Merge pull request #16090 from jeffeDurand:cuda_mog2_issue_5296
* cuda_mog2_issue_5296
This commit is contained in:
jeffeDurand
Alexander Alekhin
parent
4342657762
commit
5bf7345743
@ -47,393 +47,372 @@
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#include "opencv2/core/cuda/vec_math.hpp"
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#include "opencv2/core/cuda/limits.hpp"
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namespace cv { namespace cuda { namespace device
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#include "mog2.hpp"
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namespace cv
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{
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namespace mog2
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namespace cuda
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{
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namespace device
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{
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namespace mog2
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{
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///////////////////////////////////////////////////////////////
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// Utility
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__device__ __forceinline__ float cvt(uchar val)
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{
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return val;
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}
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__device__ __forceinline__ float3 cvt(const uchar3 &val)
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{
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return make_float3(val.x, val.y, val.z);
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}
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__device__ __forceinline__ float4 cvt(const uchar4 &val)
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{
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return make_float4(val.x, val.y, val.z, val.w);
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}
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__device__ __forceinline__ float sqr(float val)
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{
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return val * val;
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}
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__device__ __forceinline__ float sqr(const float3 &val)
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{
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return val.x * val.x + val.y * val.y + val.z * val.z;
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}
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__device__ __forceinline__ float sqr(const float4 &val)
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{
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return val.x * val.x + val.y * val.y + val.z * val.z;
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}
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__device__ __forceinline__ float sum(float val)
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{
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return val;
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}
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__device__ __forceinline__ float sum(const float3 &val)
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{
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return val.x + val.y + val.z;
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}
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__device__ __forceinline__ float sum(const float4 &val)
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{
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return val.x + val.y + val.z;
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}
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template <class Ptr2D>
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__device__ __forceinline__ void swap(Ptr2D &ptr, int x, int y, int k, int rows)
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{
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typename Ptr2D::elem_type val = ptr(k * rows + y, x);
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ptr(k * rows + y, x) = ptr((k + 1) * rows + y, x);
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ptr((k + 1) * rows + y, x) = val;
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}
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///////////////////////////////////////////////////////////////
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// MOG2
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template <bool detectShadows, typename SrcT, typename WorkT>
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__global__ void mog2(const PtrStepSz<SrcT> frame, PtrStepb fgmask, PtrStepb modesUsed,
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PtrStepf gmm_weight, PtrStepf gmm_variance, PtrStep<WorkT> gmm_mean,
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const float alphaT, const float alpha1, const float prune, const Constants *const constants)
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{
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const int x = blockIdx.x * blockDim.x + threadIdx.x;
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const int y = blockIdx.y * blockDim.y + threadIdx.y;
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if (x < frame.cols && y < frame.rows)
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{
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///////////////////////////////////////////////////////////////
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// Utility
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WorkT pix = cvt(frame(y, x));
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__device__ __forceinline__ float cvt(uchar val)
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//calculate distances to the modes (+ sort)
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//here we need to go in descending order!!!
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bool background = false; // true - the pixel classified as background
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//internal:
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bool fitsPDF = false; //if it remains zero a new GMM mode will be added
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int nmodes = modesUsed(y, x);
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const int nNewModes = nmodes; //current number of modes in GMM
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float totalWeight = 0.0f;
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//go through all modes
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for (int mode = 0; mode < nmodes; ++mode)
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{
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return val;
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}
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__device__ __forceinline__ float3 cvt(const uchar3& val)
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{
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return make_float3(val.x, val.y, val.z);
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}
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__device__ __forceinline__ float4 cvt(const uchar4& val)
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{
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return make_float4(val.x, val.y, val.z, val.w);
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}
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__device__ __forceinline__ float sqr(float val)
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{
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return val * val;
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}
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__device__ __forceinline__ float sqr(const float3& val)
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{
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return val.x * val.x + val.y * val.y + val.z * val.z;
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}
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__device__ __forceinline__ float sqr(const float4& val)
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{
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return val.x * val.x + val.y * val.y + val.z * val.z;
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}
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__device__ __forceinline__ float sum(float val)
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{
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return val;
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}
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__device__ __forceinline__ float sum(const float3& val)
