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0e43976259
opencv/modules/gpu/src/cuda/mathfunc.cu
Anatoly Baksheev 0e43976259 1) more convenient naming for samples gpu 
2) added mask support to device 'transform' function 
3) sample hog gpu: waitKey(1) -> waitKey(3), in other case image is not displayed.
2010-11-24 09:43:17 +00:00

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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*/
#include "cuda_shared.hpp"
#include "transform.hpp"
using namespace cv::gpu;
#ifndef CV_PI
#define CV_PI 3.1415926535897932384626433832795f
#endif
//////////////////////////////////////////////////////////////////////////////////////
// Cart <-> Polar
namespace cv { namespace gpu { namespace mathfunc
{
struct Nothing
{
static __device__ void calc(int, int, float, float, float*, size_t, float)
{
}
};
struct Magnitude
{
static __device__ void calc(int x, int y, float x_data, float y_data, float* dst, size_t dst_step, float)
{
dst[y * dst_step + x] = sqrtf(x_data * x_data + y_data * y_data);
}
};
struct MagnitudeSqr
{
static __device__ void calc(int x, int y, float x_data, float y_data, float* dst, size_t dst_step, float)
{
dst[y * dst_step + x] = x_data * x_data + y_data * y_data;
}
};
struct Atan2
{
static __device__ void calc(int x, int y, float x_data, float y_data, float* dst, size_t dst_step, float scale)
{
dst[y * dst_step + x] = scale * atan2f(y_data, x_data);
}
};
template <typename Mag, typename Angle>
__global__ void cartToPolar(const float* xptr, size_t x_step, const float* yptr, size_t y_step,
float* mag, size_t mag_step, float* angle, size_t angle_step, float scale, int width, int height)
{
const int x = blockDim.x * blockIdx.x + threadIdx.x;
const int y = blockDim.y * blockIdx.y + threadIdx.y;
if (x < width && y < height)
{
float x_data = xptr[y * x_step + x];
float y_data = yptr[y * y_step + x];
Mag::calc(x, y, x_data, y_data, mag, mag_step, scale);
Angle::calc(x, y, x_data, y_data, angle, angle_step, scale);
}
}
struct NonEmptyMag
{
static __device__ float get(const float* mag, size_t mag_step, int x, int y)
{
return mag[y * mag_step + x];
}
};
struct EmptyMag
{
static __device__ float get(const float*, size_t, int, int)
{
return 1.0f;
}
};
template <typename Mag>
__global__ void polarToCart(const float* mag, size_t mag_step, const float* angle, size_t angle_step, float scale,
float* xptr, size_t x_step, float* yptr, size_t y_step, int width, int height)
{
const int x = blockDim.x * blockIdx.x + threadIdx.x;
const int y = blockDim.y * blockIdx.y + threadIdx.y;
if (x < width && y < height)
{
float mag_data = Mag::get(mag, mag_step, x, y);
float angle_data = angle[y * angle_step + x];
float sin_a, cos_a;
sincosf(scale * angle_data, &sin_a, &cos_a);
xptr[y * x_step + x] = mag_data * cos_a;
yptr[y * y_step + x] = mag_data * sin_a;
}
}
template <typename Mag, typename Angle>
void cartToPolar_caller(const DevMem2Df& x, const DevMem2Df& y, const DevMem2Df& mag, const DevMem2Df& angle, bool angleInDegrees, cudaStream_t stream)
{
dim3 threads(16, 16, 1);
dim3 grid(1, 1, 1);
grid.x = divUp(x.cols, threads.x);
