--- /dev/null
+#include "perf_precomp.hpp"
+#include "opencv2/ts/ocl_perf.hpp"
+
+#ifdef HAVE_OPENCL
+
+#if defined(HAVE_XINE) || \
+defined(HAVE_GSTREAMER) || \
+defined(HAVE_QUICKTIME) || \
+defined(HAVE_AVFOUNDATION) || \
+defined(HAVE_FFMPEG) || \
+defined(WIN32)
+
+# define BUILD_WITH_VIDEO_INPUT_SUPPORT 1
+#else
+# define BUILD_WITH_VIDEO_INPUT_SUPPORT 0
+#endif
+
+#if BUILD_WITH_VIDEO_INPUT_SUPPORT
+
+namespace cvtest {
+namespace ocl {
+
+//////////////////////////// Mog2//////////////////////////
+
+typedef tuple<string, int> VideoMOG2ParamType;
+typedef TestBaseWithParam<VideoMOG2ParamType> MOG2_GetBackgroundImage;
+
+static void cvtFrameFmt(vector<Mat>& input, vector<Mat>& output)
+{
+ for(int i = 0; i< (int)(input.size()); i++)
+ {
+ cvtColor(input[i], output[i], COLOR_RGB2GRAY);
+ }
+}
+
+static void prepareData(VideoCapture& cap, int cn, vector<Mat>& frame_buffer)
+{
+ cv::Mat frame;
+ std::vector<Mat> frame_buffer_init;
+ int nFrame = (int)frame_buffer.size();
+ for(int i = 0; i < nFrame; i++)
+ {
+ cap >> frame;
+ ASSERT_FALSE(frame.empty());
+ frame_buffer_init.push_back(frame);
+ }
+
+ if(cn == 1)
+ cvtFrameFmt(frame_buffer_init, frame_buffer);
+ else
+ frame_buffer = frame_buffer_init;
+}
+
+OCL_PERF_TEST_P(MOG2_GetBackgroundImage, Mog2, Combine(Values("gpu/video/768x576.avi", "gpu/video/1920x1080.avi"), Values(1,3)))
+{
+ VideoMOG2ParamType params = GetParam();
+
+ const string inputFile = getDataPath(get<0>(params));
+
+ const int cn = get<1>(params);
+ int nFrame = 5;
+
+ vector<Mat> frame_buffer(nFrame);
+
+ cv::VideoCapture cap(inputFile);
+ ASSERT_TRUE(cap.isOpened());
+ prepareData(cap, cn, frame_buffer);
+
+ UMat u_foreground, u_background;
+
+ OCL_TEST_CYCLE()
+ {
+ Ptr<cv::BackgroundSubtractorMOG2> mog2 = createBackgroundSubtractorMOG2();
+ mog2->setDetectShadows(false);
+ u_foreground.release();
+ u_background.release();
+ for (int i = 0; i < nFrame; i++)
+ {
+ mog2->apply(frame_buffer[i], u_foreground);
+ }
+ mog2->getBackgroundImage(u_background);
+ }
+ SANITY_CHECK(u_background);
+}
+
+}}// namespace cvtest::ocl
+
+ #endif
+#endif
\ No newline at end of file
///////////*/
#include "precomp.hpp"
+#include "opencl_kernels.hpp"
namespace cv
{
fCT = defaultfCT2;
nShadowDetection = defaultnShadowDetection2;
fTau = defaultfTau;
+
+ opencl_ON = true;
}
//! the full constructor that takes the length of the history,
// the number of gaussian mixtures, the background ratio parameter and the noise strength
nShadowDetection = defaultnShadowDetection2;
fTau = defaultfTau;
name_ = "BackgroundSubtractor.MOG2";
+
+ opencl_ON = true;
}
//! the destructor
~BackgroundSubtractorMOG2Impl() {}
int nchannels = CV_MAT_CN(frameType);
CV_Assert( nchannels <= CV_CN_MAX );
- // for each gaussian mixture of each pixel bg model we store ...
