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added GoodFeaturesToTrackDetector_GPU and PyrLKOpticalFlow to gpu module

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This commit is contained in:
Vladislav Vinogradov 2012-02-13 12:57:27 +00:00
parent edc9d4f951
commit a10fed8fd1
11 changed files with 1913 additions and 6 deletions
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102
modules/gpu/include/opencv2/gpu/gpu.hpp
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@ -1717,6 +1717,108 @@ public:
GpuMat buf;
};
class CV_EXPORTS GoodFeaturesToTrackDetector_GPU
{
public:
GoodFeaturesToTrackDetector_GPU(int maxCorners_, double qualityLevel_, double minDistance_)
{
maxCorners = maxCorners_;
qualityLevel = qualityLevel_;
minDistance = minDistance_;
blockSize = 3;
useHarrisDetector = false;
harrisK = 0.04;
}
//! return 1 rows matrix with CV_32FC2 type
void operator ()(const GpuMat& image, GpuMat& corners, const GpuMat& mask = GpuMat());
int maxCorners;
double qualityLevel;
double minDistance;
int blockSize;
bool useHarrisDetector;
double harrisK;
void releaseMemory()
{
Dx_.release();
Dy_.release();
buf_.release();
eig_.release();
minMaxbuf_.release();
tmpCorners_.release();
}
private:
GpuMat Dx_;
GpuMat Dy_;
GpuMat buf_;
GpuMat eig_;
GpuMat minMaxbuf_;
GpuMat tmpCorners_;
};
class CV_EXPORTS PyrLKOpticalFlow
{
public:
PyrLKOpticalFlow()
{
winSize = Size(21, 21);
maxLevel = 3;
iters = 30;
derivLambda = 0.5;
useInitialFlow = false;
minEigThreshold = 1e-4f;
}
void sparse(const GpuMat& prevImg, const GpuMat& nextImg, const GpuMat& prevPts, GpuMat& nextPts,
GpuMat& status, GpuMat* err = 0);
void dense(const GpuMat& prevImg, const GpuMat& nextImg, GpuMat& u, GpuMat& v, GpuMat* err = 0);
Size winSize;
int maxLevel;
int iters;
double derivLambda;
bool useInitialFlow;
float minEigThreshold;
void releaseMemory()
{
dx_calcBuf_.release();
dy_calcBuf_.release();
prevPyr_.clear();
nextPyr_.clear();
dx_buf_.release();
dy_buf_.release();
uPyr_.clear();
vPyr_.clear();
}
private:
void calcSharrDeriv(const GpuMat& src, GpuMat& dx, GpuMat& dy);
void buildImagePyramid(const GpuMat& img0, vector<GpuMat>& pyr, bool withBorder);
GpuMat dx_calcBuf_;
GpuMat dy_calcBuf_;
vector<GpuMat> prevPyr_;
vector<GpuMat> nextPyr_;
GpuMat dx_buf_;
GpuMat dy_buf_;
vector<GpuMat> uPyr_;
vector<GpuMat> vPyr_;
};
//! Interpolate frames (images) using provided optical flow (displacement field).
//! frame0 - frame 0 (32-bit floating point images, single channel)
//! frame1 - frame 1 (the same type and size)

86
modules/gpu/perf/perf_video.cpp
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@ -98,21 +98,99 @@ GPU_PERF_TEST_1(CreateOpticalFlowNeedleMap, cv::gpu::DeviceInfo)
cv::gpu::GpuMat frame0(frame0_host);
cv::gpu::GpuMat frame1(frame1_host);
cv::gpu::GpuMat d_u, d_v;
cv::gpu::GpuMat u, v;
cv::gpu::BroxOpticalFlow d_flow(0.197f /*alpha*/, 50.0f /*gamma*/, 0.8f /*scale_factor*/,
10 /*inner_iterations*/, 77 /*outer_iterations*/, 10 /*solver_iterations*/);
d_flow(frame0, frame1, d_u, d_v);
d_flow(frame0, frame1, u, v);
cv::gpu::GpuMat d_vertex, d_colors;
cv::gpu::GpuMat vertex, colors;
TEST_CYCLE()
{
cv::gpu::createOpticalFlowNeedleMap(d_u, d_v, d_vertex, d_colors);
cv::gpu::createOpticalFlowNeedleMap(u, v, vertex, colors);
}
}
INSTANTIATE_TEST_CASE_P(Video, CreateOpticalFlowNeedleMap, ALL_DEVICES);
//////////////////////////////////////////////////////
// GoodFeaturesToTrack
GPU_PERF_TEST(GoodFeaturesToTrack, cv::gpu::DeviceInfo, double)
{
cv::gpu::DeviceInfo devInfo = GET_PARAM(0);
double minDistance = GET_PARAM(1);
cv::gpu::setDevice(devInfo.deviceID());
cv::Mat image_host = readImage("gpu/perf/aloe.jpg", cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(image_host.empty());
cv::gpu::GoodFeaturesToTrackDetector_GPU detector(8000, 0.01, minDistance);
cv::gpu::GpuMat image(image_host);
cv::gpu::GpuMat pts;
TEST_CYCLE()
{
detector(image, pts);
}
}
INSTANTIATE_TEST_CASE_P(Video, GoodFeaturesToTrack, testing::Combine(ALL_DEVICES, testing::Values(0.0, 3.0)));
//////////////////////////////////////////////////////
// PyrLKOpticalFlowSparse
GPU_PERF_TEST(PyrLKOpticalFlowSparse, cv::gpu::DeviceInfo, bool, int, int)
{
cv::gpu::DeviceInfo devInfo = GET_PARAM(0);
bool useGray = GET_PARAM(1);
int points = GET_PARAM(2);
int win_size = GET_PARAM(3);
cv::gpu::setDevice(devInfo.deviceID());
cv::Mat frame0_host = readImage("gpu/opticalflow/frame0.png", useGray ? cv::IMREAD_GRAYSCALE : cv::IMREAD_COLOR);
cv::Mat frame1_host = readImage("gpu/opticalflow/frame1.png", useGray ? cv::IMREAD_GRAYSCALE : cv::IMREAD_COLOR);
ASSERT_FALSE(frame0_host.empty());
ASSERT_FALSE(frame1_host.empty());
cv::Mat gray_frame;
if (useGray)
gray_frame = frame0_host;
else
cv::cvtColor(frame0_host, gray_frame, cv::COLOR_BGR2GRAY);
cv::gpu::GpuMat pts;
cv::gpu::GoodFeaturesToTrackDetector_GPU detector(points, 0.01, 0.0);
detector(cv::gpu::GpuMat(gray_frame), pts);
cv::gpu::PyrLKOpticalFlow pyrLK;
pyrLK.winSize = cv::Size(win_size, win_size);
cv::gpu::GpuMat frame0(frame0_host);
cv::gpu::GpuMat frame1(frame1_host);
cv::gpu::GpuMat nextPts;
cv::gpu::GpuMat status;
TEST_CYCLE()
{
pyrLK.sparse(frame0, frame1, pts, nextPts, status);
}
}
INSTANTIATE_TEST_CASE_P(Video, PyrLKOpticalFlowSparse, testing::Combine
(
ALL_DEVICES,
testing::Bool(),
testing::Values(1000, 2000, 4000, 8000),
testing::Values(17, 21)
));
#endif

