UAVProduct/SiftGPU/ProgramCU.cu
////////////////////////////////////////////////////////////////////////////
// File: ProgramCU.cu
// Author: Changchang Wu
// Description : implementation of ProgramCU and all CUDA kernels
//
// Copyright (c) 2007 University of North Carolina at Chapel Hill
// All Rights Reserved
//
// Permission to use, copy, modify and distribute this software and its
// documentation for educational, research and non-profit purposes, without
// fee, and without a written agreement is hereby granted, provided that the
// above copyright notice and the following paragraph appear in all copies.
//
// The University of North Carolina at Chapel Hill make no representations
// about the suitability of this software for any purpose. It is provided
// 'as is' without express or implied warranty.
//
// Please send BUG REPORTS to ccwu@cs.unc.edu
//
////////////////////////////////////////////////////////////////////////////
#if defined(CUDA_SIFTGPU_ENABLED)
#include "GL/glew.h"
#include "stdio.h"
#include "CuTexImage.h"
#include "ProgramCU.h"
#include "GlobalUtil.h"
//----------------------------------------------------------------
//Begin SiftGPU setting section.
//////////////////////////////////////////////////////////
#define IMUL(X,Y) __mul24(X,Y)
//#define FDIV(X,Y) ((X)/(Y))
#define FDIV(X,Y) __fdividef(X,Y)
/////////////////////////////////////////////////////////
//filter kernel width range (don't change this)
#define KERNEL_MAX_WIDTH 33
#define KERNEL_MIN_WIDTH 5
//////////////////////////////////////////////////////////
//horizontal filter block size (32, 64, 128, 256, 512)
#define FILTERH_TILE_WIDTH 128
//thread block for vertical filter. FILTERV_BLOCK_WIDTH can be (4, 8 or 16)
#define FILTERV_BLOCK_WIDTH 16
#define FILTERV_BLOCK_HEIGHT 32
//The corresponding image patch for a thread block
#define FILTERV_PIXEL_PER_THREAD 4
#define FILTERV_TILE_WIDTH FILTERV_BLOCK_WIDTH
#define FILTERV_TILE_HEIGHT (FILTERV_PIXEL_PER_THREAD * FILTERV_BLOCK_HEIGHT)
//////////////////////////////////////////////////////////
//thread block size for computing Difference of Gaussian
#define DOG_BLOCK_LOG_DIMX 7
#define DOG_BLOCK_LOG_DIMY 0
#define DOG_BLOCK_DIMX (1 << DOG_BLOCK_LOG_DIMX)
#define DOG_BLOCK_DIMY (1 << DOG_BLOCK_LOG_DIMY)
//////////////////////////////////////////////////////////
//thread block size for keypoint detection
#define KEY_BLOCK_LOG_DIMX 3
#define KEY_BLOCK_LOG_DIMY 3
#define KEY_BLOCK_DIMX (1<<KEY_BLOCK_LOG_DIMX)
#define KEY_BLOCK_DIMY (1<<KEY_BLOCK_LOG_DIMY)
//#define KEY_OFFSET_ONE
//make KEY_BLOCK_LOG_DIMX 4 will make the write coalesced..
//but it seems uncoalesced writes don't affect the speed
//////////////////////////////////////////////////////////
//thread block size for initializing list generation (64, 128, 256, 512 ...)
#define HIST_INIT_WIDTH 128
//thread block size for generating feature list (32, 64, 128, 256, 512, ...)
#define LISTGEN_BLOCK_DIM 128
/////////////////////////////////////////////////////////
//how many keypoint orientations to compute in a block
#define ORIENTATION_COMPUTE_PER_BLOCK 64
//how many keypoint descriptor to compute in a block (2, 4, 8, 16, 32)
#define DESCRIPTOR_COMPUTE_PER_BLOCK 4
#define DESCRIPTOR_COMPUTE_BLOCK_SIZE (16 * DESCRIPTOR_COMPUTE_PER_BLOCK)
//how many keypoint descriptor to normalized in a block (32, ...)
#define DESCRIPTOR_NORMALIZ_PER_BLOCK 32
///////////////////////////////////////////
//Thread block size for visualization
//(This doesn't affect the speed of computation)
#define BLOCK_LOG_DIM 4
#define BLOCK_DIM (1 << BLOCK_LOG_DIM)
//End SiftGPU setting section.
//----------------------------------------------------------------
__device__ __constant__ float d_kernel[KERNEL_MAX_WIDTH];
texture<float, 1, cudaReadModeElementType> texData;
texture<unsigned char, 1, cudaReadModeNormalizedFloat> texDataB;
texture<float2, 2, cudaReadModeElementType> texDataF2;
texture<float4, 1, cudaReadModeElementType> texDataF4;
texture<int4, 1, cudaReadModeElementType> texDataI4;
texture<int4, 1, cudaReadModeElementType> texDataList;
//template<int i> __device__ float Conv(float *data) { return Conv<i-1>(data) + data[i]*d_kernel[i];}
//template<> __device__ float Conv<0>(float *data) { return data[0] * d_kernel[0]; }
//////////////////////////////////////////////////////////////
template<int FW> __global__ void FilterH( float* d_result, int width)
{
const int HALF_WIDTH = FW >> 1;
const int CACHE_WIDTH = FILTERH_TILE_WIDTH + FW -1;
const int CACHE_COUNT = 2 + (CACHE_WIDTH - 2)/ FILTERH_TILE_WIDTH;
__shared__ float data[CACHE_WIDTH];
const int bcol = IMUL(blockIdx.x, FILTERH_TILE_WIDTH);
const int col = bcol + threadIdx.x;
const int index_min = IMUL(blockIdx.y, width);
const int index_max = index_min + width - 1;
int src_index = index_min + bcol - HALF_WIDTH + threadIdx.x;
int cache_index = threadIdx.x;
float value = 0;
#pragma unroll