- // the mixture weight (w),
- // the mean (nchannels values) and
- // the covariance
- bgmodel.create( 1, frameSize.height*frameSize.width*nmixtures*(2 + nchannels), CV_32F );
- //make the array for keeping track of the used modes per pixel - all zeros at start
- bgmodelUsedModes.create(frameSize,CV_8U);
- bgmodelUsedModes = Scalar::all(0);
+ if (ocl::useOpenCL() && opencl_ON)
+ {
+ kernel_apply.create("mog2_kernel", ocl::video::bgfg_mog2_oclsrc, format("-D CN=%d -D NMIXTURES=%d", nchannels, nmixtures));
+ kernel_getBg.create("getBackgroundImage2_kernel", ocl::video::bgfg_mog2_oclsrc, format( "-D CN=%d -D NMIXTURES=%d", nchannels, nmixtures));
+
+ if (kernel_apply.empty() || kernel_getBg.empty())
+ opencl_ON = false;
+ }
+ else opencl_ON = false;
+
+ if (opencl_ON)
+ {
+ u_weight.create(frameSize.height * nmixtures, frameSize.width, CV_32FC1);
+ u_weight.setTo(Scalar::all(0));
+
+ u_variance.create(frameSize.height * nmixtures, frameSize.width, CV_32FC1);
+ u_variance.setTo(Scalar::all(0));
+
+ if (nchannels==3)
+ nchannels=4;
+ u_mean.create(frameSize.height * nmixtures, frameSize.width, CV_32FC(nchannels)); //4 channels
+ u_mean.setTo(Scalar::all(0));
+
+ //make the array for keeping track of the used modes per pixel - all zeros at start
+ u_bgmodelUsedModes.create(frameSize, CV_32FC1);
+ u_bgmodelUsedModes.setTo(cv::Scalar::all(0));
+ }
+ else
+ {
+ // for each gaussian mixture of each pixel bg model we store ...
+ // the mixture weight (w),
+ // the mean (nchannels values) and
+ // the covariance
+ bgmodel.create( 1, frameSize.height*frameSize.width*nmixtures*(2 + nchannels), CV_32F );
+ //make the array for keeping track of the used modes per pixel - all zeros at start
+ bgmodelUsedModes.create(frameSize,CV_8U);
+ bgmodelUsedModes = Scalar::all(0);
+ }
}
virtual AlgorithmInfo* info() const { return 0; }
int frameType;
Mat bgmodel;
Mat bgmodelUsedModes;//keep track of number of modes per pixel
+
+ //for OCL
+
+ mutable bool opencl_ON;
+
+ UMat u_weight;
+ UMat u_variance;
+ UMat u_mean;
+ UMat u_bgmodelUsedModes;
+
+ mutable ocl::Kernel kernel_apply;
+ mutable ocl::Kernel kernel_getBg;
+
int nframes;
int history;
int nmixtures;
//See: Prati,Mikic,Trivedi,Cucchiarra,"Detecting Moving Shadows...",IEEE PAMI,2003.
