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merged 2.4 into trunk

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This commit is contained in:
Vadim Pisarevsky
2012-04-30 14:33:52 +00:00
parent 3f1c6d7357
commit d5a0088bbe
194 changed files with 10158 additions and 8225 deletions
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6
modules/gpu/include/opencv2/gpu/gpu.hpp
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@@ -1750,6 +1750,7 @@ public:
useInitialFlow = false;
minEigThreshold = 1e-4f;
getMinEigenVals = false;
isDeviceArch11_ = !DeviceInfo().supports(FEATURE_SET_COMPUTE_12);
}
void sparse(const GpuMat& prevImg, const GpuMat& nextImg, const GpuMat& prevPts, GpuMat& nextPts,
@@ -1796,6 +1797,8 @@ private:
vector<GpuMat> uPyr_;
vector<GpuMat> vPyr_;
bool isDeviceArch11_;
};
@@ -1812,6 +1815,7 @@ public:
polyN = 5;
polySigma = 1.1;
flags = 0;
isDeviceArch11_ = !DeviceInfo().supports(FEATURE_SET_COMPUTE_12);
}
int numLevels;
@@ -1859,6 +1863,8 @@ private:
GpuMat frames_[2];
GpuMat pyrLevel_[2], M_, bufM_, R_[2], blurredFrame_[2];
std::vector<GpuMat> pyramid0_, pyramid1_;
bool isDeviceArch11_;
};

43
modules/gpu/src/cuda/optical_flow_farneback.cu
View File

@@ -433,6 +433,25 @@ namespace cv { namespace gpu { namespace device { namespace optflow_farneback
}
void boxFilter5Gpu_CC11(const DevMem2Df src, int ksizeHalf, DevMem2Df dst, cudaStream_t stream)
{
int height = src.rows / 5;
int width = src.cols;
dim3 block(128);
dim3 grid(divUp(width, block.x), divUp(height, block.y));
int smem = (block.x + 2*ksizeHalf) * 5 * block.y * sizeof(float);
float boxAreaInv = 1.f / ((1 + 2*ksizeHalf) * (1 + 2*ksizeHalf));
boxFilter5<<<grid, block, smem, stream>>>(height, width, src, ksizeHalf, boxAreaInv, dst);
cudaSafeCall(cudaGetLastError());
if (stream == 0)
cudaSafeCall(cudaDeviceSynchronize());
}
__constant__ float c_gKer[MAX_KSIZE_HALF + 1];
template <typename Border>
@@ -575,14 +594,14 @@ namespace cv { namespace gpu { namespace device { namespace optflow_farneback
}
template <typename Border>
template <typename Border, int blockDimX>
void gaussianBlur5Caller(
const DevMem2Df src, int ksizeHalf, DevMem2Df dst, cudaStream_t stream)
{
int height = src.rows / 5;
int width = src.cols;
dim3 block(256);
dim3 block(blockDimX);
dim3 grid(divUp(width, block.x), divUp(height, block.y));
int smem = (block.x + 2*ksizeHalf) * 5 * block.y * sizeof(float);
Border b(height, width);
@@ -603,12 +622,26 @@ namespace cv { namespace gpu { namespace device { namespace optflow_farneback
static const caller_t callers[] =
{
gaussianBlur5Caller<BrdReflect101<float> >,
gaussianBlur5Caller<BrdReplicate<float> >,
gaussianBlur5Caller<BrdReflect101<float>,256>,
gaussianBlur5Caller<BrdReplicate<float>,256>,
};
callers[borderMode](src, ksizeHalf, dst, stream);
}
}
void gaussianBlur5Gpu_CC11(
const DevMem2Df src, int ksizeHalf, DevMem2Df dst, int borderMode, cudaStream_t stream)
{
typedef void (*caller_t)(const DevMem2Df, int, DevMem2Df, cudaStream_t);
static const caller_t callers[] =
{
gaussianBlur5Caller<BrdReflect101<float>,128>,
gaussianBlur5Caller<BrdReplicate<float>,128>,
};
callers[borderMode](src, ksizeHalf, dst, stream);
}
}}}} // namespace cv { namespace gpu { namespace device { namespace optflow_farneback

