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/*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_math.hpp"
#include "opencv2/core/cuda/limits.hpp"
#include "opencv2/core/cuda/utility.hpp"
#include "opencv2/core/cuda/reduce.hpp"
#include "opencv2/core/cuda/functional.hpp"
#include "fgd.hpp"
using namespace cv::cuda;
using namespace cv::cuda::device;
namespace fgd
{
////////////////////////////////////////////////////////////////////////////
// calcDiffHistogram
const unsigned int UINT_BITS = 32U;
const int LOG_WARP_SIZE = 5;
const int WARP_SIZE = 1 << LOG_WARP_SIZE;
#if (__CUDA_ARCH__ < 120)
const unsigned int TAG_MASK = (1U << (UINT_BITS - LOG_WARP_SIZE)) - 1U;
#endif
const int MERGE_THREADBLOCK_SIZE = 256;
__device__ __forceinline__ void addByte(unsigned int* s_WarpHist_, unsigned int data, unsigned int threadTag)
{
#if (__CUDA_ARCH__ < 120)
volatile unsigned int* s_WarpHist = s_WarpHist_;
unsigned int count;
do
{
count = s_WarpHist[data] & TAG_MASK;
count = threadTag | (count + 1);
s_WarpHist[data] = count;
} while (s_WarpHist[data] != count);
#else
atomicInc(s_WarpHist_ + data, (unsigned int)(-1));
#endif
}
template <typename PT, typename CT>
__global__ void calcPartialHistogram(const PtrStepSz<PT> prevFrame, const PtrStep<CT> curFrame, unsigned int* partialBuf0, unsigned int* partialBuf1, unsigned int* partialBuf2)
{
#if (__CUDA_ARCH__ < 200)
const int HISTOGRAM_WARP_COUNT = 4;
#else
const int HISTOGRAM_WARP_COUNT = 6;
#endif
const int HISTOGRAM_THREADBLOCK_SIZE = HISTOGRAM_WARP_COUNT * WARP_SIZE;
const int HISTOGRAM_THREADBLOCK_MEMORY = HISTOGRAM_WARP_COUNT * HISTOGRAM_BIN_COUNT;
//Per-warp subhistogram storage
__shared__ unsigned int s_Hist0[HISTOGRAM_THREADBLOCK_MEMORY];
__shared__ unsigned int s_Hist1[HISTOGRAM_THREADBLOCK_MEMORY];
__shared__ unsigned int s_Hist2[HISTOGRAM_THREADBLOCK_MEMORY];
//Clear shared memory storage for current threadblock before processing
#pragma unroll
for (int i = 0; i < (HISTOGRAM_THREADBLOCK_MEMORY / HISTOGRAM_THREADBLOCK_SIZE); ++i)
{
s_Hist0[threadIdx.x + i * HISTOGRAM_THREADBLOCK_SIZE] = 0;
s_Hist1[threadIdx.x + i * HISTOGRAM_THREADBLOCK_SIZE] = 0;
s_Hist2[threadIdx.x + i * HISTOGRAM_THREADBLOCK_SIZE] = 0;
}
__syncthreads();
const unsigned int warpId = threadIdx.x >> LOG_WARP_SIZE;
unsigned int* s_WarpHist0 = s_Hist0 + warpId * HISTOGRAM_BIN_COUNT;
unsigned int* s_WarpHist1 = s_Hist1 + warpId * HISTOGRAM_BIN_COUNT;
unsigned int* s_WarpHist2 = s_Hist2 + warpId * HISTOGRAM_BIN_COUNT;
const unsigned int tag = threadIdx.x << (UINT_BITS - LOG_WARP_SIZE);
const int dataCount = prevFrame.rows * prevFrame.cols;
for (unsigned int pos = blockIdx.x * HISTOGRAM_THREADBLOCK_SIZE + threadIdx.x; pos < dataCount; pos += HISTOGRAM_THREADBLOCK_SIZE * PARTIAL_HISTOGRAM_COUNT)
{
const unsigned int y = pos / prevFrame.cols;
const unsigned int x = pos % prevFrame.cols;
PT prevVal = prevFrame(y, x);
CT curVal = curFrame(y, x);
int3 diff = make_int3(
::abs(curVal.x - prevVal.x),
::abs(curVal.y - prevVal.y),
::abs(curVal.z - prevVal.z)
);
addByte(s_WarpHist0, diff.x, tag);
addByte(s_WarpHist1, diff.y, tag);
addByte(s_WarpHist2, diff.z, tag);
}
__syncthreads();
//Merge per-warp histograms into per-block and write to global memory
for (unsigned int bin = threadIdx.x; bin < HISTOGRAM_BIN_COUNT; bin += HISTOGRAM_THREADBLOCK_SIZE)