String name_;
+
+ bool ocl_getBackgroundImage(OutputArray backgroundImage) const;
+ bool ocl_apply(InputArray _image, OutputArray _fgmask, bool needToInitialize, double learningRate=-1);
};
struct GaussBGStatModel2Params
uchar shadowVal;
};
+bool BackgroundSubtractorMOG2Impl::ocl_apply(InputArray _image, OutputArray _fgmask, bool needToInitialize, double learningRate)
+{
+ ++nframes;
+ learningRate = learningRate >= 0 && nframes > 1 ? learningRate : 1./std::min( 2*nframes, history );
+ CV_Assert(learningRate >= 0);
+
+ UMat fgmask(_image.size(), CV_32SC1);
+
+ fgmask.setTo(cv::Scalar::all(1));
+
+ const float alpha1 = 1.0f - learningRate;
+
+ int detectShadows_flag = 0;
+ if(bShadowDetection)
+ detectShadows_flag = 1;
+
+ UMat frame = _image.getUMat();
+
+ float varMax = MAX(fVarMin, fVarMax);
+ float varMin = MIN(fVarMin, fVarMax);
+
+ int idxArg = 0;
+ idxArg = kernel_apply.set(idxArg, ocl::KernelArg::ReadOnly(frame));
+ idxArg = kernel_apply.set(idxArg, ocl::KernelArg::ReadWriteNoSize(u_bgmodelUsedModes));
+ idxArg = kernel_apply.set(idxArg, ocl::KernelArg::ReadWriteNoSize(u_weight));
+ idxArg = kernel_apply.set(idxArg, ocl::KernelArg::ReadWriteNoSize(u_mean));
+ idxArg = kernel_apply.set(idxArg, ocl::KernelArg::ReadWriteNoSize(u_variance));
+ idxArg = kernel_apply.set(idxArg, ocl::KernelArg::WriteOnlyNoSize(fgmask));
+
+ idxArg = kernel_apply.set(idxArg, (float)learningRate); //alphaT
+ idxArg = kernel_apply.set(idxArg, (float)alpha1);
+ idxArg = kernel_apply.set(idxArg, (float)(-learningRate*fCT)); //prune
+ idxArg = kernel_apply.set(idxArg, detectShadows_flag);
+
+ idxArg = kernel_apply.set(idxArg, (float)varThreshold); //c_Tb
+ idxArg = kernel_apply.set(idxArg, backgroundRatio); //c_TB
+ idxArg = kernel_apply.set(idxArg, varThresholdGen); //c_Tg
+ idxArg = kernel_apply.set(idxArg, varMin);
+ idxArg = kernel_apply.set(idxArg, varMax);
+ idxArg = kernel_apply.set(idxArg, fVarInit);
+ idxArg = kernel_apply.set(idxArg, fTau);
+ idxArg = kernel_apply.set(idxArg, nShadowDetection);
+
+ size_t globalsize[] = {frame.cols, frame.rows, 1};
+
+ if (!(kernel_apply.run(2, globalsize, NULL, true)))
+ return false;
+
+ _fgmask.create(_image.size(),CV_8U);
+ UMat temp = _fgmask.getUMat();
+ fgmask.convertTo(temp, CV_8U);
+
+ return true;
+}
+
void BackgroundSubtractorMOG2Impl::apply(InputArray _image, OutputArray _fgmask, double learningRate)
{
- Mat image = _image.getMat();
- bool needToInitialize = nframes == 0 || learningRate >= 1 || image.size() != frameSize || image.type() != frameType;
+ bool needToInitialize = nframes == 0 || learningRate >= 1 || _image.size() != frameSize || _image.type() != frameType;
if( needToInitialize )
- initialize(image.size(), image.type());
+ initialize(_image.size(), _image.type());
+ if (opencl_ON)
+ {
+ if (ocl_apply(_image,_fgmask, needToInitialize, learningRate))
+ return;
+ else
+ initialize(_image.size(), _image.type());
+ }
+ opencl_ON = false;
+
+ Mat image = _image.getMat();