grid.y = divUp(x.rows, threads.y);
const float scale = angleInDegrees ? (float)(180.0f / CV_PI) : 1.f;
cartToPolar<Mag, Angle><<<grid, threads, 0, stream>>>(
x.data, x.step/x.elemSize(), y.data, y.step/y.elemSize(),
mag.data, mag.step/mag.elemSize(), angle.data, angle.step/angle.elemSize(), scale, x.cols, x.rows);
if (stream == 0)
cudaSafeCall( cudaThreadSynchronize() );
}
void cartToPolar_gpu(const DevMem2Df& x, const DevMem2Df& y, const DevMem2Df& mag, bool magSqr, const DevMem2Df& angle, bool angleInDegrees, cudaStream_t stream)
{
typedef void (*caller_t)(const DevMem2Df& x, const DevMem2Df& y, const DevMem2Df& mag, const DevMem2Df& angle, bool angleInDegrees, cudaStream_t stream);
static const caller_t callers[2][2][2] =
{
{
{
cartToPolar_caller<Magnitude, Atan2>,
cartToPolar_caller<Magnitude, Nothing>
},
{
cartToPolar_caller<MagnitudeSqr, Atan2>,
cartToPolar_caller<MagnitudeSqr, Nothing>,
}
},
{
{
cartToPolar_caller<Nothing, Atan2>,
cartToPolar_caller<Nothing, Nothing>
},
{
cartToPolar_caller<Nothing, Atan2>,
cartToPolar_caller<Nothing, Nothing>,
}
}
};
callers[mag.data == 0][magSqr][angle.data == 0](x, y, mag, angle, angleInDegrees, stream);
}
template <typename Mag>
void polarToCart_caller(const DevMem2Df& mag, const DevMem2Df& angle, const DevMem2Df& x, const DevMem2Df& y, bool angleInDegrees, cudaStream_t stream)
{
dim3 threads(16, 16, 1);
dim3 grid(1, 1, 1);
grid.x = divUp(mag.cols, threads.x);
grid.y = divUp(mag.rows, threads.y);
const float scale = angleInDegrees ? (float)(CV_PI / 180.0f) : 1.0f;
polarToCart<Mag><<<grid, threads, 0, stream>>>(mag.data, mag.step/mag.elemSize(),
angle.data, angle.step/angle.elemSize(), scale, x.data, x.step/x.elemSize(), y.data, y.step/y.elemSize(), mag.cols, mag.rows);
if (stream == 0)
cudaSafeCall( cudaThreadSynchronize() );
}
void polarToCart_gpu(const DevMem2Df& mag, const DevMem2Df& angle, const DevMem2Df& x, const DevMem2Df& y, bool angleInDegrees, cudaStream_t stream)
{
typedef void (*caller_t)(const DevMem2Df& mag, const DevMem2Df& angle, const DevMem2Df& x, const DevMem2Df& y, bool angleInDegrees, cudaStream_t stream);
static const caller_t callers[2] =
{
polarToCart_caller<NonEmptyMag>,
polarToCart_caller<EmptyMag>
};
callers[mag.data == 0](mag, angle, x, y, angleInDegrees, stream);
}
//////////////////////////////////////////////////////////////////////////////////////
// Compare
template <typename T1, typename T2>
struct NotEqual
{
__device__ uchar operator()(const T1& src1, const T2& src2)
{
return static_cast<uchar>(static_cast<int>(src1 != src2) * 255);
}
};
template <typename T1, typename T2>
inline void compare_ne(const DevMem2D& src1, const DevMem2D& src2, const DevMem2D& dst)
{
NotEqual<T1, T2> op;
transform(static_cast< DevMem2D_<T1> >(src1), static_cast< DevMem2D_<T2> >(src2), dst, op, 0);
}
void compare_ne_8uc4(const DevMem2D& src1, const DevMem2D& src2, const DevMem2D& dst)
{
compare_ne<uint, uint>(src1, src2, dst);
}
void compare_ne_32f(const DevMem2D& src1, const DevMem2D& src2, const DevMem2D& dst)
{
compare_ne<float, float>(src1, src2, dst);
}
//////////////////////////////////////////////////////////////////////////////
