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minor formating fixes

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This commit is contained in:
Marina Kolpakova 2012-06-15 15:57:12 +00:00
parent 0942244d6f
commit 5b0d7a65e6
7 changed files with 305 additions and 305 deletions
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130
modules/gpu/src/cuda/NV12ToARGB.cu
View File

@ -1,43 +1,43 @@
/*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///////////////////////////////////////////////////////////////////////////////////////
//
// 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*/
/*
@ -50,11 +50,11 @@
* is strictly prohibited.
*
*/
/*
NV12ToARGB color space conversion CUDA kernel
This sample uses CUDA to perform a simple NV12 (YUV 4:2:0 planar)
This sample uses CUDA to perform a simple NV12 (YUV 4:2:0 planar)
source and converts to output in ARGB format
*/
@ -82,16 +82,16 @@ namespace cv { namespace gpu { namespace device {
chromaCr = (float)((int)yuvi[2] - 512.0f);
// Convert YUV To RGB with hue adjustment
*red = (luma * constHueColorSpaceMat[0]) +
(chromaCb * constHueColorSpaceMat[1]) +
*red = (luma * constHueColorSpaceMat[0]) +
(chromaCb * constHueColorSpaceMat[1]) +
(chromaCr * constHueColorSpaceMat[2]);
*green = (luma * constHueColorSpaceMat[3]) +
(chromaCb * constHueColorSpaceMat[4]) +
*green = (luma * constHueColorSpaceMat[3]) +
(chromaCb * constHueColorSpaceMat[4]) +
(chromaCr * constHueColorSpaceMat[5]);
*blue = (luma * constHueColorSpaceMat[6]) +
(chromaCb * constHueColorSpaceMat[7]) +
*blue = (luma * constHueColorSpaceMat[6]) +
(chromaCb * constHueColorSpaceMat[7]) +
(chromaCr * constHueColorSpaceMat[8]);
}
@ -105,9 +105,9 @@ namespace cv { namespace gpu { namespace device {
blue = ::fmin(::fmax(blue, 0.0f), 1023.f);
// Convert to 8 bit unsigned integers per color component
ARGBpixel = (((uint)blue >> 2) |
(((uint)green >> 2) << 8) |
(((uint)red >> 2) << 16) |
ARGBpixel = (((uint)blue >> 2) |
(((uint)green >> 2) << 8) |
(((uint)red >> 2) << 16) |
(uint)alpha);
return ARGBpixel;
@ -118,8 +118,8 @@ namespace cv { namespace gpu { namespace device {
#define COLOR_COMPONENT_BIT_SIZE 10
#define COLOR_COMPONENT_MASK 0x3FF
__global__ void NV12ToARGB(uchar* srcImage, size_t nSourcePitch,
uint* dstImage, size_t nDestPitch,
__global__ void NV12ToARGB(uchar* srcImage, size_t nSourcePitch,
uint* dstImage, size_t nDestPitch,
uint width, uint height)
{
// Pad borders with duplicate pixels, and we multiply by 2 because we process 2 pixels per thread
@ -127,7 +127,7 @@ namespace cv { namespace gpu { namespace device {
const int y = blockIdx.y * blockDim.y + threadIdx.y;
if (x >= width || y >= height)
return;
return;
// Read 2 Luma components at a time, so we don't waste processing since CbCr are decimated this way.
// if we move to texture we could read 4 luminance values
@ -142,7 +142,7 @@ namespace cv { namespace gpu { namespace device {
const int y_chroma = y >> 1;
if (y & 1) // odd scanline ?
{
{
uint chromaCb = srcImage[chromaOffset + y_chroma * nSourcePitch + x ];
uint chromaCr = srcImage[chromaOffset + y_chroma * nSourcePitch + x + 1];
@ -151,7 +151,7 @@ namespace cv { namespace gpu { namespace device {
chromaCb = (chromaCb + srcImage[chromaOffset + (y_chroma + 1) * nSourcePitch + x ] + 1) >> 1;
chromaCr = (chromaCr + srcImage[chromaOffset + (y_chroma + 1) * nSourcePitch + x + 1] + 1) >> 1;
}
yuv101010Pel[0] |= (chromaCb << ( COLOR_COMPONENT_BIT_SIZE + 2));
yuv101010Pel[0] |= (chromaCr << ((COLOR_COMPONENT_BIT_SIZE << 1) + 2));
@ -166,17 +166,17 @@ namespace cv { namespace gpu { namespace device {
yuv101010Pel[1] |= ((uint)srcImage[chromaOffset + y_chroma * nSourcePitch + x ] << ( COLOR_COMPONENT_BIT_SIZE + 2));
yuv101010Pel[1] |= ((uint)srcImage[chromaOffset + y_chroma * nSourcePitch + x + 1] << ((COLOR_COMPONENT_BIT_SIZE << 1) + 2));
}
// this steps performs the color conversion
uint yuvi[6];
float red[2], green[2], blue[2];
yuvi[0] = (yuv101010Pel[0] & COLOR_COMPONENT_MASK );
yuvi[1] = ((yuv101010Pel[0] >> COLOR_COMPONENT_BIT_SIZE) & COLOR_COMPONENT_MASK);
yuvi[0] = (yuv101010Pel[0] & COLOR_COMPONENT_MASK );
yuvi[1] = ((yuv101010Pel[0] >> COLOR_COMPONENT_BIT_SIZE) & COLOR_COMPONENT_MASK);
yuvi[2] = ((yuv101010Pel[0] >> (COLOR_COMPONENT_BIT_SIZE << 1)) & COLOR_COMPONENT_MASK);
yuvi[3] = (yuv101010Pel[1] & COLOR_COMPONENT_MASK );
yuvi[4] = ((yuv101010Pel[1] >> COLOR_COMPONENT_BIT_SIZE) & COLOR_COMPONENT_MASK);
yuvi[3] = (yuv101010Pel[1] & COLOR_COMPONENT_MASK );
yuvi[4] = ((yuv101010Pel[1] >> COLOR_COMPONENT_BIT_SIZE) & COLOR_COMPONENT_MASK);
yuvi[5] = ((yuv101010Pel[1] >> (COLOR_COMPONENT_BIT_SIZE << 1)) & COLOR_COMPONENT_MASK);
// YUV to RGB Transformation conversion
@ -184,7 +184,7 @@ namespace cv { namespace gpu { namespace device {
YUV2RGB(&yuvi[3], &red[1], &green[1], &blue[1]);
// Clamp the results to RGBA
const size_t dstImagePitch = nDestPitch >> 2;
dstImage[y * dstImagePitch + x ] = RGBAPACK_10bit(red[0], green[0], blue[0], constAlpha);
@ -194,9 +194,9 @@ namespace cv { namespace gpu { namespace device {
void NV12ToARGB_gpu(const PtrStepb decodedFrame, DevMem2D_<uint> interopFrame, cudaStream_t stream)
{
dim3 block(32, 8);
dim3 grid(divUp(interopFrame.cols, 2 * block.x), divUp(interopFrame.rows, block.y));
dim3 grid(divUp(interopFrame.cols, 2 * block.x), divUp(interopFrame.rows, block.y));
NV12ToARGB<<<grid, block, 0, stream>>>(decodedFrame.data, decodedFrame.step, interopFrame.data, interopFrame.step,
NV12ToARGB<<<grid, block, 0, stream>>>(decodedFrame.data, decodedFrame.step, interopFrame.data, interopFrame.step,
interopFrame.cols, interopFrame.rows);
cudaSafeCall( cudaGetLastError() );

172
modules/gpu/src/cuda/bf_knnmatch.cu
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@ -45,19 +45,19 @@
#include "opencv2/gpu/device/vec_distance.hpp"
#include "opencv2/gpu/device/datamov_utils.hpp"
namespace cv { namespace gpu { namespace device
namespace cv { namespace gpu { namespace device
{
namespace bf_knnmatch
namespace bf_knnmatch
{
///////////////////////////////////////////////////////////////////////////////
// Reduction
template <int BLOCK_SIZE>
__device__ void findBestMatch(float& bestDistance1, float& bestDistance2,
int& bestTrainIdx1, int& bestTrainIdx2,
template <int BLOCK_SIZE>
__device__ void findBestMatch(float& bestDistance1, float& bestDistance2,
int& bestTrainIdx1, int& bestTrainIdx2,
float* s_distance, int* s_trainIdx)
{
float myBestDistance1 = numeric_limits<float>::max();
float myBestDistance1 = numeric_limits<float>::max();
float myBestDistance2 = numeric_limits<float>::max();
int myBestTrainIdx1 = -1;
int myBestTrainIdx2 = -1;
@ -122,13 +122,13 @@ namespace cv { namespace gpu { namespace device
bestTrainIdx2 = myBestTrainIdx2;
}
template <int BLOCK_SIZE>
__device__ void findBestMatch(float& bestDistance1, float& bestDistance2,
int& bestTrainIdx1, int& bestTrainIdx2,
int& bestImgIdx1, int& bestImgIdx2,
template <int BLOCK_SIZE>
__device__ void findBestMatch(float& bestDistance1, float& bestDistance2,
int& bestTrainIdx1, int& bestTrainIdx2,
int& bestImgIdx1, int& bestImgIdx2,
float* s_distance, int* s_trainIdx, int* s_imgIdx)
{
float myBestDistance1 = numeric_limits<float>::max();
float myBestDistance1 = numeric_limits<float>::max();
float myBestDistance2 = numeric_limits<float>::max();
int myBestTrainIdx1 = -1;
int myBestTrainIdx2 = -1;
@ -208,7 +208,7 @@ namespace cv { namespace gpu { namespace device
///////////////////////////////////////////////////////////////////////////////
// Match Unrolled Cached
template <int BLOCK_SIZE, int MAX_DESC_LEN, typename T, typename U>
template <int BLOCK_SIZE, int MAX_DESC_LEN, typename T, typename U>
__device__ void loadQueryToSmem(int queryIdx, const DevMem2D_<T>& query, U* s_query)
{
#pragma unroll
@ -219,11 +219,11 @@ namespace cv { namespace gpu { namespace device
}
}
template <int BLOCK_SIZE, int MAX_DESC_LEN, typename Dist, typename T, typename Mask>
__device__ void loopUnrolledCached(int queryIdx, const DevMem2D_<T>& query, int imgIdx, const DevMem2D_<T>& train, const Mask& mask,
typename Dist::value_type* s_query, typename Dist::value_type* s_train,
float& bestDistance1, float& bestDistance2,
int& bestTrainIdx1, int& bestTrainIdx2,
template <int BLOCK_SIZE, int MAX_DESC_LEN, typename Dist, typename T, typename Mask>
__device__ void loopUnrolledCached(int queryIdx, const DevMem2D_<T>& query, int imgIdx, const DevMem2D_<T>& train, const Mask& mask,
typename Dist::value_type* s_query, typename Dist::value_type* s_train,
float& bestDistance1, float& bestDistance2,
int& bestTrainIdx1, int& bestTrainIdx2,
int& bestImgIdx1, int& bestImgIdx2)
{
for (int t = 0, endt = (train.rows + BLOCK_SIZE - 1) / BLOCK_SIZE; t < endt; ++t)
@ -280,7 +280,7 @@ namespace cv { namespace gpu { namespace device
}
}