for(int j = 0; j < CACHE_COUNT; ++j)
{
if(cache_index < CACHE_WIDTH)
{
int fetch_index = src_index < index_min? index_min : (src_index > index_max ? index_max : src_index);
data[cache_index] = tex1Dfetch(texData,fetch_index);
src_index += FILTERH_TILE_WIDTH;
cache_index += FILTERH_TILE_WIDTH;
}
}
__syncthreads();
if(col >= width) return;
#pragma unroll
for(int i = 0; i < FW; ++i)
{
value += (data[threadIdx.x + i]* d_kernel[i]);
}
// value = Conv<FW-1>(data + threadIdx.x);
d_result[index_min + col] = value;
}
////////////////////////////////////////////////////////////////////
template<int FW> __global__ void FilterV(float* d_result, int width, int height)
{
const int HALF_WIDTH = FW >> 1;
const int CACHE_WIDTH = FW + FILTERV_TILE_HEIGHT - 1;
const int TEMP = CACHE_WIDTH & 0xf;
//add some extra space to avoid bank conflict
#if FILTERV_TILE_WIDTH == 16
//make the stride 16 * n +/- 1
const int EXTRA = (TEMP == 1 || TEMP == 0) ? 1 - TEMP : 15 - TEMP;
#elif FILTERV_TILE_WIDTH == 8
//make the stride 16 * n +/- 2
const int EXTRA = (TEMP == 2 || TEMP == 1 || TEMP == 0) ? 2 - TEMP : (TEMP == 15? 3 : 14 - TEMP);
#elif FILTERV_TILE_WIDTH == 4
//make the stride 16 * n +/- 4
const int EXTRA = (TEMP >=0 && TEMP <=4) ? 4 - TEMP : (TEMP > 12? 20 - TEMP : 12 - TEMP);
#else
#error
#endif
const int CACHE_TRUE_WIDTH = CACHE_WIDTH + EXTRA;
const int CACHE_COUNT = (CACHE_WIDTH + FILTERV_BLOCK_HEIGHT - 1) / FILTERV_BLOCK_HEIGHT;
const int WRITE_COUNT = (FILTERV_TILE_HEIGHT + FILTERV_BLOCK_HEIGHT -1) / FILTERV_BLOCK_HEIGHT;
__shared__ float data[CACHE_TRUE_WIDTH * FILTERV_TILE_WIDTH];
const int row_block_first = IMUL(blockIdx.y, FILTERV_TILE_HEIGHT);
const int col = IMUL(blockIdx.x, FILTERV_TILE_WIDTH) + threadIdx.x;
const int row_first = row_block_first - HALF_WIDTH;
const int data_index_max = IMUL(height - 1, width) + col;
const int cache_col_start = threadIdx.y;
const int cache_row_start = IMUL(threadIdx.x, CACHE_TRUE_WIDTH);
int cache_index = cache_col_start + cache_row_start;
int data_index = IMUL(row_first + cache_col_start, width) + col;
if(col < width)
{
#pragma unroll
for(int i = 0; i < CACHE_COUNT; ++i)
{
if(cache_col_start < CACHE_WIDTH - i * FILTERV_BLOCK_HEIGHT)
{
int fetch_index = data_index < col ? col : (data_index > data_index_max? data_index_max : data_index);
data[cache_index + i * FILTERV_BLOCK_HEIGHT] = tex1Dfetch(texData,fetch_index);
data_index += IMUL(FILTERV_BLOCK_HEIGHT, width);
}
}
}
__syncthreads();
if(col >= width) return;
int row = row_block_first + threadIdx.y;
int index_start = cache_row_start + threadIdx.y;
#pragma unroll
for(int i = 0; i < WRITE_COUNT; ++i,
row += FILTERV_BLOCK_HEIGHT, index_start += FILTERV_BLOCK_HEIGHT)
{
if(row < height)
{
int index_dest = IMUL(row, width) + col;
float value = 0;
#pragma unroll
for(int i = 0; i < FW; ++i)
{
value += (data[index_start + i] * d_kernel[i]);
}
d_result[index_dest] = value;
}
}
}
template<int LOG_SCALE> __global__ void UpsampleKernel(float* d_result, int width)
{
const int SCALE = (1 << LOG_SCALE), SCALE_MASK = (SCALE - 1);
const float INV_SCALE = 1.0f / (float(SCALE));
int col = IMUL(blockIdx.x, FILTERH_TILE_WIDTH) + threadIdx.x;
if(col >= width) return;
int row = blockIdx.y >> LOG_SCALE;
int index = row * width + col;
int dst_row = blockIdx.y;
int dst_idx= (width * dst_row + col) * SCALE;
int helper = blockIdx.y & SCALE_MASK;
if (helper)
{
float v11 = tex1Dfetch(texData, index);
float v12 = tex1Dfetch(texData, index + 1);
index += width;
float v21 = tex1Dfetch(texData, index);
float v22 = tex1Dfetch(texData, index + 1);
float w1 = INV_SCALE * helper, w2 = 1.0 - w1;
float v1 = (v21 * w1 + w2 * v11);
float v2 = (v22 * w1 + w2 * v12);
d_result[dst_idx] = v1;
#pragma unroll
for(int i = 1; i < SCALE; ++i)
{
const float r2 = i * INV_SCALE;
const float r1 = 1.0f - r2;
d_result[dst_idx +i] = v1 * r1 + v2 * r2;
}
}else
{
float v1 = tex1Dfetch(texData, index);
float v2 = tex1Dfetch(texData, index + 1);
d_result[dst_idx] = v1;
#pragma unroll
for(int i = 1; i < SCALE; ++i)
{
const float r2 = i * INV_SCALE;
const float r1 = 1.0f - r2;
d_result[dst_idx +i] = v1 * r1 + v2 * r2;
}
}
}
////////////////////////////////////////////////////////////////////////////////////////
void ProgramCU::SampleImageU(CuTexImage *dst, CuTexImage *src, int log_scale)
{
int width = src->GetImgWidth(), height = src->GetImgHeight();
src->BindTexture(texData);
dim3 grid((width + FILTERH_TILE_WIDTH - 1)/ FILTERH_TILE_WIDTH, height << log_scale);
dim3 block(FILTERH_TILE_WIDTH);
switch(log_scale)
{
case 1 : UpsampleKernel<1> <<< grid, block>>> ((float*) dst->_cuData, width); break;
case 2 : UpsampleKernel<2> <<< grid, block>>> ((float*) dst->_cuData, width); break;
case 3 : UpsampleKernel<3> <<< grid, block>>> ((float*) dst->_cuData, width); break;
default: break;
}
}
template<int LOG_SCALE> __global__ void DownsampleKernel(float* d_result, int src_width, int dst_width)
{
const int dst_col = IMUL(blockIdx.x, FILTERH_TILE_WIDTH) + threadIdx.x;