146
modules/gpu/src/cuda/gftt.cu Normal file
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@ -0,0 +1,146 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
// Copyright (c) 2010, Paul Furgale, Chi Hay Tong
//
// The original code was written by Paul Furgale and Chi Hay Tong
// and later optimized and prepared for integration into OpenCV by Itseez.
//
//M*/
#include <thrust/sort.h>
#include "opencv2/gpu/device/common.hpp"
#include "opencv2/gpu/device/utility.hpp"
namespace cv { namespace gpu { namespace device
{
namespace gfft
{
texture<float, cudaTextureType2D, cudaReadModeElementType> eigTex(0, cudaFilterModePoint, cudaAddressModeClamp);
__device__ uint g_counter = 0;
template <class Mask> __global__ void findCorners(float threshold, const Mask mask, float2* corners, uint max_count, int rows, int cols)
{
#if __CUDA_ARCH__ >= 110
const int j = blockIdx.x * blockDim.x + threadIdx.x;
const int i = blockIdx.y * blockDim.y + threadIdx.y;
if (i > 0 && i < rows - 1 && j > 0 && j < cols - 1 && mask(i, j))
{
float val = tex2D(eigTex, j, i);
if (val > threshold)
{
float maxVal = val;
maxVal = ::fmax(tex2D(eigTex, j - 1, i - 1), maxVal);
maxVal = ::fmax(tex2D(eigTex, j , i - 1), maxVal);
maxVal = ::fmax(tex2D(eigTex, j + 1, i - 1), maxVal);
maxVal = ::fmax(tex2D(eigTex, j - 1, i), maxVal);
maxVal = ::fmax(tex2D(eigTex, j + 1, i), maxVal);
maxVal = ::fmax(tex2D(eigTex, j - 1, i + 1), maxVal);
maxVal = ::fmax(tex2D(eigTex, j , i + 1), maxVal);
maxVal = ::fmax(tex2D(eigTex, j + 1, i + 1), maxVal);
if (val == maxVal)
{
const uint ind = atomicInc(&g_counter, (uint)(-1));
if (ind < max_count)
corners[ind] = make_float2(j, i);
}
}
}
#endif // __CUDA_ARCH__ >= 110
}
int findCorners_gpu(DevMem2Df eig, float threshold, DevMem2Db mask, float2* corners, int max_count)
{
void* counter_ptr;
cudaSafeCall( cudaGetSymbolAddress(&counter_ptr, g_counter) );
cudaSafeCall( cudaMemset(counter_ptr, 0, sizeof(uint)) );
bindTexture(&eigTex, eig);
dim3 block(16, 16);
dim3 grid(divUp(eig.cols, block.x), divUp(eig.rows, block.y));
if (mask.data)
findCorners<<<grid, block>>>(threshold, SingleMask(mask), corners, max_count, eig.rows, eig.cols);
else
findCorners<<<grid, block>>>(threshold, WithOutMask(), corners, max_count, eig.rows, eig.cols);
cudaSafeCall( cudaGetLastError() );
cudaSafeCall( cudaDeviceSynchronize() );
uint count;
cudaSafeCall( cudaMemcpy(&count, counter_ptr, sizeof(uint), cudaMemcpyDeviceToHost) );
return min(count, max_count);
}
class EigGreater
{
public:
__device__ __forceinline__ bool operator()(float2 a, float2 b) const
{
return tex2D(eigTex, a.x, a.y) > tex2D(eigTex, b.x, b.y);
}
};
void sortCorners_gpu(DevMem2Df eig, float2* corners, int count)
{
bindTexture(&eigTex, eig);
thrust::device_ptr<float2> ptr(corners);
thrust::sort(ptr, ptr + count, EigGreater());
}
} // namespace optical_flow
}}}