{
unsigned int sum0 = 0;
unsigned int sum1 = 0;
unsigned int sum2 = 0;
#pragma unroll
for (int i = 0; i < HISTOGRAM_WARP_COUNT; ++i)
{
#if (__CUDA_ARCH__ < 120)
sum0 += s_Hist0[bin + i * HISTOGRAM_BIN_COUNT] & TAG_MASK;
sum1 += s_Hist1[bin + i * HISTOGRAM_BIN_COUNT] & TAG_MASK;
sum2 += s_Hist2[bin + i * HISTOGRAM_BIN_COUNT] & TAG_MASK;
#else
sum0 += s_Hist0[bin + i * HISTOGRAM_BIN_COUNT];
sum1 += s_Hist1[bin + i * HISTOGRAM_BIN_COUNT];
sum2 += s_Hist2[bin + i * HISTOGRAM_BIN_COUNT];
#endif
}
partialBuf0[blockIdx.x * HISTOGRAM_BIN_COUNT + bin] = sum0;
partialBuf1[blockIdx.x * HISTOGRAM_BIN_COUNT + bin] = sum1;
partialBuf2[blockIdx.x * HISTOGRAM_BIN_COUNT + bin] = sum2;
}
}
__global__ void mergeHistogram(const unsigned int* partialBuf0, const unsigned int* partialBuf1, const unsigned int* partialBuf2, unsigned int* hist0, unsigned int* hist1, unsigned int* hist2)
{
unsigned int sum0 = 0;
unsigned int sum1 = 0;
unsigned int sum2 = 0;
#pragma unroll
for (unsigned int i = threadIdx.x; i < PARTIAL_HISTOGRAM_COUNT; i += MERGE_THREADBLOCK_SIZE)
{
sum0 += partialBuf0[blockIdx.x + i * HISTOGRAM_BIN_COUNT];
sum1 += partialBuf1[blockIdx.x + i * HISTOGRAM_BIN_COUNT];
sum2 += partialBuf2[blockIdx.x + i * HISTOGRAM_BIN_COUNT];
}
__shared__ unsigned int data0[MERGE_THREADBLOCK_SIZE];
__shared__ unsigned int data1[MERGE_THREADBLOCK_SIZE];
__shared__ unsigned int data2[MERGE_THREADBLOCK_SIZE];
plus<unsigned int> op;
reduce<MERGE_THREADBLOCK_SIZE>(smem_tuple(data0, data1, data2), thrust::tie(sum0, sum1, sum2), threadIdx.x, thrust::make_tuple(op, op, op));
if(threadIdx.x == 0)
{
hist0[blockIdx.x] = sum0;
hist1[blockIdx.x] = sum1;
hist2[blockIdx.x] = sum2;
}
}
template <typename PT, typename CT>
void calcDiffHistogram_gpu(PtrStepSzb prevFrame, PtrStepSzb curFrame,
unsigned int* hist0, unsigned int* hist1, unsigned int* hist2,
unsigned int* partialBuf0, unsigned int* partialBuf1, unsigned int* partialBuf2,
bool cc20, cudaStream_t stream)
{
const int HISTOGRAM_WARP_COUNT = cc20 ? 6 : 4;
const int HISTOGRAM_THREADBLOCK_SIZE = HISTOGRAM_WARP_COUNT * WARP_SIZE;
calcPartialHistogram<PT, CT><<<PARTIAL_HISTOGRAM_COUNT, HISTOGRAM_THREADBLOCK_SIZE, 0, stream>>>(
(PtrStepSz<PT>)prevFrame, (PtrStepSz<CT>)curFrame, partialBuf0, partialBuf1, partialBuf2);
cudaSafeCall( cudaGetLastError() );
mergeHistogram<<<HISTOGRAM_BIN_COUNT, MERGE_THREADBLOCK_SIZE, 0, stream>>>(partialBuf0, partialBuf1, partialBuf2, hist0, hist1, hist2);
cudaSafeCall( cudaGetLastError() );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
template void calcDiffHistogram_gpu<uchar3, uchar3>(PtrStepSzb prevFrame, PtrStepSzb curFrame, unsigned int* hist0, unsigned int* hist1, unsigned int* hist2, unsigned int* partialBuf0, unsigned int* partialBuf1, unsigned int* partialBuf2, bool cc20, cudaStream_t stream);
template void calcDiffHistogram_gpu<uchar3, uchar4>(PtrStepSzb prevFrame, PtrStepSzb curFrame, unsigned int* hist0, unsigned int* hist1, unsigned int* hist2, unsigned int* partialBuf0, unsigned int* partialBuf1, unsigned int* partialBuf2, bool cc20, cudaStream_t stream);
template void calcDiffHistogram_gpu<uchar4, uchar3>(PtrStepSzb prevFrame, PtrStepSzb curFrame, unsigned int* hist0, unsigned int* hist1, unsigned int* hist2, unsigned int* partialBuf0, unsigned int* partialBuf1, unsigned int* partialBuf2, bool cc20, cudaStream_t stream);