_fgmask.create( image.size(), CV_8U );
Mat fgmask = _fgmask.getMat();
image.total()/(double)(1 << 16));
}
+bool BackgroundSubtractorMOG2Impl::ocl_getBackgroundImage(OutputArray _backgroundImage) const
+{
+ CV_Assert(frameType == CV_8UC1 || frameType == CV_8UC3);
+
+ _backgroundImage.create(frameSize, frameType);
+ UMat dst = _backgroundImage.getUMat();
+
+ int idxArg = 0;
+ idxArg = kernel_getBg.set(idxArg, ocl::KernelArg::ReadOnly(u_bgmodelUsedModes));
+ idxArg = kernel_getBg.set(idxArg, ocl::KernelArg::ReadOnlyNoSize(u_weight));
+ idxArg = kernel_getBg.set(idxArg, ocl::KernelArg::ReadOnlyNoSize(u_mean));
+ idxArg = kernel_getBg.set(idxArg, ocl::KernelArg::WriteOnlyNoSize(dst));
+ idxArg = kernel_getBg.set(idxArg, backgroundRatio);
+
+ size_t globalsize[2] = {u_bgmodelUsedModes.cols, u_bgmodelUsedModes.rows};
+
+ return kernel_getBg.run(2, globalsize, NULL, false);
+}
+
void BackgroundSubtractorMOG2Impl::getBackgroundImage(OutputArray backgroundImage) const
{
+ if (opencl_ON)
+ {
+ if (ocl_getBackgroundImage(backgroundImage));
+ return;
+
+ opencl_ON = false;
+ return;
+ }
+
int nchannels = CV_MAT_CN(frameType);
CV_Assert( nchannels == 3 );
Mat meanBackground(frameSize, CV_8UC3, Scalar::all(0));
}
}
-
Ptr<BackgroundSubtractorMOG2> createBackgroundSubtractorMOG2(int _history, double _varThreshold,
bool _bShadowDetection)
{
}
-/* End of file. */
+/* End of file. */
\ No newline at end of file
--- /dev/null
+#if CN==1
+
+#define T_MEAN float
+#define F_ZERO (0.0f)
+#define cnMode 1
+
+#define frameToMean(a, b) (b) = *(a);
+#define meanToFrame(a, b) *b = convert_uchar_sat(a);
+
+inline float sqr(float val)
+{
+ return val * val;
+}
+
+inline float sum(float val)
+{
+ return val;
+}
+
+#else
+
+#define T_MEAN float4
+#define F_ZERO (0.0f, 0.0f, 0.0f, 0.0f)
+#define cnMode 4
+
+#define meanToFrame(a, b)\
+ b[0] = convert_uchar_sat(a.x); \
+ b[1] = convert_uchar_sat(a.y); \
+ b[2] = convert_uchar_sat(a.z);
+
+#define frameToMean(a, b)\
+ b.x = a[0]; \
+ b.y = a[1]; \
+ b.z = a[2]; \
+ b.w = 0.0f;
+
+inline float sqr(const float4 val)
+{
+ return val.x * val.x + val.y * val.y + val.z * val.z;
+}
+
+inline float sum(const float4 val)
+{
+ return (val.x + val.y + val.z);
+}
+
+inline void swap4(__global float4* ptr, int x, int y, int k, int rows, int ptr_step)
+{
+ float4 val = ptr[(k * rows + y) * ptr_step + x];
+ ptr[(k * rows + y) * ptr_step + x] = ptr[((k + 1) * rows + y) * ptr_step + x];
+ ptr[((k + 1) * rows + y) * ptr_step + x] = val;
+}
+
+#endif
+
+inline void swap(__global float* ptr, int x, int y, int k, int rows, int ptr_step)
+{
+ float val = ptr[(k * rows + y) * ptr_step + x];
+ ptr[(k * rows + y) * ptr_step + x] = ptr[((k + 1) * rows + y) * ptr_step + x];
+ ptr[((k + 1) * rows + y) * ptr_step + x] = val;
+}
+
+__kernel void mog2_kernel(__global const uchar* frame, int frame_step, int frame_offset, int frame_row, int frame_col, //uchar || uchar3
+ __global uchar* modesUsed, int modesUsed_step, int modesUsed_offset, //int