599
modules/gpu/src/cuda/pyrlk.cu Normal file
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@ -0,0 +1,599 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
// Copyright (c) 2010, Paul Furgale, Chi Hay Tong
//
// The original code was written by Paul Furgale and Chi Hay Tong
// and later optimized and prepared for integration into OpenCV by Itseez.
//
//M*/
#include "opencv2/gpu/device/common.hpp"
#include "opencv2/gpu/device/utility.hpp"
#include "opencv2/gpu/device/functional.hpp"
#include "opencv2/gpu/device/limits.hpp"
namespace cv { namespace gpu { namespace device
{
namespace pyrlk
{
__constant__ int c_cn;
__constant__ float c_minEigThreshold;
__constant__ int c_winSize_x;
__constant__ int c_winSize_y;
__constant__ int c_winSize_x_cn;
__constant__ int c_halfWin_x;
__constant__ int c_halfWin_y;
__constant__ int c_iters;
void loadConstants(int cn, float minEigThreshold, int2 winSize, int iters)
{
int2 halfWin = make_int2((winSize.x - 1) / 2, (winSize.y - 1) / 2);
cudaSafeCall( cudaMemcpyToSymbol(c_cn, &cn, sizeof(int)) );
cudaSafeCall( cudaMemcpyToSymbol(c_minEigThreshold, &minEigThreshold, sizeof(float)) );
cudaSafeCall( cudaMemcpyToSymbol(c_winSize_x, &winSize.x, sizeof(int)) );
cudaSafeCall( cudaMemcpyToSymbol(c_winSize_y, &winSize.y, sizeof(int)) );
winSize.x *= cn;
cudaSafeCall( cudaMemcpyToSymbol(c_winSize_x_cn, &winSize.x, sizeof(int)) );
cudaSafeCall( cudaMemcpyToSymbol(c_halfWin_x, &halfWin.x, sizeof(int)) );
cudaSafeCall( cudaMemcpyToSymbol(c_halfWin_y, &halfWin.y, sizeof(int)) );
cudaSafeCall( cudaMemcpyToSymbol(c_iters, &iters, sizeof(int)) );
}
__global__ void calcSharrDeriv_vertical(const PtrStepb src, PtrStep<short> dx_buf, PtrStep<short> dy_buf, int rows, int colsn)
{
const int x = blockIdx.x * blockDim.x + threadIdx.x;
const int y = blockIdx.y * blockDim.y + threadIdx.y;
if (y < rows && x < colsn)
{
const uchar src_val0 = src(y > 0 ? y - 1 : 1, x);
const uchar src_val1 = src(y, x);
const uchar src_val2 = src(y < rows - 1 ? y + 1 : rows - 2, x);
dx_buf(y, x) = (src_val0 + src_val2) * 3 + src_val1 * 10;
dy_buf(y, x) = src_val2 - src_val0;
}
}
__global__ void calcSharrDeriv_horizontal(const PtrStep<short> dx_buf, const PtrStep<short> dy_buf, PtrStep<short> dIdx, PtrStep<short> dIdy, int rows, int cols)
{
const int x = blockIdx.x * blockDim.x + threadIdx.x;
const int y = blockIdx.y * blockDim.y + threadIdx.y;
const int colsn = cols * c_cn;
if (y < rows && x < colsn)
{
const short* dx_buf_row = dx_buf.ptr(y);
const short* dy_buf_row = dy_buf.ptr(y);
const int xr = x + c_cn < colsn ? x + c_cn : (cols - 2) * c_cn + x + c_cn - colsn;
const int xl = x - c_cn >= 0 ? x - c_cn : c_cn + x;
dIdx(y, x) = dx_buf_row[xr] - dx_buf_row[xl];
dIdy(y, x) = (dy_buf_row[xr] + dy_buf_row[xl]) * 3 + dy_buf_row[x] * 10;
}
}
void calcSharrDeriv_gpu(DevMem2Db src, DevMem2D_<short> dx_buf, DevMem2D_<short> dy_buf, DevMem2D_<short> dIdx, DevMem2D_<short> dIdy, int cn,
cudaStream_t stream)
{
dim3 block(32, 8);
dim3 grid(divUp(src.cols * cn, block.x), divUp(src.rows, block.y));
calcSharrDeriv_vertical<<<grid, block, 0, stream>>>(src, dx_buf, dy_buf, src.rows, src.cols * cn);
cudaSafeCall( cudaGetLastError() );
calcSharrDeriv_horizontal<<<grid, block, 0, stream>>>(dx_buf, dy_buf, dIdx, dIdy, src.rows, src.cols);
cudaSafeCall( cudaGetLastError() );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
#define W_BITS 14
#define W_BITS1 14
#define CV_DESCALE(x, n) (((x) + (1 << ((n)-1))) >> (n))
__device__ int linearFilter(const PtrStepb& src, float2 pt, int x, int y)
{
int2 ipt;
ipt.x = __float2int_rd(pt.x);
ipt.y = __float2int_rd(pt.y);
float a = pt.x - ipt.x;
float b = pt.y - ipt.y;
int iw00 = __float2int_rn((1.0f - a) * (1.0f - b) * (1 << W_BITS));
int iw01 = __float2int_rn(a * (1.0f - b) * (1 << W_BITS));
int iw10 = __float2int_rn((1.0f - a) * b * (1 << W_BITS));
int iw11 = (1 << W_BITS) - iw00 - iw01 - iw10;
const uchar* src_row = src.ptr(ipt.y + y) + ipt.x * c_cn;
const uchar* src_row1 = src.ptr(ipt.y + y + 1) + ipt.x * c_cn;
return CV_DESCALE(src_row[x] * iw00 + src_row[x + c_cn] * iw01 + src_row1[x] * iw10 + src_row1[x + c_cn] * iw11, W_BITS1 - 5);
}
__device__ int linearFilter(const PtrStep<short>& src, float2 pt, int x, int y)
{
int2 ipt;
ipt.x = __float2int_rd(pt.x);
ipt.y = __float2int_rd(pt.y);
float a = pt.x - ipt.x;
float b = pt.y - ipt.y;
int iw00 = __float2int_rn((1.0f - a) * (1.0f - b) * (1 << W_BITS));
int iw01 = __float2int_rn(a * (1.0f - b) * (1 << W_BITS));
int iw10 = __float2int_rn((1.0f - a) * b * (1 << W_BITS));
int iw11 = (1 << W_BITS) - iw00 - iw01 - iw10;
const short* src_row = src.ptr(ipt.y + y) + ipt.x * c_cn;
const short* src_row1 = src.ptr(ipt.y + y + 1) + ipt.x * c_cn;
return CV_DESCALE(src_row[x] * iw00 + src_row[x + c_cn] * iw01 + src_row1[x] * iw10 + src_row1[x + c_cn] * iw11, W_BITS1);
}
__device__ void reduce(float& val1, float& val2, float& val3, float* smem1, float* smem2, float* smem3, int tid)
{
smem1[tid] = val1;
smem2[tid] = val2;
smem3[tid] = val3;
__syncthreads();
if (tid < 128)
{
smem1[tid] = val1 += smem1[tid + 128];
smem2[tid] = val2 += smem2[tid + 128];
smem3[tid] = val3 += smem3[tid + 128];
}
__syncthreads();
if (tid < 64)
{
smem1[tid] = val1 += smem1[tid + 64];
smem2[tid] = val2 += smem2[tid + 64];
smem3[tid] = val3 += smem3[tid + 64];
}
__syncthreads();
if (tid < 32)
{
volatile float* vmem1 = smem1;
volatile float* vmem2 = smem2;
volatile float* vmem3 = smem3;
vmem1[tid] = val1 += vmem1[tid + 32];
vmem2[tid] = val2 += vmem2[tid + 32];
vmem3[tid] = val3 += vmem3[tid + 32];
vmem1[tid] = val1 += vmem1[tid + 16];
vmem2[tid] = val2 += vmem2[tid + 16];
vmem3[tid] = val3 += vmem3[tid + 16];
vmem1[tid] = val1 += vmem1[tid + 8];
vmem2[tid] = val2 += vmem2[tid + 8];
vmem3[tid] = val3 += vmem3[tid + 8];
vmem1[tid] = val1 += vmem1[tid + 4];
vmem2[tid] = val2 += vmem2[tid + 4];
vmem3[tid] = val3 += vmem3[tid + 4];
vmem1[tid] = val1 += vmem1[tid + 2];
vmem2[tid] = val2 += vmem2[tid + 2];
vmem3[tid] = val3 += vmem3[tid + 2];
vmem1[tid] = val1 += vmem1[tid + 1];
vmem2[tid] = val2 += vmem2[tid + 1];
vmem3[tid] = val3 += vmem3[tid + 1];
}
}
__device__ void reduce(float& val1, float& val2, float* smem1, float* smem2, int tid)
{
smem1[tid] = val1;
smem2[tid] = val2;
__syncthreads();
if (tid < 128)
{
smem1[tid] = val1 += smem1[tid + 128];
smem2[tid] = val2 += smem2[tid + 128];
}
__syncthreads();
if (tid < 64)
{
smem1[tid] = val1 += smem1[tid + 64];
smem2[tid] = val2 += smem2[tid + 64];
}
__syncthreads();
if (tid < 32)
{
volatile float* vmem1 = smem1;
volatile float* vmem2 = smem2;
vmem1[tid] = val1 += vmem1[tid + 32];
vmem2[tid] = val2 += vmem2[tid + 32];
vmem1[tid] = val1 += vmem1[tid + 16];
vmem2[tid] = val2 += vmem2[tid + 16];
vmem1[tid] = val1 += vmem1[tid + 8];
vmem2[tid] = val2 += vmem2[tid + 8];
vmem1[tid] = val1 += vmem1[tid + 4];
vmem2[tid] = val2 += vmem2[tid + 4];
vmem1[tid] = val1 += vmem1[tid + 2];
vmem2[tid] = val2 += vmem2[tid + 2];
vmem1[tid] = val1 += vmem1[tid + 1];
vmem2[tid] = val2 += vmem2[tid + 1];
}
}
#define SCALE (1.0f / (1 << 20))
template <int PATCH_X, int PATCH_Y, bool calcErr>
__global__ void lkSparse(const PtrStepb I, const PtrStepb J, const PtrStep<short> dIdx, const PtrStep<short> dIdy,
const float2* prevPts, float2* nextPts, uchar* status, float* err, const int level, const int rows, const int cols)
{
__shared__ float smem1[256];
__shared__ float smem2[256];
__shared__ float smem3[256];
const int tid = threadIdx.y * blockDim.x + threadIdx.x;