template <int BLOCK_SIZE, int MAX_DESC_LEN, typename Dist, typename T, typename Mask>
template <int BLOCK_SIZE, int MAX_DESC_LEN, typename Dist, typename T, typename Mask>
__global__ void matchUnrolledCached(const DevMem2D_<T> query, const DevMem2D_<T> train, const Mask mask, int2* bestTrainIdx, float2* bestDistance)
{
extern __shared__ int smem[];
@ -313,9 +313,9 @@ namespace cv { namespace gpu { namespace device
}
}
template <int BLOCK_SIZE, int MAX_DESC_LEN, typename Dist, typename T, typename Mask>
void matchUnrolledCached(const DevMem2D_<T>& query, const DevMem2D_<T>& train, const Mask& mask,
const DevMem2D_<int2>& trainIdx, const DevMem2D_<float2>& distance,
template <int BLOCK_SIZE, int MAX_DESC_LEN, typename Dist, typename T, typename Mask>
void matchUnrolledCached(const DevMem2D_<T>& query, const DevMem2D_<T>& train, const Mask& mask,
const DevMem2D_<int2>& trainIdx, const DevMem2D_<float2>& distance,
cudaStream_t stream)
{
const dim3 block(BLOCK_SIZE, BLOCK_SIZE);
@ -330,7 +330,7 @@ namespace cv { namespace gpu { namespace device
cudaSafeCall( cudaDeviceSynchronize() );
}
template <int BLOCK_SIZE, int MAX_DESC_LEN, typename Dist, typename T, typename Mask>
template <int BLOCK_SIZE, int MAX_DESC_LEN, typename Dist, typename T, typename Mask>
__global__ void matchUnrolledCached(const DevMem2D_<T> query, const DevMem2D_<T>* trains, int n, const Mask mask, int2* bestTrainIdx, int2* bestImgIdx, float2* bestDistance)
{
extern __shared__ int smem[];
@ -374,9 +374,9 @@ namespace cv { namespace gpu { namespace device
}
}
template <int BLOCK_SIZE, int MAX_DESC_LEN, typename Dist, typename T, typename Mask>
void matchUnrolledCached(const DevMem2D_<T>& query, const DevMem2D_<T>* trains, int n, const Mask& mask,
const DevMem2D_<int2>& trainIdx, const DevMem2D_<int2>& imgIdx, const DevMem2D_<float2>& distance,
template <int BLOCK_SIZE, int MAX_DESC_LEN, typename Dist, typename T, typename Mask>
void matchUnrolledCached(const DevMem2D_<T>& query, const DevMem2D_<T>* trains, int n, const Mask& mask,
const DevMem2D_<int2>& trainIdx, const DevMem2D_<int2>& imgIdx, const DevMem2D_<float2>& distance,
cudaStream_t stream)
{
const dim3 block(BLOCK_SIZE, BLOCK_SIZE);
@ -394,11 +394,11 @@ namespace cv { namespace gpu { namespace device
///////////////////////////////////////////////////////////////////////////////
// Match Unrolled
template <int BLOCK_SIZE, int MAX_DESC_LEN, typename Dist, typename T, typename Mask>
__device__ void loopUnrolled(int queryIdx, const DevMem2D_<T>& query, int imgIdx, const DevMem2D_<T>& train, const Mask& mask,
typename Dist::value_type* s_query, typename Dist::value_type* s_train,
float& bestDistance1, float& bestDistance2,
int& bestTrainIdx1, int& bestTrainIdx2,
template <int BLOCK_SIZE, int MAX_DESC_LEN, typename Dist, typename T, typename Mask>
__device__ void loopUnrolled(int queryIdx, const DevMem2D_<T>& query, int imgIdx, const DevMem2D_<T>& train, const Mask& mask,
typename Dist::value_type* s_query, typename Dist::value_type* s_train,
float& bestDistance1, float& bestDistance2,
int& bestTrainIdx1, int& bestTrainIdx2,
int& bestImgIdx1, int& bestImgIdx2)
{
for (int t = 0, endt = (train.rows + BLOCK_SIZE - 1) / BLOCK_SIZE; t < endt; ++t)
@ -459,7 +459,7 @@ namespace cv { namespace gpu { namespace device
}
}
template <int BLOCK_SIZE, int MAX_DESC_LEN, typename Dist, typename T, typename Mask>
template <int BLOCK_SIZE, int MAX_DESC_LEN, typename Dist, typename T, typename Mask>
__global__ void matchUnrolled(const DevMem2D_<T> query, const DevMem2D_<T> train, const Mask mask, int2* bestTrainIdx, float2* bestDistance)
{
extern __shared__ int smem[];
@ -490,9 +490,9 @@ namespace cv { namespace gpu { namespace device
}
}
template <int BLOCK_SIZE, int MAX_DESC_LEN, typename Dist, typename T, typename Mask>
void matchUnrolled(const DevMem2D_<T>& query, const DevMem2D_<T>& train, const Mask& mask,
const DevMem2D_<int2>& trainIdx, const DevMem2D_<float2>& distance,
template <int BLOCK_SIZE, int MAX_DESC_LEN, typename Dist, typename T, typename Mask>
void matchUnrolled(const DevMem2D_<T>& query, const DevMem2D_<T>& train, const Mask& mask,
const DevMem2D_<int2>& trainIdx, const DevMem2D_<float2>& distance,
cudaStream_t stream)
{
const dim3 block(BLOCK_SIZE, BLOCK_SIZE);
@ -507,7 +507,7 @@ namespace cv { namespace gpu { namespace device
cudaSafeCall( cudaDeviceSynchronize() );
}
template <int BLOCK_SIZE, int MAX_DESC_LEN, typename Dist, typename T, typename Mask>
template <int BLOCK_SIZE, int MAX_DESC_LEN, typename Dist, typename T, typename Mask>
__global__ void matchUnrolled(const DevMem2D_<T> query, const DevMem2D_<T>* trains, int n, const Mask mask, int2* bestTrainIdx, int2* bestImgIdx, float2* bestDistance)
{
extern __shared__ int smem[];
@ -549,9 +549,9 @@ namespace cv { namespace gpu { namespace device
}
}
template <int BLOCK_SIZE, int MAX_DESC_LEN, typename Dist, typename T, typename Mask>
void matchUnrolled(const DevMem2D_<T>& query, const DevMem2D_<T>* trains, int n, const Mask& mask,
const DevMem2D_<int2>& trainIdx, const DevMem2D_<int2>& imgIdx, const DevMem2D_<float2>& distance,
template <int BLOCK_SIZE, int MAX_DESC_LEN, typename Dist, typename T, typename Mask>
void matchUnrolled(const DevMem2D_<T>& query, const DevMem2D_<T>* trains, int n, const Mask& mask,
const DevMem2D_<int2>& trainIdx, const DevMem2D_<int2>& imgIdx, const DevMem2D_<float2>& distance,
cudaStream_t stream)
{
const dim3 block(BLOCK_SIZE, BLOCK_SIZE);
@ -569,11 +569,11 @@ namespace cv { namespace gpu { namespace device
///////////////////////////////////////////////////////////////////////////////
// Match
template <int BLOCK_SIZE, typename Dist, typename T, typename Mask>
__device__ void loop(int queryIdx, const DevMem2D_<T>& query, int imgIdx, const DevMem2D_<T>& train, const Mask& mask,
typename Dist::value_type* s_query, typename Dist::value_type* s_train,
float& bestDistance1, float& bestDistance2,
int& bestTrainIdx1, int& bestTrainIdx2,
template <int BLOCK_SIZE, typename Dist, typename T, typename Mask>
__device__ void loop(int queryIdx, const DevMem2D_<T>& query, int imgIdx, const DevMem2D_<T>& train, const Mask& mask,
typename Dist::value_type* s_query, typename Dist::value_type* s_train,
float& bestDistance1, float& bestDistance2,
int& bestTrainIdx1, int& bestTrainIdx2,
int& bestImgIdx1, int& bestImgIdx2)
{
for (int t = 0, endt = (train.rows + BLOCK_SIZE - 1) / BLOCK_SIZE; t < endt; ++t)
@ -633,7 +633,7 @@ namespace cv { namespace gpu { namespace device
}
}
template <int BLOCK_SIZE, typename Dist, typename T, typename Mask>
template <int BLOCK_SIZE, typename Dist, typename T, typename Mask>
__global__ void match(const DevMem2D_<T> query, const DevMem2D_<T> train, const Mask mask, int2* bestTrainIdx, float2* bestDistance)
{
extern __shared__ int smem[];
@ -664,9 +664,9 @@ namespace cv { namespace gpu { namespace device
}
}
template <int BLOCK_SIZE, typename Dist, typename T, typename Mask>
void match(const DevMem2D_<T>& query, const DevMem2D_<T>& train, const Mask& mask,
const DevMem2D_<int2>& trainIdx, const DevMem2D_<float2>& distance,
template <int BLOCK_SIZE, typename Dist, typename T, typename Mask>
void match(const DevMem2D_<T>& query, const DevMem2D_<T>& train, const Mask& mask,
const DevMem2D_<int2>& trainIdx, const DevMem2D_<float2>& distance,
cudaStream_t stream)
{
const dim3 block(BLOCK_SIZE, BLOCK_SIZE);
@ -681,7 +681,7 @@ namespace cv { namespace gpu { namespace device
cudaSafeCall( cudaDeviceSynchronize() );
}
template <int BLOCK_SIZE, typename Dist, typename T, typename Mask>
template <int BLOCK_SIZE, typename Dist, typename T, typename Mask>
__global__ void match(const DevMem2D_<T> query, const DevMem2D_<T>* trains, int n, const Mask mask, int2* bestTrainIdx, int2* bestImgIdx, float2* bestDistance)
{
extern __shared__ int smem[];
@ -723,9 +723,9 @@ namespace cv { namespace gpu { namespace device
}
}
template <int BLOCK_SIZE, typename Dist, typename T, typename Mask>
void match(const DevMem2D_<T>& query, const DevMem2D_<T>* trains, int n, const Mask& mask,
const DevMem2D_<int2>& trainIdx, const DevMem2D_<int2>& imgIdx, const DevMem2D_<float2>& distance,
template <int BLOCK_SIZE, typename Dist, typename T, typename Mask>
void match(const DevMem2D_<T>& query, const DevMem2D_<T>* trains, int n, const Mask& mask,
const DevMem2D_<int2>& trainIdx, const DevMem2D_<int2>& imgIdx, const DevMem2D_<float2>& distance,
cudaStream_t stream)
{
const dim3 block(BLOCK_SIZE, BLOCK_SIZE);
@ -743,9 +743,9 @@ namespace cv { namespace gpu { namespace device
///////////////////////////////////////////////////////////////////////////////
// knnMatch 2 dispatcher
template <typename Dist, typename T, typename Mask>
void match2Dispatcher(const DevMem2D_<T>& query, const DevMem2D_<T>& train, const Mask& mask,