template void calcDiffHistogram_gpu<uchar4, uchar4>(PtrStepSzb prevFrame, PtrStepSzb curFrame, unsigned int* hist0, unsigned int* hist1, unsigned int* hist2, unsigned int* partialBuf0, unsigned int* partialBuf1, unsigned int* partialBuf2, bool cc20, cudaStream_t stream);
/////////////////////////////////////////////////////////////////////////
// calcDiffThreshMask
template <typename PT, typename CT>
__global__ void calcDiffThreshMask(const PtrStepSz<PT> prevFrame, const PtrStep<CT> curFrame, uchar3 bestThres, PtrStepb changeMask)
{
const int y = blockIdx.y * blockDim.y + threadIdx.y;
const int x = blockIdx.x * blockDim.x + threadIdx.x;
if (y > prevFrame.rows || x > prevFrame.cols)
return;
PT prevVal = prevFrame(y, x);
CT curVal = curFrame(y, x);
int3 diff = make_int3(
::abs(curVal.x - prevVal.x),
::abs(curVal.y - prevVal.y),
::abs(curVal.z - prevVal.z)
);
if (diff.x > bestThres.x || diff.y > bestThres.y || diff.z > bestThres.z)
changeMask(y, x) = 255;
}
template <typename PT, typename CT>
void calcDiffThreshMask_gpu(PtrStepSzb prevFrame, PtrStepSzb curFrame, uchar3 bestThres, PtrStepSzb changeMask, cudaStream_t stream)
{
dim3 block(32, 8);
dim3 grid(divUp(prevFrame.cols, block.x), divUp(prevFrame.rows, block.y));
calcDiffThreshMask<PT, CT><<<grid, block, 0, stream>>>((PtrStepSz<PT>)prevFrame, (PtrStepSz<CT>)curFrame, bestThres, changeMask);
cudaSafeCall( cudaGetLastError() );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
template void calcDiffThreshMask_gpu<uchar3, uchar3>(PtrStepSzb prevFrame, PtrStepSzb curFrame, uchar3 bestThres, PtrStepSzb changeMask, cudaStream_t stream);
template void calcDiffThreshMask_gpu<uchar3, uchar4>(PtrStepSzb prevFrame, PtrStepSzb curFrame, uchar3 bestThres, PtrStepSzb changeMask, cudaStream_t stream);
template void calcDiffThreshMask_gpu<uchar4, uchar3>(PtrStepSzb prevFrame, PtrStepSzb curFrame, uchar3 bestThres, PtrStepSzb changeMask, cudaStream_t stream);
template void calcDiffThreshMask_gpu<uchar4, uchar4>(PtrStepSzb prevFrame, PtrStepSzb curFrame, uchar3 bestThres, PtrStepSzb changeMask, cudaStream_t stream);
/////////////////////////////////////////////////////////////////////////
// bgfgClassification
__constant__ BGPixelStat c_stat;
void setBGPixelStat(const BGPixelStat& stat)
{
cudaSafeCall( cudaMemcpyToSymbol(c_stat, &stat, sizeof(BGPixelStat)) );
}
template <typename T> struct Output;
template <> struct Output<uchar3>
{
static __device__ __forceinline__ uchar3 make(uchar v0, uchar v1, uchar v2)
{
return make_uchar3(v0, v1, v2);
}
};
template <> struct Output<uchar4>
{
static __device__ __forceinline__ uchar4 make(uchar v0, uchar v1, uchar v2)
{
return make_uchar4(v0, v1, v2, 255);
}
};
template <typename PT, typename CT, typename OT>
__global__ void bgfgClassification(const PtrStepSz<PT> prevFrame, const PtrStep<CT> curFrame,
const PtrStepb Ftd, const PtrStepb Fbd, PtrStepb foreground,
int deltaC, int deltaCC, float alpha2, int N1c, int N1cc)
{
const int i = blockIdx.y * blockDim.y + threadIdx.y;
const int j = blockIdx.x * blockDim.x + threadIdx.x;
if (i > prevFrame.rows || j > prevFrame.cols)
return;
if (Fbd(i, j) || Ftd(i, j))
{
float Pb = 0.0f;
float Pv = 0.0f;
float Pvb = 0.0f;
int val = 0;
// Is it a motion pixel?
