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opencv/modules/gpu/src/cuda/optical_flow_farneback.cu

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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 "internal_shared.hpp"
#include "opencv2/gpu/device/common.hpp"
#include "opencv2/gpu/device/border_interpolate.hpp"
#define tx threadIdx.x
#define ty threadIdx.y
#define bx blockIdx.x
#define by blockIdx.y
#define bdx blockDim.x
#define bdy blockDim.y
#define BORDER_SIZE 5
#define MAX_KSIZE_HALF 100
namespace cv { namespace gpu { namespace device { namespace optflow_farneback
{
__constant__ float c_g[8];
__constant__ float c_xg[8];
__constant__ float c_xxg[8];
__constant__ float c_ig11, c_ig03, c_ig33, c_ig55;
template <int polyN>
__global__ void polynomialExpansion(
const int height, const int width, const PtrStepf src, PtrStepf dst)
{
const int y = by * bdy + ty;
const int x = bx * (bdx - 2*polyN) + tx - polyN;
if (y < height)
{
extern __shared__ float smem[];
volatile float *row = smem + tx;
int xWarped = ::min(::max(x, 0), width - 1);
row[0] = src(y, xWarped) * c_g[0];
row[bdx] = 0.f;
row[2*bdx] = 0.f;
for (int k = 1; k <= polyN; ++k)
{
float t0 = src(::max(y - k, 0), xWarped);
float t1 = src(::min(y + k, height - 1), xWarped);
row[0] += c_g[k] * (t0 + t1);
row[bdx] += c_xg[k] * (t1 - t0);
row[2*bdx] += c_xxg[k] * (t0 + t1);
}
__syncthreads();
if (tx >= polyN && tx + polyN < bdx && x < width)
{
float b1 = c_g[0] * row[0];
float b3 = c_g[0] * row[bdx];
float b5 = c_g[0] * row[2*bdx];
float b2 = 0, b4 = 0, b6 = 0;
for (int k = 1; k <= polyN; ++k)
{
b1 += (row[k] + row[-k]) * c_g[k];
b4 += (row[k] + row[-k]) * c_xxg[k];
b2 += (row[k] - row[-k]) * c_xg[k];
b3 += (row[k + bdx] + row[-k + bdx]) * c_g[k];
b6 += (row[k + bdx] - row[-k + bdx]) * c_xg[k];
b5 += (row[k + 2*bdx] + row[-k + 2*bdx]) * c_g[k];
}
dst(y, xWarped) = b3*c_ig11;
dst(height + y, xWarped) = b2*c_ig11;
dst(2*height + y, xWarped) = b1*c_ig03 + b5*c_ig33;
dst(3*height + y, xWarped) = b1*c_ig03 + b4*c_ig33;
dst(4*height + y, xWarped) = b6*c_ig55;
}
}
}
void setPolynomialExpansionConsts(
int polyN, const float *g, const float *xg, const float *xxg,
float ig11, float ig03, float ig33, float ig55)
{
cudaSafeCall(cudaMemcpyToSymbol(c_g, g, (polyN + 1) * sizeof(*g)));
cudaSafeCall(cudaMemcpyToSymbol(c_xg, xg, (polyN + 1) * sizeof(*xg)));
cudaSafeCall(cudaMemcpyToSymbol(c_xxg, xxg, (polyN + 1) * sizeof(*xxg)));
cudaSafeCall(cudaMemcpyToSymbol(c_ig11, &ig11, sizeof(ig11)));
cudaSafeCall(cudaMemcpyToSymbol(c_ig03, &ig03, sizeof(ig03)));
cudaSafeCall(cudaMemcpyToSymbol(c_ig33, &ig33, sizeof(ig33)));
cudaSafeCall(cudaMemcpyToSymbol(c_ig55, &ig55, sizeof(ig55)));
}
void polynomialExpansionGpu(const PtrStepSzf &src, int polyN, PtrStepSzf dst, cudaStream_t stream)
{
dim3 block(256);
dim3 grid(divUp(src.cols, block.x - 2*polyN), src.rows);
int smem = 3 * block.x * sizeof(float);
if (polyN == 5)