12
modules/gpu/src/cuda/pyrlk.cu
View File

@@ -181,6 +181,7 @@ namespace cv { namespace gpu { namespace device
smem3[tid] = val3;
__syncthreads();
#if __CUDA_ARCH__ > 110
if (tid < 128)
{
smem1[tid] = val1 += smem1[tid + 128];
@@ -188,6 +189,7 @@ namespace cv { namespace gpu { namespace device
smem3[tid] = val3 += smem3[tid + 128];
}
__syncthreads();
#endif
if (tid < 64)
{
@@ -235,12 +237,14 @@ namespace cv { namespace gpu { namespace device
smem2[tid] = val2;
__syncthreads();
#if __CUDA_ARCH__ > 110
if (tid < 128)
{
smem1[tid] = val1 += smem1[tid + 128];
smem2[tid] = val2 += smem2[tid + 128];
}
__syncthreads();
#endif
if (tid < 64)
{
@@ -279,11 +283,13 @@ namespace cv { namespace gpu { namespace device
smem1[tid] = val1;
__syncthreads();
#if __CUDA_ARCH__ > 110
if (tid < 128)
{
smem1[tid] = val1 += smem1[tid + 128];
}
__syncthreads();
#endif
if (tid < 64)
{
@@ -310,9 +316,15 @@ namespace cv { namespace gpu { namespace device
__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)
{
#if __CUDA_ARCH__ <= 110
__shared__ float smem1[128];
__shared__ float smem2[128];
__shared__ float smem3[128];
#else
__shared__ float smem1[256];
__shared__ float smem2[256];
__shared__ float smem3[256];
#endif
const int tid = threadIdx.y * blockDim.x + threadIdx.x;

26
modules/gpu/src/nvidia/NCVBroxOpticalFlow.cu
View File

@@ -172,11 +172,11 @@ static void add(float *res, const float *rhs, const int count, cudaStream_t stre
///////////////////////////////////////////////////////////////////////////////
__global__ void scaleVector(float *d_res, const float *d_src, float scale, const int len)
{
const int pos = blockIdx.x * blockDim.x + threadIdx.x;
if (pos >= len) return;
d_res[pos] = d_src[pos] * scale;
const int pos = blockIdx.x * blockDim.x + threadIdx.x;
if (pos >= len) return;
d_res[pos] = d_src[pos] * scale;
}
///////////////////////////////////////////////////////////////////////////////
@@ -191,10 +191,10 @@ __global__ void scaleVector(float *d_res, const float *d_src, float scale, const
///////////////////////////////////////////////////////////////////////////////
static void ScaleVector(float *d_res, const float *d_src, float scale, const int len, cudaStream_t stream)
{
dim3 threads(256);
dim3 blocks(iDivUp(len, threads.x));
scaleVector<<<blocks, threads, 0, stream>>>(d_res, d_src, scale, len);
dim3 threads(256);
dim3 blocks(iDivUp(len, threads.x));
scaleVector<<<blocks, threads, 0, stream>>>(d_res, d_src, scale, len);
}
const int SOR_TILE_WIDTH = 32;
@@ -1128,14 +1128,14 @@ NCVStatus NCVBroxOpticalFlow(const NCVBroxOpticalFlowDescriptor desc,
ncvAssertReturnNcvStat( nppiStResize_32f_C1R (ptrU->ptr(), srcSize, kLevelStride * sizeof (float), srcROI,
ptrUNew->ptr(), dstSize, ns * sizeof (float), dstROI, 1.0f/scale_factor, 1.0f/scale_factor, nppStBicubic) );
ScaleVector(ptrUNew->ptr(), ptrUNew->ptr(), 1.0f/scale_factor, ns * nh, stream);
ScaleVector(ptrUNew->ptr(), ptrUNew->ptr(), 1.0f/scale_factor, ns * nh, stream);
ncvAssertCUDALastErrorReturn(NCV_CUDA_ERROR);
ncvAssertReturnNcvStat( nppiStResize_32f_C1R (ptrV->ptr(), srcSize, kLevelStride * sizeof (float), srcROI,
ptrVNew->ptr(), dstSize, ns * sizeof (float), dstROI, 1.0f/scale_factor, 1.0f/scale_factor, nppStBicubic) );
ScaleVector(ptrVNew->ptr(), ptrVNew->ptr(), 1.0f/scale_factor, ns * nh, stream);
ScaleVector(ptrVNew->ptr(), ptrVNew->ptr(), 1.0f/scale_factor, ns * nh, stream);
ncvAssertCUDALastErrorReturn(NCV_CUDA_ERROR);
cv::gpu::device::swap<FloatVector*>(ptrU, ptrUNew);

2
modules/gpu/src/nvidia/NPP_staging/NPP_staging.cu
View File

@@ -2508,7 +2508,7 @@ __global__ void resizeBicubic(NcvSize32u srcSize,
wsum += wx;
}
}
dst[(ix + dstROI.x)+ (iy + dstROI.y) * dstStep] = sum / wsum;
dst[(ix + dstROI.x)+ (iy + dstROI.y) * dstStep] = (!wsum)? 0 : sum / wsum;
}