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{
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return val.x + val.y + val.z;
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}
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__device__ __forceinline__ float sum(const float4& val)
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{
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return val.x + val.y + val.z;
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}
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template <class Ptr2D>
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__device__ __forceinline__ void swap(Ptr2D& ptr, int x, int y, int k, int rows)
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{
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typename Ptr2D::elem_type val = ptr(k * rows + y, x);
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ptr(k * rows + y, x) = ptr((k + 1) * rows + y, x);
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ptr((k + 1) * rows + y, x) = val;
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}
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///////////////////////////////////////////////////////////////
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// MOG2
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__constant__ int c_nmixtures;
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__constant__ float c_Tb;
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__constant__ float c_TB;
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__constant__ float c_Tg;
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__constant__ float c_varInit;
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__constant__ float c_varMin;
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__constant__ float c_varMax;
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__constant__ float c_tau;
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__constant__ unsigned char c_shadowVal;
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void loadConstants(int nmixtures, float Tb, float TB, float Tg, float varInit, float varMin, float varMax, float tau, unsigned char shadowVal)
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{
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varMin = ::fminf(varMin, varMax);
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varMax = ::fmaxf(varMin, varMax);
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cudaSafeCall( cudaMemcpyToSymbol(c_nmixtures, &nmixtures, sizeof(int)) );
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cudaSafeCall( cudaMemcpyToSymbol(c_Tb, &Tb, sizeof(float)) );
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cudaSafeCall( cudaMemcpyToSymbol(c_TB, &TB, sizeof(float)) );
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cudaSafeCall( cudaMemcpyToSymbol(c_Tg, &Tg, sizeof(float)) );
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cudaSafeCall( cudaMemcpyToSymbol(c_varInit, &varInit, sizeof(float)) );
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cudaSafeCall( cudaMemcpyToSymbol(c_varMin, &varMin, sizeof(float)) );
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cudaSafeCall( cudaMemcpyToSymbol(c_varMax, &varMax, sizeof(float)) );
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cudaSafeCall( cudaMemcpyToSymbol(c_tau, &tau, sizeof(float)) );
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cudaSafeCall( cudaMemcpyToSymbol(c_shadowVal, &shadowVal, sizeof(unsigned char)) );
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}
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template <bool detectShadows, typename SrcT, typename WorkT>
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__global__ void mog2(const PtrStepSz<SrcT> frame, PtrStepb fgmask, PtrStepb modesUsed,
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PtrStepf gmm_weight, PtrStepf gmm_variance, PtrStep<WorkT> gmm_mean,
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const float alphaT, const float alpha1, const float prune)
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{
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const int x = blockIdx.x * blockDim.x + threadIdx.x;
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const int y = blockIdx.y * blockDim.y + threadIdx.y;
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if (x >= frame.cols || y >= frame.rows)
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return;
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WorkT pix = cvt(frame(y, x));
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//calculate distances to the modes (+ sort)
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//here we need to go in descending order!!!
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bool background = false; // true - the pixel classified as background
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//internal:
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bool fitsPDF = false; //if it remains zero a new GMM mode will be added
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int nmodes = modesUsed(y, x);
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int nNewModes = nmodes; //current number of modes in GMM
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float totalWeight = 0.0f;
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//go through all modes
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for (int mode = 0; mode < nmodes; ++mode)
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{
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//need only weight if fit is found
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float weight = alpha1 * gmm_weight(mode * frame.rows + y, x) + prune;
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int swap_count = 0;
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//fit not found yet
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if (!fitsPDF)
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{
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//check if it belongs to some of the remaining modes
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float var = gmm_variance(mode * frame.rows + y, x);
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WorkT mean = gmm_mean(mode * frame.rows + y, x);
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//calculate difference and distance
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WorkT diff = mean - pix;
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float dist2 = sqr(diff);
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//background? - Tb - usually larger than Tg
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if (totalWeight < c_TB && dist2 < c_Tb * var)
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background = true;
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//check fit
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if (dist2 < c_Tg * var)
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{
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//belongs to the mode
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fitsPDF = true;
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//update distribution
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//update weight
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weight += alphaT;
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float k = alphaT / weight;
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//update mean
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gmm_mean(mode * frame.rows + y, x) = mean - k * diff;
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//update variance
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float varnew = var + k * (dist2 - var);
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//limit the variance
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varnew = ::fmaxf(varnew, c_varMin);
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varnew = ::fminf(varnew, c_varMax);
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gmm_variance(mode * frame.rows + y, x) = varnew;
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//sort
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//all other weights are at the same place and
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//only the matched (iModes) is higher -> just find the new place for it
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for (int i = mode; i > 0; --i)
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{
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//check one up
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if (weight < gmm_weight((i - 1) * frame.rows + y, x))
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break;
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swap_count++;
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//swap one up
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swap(gmm_weight, x, y, i - 1, frame.rows);
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swap(gmm_variance, x, y, i - 1, frame.rows);