polynomialExpansion<5><<<grid, block, smem, stream>>>(src.rows, src.cols, src, dst);
else if (polyN == 7)
polynomialExpansion<7><<<grid, block, smem, stream>>>(src.rows, src.cols, src, dst);
cudaSafeCall(cudaGetLastError());
if (stream == 0)
cudaSafeCall(cudaDeviceSynchronize());
}
__constant__ float c_border[BORDER_SIZE + 1];
__global__ void updateMatrices(
const int height, const int width, const PtrStepf flowx, const PtrStepf flowy,
const PtrStepf R0, const PtrStepf R1, PtrStepf M)
{
const int y = by * bdy + ty;
const int x = bx * bdx + tx;
if (y < height && x < width)
{
float dx = flowx(y, x);
float dy = flowy(y, x);
float fx = x + dx;
float fy = y + dy;
int x1 = floorf(fx);
int y1 = floorf(fy);
fx -= x1; fy -= y1;
float r2, r3, r4, r5, r6;
if (x1 >= 0 && y1 >= 0 && x1 < width - 1 && y1 < height - 1)
{
float a00 = (1.f - fx) * (1.f - fy);
float a01 = fx * (1.f - fy);
float a10 = (1.f - fx) * fy;
float a11 = fx * fy;
r2 = a00 * R1(y1, x1) +
a01 * R1(y1, x1 + 1) +
a10 * R1(y1 + 1, x1) +
a11 * R1(y1 + 1, x1 + 1);
r3 = a00 * R1(height + y1, x1) +
a01 * R1(height + y1, x1 + 1) +
a10 * R1(height + y1 + 1, x1) +
a11 * R1(height + y1 + 1, x1 + 1);
r4 = a00 * R1(2*height + y1, x1) +
a01 * R1(2*height + y1, x1 + 1) +
a10 * R1(2*height + y1 + 1, x1) +
a11 * R1(2*height + y1 + 1, x1 + 1);
r5 = a00 * R1(3*height + y1, x1) +
a01 * R1(3*height + y1, x1 + 1) +
a10 * R1(3*height + y1 + 1, x1) +
a11 * R1(3*height + y1 + 1, x1 + 1);
r6 = a00 * R1(4*height + y1, x1) +
a01 * R1(4*height + y1, x1 + 1) +
a10 * R1(4*height + y1 + 1, x1) +
a11 * R1(4*height + y1 + 1, x1 + 1);
r4 = (R0(2*height + y, x) + r4) * 0.5f;
r5 = (R0(3*height + y, x) + r5) * 0.5f;
r6 = (R0(4*height + y, x) + r6) * 0.25f;
}
else
{
r2 = r3 = 0.f;
r4 = R0(2*height + y, x);
r5 = R0(3*height + y, x);
r6 = R0(4*height + y, x) * 0.5f;
}
r2 = (R0(y, x) - r2) * 0.5f;
r3 = (R0(height + y, x) - r3) * 0.5f;
r2 += r4*dy + r6*dx;
r3 += r6*dy + r5*dx;
float scale =
c_border[::min(x, BORDER_SIZE)] *
c_border[::min(y, BORDER_SIZE)] *
c_border[::min(width - x - 1, BORDER_SIZE)] *
c_border[::min(height - y - 1, BORDER_SIZE)];
r2 *= scale; r3 *= scale; r4 *= scale;
r5 *= scale; r6 *= scale;
M(y, x) = r4*r4 + r6*r6;
M(height + y, x) = (r4 + r5)*r6;
M(2*height + y, x) = r5*r5 + r6*r6;
M(3*height + y, x) = r4*r2 + r6*r3;
M(4*height + y, x) = r6*r2 + r5*r3;
}
}
void setUpdateMatricesConsts()
{
static const float border[BORDER_SIZE + 1] = {0.14f, 0.14f, 0.4472f, 0.4472f, 0.4472f, 1.f};
cudaSafeCall(cudaMemcpyToSymbol(c_border, border, (BORDER_SIZE + 1) * sizeof(*border)));
}
void updateMatricesGpu(
const PtrStepSzf flowx, const PtrStepSzf flowy, const PtrStepSzf R0, const PtrStepSzf R1,
PtrStepSzf M, cudaStream_t stream)
{
dim3 block(32, 8);
dim3 grid(divUp(flowx.cols, block.x), divUp(flowx.rows, block.y));
updateMatrices<<<grid, block, 0, stream>>>(flowx.rows, flowx.cols, flowx, flowy, R0, R1, M);
cudaSafeCall(cudaGetLastError());
if (stream == 0)
cudaSafeCall(cudaDeviceSynchronize());
}
