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提交 88e67545 编写于 作者: V Vladislav Vinogradov
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split mog sources

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modules/gpubgsegm/src/cuda/mog.cu
浏览文件 @ 88e67545
......@@ -111,14 +111,6 @@ namespace cv { namespace gpu { namespace cudev
0.0f);
}
template <class Ptr2D>
__device__ __forceinline__ void swap(Ptr2D& ptr, int x, int y, int k, int rows)
{
typename Ptr2D::elem_type val = ptr(k * rows + y, x);
ptr(k * rows + y, x) = ptr((k + 1) * rows + y, x);
ptr((k + 1) * rows + y, x) = val;
}
///////////////////////////////////////////////////////////////
// MOG without learning
......@@ -426,337 +418,6 @@ namespace cv { namespace gpu { namespace cudev
funcs[cn](weight, mean, dst, nmixtures, backgroundRatio, stream);
}
///////////////////////////////////////////////////////////////
// MOG2
__constant__ int c_nmixtures;
__constant__ float c_Tb;
__constant__ float c_TB;
__constant__ float c_Tg;
__constant__ float c_varInit;
__constant__ float c_varMin;
__constant__ float c_varMax;
__constant__ float c_tau;
__constant__ unsigned char c_shadowVal;
void loadConstants(int nmixtures, float Tb, float TB, float Tg, float varInit, float varMin, float varMax, float tau, unsigned char shadowVal)
{
varMin = ::fminf(varMin, varMax);
varMax = ::fmaxf(varMin, varMax);
cudaSafeCall( cudaMemcpyToSymbol(c_nmixtures, &nmixtures, sizeof(int)) );
cudaSafeCall( cudaMemcpyToSymbol(c_Tb, &Tb, sizeof(float)) );
cudaSafeCall( cudaMemcpyToSymbol(c_TB, &TB, sizeof(float)) );
cudaSafeCall( cudaMemcpyToSymbol(c_Tg, &Tg, sizeof(float)) );
cudaSafeCall( cudaMemcpyToSymbol(c_varInit, &varInit, sizeof(float)) );
cudaSafeCall( cudaMemcpyToSymbol(c_varMin, &varMin, sizeof(float)) );
cudaSafeCall( cudaMemcpyToSymbol(c_varMax, &varMax, sizeof(float)) );
cudaSafeCall( cudaMemcpyToSymbol(c_tau, &tau, sizeof(float)) );
cudaSafeCall( cudaMemcpyToSymbol(c_shadowVal, &shadowVal, sizeof(unsigned char)) );
}
template <bool detectShadows, typename SrcT, typename WorkT>
__global__ void mog2(const PtrStepSz<SrcT> frame, PtrStepb fgmask, PtrStepb modesUsed,
PtrStepf gmm_weight, PtrStepf gmm_variance, PtrStep<WorkT> gmm_mean,
const float alphaT, const float alpha1, const float prune)
{
const int x = blockIdx.x * blockDim.x + threadIdx.x;
const int y = blockIdx.y * blockDim.y + threadIdx.y;
if (x >= frame.cols || y >= frame.rows)
return;
WorkT pix = cvt(frame(y, x));
//calculate distances to the modes (+ sort)
//here we need to go in descending order!!!
bool background = false; // true - the pixel classified as background
//internal:
bool fitsPDF = false; //if it remains zero a new GMM mode will be added
int nmodes = modesUsed(y, x);
int nNewModes = nmodes; //current number of modes in GMM
float totalWeight = 0.0f;
//go through all modes
for (int mode = 0; mode < nmodes; ++mode)
{
//need only weight if fit is found
float weight = alpha1 * gmm_weight(mode * frame.rows + y, x) + prune;
//fit not found yet
if (!fitsPDF)
{
//check if it belongs to some of the remaining modes
float var = gmm_variance(mode * frame.rows + y, x);
WorkT mean = gmm_mean(mode * frame.rows + y, x);
//calculate difference and distance
WorkT diff = mean - pix;
float dist2 = sqr(diff);
//background? - Tb - usually larger than Tg
if (totalWeight < c_TB && dist2 < c_Tb * var)
background = true;
//check fit
if (dist2 < c_Tg * var)
{
//belongs to the mode
fitsPDF = true;
//update distribution
//update weight
weight += alphaT;
float k = alphaT / weight;
//update mean
gmm_mean(mode * frame.rows + y, x) = mean - k * diff;
//update variance
float varnew = var + k * (dist2 - var);
//limit the variance
varnew = ::fmaxf(varnew, c_varMin);
varnew = ::fminf(varnew, c_varMax);
gmm_variance(mode * frame.rows + y, x) = varnew;
//sort
//all other weights are at the same place and
//only the matched (iModes) is higher -> just find the new place for it