18
modules/gpu/src/optical_flow_farneback.cpp
View File

@@ -81,6 +81,8 @@ namespace cv { namespace gpu { namespace device { namespace optflow_farneback
void boxFilter5Gpu(const DevMem2Df src, int ksizeHalf, DevMem2Df dst, cudaStream_t stream);
void boxFilter5Gpu_CC11(const DevMem2Df src, int ksizeHalf, DevMem2Df dst, cudaStream_t stream);
void setGaussianBlurKernel(const float *gKer, int ksizeHalf);
void gaussianBlurGpu(
@@ -89,6 +91,9 @@ namespace cv { namespace gpu { namespace device { namespace optflow_farneback
void gaussianBlur5Gpu(
const DevMem2Df src, int ksizeHalf, DevMem2Df dst, int borderType, cudaStream_t stream);
void gaussianBlur5Gpu_CC11(
const DevMem2Df src, int ksizeHalf, DevMem2Df dst, int borderType, cudaStream_t stream);
}}}} // namespace cv { namespace gpu { namespace device { namespace optflow_farneback
@@ -167,7 +172,10 @@ void cv::gpu::FarnebackOpticalFlow::updateFlow_boxFilter(
const GpuMat& R0, const GpuMat& R1, GpuMat& flowx, GpuMat &flowy,
GpuMat& M, GpuMat &bufM, int blockSize, bool updateMatrices, Stream streams[])
{
device::optflow_farneback::boxFilter5Gpu(M, blockSize/2, bufM, S(streams[0]));
if (!isDeviceArch11_)
device::optflow_farneback::boxFilter5Gpu(M, blockSize/2, bufM, S(streams[0]));
else
device::optflow_farneback::boxFilter5Gpu_CC11(M, blockSize/2, bufM, S(streams[0]));
swap(M, bufM);
for (int i = 1; i < 5; ++i)
@@ -183,8 +191,12 @@ void cv::gpu::FarnebackOpticalFlow::updateFlow_gaussianBlur(
const GpuMat& R0, const GpuMat& R1, GpuMat& flowx, GpuMat& flowy,
GpuMat& M, GpuMat &bufM, int blockSize, bool updateMatrices, Stream streams[])
{
device::optflow_farneback::gaussianBlur5Gpu(
M, blockSize/2, bufM, BORDER_REPLICATE_GPU, S(streams[0]));
if (!isDeviceArch11_)
device::optflow_farneback::gaussianBlur5Gpu(
M, blockSize/2, bufM, BORDER_REPLICATE_GPU, S(streams[0]));
else
device::optflow_farneback::gaussianBlur5Gpu_CC11(
M, blockSize/2, bufM, BORDER_REPLICATE_GPU, S(streams[0]));
swap(M, bufM);
device::optflow_farneback::updateFlowGpu(M, flowx, flowy, S(streams[0]));

3
modules/gpu/src/orb.cpp
View File

@@ -622,6 +622,9 @@ void cv::gpu::ORB_GPU::computeDescriptors(GpuMat& descriptors)
if (keyPointsCount_[level] == 0)
continue;
if (keyPointsCount_[level] == 0)
continue;
GpuMat descRange = descriptors.rowRange(offset, offset + keyPointsCount_[level]);
if (blurForDescriptor)