if (Ftd(i, j))
{
if (!c_stat.is_trained_dyn_model(i, j))
val = 1;
else
{
PT prevVal = prevFrame(i, j);
CT curVal = curFrame(i, j);
// Compare with stored CCt vectors:
for (int k = 0; k < N1cc && c_stat.PV_CC(i, j, k) > alpha2; ++k)
{
OT v1 = c_stat.V1_CC<OT>(i, j, k);
OT v2 = c_stat.V2_CC<OT>(i, j, k);
if (::abs(v1.x - prevVal.x) <= deltaCC &&
::abs(v1.y - prevVal.y) <= deltaCC &&
::abs(v1.z - prevVal.z) <= deltaCC &&
::abs(v2.x - curVal.x) <= deltaCC &&
::abs(v2.y - curVal.y) <= deltaCC &&
::abs(v2.z - curVal.z) <= deltaCC)
{
Pv += c_stat.PV_CC(i, j, k);
Pvb += c_stat.PVB_CC(i, j, k);
}
}
Pb = c_stat.Pbcc(i, j);
if (2 * Pvb * Pb <= Pv)
val = 1;
}
}
else if(c_stat.is_trained_st_model(i, j))
{
CT curVal = curFrame(i, j);
// Compare with stored Ct vectors:
for (int k = 0; k < N1c && c_stat.PV_C(i, j, k) > alpha2; ++k)
{
OT v = c_stat.V_C<OT>(i, j, k);
if (::abs(v.x - curVal.x) <= deltaC &&
::abs(v.y - curVal.y) <= deltaC &&
::abs(v.z - curVal.z) <= deltaC)
{
Pv += c_stat.PV_C(i, j, k);
Pvb += c_stat.PVB_C(i, j, k);
}
}
Pb = c_stat.Pbc(i, j);
if (2 * Pvb * Pb <= Pv)
val = 1;
}
// Update foreground:
foreground(i, j) = static_cast<uchar>(val);
} // end if( change detection...
}
template <typename PT, typename CT, typename OT>
void bgfgClassification_gpu(PtrStepSzb prevFrame, PtrStepSzb curFrame, PtrStepSzb Ftd, PtrStepSzb Fbd, PtrStepSzb foreground,
int deltaC, int deltaCC, float alpha2, int N1c, int N1cc, cudaStream_t stream)
{
dim3 block(32, 8);
dim3 grid(divUp(prevFrame.cols, block.x), divUp(prevFrame.rows, block.y));
cudaSafeCall( cudaFuncSetCacheConfig(bgfgClassification<PT, CT, OT>, cudaFuncCachePreferL1) );
bgfgClassification<PT, CT, OT><<<grid, block, 0, stream>>>((PtrStepSz<PT>)prevFrame, (PtrStepSz<CT>)curFrame,
Ftd, Fbd, foreground,
deltaC, deltaCC, alpha2, N1c, N1cc);
cudaSafeCall( cudaGetLastError() );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
template void bgfgClassification_gpu<uchar3, uchar3, uchar3>(PtrStepSzb prevFrame, PtrStepSzb curFrame, PtrStepSzb Ftd, PtrStepSzb Fbd, PtrStepSzb foreground, int deltaC, int deltaCC, float alpha2, int N1c, int N1cc, cudaStream_t stream);
template void bgfgClassification_gpu<uchar3, uchar3, uchar4>(PtrStepSzb prevFrame, PtrStepSzb curFrame, PtrStepSzb Ftd, PtrStepSzb Fbd, PtrStepSzb foreground, int deltaC, int deltaCC, float alpha2, int N1c, int N1cc, cudaStream_t stream);
template void bgfgClassification_gpu<uchar3, uchar4, uchar3>(PtrStepSzb prevFrame, PtrStepSzb curFrame, PtrStepSzb Ftd, PtrStepSzb Fbd, PtrStepSzb foreground, int deltaC, int deltaCC, float alpha2, int N1c, int N1cc, cudaStream_t stream);
template void bgfgClassification_gpu<uchar3, uchar4, uchar4>(PtrStepSzb prevFrame, PtrStepSzb curFrame, PtrStepSzb Ftd, PtrStepSzb Fbd, PtrStepSzb foreground, int deltaC, int deltaCC, float alpha2, int N1c, int N1cc, cudaStream_t stream);
template void bgfgClassification_gpu<uchar4, uchar3, uchar3>(PtrStepSzb prevFrame, PtrStepSzb curFrame, PtrStepSzb Ftd, PtrStepSzb Fbd, PtrStepSzb foreground, int deltaC, int deltaCC, float alpha2, int N1c, int N1cc, cudaStream_t stream);