for (int i = mode; i > 0; --i)
{
//check one up
if (weight < gmm_weight((i - 1) * frame.rows + y, x))
break;
//swap one up
swap(gmm_weight, x, y, i - 1, frame.rows);
swap(gmm_variance, x, y, i - 1, frame.rows);
swap(gmm_mean, x, y, i - 1, frame.rows);
}
//belongs to the mode - bFitsPDF becomes 1
}
} // !fitsPDF
//check prune
if (weight < -prune)
{
weight = 0.0;
nmodes--;
}
gmm_weight(mode * frame.rows + y, x) = weight; //update weight by the calculated value
totalWeight += weight;
}
//renormalize weights
totalWeight = 1.f / totalWeight;
for (int mode = 0; mode < nmodes; ++mode)
gmm_weight(mode * frame.rows + y, x) *= totalWeight;
nmodes = nNewModes;
//make new mode if needed and exit
if (!fitsPDF)
{
// replace the weakest or add a new one
int mode = nmodes == c_nmixtures ? c_nmixtures - 1 : nmodes++;
if (nmodes == 1)
gmm_weight(mode * frame.rows + y, x) = 1.f;
else
{
gmm_weight(mode * frame.rows + y, x) = alphaT;
// renormalize all other weights
for (int i = 0; i < nmodes - 1; ++i)
gmm_weight(i * frame.rows + y, x) *= alpha1;
}
// init
gmm_mean(mode * frame.rows + y, x) = pix;
gmm_variance(mode * frame.rows + y, x) = c_varInit;
//sort
//find the new place for it
for (int i = nmodes - 1; i > 0; --i)
{
// check one up
if (alphaT < gmm_weight((i - 1) * frame.rows + y, x))
break;
//swap one up
swap(gmm_weight, x, y, i - 1, frame.rows);
swap(gmm_variance, x, y, i - 1, frame.rows);
swap(gmm_mean, x, y, i - 1, frame.rows);
}
}
//set the number of modes
modesUsed(y, x) = nmodes;
bool isShadow = false;
if (detectShadows && !background)
{
float tWeight = 0.0f;
// check all the components marked as background:
for (int mode = 0; mode < nmodes; ++mode)
{
WorkT mean = gmm_mean(mode * frame.rows + y, x);
WorkT pix_mean = pix * mean;
float numerator = sum(pix_mean);
float denominator = sqr(mean);
// no division by zero allowed
if (denominator == 0)
break;
// if tau < a < 1 then also check the color distortion
if (numerator <= denominator && numerator >= c_tau * denominator)
{
float a = numerator / denominator;
WorkT dD = a * mean - pix;
if (sqr(dD) < c_Tb * gmm_variance(mode * frame.rows + y, x) * a * a)
{
isShadow = true;
break;
}
};
tWeight += gmm_weight(mode * frame.rows + y, x);
if (tWeight > c_TB)
break;
}
}
fgmask(y, x) = background ? 0 : isShadow ? c_shadowVal : 255;
}
template <typename SrcT, typename WorkT>
void mog2_caller(PtrStepSzb frame, PtrStepSzb fgmask, PtrStepSzb modesUsed, PtrStepSzf weight, PtrStepSzf variance, PtrStepSzb mean,
float alphaT, float prune, bool detectShadows, cudaStream_t stream)
{
dim3 block(32, 8);
dim3 grid(divUp(frame.cols, block.x), divUp(frame.rows, block.y));
const float alpha1 = 1.0f - alphaT;
if (detectShadows)
{
cudaSafeCall( cudaFuncSetCacheConfig(mog2<true, SrcT, WorkT>, cudaFuncCachePreferL1) );
mog2<true, SrcT, WorkT><<<grid, block, 0, stream>>>((PtrStepSz<SrcT>) frame, fgmask, modesUsed,
weight, variance, (PtrStepSz<WorkT>) mean,
alphaT, alpha1, prune);
}
else
{
cudaSafeCall( cudaFuncSetCacheConfig(mog2<false, SrcT, WorkT>, cudaFuncCachePreferL1) );
mog2<false, SrcT, WorkT><<<grid, block, 0, stream>>>((PtrStepSz<SrcT>) frame, fgmask, modesUsed,
weight, variance, (PtrStepSz<WorkT>) mean,
alphaT, alpha1, prune);
}
cudaSafeCall( cudaGetLastError() );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
void mog2_gpu(PtrStepSzb frame, int cn, PtrStepSzb fgmask, PtrStepSzb modesUsed, PtrStepSzf weight, PtrStepSzf variance, PtrStepSzb mean,
float alphaT, float prune, bool detectShadows, cudaStream_t stream)
{
typedef void (*func_t)(PtrStepSzb frame, PtrStepSzb fgmask, PtrStepSzb modesUsed, PtrStepSzf weight, PtrStepSzf variance, PtrStepSzb mean, float alphaT, float prune, bool detectShadows, cudaStream_t stream);
static const func_t funcs[] =
{
0, mog2_caller<uchar, float>, 0, mog2_caller<uchar3, float3>, mog2_caller<uchar4, float4>
};
funcs[cn](frame, fgmask, modesUsed, weight, variance, mean, alphaT, prune, detectShadows, stream);
}