const DevMem2Db& trainIdx, const DevMem2Db& distance,
template <typename Dist, typename T, typename Mask>
void match2Dispatcher(const DevMem2D_<T>& query, const DevMem2D_<T>& train, const Mask& mask,
const DevMem2Db& trainIdx, const DevMem2Db& distance,
int cc, cudaStream_t stream)
{
if (query.cols <= 64)
@ -761,11 +761,11 @@ namespace cv { namespace gpu { namespace device
matchUnrolled<16, 256, Dist>(query, train, mask, static_cast< DevMem2D_<int2> >(trainIdx), static_cast< DevMem2D_<float2> > (distance), stream);
}
else if (query.cols <= 512)
{
{
matchUnrolled<16, 512, Dist>(query, train, mask, static_cast< DevMem2D_<int2> >(trainIdx), static_cast< DevMem2D_<float2> > (distance), stream);
}
else if (query.cols <= 1024)
{
{
matchUnrolled<16, 1024, Dist>(query, train, mask, static_cast< DevMem2D_<int2> >(trainIdx), static_cast< DevMem2D_<float2> > (distance), stream);
}*/
else
@ -774,9 +774,9 @@ namespace cv { namespace gpu { namespace device
}
}
template <typename Dist, typename T, typename Mask>
void match2Dispatcher(const DevMem2D_<T>& query, const DevMem2D_<T>* trains, int n, const Mask& mask,
const DevMem2Db& trainIdx, const DevMem2Db& imgIdx, const DevMem2Db& distance,
template <typename Dist, typename T, typename Mask>
void match2Dispatcher(const DevMem2D_<T>& query, const DevMem2D_<T>* trains, int n, const Mask& mask,
const DevMem2Db& trainIdx, const DevMem2Db& imgIdx, const DevMem2Db& distance,
int cc, cudaStream_t stream)
{
if (query.cols <= 64)
@ -792,11 +792,11 @@ namespace cv { namespace gpu { namespace device
matchUnrolled<16, 256, Dist>(query, trains, n, mask, static_cast< DevMem2D_<int2> >(trainIdx), static_cast< DevMem2D_<int2> >(imgIdx), static_cast< DevMem2D_<float2> > (distance), stream);
}
else if (query.cols <= 512)
{
{
matchUnrolled<16, 512, Dist>(query, trains, n, mask, static_cast< DevMem2D_<int2> >(trainIdx), static_cast< DevMem2D_<int2> >(imgIdx), static_cast< DevMem2D_<float2> > (distance), stream);
}
else if (query.cols <= 1024)
{
{
matchUnrolled<16, 1024, Dist>(query, trains, n, mask, static_cast< DevMem2D_<int2> >(trainIdx), static_cast< DevMem2D_<int2> >(imgIdx), static_cast< DevMem2D_<float2> > (distance), stream);
}*/
else
@ -832,7 +832,7 @@ namespace cv { namespace gpu { namespace device
s_train[threadIdx.x * BLOCK_SIZE + threadIdx.y] = train.ptr(::min(blockIdx.x * BLOCK_SIZE + threadIdx.y, train.rows - 1))[loadX];
}
else
{
{
s_query[threadIdx.y * BLOCK_SIZE + threadIdx.x] = 0;
s_train[threadIdx.x * BLOCK_SIZE + threadIdx.y] = 0;
}
@ -857,7 +857,7 @@ namespace cv { namespace gpu { namespace device
}
}
template <int BLOCK_SIZE, int MAX_DESC_LEN, typename Dist, typename T, typename Mask>
template <int BLOCK_SIZE, int MAX_DESC_LEN, typename Dist, typename T, typename Mask>
void calcDistanceUnrolled(const DevMem2D_<T>& query, const DevMem2D_<T>& train, const Mask& mask, const DevMem2Df& allDist, cudaStream_t stream)
{
const dim3 block(BLOCK_SIZE, BLOCK_SIZE);
@ -895,7 +895,7 @@ namespace cv { namespace gpu { namespace device
s_train[threadIdx.x * BLOCK_SIZE + threadIdx.y] = train.ptr(::min(blockIdx.x * BLOCK_SIZE + threadIdx.y, train.rows - 1))[loadX];
}
else
{
{
s_query[threadIdx.y * BLOCK_SIZE + threadIdx.x] = 0;
s_train[threadIdx.x * BLOCK_SIZE + threadIdx.y] = 0;
}
@ -920,7 +920,7 @@ namespace cv { namespace gpu { namespace device
}
}
template <int BLOCK_SIZE, typename Dist, typename T, typename Mask>
template <int BLOCK_SIZE, typename Dist, typename T, typename Mask>
void calcDistance(const DevMem2D_<T>& query, const DevMem2D_<T>& train, const Mask& mask, const DevMem2Df& allDist, cudaStream_t stream)
{
const dim3 block(BLOCK_SIZE, BLOCK_SIZE);
@ -938,9 +938,9 @@ namespace cv { namespace gpu { namespace device
///////////////////////////////////////////////////////////////////////////////
// Calc Distance dispatcher
template <typename Dist, typename T, typename Mask>
void calcDistanceDispatcher(const DevMem2D_<T>& query, const DevMem2D_<T>& train, const Mask& mask,
const DevMem2Df& allDist,
template <typename Dist, typename T, typename Mask>
void calcDistanceDispatcher(const DevMem2D_<T>& query, const DevMem2D_<T>& train, const Mask& mask,
const DevMem2Df& allDist,
int cc, cudaStream_t stream)
{
if (query.cols <= 64)
@ -956,11 +956,11 @@ namespace cv { namespace gpu { namespace device
calcDistanceUnrolled<16, 256, Dist>(query, train, mask, allDist, stream);
}
else if (query.cols <= 512)
{
{
calcDistanceUnrolled<16, 512, Dist>(query, train, mask, allDist, stream);
}
else if (query.cols <= 1024)
{
{
calcDistanceUnrolled<16, 1024, Dist>(query, train, mask, allDist, stream);
}*/
else
@ -972,7 +972,7 @@ namespace cv { namespace gpu { namespace device
///////////////////////////////////////////////////////////////////////////////
// find knn match kernel
template <int BLOCK_SIZE>
template <int BLOCK_SIZE>
__global__ void findBestMatch(DevMem2Df allDist, int i, PtrStepi trainIdx, PtrStepf distance)
{
const int SMEM_SIZE = BLOCK_SIZE > 64 ? BLOCK_SIZE : 64;
@ -985,7 +985,7 @@ namespace cv { namespace gpu { namespace device
float dist = numeric_limits<float>::max();
int bestIdx = -1;
for (int i = threadIdx.x; i < allDist.cols; i += BLOCK_SIZE)
{
float reg = allDistRow[i];
@ -1013,7 +1013,7 @@ namespace cv { namespace gpu { namespace device
}
}
template <int BLOCK_SIZE>
template <int BLOCK_SIZE>
void findKnnMatch(int k, const DevMem2Di& trainIdx, const DevMem2Df& distance, const DevMem2Df& allDist, cudaStream_t stream)
{
const dim3 block(BLOCK_SIZE, 1, 1);
@ -1038,8 +1038,8 @@ namespace cv { namespace gpu { namespace device
// knn match Dispatcher
template <typename Dist, typename T, typename Mask>
void matchDispatcher(const DevMem2D_<T>& query, const DevMem2D_<T>& train, int k, const Mask& mask,
const DevMem2Db& trainIdx, const DevMem2Db& distance, const DevMem2Df& allDist,
void matchDispatcher(const DevMem2D_<T>& query, const DevMem2D_<T>& train, int k, const Mask& mask,
const DevMem2Db& trainIdx, const DevMem2Db& distance, const DevMem2Df& allDist,
int cc, cudaStream_t stream)
{
if (k == 2)
@ -1051,13 +1051,13 @@ namespace cv { namespace gpu { namespace device
calcDistanceDispatcher<Dist>(query, train, mask, allDist, cc, stream);
findKnnMatchDispatcher(k, trainIdx, distance, allDist, cc, stream);
}
}
}
///////////////////////////////////////////////////////////////////////////////
// knn match caller
template <typename T> void matchL1_gpu(const DevMem2Db& query, const DevMem2Db& train, int k, const DevMem2Db& mask,
const DevMem2Db& trainIdx, const DevMem2Db& distance, const DevMem2Df& allDist,
template <typename T> void matchL1_gpu(const DevMem2Db& query, const DevMem2Db& train, int k, const DevMem2Db& mask,
const DevMem2Db& trainIdx, const DevMem2Db& distance, const DevMem2Df& allDist,
int cc, cudaStream_t stream)
{
if (mask.data)
@ -1073,7 +1073,7 @@ namespace cv { namespace gpu { namespace device
template void matchL1_gpu<int >(const DevMem2Db& queryDescs, const DevMem2Db& trainDescs, int k, const DevMem2Db& mask, const DevMem2Db& trainIdx, const DevMem2Db& distance, const DevMem2Df& allDist, int cc, cudaStream_t stream);
template void matchL1_gpu<float >(const DevMem2Db& queryDescs, const DevMem2Db& trainDescs, int k, const DevMem2Db& mask, const DevMem2Db& trainIdx, const DevMem2Db& distance, const DevMem2Df& allDist, int cc, cudaStream_t stream);
template <typename T> void matchL2_gpu(const DevMem2Db& query, const DevMem2Db& train, int k, const DevMem2Db& mask,
template <typename T> void matchL2_gpu(const DevMem2Db& query, const DevMem2Db& train, int k, const DevMem2Db& mask,
const DevMem2Db& trainIdx, const DevMem2Db& distance, const DevMem2Df& allDist,
int cc, cudaStream_t stream)
{
@ -1091,7 +1091,7 @@ namespace cv { namespace gpu { namespace device
template void matchL2_gpu<float >(const DevMem2Db& queryDescs, const DevMem2Db& trainDescs, int k, const DevMem2Db& mask, const DevMem2Db& trainIdx, const DevMem2Db& distance, const DevMem2Df& allDist, int cc, cudaStream_t stream);
template <typename T> void matchHamming_gpu(const DevMem2Db& query, const DevMem2Db& train, int k, const DevMem2Db& mask,
const DevMem2Db& trainIdx, const DevMem2Db& distance, const DevMem2Df& allDist,
const DevMem2Db& trainIdx, const DevMem2Db& distance, const DevMem2Df& allDist,
int cc, cudaStream_t stream)
{
if (mask.data)
@ -1106,8 +1106,8 @@ namespace cv { namespace gpu { namespace device
//template void matchHamming_gpu<short >(const DevMem2Db& queryDescs, const DevMem2Db& trainDescs, int k, const DevMem2Db& mask, const DevMem2Db& trainIdx, const DevMem2Db& distance, const DevMem2Df& allDist, int cc, cudaStream_t stream);
template void matchHamming_gpu<int >(const DevMem2Db& queryDescs, const DevMem2Db& trainDescs, int k, const DevMem2Db& mask, const DevMem2Db& trainIdx, const DevMem2Db& distance, const DevMem2Df& allDist, int cc, cudaStream_t stream);
