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move obsolete algorithms from cudaoptflow to cudalegacy

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pull/3635/head
Vladislav Vinogradov 10 years ago
parent c4b2058d23
commit 19c6bbe7d9
11 changed files with 49 additions and 311 deletions
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  1. 51
      modules/cudalegacy/include/opencv2/cudalegacy.hpp
  2. 0
      modules/cudalegacy/src/bm.cpp
  3. 0
      modules/cudalegacy/src/bm_fast.cpp
  4. 0
      modules/cudalegacy/src/cuda/bm.cu
  5. 0
      modules/cudalegacy/src/cuda/bm_fast.cu
  6. 0
      modules/cudalegacy/src/cuda/needle_map.cu
  7. 0
      modules/cudalegacy/src/interpolate_frames.cpp
  8. 0
      modules/cudalegacy/src/needle_map.cpp
  9. 41
      modules/cudaoptflow/include/opencv2/cudaoptflow.hpp
  10. 150
      modules/cudaoptflow/perf/perf_optflow.cpp
  11. 118
      modules/cudaoptflow/test/test_optflow.cpp

51
modules/cudalegacy/include/opencv2/cudalegacy.hpp
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@ -71,8 +71,9 @@ public:
CV_EXPORTS Ptr<ImagePyramid> createImagePyramid(InputArray img, int nLayers = -1, Stream& stream = Stream::Null());
////////////////////////////////////////////////////
//
// GMG
//
/** @brief Background/Foreground Segmentation Algorithm.
@ -125,8 +126,9 @@ public:
CV_EXPORTS Ptr<cuda::BackgroundSubtractorGMG>
createBackgroundSubtractorGMG(int initializationFrames = 120, double decisionThreshold = 0.8);
////////////////////////////////////////////////////
//
// FGD
//
/** @brief The class discriminates between foreground and background pixels by building and maintaining a model
of the background.
@ -180,6 +182,51 @@ struct CV_EXPORTS FGDParams
CV_EXPORTS Ptr<cuda::BackgroundSubtractorFGD>
createBackgroundSubtractorFGD(const FGDParams& params = FGDParams());
//
// Optical flow
//
//! Calculates optical flow for 2 images using block matching algorithm */
CV_EXPORTS void calcOpticalFlowBM(const GpuMat& prev, const GpuMat& curr,
Size block_size, Size shift_size, Size max_range, bool use_previous,
GpuMat& velx, GpuMat& vely, GpuMat& buf,
Stream& stream = Stream::Null());
class CV_EXPORTS FastOpticalFlowBM
{
public:
void operator ()(const GpuMat& I0, const GpuMat& I1, GpuMat& flowx, GpuMat& flowy, int search_window = 21, int block_window = 7, Stream& s = Stream::Null());
private:
GpuMat buffer;
GpuMat extended_I0;
GpuMat extended_I1;
};
/** @brief Interpolates frames (images) using provided optical flow (displacement field).
@param frame0 First frame (32-bit floating point images, single channel).
@param frame1 Second frame. Must have the same type and size as frame0 .
@param fu Forward horizontal displacement.
@param fv Forward vertical displacement.
@param bu Backward horizontal displacement.
@param bv Backward vertical displacement.
@param pos New frame position.
@param newFrame Output image.
@param buf Temporary buffer, will have width x 6\*height size, CV_32FC1 type and contain 6
GpuMat: occlusion masks for first frame, occlusion masks for second, interpolated forward
horizontal flow, interpolated forward vertical flow, interpolated backward horizontal flow,
interpolated backward vertical flow.
@param stream Stream for the asynchronous version.