template <typename WorkT, typename OutT>
__global__ void getBackgroundImage2(const PtrStepSzb modesUsed, const PtrStepf gmm_weight, const PtrStep<WorkT> gmm_mean, PtrStep<OutT> dst)
{
const int x = blockIdx.x * blockDim.x + threadIdx.x;
const int y = blockIdx.y * blockDim.y + threadIdx.y;
if (x >= modesUsed.cols || y >= modesUsed.rows)
return;
int nmodes = modesUsed(y, x);
WorkT meanVal = VecTraits<WorkT>::all(0.0f);
float totalWeight = 0.0f;
for (int mode = 0; mode < nmodes; ++mode)
{
float weight = gmm_weight(mode * modesUsed.rows + y, x);
WorkT mean = gmm_mean(mode * modesUsed.rows + y, x);
meanVal = meanVal + weight * mean;
totalWeight += weight;
if(totalWeight > c_TB)
break;
}
meanVal = meanVal * (1.f / totalWeight);
dst(y, x) = saturate_cast<OutT>(meanVal);
}
template <typename WorkT, typename OutT>
void getBackgroundImage2_caller(PtrStepSzb modesUsed, PtrStepSzf weight, PtrStepSzb mean, PtrStepSzb dst, cudaStream_t stream)
{
dim3 block(32, 8);
dim3 grid(divUp(modesUsed.cols, block.x), divUp(modesUsed.rows, block.y));
cudaSafeCall( cudaFuncSetCacheConfig(getBackgroundImage2<WorkT, OutT>, cudaFuncCachePreferL1) );
getBackgroundImage2<WorkT, OutT><<<grid, block, 0, stream>>>(modesUsed, weight, (PtrStepSz<WorkT>) mean, (PtrStepSz<OutT>) dst);
cudaSafeCall( cudaGetLastError() );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
void getBackgroundImage2_gpu(int cn, PtrStepSzb modesUsed, PtrStepSzf weight, PtrStepSzb mean, PtrStepSzb dst, cudaStream_t stream)
{
typedef void (*func_t)(PtrStepSzb modesUsed, PtrStepSzf weight, PtrStepSzb mean, PtrStepSzb dst, cudaStream_t stream);
static const func_t funcs[] =
{
0, getBackgroundImage2_caller<float, uchar>, 0, getBackgroundImage2_caller<float3, uchar3>, getBackgroundImage2_caller<float4, uchar4>
};
funcs[cn](modesUsed, weight, mean, dst, stream);
}
}
}}}
......
modules/gpubgsegm/src/cuda/mog2.cu 0 → 100644
浏览文件 @ 88e67545
/*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.
//
//M*/
#if !defined CUDA_DISABLER
#include "opencv2/core/cuda/common.hpp"
#include "opencv2/core/cuda/vec_traits.hpp"
#include "opencv2/core/cuda/vec_math.hpp"
#include "opencv2/core/cuda/limits.hpp"
namespace cv { namespace gpu { namespace cudev
{
namespace mog2
{
///////////////////////////////////////////////////////////////
// Utility
__device__ __forceinline__ float cvt(uchar val)
{
return val;
}
__device__ __forceinline__ float3 cvt(const uchar3& val)
{
return make_float3(val.x, val.y, val.z);
}
__device__ __forceinline__ float4 cvt(const uchar4& val)
{
return make_float4(val.x, val.y, val.z, val.w);
}
__device__ __forceinline__ float sqr(float val)
{
return val * val;
}
__device__ __forceinline__ float sqr(const float3& val)
{
return val.x * val.x + val.y * val.y + val.z * val.z;
}
__device__ __forceinline__ float sqr(const float4& val)
{
return val.x * val.x + val.y * val.y + val.z * val.z;
}
__device__ __forceinline__ float sum(float val)
{
return val;
}
__device__ __forceinline__ float sum(const float3& val)
{
return val.x + val.y + val.z;
}
__device__ __forceinline__ float sum(const float4& val)
{
return val.x + val.y + val.z;
}
template <class Ptr2D>
__device__ __forceinline__ void swap(Ptr2D& ptr, int x, int y, int k, int rows)
{
typename Ptr2D::elem_type val = ptr(k * rows + y, x);
ptr(k * rows + y, x) = ptr((k + 1) * rows + y, x);
ptr((k + 1) * rows + y, x) = val;
}
///////////////////////////////////////////////////////////////
// MOG2
__constant__ int c_nmixtures;
__constant__ float c_Tb;
__constant__ float c_TB;
__constant__ float c_Tg;
__constant__ float c_varInit;
__constant__ float c_varMin;
__constant__ float c_varMax;
__constant__ float c_tau;
__constant__ unsigned char c_shadowVal;
void loadConstants(int nmixtures, float Tb, float TB, float Tg, float varInit, float varMin, float varMax, float tau, unsigned char shadowVal)
{
varMin = ::fminf(varMin, varMax);
varMax = ::fmaxf(varMin, varMax);
cudaSafeCall( cudaMemcpyToSymbol(c_nmixtures, &nmixtures, sizeof(int)) );