template <typename T> void match2L1_gpu(const DevMem2Db& query, const DevMem2Db& trains, const DevMem2D_<PtrStepb>& masks,
const DevMem2Db& trainIdx, const DevMem2Db& imgIdx, const DevMem2Db& distance,
template <typename T> void match2L1_gpu(const DevMem2Db& query, const DevMem2Db& trains, const DevMem2D_<PtrStepb>& masks,
const DevMem2Db& trainIdx, const DevMem2Db& imgIdx, const DevMem2Db& distance,
int cc, cudaStream_t stream)
{
if (masks.data)
@ -1123,8 +1123,8 @@ namespace cv { namespace gpu { namespace device
template void match2L1_gpu<int >(const DevMem2Db& query, const DevMem2Db& trains, const DevMem2D_<PtrStepb>& masks, const DevMem2Db& trainIdx, const DevMem2Db& imgIdx, const DevMem2Db& distance, int cc, cudaStream_t stream);
template void match2L1_gpu<float >(const DevMem2Db& query, const DevMem2Db& trains, const DevMem2D_<PtrStepb>& masks, const DevMem2Db& trainIdx, const DevMem2Db& imgIdx, const DevMem2Db& distance, int cc, cudaStream_t stream);
template <typename T> void match2L2_gpu(const DevMem2Db& query, const DevMem2Db& trains, const DevMem2D_<PtrStepb>& masks,
const DevMem2Db& trainIdx, const DevMem2Db& imgIdx, const DevMem2Db& distance,
template <typename T> void match2L2_gpu(const DevMem2Db& query, const DevMem2Db& trains, const DevMem2D_<PtrStepb>& masks,
const DevMem2Db& trainIdx, const DevMem2Db& imgIdx, const DevMem2Db& distance,
int cc, cudaStream_t stream)
{
if (masks.data)
@ -1140,8 +1140,8 @@ namespace cv { namespace gpu { namespace device
//template void match2L2_gpu<int >(const DevMem2Db& query, const DevMem2Db& trains, const DevMem2D_<PtrStepb>& masks, const DevMem2Db& trainIdx, const DevMem2Di& imgIdx, const DevMem2Db& distance, int cc, cudaStream_t stream);
template void match2L2_gpu<float >(const DevMem2Db& query, const DevMem2Db& trains, const DevMem2D_<PtrStepb>& masks, const DevMem2Db& trainIdx, const DevMem2Db& imgIdx, const DevMem2Db& distance, int cc, cudaStream_t stream);
template <typename T> void match2Hamming_gpu(const DevMem2Db& query, const DevMem2Db& trains, const DevMem2D_<PtrStepb>& masks,
const DevMem2Db& trainIdx, const DevMem2Db& imgIdx, const DevMem2Db& distance,
template <typename T> void match2Hamming_gpu(const DevMem2Db& query, const DevMem2Db& trains, const DevMem2D_<PtrStepb>& masks,
const DevMem2Db& trainIdx, const DevMem2Db& imgIdx, const DevMem2Db& distance,
int cc, cudaStream_t stream)
{
if (masks.data)

164
modules/gpu/src/cuda/bf_match.cu
View File

@ -45,14 +45,14 @@
#include "opencv2/gpu/device/vec_distance.hpp"
#include "opencv2/gpu/device/datamov_utils.hpp"
namespace cv { namespace gpu { namespace device
namespace cv { namespace gpu { namespace device
{
namespace bf_match
namespace bf_match
{
///////////////////////////////////////////////////////////////////////////////
// Reduction
template <int BLOCK_SIZE>
template <int BLOCK_SIZE>
__device__ void findBestMatch(float& bestDistance, int& bestTrainIdx, float* s_distance, int* s_trainIdx)
{
s_distance += threadIdx.y * BLOCK_SIZE;
@ -66,7 +66,7 @@ namespace cv { namespace gpu { namespace device
reducePredVal<BLOCK_SIZE>(s_distance, bestDistance, s_trainIdx, bestTrainIdx, threadIdx.x, less<volatile float>());
}
template <int BLOCK_SIZE>
template <int BLOCK_SIZE>
__device__ void findBestMatch(float& bestDistance, int& bestTrainIdx, int& bestImgIdx, float* s_distance, int* s_trainIdx, int* s_imgIdx)
{
s_distance += threadIdx.y * BLOCK_SIZE;
@ -85,7 +85,7 @@ namespace cv { namespace gpu { namespace device
///////////////////////////////////////////////////////////////////////////////
// Match Unrolled Cached
template <int BLOCK_SIZE, int MAX_DESC_LEN, typename T, typename U>
template <int BLOCK_SIZE, int MAX_DESC_LEN, typename T, typename U>
__device__ void loadQueryToSmem(int queryIdx, const DevMem2D_<T>& query, U* s_query)
{
#pragma unroll
@ -96,9 +96,9 @@ namespace cv { namespace gpu { namespace device
}
}
template <int BLOCK_SIZE, int MAX_DESC_LEN, typename Dist, typename T, typename Mask>
template <int BLOCK_SIZE, int MAX_DESC_LEN, typename Dist, typename T, typename Mask>
__device__ void loopUnrolledCached(int queryIdx, const DevMem2D_<T>& query,volatile int imgIdx, const DevMem2D_<T>& train, const Mask& mask,
typename Dist::value_type* s_query, typename Dist::value_type* s_train,
typename Dist::value_type* s_query, typename Dist::value_type* s_train,
float& bestDistance, int& bestTrainIdx, int& bestImgIdx)
{
for (int t = 0, endt = (train.rows + BLOCK_SIZE - 1) / BLOCK_SIZE; t < endt; ++t)
@ -142,7 +142,7 @@ namespace cv { namespace gpu { namespace device
}
}
template <int BLOCK_SIZE, int MAX_DESC_LEN, typename Dist, typename T, typename Mask>
template <int BLOCK_SIZE, int MAX_DESC_LEN, typename Dist, typename T, typename Mask>
__global__ void matchUnrolledCached(const DevMem2D_<T> query, const DevMem2D_<T> train, const Mask mask, int* bestTrainIdx, float* bestDistance)
{
extern __shared__ int smem[];
@ -173,9 +173,9 @@ namespace cv { namespace gpu { namespace device
}
}
template <int BLOCK_SIZE, int MAX_DESC_LEN, typename Dist, typename T, typename Mask>
void matchUnrolledCached(const DevMem2D_<T>& query, const DevMem2D_<T>& train, const Mask& mask,
const DevMem2Di& trainIdx, const DevMem2Df& distance,
template <int BLOCK_SIZE, int MAX_DESC_LEN, typename Dist, typename T, typename Mask>
void matchUnrolledCached(const DevMem2D_<T>& query, const DevMem2D_<T>& train, const Mask& mask,
const DevMem2Di& trainIdx, const DevMem2Df& distance,
cudaStream_t stream)
{
const dim3 block(BLOCK_SIZE, BLOCK_SIZE);
@ -190,8 +190,8 @@ namespace cv { namespace gpu { namespace device
cudaSafeCall( cudaDeviceSynchronize() );
}
template <int BLOCK_SIZE, int MAX_DESC_LEN, typename Dist, typename T, typename Mask>
__global__ void matchUnrolledCached(const DevMem2D_<T> query, const DevMem2D_<T>* trains, int n, const Mask mask,
template <int BLOCK_SIZE, int MAX_DESC_LEN, typename Dist, typename T, typename Mask>
__global__ void matchUnrolledCached(const DevMem2D_<T> query, const DevMem2D_<T>* trains, int n, const Mask mask,
int* bestTrainIdx, int* bestImgIdx, float* bestDistance)
{
extern __shared__ int smem[];
@ -232,9 +232,9 @@ namespace cv { namespace gpu { namespace device
}
}
template <int BLOCK_SIZE, int MAX_DESC_LEN, typename Dist, typename T, typename Mask>
void matchUnrolledCached(const DevMem2D_<T>& query, const DevMem2D_<T>* trains, int n, const Mask& mask,
const DevMem2Di& trainIdx, const DevMem2Di& imgIdx, const DevMem2Df& distance,
template <int BLOCK_SIZE, int MAX_DESC_LEN, typename Dist, typename T, typename Mask>
void matchUnrolledCached(const DevMem2D_<T>& query, const DevMem2D_<T>* trains, int n, const Mask& mask,
const DevMem2Di& trainIdx, const DevMem2Di& imgIdx, const DevMem2Df& distance,
cudaStream_t stream)
{
const dim3 block(BLOCK_SIZE, BLOCK_SIZE);
@ -252,9 +252,9 @@ namespace cv { namespace gpu { namespace device
///////////////////////////////////////////////////////////////////////////////
// Match Unrolled
template <int BLOCK_SIZE, int MAX_DESC_LEN, typename Dist, typename T, typename Mask>
template <int BLOCK_SIZE, int MAX_DESC_LEN, typename Dist, typename T, typename Mask>
__device__ void loopUnrolled(int queryIdx, const DevMem2D_<T>& query,volatile int imgIdx, const DevMem2D_<T>& train, const Mask& mask,
typename Dist::value_type* s_query, typename Dist::value_type* s_train,
typename Dist::value_type* s_query, typename Dist::value_type* s_train,
float& bestDistance, int& bestTrainIdx, int& bestImgIdx)
{
for (int t = 0, endt = (train.rows + BLOCK_SIZE - 1) / BLOCK_SIZE; t < endt; ++t)
@ -314,7 +314,7 @@ namespace cv { namespace gpu { namespace device
typename Dist::value_type* s_query = (typename Dist::value_type*)(smem);
typename Dist::value_type* s_train = (typename Dist::value_type*)(smem + BLOCK_SIZE * BLOCK_SIZE);
loopUnrolled<BLOCK_SIZE, MAX_DESC_LEN, Dist>(queryIdx, query, 0, train, mask, s_query, s_train, myBestDistance, myBestTrainIdx, myBestTrainIdx);
__syncthreads();
@ -331,9 +331,9 @@ namespace cv { namespace gpu { namespace device
}
}
template <int BLOCK_SIZE, int MAX_DESC_LEN, typename Dist, typename T, typename Mask>
void matchUnrolled(const DevMem2D_<T>& query, const DevMem2D_<T>& train, const Mask& mask,
const DevMem2Di& trainIdx, const DevMem2Df& distance,
template <int BLOCK_SIZE, int MAX_DESC_LEN, typename Dist, typename T, typename Mask>
void matchUnrolled(const DevMem2D_<T>& query, const DevMem2D_<T>& train, const Mask& mask,
const DevMem2Di& trainIdx, const DevMem2Df& distance,
cudaStream_t stream)
{
const dim3 block(BLOCK_SIZE, BLOCK_SIZE);
@ -349,7 +349,7 @@ namespace cv { namespace gpu { namespace device
}
template <int BLOCK_SIZE, int MAX_DESC_LEN, typename Dist, typename T, typename Mask>