__global__ void updateFlow(
const int height, const int width, const PtrStepf M, PtrStepf flowx, PtrStepf flowy)
{
const int y = by * bdy + ty;
const int x = bx * bdx + tx;
if (y < height && x < width)
{
float g11 = M(y, x);
float g12 = M(height + y, x);
float g22 = M(2*height + y, x);
float h1 = M(3*height + y, x);
float h2 = M(4*height + y, x);
float detInv = 1.f / (g11*g22 - g12*g12 + 1e-3f);
flowx(y, x) = (g11*h2 - g12*h1) * detInv;
flowy(y, x) = (g22*h1 - g12*h2) * detInv;
}
}
void updateFlowGpu(const PtrStepSzf M, PtrStepSzf flowx, PtrStepSzf flowy, cudaStream_t stream)
{
dim3 block(32, 8);
dim3 grid(divUp(flowx.cols, block.x), divUp(flowx.rows, block.y));
updateFlow<<<grid, block, 0, stream>>>(flowx.rows, flowx.cols, M, flowx, flowy);
cudaSafeCall(cudaGetLastError());
if (stream == 0)
cudaSafeCall(cudaDeviceSynchronize());
}
/*__global__ void boxFilter(
const int height, const int width, const PtrStepf src,
const int ksizeHalf, const float boxAreaInv, PtrStepf dst)
{
const int y = by * bdy + ty;
const int x = bx * bdx + tx;
extern __shared__ float smem[];
volatile float *row = smem + ty * (bdx + 2*ksizeHalf);
if (y < height)
{
// Vertical pass
for (int i = tx; i < bdx + 2*ksizeHalf; i += bdx)
{
int xExt = int(bx * bdx) + i - ksizeHalf;
xExt = ::min(::max(xExt, 0), width - 1);
row[i] = src(y, xExt);
for (int j = 1; j <= ksizeHalf; ++j)
row[i] += src(::max(y - j, 0), xExt) + src(::min(y + j, height - 1), xExt);
}
if (x < width)
{
__syncthreads();
// Horizontal passs
row += tx + ksizeHalf;
float res = row[0];
for (int i = 1; i <= ksizeHalf; ++i)
res += row[-i] + row[i];
dst(y, x) = res * boxAreaInv;
}
}
}
void boxFilterGpu(const PtrStepSzf src, int ksizeHalf, PtrStepSzf dst, cudaStream_t stream)
{
dim3 block(256);
dim3 grid(divUp(src.cols, block.x), divUp(src.rows, block.y));
int smem = (block.x + 2*ksizeHalf) * block.y * sizeof(float);
float boxAreaInv = 1.f / ((1 + 2*ksizeHalf) * (1 + 2*ksizeHalf));
boxFilter<<<grid, block, smem, stream>>>(src.rows, src.cols, src, ksizeHalf, boxAreaInv, dst);
cudaSafeCall(cudaGetLastError());
if (stream == 0)
cudaSafeCall(cudaDeviceSynchronize());
}*/
__global__ void boxFilter5(
const int height, const int width, const PtrStepf src,
const int ksizeHalf, const float boxAreaInv, PtrStepf dst)
{
const int y = by * bdy + ty;
const int x = bx * bdx + tx;
extern __shared__ float smem[];
const int smw = bdx + 2*ksizeHalf; // shared memory "width"
volatile float *row = smem + 5 * ty * smw;
if (y < height)
{
// Vertical pass
for (int i = tx; i < bdx + 2*ksizeHalf; i += bdx)
{
int xExt = int(bx * bdx) + i - ksizeHalf;
xExt = ::min(::max(xExt, 0), width - 1);
#pragma unroll
for (int k = 0; k < 5; ++k)
row[k*smw + i] = src(k*height + y, xExt);
for (int j = 1; j <= ksizeHalf; ++j)
#pragma unroll
for (int k = 0; k < 5; ++k)
row[k*smw + i] +=
src(k*height + ::max(y - j, 0), xExt) +
src(k*height + ::min(y + j, height - 1), xExt);
}
if (x < width)
{
__syncthreads();
// Horizontal passs
