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gpubgsegm module fixes

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This commit is contained in:
Vladislav Vinogradov 2013-04-18 10:46:51 +04:00
parent c0b3424a23
commit f531dd839c
17 changed files with 24 additions and 323 deletions
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4
modules/gpubgsegm/CMakeLists.txt
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@ -4,6 +4,6 @@ endif()
set(the_description "GPU-accelerated Background Segmentation") set(the_description "GPU-accelerated Background Segmentation")
ocv_warnings_disable(CMAKE_CXX_FLAGS -Wundef -Wmissing-declarations) ocv_warnings_disable(CMAKE_CXX_FLAGS /wd4127 /wd4324 /wd4512 -Wundef -Wmissing-declarations)
ocv_define_module(gpubgsegm opencv_video opencv_legacy opencv_gpufilters opencv_gpuimgproc) ocv_define_module(gpubgsegm opencv_video opencv_imgproc opencv_legacy opencv_gpuarithm opencv_gpufilters opencv_gpuimgproc)

291
modules/gpubgsegm/doc/background_segmentation.rst
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@ -1,296 +1,10 @@
Video Analysis Background Segmentation
============== =======================
.. highlight:: cpp .. highlight:: cpp
gpu::BroxOpticalFlow
--------------------
.. ocv:class:: gpu::BroxOpticalFlow
Class computing the optical flow for two images using Brox et al Optical Flow algorithm ([Brox2004]_). ::
class BroxOpticalFlow
{
public:
BroxOpticalFlow(float alpha_, float gamma_, float scale_factor_, int inner_iterations_, int outer_iterations_, int solver_iterations_);
//! Compute optical flow
//! frame0 - source frame (supports only CV_32FC1 type)
//! frame1 - frame to track (with the same size and type as frame0)
//! u - flow horizontal component (along x axis)
//! v - flow vertical component (along y axis)
void operator ()(const GpuMat& frame0, const GpuMat& frame1, GpuMat& u, GpuMat& v, Stream& stream = Stream::Null());
//! flow smoothness
float alpha;
//! gradient constancy importance
float gamma;
//! pyramid scale factor
float scale_factor;
//! number of lagged non-linearity iterations (inner loop)
int inner_iterations;
//! number of warping iterations (number of pyramid levels)
int outer_iterations;
//! number of linear system solver iterations
int solver_iterations;
GpuMat buf;
};
gpu::GoodFeaturesToTrackDetector_GPU
------------------------------------
.. ocv:class:: gpu::GoodFeaturesToTrackDetector_GPU
Class used for strong corners detection on an image. ::
class GoodFeaturesToTrackDetector_GPU
{
public:
explicit GoodFeaturesToTrackDetector_GPU(int maxCorners_ = 1000, double qualityLevel_ = 0.01, double minDistance_ = 0.0,
int blockSize_ = 3, bool useHarrisDetector_ = false, double harrisK_ = 0.04);
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();
};
The class finds the most prominent corners in the image.
.. seealso:: :ocv:func:`goodFeaturesToTrack`
gpu::GoodFeaturesToTrackDetector_GPU::GoodFeaturesToTrackDetector_GPU
---------------------------------------------------------------------
Constructor.
.. ocv:function:: gpu::GoodFeaturesToTrackDetector_GPU::GoodFeaturesToTrackDetector_GPU(int maxCorners = 1000, double qualityLevel = 0.01, double minDistance = 0.0, int blockSize = 3, bool useHarrisDetector = false, double harrisK = 0.04)
:param maxCorners: Maximum number of corners to return. If there are more corners than are found, the strongest of them is returned.
:param qualityLevel: Parameter characterizing the minimal accepted quality of image corners. The parameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue (see :ocv:func:`gpu::cornerMinEigenVal` ) or the Harris function response (see :ocv:func:`gpu::cornerHarris` ). The corners with the quality measure less than the product are rejected. For example, if the best corner has the quality measure = 1500, and the ``qualityLevel=0.01`` , then all the corners with the quality measure less than 15 are rejected.