__global__ void matchUnrolled(const DevMem2D_<T> query, const DevMem2D_<T>* trains, int n, const Mask mask,
__global__ void matchUnrolled(const DevMem2D_<T> query, const DevMem2D_<T>* trains, int n, const Mask mask,
int* bestTrainIdx, int* bestImgIdx, float* bestDistance)
{
extern __shared__ int smem[];
@ -364,7 +364,7 @@ namespace cv { namespace gpu { namespace device
typename Dist::value_type* s_train = (typename Dist::value_type*)(smem + BLOCK_SIZE * BLOCK_SIZE);
Mask m = mask;
for (int imgIdx = 0; imgIdx < n; ++imgIdx)
{
const DevMem2D_<T> train = trains[imgIdx];
@ -388,9 +388,9 @@ namespace cv { namespace gpu { namespace device
}
}
template <int BLOCK_SIZE, int MAX_DESC_LEN, typename Dist, typename T, typename Mask>
void matchUnrolled(const DevMem2D_<T>& query, const DevMem2D_<T>* trains, int n, const Mask& mask,
const DevMem2Di& trainIdx, const DevMem2Di& imgIdx, const DevMem2Df& distance,
template <int BLOCK_SIZE, int MAX_DESC_LEN, typename Dist, typename T, typename Mask>
void matchUnrolled(const DevMem2D_<T>& query, const DevMem2D_<T>* trains, int n, const Mask& mask,
const DevMem2Di& trainIdx, const DevMem2Di& imgIdx, const DevMem2Df& distance,
cudaStream_t stream)
{
const dim3 block(BLOCK_SIZE, BLOCK_SIZE);
@ -408,9 +408,9 @@ namespace cv { namespace gpu { namespace device
///////////////////////////////////////////////////////////////////////////////
// Match
template <int BLOCK_SIZE, typename Dist, typename T, typename Mask>
template <int BLOCK_SIZE, typename Dist, typename T, typename Mask>
__device__ void loop(int queryIdx, const DevMem2D_<T>& query, volatile int imgIdx, const DevMem2D_<T>& train, const Mask& mask,
typename Dist::value_type* s_query, typename Dist::value_type* s_train,
typename Dist::value_type* s_query, typename Dist::value_type* s_train,
float& bestDistance, int& bestTrainIdx, int& bestImgIdx)
{
for (int t = 0, endt = (train.rows + BLOCK_SIZE - 1) / BLOCK_SIZE; t < endt; ++t)
@ -469,7 +469,7 @@ namespace cv { namespace gpu { namespace device
typename Dist::value_type* s_query = (typename Dist::value_type*)(smem);
typename Dist::value_type* s_train = (typename Dist::value_type*)(smem + BLOCK_SIZE * BLOCK_SIZE);
loop<BLOCK_SIZE, Dist>(queryIdx, query, 0, train, mask, s_query, s_train, myBestDistance, myBestTrainIdx, myBestTrainIdx);
__syncthreads();
@ -486,9 +486,9 @@ namespace cv { namespace gpu { namespace device
}
}
template <int BLOCK_SIZE, typename Dist, typename T, typename Mask>
void match(const DevMem2D_<T>& query, const DevMem2D_<T>& train, const Mask& mask,
const DevMem2Di& trainIdx, const DevMem2Df& distance,
template <int BLOCK_SIZE, typename Dist, typename T, typename Mask>
void match(const DevMem2D_<T>& query, const DevMem2D_<T>& train, const Mask& mask,
const DevMem2Di& trainIdx, const DevMem2Df& distance,
cudaStream_t stream)
{
const dim3 block(BLOCK_SIZE, BLOCK_SIZE);
@ -504,7 +504,7 @@ namespace cv { namespace gpu { namespace device
}
template <int BLOCK_SIZE, typename Dist, typename T, typename Mask>
__global__ void match(const DevMem2D_<T> query, const DevMem2D_<T>* trains, int n, const Mask mask,
__global__ void match(const DevMem2D_<T> query, const DevMem2D_<T>* trains, int n, const Mask mask,
int* bestTrainIdx, int* bestImgIdx, float* bestDistance)
{
extern __shared__ int smem[];
@ -542,9 +542,9 @@ namespace cv { namespace gpu { namespace device
}
}
template <int BLOCK_SIZE, typename Dist, typename T, typename Mask>
void match(const DevMem2D_<T>& query, const DevMem2D_<T>* trains, int n, const Mask& mask,
const DevMem2Di& trainIdx, const DevMem2Di& imgIdx, const DevMem2Df& distance,
template <int BLOCK_SIZE, typename Dist, typename T, typename Mask>
void match(const DevMem2D_<T>& query, const DevMem2D_<T>* trains, int n, const Mask& mask,
const DevMem2Di& trainIdx, const DevMem2Di& imgIdx, const DevMem2Df& distance,
cudaStream_t stream)
{
const dim3 block(BLOCK_SIZE, BLOCK_SIZE);
@ -562,9 +562,9 @@ namespace cv { namespace gpu { namespace device
///////////////////////////////////////////////////////////////////////////////
// Match dispatcher
template <typename Dist, typename T, typename Mask>
void matchDispatcher(const DevMem2D_<T>& query, const DevMem2D_<T>& train, const Mask& mask,
const DevMem2Di& trainIdx, const DevMem2Df& distance,
template <typename Dist, typename T, typename Mask>
void matchDispatcher(const DevMem2D_<T>& query, const DevMem2D_<T>& train, const Mask& mask,
const DevMem2Di& trainIdx, const DevMem2Df& distance,
int cc, cudaStream_t stream)
{
if (query.cols <= 64)
@ -580,11 +580,11 @@ namespace cv { namespace gpu { namespace device
matchUnrolled<16, 256, Dist>(query, train, mask, trainIdx, distance, stream);
}
else if (query.cols <= 512)
{
{
matchUnrolled<16, 512, Dist>(query, train, mask, trainIdx, distance, stream);
}
else if (query.cols <= 1024)
{
{
matchUnrolled<16, 1024, Dist>(query, train, mask, trainIdx, distance, stream);
}*/
else
@ -593,9 +593,9 @@ namespace cv { namespace gpu { namespace device
}
}
template <typename Dist, typename T, typename Mask>
void matchDispatcher(const DevMem2D_<T>& query, const DevMem2D_<T>* trains, int n, const Mask& mask,
const DevMem2Di& trainIdx, const DevMem2Di& imgIdx, const DevMem2Df& distance,
template <typename Dist, typename T, typename Mask>
void matchDispatcher(const DevMem2D_<T>& query, const DevMem2D_<T>* trains, int n, const Mask& mask,
const DevMem2Di& trainIdx, const DevMem2Di& imgIdx, const DevMem2Df& distance,
int cc, cudaStream_t stream)
{
if (query.cols <= 64)
@ -611,11 +611,11 @@ namespace cv { namespace gpu { namespace device
matchUnrolled<16, 256, Dist>(query, trains, n, mask, trainIdx, imgIdx, distance, stream);
}
else if (query.cols <= 512)
{
{
matchUnrolled<16, 512, Dist>(query, trains, n, mask, trainIdx, imgIdx, distance, stream);
}
else if (query.cols <= 1024)
{
{
matchUnrolled<16, 1024, Dist>(query, trains, n, mask, trainIdx, imgIdx, distance, stream);
}*/
else
@ -627,20 +627,20 @@ namespace cv { namespace gpu { namespace device
///////////////////////////////////////////////////////////////////////////////
// Match caller
template <typename T> void matchL1_gpu(const DevMem2Db& query, const DevMem2Db& train, const DevMem2Db& mask,
template <typename T> void matchL1_gpu(const DevMem2Db& query, const DevMem2Db& train, const DevMem2Db& mask,
const DevMem2Di& trainIdx, const DevMem2Df& distance,
int cc, cudaStream_t stream)
{
if (mask.data)
{
matchDispatcher< L1Dist<T> >(static_cast< DevMem2D_<T> >(query), static_cast< DevMem2D_<T> >(train), SingleMask(mask),
trainIdx, distance,
matchDispatcher< L1Dist<T> >(static_cast< DevMem2D_<T> >(query), static_cast< DevMem2D_<T> >(train), SingleMask(mask),
trainIdx, distance,
cc, stream);
}
else
{
matchDispatcher< L1Dist<T> >(static_cast< DevMem2D_<T> >(query), static_cast< DevMem2D_<T> >(train), WithOutMask(),
trainIdx, distance,
matchDispatcher< L1Dist<T> >(static_cast< DevMem2D_<T> >(query), static_cast< DevMem2D_<T> >(train), WithOutMask(),
trainIdx, distance,
cc, stream);
}
}
@ -652,20 +652,20 @@ namespace cv { namespace gpu { namespace device
template void matchL1_gpu<int >(const DevMem2Db& queryDescs, const DevMem2Db& trainDescs, const DevMem2Db& mask, const DevMem2Di& trainIdx, const DevMem2Df& distance, int cc, cudaStream_t stream);
template void matchL1_gpu<float >(const DevMem2Db& queryDescs, const DevMem2Db& trainDescs, const DevMem2Db& mask, const DevMem2Di& trainIdx, const DevMem2Df& distance, int cc, cudaStream_t stream);
template <typename T> void matchL2_gpu(const DevMem2Db& query, const DevMem2Db& train, const DevMem2Db& mask,
const DevMem2Di& trainIdx, const DevMem2Df& distance,
template <typename T> void matchL2_gpu(const DevMem2Db& query, const DevMem2Db& train, const DevMem2Db& mask,
const DevMem2Di& trainIdx, const DevMem2Df& distance,
int cc, cudaStream_t stream)
{
if (mask.data)
{
matchDispatcher<L2Dist>(static_cast< DevMem2D_<T> >(query), static_cast< DevMem2D_<T> >(train), SingleMask(mask),
trainIdx, distance,
matchDispatcher<L2Dist>(static_cast< DevMem2D_<T> >(query), static_cast< DevMem2D_<T> >(train), SingleMask(mask),
trainIdx, distance,
cc, stream);
}
else
{
matchDispatcher<L2Dist>(static_cast< DevMem2D_<T> >(query), static_cast< DevMem2D_<T> >(train), WithOutMask(),
trainIdx, distance,
matchDispatcher<L2Dist>(static_cast< DevMem2D_<T> >(query), static_cast< DevMem2D_<T> >(train), WithOutMask(),
trainIdx, distance,
cc, stream);
}
}
@ -677,20 +677,20 @@ namespace cv { namespace gpu { namespace device
//template void matchL2_gpu<int >(const DevMem2Db& queryDescs, const DevMem2Db& trainDescs, const DevMem2Db& mask, const DevMem2Di& trainIdx, const DevMem2Df& distance, int cc, cudaStream_t stream);