*/
CV_EXPORTS void interpolateFrames(const GpuMat& frame0, const GpuMat& frame1,
const GpuMat& fu, const GpuMat& fv,
const GpuMat& bu, const GpuMat& bv,
float pos, GpuMat& newFrame, GpuMat& buf,
Stream& stream = Stream::Null());
CV_EXPORTS void createOpticalFlowNeedleMap(const GpuMat& u, const GpuMat& v, GpuMat& vertex, GpuMat& colors);
//! @}
}}

0
modules/cudaoptflow/src/bm.cpp → modules/cudalegacy/src/bm.cpp
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0
modules/cudaoptflow/src/bm_fast.cpp → modules/cudalegacy/src/bm_fast.cpp
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0
modules/cudaoptflow/src/cuda/bm.cu → modules/cudalegacy/src/cuda/bm.cu
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0
modules/cudaoptflow/src/cuda/bm_fast.cu → modules/cudalegacy/src/cuda/bm_fast.cu
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0
modules/cudaoptflow/src/cuda/needle_map.cu → modules/cudalegacy/src/cuda/needle_map.cu
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0
modules/cudaoptflow/src/interpolate_frames.cpp → modules/cudalegacy/src/interpolate_frames.cpp
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0
modules/cudaoptflow/src/needle_map.cpp → modules/cudalegacy/src/needle_map.cpp
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41
modules/cudaoptflow/include/opencv2/cudaoptflow.hpp
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@ -347,47 +347,6 @@ private:
GpuMat norm_buf;
};
//! Calculates optical flow for 2 images using block matching algorithm */
CV_EXPORTS void calcOpticalFlowBM(const GpuMat& prev, const GpuMat& curr,
Size block_size, Size shift_size, Size max_range, bool use_previous,
GpuMat& velx, GpuMat& vely, GpuMat& buf,
Stream& stream = Stream::Null());
class CV_EXPORTS FastOpticalFlowBM
{
public:
void operator ()(const GpuMat& I0, const GpuMat& I1, GpuMat& flowx, GpuMat& flowy, int search_window = 21, int block_window = 7, Stream& s = Stream::Null());
private:
GpuMat buffer;
GpuMat extended_I0;
GpuMat extended_I1;
};
/** @brief Interpolates frames (images) using provided optical flow (displacement field).
@param frame0 First frame (32-bit floating point images, single channel).
@param frame1 Second frame. Must have the same type and size as frame0 .
@param fu Forward horizontal displacement.
@param fv Forward vertical displacement.
@param bu Backward horizontal displacement.
@param bv Backward vertical displacement.
@param pos New frame position.
@param newFrame Output image.
@param buf Temporary buffer, will have width x 6\*height size, CV_32FC1 type and contain 6
GpuMat: occlusion masks for first frame, occlusion masks for second, interpolated forward
horizontal flow, interpolated forward vertical flow, interpolated backward horizontal flow,
interpolated backward vertical flow.
@param stream Stream for the asynchronous version.
*/
CV_EXPORTS void interpolateFrames(const GpuMat& frame0, const GpuMat& frame1,
const GpuMat& fu, const GpuMat& fv,
const GpuMat& bu, const GpuMat& bv,
float pos, GpuMat& newFrame, GpuMat& buf,
Stream& stream = Stream::Null());
CV_EXPORTS void createOpticalFlowNeedleMap(const GpuMat& u, const GpuMat& v, GpuMat& vertex, GpuMat& colors);
//! @}
}} // namespace cv { namespace cuda {

150
modules/cudaoptflow/perf/perf_optflow.cpp
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@ -46,91 +46,10 @@ using namespace std;
using namespace testing;
using namespace perf;
//////////////////////////////////////////////////////
// InterpolateFrames
typedef pair<string, string> pair_string;