float2 prevPt = prevPts[blockIdx.x];
prevPt.x *= (1.0f / (1 << level));
prevPt.y *= (1.0f / (1 << level));
prevPt.x -= c_halfWin_x;
prevPt.y -= c_halfWin_y;
if (prevPt.x < -c_winSize_x || prevPt.x >= cols || prevPt.y < -c_winSize_y || prevPt.y >= rows)
{
if (level == 0 && tid == 0)
{
status[blockIdx.x] = 0;
if (calcErr)
err[blockIdx.x] = 0;
}
return;
}
// extract the patch from the first image, compute covariation matrix of derivatives
float A11 = 0;
float A12 = 0;
float A22 = 0;
int I_patch[PATCH_Y][PATCH_X];
int dIdx_patch[PATCH_Y][PATCH_X];
int dIdy_patch[PATCH_Y][PATCH_X];
for (int y = threadIdx.y, i = 0; y < c_winSize_y; y += blockDim.y, ++i)
{
for (int x = threadIdx.x, j = 0; x < c_winSize_x_cn; x += blockDim.x, ++j)
{
I_patch[i][j] = linearFilter(I, prevPt, x, y);
int ixval = linearFilter(dIdx, prevPt, x, y);
int iyval = linearFilter(dIdy, prevPt, x, y);
dIdx_patch[i][j] = ixval;
dIdy_patch[i][j] = iyval;
A11 += ixval * ixval;
A12 += ixval * iyval;
A22 += iyval * iyval;
}
}
reduce(A11, A12, A22, smem1, smem2, smem3, tid);
__syncthreads();
A11 = smem1[0];
A12 = smem2[0];
A22 = smem3[0];
A11 *= SCALE;
A12 *= SCALE;
A22 *= SCALE;
{
float D = A11 * A22 - A12 * A12;
float minEig = (A22 + A11 - ::sqrtf((A11 - A22) * (A11 - A22) + 4.f * A12 * A12)) / (2 * c_winSize_x * c_winSize_y);
if (calcErr && tid == 0)
err[blockIdx.x] = minEig;
if (minEig < c_minEigThreshold || D < numeric_limits<float>::epsilon())
{
if (level == 0 && tid == 0)
status[blockIdx.x] = 0;
return;
}
D = 1.f / D;
A11 *= D;
A12 *= D;
A22 *= D;
}
float2 nextPt = nextPts[blockIdx.x];
nextPt.x *= 2.f;
nextPt.y *= 2.f;
nextPt.x -= c_halfWin_x;
nextPt.y -= c_halfWin_y;
bool status_ = true;
for (int k = 0; k < c_iters; ++k)
{
if (nextPt.x < -c_winSize_x || nextPt.x >= cols || nextPt.y < -c_winSize_y || nextPt.y >= rows)
{
status_ = false;
break;
}
float b1 = 0;
float b2 = 0;
for (int y = threadIdx.y, i = 0; y < c_winSize_y; y += blockDim.y, ++i)
{
for (int x = threadIdx.x, j = 0; x < c_winSize_x_cn; x += blockDim.x, ++j)
{
int diff = linearFilter(J, nextPt, x, y) - I_patch[i][j];
b1 += diff * dIdx_patch[i][j];
b2 += diff * dIdy_patch[i][j];
}
}
reduce(b1, b2, smem1, smem2, tid);
__syncthreads();
b1 = smem1[0];
b2 = smem2[0];
b1 *= SCALE;
b2 *= SCALE;
float2 delta;
delta.x = A12 * b2 - A22 * b1;
delta.y = A12 * b1 - A11 * b2;
nextPt.x += delta.x;
nextPt.y += delta.y;
if (::fabs(delta.x) < 0.01f && ::fabs(delta.y) < 0.01f)
break;
}
if (tid == 0)
{
nextPt.x += c_halfWin_x;
nextPt.y += c_halfWin_y;
nextPts[blockIdx.x] = nextPt;
status[blockIdx.x] = status_;
}
}
template <int PATCH_X, int PATCH_Y>
void lkSparse_caller(DevMem2Db I, DevMem2Db J, DevMem2D_<short> dIdx, DevMem2D_<short> dIdy,
const float2* prevPts, float2* nextPts, uchar* status, float* err, int ptcount,
int level, dim3 block, cudaStream_t stream)
{
dim3 grid(ptcount);
if (err)
{
cudaSafeCall( cudaFuncSetCacheConfig(lkSparse<PATCH_X, PATCH_Y, true>, cudaFuncCachePreferL1) );
lkSparse<PATCH_X, PATCH_Y, true><<<grid, block>>>(I, J, dIdx, dIdy,
prevPts, nextPts, status, err, level, I.rows, I.cols);
}
else
{
cudaSafeCall( cudaFuncSetCacheConfig(lkSparse<PATCH_X, PATCH_Y, false>, cudaFuncCachePreferL1) );
lkSparse<PATCH_X, PATCH_Y, false><<<grid, block>>>(I, J, dIdx, dIdy,
prevPts, nextPts, status, err, level, I.rows, I.cols);
}
cudaSafeCall( cudaGetLastError() );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
void lkSparse_gpu(DevMem2Db I, DevMem2Db J, DevMem2D_<short> dIdx, DevMem2D_<short> dIdy,
const float2* prevPts, float2* nextPts, uchar* status, float* err, int ptcount,
int level, dim3 block, dim3 patch, cudaStream_t stream)
{
typedef void (*func_t)(DevMem2Db I, DevMem2Db J, DevMem2D_<short> dIdx, DevMem2D_<short> dIdy,
const float2* prevPts, float2* nextPts, uchar* status, float* err, int ptcount,
int level, dim3 block, cudaStream_t stream);
static const func_t funcs[5][5] =
{
{lkSparse_caller<1, 1>, lkSparse_caller<2, 1>, lkSparse_caller<3, 1>, lkSparse_caller<4, 1>, lkSparse_caller<5, 1>},
{lkSparse_caller<1, 2>, lkSparse_caller<2, 2>, lkSparse_caller<3, 2>, lkSparse_caller<4, 2>, lkSparse_caller<5, 2>},
{lkSparse_caller<1, 3>, lkSparse_caller<2, 3>, lkSparse_caller<3, 3>, lkSparse_caller<4, 3>, lkSparse_caller<5, 3>},
{lkSparse_caller<1, 4>, lkSparse_caller<2, 4>, lkSparse_caller<3, 4>, lkSparse_caller<4, 4>, lkSparse_caller<5, 4>},
{lkSparse_caller<1, 5>, lkSparse_caller<2, 5>, lkSparse_caller<3, 5>, lkSparse_caller<4, 5>, lkSparse_caller<5, 5>}
};
funcs[patch.y - 1][patch.x - 1](I, J, dIdx, dIdy,
prevPts, nextPts, status, err, ptcount,
level, block, stream);
}
template <bool calcErr>
__global__ void lkDense(const PtrStepb I, const PtrStepb J, const PtrStep<short> dIdx, const PtrStep<short> dIdy,
PtrStepf u, PtrStepf v, PtrStepf err, const int rows, const int cols)
{
const int x = blockIdx.x * blockDim.x + threadIdx.x;
const int y = blockIdx.y * blockDim.y + threadIdx.y;
if (x >= cols || y >= rows)
return;
// extract the patch from the first image, compute covariation matrix of derivatives
float A11 = 0;
float A12 = 0;
float A22 = 0;
for (int i = 0; i < c_winSize_y; ++i)
{
for (int j = 0; j < c_winSize_x; ++j)
{
int ixval = dIdx(y - c_halfWin_y + i, x - c_halfWin_x + j);
int iyval = dIdy(y - c_halfWin_y + i, x - c_halfWin_x + j);
A11 += ixval * ixval;
A12 += ixval * iyval;
A22 += iyval * iyval;
}
}
A11 *= SCALE;
A12 *= SCALE;
A22 *= SCALE;
{
float D = A11 * A22 - A12 * A12;
float minEig = (A22 + A11 - ::sqrtf((A11 - A22) * (A11 - A22) + 4.f * A12 * A12)) / (2 * c_winSize_x * c_winSize_y);
if (calcErr)
err(y, x) = minEig;
if (minEig < c_minEigThreshold || D < numeric_limits<float>::epsilon())
return;
D = 1.f / D;
A11 *= D;
A12 *= D;
A22 *= D;
}
float2 nextPt;
nextPt.x = x - c_halfWin_x + u(y, x);
nextPt.y = y - c_halfWin_y + v(y, x);
for (int k = 0; k < c_iters; ++k)
{
if (nextPt.x < -c_winSize_x || nextPt.x >= cols || nextPt.y < -c_winSize_y || nextPt.y >= rows)
break;
float b1 = 0;
float b2 = 0;
for (int i = 0; i < c_winSize_y; ++i)
{
for (int j = 0; j < c_winSize_x; ++j)
{
int I_val = I(y - c_halfWin_y + i, x - c_halfWin_x + j);
int diff = linearFilter(J, nextPt, j, i) - CV_DESCALE(I_val * (1 << W_BITS), W_BITS1 - 5);
b1 += diff * dIdx(y - c_halfWin_y + i, x - c_halfWin_x + j);
b2 += diff * dIdy(y - c_halfWin_y + i, x - c_halfWin_x + j);
}
}
b1 *= SCALE;
b2 *= SCALE;
float2 delta;
delta.x = A12 * b2 - A22 * b1;
delta.y = A12 * b1 - A11 * b2;
nextPt.x += delta.x;
nextPt.y += delta.y;
if (::fabs(delta.x) < 0.01f && ::fabs(delta.y) < 0.01f)
break;
}
u(y, x) = nextPt.x - x + c_halfWin_x;
v(y, x) = nextPt.y - y + c_halfWin_y;
}
void lkDense_gpu(DevMem2Db I, DevMem2Db J, DevMem2D_<short> dIdx, DevMem2D_<short> dIdy,
DevMem2Df u, DevMem2Df v, DevMem2Df* err, cudaStream_t stream)
{
dim3 block(32, 8);
dim3 grid(divUp(I.cols, block.x), divUp(I.rows, block.y));
if (err)
{
cudaSafeCall( cudaFuncSetCacheConfig(lkDense<true>, cudaFuncCachePreferL1) );
lkDense<true><<<grid, block, 0, stream>>>(I, J, dIdx, dIdy, u, v, *err, I.rows, I.cols);
cudaSafeCall( cudaGetLastError() );
}
else
{
cudaSafeCall( cudaFuncSetCacheConfig(lkDense<false>, cudaFuncCachePreferL1) );
lkDense<false><<<grid, block, 0, stream>>>(I, J, dIdx, dIdy, u, v, PtrStepf(), I.rows, I.cols);
cudaSafeCall( cudaGetLastError() );
}
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
}
}}}