915
modules/gpu/test/test_video.cpp
View File

@@ -1,499 +1,416 @@
/*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.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, 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 Intel Corporation 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.
//
//M*/
#include "precomp.hpp"
namespace {
//#define DUMP
/////////////////////////////////////////////////////////////////////////////////////////////////
// BroxOpticalFlow
#define BROX_OPTICAL_FLOW_DUMP_FILE "opticalflow/brox_optical_flow.bin"
#define BROX_OPTICAL_FLOW_DUMP_FILE_CC20 "opticalflow/brox_optical_flow_cc20.bin"
struct BroxOpticalFlow : testing::TestWithParam<cv::gpu::DeviceInfo>
{
cv::gpu::DeviceInfo devInfo;
virtual void SetUp()
{
devInfo = GetParam();
cv::gpu::setDevice(devInfo.deviceID());
}
};
TEST_P(BroxOpticalFlow, Regression)
{
cv::Mat frame0 = readImageType("opticalflow/frame0.png", CV_32FC1);
ASSERT_FALSE(frame0.empty());
cv::Mat frame1 = readImageType("opticalflow/frame1.png", CV_32FC1);
ASSERT_FALSE(frame1.empty());
cv::gpu::BroxOpticalFlow brox(0.197f /*alpha*/, 50.0f /*gamma*/, 0.8f /*scale_factor*/,
10 /*inner_iterations*/, 77 /*outer_iterations*/, 10 /*solver_iterations*/);
cv::gpu::GpuMat u;
cv::gpu::GpuMat v;
brox(loadMat(frame0), loadMat(frame1), u, v);
#ifndef DUMP
std::string fname(cvtest::TS::ptr()->get_data_path());
if (devInfo.majorVersion() >= 2)
fname += BROX_OPTICAL_FLOW_DUMP_FILE_CC20;
else
fname += BROX_OPTICAL_FLOW_DUMP_FILE;
std::ifstream f(fname.c_str(), std::ios_base::binary);
int rows, cols;
f.read((char*)&rows, sizeof(rows));
f.read((char*)&cols, sizeof(cols));
cv::Mat u_gold(rows, cols, CV_32FC1);
for (int i = 0; i < u_gold.rows; ++i)
f.read(u_gold.ptr<char>(i), u_gold.cols * sizeof(float));
cv::Mat v_gold(rows, cols, CV_32FC1);
for (int i = 0; i < v_gold.rows; ++i)
f.read(v_gold.ptr<char>(i), v_gold.cols * sizeof(float));
EXPECT_MAT_NEAR(u_gold, u, 0);
EXPECT_MAT_NEAR(v_gold, v, 0);
#else
std::string fname(cvtest::TS::ptr()->get_data_path());
if (devInfo.majorVersion() >= 2)
fname += BROX_OPTICAL_FLOW_DUMP_FILE_CC20;
else
fname += BROX_OPTICAL_FLOW_DUMP_FILE;
std::ofstream f(fname.c_str(), std::ios_base::binary);
f.write((char*)&u.rows, sizeof(u.rows));
f.write((char*)&u.cols, sizeof(u.cols));
cv::Mat h_u(u);
cv::Mat h_v(v);
for (int i = 0; i < u.rows; ++i)
f.write(h_u.ptr<char>(i), u.cols * sizeof(float));
for (int i = 0; i < v.rows; ++i)
f.write(h_v.ptr<char>(i), v.cols * sizeof(float));
#endif
}
INSTANTIATE_TEST_CASE_P(GPU_Video, BroxOpticalFlow, ALL_DEVICES);
/////////////////////////////////////////////////////////////////////////////////////////////////
// GoodFeaturesToTrack
IMPLEMENT_PARAM_CLASS(MinDistance, double)
PARAM_TEST_CASE(GoodFeaturesToTrack, cv::gpu::DeviceInfo, MinDistance)
{
cv::gpu::DeviceInfo devInfo;
double minDistance;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
minDistance = GET_PARAM(1);
cv::gpu::setDevice(devInfo.deviceID());
}
};
TEST_P(GoodFeaturesToTrack, Accuracy)
{
cv::Mat image = readImage("opticalflow/frame0.png", cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(image.empty());
int maxCorners = 1000;
double qualityLevel = 0.01;
cv::gpu::GoodFeaturesToTrackDetector_GPU detector(maxCorners, qualityLevel, minDistance);
if (!supportFeature(devInfo, cv::gpu::GLOBAL_ATOMICS))
{
try
{
cv::gpu::GpuMat d_pts;
detector(loadMat(image), d_pts);
}
catch (const cv::Exception& e)
{
ASSERT_EQ(CV_StsNotImplemented, e.code);
}
}
else
{
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);
std::vector<cv::Point2f> pts_gold;