if(dst_col >= dst_width) return;
const int src_col = min((dst_col << LOG_SCALE), (src_width - 1));
const int dst_row = blockIdx.y;
const int src_row = blockIdx.y << LOG_SCALE;
const int src_idx = IMUL(src_row, src_width) + src_col;
const int dst_idx = IMUL(dst_width, dst_row) + dst_col;
d_result[dst_idx] = tex1Dfetch(texData, src_idx);
}
__global__ void DownsampleKernel(float* d_result, int src_width, int dst_width, const int log_scale)
{
const int dst_col = IMUL(blockIdx.x, FILTERH_TILE_WIDTH) + threadIdx.x;
if(dst_col >= dst_width) return;
const int src_col = min((dst_col << log_scale), (src_width - 1));
const int dst_row = blockIdx.y;
const int src_row = blockIdx.y << log_scale;
const int src_idx = IMUL(src_row, src_width) + src_col;
const int dst_idx = IMUL(dst_width, dst_row) + dst_col;
d_result[dst_idx] = tex1Dfetch(texData, src_idx);
}
void ProgramCU::SampleImageD(CuTexImage *dst, CuTexImage *src, int log_scale)
{
int src_width = src->GetImgWidth(), dst_width = dst->GetImgWidth() ;
src->BindTexture(texData);
dim3 grid((dst_width + FILTERH_TILE_WIDTH - 1)/ FILTERH_TILE_WIDTH, dst->GetImgHeight());
dim3 block(FILTERH_TILE_WIDTH);
switch(log_scale)
{
case 1 : DownsampleKernel<1> <<< grid, block>>> ((float*) dst->_cuData, src_width, dst_width); break;
case 2 : DownsampleKernel<2> <<< grid, block>>> ((float*) dst->_cuData, src_width, dst_width); break;
case 3 : DownsampleKernel<3> <<< grid, block>>> ((float*) dst->_cuData, src_width, dst_width); break;
default: DownsampleKernel <<< grid, block>>> ((float*) dst->_cuData, src_width, dst_width, log_scale);
}
}
__global__ void ChannelReduce_Kernel(float* d_result)
{
int index = IMUL(blockIdx.x, FILTERH_TILE_WIDTH) + threadIdx.x;
d_result[index] = tex1Dfetch(texData, index*4);
}
__global__ void ChannelReduce_Convert_Kernel(float* d_result)
{
int index = IMUL(blockIdx.x, FILTERH_TILE_WIDTH) + threadIdx.x;
float4 rgba = tex1Dfetch(texDataF4, index);
d_result[index] = 0.299f * rgba.x + 0.587f* rgba.y + 0.114f * rgba.z;
}
void ProgramCU::ReduceToSingleChannel(CuTexImage* dst, CuTexImage* src, int convert_rgb)
{
int width = src->GetImgWidth(), height = dst->GetImgHeight() ;
dim3 grid((width * height + FILTERH_TILE_WIDTH - 1)/ FILTERH_TILE_WIDTH);
dim3 block(FILTERH_TILE_WIDTH);
if(convert_rgb)
{
src->BindTexture(texDataF4);
ChannelReduce_Convert_Kernel<<<grid, block>>>((float*)dst->_cuData);
}else
{
src->BindTexture(texData);
ChannelReduce_Kernel<<<grid, block>>>((float*)dst->_cuData);
}
}
__global__ void ConvertByteToFloat_Kernel(float* d_result)
{
int index = IMUL(blockIdx.x, FILTERH_TILE_WIDTH) + threadIdx.x;
d_result[index] = tex1Dfetch(texDataB, index);
}
void ProgramCU::ConvertByteToFloat(CuTexImage*src, CuTexImage* dst)
{
int width = src->GetImgWidth(), height = dst->GetImgHeight() ;
dim3 grid((width * height + FILTERH_TILE_WIDTH - 1)/ FILTERH_TILE_WIDTH);
dim3 block(FILTERH_TILE_WIDTH);
src->BindTexture(texDataB);
ConvertByteToFloat_Kernel<<<grid, block>>>((float*)dst->_cuData);
}
void ProgramCU::CreateFilterKernel(float sigma, float* kernel, int& width)
{
int i, sz = int( ceil( GlobalUtil::_FilterWidthFactor * sigma -0.5) ) ;//
width = 2*sz + 1;
if(width > KERNEL_MAX_WIDTH)
{
//filter size truncation
sz = KERNEL_MAX_WIDTH >> 1;
width =KERNEL_MAX_WIDTH;
}else if(width < KERNEL_MIN_WIDTH)
{
sz = KERNEL_MIN_WIDTH >> 1;
width =KERNEL_MIN_WIDTH;
}
float rv = 1.0f/(sigma*sigma), v, ksum =0;
// pre-compute filter
for( i = -sz ; i <= sz ; ++i)
{
kernel[i+sz] = v = exp(-0.5f * i * i *rv) ;
ksum += v;
}
//normalize the kernel
rv = 1.0f/ksum;
for(i = 0; i< width ;i++) kernel[i]*=rv;
}
template<int FW> void ProgramCU::FilterImage(CuTexImage *dst, CuTexImage *src, CuTexImage* buf)
{
int width = src->GetImgWidth(), height = src->GetImgHeight();
//horizontal filtering
src->BindTexture(texData);
dim3 gridh((width + FILTERH_TILE_WIDTH - 1)/ FILTERH_TILE_WIDTH, height);
dim3 blockh(FILTERH_TILE_WIDTH);
FilterH<FW><<<gridh, blockh>>>((float*)buf->_cuData, width);
CheckErrorCUDA("FilterH");
///vertical filtering
buf->BindTexture(texData);
dim3 gridv((width + FILTERV_TILE_WIDTH - 1)/ FILTERV_TILE_WIDTH, (height + FILTERV_TILE_HEIGHT - 1)/FILTERV_TILE_HEIGHT);
dim3 blockv(FILTERV_TILE_WIDTH, FILTERV_BLOCK_HEIGHT);
FilterV<FW><<<gridv, blockv>>>((float*)dst->_cuData, width, height);
CheckErrorCUDA("FilterV");
}
//////////////////////////////////////////////////////////////////////
// tested on 2048x1500 image, the time on pyramid construction is
// OpenGL version : 18ms
// CUDA version: 28 ms
void ProgramCU::FilterImage(CuTexImage *dst, CuTexImage *src, CuTexImage* buf, float sigma)
{
float filter_kernel[KERNEL_MAX_WIDTH]; int width;
CreateFilterKernel(sigma, filter_kernel, width);
cudaMemcpyToSymbol(d_kernel, filter_kernel, width * sizeof(float), 0, cudaMemcpyHostToDevice);
switch(width)
{
case 5: FilterImage< 5>(dst, src, buf); break;