:param minDistance: Minimum possible Euclidean distance between the returned corners.
:param blockSize: Size of an average block for computing a derivative covariation matrix over each pixel neighborhood. See :ocv:func:`cornerEigenValsAndVecs` .
:param useHarrisDetector: Parameter indicating whether to use a Harris detector (see :ocv:func:`gpu::cornerHarris`) or :ocv:func:`gpu::cornerMinEigenVal`.
:param harrisK: Free parameter of the Harris detector.
gpu::GoodFeaturesToTrackDetector_GPU::operator ()
-------------------------------------------------
Finds the most prominent corners in the image.
.. ocv:function:: void gpu::GoodFeaturesToTrackDetector_GPU::operator ()(const GpuMat& image, GpuMat& corners, const GpuMat& mask = GpuMat())
:param image: Input 8-bit, single-channel image.
:param corners: Output vector of detected corners (it will be one row matrix with CV_32FC2 type).
:param mask: Optional region of interest. If the image is not empty (it needs to have the type ``CV_8UC1`` and the same size as ``image`` ), it specifies the region in which the corners are detected.
.. seealso:: :ocv:func:`goodFeaturesToTrack`
gpu::GoodFeaturesToTrackDetector_GPU::releaseMemory
---------------------------------------------------
Releases inner buffers memory.
.. ocv:function:: void gpu::GoodFeaturesToTrackDetector_GPU::releaseMemory()
gpu::FarnebackOpticalFlow
-------------------------
.. ocv:class:: gpu::FarnebackOpticalFlow
Class computing a dense optical flow using the Gunnar Farneback’s algorithm. ::
class CV_EXPORTS FarnebackOpticalFlow
{
public:
FarnebackOpticalFlow()
{
numLevels = 5;
pyrScale = 0.5;
fastPyramids = false;
winSize = 13;
numIters = 10;
polyN = 5;
polySigma = 1.1;
flags = 0;
}
int numLevels;
double pyrScale;
bool fastPyramids;
int winSize;
int numIters;
int polyN;
double polySigma;
int flags;
void operator ()(const GpuMat &frame0, const GpuMat &frame1, GpuMat &flowx, GpuMat &flowy, Stream &s = Stream::Null());
void releaseMemory();
private:
/* hidden */
};
gpu::FarnebackOpticalFlow::operator ()
--------------------------------------
Computes a dense optical flow using the Gunnar Farneback’s algorithm.
.. ocv:function:: void gpu::FarnebackOpticalFlow::operator ()(const GpuMat &frame0, const GpuMat &frame1, GpuMat &flowx, GpuMat &flowy, Stream &s = Stream::Null())
:param frame0: First 8-bit gray-scale input image
:param frame1: Second 8-bit gray-scale input image
:param flowx: Flow horizontal component
:param flowy: Flow vertical component
:param s: Stream
.. seealso:: :ocv:func:`calcOpticalFlowFarneback`
gpu::FarnebackOpticalFlow::releaseMemory
----------------------------------------
Releases unused auxiliary memory buffers.
.. ocv:function:: void gpu::FarnebackOpticalFlow::releaseMemory()
gpu::PyrLKOpticalFlow
---------------------
.. ocv:class:: gpu::PyrLKOpticalFlow
Class used for calculating an optical flow. ::
class PyrLKOpticalFlow
{
public:
PyrLKOpticalFlow();
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;
bool useInitialFlow;
void releaseMemory();
};
The class can calculate an optical flow for a sparse feature set or dense optical flow using the iterative Lucas-Kanade method with pyramids.
.. seealso:: :ocv:func:`calcOpticalFlowPyrLK`
gpu::PyrLKOpticalFlow::sparse
-----------------------------
Calculate an optical flow for a sparse feature set.
.. ocv:function:: void gpu::PyrLKOpticalFlow::sparse(const GpuMat& prevImg, const GpuMat& nextImg, const GpuMat& prevPts, GpuMat& nextPts, GpuMat& status, GpuMat* err = 0)