// Per-element bit-wise logical matrix operations
struct Mask8U
{
explicit Mask8U(PtrStep mask): mask(mask) {}
__device__ bool operator()(int y, int x) { return mask.ptr(y)[x]; }
PtrStep mask;
};
struct MaskTrue { __device__ bool operator()(int y, int x) { return true; } };
// Unary operations
enum { UN_OP_NOT };
template <typename T, int opid>
struct UnOp { __device__ T operator()(T lhs, T rhs); };
template <typename T>
struct UnOp<T, UN_OP_NOT>{ __device__ T operator()(T x) { return ~x; } };
template <typename T, int cn, typename UnOp, typename Mask>
__global__ void bitwise_un_op(int rows, int cols, const PtrStep src, PtrStep dst, UnOp op, Mask mask)
{
const int x = blockDim.x * blockIdx.x + threadIdx.x;
const int y = blockDim.y * blockIdx.y + threadIdx.y;
if (x < cols && y < rows && mask(y, x))
{
T* dsty = (T*)dst.ptr(y);
const T* srcy = (const T*)src.ptr(y);
#pragma unroll
for (int i = 0; i < cn; ++i)
dsty[cn * x + i] = op(srcy[cn * x + i]);
}
}
template <int opid, typename Mask>
void bitwise_un_op(int rows, int cols, const PtrStep src, PtrStep dst, int elem_size, Mask mask, cudaStream_t stream)
{
dim3 threads(16, 16);
dim3 grid(divUp(cols, threads.x), divUp(rows, threads.y));
switch (elem_size)
{
case 1: bitwise_un_op<unsigned char, 1><<<grid, threads>>>(rows, cols, src, dst, UnOp<unsigned char, opid>(), mask); break;
case 2: bitwise_un_op<unsigned short, 1><<<grid, threads>>>(rows, cols, src, dst, UnOp<unsigned short, opid>(), mask); break;
case 3: bitwise_un_op<unsigned char, 3><<<grid, threads>>>(rows, cols, src, dst, UnOp<unsigned char, opid>(), mask); break;
case 4: bitwise_un_op<unsigned int, 1><<<grid, threads>>>(rows, cols, src, dst, UnOp<unsigned int, opid>(), mask); break;
case 6: bitwise_un_op<unsigned short, 3><<<grid, threads>>>(rows, cols, src, dst, UnOp<unsigned short, opid>(), mask); break;
case 8: bitwise_un_op<unsigned int, 2><<<grid, threads>>>(rows, cols, src, dst, UnOp<unsigned int, opid>(), mask); break;
case 12: bitwise_un_op<unsigned int, 3><<<grid, threads>>>(rows, cols, src, dst, UnOp<unsigned int, opid>(), mask); break;
case 16: bitwise_un_op<unsigned int, 4><<<grid, threads>>>(rows, cols, src, dst, UnOp<unsigned int, opid>(), mask); break;
case 24: bitwise_un_op<unsigned int, 6><<<grid, threads>>>(rows, cols, src, dst, UnOp<unsigned int, opid>(), mask); break;
case 32: bitwise_un_op<unsigned int, 8><<<grid, threads>>>(rows, cols, src, dst, UnOp<unsigned int, opid>(), mask); break;
}
if (stream == 0) cudaSafeCall(cudaThreadSynchronize());
}
void bitwise_not_caller(int rows, int cols,const PtrStep src, int elem_size, PtrStep dst, cudaStream_t stream)
{
bitwise_un_op<UN_OP_NOT>(rows, cols, src, dst, elem_size, MaskTrue(), stream);
}
void bitwise_not_caller(int rows, int cols,const PtrStep src, int elem_size, PtrStep dst, const PtrStep mask, cudaStream_t stream)
{
bitwise_un_op<UN_OP_NOT>(rows, cols, src, dst, elem_size, Mask8U(mask), stream);
}
// Binary operations
enum { BIN_OP_OR, BIN_OP_AND, BIN_OP_XOR };
template <typename T, int opid>
struct BinOp { __device__ T operator()(T lhs, T rhs); };
template <typename T>