+ __global uchar* weight, int weight_step, int weight_offset, //float
+ __global uchar* mean, int mean_step, int mean_offset, //T_MEAN=float || float4
+ __global uchar* variance, int var_step, int var_offset, //float
+ __global uchar* fgmask, int fgmask_step, int fgmask_offset, //int
+ float alphaT, float alpha1, float prune,
+ int detectShadows_flag,
+ float c_Tb, float c_TB, float c_Tg, float c_varMin, //constants
+ float c_varMax, float c_varInit, float c_tau, uchar c_shadowVal)
+{
+ int x = get_global_id(0);
+ int y = get_global_id(1);
+
+ weight_step/= sizeof(float);
+ var_step /= sizeof(float);
+ mean_step /= (sizeof(float)*cnMode);
+
+ if( x < frame_col && y < frame_row)
+ {
+ __global const uchar* _frame = (frame + mad24( y, frame_step, x*CN + frame_offset));
+ T_MEAN pix;
+ frameToMean(_frame, pix);
+
+ bool background = false; // true - the pixel classified as background
+
+ bool fitsPDF = false; //if it remains zero a new GMM mode will be added
+
+ __global int* _modesUsed = (__global int*)(modesUsed + mad24( y, modesUsed_step, x*(int)(sizeof(int))));
+ int nmodes = _modesUsed[0];
+ int nNewModes = nmodes; //current number of modes in GMM
+
+ float totalWeight = 0.0f;
+
+ __global float* _weight = (__global float*)(weight);
+ __global float* _variance = (__global float*)(variance);
+ __global T_MEAN* _mean = (__global T_MEAN*)(mean);
+
+ for (int mode = 0; mode < nmodes; ++mode)
+ {
+
+ float c_weight = alpha1 * _weight[(mode * frame_row + y) * weight_step + x] + prune;
+
+ if (!fitsPDF)
+ {
+ float c_var = _variance[(mode * frame_row + y) * var_step + x];
+
+ T_MEAN c_mean = _mean[(mode * frame_row + y) * mean_step + x];
+
+ T_MEAN diff = c_mean - pix;
+ float dist2 = sqr(diff);
+
+ if (totalWeight < c_TB && dist2 < c_Tb * c_var)
+ background = true;
+
+ if (dist2 < c_Tg * c_var)
+ {
+ fitsPDF = true;
+ c_weight += alphaT;
+ float k = alphaT / c_weight;
+
+ _mean[(mode * frame_row + y) * mean_step + x] = c_mean - k * diff;
+
+ float varnew = c_var + k * (dist2 - c_var);
+ varnew = fmax(varnew, c_varMin);
+ varnew = fmin(varnew, c_varMax);
+
+ _variance[(mode * frame_row + y) * var_step + x] = varnew;
+ for (int i = mode; i > 0; --i)
+ {
+ if (c_weight < _weight[((i - 1) * frame_row + y) * weight_step + x])
+ break;
+ swap(_weight, x, y, i - 1, frame_row, weight_step);
+ swap(_variance, x, y, i - 1, frame_row, var_step);
+ #if (CN==1)
+ swap(_mean, x, y, i - 1, frame_row, mean_step);
+ #else
+ swap4(_mean, x, y, i - 1, frame_row, mean_step);
+ #endif
+ }
+ }
+ } // !fitsPDF
+
+ if (c_weight < -prune)
+ {
+ c_weight = 0.0f;
+ nmodes--;
+ }
+
+ _weight[(mode * frame_row + y) * weight_step + x] = c_weight; //update weight by the calculated value
+ totalWeight += c_weight;
+ }
+
+ totalWeight = 1.f / totalWeight;
+ for (int mode = 0; mode < nmodes; ++mode)
+ _weight[(mode * frame_row + y) * weight_step + x] *= totalWeight;
+
+ nmodes = nNewModes;
+
+ if (!fitsPDF)
+ {
+ int mode = nmodes == (NMIXTURES) ? (NMIXTURES) - 1 : nmodes++;