cudaSafeCall( cudaMemcpyToSymbol(c_Tb, &Tb, sizeof(float)) );
cudaSafeCall( cudaMemcpyToSymbol(c_TB, &TB, sizeof(float)) );
cudaSafeCall( cudaMemcpyToSymbol(c_Tg, &Tg, sizeof(float)) );
cudaSafeCall( cudaMemcpyToSymbol(c_varInit, &varInit, sizeof(float)) );
cudaSafeCall( cudaMemcpyToSymbol(c_varMin, &varMin, sizeof(float)) );
cudaSafeCall( cudaMemcpyToSymbol(c_varMax, &varMax, sizeof(float)) );
cudaSafeCall( cudaMemcpyToSymbol(c_tau, &tau, sizeof(float)) );
cudaSafeCall( cudaMemcpyToSymbol(c_shadowVal, &shadowVal, sizeof(unsigned char)) );
}
template <bool detectShadows, typename SrcT, typename WorkT>
__global__ void mog2(const PtrStepSz<SrcT> frame, PtrStepb fgmask, PtrStepb modesUsed,
PtrStepf gmm_weight, PtrStepf gmm_variance, PtrStep<WorkT> gmm_mean,
const float alphaT, const float alpha1, const float prune)
{
const int x = blockIdx.x * blockDim.x + threadIdx.x;
const int y = blockIdx.y * blockDim.y + threadIdx.y;
if (x >= frame.cols || y >= frame.rows)
return;
WorkT pix = cvt(frame(y, x));
//calculate distances to the modes (+ sort)
//here we need to go in descending order!!!
bool background = false; // true - the pixel classified as background
//internal:
bool fitsPDF = false; //if it remains zero a new GMM mode will be added
int nmodes = modesUsed(y, x);
int nNewModes = nmodes; //current number of modes in GMM
float totalWeight = 0.0f;
//go through all modes
for (int mode = 0; mode < nmodes; ++mode)
{
//need only weight if fit is found
float weight = alpha1 * gmm_weight(mode * frame.rows + y, x) + prune;
//fit not found yet
if (!fitsPDF)
{
//check if it belongs to some of the remaining modes
float var = gmm_variance(mode * frame.rows + y, x);
WorkT mean = gmm_mean(mode * frame.rows + y, x);
//calculate difference and distance
WorkT diff = mean - pix;
float dist2 = sqr(diff);
//background? - Tb - usually larger than Tg
if (totalWeight < c_TB && dist2 < c_Tb * var)
background = true;
//check fit
if (dist2 < c_Tg * var)
{
//belongs to the mode
fitsPDF = true;
//update distribution
//update weight
weight += alphaT;
float k = alphaT / weight;
//update mean
gmm_mean(mode * frame.rows + y, x) = mean - k * diff;
//update variance
float varnew = var + k * (dist2 - var);
//limit the variance
varnew = ::fmaxf(varnew, c_varMin);
varnew = ::fminf(varnew, c_varMax);
gmm_variance(mode * frame.rows + y, x) = varnew;
//sort
//all other weights are at the same place and
//only the matched (iModes) is higher -> just find the new place for it
for (int i = mode; i > 0; --i)
{
//check one up
if (weight < gmm_weight((i - 1) * frame.rows + y, x))
break;
//swap one up
swap(gmm_weight, x, y, i - 1, frame.rows);
swap(gmm_variance, x, y, i - 1, frame.rows);
swap(gmm_mean, x, y, i - 1, frame.rows);
}
//belongs to the mode - bFitsPDF becomes 1
}
} // !fitsPDF
//check prune
if (weight < -prune)
{
weight = 0.0;
nmodes--;
}
gmm_weight(mode * frame.rows + y, x) = weight; //update weight by the calculated value
totalWeight += weight;
}
//renormalize weights
totalWeight = 1.f / totalWeight;
for (int mode = 0; mode < nmodes; ++mode)
gmm_weight(mode * frame.rows + y, x) *= totalWeight;
nmodes = nNewModes;
//make new mode if needed and exit
if (!fitsPDF)
{
// replace the weakest or add a new one
int mode = nmodes == c_nmixtures ? c_nmixtures - 1 : nmodes++;
if (nmodes == 1)
gmm_weight(mode * frame.rows + y, x) = 1.f;
else
{
gmm_weight(mode * frame.rows + y, x) = alphaT;
// renormalize all other weights
for (int i = 0; i < nmodes - 1; ++i)
gmm_weight(i * frame.rows + y, x) *= alpha1;
}
// init
gmm_mean(mode * frame.rows + y, x) = pix;
gmm_variance(mode * frame.rows + y, x) = c_varInit;
//sort
//find the new place for it
for (int i = nmodes - 1; i > 0; --i)
{
// check one up
if (alphaT < gmm_weight((i - 1) * frame.rows + y, x))
break;
//swap one up
swap(gmm_weight, x, y, i - 1, frame.rows);
swap(gmm_variance, x, y, i - 1, frame.rows);
swap(gmm_mean, x, y, i - 1, frame.rows);