struct BinOp<T, BIN_OP_OR>{ __device__ T operator()(T lhs, T rhs) { return lhs | rhs; } };
template <typename T>
struct BinOp<T, BIN_OP_AND>{ __device__ T operator()(T lhs, T rhs) { return lhs & rhs; } };
template <typename T>
struct BinOp<T, BIN_OP_XOR>{ __device__ T operator()(T lhs, T rhs) { return lhs ^ rhs; } };
template <typename T, int cn, typename BinOp, typename Mask>
__global__ void bitwise_bin_op(int rows, int cols, const PtrStep src1, const PtrStep src2, PtrStep dst, BinOp op, Mask mask)
{
const int x = blockDim.x * blockIdx.x + threadIdx.x;
const int y = blockDim.y * blockIdx.y + threadIdx.y;
if (x < cols && y < rows && mask(y, x))
{
T* dsty = (T*)dst.ptr(y);
const T* src1y = (const T*)src1.ptr(y);
const T* src2y = (const T*)src2.ptr(y);
#pragma unroll
for (int i = 0; i < cn; ++i)
dsty[cn * x + i] = op(src1y[cn * x + i], src2y[cn * x + i]);
}
}
template <int opid, typename Mask>
void bitwise_bin_op(int rows, int cols, const PtrStep src1, const PtrStep src2, PtrStep dst, int elem_size, Mask mask, cudaStream_t stream)
{
dim3 threads(16, 16);
dim3 grid(divUp(cols, threads.x), divUp(rows, threads.y));
switch (elem_size)
{
case 1: bitwise_bin_op<unsigned char, 1><<<grid, threads>>>(rows, cols, src1, src2, dst, BinOp<unsigned char, opid>(), mask); break;
case 2: bitwise_bin_op<unsigned short, 1><<<grid, threads>>>(rows, cols, src1, src2, dst, BinOp<unsigned short, opid>(), mask); break;
case 3: bitwise_bin_op<unsigned char, 3><<<grid, threads>>>(rows, cols, src1, src2, dst, BinOp<unsigned char, opid>(), mask); break;
case 4: bitwise_bin_op<unsigned int, 1><<<grid, threads>>>(rows, cols, src1, src2, dst, BinOp<unsigned int, opid>(), mask); break;
case 6: bitwise_bin_op<unsigned short, 3><<<grid, threads>>>(rows, cols, src1, src2, dst, BinOp<unsigned short, opid>(), mask); break;
case 8: bitwise_bin_op<unsigned int, 2><<<grid, threads>>>(rows, cols, src1, src2, dst, BinOp<unsigned int, opid>(), mask); break;
case 12: bitwise_bin_op<unsigned int, 3><<<grid, threads>>>(rows, cols, src1, src2, dst, BinOp<unsigned int, opid>(), mask); break;
case 16: bitwise_bin_op<unsigned int, 4><<<grid, threads>>>(rows, cols, src1, src2, dst, BinOp<unsigned int, opid>(), mask); break;
case 24: bitwise_bin_op<unsigned int, 6><<<grid, threads>>>(rows, cols, src1, src2, dst, BinOp<unsigned int, opid>(), mask); break;
case 32: bitwise_bin_op<unsigned int, 8><<<grid, threads>>>(rows, cols, src1, src2, dst, BinOp<unsigned int, opid>(), mask); break;
}
if (stream == 0) cudaSafeCall(cudaThreadSynchronize());
}
void bitwise_or_caller(int rows, int cols, const PtrStep src1, const PtrStep src2, int elem_size, PtrStep dst, cudaStream_t stream)
{
bitwise_bin_op<BIN_OP_OR>(rows, cols, src1, src2, dst, elem_size, MaskTrue(), stream);
}
void bitwise_or_caller(int rows, int cols, const PtrStep src1, const PtrStep src2, int elem_size, PtrStep dst, const PtrStep mask, cudaStream_t stream)
{
bitwise_bin_op<BIN_OP_OR>(rows, cols, src1, src2, dst, elem_size, Mask8U(mask), stream);
}
void bitwise_and_caller(int rows, int cols, const PtrStep src1, const PtrStep src2, int elem_size, PtrStep dst, cudaStream_t stream)