:param prevImg: First 8-bit input image (supports both grayscale and color images).
:param nextImg: Second input image of the same size and the same type as ``prevImg`` .
:param prevPts: Vector of 2D points for which the flow needs to be found. It must be one row matrix with CV_32FC2 type.
:param nextPts: Output vector of 2D points (with single-precision floating-point coordinates) containing the calculated new positions of input features in the second image. When ``useInitialFlow`` is true, the vector must have the same size as in the input.
:param status: Output status vector (CV_8UC1 type). Each element of the vector is set to 1 if the flow for the corresponding features has been found. Otherwise, it is set to 0.
:param err: Output vector (CV_32FC1 type) that contains the difference between patches around the original and moved points or min eigen value if ``getMinEigenVals`` is checked. It can be NULL, if not needed.
.. seealso:: :ocv:func:`calcOpticalFlowPyrLK`
gpu::PyrLKOpticalFlow::dense
-----------------------------
Calculate dense optical flow.
.. ocv:function:: void gpu::PyrLKOpticalFlow::dense(const GpuMat& prevImg, const GpuMat& nextImg, GpuMat& u, GpuMat& v, GpuMat* err = 0)
:param prevImg: First 8-bit grayscale input image.
:param nextImg: Second input image of the same size and the same type as ``prevImg`` .
:param u: Horizontal component of the optical flow of the same size as input images, 32-bit floating-point, single-channel
:param v: Vertical component of the optical flow of the same size as input images, 32-bit floating-point, single-channel
:param err: Output vector (CV_32FC1 type) that contains the difference between patches around the original and moved points or min eigen value if ``getMinEigenVals`` is checked. It can be NULL, if not needed.
gpu::PyrLKOpticalFlow::releaseMemory
------------------------------------
Releases inner buffers memory.
.. ocv:function:: void gpu::PyrLKOpticalFlow::releaseMemory()
gpu::interpolateFrames
----------------------
Interpolates frames (images) using provided optical flow (displacement field).
.. ocv:function:: void gpu::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())
: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.
gpu::FGDStatModel gpu::FGDStatModel
----------------- -----------------
.. ocv:class:: gpu::FGDStatModel .. ocv:class:: gpu::FGDStatModel
@ -687,7 +401,6 @@ Releases all inner buffer's memory.
.. [Brox2004] T. Brox, A. Bruhn, N. Papenberg, J. Weickert. *High accuracy optical flow estimation based on a theory for warping*. ECCV 2004.
.. [FGD2003] Liyuan Li, Weimin Huang, Irene Y.H. Gu, and Qi Tian. *Foreground Object Detection from Videos Containing Complex Background*. ACM MM2003 9p, 2003. .. [FGD2003] Liyuan Li, Weimin Huang, Irene Y.H. Gu, and Qi Tian. *Foreground Object Detection from Videos Containing Complex Background*. ACM MM2003 9p, 2003.
.. [MOG2001] P. KadewTraKuPong and R. Bowden. *An improved adaptive background mixture model for real-time tracking with shadow detection*. Proc. 2nd European Workshop on Advanced Video-Based Surveillance Systems, 2001 .. [MOG2001] P. KadewTraKuPong and R. Bowden. *An improved adaptive background mixture model for real-time tracking with shadow detection*. Proc. 2nd European Workshop on Advanced Video-Based Surveillance Systems, 2001
.. [MOG2004] Z. Zivkovic. *Improved adaptive Gausian mixture model for background subtraction*. International Conference Pattern Recognition, UK, August, 2004 .. [MOG2004] Z. Zivkovic. *Improved adaptive Gausian mixture model for background subtraction*. International Conference Pattern Recognition, UK, August, 2004