cv::goodFeaturesToTrack(image, pts_gold, maxCorners, qualityLevel, minDistance);
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(GPU_Video, GoodFeaturesToTrack, testing::Combine(
ALL_DEVICES,
testing::Values(MinDistance(0.0), MinDistance(3.0))));
/////////////////////////////////////////////////////////////////////////////////////////////////
// PyrLKOpticalFlow
IMPLEMENT_PARAM_CLASS(UseGray, bool)
PARAM_TEST_CASE(PyrLKOpticalFlow, cv::gpu::DeviceInfo, UseGray)
{
cv::gpu::DeviceInfo devInfo;
bool useGray;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
useGray = GET_PARAM(1);
cv::gpu::setDevice(devInfo.deviceID());
}
};
TEST_P(PyrLKOpticalFlow, Sparse)
{
cv::Mat frame0 = readImage("opticalflow/frame0.png", useGray ? cv::IMREAD_GRAYSCALE : cv::IMREAD_COLOR);
ASSERT_FALSE(frame0.empty());
cv::Mat 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);
std::vector<cv::Point2f> pts;
cv::goodFeaturesToTrack(gray_frame, pts, 1000, 0.01, 0.0);
cv::gpu::GpuMat d_pts;
cv::Mat pts_mat(1, (int)pts.size(), CV_32FC2, (void*)&pts[0]);
d_pts.upload(pts_mat);
cv::gpu::PyrLKOpticalFlow pyrLK;
cv::gpu::GpuMat d_nextPts;
cv::gpu::GpuMat d_status;
cv::gpu::GpuMat d_err;
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);
std::vector<cv::Point2f> nextPts_gold;
std::vector<unsigned char> status_gold;
std::vector<float> err_gold;
cv::calcOpticalFlowPyrLK(frame0, frame1, pts, nextPts_gold, status_gold, err_gold);
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-1)
++mistmatch;
}
}
double bad_ratio = static_cast<double>(mistmatch) / nextPts.size();
ASSERT_LE(bad_ratio, 0.01);
}
INSTANTIATE_TEST_CASE_P(GPU_Video, PyrLKOpticalFlow, testing::Combine(
ALL_DEVICES,
testing::Values(UseGray(true), UseGray(false))));
/////////////////////////////////////////////////////////////////////////////////////////////////
// FarnebackOpticalFlow
IMPLEMENT_PARAM_CLASS(PyrScale, double)
IMPLEMENT_PARAM_CLASS(PolyN, int)
CV_FLAGS(FarnebackOptFlowFlags, 0, cv::OPTFLOW_FARNEBACK_GAUSSIAN)
IMPLEMENT_PARAM_CLASS(UseInitFlow, bool)
PARAM_TEST_CASE(FarnebackOpticalFlow, cv::gpu::DeviceInfo, PyrScale, PolyN, FarnebackOptFlowFlags, UseInitFlow)
{
cv::gpu::DeviceInfo devInfo;
double pyrScale;
int polyN;
int flags;
bool useInitFlow;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
pyrScale = GET_PARAM(1);
polyN = GET_PARAM(2);
flags = GET_PARAM(3);
useInitFlow = GET_PARAM(4);
cv::gpu::setDevice(devInfo.deviceID());
}
};
TEST_P(FarnebackOpticalFlow, Accuracy)
{
cv::Mat frame0 = readImage("opticalflow/rubberwhale1.png", cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(frame0.empty());
cv::Mat frame1 = readImage("opticalflow/rubberwhale2.png", cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(frame1.empty());
double polySigma = polyN <= 5 ? 1.1 : 1.5;
cv::gpu::FarnebackOpticalFlow calc;
calc.pyrScale = pyrScale;
calc.polyN = polyN;
calc.polySigma = polySigma;
calc.flags = flags;
cv::gpu::GpuMat d_flowx, d_flowy;
calc(loadMat(frame0), loadMat(frame1), d_flowx, d_flowy);
cv::Mat flow;
if (useInitFlow)
{
cv::Mat flowxy[] = {cv::Mat(d_flowx), cv::Mat(d_flowy)};
cv::merge(flowxy, 2, flow);
}
if (useInitFlow)
{
calc.flags |= cv::OPTFLOW_USE_INITIAL_FLOW;
calc(loadMat(frame0), loadMat(frame1), d_flowx, d_flowy);
}
cv::calcOpticalFlowFarneback(
frame0, frame1, flow, calc.pyrScale, calc.numLevels, calc.winSize,
calc.numIters, calc.polyN, calc.polySigma, calc.flags);
std::vector<cv::Mat> flowxy;
cv::split(flow, flowxy);
EXPECT_MAT_SIMILAR(flowxy[0], d_flowx, 0.1);
EXPECT_MAT_SIMILAR(flowxy[1], d_flowy, 0.1);
}
INSTANTIATE_TEST_CASE_P(GPU_Video, FarnebackOpticalFlow, testing::Combine(