case 7: FilterImage< 7>(dst, src, buf); break;
case 9: FilterImage< 9>(dst, src, buf); break;
case 11: FilterImage<11>(dst, src, buf); break;
case 13: FilterImage<13>(dst, src, buf); break;
case 15: FilterImage<15>(dst, src, buf); break;
case 17: FilterImage<17>(dst, src, buf); break;
case 19: FilterImage<19>(dst, src, buf); break;
case 21: FilterImage<21>(dst, src, buf); break;
case 23: FilterImage<23>(dst, src, buf); break;
case 25: FilterImage<25>(dst, src, buf); break;
case 27: FilterImage<27>(dst, src, buf); break;
case 29: FilterImage<29>(dst, src, buf); break;
case 31: FilterImage<31>(dst, src, buf); break;
case 33: FilterImage<33>(dst, src, buf); break;
default: break;
}
}
texture<float, 1, cudaReadModeElementType> texC;
texture<float, 1, cudaReadModeElementType> texP;
texture<float, 1, cudaReadModeElementType> texN;
void __global__ ComputeDOG_Kernel(float* d_dog, float2* d_got, int width, int height)
{
int row = (blockIdx.y << DOG_BLOCK_LOG_DIMY) + threadIdx.y;
int col = (blockIdx.x << DOG_BLOCK_LOG_DIMX) + threadIdx.x;
if(col < width && row < height)
{
int index = IMUL(row, width) + col;
float vp = tex1Dfetch(texP, index);
float v = tex1Dfetch(texC, index);
d_dog[index] = v - vp;
float vxn = tex1Dfetch(texC, index + 1);
float vxp = tex1Dfetch(texC, index - 1);
float vyp = tex1Dfetch(texC, index - width);
float vyn = tex1Dfetch(texC, index + width);
float dx = vxn - vxp, dy = vyn - vyp;
float grd = 0.5f * sqrt(dx * dx + dy * dy);
float rot = (grd == 0.0f? 0.0f : atan2(dy, dx));
d_got[index] = make_float2(grd, rot);
}
}
void __global__ ComputeDOG_Kernel(float* d_dog, int width, int height)
{
int row = (blockIdx.y << DOG_BLOCK_LOG_DIMY) + threadIdx.y;
int col = (blockIdx.x << DOG_BLOCK_LOG_DIMX) + threadIdx.x;
if(col < width && row < height)
{
int index = IMUL(row, width) + col;
float vp = tex1Dfetch(texP, index);
float v = tex1Dfetch(texC, index);
d_dog[index] = v - vp;
}
}
void ProgramCU::ComputeDOG(CuTexImage* gus, CuTexImage* dog, CuTexImage* got)
{
int width = gus->GetImgWidth(), height = gus->GetImgHeight();
dim3 grid((width + DOG_BLOCK_DIMX - 1)/ DOG_BLOCK_DIMX, (height + DOG_BLOCK_DIMY - 1)/DOG_BLOCK_DIMY);
dim3 block(DOG_BLOCK_DIMX, DOG_BLOCK_DIMY);
gus->BindTexture(texC);
(gus -1)->BindTexture(texP);
if(got->_cuData)
ComputeDOG_Kernel<<<grid, block>>>((float*) dog->_cuData, (float2*) got->_cuData, width, height);
else
ComputeDOG_Kernel<<<grid, block>>>((float*) dog->_cuData, width, height);
}
#define READ_CMP_DOG_DATA(datai, tex, idx) \
datai[0] = tex1Dfetch(tex, idx - 1);\
datai[1] = tex1Dfetch(tex, idx);\
datai[2] = tex1Dfetch(tex, idx + 1);\
if(v > nmax)\
{\
nmax = max(nmax, datai[0]);\
nmax = max(nmax, datai[1]);\
nmax = max(nmax, datai[2]);\
if(v < nmax) goto key_finish;\
}else\
{\
nmin = min(nmin, datai[0]);\
nmin = min(nmin, datai[1]);\
nmin = min(nmin, datai[2]);\
if(v > nmin) goto key_finish;\
}
void __global__ ComputeKEY_Kernel(float4* d_key, int width, int colmax, int rowmax,
float dog_threshold0, float dog_threshold, float edge_threshold, int subpixel_localization)
{
float data[3][3], v;
float datap[3][3], datan[3][3];
#ifdef KEY_OFFSET_ONE
int row = (blockIdx.y << KEY_BLOCK_LOG_DIMY) + threadIdx.y + 1;
int col = (blockIdx.x << KEY_BLOCK_LOG_DIMX) + threadIdx.x + 1;
#else
int row = (blockIdx.y << KEY_BLOCK_LOG_DIMY) + threadIdx.y;
int col = (blockIdx.x << KEY_BLOCK_LOG_DIMX) + threadIdx.x;
#endif
int index = IMUL(row, width) + col;
int idx[3] ={index - width, index, index + width};
int in_image =0;
float nmax, nmin, result = 0.0f;
float dx = 0, dy = 0, ds = 0;
bool offset_test_passed = true;
#ifdef KEY_OFFSET_ONE
if(row < rowmax && col < colmax)
#else
if(row > 0 && col > 0 && row < rowmax && col < colmax)
#endif
{
in_image = 1;
data[1][1] = v = tex1Dfetch(texC, idx[1]);
if(fabs(v) <= dog_threshold0) goto key_finish;
data[1][0] = tex1Dfetch(texC, idx[1] - 1);
data[1][2] = tex1Dfetch(texC, idx[1] + 1);
nmax = max(data[1][0], data[1][2]);
nmin = min(data[1][0], data[1][2]);
if(v <=nmax && v >= nmin) goto key_finish;
//if((v > nmax && v < 0 )|| (v < nmin && v > 0)) goto key_finish;
READ_CMP_DOG_DATA(data[0], texC, idx[0]);
READ_CMP_DOG_DATA(data[2], texC, idx[2]);
//edge supression
float vx2 = v * 2.0f;
float fxx = data[1][0] + data[1][2] - vx2;
float fyy = data[0][1] + data[2][1] - vx2;
float fxy = 0.25f * (data[2][2] + data[0][0] - data[2][0] - data[0][2]);
float temp1 = fxx * fyy - fxy * fxy;
float temp2 = (fxx + fyy) * (fxx + fyy);
if(temp1 <=0 || temp2 > edge_threshold * temp1) goto key_finish;
//read the previous level
READ_CMP_DOG_DATA(datap[0], texP, idx[0]);
READ_CMP_DOG_DATA(datap[1], texP, idx[1]);
READ_CMP_DOG_DATA(datap[2], texP, idx[2]);
//read the next level
READ_CMP_DOG_DATA(datan[0], texN, idx[0]);
READ_CMP_DOG_DATA(datan[1], texN, idx[1]);
READ_CMP_DOG_DATA(datan[2], texN, idx[2]);
if(subpixel_localization)
{
//subpixel localization
float fx = 0.5f * (data[1][2] - data[1][0]);