row += tx + ksizeHalf;
float res[5];
#pragma unroll
for (int k = 0; k < 5; ++k)
res[k] = row[k*smw];
for (int i = 1; i <= ksizeHalf; ++i)
#pragma unroll
for (int k = 0; k < 5; ++k)
res[k] += row[k*smw - i] + row[k*smw + i];
#pragma unroll
for (int k = 0; k < 5; ++k)
dst(k*height + y, x) = res[k] * boxAreaInv;
}
}
}
void boxFilter5Gpu(const PtrStepSzf src, int ksizeHalf, PtrStepSzf dst, cudaStream_t stream)
{
int height = src.rows / 5;
int width = src.cols;
dim3 block(256);
dim3 grid(divUp(width, block.x), divUp(height, block.y));
int smem = (block.x + 2*ksizeHalf) * 5 * block.y * sizeof(float);
float boxAreaInv = 1.f / ((1 + 2*ksizeHalf) * (1 + 2*ksizeHalf));
boxFilter5<<<grid, block, smem, stream>>>(height, width, src, ksizeHalf, boxAreaInv, dst);
cudaSafeCall(cudaGetLastError());
if (stream == 0)
cudaSafeCall(cudaDeviceSynchronize());
}
void boxFilter5Gpu_CC11(const PtrStepSzf src, int ksizeHalf, PtrStepSzf dst, cudaStream_t stream)
{
int height = src.rows / 5;
int width = src.cols;
dim3 block(128);
dim3 grid(divUp(width, block.x), divUp(height, block.y));
int smem = (block.x + 2*ksizeHalf) * 5 * block.y * sizeof(float);
float boxAreaInv = 1.f / ((1 + 2*ksizeHalf) * (1 + 2*ksizeHalf));
boxFilter5<<<grid, block, smem, stream>>>(height, width, src, ksizeHalf, boxAreaInv, dst);
cudaSafeCall(cudaGetLastError());
if (stream == 0)
cudaSafeCall(cudaDeviceSynchronize());
}
__constant__ float c_gKer[MAX_KSIZE_HALF + 1];
template <typename Border>
__global__ void gaussianBlur(
const int height, const int width, const PtrStepf src, const int ksizeHalf,
const Border b, PtrStepf dst)
{
const int y = by * bdy + ty;
const int x = bx * bdx + tx;
extern __shared__ float smem[];
volatile float *row = smem + ty * (bdx + 2*ksizeHalf);
if (y < height)
{
// Vertical pass
for (int i = tx; i < bdx + 2*ksizeHalf; i += bdx)
{
int xExt = int(bx * bdx) + i - ksizeHalf;
xExt = b.idx_col(xExt);
row[i] = src(y, xExt) * c_gKer[0];
for (int j = 1; j <= ksizeHalf; ++j)
row[i] +=
(src(b.idx_row_low(y - j), xExt) +
src(b.idx_row_high(y + j), xExt)) * c_gKer[j];
}
if (x < width)
{
__syncthreads();
// Horizontal pass
row += tx + ksizeHalf;
float res = row[0] * c_gKer[0];
for (int i = 1; i <= ksizeHalf; ++i)
res += (row[-i] + row[i]) * c_gKer[i];
dst(y, x) = res;
}
}
}
void setGaussianBlurKernel(const float *gKer, int ksizeHalf)
{
cudaSafeCall(cudaMemcpyToSymbol(c_gKer, gKer, (ksizeHalf + 1) * sizeof(*gKer)));
}
template <typename Border>
void gaussianBlurCaller(const PtrStepSzf src, int ksizeHalf, PtrStepSzf dst, cudaStream_t stream)
{
int height = src.rows;
int width = src.cols;
dim3 block(256);
dim3 grid(divUp(width, block.x), divUp(height, block.y));
int smem = (block.x + 2*ksizeHalf) * block.y * sizeof(float);
Border b(height, width);
gaussianBlur<<<grid, block, smem, stream>>>(height, width, src, ksizeHalf, b, dst);
cudaSafeCall(cudaGetLastError());
if (stream == 0)
cudaSafeCall(cudaDeviceSynchronize());
}
void gaussianBlurGpu(