6
modules/gpubgsegm/doc/gpubgsegm.rst
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@ -1,6 +1,6 @@
******************************************** **************************************************
gpu. GPU-accelerated Background Segmentation gpubgsegm. GPU-accelerated Background Segmentation
******************************************** **************************************************
.. toctree:: .. toctree::
:maxdepth: 1 :maxdepth: 1

4
modules/gpubgsegm/include/opencv2/gpubgsegm.hpp
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@ -43,6 +43,10 @@
#ifndef __OPENCV_GPUBGSEGM_HPP__ #ifndef __OPENCV_GPUBGSEGM_HPP__
#define __OPENCV_GPUBGSEGM_HPP__ #define __OPENCV_GPUBGSEGM_HPP__
#ifndef __cplusplus
# error gpubgsegm.hpp header must be compiled as C++
#endif
#include <memory> #include <memory>
#include "opencv2/core/gpumat.hpp" #include "opencv2/core/gpumat.hpp"

11
modules/gpubgsegm/perf/perf_bgfg.cpp → modules/gpubgsegm/perf/perf_bgsegm.cpp
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@ -41,6 +41,7 @@
//M*/ //M*/
#include "perf_precomp.hpp" #include "perf_precomp.hpp"
#include "opencv2/legacy.hpp"
using namespace std; using namespace std;
using namespace testing; using namespace testing;
@ -73,7 +74,7 @@ namespace cv
DEF_PARAM_TEST_1(Video, string); DEF_PARAM_TEST_1(Video, string);
PERF_TEST_P(Video, Video_FGDStatModel, PERF_TEST_P(Video, FGDStatModel,
Values(string("gpu/video/768x576.avi"))) Values(string("gpu/video/768x576.avi")))
{ {
declare.time(60); declare.time(60);
@ -146,7 +147,7 @@ PERF_TEST_P(Video, Video_FGDStatModel,
DEF_PARAM_TEST(Video_Cn_LearningRate, string, MatCn, double); DEF_PARAM_TEST(Video_Cn_LearningRate, string, MatCn, double);
PERF_TEST_P(Video_Cn_LearningRate, Video_MOG, PERF_TEST_P(Video_Cn_LearningRate, MOG,
Combine(Values("gpu/video/768x576.avi", "gpu/video/1920x1080.avi"), Combine(Values("gpu/video/768x576.avi", "gpu/video/1920x1080.avi"),
GPU_CHANNELS_1_3_4, GPU_CHANNELS_1_3_4,
Values(0.0, 0.01))) Values(0.0, 0.01)))
@ -245,7 +246,7 @@ PERF_TEST_P(Video_Cn_LearningRate, Video_MOG,
DEF_PARAM_TEST(Video_Cn, string, int); DEF_PARAM_TEST(Video_Cn, string, int);
PERF_TEST_P(Video_Cn, Video_MOG2, PERF_TEST_P(Video_Cn, MOG2,
Combine(Values("gpu/video/768x576.avi", "gpu/video/1920x1080.avi"), Combine(Values("gpu/video/768x576.avi", "gpu/video/1920x1080.avi"),
GPU_CHANNELS_1_3_4)) GPU_CHANNELS_1_3_4))
{ {
@ -344,7 +345,7 @@ PERF_TEST_P(Video_Cn, Video_MOG2,
#if BUILD_WITH_VIDEO_INPUT_SUPPORT #if BUILD_WITH_VIDEO_INPUT_SUPPORT
PERF_TEST_P(Video_Cn, Video_MOG2GetBackgroundImage, PERF_TEST_P(Video_Cn, MOG2GetBackgroundImage,
Combine(Values("gpu/video/768x576.avi", "gpu/video/1920x1080.avi"), Combine(Values("gpu/video/768x576.avi", "gpu/video/1920x1080.avi"),
GPU_CHANNELS_1_3_4)) GPU_CHANNELS_1_3_4))
{ {
@ -428,7 +429,7 @@ PERF_TEST_P(Video_Cn, Video_MOG2GetBackgroundImage,
DEF_PARAM_TEST(Video_Cn_MaxFeatures, string, MatCn, int); DEF_PARAM_TEST(Video_Cn_MaxFeatures, string, MatCn, int);
PERF_TEST_P(Video_Cn_MaxFeatures, Video_GMG, PERF_TEST_P(Video_Cn_MaxFeatures, GMG,
Combine(Values(string("gpu/video/768x576.avi")), Combine(Values(string("gpu/video/768x576.avi")),
GPU_CHANNELS_1_3_4, GPU_CHANNELS_1_3_4,
Values(20, 40, 60))) Values(20, 40, 60)))