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other GpuMaterials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or bpied warranties, including, but not limited to, the bpied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "precomp.hpp"
using namespace std;
using namespace cv;
using namespace cv::gpu;
#if !defined (HAVE_CUDA)
void cv::gpu::GoodFeaturesToTrackDetector_GPU::operator ()(const GpuMat&, GpuMat&, const GpuMat&) { throw_nogpu(); }
#else /* !defined (HAVE_CUDA) */
namespace cv { namespace gpu { namespace device
{
namespace gfft
{
int findCorners_gpu(DevMem2Df eig, float threshold, DevMem2Db mask, float2* corners, int max_count);
void sortCorners_gpu(DevMem2Df eig, float2* corners, int count);
}
}}}
void cv::gpu::GoodFeaturesToTrackDetector_GPU::operator ()(const GpuMat& image, GpuMat& corners, const GpuMat& mask)
{
using namespace cv::gpu::device::gfft;
CV_Assert(qualityLevel > 0 && minDistance >= 0 && maxCorners >= 0);
CV_Assert(mask.empty() || (mask.type() == CV_8UC1 && mask.size() == image.size()));
CV_Assert(TargetArchs::builtWith(GLOBAL_ATOMICS) && DeviceInfo().supports(GLOBAL_ATOMICS));
ensureSizeIsEnough(image.size(), CV_32F, eig_);
if (useHarrisDetector)
cornerHarris(image, eig_, Dx_, Dy_, buf_, blockSize, 3, harrisK);
else
cornerMinEigenVal(image, eig_, Dx_, Dy_, buf_, blockSize, 3);
double maxVal = 0;
minMax(eig_, 0, &maxVal, GpuMat(), minMaxbuf_);
ensureSizeIsEnough(1, std::max(1000, static_cast<int>(image.size().area() * 0.05)), CV_32FC2, tmpCorners_);
int total = findCorners_gpu(eig_, static_cast<float>(maxVal * qualityLevel), mask, tmpCorners_.ptr<float2>(), tmpCorners_.cols);
sortCorners_gpu(eig_, tmpCorners_.ptr<float2>(), total);
if (minDistance < 1)
tmpCorners_.colRange(0, maxCorners > 0 ? std::min(maxCorners, total) : total).copyTo(corners);
else
{
vector<Point2f> tmp(total);
Mat tmpMat(1, total, CV_32FC2, (void*)&tmp[0]);
tmpCorners_.colRange(0, total).download(tmpMat);
vector<Point2f> tmp2;
tmp2.reserve(total);
const int cell_size = cvRound(minDistance);
const int grid_width = (image.cols + cell_size - 1) / cell_size;
const int grid_height = (image.rows + cell_size - 1) / cell_size;
std::vector< std::vector<Point2f> > grid(grid_width * grid_height);
for (int i = 0; i < total; ++i)
{
Point2f p = tmp[i];
bool good = true;
int x_cell = static_cast<int>(p.x / cell_size);
int y_cell = static_cast<int>(p.y / cell_size);
int x1 = x_cell - 1;
int y1 = y_cell - 1;
int x2 = x_cell + 1;
int y2 = y_cell + 1;
// boundary check
x1 = std::max(0, x1);
y1 = std::max(0, y1);
x2 = std::min(grid_width - 1, x2);
y2 = std::min(grid_height - 1, y2);
for (int yy = y1; yy <= y2; yy++)
{
for (int xx = x1; xx <= x2; xx++)
{
vector<Point2f>& m = grid[yy * grid_width + xx];
if (!m.empty())
{
for(int j = 0; j < m.size(); j++)
{
float dx = p.x - m[j].x;
float dy = p.y - m[j].y;
if (dx * dx + dy * dy < minDistance * minDistance)
{
good = false;
goto break_out;
}
}
}
}
}
break_out:
if(good)
{
grid[y_cell * grid_width + x_cell].push_back(p);
tmp2.push_back(p);
if (maxCorners > 0 && tmp2.size() == maxCorners)
break;
}
}
corners.upload(Mat(1, tmp2.size(), CV_32FC2, &tmp2[0]));
}
}
#endif /* !defined (HAVE_CUDA) */