{
bitwise_bin_op<BIN_OP_AND>(rows, cols, src1, src2, dst, elem_size, MaskTrue(), stream);
}
void bitwise_and_caller(int rows, int cols, const PtrStep src1, const PtrStep src2, int elem_size, PtrStep dst, const PtrStep mask, cudaStream_t stream)
{
bitwise_bin_op<BIN_OP_AND>(rows, cols, src1, src2, dst, elem_size, Mask8U(mask), stream);
}
void bitwise_xor_caller(int rows, int cols, const PtrStep src1, const PtrStep src2, int elem_size, PtrStep dst, cudaStream_t stream)
{
bitwise_bin_op<BIN_OP_XOR>(rows, cols, src1, src2, dst, elem_size, MaskTrue(), stream);
}
void bitwise_xor_caller(int rows, int cols, const PtrStep src1, const PtrStep src2, int elem_size, PtrStep dst, const PtrStep mask, cudaStream_t stream)
{
bitwise_bin_op<BIN_OP_XOR>(rows, cols, src1, src2, dst, elem_size, Mask8U(mask), stream);
}
//////////////////////////////////////////////////////////////////////////////
// Min max
enum { MIN, MAX };
template <typename T> struct MinMaxTypeTraits {};
template <> struct MinMaxTypeTraits<unsigned char> { typedef int best_type; };
template <> struct MinMaxTypeTraits<signed char> { typedef int best_type; };
template <> struct MinMaxTypeTraits<unsigned short> { typedef int best_type; };
template <> struct MinMaxTypeTraits<signed short> { typedef int best_type; };
template <> struct MinMaxTypeTraits<int> { typedef int best_type; };
template <> struct MinMaxTypeTraits<float> { typedef float best_type; };
template <> struct MinMaxTypeTraits<double> { typedef double best_type; };
template <typename T, int op> struct Cmp {};
template <typename T>
struct Cmp<T, MIN>
{
static __device__ void call(unsigned int tid, unsigned int offset, volatile T* optval)
{
optval[tid] = min(optval[tid], optval[tid + offset]);
}
};
template <typename T>
struct Cmp<T, MAX>
{
static __device__ void call(unsigned int tid, unsigned int offset, volatile T* optval)
{
optval[tid] = max(optval[tid], optval[tid + offset]);
}
};
template <int nthreads, int op, typename T>
__global__ void opt_kernel(int cols, int rows, const PtrStep src, PtrStep optval)
{
typedef typename MinMaxTypeTraits<T>::best_type best_type;
__shared__ best_type soptval[nthreads];
unsigned int x0 = blockIdx.x * blockDim.x;
unsigned int y0 = blockIdx.y * blockDim.y;
unsigned int tid = threadIdx.y * blockDim.x + threadIdx.x;
if (x0 + threadIdx.x < cols && y0 + threadIdx.y < rows)
soptval[tid] = ((const T*)src.ptr(y0 + threadIdx.y))[x0 + threadIdx.x];
else
soptval[tid] = ((const T*)src.ptr(y0))[x0];
__syncthreads();
if (nthreads >= 512) if (tid < 256) { Cmp<best_type, op>::call(tid, 256, soptval); __syncthreads(); }
if (nthreads >= 256) if (tid < 128) { Cmp<best_type, op>::call(tid, 128, soptval); __syncthreads(); }
if (nthreads >= 128) if (tid < 64) { Cmp<best_type, op>::call(tid, 64, soptval); __syncthreads(); }
if (tid < 32)
{
if (nthreads >= 64) Cmp<best_type, op>::call(tid, 32, soptval);
if (nthreads >= 32) Cmp<best_type, op>::call(tid, 16, soptval);
if (nthreads >= 16) Cmp<best_type, op>::call(tid, 8, soptval);
if (nthreads >= 8) Cmp<best_type, op>::call(tid, 4, soptval);