const PtrStepSzf src, int ksizeHalf, PtrStepSzf dst, int borderMode, cudaStream_t stream)
{
typedef void (*caller_t)(const PtrStepSzf, int, PtrStepSzf, cudaStream_t);
static const caller_t callers[] =
{
gaussianBlurCaller<BrdReflect101<float> >,
gaussianBlurCaller<BrdReplicate<float> >,
};
callers[borderMode](src, ksizeHalf, dst, stream);
}
template <typename Border>
__global__ void gaussianBlur5(
const int height, const int width, const PtrStepf src, const int ksizeHalf,
const Border b, PtrStepf dst)
{
const int y = by * bdy + ty;
const int x = bx * bdx + tx;
extern __shared__ float smem[];
const int smw = bdx + 2*ksizeHalf; // shared memory "width"
volatile float *row = smem + 5 * ty * smw;
if (y < height)
{
// Vertical pass
for (int i = tx; i < bdx + 2*ksizeHalf; i += bdx)
{
int xExt = int(bx * bdx) + i - ksizeHalf;
xExt = b.idx_col(xExt);
#pragma unroll
for (int k = 0; k < 5; ++k)
row[k*smw + i] = src(k*height + y, xExt) * c_gKer[0];
for (int j = 1; j <= ksizeHalf; ++j)
#pragma unroll
for (int k = 0; k < 5; ++k)
row[k*smw + i] +=
(src(k*height + b.idx_row_low(y - j), xExt) +
src(k*height + b.idx_row_high(y + j), xExt)) * c_gKer[j];
}
if (x < width)
{
__syncthreads();
// Horizontal pass
row += tx + ksizeHalf;
float res[5];
#pragma unroll
for (int k = 0; k < 5; ++k)
res[k] = row[k*smw] * c_gKer[0];
for (int i = 1; i <= ksizeHalf; ++i)
#pragma unroll
for (int k = 0; k < 5; ++k)
res[k] += (row[k*smw - i] + row[k*smw + i]) * c_gKer[i];
#pragma unroll
for (int k = 0; k < 5; ++k)
dst(k*height + y, x) = res[k];
}
}
}
template <typename Border, int blockDimX>
void gaussianBlur5Caller(
const PtrStepSzf src, int ksizeHalf, PtrStepSzf dst, cudaStream_t stream)
{
int height = src.rows / 5;
int width = src.cols;
dim3 block(blockDimX);
dim3 grid(divUp(width, block.x), divUp(height, block.y));
int smem = (block.x + 2*ksizeHalf) * 5 * block.y * sizeof(float);
Border b(height, width);
gaussianBlur5<<<grid, block, smem, stream>>>(height, width, src, ksizeHalf, b, dst);
cudaSafeCall(cudaGetLastError());
if (stream == 0)
cudaSafeCall(cudaDeviceSynchronize());
}
void gaussianBlur5Gpu(
const PtrStepSzf src, int ksizeHalf, PtrStepSzf dst, int borderMode, cudaStream_t stream)
{
typedef void (*caller_t)(const PtrStepSzf, int, PtrStepSzf, cudaStream_t);
static const caller_t callers[] =
{
gaussianBlur5Caller<BrdReflect101<float>,256>,
gaussianBlur5Caller<BrdReplicate<float>,256>,
};
callers[borderMode](src, ksizeHalf, dst, stream);
}
void gaussianBlur5Gpu_CC11(
const PtrStepSzf src, int ksizeHalf, PtrStepSzf dst, int borderMode, cudaStream_t stream)
{
typedef void (*caller_t)(const PtrStepSzf, int, PtrStepSzf, cudaStream_t);
static const caller_t callers[] =
{
gaussianBlur5Caller<BrdReflect101<float>,128>,
gaussianBlur5Caller<BrdReplicate<float>,128>,
};
callers[borderMode](src, ksizeHalf, dst, stream);
}
}}}} // namespace cv { namespace gpu { namespace device { namespace optflow_farneback
#endif /* CUDA_DISABLER */