template void bgfgClassification_gpu<uchar4, uchar3, uchar4>(PtrStepSzb prevFrame, PtrStepSzb curFrame, PtrStepSzb Ftd, PtrStepSzb Fbd, PtrStepSzb foreground, int deltaC, int deltaCC, float alpha2, int N1c, int N1cc, cudaStream_t stream);
template void bgfgClassification_gpu<uchar4, uchar4, uchar3>(PtrStepSzb prevFrame, PtrStepSzb curFrame, PtrStepSzb Ftd, PtrStepSzb Fbd, PtrStepSzb foreground, int deltaC, int deltaCC, float alpha2, int N1c, int N1cc, cudaStream_t stream);
template void bgfgClassification_gpu<uchar4, uchar4, uchar4>(PtrStepSzb prevFrame, PtrStepSzb curFrame, PtrStepSzb Ftd, PtrStepSzb Fbd, PtrStepSzb foreground, int deltaC, int deltaCC, float alpha2, int N1c, int N1cc, cudaStream_t stream);
////////////////////////////////////////////////////////////////////////////
// updateBackgroundModel
template <typename PT, typename CT, typename OT, class PrevFramePtr2D, class CurFramePtr2D, class FtdPtr2D, class FbdPtr2D>
__global__ void updateBackgroundModel(int cols, int rows, const PrevFramePtr2D prevFrame, const CurFramePtr2D curFrame, const FtdPtr2D Ftd, const FbdPtr2D Fbd,
PtrStepb foreground, PtrStep<OT> background,
int deltaC, int deltaCC, float alpha1, float alpha2, float alpha3, int N1c, int N1cc, int N2c, int N2cc, float T)
{
const int i = blockIdx.y * blockDim.y + threadIdx.y;
const int j = blockIdx.x * blockDim.x + threadIdx.x;
if (i > rows || j > cols)
return;
const float MIN_PV = 1e-10f;
const uchar is_trained_dyn_model = c_stat.is_trained_dyn_model(i, j);
if (Ftd(i, j) || !is_trained_dyn_model)
{
const float alpha = is_trained_dyn_model ? alpha2 : alpha3;
float Pbcc = c_stat.Pbcc(i, j);
//update Pb
Pbcc *= (1.0f - alpha);
if (!foreground(i, j))
{
Pbcc += alpha;
}
int min_dist = numeric_limits<int>::max();
int indx = -1;
PT prevVal = prevFrame(i, j);
CT curVal = curFrame(i, j);
// Find best Vi match:
for (int k = 0; k < N2cc; ++k)
{
float PV_CC = c_stat.PV_CC(i, j, k);
if (!PV_CC)
break;
if (PV_CC < MIN_PV)
{
c_stat.PV_CC(i, j, k) = 0;
c_stat.PVB_CC(i, j, k) = 0;
continue;
}
c_stat.PV_CC(i, j, k) = PV_CC * (1.0f - alpha);
c_stat.PVB_CC(i, j, k) = c_stat.PVB_CC(i, j, k) * (1.0f - alpha);
OT v1 = c_stat.V1_CC<OT>(i, j, k);
int3 val1 = make_int3(
::abs(v1.x - prevVal.x),
::abs(v1.y - prevVal.y),
::abs(v1.z - prevVal.z)
);
OT v2 = c_stat.V2_CC<OT>(i, j, k);
int3 val2 = make_int3(
::abs(v2.x - curVal.x),
::abs(v2.y - curVal.y),
::abs(v2.z - curVal.z)
);
int dist = val1.x + val1.y + val1.z + val2.x + val2.y + val2.z;
if (dist < min_dist &&
val1.x <= deltaCC && val1.y <= deltaCC && val1.z <= deltaCC &&
val2.x <= deltaCC && val2.y <= deltaCC && val2.z <= deltaCC)
{
min_dist = dist;
indx = k;
}
}
if (indx < 0)
{
// Replace N2th elem in the table by new feature:
indx = N2cc - 1;
c_stat.PV_CC(i, j, indx) = alpha;
c_stat.PVB_CC(i, j, indx) = alpha;
//udate Vt
c_stat.V1_CC<OT>(i, j, indx) = Output<OT>::make(prevVal.x, prevVal.y, prevVal.z);