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swap(gmm_mean, x, y, i - 1, frame.rows);
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}
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//belongs to the mode - bFitsPDF becomes 1
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}
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} // !fitsPDF
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//check prune
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if (weight < -prune)
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{
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weight = 0.0f;
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nmodes--;
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}
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gmm_weight((mode - swap_count) * frame.rows + y, x) = weight; //update weight by the calculated value
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totalWeight += weight;
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}
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//renormalize weights
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totalWeight = 1.f / totalWeight;
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for (int mode = 0; mode < nmodes; ++mode)
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gmm_weight(mode * frame.rows + y, x) *= totalWeight;
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nmodes = nNewModes;
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//make new mode if needed and exit
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//need only weight if fit is found
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float weight = alpha1 * gmm_weight(mode * frame.rows + y, x) + prune;
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int swap_count = 0;
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//fit not found yet
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if (!fitsPDF)
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{
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// replace the weakest or add a new one
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int mode = nmodes == c_nmixtures ? c_nmixtures - 1 : nmodes++;
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//check if it belongs to some of the remaining modes
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const float var = gmm_variance(mode * frame.rows + y, x);
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if (nmodes == 1)
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gmm_weight(mode * frame.rows + y, x) = 1.f;
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else
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const WorkT mean = gmm_mean(mode * frame.rows + y, x);
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//calculate difference and distance
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const WorkT diff = mean - pix;
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const float dist2 = sqr(diff);
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//background? - Tb - usually larger than Tg
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if (totalWeight < constants->TB_ && dist2 < constants->Tb_ * var)
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background = true;
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//check fit
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if (dist2 < constants->Tg_ * var)
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{
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gmm_weight(mode * frame.rows + y, x) = alphaT;
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//belongs to the mode
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fitsPDF = true;
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// renormalize all other weights
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//update distribution
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for (int i = 0; i < nmodes - 1; ++i)
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gmm_weight(i * frame.rows + y, x) *= alpha1;
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}
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//update weight
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weight += alphaT;
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float k = alphaT / weight;
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// init
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//update mean
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gmm_mean(mode * frame.rows + y, x) = mean - k * diff;
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gmm_mean(mode * frame.rows + y, x) = pix;
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gmm_variance(mode * frame.rows + y, x) = c_varInit;
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//update variance
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float varnew = var + k * (dist2 - var);
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//sort
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//find the new place for it
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//limit the variance
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varnew = ::fmaxf(varnew, constants->varMin_);
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varnew = ::fminf(varnew, constants->varMax_);
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for (int i = nmodes - 1; i > 0; --i)
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{
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// check one up
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if (alphaT < gmm_weight((i - 1) * frame.rows + y, x))
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break;
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gmm_variance(mode * frame.rows + y, x) = varnew;
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//swap one up
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swap(gmm_weight, x, y, i - 1, frame.rows);
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swap(gmm_variance, x, y, i - 1, frame.rows);
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swap(gmm_mean, x, y, i - 1, frame.rows);
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}
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}
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//sort
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//all other weights are at the same place and
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//only the matched (iModes) is higher -> just find the new place for it
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//set the number of modes
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modesUsed(y, x) = nmodes;
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bool isShadow = false;
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if (detectShadows && !background)
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{
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float tWeight = 0.0f;
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// check all the components marked as background:
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for (int mode = 0; mode < nmodes; ++mode)
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{
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WorkT mean = gmm_mean(mode * frame.rows + y, x);
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WorkT pix_mean = pix * mean;
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float numerator = sum(pix_mean);
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float denominator = sqr(mean);
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// no division by zero allowed
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if (denominator == 0)
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break;
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// if tau < a < 1 then also check the color distortion
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if (numerator <= denominator && numerator >= c_tau * denominator)
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for (int i = mode; i > 0; --i)
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{
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float a = numerator / denominator;
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WorkT dD = a * mean - pix;
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if (sqr(dD) < c_Tb * gmm_variance(mode * frame.rows + y, x) * a * a)
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{
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isShadow = true;
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//check one up
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if (weight < gmm_weight((i - 1) * frame.rows + y, x))
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break;
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}
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};
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tWeight += gmm_weight(mode * frame.rows + y, x);
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if (tWeight > c_TB)