295
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other GpuMaterials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or bpied warranties, including, but not limited to, the bpied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "precomp.hpp"
using namespace std;
using namespace cv;
using namespace cv::gpu;
#if !defined (HAVE_CUDA)
void cv::gpu::PyrLKOpticalFlow::sparse(const GpuMat&, const GpuMat&, const GpuMat&, GpuMat&, GpuMat&, GpuMat*) { throw_nogpu(); }
void cv::gpu::PyrLKOpticalFlow::dense(const GpuMat&, const GpuMat&, GpuMat&, GpuMat&, GpuMat*) { throw_nogpu(); }
#else /* !defined (HAVE_CUDA) */
namespace cv { namespace gpu { namespace device
{
namespace pyrlk
{
void loadConstants(int cn, float minEigThreshold, int2 winSize, int iters);
void calcSharrDeriv_gpu(DevMem2Db src, DevMem2D_<short> dx_buf, DevMem2D_<short> dy_buf, DevMem2D_<short> dIdx, DevMem2D_<short> dIdy, int cn,
cudaStream_t stream = 0);
void lkSparse_gpu(DevMem2Db I, DevMem2Db J, DevMem2D_<short> dIdx, DevMem2D_<short> dIdy,
const float2* prevPts, float2* nextPts, uchar* status, float* err, int ptcount,
int level, dim3 block, dim3 patch, cudaStream_t stream = 0);
void lkDense_gpu(DevMem2Db I, DevMem2Db J, DevMem2D_<short> dIdx, DevMem2D_<short> dIdy,
DevMem2Df u, DevMem2Df v, DevMem2Df* err, cudaStream_t stream = 0);
}
}}}
void cv::gpu::PyrLKOpticalFlow::calcSharrDeriv(const GpuMat& src, GpuMat& dIdx, GpuMat& dIdy)
{
using namespace cv::gpu::device::pyrlk;
CV_Assert(src.rows > 1 && src.cols > 1);
CV_Assert(src.depth() == CV_8U);
const int cn = src.channels();
ensureSizeIsEnough(src.size(), CV_MAKETYPE(CV_16S, cn), dx_calcBuf_);
ensureSizeIsEnough(src.size(), CV_MAKETYPE(CV_16S, cn), dy_calcBuf_);
const int colsn = src.cols * cn;
calcSharrDeriv_gpu(src, dx_calcBuf_, dy_calcBuf_, dIdx, dIdy, cn);
}
void cv::gpu::PyrLKOpticalFlow::buildImagePyramid(const GpuMat& img0, vector<GpuMat>& pyr, bool withBorder)
{
pyr.resize(maxLevel + 1);
Size sz = img0.size();
for (int level = 0; level <= maxLevel; ++level)
{
GpuMat temp;
if (withBorder)
{
temp.create(sz.height + winSize.height * 2, sz.width + winSize.width * 2, img0.type());
pyr[level] = temp(Rect(winSize.width, winSize.height, sz.width, sz.height));
}
else
{
ensureSizeIsEnough(sz, img0.type(), pyr[level]);
}
if (level == 0)
img0.copyTo(pyr[level]);
else
pyrDown(pyr[level - 1], pyr[level]);
if (withBorder)
copyMakeBorder(pyr[level], temp, winSize.height, winSize.height, winSize.width, winSize.width, BORDER_REFLECT_101);
sz = Size((sz.width + 1) / 2, (sz.height + 1) / 2);
if (sz.width <= winSize.width || sz.height <= winSize.height)
{
maxLevel = level;
break;
}
}
}
void cv::gpu::PyrLKOpticalFlow::sparse(const GpuMat& prevImg, const GpuMat& nextImg, const GpuMat& prevPts, GpuMat& nextPts, GpuMat& status, GpuMat* err)
{
using namespace cv::gpu::device::pyrlk;
if (prevPts.empty())
{
nextPts.release();
status.release();
if (err) err->release();
return;
}
derivLambda = std::min(std::max(derivLambda, 0.0), 1.0);
iters = std::min(std::max(iters, 0), 100);
const int cn = prevImg.channels();
dim3 block;
if (winSize.width * cn > 32)
{
block.x = 32;
block.y = 8;
}
else
{
block.x = block.y = 16;
}
dim3 patch((winSize.width * cn + block.x - 1) / block.x, (winSize.height + block.y - 1) / block.y);
CV_Assert(derivLambda >= 0);
CV_Assert(maxLevel >= 0 && winSize.width > 2 && winSize.height > 2);
CV_Assert(prevImg.size() == nextImg.size() && prevImg.type() == nextImg.type());
CV_Assert(patch.x > 0 && patch.x < 6 && patch.y > 0 && patch.y < 6);
CV_Assert(prevPts.rows == 1 && prevPts.type() == CV_32FC2);
if (useInitialFlow)
CV_Assert(nextPts.size() == prevPts.size() && nextPts.type() == CV_32FC2);
else
ensureSizeIsEnough(1, prevPts.cols, prevPts.type(), nextPts);
GpuMat temp1 = (useInitialFlow ? nextPts : prevPts).reshape(1);
GpuMat temp2 = nextPts.reshape(1);
multiply(temp1, Scalar::all(1.0 / (1 << maxLevel) / 2.0), temp2);
ensureSizeIsEnough(1, prevPts.cols, CV_8UC1, status);
status.setTo(Scalar::all(1));
if (err)
ensureSizeIsEnough(1, prevPts.cols, CV_32FC1, *err);
// build the image pyramids.
// we pad each level with +/-winSize.{width|height}
// pixels to simplify the further patch extraction.
buildImagePyramid(prevImg, prevPyr_, true);
buildImagePyramid(nextImg, nextPyr_, true);
// dI/dx ~ Ix, dI/dy ~ Iy
ensureSizeIsEnough(prevImg.rows + winSize.height * 2, prevImg.cols + winSize.width * 2, CV_MAKETYPE(CV_16S, cn), dx_buf_);
ensureSizeIsEnough(prevImg.rows + winSize.height * 2, prevImg.cols + winSize.width * 2, CV_MAKETYPE(CV_16S, cn), dy_buf_);
loadConstants(cn, minEigThreshold, make_int2(winSize.width, winSize.height), iters);
for (int level = maxLevel; level >= 0; level--)
{
Size imgSize = prevPyr_[level].size();
GpuMat dxWhole(imgSize.height + winSize.height * 2, imgSize.width + winSize.width * 2, dx_buf_.type(), dx_buf_.data, dx_buf_.step);
GpuMat dyWhole(imgSize.height + winSize.height * 2, imgSize.width + winSize.width * 2, dy_buf_.type(), dy_buf_.data, dy_buf_.step);
dxWhole.setTo(Scalar::all(0));
dyWhole.setTo(Scalar::all(0));
GpuMat dIdx = dxWhole(Rect(winSize.width, winSize.height, imgSize.width, imgSize.height));
GpuMat dIdy = dyWhole(Rect(winSize.width, winSize.height, imgSize.width, imgSize.height));
calcSharrDeriv(prevPyr_[level], dIdx, dIdy);
lkSparse_gpu(prevPyr_[level], nextPyr_[level], dIdx, dIdy,
prevPts.ptr<float2>(), nextPts.ptr<float2>(), status.ptr(), level == 0 && err ? err->ptr<float>() : 0, prevPts.cols,
level, block, patch);
}
}
void cv::gpu::PyrLKOpticalFlow::dense(const GpuMat& prevImg, const GpuMat& nextImg, GpuMat& u, GpuMat& v, GpuMat* err)
{
using namespace cv::gpu::device::pyrlk;
derivLambda = std::min(std::max(derivLambda, 0.0), 1.0);
iters = std::min(std::max(iters, 0), 100);
CV_Assert(prevImg.type() == CV_8UC1);
CV_Assert(prevImg.size() == nextImg.size() && prevImg.type() == nextImg.type());
CV_Assert(derivLambda >= 0);
CV_Assert(maxLevel >= 0 && winSize.width > 2 && winSize.height > 2);
if (useInitialFlow)
{
CV_Assert(u.size() == prevImg.size() && u.type() == CV_32FC1);
CV_Assert(v.size() == prevImg.size() && v.type() == CV_32FC1);
}
else
{
u.create(prevImg.size(), CV_32FC1);
v.create(prevImg.size(), CV_32FC1);
u.setTo(Scalar::all(0));
v.setTo(Scalar::all(0));
}
if (err)
err->create(prevImg.size(), CV_32FC1);
// build the image pyramids.
// we pad each level with +/-winSize.{width|height}
// pixels to simplify the further patch extraction.
buildImagePyramid(prevImg, prevPyr_, true);
buildImagePyramid(nextImg, nextPyr_, true);
buildImagePyramid(u, uPyr_, false);
buildImagePyramid(v, vPyr_, false);
// dI/dx ~ Ix, dI/dy ~ Iy
ensureSizeIsEnough(prevImg.rows + winSize.height * 2, prevImg.cols + winSize.width * 2, CV_16SC1, dx_buf_);
ensureSizeIsEnough(prevImg.rows + winSize.height * 2, prevImg.cols + winSize.width * 2, CV_16SC1, dy_buf_);
loadConstants(1, minEigThreshold, make_int2(winSize.width, winSize.height), iters);
DevMem2Df derr = err ? *err : DevMem2Df();
for (int level = maxLevel; level >= 0; level--)
{
Size imgSize = prevPyr_[level].size();
GpuMat dxWhole(imgSize.height + winSize.height * 2, imgSize.width + winSize.width * 2, dx_buf_.type(), dx_buf_.data, dx_buf_.step);
GpuMat dyWhole(imgSize.height + winSize.height * 2, imgSize.width + winSize.width * 2, dy_buf_.type(), dy_buf_.data, dy_buf_.step);
dxWhole.setTo(Scalar::all(0));
dyWhole.setTo(Scalar::all(0));
GpuMat dIdx = dxWhole(Rect(winSize.width, winSize.height, imgSize.width, imgSize.height));
GpuMat dIdy = dyWhole(Rect(winSize.width, winSize.height, imgSize.width, imgSize.height));
calcSharrDeriv(prevPyr_[level], dIdx, dIdy);
lkDense_gpu(prevPyr_[level], nextPyr_[level], dIdx, dIdy, uPyr_[level], vPyr_[level],
level == 0 && err ? &derr : 0);
if (level == 0)
{
uPyr_[0].copyTo(u);
vPyr_[0].copyTo(v);
}
else
{
pyrUp(uPyr_[level], uPyr_[level - 1]);
pyrUp(vPyr_[level], vPyr_[level - 1]);
multiply(uPyr_[level - 1], Scalar::all(2), uPyr_[level - 1]);
multiply(vPyr_[level - 1], Scalar::all(2), vPyr_[level - 1]);
}
}
}
#endif /* !defined (HAVE_CUDA) */