template void matchL2_gpu<float >(const DevMem2Db& queryDescs, const DevMem2Db& trainDescs, const DevMem2Db& mask, const DevMem2Di& trainIdx, const DevMem2Df& distance, int cc, cudaStream_t stream);
template <typename T> void matchHamming_gpu(const DevMem2Db& query, const DevMem2Db& train, const DevMem2Db& mask,
const DevMem2Di& trainIdx, const DevMem2Df& distance,
template <typename T> void matchHamming_gpu(const DevMem2Db& query, const DevMem2Db& train, const DevMem2Db& mask,
const DevMem2Di& trainIdx, const DevMem2Df& distance,
int cc, cudaStream_t stream)
{
if (mask.data)
{
matchDispatcher<HammingDist>(static_cast< DevMem2D_<T> >(query), static_cast< DevMem2D_<T> >(train), SingleMask(mask),
trainIdx, distance,
matchDispatcher<HammingDist>(static_cast< DevMem2D_<T> >(query), static_cast< DevMem2D_<T> >(train), SingleMask(mask),
trainIdx, distance,
cc, stream);
}
else
{
matchDispatcher<HammingDist>(static_cast< DevMem2D_<T> >(query), static_cast< DevMem2D_<T> >(train), WithOutMask(),
trainIdx, distance,
matchDispatcher<HammingDist>(static_cast< DevMem2D_<T> >(query), static_cast< DevMem2D_<T> >(train), WithOutMask(),
trainIdx, distance,
cc, stream);
}
}
@ -701,20 +701,20 @@ namespace cv { namespace gpu { namespace device
//template void matchHamming_gpu<short >(const DevMem2Db& queryDescs, const DevMem2Db& trainDescs, const DevMem2Db& mask, const DevMem2Di& trainIdx, const DevMem2Df& distance, int cc, cudaStream_t stream);
template void matchHamming_gpu<int >(const DevMem2Db& queryDescs, const DevMem2Db& trainDescs, const DevMem2Db& mask, const DevMem2Di& trainIdx, const DevMem2Df& distance, int cc, cudaStream_t stream);
template <typename T> void matchL1_gpu(const DevMem2Db& query, const DevMem2Db& trains, const DevMem2D_<PtrStepb>& masks,
const DevMem2Di& trainIdx, const DevMem2Di& imgIdx, const DevMem2Df& distance,
template <typename T> void matchL1_gpu(const DevMem2Db& query, const DevMem2Db& trains, const DevMem2D_<PtrStepb>& masks,
const DevMem2Di& trainIdx, const DevMem2Di& imgIdx, const DevMem2Df& distance,
int cc, cudaStream_t stream)
{
if (masks.data)
{
matchDispatcher< L1Dist<T> >(static_cast< DevMem2D_<T> >(query), (const DevMem2D_<T>*)trains.ptr(), trains.cols, MaskCollection(masks.data),
trainIdx, imgIdx, distance,
matchDispatcher< L1Dist<T> >(static_cast< DevMem2D_<T> >(query), (const DevMem2D_<T>*)trains.ptr(), trains.cols, MaskCollection(masks.data),
trainIdx, imgIdx, distance,
cc, stream);
}
else
{
matchDispatcher< L1Dist<T> >(static_cast< DevMem2D_<T> >(query), (const DevMem2D_<T>*)trains.ptr(), trains.cols, WithOutMask(),
trainIdx, imgIdx, distance,
matchDispatcher< L1Dist<T> >(static_cast< DevMem2D_<T> >(query), (const DevMem2D_<T>*)trains.ptr(), trains.cols, WithOutMask(),
trainIdx, imgIdx, distance,
cc, stream);
}
}
@ -726,20 +726,20 @@ namespace cv { namespace gpu { namespace device
template void matchL1_gpu<int >(const DevMem2Db& query, const DevMem2Db& trains, const DevMem2D_<PtrStepb>& masks, const DevMem2Di& trainIdx, const DevMem2Di& imgIdx, const DevMem2Df& distance, int cc, cudaStream_t stream);
template void matchL1_gpu<float >(const DevMem2Db& query, const DevMem2Db& trains, const DevMem2D_<PtrStepb>& masks, const DevMem2Di& trainIdx, const DevMem2Di& imgIdx, const DevMem2Df& distance, int cc, cudaStream_t stream);
template <typename T> void matchL2_gpu(const DevMem2Db& query, const DevMem2Db& trains, const DevMem2D_<PtrStepb>& masks,
const DevMem2Di& trainIdx, const DevMem2Di& imgIdx, const DevMem2Df& distance,
template <typename T> void matchL2_gpu(const DevMem2Db& query, const DevMem2Db& trains, const DevMem2D_<PtrStepb>& masks,
const DevMem2Di& trainIdx, const DevMem2Di& imgIdx, const DevMem2Df& distance,
int cc, cudaStream_t stream)
{
if (masks.data)
{
matchDispatcher<L2Dist>(static_cast< DevMem2D_<T> >(query), (const DevMem2D_<T>*)trains.ptr(), trains.cols, MaskCollection(masks.data),
trainIdx, imgIdx, distance,
matchDispatcher<L2Dist>(static_cast< DevMem2D_<T> >(query), (const DevMem2D_<T>*)trains.ptr(), trains.cols, MaskCollection(masks.data),
trainIdx, imgIdx, distance,
cc, stream);
}
else
{
matchDispatcher<L2Dist>(static_cast< DevMem2D_<T> >(query), (const DevMem2D_<T>*)trains.ptr(), trains.cols, WithOutMask(),
trainIdx, imgIdx, distance,
matchDispatcher<L2Dist>(static_cast< DevMem2D_<T> >(query), (const DevMem2D_<T>*)trains.ptr(), trains.cols, WithOutMask(),
trainIdx, imgIdx, distance,
cc, stream);
}
}
@ -751,20 +751,20 @@ namespace cv { namespace gpu { namespace device
//template void matchL2_gpu<int >(const DevMem2Db& query, const DevMem2Db& trains, const DevMem2D_<PtrStepb>& masks, const DevMem2Di& trainIdx, const DevMem2Di& imgIdx, const DevMem2Df& distance, int cc, cudaStream_t stream);
template void matchL2_gpu<float >(const DevMem2Db& query, const DevMem2Db& trains, const DevMem2D_<PtrStepb>& maskCollection, const DevMem2Di& trainIdx, const DevMem2Di& imgIdx, const DevMem2Df& distance, int cc, cudaStream_t stream);
template <typename T> void matchHamming_gpu(const DevMem2Db& query, const DevMem2Db& trains, const DevMem2D_<PtrStepb>& masks,
const DevMem2Di& trainIdx, const DevMem2Di& imgIdx, const DevMem2Df& distance,
template <typename T> void matchHamming_gpu(const DevMem2Db& query, const DevMem2Db& trains, const DevMem2D_<PtrStepb>& masks,
const DevMem2Di& trainIdx, const DevMem2Di& imgIdx, const DevMem2Df& distance,
int cc, cudaStream_t stream)
{
if (masks.data)
{
matchDispatcher<HammingDist>(static_cast< DevMem2D_<T> >(query), (const DevMem2D_<T>*)trains.ptr(), trains.cols, MaskCollection(masks.data),
trainIdx, imgIdx, distance,
matchDispatcher<HammingDist>(static_cast< DevMem2D_<T> >(query), (const DevMem2D_<T>*)trains.ptr(), trains.cols, MaskCollection(masks.data),
trainIdx, imgIdx, distance,
cc, stream);
}
else
{
matchDispatcher<HammingDist>(static_cast< DevMem2D_<T> >(query), (const DevMem2D_<T>*)trains.ptr(), trains.cols, WithOutMask(),
trainIdx, imgIdx, distance,
matchDispatcher<HammingDist>(static_cast< DevMem2D_<T> >(query), (const DevMem2D_<T>*)trains.ptr(), trains.cols, WithOutMask(),
trainIdx, imgIdx, distance,
cc, stream);
}
}

122
modules/gpu/src/cuda/bf_radius_match.cu
View File

@ -45,9 +45,9 @@
#include "opencv2/gpu/device/vec_distance.hpp"
#include "opencv2/gpu/device/datamov_utils.hpp"
namespace cv { namespace gpu { namespace device
namespace cv { namespace gpu { namespace device
{
namespace bf_radius_match
namespace bf_radius_match
{
///////////////////////////////////////////////////////////////////////////////
// Match Unrolled
@ -112,8 +112,8 @@ namespace cv { namespace gpu { namespace device
#endif
}
template <int BLOCK_SIZE, int MAX_DESC_LEN, typename Dist, typename T, typename Mask>
void matchUnrolled(const DevMem2D_<T>& query, const DevMem2D_<T>& train, float maxDistance, const Mask& mask,
template <int BLOCK_SIZE, int MAX_DESC_LEN, typename Dist, typename T, typename Mask>
void matchUnrolled(const DevMem2D_<T>& query, const DevMem2D_<T>& train, float maxDistance, const Mask& mask,
const DevMem2Di& trainIdx, const DevMem2Df& distance, const DevMem2D_<unsigned int>& nMatches, cudaStream_t stream)
{
const dim3 block(BLOCK_SIZE, BLOCK_SIZE);
@ -121,17 +121,17 @@ namespace cv { namespace gpu { namespace device
const size_t smemSize = (2 * BLOCK_SIZE * BLOCK_SIZE) * sizeof(int);
matchUnrolled<BLOCK_SIZE, MAX_DESC_LEN, false, Dist><<<grid, block, smemSize, stream>>>(query, 0, train, maxDistance, mask,
matchUnrolled<BLOCK_SIZE, MAX_DESC_LEN, false, Dist><<<grid, block, smemSize, stream>>>(query, 0, train, maxDistance, mask,
trainIdx, PtrStepi(), distance, nMatches.data, trainIdx.cols);
cudaSafeCall( cudaGetLastError() );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
}
template <int BLOCK_SIZE, int MAX_DESC_LEN, typename Dist, typename T>
void matchUnrolled(const DevMem2D_<T>& query, const DevMem2D_<T>* trains, int n, float maxDistance, const DevMem2Db* masks,
const DevMem2Di& trainIdx, const DevMem2Di& imgIdx, const DevMem2Df& distance, const DevMem2D_<unsigned int>& nMatches,
template <int BLOCK_SIZE, int MAX_DESC_LEN, typename Dist, typename T>
void matchUnrolled(const DevMem2D_<T>& query, const DevMem2D_<T>* trains, int n, float maxDistance, const DevMem2Db* masks,
const DevMem2Di& trainIdx, const DevMem2Di& imgIdx, const DevMem2Df& distance, const DevMem2D_<unsigned int>& nMatches,
cudaStream_t stream)
{
const dim3 block(BLOCK_SIZE, BLOCK_SIZE);
@ -146,12 +146,12 @@ namespace cv { namespace gpu { namespace device
if (masks != 0 && masks[i].data)
{
matchUnrolled<BLOCK_SIZE, MAX_DESC_LEN, true, Dist><<<grid, block, smemSize, stream>>>(query, i, train, maxDistance, SingleMask(masks[i]),
matchUnrolled<BLOCK_SIZE, MAX_DESC_LEN, true, Dist><<<grid, block, smemSize, stream>>>(query, i, train, maxDistance, SingleMask(masks[i]),
trainIdx, imgIdx, distance, nMatches.data, trainIdx.cols);
}
else
{