if (nthreads >= 4) Cmp<best_type, op>::call(tid, 2, soptval);
if (nthreads >= 2) Cmp<best_type, op>::call(tid, 1, soptval);
}
if (tid == 0) ((T*)optval.ptr(blockIdx.y))[blockIdx.x] = (T)soptval[0];
}
template <typename T>
void min_max_caller(const DevMem2D src, double* minval, double* maxval)
{
dim3 threads(32, 8);
// Allocate memory for aux. buffers
DevMem2D minval_buf[2]; DevMem2D maxval_buf[2];
minval_buf[0].cols = divUp(src.cols, threads.x);
minval_buf[0].rows = divUp(src.rows, threads.y);
minval_buf[1].cols = divUp(minval_buf[0].cols, threads.x);
minval_buf[1].rows = divUp(minval_buf[0].rows, threads.y);
maxval_buf[0].cols = divUp(src.cols, threads.x);
maxval_buf[0].rows = divUp(src.rows, threads.y);
maxval_buf[1].cols = divUp(maxval_buf[0].cols, threads.x);
maxval_buf[1].rows = divUp(maxval_buf[0].rows, threads.y);
cudaSafeCall(cudaMallocPitch(&minval_buf[0].data, &minval_buf[0].step, minval_buf[0].cols * sizeof(T), minval_buf[0].rows));
cudaSafeCall(cudaMallocPitch(&minval_buf[1].data, &minval_buf[1].step, minval_buf[1].cols * sizeof(T), minval_buf[1].rows));
cudaSafeCall(cudaMallocPitch(&maxval_buf[0].data, &maxval_buf[0].step, maxval_buf[0].cols * sizeof(T), maxval_buf[0].rows));
cudaSafeCall(cudaMallocPitch(&maxval_buf[1].data, &maxval_buf[1].step, maxval_buf[1].cols * sizeof(T), maxval_buf[1].rows));
int curbuf = 0;
dim3 cursize(src.cols, src.rows);
dim3 grid(divUp(cursize.x, threads.x), divUp(cursize.y, threads.y));
opt_kernel<256, MIN, T><<<grid, threads>>>(cursize.x, cursize.y, src, minval_buf[curbuf]);
opt_kernel<256, MAX, T><<<grid, threads>>>(cursize.x, cursize.y, src, maxval_buf[curbuf]);
cursize = grid;
while (cursize.x > 1 || cursize.y > 1)
{
grid.x = divUp(cursize.x, threads.x);
grid.y = divUp(cursize.y, threads.y);
opt_kernel<256, MIN, T><<<grid, threads>>>(cursize.x, cursize.y, minval_buf[curbuf], minval_buf[1 - curbuf]);
opt_kernel<256, MAX, T><<<grid, threads>>>(cursize.x, cursize.y, maxval_buf[curbuf], maxval_buf[1 - curbuf]);
curbuf = 1 - curbuf;
cursize = grid;
}
cudaSafeCall(cudaThreadSynchronize());
// Copy results from device to host
T minval_, maxval_;
cudaSafeCall(cudaMemcpy(&minval_, minval_buf[curbuf].ptr(0), sizeof(T), cudaMemcpyDeviceToHost));
cudaSafeCall(cudaMemcpy(&maxval_, maxval_buf[curbuf].ptr(0), sizeof(T), cudaMemcpyDeviceToHost));
*minval = minval_;
*maxval = maxval_;
// Release aux. buffers
cudaSafeCall(cudaFree(minval_buf[0].data));
cudaSafeCall(cudaFree(minval_buf[1].data));
cudaSafeCall(cudaFree(maxval_buf[0].data));
cudaSafeCall(cudaFree(maxval_buf[1].data));
}
template void min_max_caller<unsigned char>(const DevMem2D, double*, double*);
template void min_max_caller<signed char>(const DevMem2D, double*, double*);
template void min_max_caller<unsigned short>(const DevMem2D, double*, double*);
template void min_max_caller<signed short>(const DevMem2D, double*, double*);
template void min_max_caller<int>(const DevMem2D, double*, double*);
template void min_max_caller<float>(const DevMem2D, double*, double*);
template void min_max_caller<double>(const DevMem2D, double*, double*);
}}}
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