+
+ if (nmodes == 1)
+ _weight[(mode * frame_row + y) * weight_step + x] = 1.f;
+ else
+ {
+ _weight[(mode * frame_row + y) * weight_step + x] = alphaT;
+
+ for (int i = 0; i < nmodes - 1; ++i)
+ _weight[(i * frame_row + y) * weight_step + x] *= alpha1;
+ }
+
+ _mean[(mode * frame_row + y) * mean_step + x] = pix;
+ _variance[(mode * frame_row + y) * var_step + x] = c_varInit;
+
+ for (int i = nmodes - 1; i > 0; --i)
+ {
+ if (alphaT < _weight[((i - 1) * frame_row + y) * weight_step + x])
+ break;
+
+ swap(_weight, x, y, i - 1, frame_row, weight_step);
+ swap(_variance, x, y, i - 1, frame_row, var_step);
+ #if (CN==1)
+ swap(_mean, x, y, i - 1, frame_row, mean_step);
+ #else
+ swap4(_mean, x, y, i - 1, frame_row, mean_step);
+ #endif
+ }
+ }
+
+ _modesUsed[0] = nmodes;
+ bool isShadow = false;
+ if (detectShadows_flag && !background)
+ {
+ float tWeight = 0.0f;
+
+ for (int mode = 0; mode < nmodes; ++mode)
+ {
+ T_MEAN c_mean = _mean[(mode * frame_row + y) * mean_step + x];
+
+ T_MEAN pix_mean = pix * c_mean;
+
+ float numerator = sum(pix_mean);
+ float denominator = sqr(c_mean);
+
+ if (denominator == 0)
+ break;
+
+ if (numerator <= denominator && numerator >= c_tau * denominator)
+ {
+ float a = numerator / denominator;
+
+ T_MEAN dD = a * c_mean - pix;
+
+ if (sqr(dD) < c_Tb * _variance[(mode * frame_row + y) * var_step + x] * a * a)
+ {
+ isShadow = true;
+ break;
+ }
+ }
+
+ tWeight += _weight[(mode * frame_row + y) * weight_step + x];
+ if (tWeight > c_TB)
+ break;
+ }
+ }
+ __global int* _fgmask = (__global int*)(fgmask + mad24(y, fgmask_step, x*(int)(sizeof(int)) + fgmask_offset));
+ *_fgmask = background ? 0 : isShadow ? c_shadowVal : 255;
+ }
+}
+
+__kernel void getBackgroundImage2_kernel(__global const uchar* modesUsed, int modesUsed_step, int modesUsed_offset, int modesUsed_row, int modesUsed_col,
+ __global const uchar* weight, int weight_step, int weight_offset,
+ __global const uchar* mean, int mean_step, int mean_offset,
+ __global uchar* dst, int dst_step, int dst_offset,
+ float c_TB)
+{
+ int x = get_global_id(0);
+ int y = get_global_id(1);
+
+ if(x < modesUsed_col && y < modesUsed_row)
+ {
+ __global int* _modesUsed = (__global int*)(modesUsed + mad24( y, modesUsed_step, x*(int)(sizeof(int))));
+ int nmodes = _modesUsed[0];
+
+ T_MEAN meanVal = (T_MEAN)F_ZERO;
+
+ float totalWeight = 0.0f;
+
+ for (int mode = 0; mode < nmodes; ++mode)
+ {
+ __global const float* _weight = (__global const float*)(weight + mad24(mode * modesUsed_row + y, weight_step, x*(int)(sizeof(float))));
+ float c_weight = _weight[0];
+
+ __global const T_MEAN* _mean = (__global const T_MEAN*)(mean + mad24(mode * modesUsed_row + y, mean_step, x*(int)(sizeof(float))*cnMode));
+ T_MEAN c_mean = _mean[0];
+ meanVal = meanVal + c_weight * c_mean;
+
+ totalWeight += c_weight;
+
+ if(totalWeight > c_TB)
+ break;
+ }
+
+ meanVal = meanVal * (1.f / totalWeight);
+ __global uchar* _dst = dst + y * dst_step + x*CN + dst_offset;