matchUnrolled<BLOCK_SIZE, MAX_DESC_LEN, true, Dist><<<grid, block, smemSize, stream>>>(query, i, train, maxDistance, WithOutMask(),
matchUnrolled<BLOCK_SIZE, MAX_DESC_LEN, true, Dist><<<grid, block, smemSize, stream>>>(query, i, train, maxDistance, WithOutMask(),
trainIdx, imgIdx, distance, nMatches.data, trainIdx.cols);
}
cudaSafeCall( cudaGetLastError() );
@ -223,9 +223,9 @@ namespace cv { namespace gpu { namespace device
#endif
}
template <int BLOCK_SIZE, typename Dist, typename T, typename Mask>
void match(const DevMem2D_<T>& query, const DevMem2D_<T>& train, float maxDistance, const Mask& mask,
const DevMem2Di& trainIdx, const DevMem2Df& distance, const DevMem2D_<unsigned int>& nMatches,
template <int BLOCK_SIZE, typename Dist, typename T, typename Mask>
void match(const DevMem2D_<T>& query, const DevMem2D_<T>& train, float maxDistance, const Mask& mask,
const DevMem2Di& trainIdx, const DevMem2Df& distance, const DevMem2D_<unsigned int>& nMatches,
cudaStream_t stream)
{
const dim3 block(BLOCK_SIZE, BLOCK_SIZE);
@ -233,7 +233,7 @@ namespace cv { namespace gpu { namespace device
const size_t smemSize = (2 * BLOCK_SIZE * BLOCK_SIZE) * sizeof(int);
match<BLOCK_SIZE, false, Dist><<<grid, block, smemSize, stream>>>(query, 0, train, maxDistance, mask,
match<BLOCK_SIZE, false, Dist><<<grid, block, smemSize, stream>>>(query, 0, train, maxDistance, mask,
trainIdx, PtrStepi(), distance, nMatches.data, trainIdx.cols);
cudaSafeCall( cudaGetLastError() );
@ -241,9 +241,9 @@ namespace cv { namespace gpu { namespace device
cudaSafeCall( cudaDeviceSynchronize() );
}
template <int BLOCK_SIZE, typename Dist, typename T>
void match(const DevMem2D_<T>& query, const DevMem2D_<T>* trains, int n, float maxDistance, const DevMem2Db* masks,
const DevMem2Di& trainIdx, const DevMem2Di& imgIdx, const DevMem2Df& distance, const DevMem2D_<unsigned int>& nMatches,
template <int BLOCK_SIZE, typename Dist, typename T>
void match(const DevMem2D_<T>& query, const DevMem2D_<T>* trains, int n, float maxDistance, const DevMem2Db* masks,
const DevMem2Di& trainIdx, const DevMem2Di& imgIdx, const DevMem2Df& distance, const DevMem2D_<unsigned int>& nMatches,
cudaStream_t stream)
{
const dim3 block(BLOCK_SIZE, BLOCK_SIZE);
@ -258,12 +258,12 @@ namespace cv { namespace gpu { namespace device
if (masks != 0 && masks[i].data)
{
match<BLOCK_SIZE, true, Dist><<<grid, block, smemSize, stream>>>(query, i, train, maxDistance, SingleMask(masks[i]),
match<BLOCK_SIZE, true, Dist><<<grid, block, smemSize, stream>>>(query, i, train, maxDistance, SingleMask(masks[i]),
trainIdx, imgIdx, distance, nMatches.data, trainIdx.cols);
}
else
{
match<BLOCK_SIZE, true, Dist><<<grid, block, smemSize, stream>>>(query, i, train, maxDistance, WithOutMask(),
match<BLOCK_SIZE, true, Dist><<<grid, block, smemSize, stream>>>(query, i, train, maxDistance, WithOutMask(),
trainIdx, imgIdx, distance, nMatches.data, trainIdx.cols);
}
cudaSafeCall( cudaGetLastError() );
@ -276,9 +276,9 @@ namespace cv { namespace gpu { namespace device
///////////////////////////////////////////////////////////////////////////////
// Match dispatcher
template <typename Dist, typename T, typename Mask>
void matchDispatcher(const DevMem2D_<T>& query, const DevMem2D_<T>& train, float maxDistance, const Mask& mask,
const DevMem2Di& trainIdx, const DevMem2Df& distance, const DevMem2D_<unsigned int>& nMatches,
template <typename Dist, typename T, typename Mask>
void matchDispatcher(const DevMem2D_<T>& query, const DevMem2D_<T>& train, float maxDistance, const Mask& mask,
const DevMem2Di& trainIdx, const DevMem2Df& distance, const DevMem2D_<unsigned int>& nMatches,
int cc, cudaStream_t stream)
{
if (query.cols <= 64)
@ -294,11 +294,11 @@ namespace cv { namespace gpu { namespace device
matchUnrolled<16, 256, Dist>(query, train, maxDistance, mask, trainIdx, distance, nMatches, stream);
}
else if (query.cols <= 512)
{
{
matchUnrolled<16, 512, Dist>(query, train, maxDistance, mask, trainIdx, distance, nMatches, stream);
}
else if (query.cols <= 1024)
{
{
matchUnrolled<16, 1024, Dist>(query, train, maxDistance, mask, trainIdx, distance, nMatches, stream);
}*/
else
@ -307,9 +307,9 @@ namespace cv { namespace gpu { namespace device
}
}
template <typename Dist, typename T>
void matchDispatcher(const DevMem2D_<T>& query, const DevMem2D_<T>* trains, int n, float maxDistance, const DevMem2Db* masks,
const DevMem2Di& trainIdx, const DevMem2Di& imgIdx, const DevMem2Df& distance, const DevMem2D_<unsigned int>& nMatches,
template <typename Dist, typename T>
void matchDispatcher(const DevMem2D_<T>& query, const DevMem2D_<T>* trains, int n, float maxDistance, const DevMem2Db* masks,
const DevMem2Di& trainIdx, const DevMem2Di& imgIdx, const DevMem2Df& distance, const DevMem2D_<unsigned int>& nMatches,
int cc, cudaStream_t stream)
{
if (query.cols <= 64)
@ -325,36 +325,36 @@ namespace cv { namespace gpu { namespace device
matchUnrolled<16, 256, Dist>(query, trains, n, maxDistance, masks, trainIdx, imgIdx, distance, nMatches, stream);
}
else if (query.cols <= 512)
{
{
matchUnrolled<16, 512, Dist>(query, trains, n, maxDistance, masks, trainIdx, imgIdx, distance, nMatches, stream);
}
else if (query.cols <= 1024)
{
{
matchUnrolled<16, 1024, Dist>(query, trains, n, maxDistance, masks, trainIdx, imgIdx, distance, nMatches, stream);
}*/
else
{
match<16, Dist>(query, trains, n, maxDistance, masks, trainIdx, imgIdx, distance, nMatches, stream);
}
}
}
///////////////////////////////////////////////////////////////////////////////
// Radius Match caller
template <typename T> void matchL1_gpu(const DevMem2Db& query, const DevMem2Db& train, float maxDistance, const DevMem2Db& mask,
const DevMem2Di& trainIdx, const DevMem2Df& distance, const DevMem2D_<unsigned int>& nMatches,
template <typename T> void matchL1_gpu(const DevMem2Db& query, const DevMem2Db& train, float maxDistance, const DevMem2Db& mask,
const DevMem2Di& trainIdx, const DevMem2Df& distance, const DevMem2D_<unsigned int>& nMatches,
int cc, cudaStream_t stream)
{
if (mask.data)
{
matchDispatcher< L1Dist<T> >(static_cast< DevMem2D_<T> >(query), static_cast< DevMem2D_<T> >(train), maxDistance, SingleMask(mask),
trainIdx, distance, nMatches,
matchDispatcher< L1Dist<T> >(static_cast< DevMem2D_<T> >(query), static_cast< DevMem2D_<T> >(train), maxDistance, SingleMask(mask),
trainIdx, distance, nMatches,
cc, stream);
}
else
{
matchDispatcher< L1Dist<T> >(static_cast< DevMem2D_<T> >(query), static_cast< DevMem2D_<T> >(train), maxDistance, WithOutMask(),
trainIdx, distance, nMatches,
matchDispatcher< L1Dist<T> >(static_cast< DevMem2D_<T> >(query), static_cast< DevMem2D_<T> >(train), maxDistance, WithOutMask(),
trainIdx, distance, nMatches,
cc, stream);
}
}
@ -366,20 +366,20 @@ namespace cv { namespace gpu { namespace device
template void matchL1_gpu<int >(const DevMem2Db& queryDescs, const DevMem2Db& trainDescs, float maxDistance, const DevMem2Db& mask, const DevMem2Di& trainIdx, const DevMem2Df& distance, const DevMem2D_<unsigned int>& nMatches, int cc, cudaStream_t stream);
template void matchL1_gpu<float >(const DevMem2Db& queryDescs, const DevMem2Db& trainDescs, float maxDistance, const DevMem2Db& mask, const DevMem2Di& trainIdx, const DevMem2Df& distance, const DevMem2D_<unsigned int>& nMatches, int cc, cudaStream_t stream);
template <typename T> void matchL2_gpu(const DevMem2Db& query, const DevMem2Db& train, float maxDistance, const DevMem2Db& mask,
const DevMem2Di& trainIdx, const DevMem2Df& distance, const DevMem2D_<unsigned int>& nMatches,
template <typename T> void matchL2_gpu(const DevMem2Db& query, const DevMem2Db& train, float maxDistance, const DevMem2Db& mask,
const DevMem2Di& trainIdx, const DevMem2Df& distance, const DevMem2D_<unsigned int>& nMatches,
int cc, cudaStream_t stream)
{
if (mask.data)
{
matchDispatcher<L2Dist>(static_cast< DevMem2D_<T> >(query), static_cast< DevMem2D_<T> >(train), maxDistance, SingleMask(mask),
trainIdx, distance, nMatches,
matchDispatcher<L2Dist>(static_cast< DevMem2D_<T> >(query), static_cast< DevMem2D_<T> >(train), maxDistance, SingleMask(mask),
trainIdx, distance, nMatches,
cc, stream);
}
else
{
matchDispatcher<L2Dist>(static_cast< DevMem2D_<T> >(query), static_cast< DevMem2D_<T> >(train), maxDistance, WithOutMask(),
trainIdx, distance, nMatches,
matchDispatcher<L2Dist>(static_cast< DevMem2D_<T> >(query), static_cast< DevMem2D_<T> >(train), maxDistance, WithOutMask(),
trainIdx, distance, nMatches,
cc, stream);
}
}
@ -391,20 +391,20 @@ namespace cv { namespace gpu { namespace device
//template void matchL2_gpu<int >(const DevMem2Db& queryDescs, const DevMem2Db& trainDescs, float maxDistance, const DevMem2Db& mask, const DevMem2Di& trainIdx, const DevMem2Df& distance, const DevMem2D_<unsigned int>& nMatches, int cc, cudaStream_t stream);