c_stat.V2_CC<OT>(i, j, indx) = Output<OT>::make(curVal.x, curVal.y, curVal.z);
}
else
{
// Update:
c_stat.PV_CC(i, j, indx) += alpha;
if (!foreground(i, j))
{
c_stat.PVB_CC(i, j, indx) += alpha;
}
}
//re-sort CCt table by Pv
const float PV_CC_indx = c_stat.PV_CC(i, j, indx);
const float PVB_CC_indx = c_stat.PVB_CC(i, j, indx);
const OT V1_CC_indx = c_stat.V1_CC<OT>(i, j, indx);
const OT V2_CC_indx = c_stat.V2_CC<OT>(i, j, indx);
for (int k = 0; k < indx; ++k)
{
if (c_stat.PV_CC(i, j, k) <= PV_CC_indx)
{
//shift elements
float Pv_tmp1;
float Pv_tmp2 = PV_CC_indx;
float Pvb_tmp1;
float Pvb_tmp2 = PVB_CC_indx;
OT v1_tmp1;
OT v1_tmp2 = V1_CC_indx;
OT v2_tmp1;
OT v2_tmp2 = V2_CC_indx;
for (int l = k; l <= indx; ++l)
{
Pv_tmp1 = c_stat.PV_CC(i, j, l);
c_stat.PV_CC(i, j, l) = Pv_tmp2;
Pv_tmp2 = Pv_tmp1;
Pvb_tmp1 = c_stat.PVB_CC(i, j, l);
c_stat.PVB_CC(i, j, l) = Pvb_tmp2;
Pvb_tmp2 = Pvb_tmp1;
v1_tmp1 = c_stat.V1_CC<OT>(i, j, l);
c_stat.V1_CC<OT>(i, j, l) = v1_tmp2;
v1_tmp2 = v1_tmp1;
v2_tmp1 = c_stat.V2_CC<OT>(i, j, l);
c_stat.V2_CC<OT>(i, j, l) = v2_tmp2;
v2_tmp2 = v2_tmp1;
}
break;
}
}
float sum1 = 0.0f;
float sum2 = 0.0f;
//check "once-off" changes
for (int k = 0; k < N1cc; ++k)
{
const float PV_CC = c_stat.PV_CC(i, j, k);
if (!PV_CC)
break;
sum1 += PV_CC;
sum2 += c_stat.PVB_CC(i, j, k);
}
if (sum1 > T)
c_stat.is_trained_dyn_model(i, j) = 1;
float diff = sum1 - Pbcc * sum2;
// Update stat table:
if (diff > T)
{
//new BG features are discovered
for (int k = 0; k < N1cc; ++k)
{
const float PV_CC = c_stat.PV_CC(i, j, k);
if (!PV_CC)
break;
c_stat.PVB_CC(i, j, k) = (PV_CC - Pbcc * c_stat.PVB_CC(i, j, k)) / (1.0f - Pbcc);
}
}
c_stat.Pbcc(i, j) = Pbcc;
}
// Handle "stationary" pixel:
if (!Ftd(i, j))
{
const float alpha = c_stat.is_trained_st_model(i, j) ? alpha2 : alpha3;
float Pbc = c_stat.Pbc(i, j);
//update Pb
Pbc *= (1.0f - alpha);
if (!foreground(i, j))
{
Pbc += alpha;
}
int min_dist = numeric_limits<int>::max();
int indx = -1;
CT curVal = curFrame(i, j);
//find best Vi match
for (int k = 0; k < N2c; ++k)
{
float PV_C = c_stat.PV_C(i, j, k);
if (PV_C < MIN_PV)
{
c_stat.PV_C(i, j, k) = 0;
c_stat.PVB_C(i, j, k) = 0;
continue;
}
// Exponential decay of memory
c_stat.PV_C(i, j, k) = PV_C * (1.0f - alpha);
c_stat.PVB_C(i, j, k) = c_stat.PVB_C(i, j, k) * (1.0f - alpha);
OT v = c_stat.V_C<OT>(i, j, k);
int3 val = make_int3(
::abs(v.x - curVal.x),
::abs(v.y - curVal.y),
::abs(v.z - curVal.z)
);
int dist = val.x + val.y + val.z;
if (dist < min_dist && val.x <= deltaC && val.y <= deltaC && val.z <= deltaC)
{
min_dist = dist;
indx = k;
}
}
if (indx < 0)
{
//N2th elem in the table is replaced by a new features
indx = N2c - 1;
c_stat.PV_C(i, j, indx) = alpha;
c_stat.PVB_C(i, j, indx) = alpha;
//udate Vt
c_stat.V_C<OT>(i, j, indx) = Output<OT>::make(curVal.x, curVal.y, curVal.z);
}
else
{
//update
c_stat.PV_C(i, j, indx) += alpha;
if (!foreground(i, j))
{
c_stat.PVB_C(i, j, indx) += alpha;
}
}
//re-sort Ct table by Pv