1
modules/gpu/test/test_precomp.hpp
View File

@ -55,6 +55,7 @@
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/calib3d/calib3d.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/video/video.hpp"
#include "opencv2/ts/ts.hpp"
#include "opencv2/ts/ts_perf.hpp"
#include "opencv2/gpu/gpu.hpp"

170
modules/gpu/test/test_video.cpp
View File

@ -254,4 +254,172 @@ TEST_P(InterpolateFrames, Regression)
INSTANTIATE_TEST_CASE_P(Video, InterpolateFrames, ALL_DEVICES);
#endif
/////////////////////////////////////////////////////////////////////////////////////////////////
// GoodFeaturesToTrack
PARAM_TEST_CASE(GoodFeaturesToTrack, cv::gpu::DeviceInfo, double)
{
cv::gpu::DeviceInfo devInfo;
cv::Mat image;
int maxCorners;
double qualityLevel;
double minDistance;
std::vector<cv::Point2f> pts_gold;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
minDistance = GET_PARAM(1);
cv::gpu::setDevice(devInfo.deviceID());
image = readImage("opticalflow/frame0.png", cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(image.empty());
maxCorners = 1000;
qualityLevel= 0.01;
cv::goodFeaturesToTrack(image, pts_gold, maxCorners, qualityLevel, minDistance);
}
};
TEST_P(GoodFeaturesToTrack, Accuracy)
{
cv::gpu::GoodFeaturesToTrackDetector_GPU detector(maxCorners, qualityLevel, minDistance);
cv::gpu::GpuMat d_pts;
detector(loadMat(image), d_pts);
std::vector<cv::Point2f> pts(d_pts.cols);
cv::Mat pts_mat(1, d_pts.cols, CV_32FC2, (void*)&pts[0]);
d_pts.download(pts_mat);
ASSERT_EQ(pts_gold.size(), pts.size());
size_t mistmatch = 0;
for (size_t i = 0; i < pts.size(); ++i)
{
cv::Point2i a = pts_gold[i];
cv::Point2i b = pts[i];
bool eq = std::abs(a.x - b.x) < 1 && std::abs(a.y - b.y) < 1;
if (!eq)
++mistmatch;
}
double bad_ratio = static_cast<double>(mistmatch) / pts.size();
ASSERT_LE(bad_ratio, 0.01);
}
INSTANTIATE_TEST_CASE_P(Video, GoodFeaturesToTrack, Combine(ALL_DEVICES, Values(0.0, 3.0)));
/////////////////////////////////////////////////////////////////////////////////////////////////
// PyrLKOpticalFlow
PARAM_TEST_CASE(PyrLKOpticalFlowSparse, cv::gpu::DeviceInfo, bool)
{
cv::gpu::DeviceInfo devInfo;
cv::Mat frame0;
cv::Mat frame1;
std::vector<cv::Point2f> pts;
std::vector<cv::Point2f> nextPts_gold;
std::vector<unsigned char> status_gold;
std::vector<float> err_gold;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
bool useGray = GET_PARAM(1);
cv::gpu::setDevice(devInfo.deviceID());
frame0 = readImage("opticalflow/frame0.png", useGray ? cv::IMREAD_GRAYSCALE : cv::IMREAD_COLOR);
ASSERT_FALSE(frame0.empty());
frame1 = readImage("opticalflow/frame1.png", useGray ? cv::IMREAD_GRAYSCALE : cv::IMREAD_COLOR);
ASSERT_FALSE(frame1.empty());
cv::Mat gray_frame;
if (useGray)
gray_frame = frame0;
else
cv::cvtColor(frame0, gray_frame, cv::COLOR_BGR2GRAY);
cv::goodFeaturesToTrack(gray_frame, pts, 1000, 0.01, 0.0);
cv::calcOpticalFlowPyrLK(frame0, frame1, pts, nextPts_gold, status_gold, err_gold, cv::Size(21, 21), 3,
cv::TermCriteria(cv::TermCriteria::COUNT + cv::TermCriteria::EPS, 30, 0.01), 0.5, CV_LKFLOW_GET_MIN_EIGENVALS);
}
};
TEST_P(PyrLKOpticalFlowSparse, Accuracy)
{
cv::gpu::PyrLKOpticalFlow d_pyrLK;
cv::gpu::GpuMat d_pts;
cv::Mat pts_mat(1, pts.size(), CV_32FC2, (void*)&pts[0]);
d_pts.upload(pts_mat);
cv::gpu::GpuMat d_nextPts;
cv::gpu::GpuMat d_status;
cv::gpu::GpuMat d_err;
d_pyrLK.sparse(loadMat(frame0), loadMat(frame1), d_pts, d_nextPts, d_status, &d_err);
std::vector<cv::Point2f> nextPts(d_nextPts.cols);
cv::Mat nextPts_mat(1, d_nextPts.cols, CV_32FC2, (void*)&nextPts[0]);
d_nextPts.download(nextPts_mat);
std::vector<unsigned char> status(d_status.cols);
cv::Mat status_mat(1, d_status.cols, CV_8UC1, (void*)&status[0]);
d_status.download(status_mat);
std::vector<float> err(d_err.cols);
cv::Mat err_mat(1, d_err.cols, CV_32FC1, (void*)&err[0]);
d_err.download(err_mat);
ASSERT_EQ(nextPts_gold.size(), nextPts.size());
ASSERT_EQ(status_gold.size(), status.size());
ASSERT_EQ(err_gold.size(), err.size());
size_t mistmatch = 0;
for (size_t i = 0; i < nextPts.size(); ++i)
{
if (status[i] != status_gold[i])
{
++mistmatch;
continue;
}
if (status[i])
{
cv::Point2i a = nextPts[i];
cv::Point2i b = nextPts_gold[i];
bool eq = std::abs(a.x - b.x) < 1 && std::abs(a.y - b.y) < 1;
float errdiff = std::abs(err[i] - err_gold[i]);
if (!eq || errdiff > 1e-4)
++mistmatch;
}
}
double bad_ratio = static_cast<double>(mistmatch) / nextPts.size();
ASSERT_LE(bad_ratio, 0.01);
}
INSTANTIATE_TEST_CASE_P(Video, PyrLKOpticalFlowSparse, Combine(ALL_DEVICES, Bool()));
#endif // HAVE_CUDA

2
samples/gpu/optical_flow.cpp → samples/gpu/brox_optical_flow.cpp
View File

@ -40,7 +40,7 @@ int main(int argc, const char* argv[])
if (cmd.get<bool>("help"))
{
cout << "Usage: optical_flow [options]" << endl;
cout << "Usage: brox_optical_flow [options]" << endl;
cout << "Avaible options:" << endl;
cmd.printParams();
return 0;

74
samples/gpu/performance/tests.cpp
View File

@ -2,6 +2,7 @@
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/calib3d/calib3d.hpp"
#include "opencv2/video/video.hpp"
#include "opencv2/gpu/gpu.hpp"
#include "performance.h"
@ -1109,3 +1110,76 @@ TEST(gemm)
GPU_OFF;
}
}
TEST(GoodFeaturesToTrack)
{
Mat src = imread(abspath("aloeL.jpg"), IMREAD_GRAYSCALE);
if (src.empty()) throw runtime_error("can't open aloeL.jpg");
vector<Point2f> pts;
goodFeaturesToTrack(src, pts, 8000, 0.01, 0.0);
CPU_ON;
goodFeaturesToTrack(src, pts, 8000, 0.01, 0.0);
CPU_OFF;
gpu::GoodFeaturesToTrackDetector_GPU detector(8000, 0.01, 0.0);
gpu::GpuMat d_src(src);
gpu::GpuMat d_pts;
detector(d_src, d_pts);
GPU_ON;
detector(d_src, d_pts);
GPU_OFF;
}
TEST(PyrLKOpticalFlow)
{
Mat frame0 = imread(abspath("rubberwhale1.png"));
if (frame0.empty()) throw runtime_error("can't open rubberwhale1.png");
Mat frame1 = imread(abspath("rubberwhale2.png"));
if (frame1.empty()) throw runtime_error("can't open rubberwhale2.png");
Mat gray_frame;
cvtColor(frame0, gray_frame, COLOR_BGR2GRAY);
for (int points = 1000; points <= 8000; points *= 2)
{
SUBTEST << points;
vector<Point2f> pts;
goodFeaturesToTrack(gray_frame, pts, points, 0.01, 0.0);
vector<Point2f> nextPts;
vector<unsigned char> status;
calcOpticalFlowPyrLK(frame0, frame1, pts, nextPts, status, noArray());
CPU_ON;
calcOpticalFlowPyrLK(frame0, frame1, pts, nextPts, status, noArray());
CPU_OFF;
gpu::PyrLKOpticalFlow d_pyrLK;
gpu::GpuMat d_frame0(frame0);
gpu::GpuMat d_frame1(frame1);
gpu::GpuMat d_pts;
Mat pts_mat(1, pts.size(), CV_32FC2, (void*)&pts[0]);
d_pts.upload(pts_mat);
gpu::GpuMat d_nextPts;
gpu::GpuMat d_status;
gpu::GpuMat d_err;
d_pyrLK.sparse(d_frame0, d_frame1, d_pts, d_nextPts, d_status);
GPU_ON;
d_pyrLK.sparse(d_frame0, d_frame1, d_pts, d_nextPts, d_status);
GPU_OFF;
}
}