float fy = 0.5f * (data[2][1] - data[0][1]);
float fs = 0.5f * (datan[1][1] - datap[1][1]);
float fss = (datan[1][1] + datap[1][1] - vx2);
float fxs = 0.25f* (datan[1][2] + datap[1][0] - datan[1][0] - datap[1][2]);
float fys = 0.25f* (datan[2][1] + datap[0][1] - datan[0][1] - datap[2][1]);
//need to solve dx, dy, ds;
// |-fx| | fxx fxy fxs | |dx|
// |-fy| = | fxy fyy fys | * |dy|
// |-fs| | fxs fys fss | |ds|
float4 A0 = fxx > 0? make_float4(fxx, fxy, fxs, -fx) : make_float4(-fxx, -fxy, -fxs, fx);
float4 A1 = fxy > 0? make_float4(fxy, fyy, fys, -fy) : make_float4(-fxy, -fyy, -fys, fy);
float4 A2 = fxs > 0? make_float4(fxs, fys, fss, -fs) : make_float4(-fxs, -fys, -fss, fs);
float maxa = max(max(A0.x, A1.x), A2.x);
if(maxa >= 1e-10)
{
if(maxa == A1.x)
{
float4 TEMP = A1; A1 = A0; A0 = TEMP;
}else if(maxa == A2.x)
{
float4 TEMP = A2; A2 = A0; A0 = TEMP;
}
A0.y /= A0.x; A0.z /= A0.x; A0.w/= A0.x;
A1.y -= A1.x * A0.y; A1.z -= A1.x * A0.z; A1.w -= A1.x * A0.w;
A2.y -= A2.x * A0.y; A2.z -= A2.x * A0.z; A2.w -= A2.x * A0.w;
if(abs(A2.y) > abs(A1.y))
{
float4 TEMP = A2; A2 = A1; A1 = TEMP;
}
if(abs(A1.y) >= 1e-10)
{
A1.z /= A1.y; A1.w /= A1.y;
A2.z -= A2.y * A1.z; A2.w -= A2.y * A1.w;
if(abs(A2.z) >= 1e-10)
{
ds = A2.w / A2.z;
dy = A1.w - ds * A1.z;
dx = A0.w - ds * A0.z - dy * A0.y;
offset_test_passed =
fabs(data[1][1] + 0.5f * (dx * fx + dy * fy + ds * fs)) > dog_threshold
&&fabs(ds) < 1.0f && fabs(dx) < 1.0f && fabs(dy) < 1.0f;
}
}
}
}
if(offset_test_passed) result = v > nmax ? 1.0 : -1.0;
}
key_finish:
if(in_image) d_key[index] = make_float4(result, dx, dy, ds);
}
void ProgramCU::ComputeKEY(CuTexImage* dog, CuTexImage* key, float Tdog, float Tedge)
{
int width = dog->GetImgWidth(), height = dog->GetImgHeight();
float Tdog1 = (GlobalUtil::_SubpixelLocalization? 0.8f : 1.0f) * Tdog;
CuTexImage* dogp = dog - 1;
CuTexImage* dogn = dog + 1;
#ifdef KEY_OFFSET_ONE
dim3 grid((width - 1 + KEY_BLOCK_DIMX - 1)/ KEY_BLOCK_DIMX, (height - 1 + KEY_BLOCK_DIMY - 1)/KEY_BLOCK_DIMY);
#else
dim3 grid((width + KEY_BLOCK_DIMX - 1)/ KEY_BLOCK_DIMX, (height + KEY_BLOCK_DIMY - 1)/KEY_BLOCK_DIMY);
#endif
dim3 block(KEY_BLOCK_DIMX, KEY_BLOCK_DIMY);
dogp->BindTexture(texP);
dog ->BindTexture(texC);
dogn->BindTexture(texN);
Tedge = (Tedge+1)*(Tedge+1)/Tedge;
ComputeKEY_Kernel<<<grid, block>>>((float4*) key->_cuData, width,
width -1, height -1, Tdog1, Tdog, Tedge, GlobalUtil::_SubpixelLocalization);
}
void __global__ InitHist_Kernel(int4* hist, int ws, int wd, int height)
{
int row = IMUL(blockIdx.y, blockDim.y) + threadIdx.y;
int col = IMUL(blockIdx.x, blockDim.x) + threadIdx.x;
if(row < height && col < wd)
{
int hidx = IMUL(row, wd) + col;
int scol = col << 2;
int sidx = IMUL(row, ws) + scol;
int v[4] = {0, 0, 0, 0};
if(row > 0 && row < height -1)
{
#pragma unroll
for(int i = 0; i < 4 ; ++i, ++scol)
{
float4 temp = tex1Dfetch(texDataF4, sidx +i);
v[i] = (scol < ws -1 && scol > 0 && temp.x!=0) ? 1 : 0;
}
}
hist[hidx] = make_int4(v[0], v[1], v[2], v[3]);
}
}
void ProgramCU::InitHistogram(CuTexImage* key, CuTexImage* hist)
{
int ws = key->GetImgWidth(), hs = key->GetImgHeight();
int wd = hist->GetImgWidth(), hd = hist->GetImgHeight();
dim3 grid((wd + HIST_INIT_WIDTH - 1)/ HIST_INIT_WIDTH, hd);
dim3 block(HIST_INIT_WIDTH, 1);
key->BindTexture(texDataF4);
InitHist_Kernel<<<grid, block>>>((int4*) hist->_cuData, ws, wd, hd);
}
void __global__ ReduceHist_Kernel(int4* d_hist, int ws, int wd, int height)
{
int row = IMUL(blockIdx.y, blockDim.y) + threadIdx.y;
int col = IMUL(blockIdx.x, blockDim.x) + threadIdx.x;
if(row < height && col < wd)
{
int hidx = IMUL(row, wd) + col;
int scol = col << 2;
int sidx = IMUL(row, ws) + scol;
int v[4] = {0, 0, 0, 0};
#pragma unroll
for(int i = 0; i < 4 && scol < ws; ++i, ++scol)
{
int4 temp = tex1Dfetch(texDataI4, sidx + i);
v[i] = temp.x + temp.y + temp.z + temp.w;
}
d_hist[hidx] = make_int4(v[0], v[1], v[2], v[3]);
}
}
void ProgramCU::ReduceHistogram(CuTexImage*hist1, CuTexImage* hist2)
{
int ws = hist1->GetImgWidth(), hs = hist1->GetImgHeight();
int wd = hist2->GetImgWidth(), hd = hist2->GetImgHeight();
int temp = (int)floor(logf(float(wd * 2/ 3)) / logf(2.0f));
const int wi = min(7, max(temp , 0));
hist1->BindTexture(texDataI4);
const int BW = 1 << wi, BH = 1 << (7 - wi);
dim3 grid((wd + BW - 1)/ BW, (hd + BH -1) / BH);
dim3 block(BW, BH);
ReduceHist_Kernel<<<grid, block>>>((int4*)hist2->_cuData, ws, wd, hd);
}
void __global__ ListGen_Kernel(int4* d_list, int width)
{
int idx1 = IMUL(blockIdx.x, blockDim.x) + threadIdx.x;
int4 pos = tex1Dfetch(texDataList, idx1);
int idx2 = IMUL(pos.y, width) + pos.x;
int4 temp = tex1Dfetch(texDataI4, idx2);
int sum1 = temp.x + temp.y;
int sum2 = sum1 + temp.z;
pos.x <<= 2;
if(pos.z >= sum2)
{
pos.x += 3;
pos.z -= sum2;
}else if(pos.z >= sum1)
{
pos.x += 2;
pos.z -= sum1;
}else if(pos.z >= temp.x)
{
pos.x += 1;
pos.z -= temp.x;
}
d_list[idx1] = pos;
}
//input list (x, y) (x, y) ....