+ meanToFrame(meanVal, _dst);
+ }
+}
\ No newline at end of file
--- /dev/null
+#include "test_precomp.hpp"
+#include "opencv2/ts/ocl_test.hpp"
+
+#ifdef HAVE_OPENCL
+
+#if defined(HAVE_XINE) || \
+defined(HAVE_GSTREAMER) || \
+defined(HAVE_QUICKTIME) || \
+defined(HAVE_AVFOUNDATION) || \
+defined(HAVE_FFMPEG) || \
+defined(WIN32)
+
+# define BUILD_WITH_VIDEO_INPUT_SUPPORT 1
+#else
+# define BUILD_WITH_VIDEO_INPUT_SUPPORT 0
+#endif
+
+#if BUILD_WITH_VIDEO_INPUT_SUPPORT
+
+namespace cvtest {
+namespace ocl {
+
+////////////////////////// MOG2///////////////////////////////////
+
+namespace
+{
+ IMPLEMENT_PARAM_CLASS(UseGray, bool)
+ IMPLEMENT_PARAM_CLASS(DetectShadow, bool)
+}
+
+PARAM_TEST_CASE(Mog2, UseGray, DetectShadow, bool)
+{
+ bool useGray;
+ bool detectShadow;
+ bool useRoi;
+ virtual void SetUp()
+ {
+ useGray = GET_PARAM(0);
+ detectShadow = GET_PARAM(1);
+ useRoi = GET_PARAM(2);
+ }
+};
+
+OCL_TEST_P(Mog2, Update)
+{
+ string inputFile = string(TS::ptr()->get_data_path()) + "video/768x576.avi";
+ VideoCapture cap(inputFile);
+ ASSERT_TRUE(cap.isOpened());
+
+ Ptr<BackgroundSubtractorMOG2> mog2_cpu = createBackgroundSubtractorMOG2();
+ Ptr<BackgroundSubtractorMOG2> mog2_ocl = createBackgroundSubtractorMOG2();
+
+ mog2_cpu->setDetectShadows(detectShadow);
+ mog2_ocl->setDetectShadows(detectShadow);
+
+ Mat frame, foreground;
+ UMat u_foreground;
+
+ for (int i = 0; i < 10; ++i)
+ {
+ cap >> frame;
+ ASSERT_FALSE(frame.empty());
+
+ if (useGray)
+ {
+ Mat temp;
+ cvtColor(frame, temp, COLOR_BGR2GRAY);
+ swap(temp, frame);
+ }
+
+ OCL_OFF(mog2_cpu->apply(frame, foreground));
+ OCL_ON (mog2_ocl->apply(frame, u_foreground));
+
+ if (detectShadow)
+ EXPECT_MAT_SIMILAR(foreground, u_foreground, 15e-3)
+ else
+ EXPECT_MAT_NEAR(foreground, u_foreground, 0);
+ }
+}
+
+OCL_TEST_P(Mog2, getBackgroundImage)
+{
+ if (useGray)
+ return;
+
+ string inputFile = string(TS::ptr()->get_data_path()) + "video/768x576.avi";
+ VideoCapture cap(inputFile);
+ ASSERT_TRUE(cap.isOpened());
+
+ Ptr<BackgroundSubtractorMOG2> mog2_cpu = createBackgroundSubtractorMOG2();
+ Ptr<BackgroundSubtractorMOG2> mog2_ocl = createBackgroundSubtractorMOG2();
+
+ mog2_cpu->setDetectShadows(detectShadow);
+ mog2_ocl->setDetectShadows(detectShadow);
+
+ Mat frame, foreground;
+ UMat u_foreground;
+
+ for (int i = 0; i < 10; ++i)
+ {
+ cap >> frame;
+ ASSERT_FALSE(frame.empty());
+
+ OCL_OFF(mog2_cpu->apply(frame, foreground));
+ OCL_ON (mog2_ocl->apply(frame, u_foreground));
+ }
+
+ Mat background;
+ OCL_OFF(mog2_cpu->getBackgroundImage(background));
+
+ UMat u_background;
+ OCL_ON (mog2_ocl->getBackgroundImage(u_background));
+
+ EXPECT_MAT_NEAR(background, u_background, 1.0);
+}
+
+OCL_INSTANTIATE_TEST_CASE_P(OCL_Video, Mog2, Combine(
+ Values(UseGray(true), UseGray(false)),
+ Values(DetectShadow(true), DetectShadow(false)),
+ Bool())
+ );
+}}// namespace cvtest::ocl
+
+ #endif
+#endif
projects / profile / ivi / opencv.git / commitdiff