DEF_PARAM_TEST_1(ImagePair, pair_string);
PERF_TEST_P(ImagePair, InterpolateFrames,
Values<pair_string>(make_pair("gpu/opticalflow/frame0.png", "gpu/opticalflow/frame1.png")))
{
cv::Mat frame0 = readImage(GetParam().first, cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(frame0.empty());
cv::Mat frame1 = readImage(GetParam().second, cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(frame1.empty());
frame0.convertTo(frame0, CV_32FC1, 1.0 / 255.0);
frame1.convertTo(frame1, CV_32FC1, 1.0 / 255.0);
if (PERF_RUN_CUDA())
{
const cv::cuda::GpuMat d_frame0(frame0);
const cv::cuda::GpuMat d_frame1(frame1);
cv::cuda::GpuMat d_fu, d_fv;
cv::cuda::GpuMat d_bu, d_bv;
cv::cuda::BroxOpticalFlow d_flow(0.197f /*alpha*/, 50.0f /*gamma*/, 0.8f /*scale_factor*/,
10 /*inner_iterations*/, 77 /*outer_iterations*/, 10 /*solver_iterations*/);
d_flow(d_frame0, d_frame1, d_fu, d_fv);
d_flow(d_frame1, d_frame0, d_bu, d_bv);
cv::cuda::GpuMat newFrame;
cv::cuda::GpuMat d_buf;
TEST_CYCLE() cv::cuda::interpolateFrames(d_frame0, d_frame1, d_fu, d_fv, d_bu, d_bv, 0.5f, newFrame, d_buf);
CUDA_SANITY_CHECK(newFrame, 1e-4);
}
else
{
FAIL_NO_CPU();
}
}
//////////////////////////////////////////////////////
// CreateOpticalFlowNeedleMap
PERF_TEST_P(ImagePair, CreateOpticalFlowNeedleMap,
Values<pair_string>(make_pair("gpu/opticalflow/frame0.png", "gpu/opticalflow/frame1.png")))
{
cv::Mat frame0 = readImage(GetParam().first, cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(frame0.empty());
cv::Mat frame1 = readImage(GetParam().second, cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(frame1.empty());
frame0.convertTo(frame0, CV_32FC1, 1.0 / 255.0);
frame1.convertTo(frame1, CV_32FC1, 1.0 / 255.0);
if (PERF_RUN_CUDA())
{
const cv::cuda::GpuMat d_frame0(frame0);
const cv::cuda::GpuMat d_frame1(frame1);
cv::cuda::GpuMat u;
cv::cuda::GpuMat v;
cv::cuda::BroxOpticalFlow d_flow(0.197f /*alpha*/, 50.0f /*gamma*/, 0.8f /*scale_factor*/,
10 /*inner_iterations*/, 77 /*outer_iterations*/, 10 /*solver_iterations*/);
d_flow(d_frame0, d_frame1, u, v);
cv::cuda::GpuMat vertex, colors;
TEST_CYCLE() cv::cuda::createOpticalFlowNeedleMap(u, v, vertex, colors);
CUDA_SANITY_CHECK(vertex, 1e-6);
CUDA_SANITY_CHECK(colors);
}
else
{
FAIL_NO_CPU();
}
}
//////////////////////////////////////////////////////
// BroxOpticalFlow
@ -383,72 +302,3 @@ PERF_TEST_P(ImagePair, OpticalFlowDual_TVL1,
CPU_SANITY_CHECK(flow);
}
}
//////////////////////////////////////////////////////
// OpticalFlowBM
PERF_TEST_P(ImagePair, OpticalFlowBM,
Values<pair_string>(make_pair("gpu/opticalflow/frame0.png", "gpu/opticalflow/frame1.png")))
{
declare.time(400);
const cv::Mat frame0 = readImage(GetParam().first, cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(frame0.empty());
const cv::Mat frame1 = readImage(GetParam().second, cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(frame1.empty());
const cv::Size block_size(16, 16);
const cv::Size shift_size(1, 1);
const cv::Size max_range(16, 16);
if (PERF_RUN_CUDA())
{
const cv::cuda::GpuMat d_frame0(frame0);
const cv::cuda::GpuMat d_frame1(frame1);
cv::cuda::GpuMat u, v, buf;
TEST_CYCLE() cv::cuda::calcOpticalFlowBM(d_frame0, d_frame1, block_size, shift_size, max_range, false, u, v, buf);
CUDA_SANITY_CHECK(u);
CUDA_SANITY_CHECK(v);
}
else
{
FAIL_NO_CPU();
}
}
PERF_TEST_P(ImagePair, DISABLED_FastOpticalFlowBM,
Values<pair_string>(make_pair("gpu/opticalflow/frame0.png", "gpu/opticalflow/frame1.png")))
{
declare.time(400);