void ProgramCU::GenerateList(CuTexImage* list, CuTexImage* hist)
{
int len = list->GetImgWidth();
list->BindTexture(texDataList);
hist->BindTexture(texDataI4);
dim3 grid((len + LISTGEN_BLOCK_DIM -1) /LISTGEN_BLOCK_DIM);
dim3 block(LISTGEN_BLOCK_DIM);
ListGen_Kernel<<<grid, block>>>((int4*) list->_cuData, hist->GetImgWidth());
}
void __global__ ComputeOrientation_Kernel(float4* d_list,
int list_len,
int width, int height,
float sigma, float sigma_step,
float gaussian_factor, float sample_factor,
int num_orientation,
int existing_keypoint,
int subpixel,
int keepsign)
{
const float ten_degree_per_radius = 5.7295779513082320876798154814105;
const float radius_per_ten_degrees = 1.0 / 5.7295779513082320876798154814105;
int idx = IMUL(blockDim.x, blockIdx.x) + threadIdx.x;
if(idx >= list_len) return;
float4 key;
if(existing_keypoint)
{
key = tex1Dfetch(texDataF4, idx);
}else
{
int4 ikey = tex1Dfetch(texDataList, idx);
key.x = ikey.x + 0.5f;
key.y = ikey.y + 0.5f;
key.z = sigma;
if(subpixel || keepsign)
{
float4 offset = tex1Dfetch(texDataF4, IMUL(width, ikey.y) + ikey.x);
if(subpixel)
{
key.x += offset.y;
key.y += offset.z;
key.z *= pow(sigma_step, offset.w);
}
if(keepsign) key.z *= offset.x;
}
}
if(num_orientation == 0)
{
key.w = 0;
d_list[idx] = key;
return;
}
float vote[37];
float gsigma = key.z * gaussian_factor;
float win = fabs(key.z) * sample_factor;
float dist_threshold = win * win + 0.5;
float factor = -0.5f / (gsigma * gsigma);
float xmin = max(1.5f, floor(key.x - win) + 0.5f);
float ymin = max(1.5f, floor(key.y - win) + 0.5f);
float xmax = min(width - 1.5f, floor(key.x + win) + 0.5f);
float ymax = min(height -1.5f, floor(key.y + win) + 0.5f);
#pragma unroll
for(int i = 0; i < 36; ++i) vote[i] = 0.0f;
for(float y = ymin; y <= ymax; y += 1.0f)
{
for(float x = xmin; x <= xmax; x += 1.0f)
{
float dx = x - key.x;
float dy = y - key.y;
float sq_dist = dx * dx + dy * dy;
if(sq_dist >= dist_threshold) continue;
float2 got = tex2D(texDataF2, x, y);
float weight = got.x * exp(sq_dist * factor);
float fidx = floor(got.y * ten_degree_per_radius);
int oidx = fidx;
if(oidx < 0) oidx += 36;
vote[oidx] += weight;
}
}
//filter the vote
const float one_third = 1.0 /3.0;
#pragma unroll
for(int i = 0; i < 6; ++i)
{
vote[36] = vote[0];
float pre = vote[35];
#pragma unroll
for(int j = 0; j < 36; ++j)
{
float temp = one_third * (pre + vote[j] + vote[j + 1]);
pre = vote[j]; vote[j] = temp;
}
}
vote[36] = vote[0];
if(num_orientation == 1 || existing_keypoint)
{
int index_max = 0;
float max_vote = vote[0];
#pragma unroll
for(int i = 1; i < 36; ++i)
{
index_max = vote[i] > max_vote? i : index_max;
max_vote = max(max_vote, vote[i]);
}
float pre = vote[index_max == 0? 35 : index_max -1];
float next = vote[index_max + 1];
float weight = max_vote;
float off = 0.5f * FDIV(next - pre, weight + weight - next - pre);
key.w = radius_per_ten_degrees * (index_max + 0.5f + off);
d_list[idx] = key;
}else
{
float max_vote = vote[0];
#pragma unroll
for(int i = 1; i < 36; ++i) max_vote = max(max_vote, vote[i]);
float vote_threshold = max_vote * 0.8f;
float pre = vote[35];
float max_rot[2], max_vot[2] = {0, 0};
int ocount = 0;
#pragma unroll
for(int i =0; i < 36; ++i)
{
float next = vote[i + 1];
if(vote[i] > vote_threshold && vote[i] > pre && vote[i] > next)
{
float di = 0.5f * FDIV(next - pre, vote[i] + vote[i] - next - pre);
float rot = i + di + 0.5f;
float weight = vote[i];
///
if(weight > max_vot[1])
{
if(weight > max_vot[0])
{
max_vot[1] = max_vot[0];
max_rot[1] = max_rot[0];
max_vot[0] = weight;
max_rot[0] = rot;
}
else
{
max_vot[1] = weight;
max_rot[1] = rot;
}
ocount ++;
}
}
pre = vote[i];
}
float fr1 = max_rot[0] / 36.0f;
if(fr1 < 0) fr1 += 1.0f;
unsigned short us1 = ocount == 0? 65535 : ((unsigned short )floor(fr1 * 65535.0f));
unsigned short us2 = 65535;
if(ocount > 1)
{
float fr2 = max_rot[1] / 36.0f;
if(fr2 < 0) fr2 += 1.0f;
us2 = (unsigned short ) floor(fr2 * 65535.0f);
}
unsigned int uspack = (us2 << 16) | us1;
key.w = __int_as_float(uspack);
d_list[idx] = key;
}
}
void ProgramCU::ComputeOrientation(CuTexImage* list, CuTexImage* got, CuTexImage*key,
float sigma, float sigma_step, int existing_keypoint)
{
int len = list->GetImgWidth();
if(len <= 0) return;
int width = got->GetImgWidth(), height = got->GetImgHeight();
if(existing_keypoint)
{
list->BindTexture(texDataF4);
}else
{
list->BindTexture(texDataList);
if(GlobalUtil::_SubpixelLocalization) key->BindTexture(texDataF4);
}
got->BindTexture2D(texDataF2);
const int block_width = len < ORIENTATION_COMPUTE_PER_BLOCK ? 16 : ORIENTATION_COMPUTE_PER_BLOCK;
dim3 grid((len + block_width -1) / block_width);
dim3 block(block_width);
ComputeOrientation_Kernel<<<grid, block>>>((float4*) list->_cuData,
len, width, height, sigma, sigma_step,
GlobalUtil::_OrientationGaussianFactor,
GlobalUtil::_OrientationGaussianFactor * GlobalUtil::_OrientationWindowFactor,
GlobalUtil::_FixedOrientation? 0 : GlobalUtil::_MaxOrientation,
existing_keypoint, GlobalUtil::_SubpixelLocalization, GlobalUtil::_KeepExtremumSign);
ProgramCU::CheckErrorCUDA("ComputeOrientation");
}