ALL_DEVICES,
testing::Values(PyrScale(0.3), PyrScale(0.5), PyrScale(0.8)),
testing::Values(PolyN(5), PolyN(7)),
testing::Values(FarnebackOptFlowFlags(0), FarnebackOptFlowFlags(cv::OPTFLOW_FARNEBACK_GAUSSIAN)),
testing::Values(UseInitFlow(false), UseInitFlow(true))));
/////////////////////////////////////////////////////////////////////////////////////////////////
// VideoWriter
#ifdef WIN32
PARAM_TEST_CASE(VideoWriter, cv::gpu::DeviceInfo, std::string)
{
cv::gpu::DeviceInfo devInfo;
std::string inputFile;
std::string outputFile;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
inputFile = GET_PARAM(1);
cv::gpu::setDevice(devInfo.deviceID());
inputFile = std::string(cvtest::TS::ptr()->get_data_path()) + "video/" + inputFile;
outputFile = inputFile.substr(0, inputFile.find('.')) + "_test.avi";
}
};
TEST_P(VideoWriter, Regression)
{
const double FPS = 25.0;
cv::VideoCapture reader(inputFile);
ASSERT_TRUE( reader.isOpened() );
cv::gpu::VideoWriter_GPU d_writer;
cv::Mat frame;
std::vector<cv::Mat> frames;
cv::gpu::GpuMat d_frame;
for (int i = 1; i < 10; ++i)
{
reader >> frame;
if (frame.empty())
break;
frames.push_back(frame.clone());
d_frame.upload(frame);
if (!d_writer.isOpened())
d_writer.open(outputFile, frame.size(), FPS);
d_writer.write(d_frame);
}
reader.release();
d_writer.close();
reader.open(outputFile);
ASSERT_TRUE( reader.isOpened() );
for (int i = 0; i < 5; ++i)
{
reader >> frame;
ASSERT_FALSE( frame.empty() );
}
}
INSTANTIATE_TEST_CASE_P(GPU_Video, VideoWriter, testing::Combine(
ALL_DEVICES,
testing::Values(std::string("VID00003-20100701-2204.mpg"), std::string("big_buck_bunny.mpg"))));
#endif // WIN32
/////////////////////////////////////////////////////////////////////////////////////////////////
// VideoReader
PARAM_TEST_CASE(VideoReader, cv::gpu::DeviceInfo, std::string)
{
cv::gpu::DeviceInfo devInfo;
std::string inputFile;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
inputFile = GET_PARAM(1);
cv::gpu::setDevice(devInfo.deviceID());
inputFile = std::string(cvtest::TS::ptr()->get_data_path()) + "video/" + inputFile;
}
};
TEST_P(VideoReader, Regression)
{
cv::gpu::VideoReader_GPU reader(inputFile);
ASSERT_TRUE( reader.isOpened() );
cv::gpu::GpuMat frame;
for (int i = 0; i < 5; ++i)
{
ASSERT_TRUE( reader.read(frame) );
ASSERT_FALSE( frame.empty() );
}
reader.close();
ASSERT_FALSE( reader.isOpened() );
}
INSTANTIATE_TEST_CASE_P(GPU_Video, VideoReader, testing::Combine(
ALL_DEVICES,
testing::Values(std::string("VID00003-20100701-2204.mpg"))));
} // namespace
/*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.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, 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 Intel Corporation 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.
//
//M*/
#include "precomp.hpp"
namespace {
//#define DUMP
/////////////////////////////////////////////////////////////////////////////////////////////////
// BroxOpticalFlow
#define BROX_OPTICAL_FLOW_DUMP_FILE "opticalflow/brox_optical_flow.bin"
#define BROX_OPTICAL_FLOW_DUMP_FILE_CC20 "opticalflow/brox_optical_flow_cc20.bin"
struct BroxOpticalFlow : testing::TestWithParam<cv::gpu::DeviceInfo>
{
cv::gpu::DeviceInfo devInfo;
virtual void SetUp()
{
devInfo = GetParam();
cv::gpu::setDevice(devInfo.deviceID());
}
};
TEST_P(BroxOpticalFlow, Regression)
{
cv::Mat frame0 = readImageType("opticalflow/frame0.png", CV_32FC1);
ASSERT_FALSE(frame0.empty());
cv::Mat frame1 = readImageType("opticalflow/frame1.png", CV_32FC1);
ASSERT_FALSE(frame1.empty());
cv::gpu::BroxOpticalFlow brox(0.197f /*alpha*/, 50.0f /*gamma*/, 0.8f /*scale_factor*/,