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break;
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swap_count++;
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//swap one up
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swap(gmm_weight, x, y, i - 1, frame.rows);
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swap(gmm_variance, x, y, i - 1, frame.rows);
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swap(gmm_mean, x, y, i - 1, frame.rows);
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}
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//belongs to the mode - bFitsPDF becomes 1
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}
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} // !fitsPDF
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//check prune
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if (weight < -prune)
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{
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weight = 0.0f;
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nmodes--;
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}
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fgmask(y, x) = background ? 0 : isShadow ? c_shadowVal : 255;
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gmm_weight((mode - swap_count) * frame.rows + y, x) = weight; //update weight by the calculated value
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totalWeight += weight;
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}
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template <typename SrcT, typename WorkT>
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void mog2_caller(PtrStepSzb frame, PtrStepSzb fgmask, PtrStepSzb modesUsed, PtrStepSzf weight, PtrStepSzf variance, PtrStepSzb mean,
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float alphaT, float prune, bool detectShadows, cudaStream_t stream)
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//renormalize weights
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totalWeight = 1.f / totalWeight;
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for (int mode = 0; mode < nmodes; ++mode)
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gmm_weight(mode * frame.rows + y, x) *= totalWeight;
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nmodes = nNewModes;
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//make new mode if needed and exit
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if (!fitsPDF)
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{
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dim3 block(32, 8);
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dim3 grid(divUp(frame.cols, block.x), divUp(frame.rows, block.y));
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// replace the weakest or add a new one
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const int mode = nmodes == constants->nmixtures_ ? constants->nmixtures_ - 1 : nmodes++;
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const float alpha1 = 1.0f - alphaT;
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if (detectShadows)
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{
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cudaSafeCall( cudaFuncSetCacheConfig(mog2<true, SrcT, WorkT>, cudaFuncCachePreferL1) );
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mog2<true, SrcT, WorkT><<<grid, block, 0, stream>>>((PtrStepSz<SrcT>) frame, fgmask, modesUsed,
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weight, variance, (PtrStepSz<WorkT>) mean,
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alphaT, alpha1, prune);
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}
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if (nmodes == 1)
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gmm_weight(mode * frame.rows + y, x) = 1.f;
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else
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{
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cudaSafeCall( cudaFuncSetCacheConfig(mog2<false, SrcT, WorkT>, cudaFuncCachePreferL1) );
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gmm_weight(mode * frame.rows + y, x) = alphaT;
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mog2<false, SrcT, WorkT><<<grid, block, 0, stream>>>((PtrStepSz<SrcT>) frame, fgmask, modesUsed,
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weight, variance, (PtrStepSz<WorkT>) mean,
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alphaT, alpha1, prune);
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// renormalize all other weights
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for (int i = 0; i < nmodes - 1; ++i)
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gmm_weight(i * frame.rows + y, x) *= alpha1;
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}
|
||||
|
||||
cudaSafeCall( cudaGetLastError() );
|
||||
// init
|
||||
|
||||
if (stream == 0)
|
||||
cudaSafeCall( cudaDeviceSynchronize() );
|
||||
}
|
||||
gmm_mean(mode * frame.rows + y, x) = pix;
|
||||
gmm_variance(mode * frame.rows + y, x) = constants->varInit_;
|
||||
|
||||
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);
|
||||
//sort
|
||||
//find the new place for it
|
||||
|
||||
static const func_t funcs[] =
|
||||
for (int i = nmodes - 1; i > 0; --i)
|
||||
{
|
||||
0, mog2_caller<uchar, float>, 0, mog2_caller<uchar3, float3>, mog2_caller<uchar4, float4>
|
||||
};
|
||||
// check one up
|
||||
if (alphaT < gmm_weight((i - 1) * frame.rows + y, x))
|
||||
break;
|
||||
|
||||
funcs[cn](frame, fgmask, modesUsed, weight, variance, mean, alphaT, prune, detectShadows, stream);
|
||||
//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);
|
||||
}
|
||||
}
|
||||
|
||||
template <typename WorkT, typename OutT>
|
||||
__global__ void getBackgroundImage2(const PtrStepSzb modesUsed, const PtrStepf gmm_weight, const PtrStep<WorkT> gmm_mean, PtrStep<OutT> dst)
|
||||
//set the number of modes
|
||||
modesUsed(y, x) = nmodes;
|
||||
|
||||
bool isShadow = false;
|
||||
if (detectShadows && !background)
|
||||
{
|
||||
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;
|
||||
float tWeight = 0.0f;
|
||||
|
||||
// check all the components marked as background:
|
||||
for (int mode = 0; mode < nmodes; ++mode)
|
||||
{
|
||||
float weight = gmm_weight(mode * modesUsed.rows + y, x);
|
||||
const WorkT mean = gmm_mean(mode * frame.rows + y, x);
|
||||
|
||||
WorkT mean = gmm_mean(mode * modesUsed.rows + y, x);
|
||||
meanVal = meanVal + weight * mean;
|
||||
const WorkT pix_mean = pix * mean;
|
||||
|
||||
totalWeight += weight;
|
||||
const float numerator = sum(pix_mean);
|
||||
const float denominator = sqr(mean);
|
||||
|
||||
if(totalWeight > c_TB)
|
||||
// no division by zero allowed
|
||||
if (denominator == 0)
|
||||
break;
|
||||
|
||||
// if tau < a < 1 then also check the color distortion
|
||||
else if (numerator <= denominator && numerator >= constants->tau_ * denominator)
|
||||
{
|
||||
const float a = numerator / denominator;
|
||||
|
||||
WorkT dD = a * mean - pix;
|
||||
|
||||
if (sqr(dD) < constants->Tb_ * gmm_variance(mode * frame.rows + y, x) * a * a)
|
||||
{
|
||||
isShadow = true;
|
||||
break;
|
||||
}
|
||||
};
|
||||
|
||||
tWeight += gmm_weight(mode * frame.rows + y, x);
|
||||
if (tWeight > constants->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);
|
||||
}
|
||||
fgmask(y, x) = background ? 0 : isShadow ? constants->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, const Constants *const constants, 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, constants);
|
||||
}
|
||||
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, constants);
|
||||
}
|
||||
|
||||
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, const Constants *const constants, 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, const Constants *const constants, 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, constants, 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 Constants *const constants)
|
||||
{
|
||||
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 > constants->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, const Constants *const constants, 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, constants);
|
||||
cudaSafeCall(cudaGetLastError());
|
||||
|
||||
if (stream == 0)
|
||||
cudaSafeCall(cudaDeviceSynchronize());
|
||||
}
|
||||
|
||||
void getBackgroundImage2_gpu(int cn, PtrStepSzb modesUsed, PtrStepSzf weight, PtrStepSzb mean, PtrStepSzb dst, const Constants *const constants, cudaStream_t stream)
|
||||
{
|
||||
typedef void (*func_t)(PtrStepSzb modesUsed, PtrStepSzf weight, PtrStepSzb mean, PtrStepSzb dst, const Constants *const constants, 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, constants, stream);
|
||||
}
|
||||
} // namespace mog2
|
||||
} // namespace device
|
||||
} // namespace cuda
|
||||
} // namespace cv
|
||||
|
||||
#endif /* CUDA_DISABLER */
|
||||
|
||||
37
modules/cudabgsegm/src/cuda/mog2.hpp
Normal file
37
modules/cudabgsegm/src/cuda/mog2.hpp
Normal file
@ -0,0 +1,37 @@
|
||||
// This file is part of OpenCV project.