const float PV_C_indx = c_stat.PV_C(i, j, indx);
const float PVB_C_indx = c_stat.PVB_C(i, j, indx);
OT V_C_indx = c_stat.V_C<OT>(i, j, indx);
for (int k = 0; k < indx; ++k)
{
if (c_stat.PV_C(i, j, k) <= PV_C_indx)
{
//shift elements
float Pv_tmp1;
float Pv_tmp2 = PV_C_indx;
float Pvb_tmp1;
float Pvb_tmp2 = PVB_C_indx;
OT v_tmp1;
OT v_tmp2 = V_C_indx;
for (int l = k; l <= indx; ++l)
{
Pv_tmp1 = c_stat.PV_C(i, j, l);
c_stat.PV_C(i, j, l) = Pv_tmp2;
Pv_tmp2 = Pv_tmp1;
Pvb_tmp1 = c_stat.PVB_C(i, j, l);
c_stat.PVB_C(i, j, l) = Pvb_tmp2;
Pvb_tmp2 = Pvb_tmp1;
v_tmp1 = c_stat.V_C<OT>(i, j, l);
c_stat.V_C<OT>(i, j, l) = v_tmp2;
v_tmp2 = v_tmp1;
}
break;
}
}
// Check "once-off" changes:
float sum1 = 0.0f;
float sum2 = 0.0f;
for (int k = 0; k < N1c; ++k)
{
const float PV_C = c_stat.PV_C(i, j, k);
if (!PV_C)
break;
sum1 += PV_C;
sum2 += c_stat.PVB_C(i, j, k);
}
if (sum1 > T)
c_stat.is_trained_st_model(i, j) = 1;
float diff = sum1 - Pbc * sum2;
// Update stat table:
if (diff > T)
{
//new BG features are discovered
for (int k = 0; k < N1c; ++k)
{
const float PV_C = c_stat.PV_C(i, j, k);
if (!PV_C)
break;
c_stat.PVB_C(i, j, k) = (PV_C - Pbc * c_stat.PVB_C(i, j, k)) / (1.0f - Pbc);
}
c_stat.Pbc(i, j) = 1.0f - Pbc;
}
else
{
c_stat.Pbc(i, j) = Pbc;
}
} // if !(change detection) at pixel (i,j)
// Update the reference BG image:
if (!foreground(i, j))
{
CT curVal = curFrame(i, j);
if (!Ftd(i, j) && !Fbd(i, j))
{
// Apply IIR filter:
OT oldVal = background(i, j);
int3 newVal = make_int3(
__float2int_rn(oldVal.x * (1.0f - alpha1) + curVal.x * alpha1),
__float2int_rn(oldVal.y * (1.0f - alpha1) + curVal.y * alpha1),
__float2int_rn(oldVal.z * (1.0f - alpha1) + curVal.z * alpha1)
);
background(i, j) = Output<OT>::make(
static_cast<uchar>(newVal.x),
static_cast<uchar>(newVal.y),
static_cast<uchar>(newVal.z)
);
}
else
{
background(i, j) = Output<OT>::make(curVal.x, curVal.y, curVal.z);
}
}
}
template <typename PT, typename CT, typename OT>
struct UpdateBackgroundModel
{
static void call(PtrStepSz<PT> prevFrame, PtrStepSz<CT> curFrame, PtrStepSzb Ftd, PtrStepSzb Fbd, PtrStepSzb foreground, PtrStepSz<OT> background,
int deltaC, int deltaCC, float alpha1, float alpha2, float alpha3, int N1c, int N1cc, int N2c, int N2cc, float T,
cudaStream_t stream)
{
dim3 block(32, 8);
dim3 grid(divUp(prevFrame.cols, block.x), divUp(prevFrame.rows, block.y));
cudaSafeCall( cudaFuncSetCacheConfig(updateBackgroundModel<PT, CT, OT, PtrStep<PT>, PtrStep<CT>, PtrStepb, PtrStepb>, cudaFuncCachePreferL1) );
updateBackgroundModel<PT, CT, OT, PtrStep<PT>, PtrStep<CT>, PtrStepb, PtrStepb><<<grid, block, 0, stream>>>(
prevFrame.cols, prevFrame.rows,
prevFrame, curFrame,
Ftd, Fbd, foreground, background,
deltaC, deltaCC, alpha1, alpha2, alpha3, N1c, N1cc, N2c, N2cc, T);
cudaSafeCall( cudaGetLastError() );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
};
template <typename PT, typename CT, typename OT>
void updateBackgroundModel_gpu(PtrStepSzb prevFrame, PtrStepSzb curFrame, PtrStepSzb Ftd, PtrStepSzb Fbd, PtrStepSzb foreground, PtrStepSzb background,