2
modules/gpubgsegm/perf/perf_main.cpp
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@ -44,4 +44,4 @@
using namespace perf; using namespace perf;
CV_PERF_TEST_MAIN(gpuarithm, printCudaInfo()) CV_PERF_TEST_MAIN(gpubgsegm, printCudaInfo())

2
modules/gpubgsegm/perf/perf_precomp.hpp
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@ -55,9 +55,7 @@
#include "opencv2/ts/gpu_perf.hpp" #include "opencv2/ts/gpu_perf.hpp"
#include "opencv2/gpubgsegm.hpp" #include "opencv2/gpubgsegm.hpp"
#include "opencv2/video.hpp" #include "opencv2/video.hpp"
#include "opencv2/legacy.hpp"
#ifdef GTEST_CREATE_SHARED_LIBRARY #ifdef GTEST_CREATE_SHARED_LIBRARY
#error no modules except ts should have GTEST_CREATE_SHARED_LIBRARY defined #error no modules except ts should have GTEST_CREATE_SHARED_LIBRARY defined

2
modules/gpubgsegm/src/cuda/fgd_bgfg.cu → modules/gpubgsegm/src/cuda/fgd.cu
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@ -48,7 +48,7 @@
#include "opencv2/core/cuda/utility.hpp" #include "opencv2/core/cuda/utility.hpp"
#include "opencv2/core/cuda/reduce.hpp" #include "opencv2/core/cuda/reduce.hpp"
#include "opencv2/core/cuda/functional.hpp" #include "opencv2/core/cuda/functional.hpp"
#include "fgd_bgfg_common.hpp" #include "fgd.hpp"
using namespace cv::gpu; using namespace cv::gpu;
using namespace cv::gpu::cudev; using namespace cv::gpu::cudev;

0
modules/gpubgsegm/src/cuda/fgd_bgfg_common.hpp → modules/gpubgsegm/src/cuda/fgd.hpp
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0
modules/gpubgsegm/src/cuda/bgfg_gmg.cu → modules/gpubgsegm/src/cuda/gmg.cu
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0
modules/gpubgsegm/src/cuda/bgfg_mog.cu → modules/gpubgsegm/src/cuda/mog.cu
View File

2
modules/gpubgsegm/src/fgd_bgfg.cpp → modules/gpubgsegm/src/fgd.cpp
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@ -59,7 +59,7 @@ int cv::gpu::FGDStatModel::update(const cv::gpu::GpuMat&) { throw_no_cuda(); ret
#else #else
#include "fgd_bgfg_common.hpp" #include "cuda/fgd.hpp"
#include "opencv2/imgproc/imgproc_c.h" #include "opencv2/imgproc/imgproc_c.h"
namespace namespace

0
modules/gpubgsegm/src/bgfg_gmg.cpp → modules/gpubgsegm/src/gmg.cpp
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0
modules/gpubgsegm/src/bgfg_mog.cpp → modules/gpubgsegm/src/mog.cpp
View File

