo_bp_gpu_operator(ndisp, iters, levels, disc_cost, data_cost, lambda, u, d, l, r, u2, d2, l2, r2, datas, left, right, disp, StreamAccessor::getStream(stream));
::stereo_bp_gpu_operator(ndisp, iters, levels, disc_cost, data_cost, lambda, u, d, l, r, u2, d2, l2, r2, datas, out, left, right, disp, StreamAccessor::getStream(stream));
}
bool cv::gpu::StereoBeliefPropagation_GPU::checkIfGpuCallReasonable()

49
modules/gpu/src/cuda/beliefpropagation.cu
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@ -353,42 +353,41 @@ namespace cv { namespace gpu { namespace impl {
namespace beliefpropagation_gpu
{
__global__ void output(int cols, int rows, float *u, float *d, float *l, float *r, float* data, size_t step, unsigned char *disp, size_t res_step)
__global__ void output(int cols, int rows, float *u, float *d, float *l, float *r, float* data, size_t step, int *disp, size_t res_step)
{
int x = blockIdx.x * blockDim.x + threadIdx.x;
int y = blockIdx.y * blockDim.y + threadIdx.y;
if (y > 0 && y < rows - 1)
if (x > 0 && x < cols - 1)
if (y > 0 && y < rows - 1 && x > 0 && x < cols - 1)
{
float *us = u + (y + 1) * step + x;
float *ds = d + (y - 1) * step + x;
float *ls = l + y * step + (x + 1);
float *rs = r + y * step + (x - 1);
float *dt = data + y * step + x;
size_t disp_step = rows * step;
int best = 0;
float best_val = FLT_MAX;
for (int d = 0; d < cndisp; ++d)
{
float *us = u + (y + 1) * step + x;
float *ds = d + (y - 1) * step + x;
float *ls = l + y * step + (x + 1);
float *rs = r + y * step + (x - 1);
float *dt = data + y * step + x;
float val = us[d * disp_step] + ds[d * disp_step] + ls[d * disp_step] + rs[d * disp_step] + dt[d * disp_step];
size_t disp_step = rows * step;
int best = 0;
float best_val = FLT_MAX;
for (int d = 0; d < cndisp; ++d)
if (val < best_val)
{
float val = us[d * disp_step] + ds[d * disp_step] + ls[d * disp_step] + rs[d * disp_step] + dt[d * disp_step];
if (val < best_val)
{
best_val = val;
best = d;
}
best_val = val;
best = d;
}
disp[res_step * y + x] = best & 0xFF;
}
disp[res_step * y + x] = best;
}
}
}
namespace cv { namespace gpu { namespace impl {
extern "C" void output_caller(const DevMem2D_<float>& u, const DevMem2D_<float>& d, const DevMem2D_<float>& l, const DevMem2D_<float>& r, const DevMem2D_<float>& data, DevMem2D disp, const cudaStream_t& stream)
extern "C" void output_caller(const DevMem2D_<float>& u, const DevMem2D_<float>& d, const DevMem2D_<float>& l, const DevMem2D_<float>& r, const DevMem2D_<float>& data, DevMem2D_<int> disp, const cudaStream_t& stream)
{
dim3 threads(32, 8, 1);
dim3 grid(1, 1, 1);
@ -398,12 +397,12 @@ namespace cv { namespace gpu { namespace impl {
if (stream == 0)
{
beliefpropagation_gpu::output<<<grid, threads>>>(disp.cols, disp.rows, u.ptr, d.ptr, l.ptr, r.ptr, data.ptr, u.step/sizeof(float), disp.ptr, disp.step);
beliefpropagation_gpu::output<<<grid, threads>>>(disp.cols, disp.rows, u.ptr, d.ptr, l.ptr, r.ptr, data.ptr, u.step/sizeof(float), disp.ptr, disp.step/sizeof(int));
cudaSafeCall( cudaThreadSynchronize() );
}
else
{
beliefpropagation_gpu::output<<<grid, threads, 0, stream>>>(disp.cols, disp.rows, u.ptr, d.ptr, l.ptr, r.ptr, data.ptr, u.step/sizeof(float), disp.ptr, disp.step);
beliefpropagation_gpu::output<<<grid, threads, 0, stream>>>(disp.cols, disp.rows, u.ptr, d.ptr, l.ptr, r.ptr, data.ptr, u.step/sizeof(float), disp.ptr, disp.step/sizeof(int));
}
}
}}}