}
}
//set the number of modes
modesUsed(y, x) = nmodes;
bool isShadow = false;
if (detectShadows && !background)
{
float tWeight = 0.0f;
// check all the components marked as background:
for (int mode = 0; mode < nmodes; ++mode)
{
WorkT mean = gmm_mean(mode * frame.rows + y, x);
WorkT pix_mean = pix * mean;
float numerator = sum(pix_mean);
float denominator = sqr(mean);
// no division by zero allowed
if (denominator == 0)
break;
// if tau < a < 1 then also check the color distortion
if (numerator <= denominator && numerator >= c_tau * denominator)
{
float a = numerator / denominator;
WorkT dD = a * mean - pix;
if (sqr(dD) < c_Tb * gmm_variance(mode * frame.rows + y, x) * a * a)
{
isShadow = true;
break;
}
};
tWeight += gmm_weight(mode * frame.rows + y, x);
if (tWeight > c_TB)
break;
}
}
fgmask(y, x) = background ? 0 : isShadow ? c_shadowVal : 255;
}
template <typename SrcT, typename WorkT>
void mog2_caller(PtrStepSzb frame, PtrStepSzb fgmask, PtrStepSzb modesUsed, PtrStepSzf weight, PtrStepSzf variance, PtrStepSzb mean,
float alphaT, float prune, bool detectShadows, cudaStream_t stream)
{
dim3 block(32, 8);
dim3 grid(divUp(frame.cols, block.x), divUp(frame.rows, block.y));
const float alpha1 = 1.0f - alphaT;
if (detectShadows)
{
cudaSafeCall( cudaFuncSetCacheConfig(mog2<true, SrcT, WorkT>, cudaFuncCachePreferL1) );
mog2<true, SrcT, WorkT><<<grid, block, 0, stream>>>((PtrStepSz<SrcT>) frame, fgmask, modesUsed,
weight, variance, (PtrStepSz<WorkT>) mean,
alphaT, alpha1, prune);
}
else
{
cudaSafeCall( cudaFuncSetCacheConfig(mog2<false, SrcT, WorkT>, cudaFuncCachePreferL1) );
mog2<false, SrcT, WorkT><<<grid, block, 0, stream>>>((PtrStepSz<SrcT>) frame, fgmask, modesUsed,
weight, variance, (PtrStepSz<WorkT>) mean,
alphaT, alpha1, prune);
}
cudaSafeCall( cudaGetLastError() );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
void mog2_gpu(PtrStepSzb frame, int cn, PtrStepSzb fgmask, PtrStepSzb modesUsed, PtrStepSzf weight, PtrStepSzf variance, PtrStepSzb mean,
float alphaT, float prune, bool detectShadows, cudaStream_t stream)
{
typedef void (*func_t)(PtrStepSzb frame, PtrStepSzb fgmask, PtrStepSzb modesUsed, PtrStepSzf weight, PtrStepSzf variance, PtrStepSzb mean, float alphaT, float prune, bool detectShadows, cudaStream_t stream);
static const func_t funcs[] =
{
0, mog2_caller<uchar, float>, 0, mog2_caller<uchar3, float3>, mog2_caller<uchar4, float4>
};
funcs[cn](frame, fgmask, modesUsed, weight, variance, mean, alphaT, prune, detectShadows, stream);
}
template <typename WorkT, typename OutT>
__global__ void getBackgroundImage2(const PtrStepSzb modesUsed, const PtrStepf gmm_weight, const PtrStep<WorkT> gmm_mean, PtrStep<OutT> dst)
{
const int x = blockIdx.x * blockDim.x + threadIdx.x;
const int y = blockIdx.y * blockDim.y + threadIdx.y;
if (x >= modesUsed.cols || y >= modesUsed.rows)
return;
int nmodes = modesUsed(y, x);
WorkT meanVal = VecTraits<WorkT>::all(0.0f);
float totalWeight = 0.0f;
for (int mode = 0; mode < nmodes; ++mode)
{
float weight = gmm_weight(mode * modesUsed.rows + y, x);
WorkT mean = gmm_mean(mode * modesUsed.rows + y, x);
meanVal = meanVal + weight * mean;
totalWeight += weight;
if(totalWeight > c_TB)
break;
}
meanVal = meanVal * (1.f / totalWeight);
dst(y, x) = saturate_cast<OutT>(meanVal);
}
template <typename WorkT, typename OutT>
void getBackgroundImage2_caller(PtrStepSzb modesUsed, PtrStepSzf weight, PtrStepSzb mean, PtrStepSzb dst, cudaStream_t stream)
{
dim3 block(32, 8);
dim3 grid(divUp(modesUsed.cols, block.x), divUp(modesUsed.rows, block.y));
cudaSafeCall( cudaFuncSetCacheConfig(getBackgroundImage2<WorkT, OutT>, cudaFuncCachePreferL1) );
getBackgroundImage2<WorkT, OutT><<<grid, block, 0, stream>>>(modesUsed, weight, (PtrStepSz<WorkT>) mean, (PtrStepSz<OutT>) dst);
cudaSafeCall( cudaGetLastError() );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
void getBackgroundImage2_gpu(int cn, PtrStepSzb modesUsed, PtrStepSzf weight, PtrStepSzb mean, PtrStepSzb dst, cudaStream_t stream)