10 /*inner_iterations*/, 77 /*outer_iterations*/, 10 /*solver_iterations*/);
cv::gpu::GpuMat u;
cv::gpu::GpuMat v;
brox(loadMat(frame0), loadMat(frame1), u, v);
#ifndef DUMP
std::string fname(cvtest::TS::ptr()->get_data_path());
if (devInfo.majorVersion() >= 2)
fname += BROX_OPTICAL_FLOW_DUMP_FILE_CC20;
else
fname += BROX_OPTICAL_FLOW_DUMP_FILE;
std::ifstream f(fname.c_str(), std::ios_base::binary);
int rows, cols;
f.read((char*)&rows, sizeof(rows));
f.read((char*)&cols, sizeof(cols));
cv::Mat u_gold(rows, cols, CV_32FC1);
for (int i = 0; i < u_gold.rows; ++i)
f.read(u_gold.ptr<char>(i), u_gold.cols * sizeof(float));
cv::Mat v_gold(rows, cols, CV_32FC1);
for (int i = 0; i < v_gold.rows; ++i)
f.read(v_gold.ptr<char>(i), v_gold.cols * sizeof(float));
EXPECT_MAT_NEAR(u_gold, u, 0);
EXPECT_MAT_NEAR(v_gold, v, 0);
#else
std::string fname(cvtest::TS::ptr()->get_data_path());
if (devInfo.majorVersion() >= 2)
fname += BROX_OPTICAL_FLOW_DUMP_FILE_CC20;
else
fname += BROX_OPTICAL_FLOW_DUMP_FILE;
std::ofstream f(fname.c_str(), std::ios_base::binary);
f.write((char*)&u.rows, sizeof(u.rows));
f.write((char*)&u.cols, sizeof(u.cols));
cv::Mat h_u(u);
cv::Mat h_v(v);
for (int i = 0; i < u.rows; ++i)
f.write(h_u.ptr<char>(i), u.cols * sizeof(float));
for (int i = 0; i < v.rows; ++i)
f.write(h_v.ptr<char>(i), v.cols * sizeof(float));
#endif
}
INSTANTIATE_TEST_CASE_P(GPU_Video, BroxOpticalFlow, ALL_DEVICES);
/////////////////////////////////////////////////////////////////////////////////////////////////
// GoodFeaturesToTrack
IMPLEMENT_PARAM_CLASS(MinDistance, double)
PARAM_TEST_CASE(GoodFeaturesToTrack, cv::gpu::DeviceInfo, MinDistance)
{
cv::gpu::DeviceInfo devInfo;
double minDistance;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
minDistance = GET_PARAM(1);
cv::gpu::setDevice(devInfo.deviceID());
}
};
TEST_P(GoodFeaturesToTrack, Accuracy)
{
cv::Mat image = readImage("opticalflow/frame0.png", cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(image.empty());
int maxCorners = 1000;
double qualityLevel = 0.01;
cv::gpu::GoodFeaturesToTrackDetector_GPU detector(maxCorners, qualityLevel, minDistance);
if (!supportFeature(devInfo, cv::gpu::GLOBAL_ATOMICS))
{
try
{
cv::gpu::GpuMat d_pts;
detector(loadMat(image), d_pts);
}
catch (const cv::Exception& e)
{
ASSERT_EQ(CV_StsNotImplemented, e.code);
}
}
else
{
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);
std::vector<cv::Point2f> pts_gold;
cv::goodFeaturesToTrack(image, pts_gold, maxCorners, qualityLevel, minDistance);
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(GPU_Video, GoodFeaturesToTrack, testing::Combine(
ALL_DEVICES,
testing::Values(MinDistance(0.0), MinDistance(3.0))));
/////////////////////////////////////////////////////////////////////////////////////////////////
// PyrLKOpticalFlow
IMPLEMENT_PARAM_CLASS(UseGray, bool)
PARAM_TEST_CASE(PyrLKOpticalFlow, cv::gpu::DeviceInfo, UseGray)
{
cv::gpu::DeviceInfo devInfo;
bool useGray;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
useGray = GET_PARAM(1);
cv::gpu::setDevice(devInfo.deviceID());
}
};
TEST_P(PyrLKOpticalFlow, Sparse)
{
cv::Mat frame0 = readImage("opticalflow/frame0.png", useGray ? cv::IMREAD_GRAYSCALE : cv::IMREAD_COLOR);
ASSERT_FALSE(frame0.empty());
cv::Mat 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);
std::vector<cv::Point2f> pts;
cv::goodFeaturesToTrack(gray_frame, pts, 1000, 0.01, 0.0);
cv::gpu::GpuMat d_pts;
cv::Mat pts_mat(1, (int)pts.size(), CV_32FC2, (void*)&pts[0]);
d_pts.upload(pts_mat);
cv::gpu::PyrLKOpticalFlow pyrLK;
cv::gpu::GpuMat d_nextPts;
cv::gpu::GpuMat d_status;