|
||||
// It is subject to the license terms in the LICENSE file found in the top-level directory
|
||||
// of this distribution and at http://opencv.org/license.html.
|
||||
|
||||
#ifndef OPENCV_CUDA_MOG2_H
|
||||
#define OPENCV_CUDA_MOG2_H
|
||||
|
||||
#include "opencv2/core/cuda.hpp"
|
||||
|
||||
struct CUstream_st;
|
||||
typedef struct CUstream_st *cudaStream_t;
|
||||
|
||||
namespace cv { namespace cuda {
|
||||
|
||||
class Stream;
|
||||
|
||||
namespace device { namespace mog2 {
|
||||
|
||||
typedef struct
|
||||
{
|
||||
float Tb_;
|
||||
float TB_;
|
||||
float Tg_;
|
||||
float varInit_;
|
||||
float varMin_;
|
||||
float varMax_;
|
||||
float tau_;
|
||||
int nmixtures_;
|
||||
unsigned char shadowVal_;
|
||||
} Constants;
|
||||
|
||||
void mog2_gpu(PtrStepSzb frame, int cn, PtrStepSzb fgmask, PtrStepSzb modesUsed, PtrStepSzf weight, PtrStepSzf variance, PtrStepSzb mean, float alphaT, float prune, bool detectShadows, const Constants *const constants, cudaStream_t stream);
|
||||
void getBackgroundImage2_gpu(int cn, PtrStepSzb modesUsed, PtrStepSzf weight, PtrStepSzb mean, PtrStepSzb dst, const Constants *const constants, cudaStream_t stream);
|
||||
|
||||
} } } }
|
||||
|
||||
#endif /* OPENCV_CUDA_MOG2_H */
|
||||
@ -41,210 +41,208 @@
|
||||
//M*/
|
||||
|
||||
#include "precomp.hpp"
|
||||
#include "cuda/mog2.hpp"
|
||||
|
||||
using namespace cv;
|
||||
using namespace cv::cuda;
|
||||
using namespace cv::cuda::device::mog2;
|
||||
|
||||
#if !defined HAVE_CUDA || defined(CUDA_DISABLER)
|
||||
|
||||
Ptr<cuda::BackgroundSubtractorMOG2> cv::cuda::createBackgroundSubtractorMOG2(int, double, bool) { throw_no_cuda(); return Ptr<cuda::BackgroundSubtractorMOG2>(); }
|
||||
Ptr<cuda::BackgroundSubtractorMOG2> cv::cuda::createBackgroundSubtractorMOG2(int, double, bool)
|
||||
{
|
||||
throw_no_cuda();
|
||||
return Ptr<cuda::BackgroundSubtractorMOG2>();
|
||||
}
|
||||
|
||||
#else
|
||||
|
||||
namespace cv { namespace cuda { namespace device
|
||||
{
|
||||
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
|
||||
{
|
||||
// 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;
|
||||
// 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 defaultCT = 0.05f; // complexity reduction prior constant 0 - no reduction of number of components
|
||||
const unsigned char defaultShadowValue = 127; // value to use in the segmentation mask for shadows, set 0 not to do shadow detection
|
||||
const float defaultShadowThreshold = 0.5f; // Tau - shadow threshold, see the paper for explanation
|
||||
// additional parameters
|
||||
const float defaultCT = 0.05f; // complexity reduction prior constant 0 - no reduction of number of components
|
||||
const unsigned char defaultShadowValue = 127; // value to use in the segmentation mask for shadows, set 0 not to do shadow detection
|
||||
const float defaultShadowThreshold = 0.5f; // Tau - shadow threshold, see the paper for explanation
|
||||
|
||||
class MOG2Impl CV_FINAL : public cuda::BackgroundSubtractorMOG2
|
||||
{
|
||||
public:
|
||||
MOG2Impl(int history, double varThreshold, bool detectShadows);
|
||||
class MOG2Impl CV_FINAL : public cuda::BackgroundSubtractorMOG2
|
||||
{
|
||||
public:
|
||||
MOG2Impl(int history, double varThreshold, bool detectShadows);
|
||||
~MOG2Impl();
|
||||
|
||||
void apply(InputArray image, OutputArray fgmask, double learningRate=-1) CV_OVERRIDE;
|
||||
void apply(InputArray image, OutputArray fgmask, double learningRate, Stream& stream) CV_OVERRIDE;
|
||||
void apply(InputArray image, OutputArray fgmask, double learningRate = -1) CV_OVERRIDE;
|
||||
void apply(InputArray image, OutputArray fgmask, double learningRate, Stream &stream) CV_OVERRIDE;
|
||||
|
||||
void getBackgroundImage(OutputArray backgroundImage) const CV_OVERRIDE;
|
||||
void getBackgroundImage(OutputArray backgroundImage, Stream& stream) const CV_OVERRIDE;
|
||||
void getBackgroundImage(OutputArray backgroundImage) const CV_OVERRIDE;
|
||||