{
typedef void (*func_t)(PtrStepSzb modesUsed, PtrStepSzf weight, PtrStepSzb mean, PtrStepSzb dst, cudaStream_t stream);
static const func_t funcs[] =
{
0, getBackgroundImage2_caller<float, uchar>, 0, getBackgroundImage2_caller<float3, uchar3>, getBackgroundImage2_caller<float4, uchar4>
};
funcs[cn](modesUsed, weight, mean, dst, stream);
}
}
}}}
#endif /* CUDA_DISABLER */
modules/gpubgsegm/src/mog.cpp
浏览文件 @ 88e67545
......@@ -50,12 +50,6 @@ void cv::gpu::MOG_GPU::operator()(const cv::gpu::GpuMat&, cv::gpu::GpuMat&, floa
void cv::gpu::MOG_GPU::getBackgroundImage(GpuMat&, Stream&) const { throw_no_cuda(); }
void cv::gpu::MOG_GPU::release() {}
cv::gpu::MOG2_GPU::MOG2_GPU(int) { throw_no_cuda(); }
void cv::gpu::MOG2_GPU::initialize(cv::Size, int) { throw_no_cuda(); }
void cv::gpu::MOG2_GPU::operator()(const GpuMat&, GpuMat&, float, Stream&) { throw_no_cuda(); }
void cv::gpu::MOG2_GPU::getBackgroundImage(GpuMat&, Stream&) const { throw_no_cuda(); }
void cv::gpu::MOG2_GPU::release() {}
#else
namespace cv { namespace gpu { namespace cudev
......@@ -66,10 +60,6 @@ namespace cv { namespace gpu { namespace cudev
int nmixtures, float varThreshold, float learningRate, float backgroundRatio, float noiseSigma,
cudaStream_t stream);
void getBackgroundImage_gpu(int cn, PtrStepSzf weight, PtrStepSzb mean, PtrStepSzb dst, int nmixtures, float backgroundRatio, cudaStream_t stream);
void loadConstants(int nmixtures, float Tb, float TB, float Tg, float varInit, float varMin, float varMax, float tau, unsigned char shadowVal);
void mog2_gpu(PtrStepSzb frame, int cn, PtrStepSzb fgmask, PtrStepSzb modesUsed, PtrStepSzf weight, PtrStepSzf variance, PtrStepSzb mean, float alphaT, float prune, bool detectShadows, cudaStream_t stream);
void getBackgroundImage2_gpu(int cn, PtrStepSzb modesUsed, PtrStepSzf weight, PtrStepSzb mean, PtrStepSzb dst, cudaStream_t stream);
}
}}}
......@@ -165,115 +155,4 @@ void cv::gpu::MOG_GPU::release()
var_.release();
}
/////////////////////////////////////////////////////////////////
// MOG2
namespace mog2
{
// default parameters of gaussian background detection algorithm
const int defaultHistory = 500; // Learning rate; alpha = 1/defaultHistory2
const float defaultVarThreshold = 4.0f * 4.0f;
const int defaultNMixtures = 5; // maximal number of Gaussians in mixture
const float defaultBackgroundRatio = 0.9f; // threshold sum of weights for background test
const float defaultVarThresholdGen = 3.0f * 3.0f;
const float defaultVarInit = 15.0f; // initial variance for new components
const float defaultVarMax = 5.0f * defaultVarInit;
const float defaultVarMin = 4.0f;
// additional parameters
const float defaultfCT = 0.05f; // complexity reduction prior constant 0 - no reduction of number of components
const unsigned char defaultnShadowDetection = 127; // value to use in the segmentation mask for shadows, set 0 not to do shadow detection
const float defaultfTau = 0.5f; // Tau - shadow threshold, see the paper for explanation
}
cv::gpu::MOG2_GPU::MOG2_GPU(int nmixtures) :
frameSize_(0, 0), frameType_(0), nframes_(0)
{
nmixtures_ = nmixtures > 0 ? nmixtures : mog2::defaultNMixtures;
history = mog2::defaultHistory;
varThreshold = mog2::defaultVarThreshold;
bShadowDetection = true;
backgroundRatio = mog2::defaultBackgroundRatio;
fVarInit = mog2::defaultVarInit;
fVarMax = mog2::defaultVarMax;
fVarMin = mog2::defaultVarMin;
varThresholdGen = mog2::defaultVarThresholdGen;
fCT = mog2::defaultfCT;
nShadowDetection = mog2::defaultnShadowDetection;
fTau = mog2::defaultfTau;
}
void cv::gpu::MOG2_GPU::initialize(cv::Size frameSize, int frameType)
{
using namespace cv::gpu::cudev::mog;
CV_Assert(frameType == CV_8UC1 || frameType == CV_8UC3 || frameType == CV_8UC4);
frameSize_ = frameSize;
frameType_ = frameType;
nframes_ = 0;
int ch = CV_MAT_CN(frameType);
int work_ch = ch;
// for each gaussian mixture of each pixel bg model we store ...