template <bool DYNAMIC_INDEXING> void __global__ ComputeDescriptor_Kernel(float4* d_des, int num,
int width, int height, float window_factor)
{
const float rpi = 4.0/ 3.14159265358979323846;
int idx = IMUL(blockIdx.x, blockDim.x) + threadIdx.x;
int fidx = idx >> 4;
if(fidx >= num) return;
float4 key = tex1Dfetch(texDataF4, fidx);
int bidx = idx& 0xf, ix = bidx & 0x3, iy = bidx >> 2;
float spt = fabs(key.z * window_factor);
float s, c; __sincosf(key.w, &s, &c);
float anglef = key.w > 3.14159265358979323846? key.w - (2.0 * 3.14159265358979323846) : key.w ;
float cspt = c * spt, sspt = s * spt;
float crspt = c / spt, srspt = s / spt;
float2 offsetpt, pt;
float xmin, ymin, xmax, ymax, bsz;
offsetpt.x = ix - 1.5f;
offsetpt.y = iy - 1.5f;
pt.x = cspt * offsetpt.x - sspt * offsetpt.y + key.x;
pt.y = cspt * offsetpt.y + sspt * offsetpt.x + key.y;
bsz = fabs(cspt) + fabs(sspt);
xmin = max(1.5f, floor(pt.x - bsz) + 0.5f);
ymin = max(1.5f, floor(pt.y - bsz) + 0.5f);
xmax = min(width - 1.5f, floor(pt.x + bsz) + 0.5f);
ymax = min(height - 1.5f, floor(pt.y + bsz) + 0.5f);
float des[9];
#pragma unroll
for(int i =0; i < 9; ++i) des[i] = 0.0f;
for(float y = ymin; y <= ymax; y += 1.0f)
{
for(float x = xmin; x <= xmax; x += 1.0f)
{
float dx = x - pt.x;
float dy = y - pt.y;
float nx = crspt * dx + srspt * dy;
float ny = crspt * dy - srspt * dx;
float nxn = fabs(nx);
float nyn = fabs(ny);
if(nxn < 1.0f && nyn < 1.0f)
{
float2 cc = tex2D(texDataF2, x, y);
float dnx = nx + offsetpt.x;
float dny = ny + offsetpt.y;
float ww = exp(-0.125f * (dnx * dnx + dny * dny));
float wx = 1.0 - nxn;
float wy = 1.0 - nyn;
float weight = ww * wx * wy * cc.x;
float theta = (anglef - cc.y) * rpi;
if(theta < 0) theta += 8.0f;
float fo = floor(theta);
int fidx = fo;
float weight1 = fo + 1.0f - theta;
float weight2 = theta - fo;
if(DYNAMIC_INDEXING)
{
des[fidx] += (weight1 * weight);
des[fidx + 1] += (weight2 * weight);
//this dynamic indexing part might be slow
}else
{
#pragma unroll
for(int k = 0; k < 8; ++k)
{
if(k == fidx)
{
des[k] += (weight1 * weight);
des[k+1] += (weight2 * weight);
}
}
}
}
}
}
des[0] += des[8];
int didx = idx << 1;
d_des[didx] = make_float4(des[0], des[1], des[2], des[3]);
d_des[didx+1] = make_float4(des[4], des[5], des[6], des[7]);
}
template <bool DYNAMIC_INDEXING> void __global__ ComputeDescriptorRECT_Kernel(float4* d_des, int num,
int width, int height, float window_factor)
{
const float rpi = 4.0/ 3.14159265358979323846;
int idx = IMUL(blockIdx.x, blockDim.x) + threadIdx.x;
int fidx = idx >> 4;
if(fidx >= num) return;
float4 key = tex1Dfetch(texDataF4, fidx);
int bidx = idx& 0xf, ix = bidx & 0x3, iy = bidx >> 2;
//float aspect_ratio = key.w / key.z;
//float aspect_sq = aspect_ratio * aspect_ratio;
float sptx = key.z * 0.25, spty = key.w * 0.25;
float xmin, ymin, xmax, ymax; float2 pt;
pt.x = sptx * (ix + 0.5f) + key.x;
pt.y = spty * (iy + 0.5f) + key.y;
xmin = max(1.5f, floor(pt.x - sptx) + 0.5f);
ymin = max(1.5f, floor(pt.y - spty) + 0.5f);
xmax = min(width - 1.5f, floor(pt.x + sptx) + 0.5f);
ymax = min(height - 1.5f, floor(pt.y + spty) + 0.5f);
float des[9];
#pragma unroll
for(int i =0; i < 9; ++i) des[i] = 0.0f;
for(float y = ymin; y <= ymax; y += 1.0f)
{
for(float x = xmin; x <= xmax; x += 1.0f)
{
float nx = (x - pt.x) / sptx;
float ny = (y - pt.y) / spty;
float nxn = fabs(nx);
float nyn = fabs(ny);
if(nxn < 1.0f && nyn < 1.0f)
{
float2 cc = tex2D(texDataF2, x, y);
float wx = 1.0 - nxn;
float wy = 1.0 - nyn;
float weight = wx * wy * cc.x;
float theta = (- cc.y) * rpi;
if(theta < 0) theta += 8.0f;
float fo = floor(theta);
int fidx = fo;
float weight1 = fo + 1.0f - theta;
float weight2 = theta - fo;
if(DYNAMIC_INDEXING)
{
des[fidx] += (weight1 * weight);
des[fidx + 1] += (weight2 * weight);
//this dynamic indexing part might be slow
}else
{
#pragma unroll
for(int k = 0; k < 8; ++k)
{
if(k == fidx)
{
des[k] += (weight1 * weight);
des[k+1] += (weight2 * weight);
}
}
}
}
}
}
des[0] += des[8];
int didx = idx << 1;
d_des[didx] = make_float4(des[0], des[1], des[2], des[3]);
d_des[didx+1] = make_float4(des[4], des[5], des[6], des[7]);
}
void __global__ NormalizeDescriptor_Kernel(float4* d_des, int num)
{
float4 temp[32];
int idx = IMUL(blockIdx.x, blockDim.x) + threadIdx.x;
if(idx >= num) return;
int sidx = idx << 5;
float norm1 = 0, norm2 = 0;
#pragma unroll
for(int i = 0; i < 32; ++i)
{
temp[i] = tex1Dfetch(texDataF4, sidx +i);
norm1 += (temp[i].x * temp[i].x + temp[i].y * temp[i].y +
temp[i].z * temp[i].z + temp[i].w * temp[i].w);
}
norm1 = rsqrt(norm1);
#pragma unroll
for(int i = 0; i < 32; ++i)
{
temp[i].x = min(0.2f, temp[i].x * norm1);
temp[i].y = min(0.2f, temp[i].y * norm1);
temp[i].z = min(0.2f, temp[i].z * norm1);
temp[i].w = min(0.2f, temp[i].w * norm1);
norm2 += (temp[i].x * temp[i].x + temp[i].y * temp[i].y +
temp[i].z * temp[i].z + temp[i].w * temp[i].w);
}
norm2 = rsqrt(norm2);
#pragma unroll
for(int i = 0; i < 32; ++i)
{
temp[i].x *= norm2; temp[i].y *= norm2;
temp[i].z *= norm2; temp[i].w *= norm2;
d_des[sidx + i] = temp[i];
}
}
void ProgramCU::ComputeDescriptor(CuTexImage*list, CuTexImage* got, CuTexImage* dtex, int rect, int stream)