template void matchL2_gpu<float >(const DevMem2Db& queryDescs, const DevMem2Db& trainDescs, float maxDistance, const DevMem2Db& mask, const DevMem2Di& trainIdx, const DevMem2Df& distance, const DevMem2D_<unsigned int>& nMatches, int cc, cudaStream_t stream);
template <typename T> void matchHamming_gpu(const DevMem2Db& query, const DevMem2Db& train, float maxDistance, const DevMem2Db& mask,
const DevMem2Di& trainIdx, const DevMem2Df& distance, const DevMem2D_<unsigned int>& nMatches,
template <typename T> void matchHamming_gpu(const DevMem2Db& query, const DevMem2Db& train, float maxDistance, const DevMem2Db& mask,
const DevMem2Di& trainIdx, const DevMem2Df& distance, const DevMem2D_<unsigned int>& nMatches,
int cc, cudaStream_t stream)
{
if (mask.data)
{
matchDispatcher<HammingDist>(static_cast< DevMem2D_<T> >(query), static_cast< DevMem2D_<T> >(train), maxDistance, SingleMask(mask),
trainIdx, distance, nMatches,
matchDispatcher<HammingDist>(static_cast< DevMem2D_<T> >(query), static_cast< DevMem2D_<T> >(train), maxDistance, SingleMask(mask),
trainIdx, distance, nMatches,
cc, stream);
}
else
{
matchDispatcher<HammingDist>(static_cast< DevMem2D_<T> >(query), static_cast< DevMem2D_<T> >(train), maxDistance, WithOutMask(),
trainIdx, distance, nMatches,
matchDispatcher<HammingDist>(static_cast< DevMem2D_<T> >(query), static_cast< DevMem2D_<T> >(train), maxDistance, WithOutMask(),
trainIdx, distance, nMatches,
cc, stream);
}
}
@ -415,12 +415,12 @@ namespace cv { namespace gpu { namespace device
//template void matchHamming_gpu<short >(const DevMem2Db& queryDescs, const DevMem2Db& trainDescs, float maxDistance, const DevMem2Db& mask, const DevMem2Di& trainIdx, const DevMem2Df& distance, const DevMem2D_<unsigned int>& nMatches, int cc, cudaStream_t stream);
template void matchHamming_gpu<int >(const DevMem2Db& queryDescs, const DevMem2Db& trainDescs, float maxDistance, const DevMem2Db& mask, const DevMem2Di& trainIdx, const DevMem2Df& distance, const DevMem2D_<unsigned int>& nMatches, int cc, cudaStream_t stream);
template <typename T> void matchL1_gpu(const DevMem2Db& query, const DevMem2Db* trains, int n, float maxDistance, const DevMem2Db* masks,
const DevMem2Di& trainIdx, const DevMem2Di& imgIdx, const DevMem2Df& distance, const DevMem2D_<unsigned int>& nMatches,
template <typename T> void matchL1_gpu(const DevMem2Db& query, const DevMem2Db* trains, int n, float maxDistance, const DevMem2Db* masks,
const DevMem2Di& trainIdx, const DevMem2Di& imgIdx, const DevMem2Df& distance, const DevMem2D_<unsigned int>& nMatches,
int cc, cudaStream_t stream)
{
matchDispatcher< L1Dist<T> >(static_cast< DevMem2D_<T> >(query), (const DevMem2D_<T>*)trains, n, maxDistance, masks,
trainIdx, imgIdx, distance, nMatches,
matchDispatcher< L1Dist<T> >(static_cast< DevMem2D_<T> >(query), (const DevMem2D_<T>*)trains, n, maxDistance, masks,
trainIdx, imgIdx, distance, nMatches,
cc, stream);
}
@ -431,12 +431,12 @@ namespace cv { namespace gpu { namespace device
template void matchL1_gpu<int >(const DevMem2Db& query, const DevMem2Db* trains, int n, float maxDistance, const DevMem2Db* masks, const DevMem2Di& trainIdx, const DevMem2Di& imgIdx, const DevMem2Df& distance, const DevMem2D_<unsigned int>& nMatches, int cc, cudaStream_t stream);
template void matchL1_gpu<float >(const DevMem2Db& query, const DevMem2Db* trains, int n, float maxDistance, const DevMem2Db* masks, const DevMem2Di& trainIdx, const DevMem2Di& imgIdx, const DevMem2Df& distance, const DevMem2D_<unsigned int>& nMatches, int cc, cudaStream_t stream);
template <typename T> void matchL2_gpu(const DevMem2Db& query, const DevMem2Db* trains, int n, float maxDistance, const DevMem2Db* masks,
const DevMem2Di& trainIdx, const DevMem2Di& imgIdx, const DevMem2Df& distance, const DevMem2D_<unsigned int>& nMatches,
template <typename T> void matchL2_gpu(const DevMem2Db& query, const DevMem2Db* trains, int n, float maxDistance, const DevMem2Db* masks,
const DevMem2Di& trainIdx, const DevMem2Di& imgIdx, const DevMem2Df& distance, const DevMem2D_<unsigned int>& nMatches,
int cc, cudaStream_t stream)
{
matchDispatcher<L2Dist>(static_cast< DevMem2D_<T> >(query), (const DevMem2D_<T>*)trains, n, maxDistance, masks,
trainIdx, imgIdx, distance, nMatches,
matchDispatcher<L2Dist>(static_cast< DevMem2D_<T> >(query), (const DevMem2D_<T>*)trains, n, maxDistance, masks,
trainIdx, imgIdx, distance, nMatches,
cc, stream);
}
@ -447,12 +447,12 @@ namespace cv { namespace gpu { namespace device
//template void matchL2_gpu<int >(const DevMem2Db& query, const DevMem2Db* trains, int n, float maxDistance, const DevMem2Db* masks, const DevMem2Di& trainIdx, const DevMem2Di& imgIdx, const DevMem2Df& distance, const DevMem2D_<unsigned int>& nMatches, int cc, cudaStream_t stream);
template void matchL2_gpu<float >(const DevMem2Db& query, const DevMem2Db* trains, int n, float maxDistance, const DevMem2Db* masks, const DevMem2Di& trainIdx, const DevMem2Di& imgIdx, const DevMem2Df& distance, const DevMem2D_<unsigned int>& nMatches, int cc, cudaStream_t stream);
template <typename T> void matchHamming_gpu(const DevMem2Db& query, const DevMem2Db* trains, int n, float maxDistance, const DevMem2Db* masks,
const DevMem2Di& trainIdx, const DevMem2Di& imgIdx, const DevMem2Df& distance, const DevMem2D_<unsigned int>& nMatches,
template <typename T> void matchHamming_gpu(const DevMem2Db& query, const DevMem2Db* trains, int n, float maxDistance, const DevMem2Db* masks,
const DevMem2Di& trainIdx, const DevMem2Di& imgIdx, const DevMem2Df& distance, const DevMem2D_<unsigned int>& nMatches,
int cc, cudaStream_t stream)
{
matchDispatcher<HammingDist>(static_cast< DevMem2D_<T> >(query), (const DevMem2D_<T>*)trains, n, maxDistance, masks,
trainIdx, imgIdx, distance, nMatches,
matchDispatcher<HammingDist>(static_cast< DevMem2D_<T> >(query), (const DevMem2D_<T>*)trains, n, maxDistance, masks,
trainIdx, imgIdx, distance, nMatches,
cc, stream);
}

8
modules/gpu/src/cuda/bilateral_filter.cu
View File

@ -43,9 +43,9 @@
#include "internal_shared.hpp"
#include "opencv2/gpu/device/limits.hpp"
namespace cv { namespace gpu { namespace device
namespace cv { namespace gpu { namespace device
{
namespace bilateral_filter
namespace bilateral_filter
{
__constant__ float* ctable_color;
__constant__ float* ctable_space;
@ -108,7 +108,7 @@ namespace cv { namespace gpu { namespace device
dp[3] = *(disp + (y+1) * disp_step + x + 0);
dp[4] = *(disp + (y ) * disp_step + x + 1);
if(::abs(dp[1] - dp[0]) >= cedge_disc || ::abs(dp[2] - dp[0]) >= cedge_disc || ::abs(dp[3] - dp[0]) >= cedge_disc || ::abs(dp[4] - dp[0]) >= cedge_disc)
if(::abs(dp[1] - dp[0]) >= cedge_disc || ::abs(dp[2] - dp[0]) >= cedge_disc || ::abs(dp[3] - dp[0]) >= cedge_disc || ::abs(dp[4] - dp[0]) >= cedge_disc)
{
const int ymin = ::max(0, y - cradius);
const int xmin = ::max(0, x - cradius);
@ -175,7 +175,7 @@ namespace cv { namespace gpu { namespace device
}
}
template <typename T>
template <typename T>
void bilateral_filter_caller(DevMem2D_<T> disp, DevMem2Db img, int channels, int iters, cudaStream_t stream)
{
dim3 threads(32, 8, 1);

12
modules/gpu/src/cuda/blend.cu
View File

@ -42,9 +42,9 @@
#include "internal_shared.hpp"
namespace cv { namespace gpu { namespace device
namespace cv { namespace gpu { namespace device
{
namespace blend
namespace blend
{
template <typename T>
__global__ void blendLinearKernel(int rows, int cols, int cn, const PtrStep<T> img1, const PtrStep<T> img2,
@ -62,14 +62,14 @@ namespace cv { namespace gpu { namespace device
T p2 = img2.ptr(y)[x];
result.ptr(y)[x] = (p1 * w1 + p2 * w2) / (w1 + w2 + 1e-5f);
}
}
}
template <typename T>
void blendLinearCaller(int rows, int cols, int cn, PtrStep<T> img1, PtrStep<T> img2, PtrStepf weights1, PtrStepf weights2, PtrStep<T> result, cudaStream_t stream)
{
dim3 threads(16, 16);
dim3 grid(divUp(cols * cn, threads.x), divUp(rows, threads.y));
blendLinearKernel<<<grid, threads, 0, stream>>>(rows, cols * cn, cn, img1, img2, weights1, weights2, result);
cudaSafeCall( cudaGetLastError() );
@ -105,12 +105,12 @@ namespace cv { namespace gpu { namespace device
{
dim3 threads(16, 16);
dim3 grid(divUp(cols, threads.x), divUp(rows, threads.y));
blendLinearKernel8UC4<<<grid, threads, 0, stream>>>(rows, cols, img1, img2, weights1, weights2, result);
cudaSafeCall( cudaGetLastError() );
if (stream == 0)
cudaSafeCall(cudaDeviceSynchronize());
}
} // namespace blend
} // namespace blend
}}} // namespace cv { namespace gpu { namespace device

2
modules/gpu/src/cuda/calib3d.cu
View File

@ -44,7 +44,7 @@
#include "opencv2/gpu/device/transform.hpp"
#include "opencv2/gpu/device/functional.hpp"
namespace cv { namespace gpu { namespace device
namespace cv { namespace gpu { namespace device
{
#define SOLVE_PNP_RANSAC_MAX_NUM_ITERS 200