void getBackgroundImage(OutputArray backgroundImage, Stream &stream) const CV_OVERRIDE;
|
||||
|
||||
int getHistory() const CV_OVERRIDE { return history_; }
|
||||
void setHistory(int history) CV_OVERRIDE { history_ = history; }
|
||||
int getHistory() const CV_OVERRIDE { return history_; }
|
||||
void setHistory(int history) CV_OVERRIDE { history_ = history; }
|
||||
|
||||
int getNMixtures() const CV_OVERRIDE { return nmixtures_; }
|
||||
void setNMixtures(int nmixtures) CV_OVERRIDE { nmixtures_ = nmixtures; }
|
||||
int getNMixtures() const CV_OVERRIDE { return constantsHost_.nmixtures_; }
|
||||
void setNMixtures(int nmixtures) CV_OVERRIDE { constantsHost_.nmixtures_ = nmixtures; }
|
||||
|
||||
double getBackgroundRatio() const CV_OVERRIDE { return backgroundRatio_; }
|
||||
void setBackgroundRatio(double ratio) CV_OVERRIDE { backgroundRatio_ = (float) ratio; }
|
||||
double getBackgroundRatio() const CV_OVERRIDE { return constantsHost_.TB_; }
|
||||
void setBackgroundRatio(double ratio) CV_OVERRIDE { constantsHost_.TB_ = (float)ratio; }
|
||||
|
||||
double getVarThreshold() const CV_OVERRIDE { return varThreshold_; }
|
||||
void setVarThreshold(double varThreshold) CV_OVERRIDE { varThreshold_ = (float) varThreshold; }
|
||||
double getVarThreshold() const CV_OVERRIDE { return constantsHost_.Tb_; }
|
||||
void setVarThreshold(double varThreshold) CV_OVERRIDE { constantsHost_.Tb_ = (float)varThreshold; }
|
||||
|
||||
double getVarThresholdGen() const CV_OVERRIDE { return varThresholdGen_; }
|
||||
void setVarThresholdGen(double varThresholdGen) CV_OVERRIDE { varThresholdGen_ = (float) varThresholdGen; }
|
||||
double getVarThresholdGen() const CV_OVERRIDE { return constantsHost_.Tg_; }
|
||||
void setVarThresholdGen(double varThresholdGen) CV_OVERRIDE { constantsHost_.Tg_ = (float)varThresholdGen; }
|
||||
|
||||
double getVarInit() const CV_OVERRIDE { return varInit_; }
|
||||
void setVarInit(double varInit) CV_OVERRIDE { varInit_ = (float) varInit; }
|
||||
double getVarInit() const CV_OVERRIDE { return constantsHost_.varInit_; }
|
||||
void setVarInit(double varInit) CV_OVERRIDE { constantsHost_.varInit_ = (float)varInit; }
|
||||
|
||||
double getVarMin() const CV_OVERRIDE { return varMin_; }
|
||||
void setVarMin(double varMin) CV_OVERRIDE { varMin_ = (float) varMin; }
|
||||
double getVarMin() const CV_OVERRIDE { return constantsHost_.varMin_; }
|
||||
void setVarMin(double varMin) CV_OVERRIDE { constantsHost_.varMin_ = ::fminf((float)varMin, constantsHost_.varMax_); }
|
||||
|
||||
double getVarMax() const CV_OVERRIDE { return varMax_; }
|
||||
void setVarMax(double varMax) CV_OVERRIDE { varMax_ = (float) varMax; }
|
||||
double getVarMax() const CV_OVERRIDE { return constantsHost_.varMax_; }
|
||||
void setVarMax(double varMax) CV_OVERRIDE { constantsHost_.varMax_ = ::fmaxf(constantsHost_.varMin_, (float)varMax); }
|
||||
|
||||
double getComplexityReductionThreshold() const CV_OVERRIDE { return ct_; }
|
||||
void setComplexityReductionThreshold(double ct) CV_OVERRIDE { ct_ = (float) ct; }
|
||||
double getComplexityReductionThreshold() const CV_OVERRIDE { return ct_; }
|
||||
void setComplexityReductionThreshold(double ct) CV_OVERRIDE { ct_ = (float)ct; }
|
||||
|
||||
bool getDetectShadows() const CV_OVERRIDE { return detectShadows_; }
|
||||
void setDetectShadows(bool detectShadows) CV_OVERRIDE { detectShadows_ = detectShadows; }
|
||||
bool getDetectShadows() const CV_OVERRIDE { return detectShadows_; }
|
||||
void setDetectShadows(bool detectShadows) CV_OVERRIDE { detectShadows_ = detectShadows; }
|
||||
|
||||
int getShadowValue() const CV_OVERRIDE { return shadowValue_; }
|
||||
void setShadowValue(int value) CV_OVERRIDE { shadowValue_ = (uchar) value; }
|
||||
int getShadowValue() const CV_OVERRIDE { return constantsHost_.shadowVal_; }
|
||||
void setShadowValue(int value) CV_OVERRIDE { constantsHost_.shadowVal_ = (uchar)value; }
|
||||
|
||||
double getShadowThreshold() const CV_OVERRIDE { return shadowThreshold_; }
|
||||