279
samples/gpu/pyrlk_optical_flow.cpp Normal file
View File

@ -0,0 +1,279 @@
#include <iostream>
#include <vector>
#include "cvconfig.h"
#include "opencv2/core/core.hpp"
#include "opencv2/core/opengl_interop.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/video/video.hpp"
#include "opencv2/gpu/gpu.hpp"
using namespace std;
using namespace cv;
using namespace cv::gpu;
void download(const GpuMat& d_mat, vector<Point2f>& vec)
{
vec.resize(d_mat.cols);
Mat mat(1, d_mat.cols, CV_32FC2, (void*)&vec[0]);
d_mat.download(mat);
}
void download(const GpuMat& d_mat, vector<uchar>& vec)
{
vec.resize(d_mat.cols);
Mat mat(1, d_mat.cols, CV_8UC1, (void*)&vec[0]);
d_mat.download(mat);
}
void drawArrows(Mat& frame, const vector<Point2f>& prevPts, const vector<Point2f>& nextPts, const vector<uchar>& status, Scalar line_color = Scalar(0, 0, 255))
{
for (size_t i = 0; i < prevPts.size(); ++i)
{
if (status[i])
{
int line_thickness = 1;
Point p = prevPts[i];
Point q = nextPts[i];
double angle = atan2((double) p.y - q.y, (double) p.x - q.x);
double hypotenuse = sqrt( (double)(p.y - q.y)*(p.y - q.y) + (double)(p.x - q.x)*(p.x - q.x) );
if (hypotenuse < 1.0)
continue;
// Here we lengthen the arrow by a factor of three.
q.x = (int) (p.x - 3 * hypotenuse * cos(angle));
q.y = (int) (p.y - 3 * hypotenuse * sin(angle));
// Now we draw the main line of the arrow.
line(frame, p, q, line_color, line_thickness);
// Now draw the tips of the arrow. I do some scaling so that the
// tips look proportional to the main line of the arrow.
p.x = (int) (q.x + 9 * cos(angle + CV_PI / 4));
p.y = (int) (q.y + 9 * sin(angle + CV_PI / 4));
line(frame, p, q, line_color, line_thickness);
p.x = (int) (q.x + 9 * cos(angle - CV_PI / 4));
p.y = (int) (q.y + 9 * sin(angle - CV_PI / 4));
line(frame, p, q, line_color, line_thickness);
}
}
}
#ifdef HAVE_OPENGL
struct DrawData
{
GlTexture tex;
GlArrays arr;
};
void drawCallback(void* userdata)
{
DrawData* data = static_cast<DrawData*>(userdata);
if (data->tex.empty() || data->arr.empty())
return;
static GlCamera camera;
static bool init_camera = true;
if (init_camera)
{
camera.setOrthoProjection(0.0, 1.0, 1.0, 0.0, 0.0, 1.0);
camera.lookAt(Point3d(0.0, 0.0, 1.0), Point3d(0.0, 0.0, 0.0), Point3d(0.0, 1.0, 0.0));
init_camera = false;
}
camera.setupProjectionMatrix();
camera.setupModelViewMatrix();
render(data->tex);
render(data->arr, RenderMode::TRIANGLES);
}
#endif
template <typename T> inline T clamp (T x, T a, T b)
{
return ((x) > (a) ? ((x) < (b) ? (x) : (b)) : (a));
}
template <typename T> inline T mapValue(T x, T a, T b, T c, T d)
{
x = clamp(x, a, b);
return c + (d - c) * (x - a) / (b - a);
}
void getFlowField(const Mat& u, const Mat& v, Mat& flowField)
{
float maxDisplacement = 1.0f;
for (int i = 0; i < u.rows; ++i)
{
const float* ptr_u = u.ptr<float>(i);
const float* ptr_v = v.ptr<float>(i);
for (int j = 0; j < u.cols; ++j)
{
float d = max(fabsf(ptr_u[j]), fabsf(ptr_v[j]));
if (d > maxDisplacement)
maxDisplacement = d;
}
}
flowField.create(u.size(), CV_8UC4);
for (int i = 0; i < flowField.rows; ++i)
{
const float* ptr_u = u.ptr<float>(i);
const float* ptr_v = v.ptr<float>(i);
Vec4b* row = flowField.ptr<Vec4b>(i);
for (int j = 0; j < flowField.cols; ++j)
{
row[j][0] = 0;
row[j][1] = static_cast<unsigned char> (mapValue (-ptr_v[j], -maxDisplacement, maxDisplacement, 0.0f, 255.0f));
row[j][2] = static_cast<unsigned char> (mapValue ( ptr_u[j], -maxDisplacement, maxDisplacement, 0.0f, 255.0f));
row[j][3] = 255;
}
}
}
int main(int argc, const char* argv[])
{
const char* keys =
"{ h | help | false | print help message }"
"{ l | left | | specify left image }"
"{ r | right | | specify right image }"
"{ g | gray | false | use grayscale sources [PyrLK Sparse] }"
"{ p | points | 4000 | specify points count [GoodFeatureToTrack] }";
CommandLineParser cmd(argc, argv, keys);
if (cmd.get<bool>("help"))
{
cout << "Usage: pyrlk_optical_flow [options]" << endl;
cout << "Avaible options:" << endl;
cmd.printParams();
return 0;
}
string fname0 = cmd.get<string>("left");
string fname1 = cmd.get<string>("right");
if (fname0.empty() || fname1.empty())
{
cerr << "Missing input file names" << endl;
return -1;
}
bool useGray = cmd.get<bool>("gray");
int points = cmd.get<int>("points");
Mat frame0 = imread(fname0);
Mat frame1 = imread(fname1);
if (frame0.empty() || frame1.empty())
{
cout << "Can't load input images" << endl;
return -1;
}
namedWindow("PyrLK [Sparse]", WINDOW_NORMAL);
namedWindow("PyrLK [Dense] Flow Field", WINDOW_NORMAL);
#ifdef HAVE_OPENGL
namedWindow("PyrLK [Dense]", WINDOW_OPENGL);
setGlDevice();
#endif
cout << "Image size : " << frame0.cols << " x " << frame0.rows << endl;
cout << "Points count : " << points << endl;
cout << endl;
Mat frame0Gray;
cvtColor(frame0, frame0Gray, COLOR_BGR2GRAY);
Mat frame1Gray;
cvtColor(frame1, frame1Gray, COLOR_BGR2GRAY);
// goodFeaturesToTrack
GoodFeaturesToTrackDetector_GPU detector(points, 0.01, 0.0);
GpuMat d_frame0Gray(frame0Gray);
GpuMat d_prevPts;
detector(d_frame0Gray, d_prevPts);
// Sparse
PyrLKOpticalFlow d_pyrLK;
GpuMat d_frame0(frame0);
GpuMat d_frame1(frame1);
GpuMat d_frame1Gray(frame1Gray);
GpuMat d_nextPts;
GpuMat d_status;
d_pyrLK.sparse(useGray ? d_frame0Gray : d_frame0, useGray ? d_frame1Gray : d_frame1, d_prevPts, d_nextPts, d_status);
// Draw arrows
vector<Point2f> prevPts(d_prevPts.cols);
download(d_prevPts, prevPts);
vector<Point2f> nextPts(d_nextPts.cols);
download(d_nextPts, nextPts);
vector<uchar> status(d_status.cols);
download(d_status, status);
drawArrows(frame0, prevPts, nextPts, status, Scalar(255, 0, 0));
imshow("PyrLK [Sparse]", frame0);
// Dense
GpuMat d_u;
GpuMat d_v;
d_pyrLK.dense(d_frame0Gray, d_frame1Gray, d_u, d_v);
// Draw flow field
Mat flowField;
getFlowField(Mat(d_u), Mat(d_v), flowField);
imshow("PyrLK [Dense] Flow Field", flowField);
#ifdef HAVE_OPENGL
setOpenGlContext("PyrLK [Dense]");
GpuMat d_vertex, d_colors;
createOpticalFlowNeedleMap(d_u, d_v, d_vertex, d_colors);
DrawData drawData;
drawData.tex.copyFrom(d_frame0Gray);
drawData.arr.setVertexArray(d_vertex);
drawData.arr.setColorArray(d_colors, false);
setOpenGlDrawCallback("PyrLK [Dense]", drawCallback, &drawData);
#endif
waitKey();
return 0;
}