int deltaC, int deltaCC, float alpha1, float alpha2, float alpha3, int N1c, int N1cc, int N2c, int N2cc, float T,
cudaStream_t stream)
{
UpdateBackgroundModel<PT, CT, OT>::call(PtrStepSz<PT>(prevFrame), PtrStepSz<CT>(curFrame), Ftd, Fbd, foreground, PtrStepSz<OT>(background),
deltaC, deltaCC, alpha1, alpha2, alpha3, N1c, N1cc, N2c, N2cc, T, stream);
}
template void updateBackgroundModel_gpu<uchar3, uchar3, uchar3>(PtrStepSzb prevFrame, PtrStepSzb curFrame, PtrStepSzb Ftd, PtrStepSzb Fbd, PtrStepSzb foreground, PtrStepSzb background, int deltaC, int deltaCC, float alpha1, float alpha2, float alpha3, int N1c, int N1cc, int N2c, int N2cc, float T, cudaStream_t stream);
template void updateBackgroundModel_gpu<uchar3, uchar3, uchar4>(PtrStepSzb prevFrame, PtrStepSzb curFrame, PtrStepSzb Ftd, PtrStepSzb Fbd, PtrStepSzb foreground, PtrStepSzb background, int deltaC, int deltaCC, float alpha1, float alpha2, float alpha3, int N1c, int N1cc, int N2c, int N2cc, float T, cudaStream_t stream);
template void updateBackgroundModel_gpu<uchar3, uchar4, uchar3>(PtrStepSzb prevFrame, PtrStepSzb curFrame, PtrStepSzb Ftd, PtrStepSzb Fbd, PtrStepSzb foreground, PtrStepSzb background, int deltaC, int deltaCC, float alpha1, float alpha2, float alpha3, int N1c, int N1cc, int N2c, int N2cc, float T, cudaStream_t stream);
template void updateBackgroundModel_gpu<uchar3, uchar4, uchar4>(PtrStepSzb prevFrame, PtrStepSzb curFrame, PtrStepSzb Ftd, PtrStepSzb Fbd, PtrStepSzb foreground, PtrStepSzb background, int deltaC, int deltaCC, float alpha1, float alpha2, float alpha3, int N1c, int N1cc, int N2c, int N2cc, float T, cudaStream_t stream);
template void updateBackgroundModel_gpu<uchar4, uchar3, uchar3>(PtrStepSzb prevFrame, PtrStepSzb curFrame, PtrStepSzb Ftd, PtrStepSzb Fbd, PtrStepSzb foreground, PtrStepSzb background, int deltaC, int deltaCC, float alpha1, float alpha2, float alpha3, int N1c, int N1cc, int N2c, int N2cc, float T, cudaStream_t stream);
template void updateBackgroundModel_gpu<uchar4, uchar3, uchar4>(PtrStepSzb prevFrame, PtrStepSzb curFrame, PtrStepSzb Ftd, PtrStepSzb Fbd, PtrStepSzb foreground, PtrStepSzb background, int deltaC, int deltaCC, float alpha1, float alpha2, float alpha3, int N1c, int N1cc, int N2c, int N2cc, float T, cudaStream_t stream);
template void updateBackgroundModel_gpu<uchar4, uchar4, uchar3>(PtrStepSzb prevFrame, PtrStepSzb curFrame, PtrStepSzb Ftd, PtrStepSzb Fbd, PtrStepSzb foreground, PtrStepSzb background, int deltaC, int deltaCC, float alpha1, float alpha2, float alpha3, int N1c, int N1cc, int N2c, int N2cc, float T, cudaStream_t stream);
template void updateBackgroundModel_gpu<uchar4, uchar4, uchar4>(PtrStepSzb prevFrame, PtrStepSzb curFrame, PtrStepSzb Ftd, PtrStepSzb Fbd, PtrStepSzb foreground, PtrStepSzb background, int deltaC, int deltaCC, float alpha1, float alpha2, float alpha3, int N1c, int N1cc, int N2c, int N2cc, float T, cudaStream_t stream);
}
#endif /* CUDA_DISABLER */