void setShadowThreshold(double threshold) CV_OVERRIDE { shadowThreshold_ = (float) threshold; }
|
||||
double getShadowThreshold() const CV_OVERRIDE { return constantsHost_.tau_; }
|
||||
void setShadowThreshold(double threshold) CV_OVERRIDE { constantsHost_.tau_ = (float)threshold; }
|
||||
|
||||
private:
|
||||
void initialize(Size frameSize, int frameType);
|
||||
private:
|
||||
void initialize(Size frameSize, int frameType, Stream &stream);
|
||||
|
||||
int history_;
|
||||
int nmixtures_;
|
||||
float backgroundRatio_;
|
||||
float varThreshold_;
|
||||
float varThresholdGen_;
|
||||
float varInit_;
|
||||
float varMin_;
|
||||
float varMax_;
|
||||
float ct_;
|
||||
bool detectShadows_;
|
||||
uchar shadowValue_;
|
||||
float shadowThreshold_;
|
||||
Constants constantsHost_;
|
||||
Constants *constantsDevice_;
|
||||
|
||||
Size frameSize_;
|
||||
int frameType_;
|
||||
int nframes_;
|
||||
int history_;
|
||||
float ct_;
|
||||
bool detectShadows_;
|
||||
|
||||
GpuMat weight_;
|
||||
GpuMat variance_;
|
||||
GpuMat mean_;
|
||||
Size frameSize_;
|
||||
int frameType_;
|
||||
int nframes_;
|
||||
|
||||
//keep track of number of modes per pixel
|
||||
GpuMat bgmodelUsedModes_;
|
||||
};
|
||||
GpuMat weight_;
|
||||
GpuMat variance_;
|
||||
GpuMat mean_;
|
||||
|
||||
MOG2Impl::MOG2Impl(int history, double varThreshold, bool detectShadows) :
|
||||
frameSize_(0, 0), frameType_(0), nframes_(0)
|
||||
{
|
||||
history_ = history > 0 ? history : defaultHistory;
|
||||
varThreshold_ = varThreshold > 0 ? (float) varThreshold : defaultVarThreshold;
|
||||
detectShadows_ = detectShadows;
|
||||
//keep track of number of modes per pixel
|
||||
GpuMat bgmodelUsedModes_;
|
||||
};
|
||||
|
||||
nmixtures_ = defaultNMixtures;
|
||||
backgroundRatio_ = defaultBackgroundRatio;
|
||||
varInit_ = defaultVarInit;
|
||||
varMax_ = defaultVarMax;
|
||||
varMin_ = defaultVarMin;
|
||||
varThresholdGen_ = defaultVarThresholdGen;
|
||||
ct_ = defaultCT;
|
||||
shadowValue_ = defaultShadowValue;
|
||||
shadowThreshold_ = defaultShadowThreshold;
|
||||
}
|
||||
MOG2Impl::MOG2Impl(int history, double varThreshold, bool detectShadows) : frameSize_(0, 0), frameType_(0), nframes_(0)
|
||||
{
|
||||
history_ = history > 0 ? history : defaultHistory;
|
||||
detectShadows_ = detectShadows;
|
||||
ct_ = defaultCT;
|
||||
|
||||
void MOG2Impl::apply(InputArray image, OutputArray fgmask, double learningRate)
|
||||
{
|
||||
apply(image, fgmask, learningRate, Stream::Null());
|
||||
}
|
||||
setNMixtures(defaultNMixtures);
|
||||
setBackgroundRatio(defaultBackgroundRatio);
|
||||
setVarInit(defaultVarInit);
|
||||
setVarMin(defaultVarMin);
|
||||
setVarMax(defaultVarMax);
|
||||
setVarThreshold(varThreshold > 0 ? (float)varThreshold : defaultVarThreshold);
|
||||
setVarThresholdGen(defaultVarThresholdGen);
|
||||
|
||||
void MOG2Impl::apply(InputArray _frame, OutputArray _fgmask, double learningRate, Stream& stream)
|
||||
{
|
||||
using namespace cv::cuda::device::mog2;
|
||||
setShadowValue(defaultShadowValue);
|
||||
setShadowThreshold(defaultShadowThreshold);
|
||||
|
||||
GpuMat frame = _frame.getGpuMat();
|
||||
|
||||
int ch = frame.channels();
|
||||
int work_ch = ch;
|
||||
|
||||
if (nframes_ == 0 || learningRate >= 1.0 || frame.size() != frameSize_ || work_ch != mean_.channels())
|
||||
initialize(frame.size(), frame.type());
|
||||
|
||||
_fgmask.create(frameSize_, CV_8UC1);
|
||||
GpuMat fgmask = _fgmask.getGpuMat();
|
||||
|
||||
fgmask.setTo(Scalar::all(0), stream);
|
||||
|
||||
++nframes_;
|
||||
learningRate = learningRate >= 0 && nframes_ > 1 ? learningRate : 1.0 / std::min(2 * nframes_, history_);
|
||||
CV_Assert( learningRate >= 0 );
|
||||
|
||||
mog2_gpu(frame, frame.channels(), fgmask, bgmodelUsedModes_, weight_, variance_, mean_,
|
||||
(float) learningRate, static_cast<float>(-learningRate * ct_), detectShadows_, StreamAccessor::getStream(stream));
|
||||
}
|
||||
|
||||
void MOG2Impl::getBackgroundImage(OutputArray backgroundImage) const
|
||||
{
|
||||
getBackgroundImage(backgroundImage, Stream::Null());