13
modules/gpubgsegm/src/precomp.hpp
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@ -46,23 +46,10 @@
#include <limits> #include <limits>
#include "opencv2/gpubgsegm.hpp" #include "opencv2/gpubgsegm.hpp"
#include "opencv2/gpuarithm.hpp" #include "opencv2/gpuarithm.hpp"
#include "opencv2/gpufilters.hpp" #include "opencv2/gpufilters.hpp"
#include "opencv2/gpuimgproc.hpp" #include "opencv2/gpuimgproc.hpp"
#include "opencv2/imgproc/imgproc_c.h"
#include "opencv2/video.hpp"
#include "opencv2/core/gpu_private.hpp" #include "opencv2/core/gpu_private.hpp"
#include "opencv2/opencv_modules.hpp"
#ifdef HAVE_OPENCV_GPULEGACY
# include "opencv2/gpulegacy/private.hpp"
#endif
#ifdef HAVE_CUDA
# include "cuda/fgd_bgfg_common.hpp"
#endif
#endif /* __OPENCV_PRECOMP_H__ */ #endif /* __OPENCV_PRECOMP_H__ */

8
modules/gpubgsegm/test/test_bgfg.cpp → modules/gpubgsegm/test/test_bgsegm.cpp
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@ -143,7 +143,7 @@ GPU_TEST_P(FGDStatModel, Update)
} }
} }
INSTANTIATE_TEST_CASE_P(GPU_Video, FGDStatModel, testing::Combine( INSTANTIATE_TEST_CASE_P(GPU_BgSegm, FGDStatModel, testing::Combine(
ALL_DEVICES, ALL_DEVICES,
testing::Values(std::string("768x576.avi")), testing::Values(std::string("768x576.avi")),
testing::Values(Channels(3), Channels(4)))); testing::Values(Channels(3), Channels(4))));
@ -219,7 +219,7 @@ GPU_TEST_P(MOG, Update)
} }
} }
INSTANTIATE_TEST_CASE_P(GPU_Video, MOG, testing::Combine( INSTANTIATE_TEST_CASE_P(GPU_BgSegm, MOG, testing::Combine(
ALL_DEVICES, ALL_DEVICES,
testing::Values(std::string("768x576.avi")), testing::Values(std::string("768x576.avi")),
testing::Values(UseGray(true), UseGray(false)), testing::Values(UseGray(true), UseGray(false)),
@ -339,7 +339,7 @@ GPU_TEST_P(MOG2, getBackgroundImage)
ASSERT_MAT_NEAR(background_gold, background, 0); ASSERT_MAT_NEAR(background_gold, background, 0);
} }
INSTANTIATE_TEST_CASE_P(GPU_Video, MOG2, testing::Combine( INSTANTIATE_TEST_CASE_P(GPU_BgSegm, MOG2, testing::Combine(
ALL_DEVICES, ALL_DEVICES,
testing::Values(std::string("768x576.avi")), testing::Values(std::string("768x576.avi")),
testing::Values(UseGray(true), UseGray(false)), testing::Values(UseGray(true), UseGray(false)),
@ -395,7 +395,7 @@ GPU_TEST_P(GMG, Accuracy)
ASSERT_MAT_NEAR(fullfg, d_fgmask, 0); ASSERT_MAT_NEAR(fullfg, d_fgmask, 0);
} }
INSTANTIATE_TEST_CASE_P(GPU_Video, GMG, testing::Combine( INSTANTIATE_TEST_CASE_P(GPU_BgSegm, GMG, testing::Combine(
ALL_DEVICES, ALL_DEVICES,
DIFFERENT_SIZES, DIFFERENT_SIZES,
testing::Values(MatType(CV_8U), MatType(CV_16U), MatType(CV_32F)), testing::Values(MatType(CV_8U), MatType(CV_16U), MatType(CV_32F)),

2
modules/gpubgsegm/test/test_precomp.hpp
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@ -57,8 +57,6 @@
#include "opencv2/ts/gpu_test.hpp" #include "opencv2/ts/gpu_test.hpp"
#include "opencv2/gpubgsegm.hpp" #include "opencv2/gpubgsegm.hpp"
#include "opencv2/video.hpp" #include "opencv2/video.hpp"
#include "opencv2/legacy.hpp"
#endif #endif