const cv::Mat frame0 = readImage(GetParam().first, cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(frame0.empty());
const cv::Mat frame1 = readImage(GetParam().second, cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(frame1.empty());
const cv::Size block_size(16, 16);
const cv::Size shift_size(1, 1);
const cv::Size max_range(16, 16);
if (PERF_RUN_CUDA())
{
const cv::cuda::GpuMat d_frame0(frame0);
const cv::cuda::GpuMat d_frame1(frame1);
cv::cuda::GpuMat u, v;
cv::cuda::FastOpticalFlowBM fastBM;
TEST_CYCLE() fastBM(d_frame0, d_frame1, u, v, max_range.width, block_size.width);
CUDA_SANITY_CHECK(u, 2);
CUDA_SANITY_CHECK(v, 2);
}
else
{
FAIL_NO_CPU();
}
}

118
modules/cudaoptflow/test/test_optflow.cpp
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@ -378,122 +378,4 @@ INSTANTIATE_TEST_CASE_P(CUDA_OptFlow, OpticalFlowDual_TVL1, testing::Combine(
ALL_DEVICES,
testing::Values(Gamma(0.0), Gamma(1.0))));
//////////////////////////////////////////////////////
// FastOpticalFlowBM
namespace
{
void FastOpticalFlowBM_gold(const cv::Mat_<uchar>& I0, const cv::Mat_<uchar>& I1, cv::Mat_<float>& velx, cv::Mat_<float>& vely, int search_window, int block_window)
{
velx.create(I0.size());
vely.create(I0.size());
int search_radius = search_window / 2;
int block_radius = block_window / 2;
for (int y = 0; y < I0.rows; ++y)
{
for (int x = 0; x < I0.cols; ++x)
{
int bestDist = std::numeric_limits<int>::max();
int bestDx = 0;
int bestDy = 0;
for (int dy = -search_radius; dy <= search_radius; ++dy)
{
for (int dx = -search_radius; dx <= search_radius; ++dx)
{
int dist = 0;
for (int by = -block_radius; by <= block_radius; ++by)
{
for (int bx = -block_radius; bx <= block_radius; ++bx)
{
int I0_val = I0(cv::borderInterpolate(y + by, I0.rows, cv::BORDER_DEFAULT), cv::borderInterpolate(x + bx, I0.cols, cv::BORDER_DEFAULT));
int I1_val = I1(cv::borderInterpolate(y + dy + by, I0.rows, cv::BORDER_DEFAULT), cv::borderInterpolate(x + dx + bx, I0.cols, cv::BORDER_DEFAULT));
dist += std::abs(I0_val - I1_val);
}
}
if (dist < bestDist)
{
bestDist = dist;
bestDx = dx;
bestDy = dy;
}
}
}
velx(y, x) = (float) bestDx;
vely(y, x) = (float) bestDy;
}
}
}
double calc_rmse(const cv::Mat_<float>& flow1, const cv::Mat_<float>& flow2)
{
double sum = 0.0;
for (int y = 0; y < flow1.rows; ++y)
{
for (int x = 0; x < flow1.cols; ++x)
{
double diff = flow1(y, x) - flow2(y, x);
sum += diff * diff;
}
}
return std::sqrt(sum / flow1.size().area());
}
}
struct FastOpticalFlowBM : testing::TestWithParam<cv::cuda::DeviceInfo>
{
};
CUDA_TEST_P(FastOpticalFlowBM, Accuracy)
{
const double MAX_RMSE = 0.6;
int search_window = 15;
int block_window = 5;
cv::cuda::DeviceInfo devInfo = GetParam();
cv::cuda::setDevice(devInfo.deviceID());
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());
cv::Size smallSize(320, 240);
cv::Mat frame0_small;
cv::Mat frame1_small;
cv::resize(frame0, frame0_small, smallSize);
cv::resize(frame1, frame1_small, smallSize);
cv::cuda::GpuMat d_flowx;
cv::cuda::GpuMat d_flowy;
cv::cuda::FastOpticalFlowBM fastBM;
fastBM(loadMat(frame0_small), loadMat(frame1_small), d_flowx, d_flowy, search_window, block_window);
cv::Mat_<float> flowx;
cv::Mat_<float> flowy;
FastOpticalFlowBM_gold(frame0_small, frame1_small, flowx, flowy, search_window, block_window);
double err;
err = calc_rmse(flowx, cv::Mat(d_flowx));
EXPECT_LE(err, MAX_RMSE);
err = calc_rmse(flowy, cv::Mat(d_flowy));
EXPECT_LE(err, MAX_RMSE);
}
INSTANTIATE_TEST_CASE_P(CUDA_OptFlow, FastOpticalFlowBM, ALL_DEVICES);
#endif // HAVE_CUDA

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