cv::gpu::GpuMat d_err;
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);
std::vector<cv::Point2f> nextPts_gold;
std::vector<unsigned char> status_gold;
std::vector<float> err_gold;
cv::calcOpticalFlowPyrLK(frame0, frame1, pts, nextPts_gold, status_gold, err_gold);
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-1)
++mistmatch;
}
}
double bad_ratio = static_cast<double>(mistmatch) / nextPts.size();
ASSERT_LE(bad_ratio, 0.01);
}
INSTANTIATE_TEST_CASE_P(GPU_Video, PyrLKOpticalFlow, testing::Combine(
ALL_DEVICES,
testing::Values(UseGray(true), UseGray(false))));
/////////////////////////////////////////////////////////////////////////////////////////////////
// FarnebackOpticalFlow
IMPLEMENT_PARAM_CLASS(PyrScale, double)
IMPLEMENT_PARAM_CLASS(PolyN, int)
CV_FLAGS(FarnebackOptFlowFlags, 0, cv::OPTFLOW_FARNEBACK_GAUSSIAN)
IMPLEMENT_PARAM_CLASS(UseInitFlow, bool)
PARAM_TEST_CASE(FarnebackOpticalFlow, cv::gpu::DeviceInfo, PyrScale, PolyN, FarnebackOptFlowFlags, UseInitFlow)
{
cv::gpu::DeviceInfo devInfo;
double pyrScale;
int polyN;
int flags;
bool useInitFlow;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
pyrScale = GET_PARAM(1);
polyN = GET_PARAM(2);
flags = GET_PARAM(3);
useInitFlow = GET_PARAM(4);
cv::gpu::setDevice(devInfo.deviceID());
}
};
TEST_P(FarnebackOpticalFlow, Accuracy)
{
cv::Mat frame0 = readImage("opticalflow/rubberwhale1.png", cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(frame0.empty());
cv::Mat frame1 = readImage("opticalflow/rubberwhale2.png", cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(frame1.empty());
double polySigma = polyN <= 5 ? 1.1 : 1.5;
cv::gpu::FarnebackOpticalFlow calc;
calc.pyrScale = pyrScale;
calc.polyN = polyN;
calc.polySigma = polySigma;
calc.flags = flags;
cv::gpu::GpuMat d_flowx, d_flowy;
calc(loadMat(frame0), loadMat(frame1), d_flowx, d_flowy);
cv::Mat flow;
if (useInitFlow)
{
cv::Mat flowxy[] = {cv::Mat(d_flowx), cv::Mat(d_flowy)};
cv::merge(flowxy, 2, flow);
}
if (useInitFlow)
{
calc.flags |= cv::OPTFLOW_USE_INITIAL_FLOW;
calc(loadMat(frame0), loadMat(frame1), d_flowx, d_flowy);
}
cv::calcOpticalFlowFarneback(
frame0, frame1, flow, calc.pyrScale, calc.numLevels, calc.winSize,
calc.numIters, calc.polyN, calc.polySigma, calc.flags);
std::vector<cv::Mat> flowxy;
cv::split(flow, flowxy);
EXPECT_MAT_SIMILAR(flowxy[0], d_flowx, 0.1);
EXPECT_MAT_SIMILAR(flowxy[1], d_flowy, 0.1);
};
INSTANTIATE_TEST_CASE_P(GPU_Video, FarnebackOpticalFlow, testing::Combine(
ALL_DEVICES,
testing::Values(PyrScale(0.3), PyrScale(0.5), PyrScale(0.8)),
testing::Values(PolyN(5), PolyN(7)),
testing::Values(FarnebackOptFlowFlags(0), FarnebackOptFlowFlags(cv::OPTFLOW_FARNEBACK_GAUSSIAN)),
testing::Values(UseInitFlow(false), UseInitFlow(true))));
struct OpticalFlowNan : public BroxOpticalFlow {};
TEST_P(OpticalFlowNan, Regression)
{
cv::Mat frame0 = readImageType("opticalflow/frame0.png", CV_32FC1);
ASSERT_FALSE(frame0.empty());
cv::Mat r_frame0, r_frame1;
cv::resize(frame0, r_frame0, cv::Size(1380,1000));
cv::Mat frame1 = readImageType("opticalflow/frame1.png", CV_32FC1);
ASSERT_FALSE(frame1.empty());
cv::resize(frame1, r_frame1, cv::Size(1380,1000));
cv::gpu::BroxOpticalFlow brox(0.197f /*alpha*/, 50.0f /*gamma*/, 0.8f /*scale_factor*/,
5 /*inner_iterations*/, 150 /*outer_iterations*/, 10 /*solver_iterations*/);
cv::gpu::GpuMat u;
cv::gpu::GpuMat v;
brox(loadMat(r_frame0), loadMat(r_frame1), u, v);
cv::Mat h_u, h_v;
u.download(h_u);
v.download(h_v);
EXPECT_TRUE(cv::checkRange(h_u));
EXPECT_TRUE(cv::checkRange(h_v));
};
INSTANTIATE_TEST_CASE_P(GPU_Video, OpticalFlowNan, ALL_DEVICES);
} // namespace