// the mixture weight (w),
// the mean (nchannels values) and
// the covariance
weight_.create(frameSize.height * nmixtures_, frameSize_.width, CV_32FC1);
variance_.create(frameSize.height * nmixtures_, frameSize_.width, CV_32FC1);
mean_.create(frameSize.height * nmixtures_, frameSize_.width, CV_32FC(work_ch));
//make the array for keeping track of the used modes per pixel - all zeros at start
bgmodelUsedModes_.create(frameSize_, CV_8UC1);
bgmodelUsedModes_.setTo(cv::Scalar::all(0));
loadConstants(nmixtures_, varThreshold, backgroundRatio, varThresholdGen, fVarInit, fVarMin, fVarMax, fTau, nShadowDetection);
}
void cv::gpu::MOG2_GPU::operator()(const GpuMat& frame, GpuMat& fgmask, float learningRate, Stream& stream)
{
using namespace cv::gpu::cudev::mog;
int ch = frame.channels();
int work_ch = ch;
if (nframes_ == 0 || learningRate >= 1.0f || frame.size() != frameSize_ || work_ch != mean_.channels())
initialize(frame.size(), frame.type());
fgmask.create(frameSize_, CV_8UC1);
fgmask.setTo(cv::Scalar::all(0));
++nframes_;
learningRate = learningRate >= 0.0f && nframes_ > 1 ? learningRate : 1.0f / std::min(2 * nframes_, history);
CV_Assert(learningRate >= 0.0f);
mog2_gpu(frame, frame.channels(), fgmask, bgmodelUsedModes_, weight_, variance_, mean_, learningRate, -learningRate * fCT, bShadowDetection, StreamAccessor::getStream(stream));
}
void cv::gpu::MOG2_GPU::getBackgroundImage(GpuMat& backgroundImage, Stream& stream) const
{
using namespace cv::gpu::cudev::mog;
backgroundImage.create(frameSize_, frameType_);
getBackgroundImage2_gpu(backgroundImage.channels(), bgmodelUsedModes_, weight_, mean_, backgroundImage, StreamAccessor::getStream(stream));
}
void cv::gpu::MOG2_GPU::release()
{
frameSize_ = Size(0, 0);
frameType_ = 0;
nframes_ = 0;
weight_.release();
variance_.release();
mean_.release();
bgmodelUsedModes_.release();
}
#endif
modules/gpubgsegm/src/mog2.cpp 0 → 100644
浏览文件 @ 88e67545
/*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.