|
||||
}
|
||||
|
||||
void MOG2Impl::getBackgroundImage(OutputArray _backgroundImage, Stream& stream) const
|
||||
{
|
||||
using namespace cv::cuda::device::mog2;
|
||||
|
||||
_backgroundImage.create(frameSize_, frameType_);
|
||||
GpuMat backgroundImage = _backgroundImage.getGpuMat();
|
||||
|
||||
getBackgroundImage2_gpu(backgroundImage.channels(), bgmodelUsedModes_, weight_, mean_, backgroundImage, StreamAccessor::getStream(stream));
|
||||
}
|
||||
|
||||
void MOG2Impl::initialize(cv::Size frameSize, int frameType)
|
||||
{
|
||||
using namespace cv::cuda::device::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(Scalar::all(0));
|
||||
|
||||
loadConstants(nmixtures_, varThreshold_, backgroundRatio_, varThresholdGen_, varInit_, varMin_, varMax_, shadowThreshold_, shadowValue_);
|
||||
}
|
||||
cudaSafeCall(cudaMalloc((void **)&constantsDevice_, sizeof(Constants)));
|
||||
}
|
||||
|
||||
MOG2Impl::~MOG2Impl()
|
||||
{
|
||||
cudaFree(constantsDevice_);
|
||||
}
|
||||
|
||||
void MOG2Impl::apply(InputArray image, OutputArray fgmask, double learningRate)
|
||||
{
|
||||
apply(image, fgmask, learningRate, Stream::Null());
|
||||
}
|
||||
|
||||
void MOG2Impl::apply(InputArray _frame, OutputArray _fgmask, double learningRate, Stream &stream)
|
||||
{
|
||||
using namespace cv::cuda::device::mog2;
|
||||
|
||||
GpuMat frame = _frame.getGpuMat();
|
||||
|
||||
int ch = frame.channels();
|
||||
int work_ch = ch;
|
||||
|
||||
if (nframes_ == 0 || learningRate >= 1.0 || frame.size() != frameSize_ || work_ch != mean_.channels())
|
||||
initialize(frame.size(), frame.type(), stream);
|
||||
|
||||
_fgmask.create(frameSize_, CV_8UC1);
|
||||
GpuMat fgmask = _fgmask.getGpuMat();
|
||||
|
||||
fgmask.setTo(Scalar::all(0), stream);
|
||||
|
||||
++nframes_;
|
||||
learningRate = learningRate >= 0 && nframes_ > 1 ? learningRate : 1.0 / std::min(2 * nframes_, history_);
|
||||
CV_Assert(learningRate >= 0);
|
||||
|
||||
mog2_gpu(frame, frame.channels(), fgmask, bgmodelUsedModes_, weight_, variance_, mean_,
|
||||
(float)learningRate, static_cast<float>(-learningRate * ct_), detectShadows_, constantsDevice_, StreamAccessor::getStream(stream));
|
||||
}
|
||||
|
||||
void MOG2Impl::getBackgroundImage(OutputArray backgroundImage) const
|
||||
{
|
||||
getBackgroundImage(backgroundImage, Stream::Null());
|
||||
}
|
||||
|
||||
void MOG2Impl::getBackgroundImage(OutputArray _backgroundImage, Stream &stream) const
|
||||
{
|
||||
using namespace cv::cuda::device::mog2;
|
||||
|
||||
_backgroundImage.create(frameSize_, frameType_);
|
||||
GpuMat backgroundImage = _backgroundImage.getGpuMat();
|
||||
|
||||
getBackgroundImage2_gpu(backgroundImage.channels(), bgmodelUsedModes_, weight_, mean_, backgroundImage, constantsDevice_, StreamAccessor::getStream(stream));
|
||||
}
|
||||
|
||||
void MOG2Impl::initialize(cv::Size frameSize, int frameType, Stream &stream)
|
||||
{
|
||||
using namespace cv::cuda::device::mog2;
|
||||
|
||||
CV_Assert(frameType == CV_8UC1 || frameType == CV_8UC3 || frameType == CV_8UC4);
|
||||
|
||||
frameSize_ = frameSize;
|
||||
frameType_ = frameType;
|
||||
nframes_ = 0;
|
||||
|
||||
const int ch = CV_MAT_CN(frameType);
|
||||
const 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 * getNMixtures(), frameSize_.width, CV_32FC1);
|
||||
variance_.create(frameSize.height * getNMixtures(), frameSize_.width, CV_32FC1);
|
||||
mean_.create(frameSize.height * getNMixtures(), 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(Scalar::all(0));
|
||||
|
||||
cudaSafeCall(cudaMemcpyAsync(constantsDevice_, &constantsHost_, sizeof(Constants), cudaMemcpyHostToDevice, StreamAccessor::getStream(stream)));
|
||||
}
|
||||
} // namespace
|
||||
|
||||
Ptr<cuda::BackgroundSubtractorMOG2> cv::cuda::createBackgroundSubtractorMOG2(int history, double varThreshold, bool detectShadows)
|
||||
{
|
||||
return makePtr<MOG2Impl>(history, varThreshold, detectShadows);
|
||||
|
||||
Loading…
Reference in New Issue
Block a user