//
//M*/
#include "precomp.hpp"
#if !defined HAVE_CUDA || defined(CUDA_DISABLER)
cv::gpu::MOG2_GPU::MOG2_GPU(int) { throw_no_cuda(); }
void cv::gpu::MOG2_GPU::initialize(cv::Size, int) { throw_no_cuda(); }
void cv::gpu::MOG2_GPU::operator()(const GpuMat&, GpuMat&, float, Stream&) { throw_no_cuda(); }
void cv::gpu::MOG2_GPU::getBackgroundImage(GpuMat&, Stream&) const { throw_no_cuda(); }
void cv::gpu::MOG2_GPU::release() {}
#else
namespace cv { namespace gpu { namespace cudev
{
namespace mog2
{
void loadConstants(int nmixtures, float Tb, float TB, float Tg, float varInit, float varMin, float varMax, float tau, unsigned char shadowVal);
void mog2_gpu(PtrStepSzb frame, int cn, PtrStepSzb fgmask, PtrStepSzb modesUsed, PtrStepSzf weight, PtrStepSzf variance, PtrStepSzb mean, float alphaT, float prune, bool detectShadows, cudaStream_t stream);
void getBackgroundImage2_gpu(int cn, PtrStepSzb modesUsed, PtrStepSzf weight, PtrStepSzb mean, PtrStepSzb dst, cudaStream_t stream);
}
}}}
namespace mog2
{
// default parameters of gaussian background detection algorithm
const int defaultHistory = 500; // Learning rate; alpha = 1/defaultHistory2
const float defaultVarThreshold = 4.0f * 4.0f;
const int defaultNMixtures = 5; // maximal number of Gaussians in mixture
const float defaultBackgroundRatio = 0.9f; // threshold sum of weights for background test
const float defaultVarThresholdGen = 3.0f * 3.0f;
const float defaultVarInit = 15.0f; // initial variance for new components
const float defaultVarMax = 5.0f * defaultVarInit;
const float defaultVarMin = 4.0f;
// additional parameters
const float defaultfCT = 0.05f; // complexity reduction prior constant 0 - no reduction of number of components
const unsigned char defaultnShadowDetection = 127; // value to use in the segmentation mask for shadows, set 0 not to do shadow detection
const float defaultfTau = 0.5f; // Tau - shadow threshold, see the paper for explanation
}
cv::gpu::MOG2_GPU::MOG2_GPU(int nmixtures) :
frameSize_(0, 0), frameType_(0), nframes_(0)
{
nmixtures_ = nmixtures > 0 ? nmixtures : mog2::defaultNMixtures;
history = mog2::defaultHistory;
varThreshold = mog2::defaultVarThreshold;
bShadowDetection = true;
backgroundRatio = mog2::defaultBackgroundRatio;
fVarInit = mog2::defaultVarInit;
fVarMax = mog2::defaultVarMax;
fVarMin = mog2::defaultVarMin;
varThresholdGen = mog2::defaultVarThresholdGen;
fCT = mog2::defaultfCT;
nShadowDetection = mog2::defaultnShadowDetection;
fTau = mog2::defaultfTau;
}
void cv::gpu::MOG2_GPU::initialize(cv::Size frameSize, int frameType)
{
using namespace cv::gpu::cudev::mog2;
CV_Assert(frameType == CV_8UC1 || frameType == CV_8UC3 || frameType == CV_8UC4);
frameSize_ = frameSize;
frameType_ = frameType;
nframes_ = 0;
int ch = CV_MAT_CN(frameType);
int work_ch = ch;
// for each gaussian mixture of each pixel bg model we store ...
// the mixture weight (w),
// the mean (nchannels values) and
// the covariance
weight_.create(frameSize.height * nmixtures_, frameSize_.width, CV_32FC1);
variance_.create(frameSize.height * nmixtures_, frameSize_.width, CV_32FC1);
mean_.create(frameSize.height * nmixtures_, frameSize_.width, CV_32FC(work_ch));
//make the array for keeping track of the used modes per pixel - all zeros at start
bgmodelUsedModes_.create(frameSize_, CV_8UC1);
bgmodelUsedModes_.setTo(cv::Scalar::all(0));
loadConstants(nmixtures_, varThreshold, backgroundRatio, varThresholdGen, fVarInit, fVarMin, fVarMax, fTau, nShadowDetection);
}
void cv::gpu::MOG2_GPU::operator()(const GpuMat& frame, GpuMat& fgmask, float learningRate, Stream& stream)
{
using namespace cv::gpu::cudev::mog2;
int ch = frame.channels();
int work_ch = ch;
if (nframes_ == 0 || learningRate >= 1.0f || frame.size() != frameSize_ || work_ch != mean_.channels())
initialize(frame.size(), frame.type());
fgmask.create(frameSize_, CV_8UC1);
fgmask.setTo(cv::Scalar::all(0));
++nframes_;
learningRate = learningRate >= 0.0f && nframes_ > 1 ? learningRate : 1.0f / std::min(2 * nframes_, history);
CV_Assert(learningRate >= 0.0f);
mog2_gpu(frame, frame.channels(), fgmask, bgmodelUsedModes_, weight_, variance_, mean_, learningRate, -learningRate * fCT, bShadowDetection, StreamAccessor::getStream(stream));
}
void cv::gpu::MOG2_GPU::getBackgroundImage(GpuMat& backgroundImage, Stream& stream) const
{
using namespace cv::gpu::cudev::mog2;
backgroundImage.create(frameSize_, frameType_);
getBackgroundImage2_gpu(backgroundImage.channels(), bgmodelUsedModes_, weight_, mean_, backgroundImage, StreamAccessor::getStream(stream));
}
void cv::gpu::MOG2_GPU::release()
{
frameSize_ = Size(0, 0);
frameType_ = 0;
nframes_ = 0;
weight_.release();
variance_.release();
mean_.release();
bgmodelUsedModes_.release();
}
#endif
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