{
int num = list->GetImgWidth();
int width = got->GetImgWidth();
int height = got->GetImgHeight();
dtex->InitTexture(num * 128, 1, 1);
got->BindTexture2D(texDataF2);
list->BindTexture(texDataF4);
int block_width = DESCRIPTOR_COMPUTE_BLOCK_SIZE;
dim3 grid((num * 16 + block_width -1) / block_width);
dim3 block(block_width);
if(rect)
{
if(GlobalUtil::_UseDynamicIndexing)
ComputeDescriptorRECT_Kernel<true><<<grid, block>>>((float4*) dtex->_cuData, num, width, height, GlobalUtil::_DescriptorWindowFactor);
else
ComputeDescriptorRECT_Kernel<false><<<grid, block>>>((float4*) dtex->_cuData, num, width, height, GlobalUtil::_DescriptorWindowFactor);
}else
{
if(GlobalUtil::_UseDynamicIndexing)
ComputeDescriptor_Kernel<true><<<grid, block>>>((float4*) dtex->_cuData, num, width, height, GlobalUtil::_DescriptorWindowFactor);
else
ComputeDescriptor_Kernel<false><<<grid, block>>>((float4*) dtex->_cuData, num, width, height, GlobalUtil::_DescriptorWindowFactor);
}
if(GlobalUtil::_NormalizedSIFT)
{
dtex->BindTexture(texDataF4);
const int block_width = DESCRIPTOR_NORMALIZ_PER_BLOCK;
dim3 grid((num + block_width -1) / block_width);
dim3 block(block_width);
NormalizeDescriptor_Kernel<<<grid, block>>>((float4*) dtex->_cuData, num);
}
CheckErrorCUDA("ComputeDescriptor");
}
//////////////////////////////////////////////////////
void ProgramCU::FinishCUDA()
{
cudaThreadSynchronize();
}
int ProgramCU::CheckErrorCUDA(const char* location)
{
cudaError_t e = cudaGetLastError();
if(e)
{
if(location) fprintf(stderr, "%s:\t", location);
fprintf(stderr, "%s\n", cudaGetErrorString(e));
//assert(0);
return 1;
}else
{
return 0;
}
}
void __global__ ConvertDOG_Kernel(float* d_result, int width, int height)
{
int row = (blockIdx.y << BLOCK_LOG_DIM) + threadIdx.y;
int col = (blockIdx.x << BLOCK_LOG_DIM) + threadIdx.x;
if(col < width && row < height)
{
int index = row * width + col;
float v = tex1Dfetch(texData, index);
d_result[index] = (col == 0 || row == 0 || col == width -1 || row == height -1)?
0.5 : saturate(0.5+20.0*v);
}
}
///
void ProgramCU::DisplayConvertDOG(CuTexImage* dog, CuTexImage* out)
{
if(out->_cuData == NULL) return;
int width = dog->GetImgWidth(), height = dog ->GetImgHeight();
dog->BindTexture(texData);
dim3 grid((width + BLOCK_DIM - 1)/ BLOCK_DIM, (height + BLOCK_DIM - 1)/BLOCK_DIM);
dim3 block(BLOCK_DIM, BLOCK_DIM);
ConvertDOG_Kernel<<<grid, block>>>((float*) out->_cuData, width, height);
ProgramCU::CheckErrorCUDA("DisplayConvertDOG");
}
void __global__ ConvertGRD_Kernel(float* d_result, int width, int height)
{
int row = (blockIdx.y << BLOCK_LOG_DIM) + threadIdx.y;
int col = (blockIdx.x << BLOCK_LOG_DIM) + threadIdx.x;
if(col < width && row < height)
{
int index = row * width + col;
float v = tex1Dfetch(texData, index << 1);
d_result[index] = (col == 0 || row == 0 || col == width -1 || row == height -1)?
0 : saturate(5 * v);
}
}
void ProgramCU::DisplayConvertGRD(CuTexImage* got, CuTexImage* out)
{
if(out->_cuData == NULL) return;
int width = got->GetImgWidth(), height = got ->GetImgHeight();
got->BindTexture(texData);
dim3 grid((width + BLOCK_DIM - 1)/ BLOCK_DIM, (height + BLOCK_DIM - 1)/BLOCK_DIM);
dim3 block(BLOCK_DIM, BLOCK_DIM);
ConvertGRD_Kernel<<<grid, block>>>((float*) out->_cuData, width, height);
ProgramCU::CheckErrorCUDA("DisplayConvertGRD");
}
void __global__ ConvertKEY_Kernel(float4* d_result, int width, int height)
{
int row = (blockIdx.y << BLOCK_LOG_DIM) + threadIdx.y;
int col = (blockIdx.x << BLOCK_LOG_DIM) + threadIdx.x;
if(col < width && row < height)
{
int index = row * width + col;
float4 keyv = tex1Dfetch(texDataF4, index);
int is_key = (keyv.x == 1.0f || keyv.x == -1.0f);
int inside = col > 0 && row > 0 && row < height -1 && col < width - 1;
float v = inside? saturate(0.5 + 20 * tex1Dfetch(texData, index)) : 0.5;
d_result[index] = is_key && inside ?
(keyv.x > 0? make_float4(1.0f, 0, 0, 1.0f) : make_float4(0.0f, 1.0f, 0.0f, 1.0f)):
make_float4(v, v, v, 1.0f) ;
}
}
void ProgramCU::DisplayConvertKEY(CuTexImage* key, CuTexImage* dog, CuTexImage* out)
{
if(out->_cuData == NULL) return;
int width = key->GetImgWidth(), height = key ->GetImgHeight();
dog->BindTexture(texData);
key->BindTexture(texDataF4);
dim3 grid((width + BLOCK_DIM - 1)/ BLOCK_DIM, (height + BLOCK_DIM - 1)/BLOCK_DIM);
dim3 block(BLOCK_DIM, BLOCK_DIM);
ConvertKEY_Kernel<<<grid, block>>>((float4*) out->_cuData, width, height);
}
void __global__ DisplayKeyPoint_Kernel(float4 * d_result, int num)
{
int idx = IMUL(blockIdx.x, blockDim.x) + threadIdx.x;
if(idx >= num) return;
float4 v = tex1Dfetch(texDataF4, idx);
d_result[idx] = make_float4(v.x, v.y, 0, 1.0f);
}
void ProgramCU::DisplayKeyPoint(CuTexImage* ftex, CuTexImage* out)
{
int num = ftex->GetImgWidth();
int block_width = 64;
dim3 grid((num + block_width -1) /block_width);
dim3 block(block_width);
ftex->BindTexture(texDataF4);
DisplayKeyPoint_Kernel<<<grid, block>>>((float4*) out->_cuData, num);
ProgramCU::CheckErrorCUDA("DisplayKeyPoint");
}
void __global__ DisplayKeyBox_Kernel(float4* d_result, int num)
{
int idx = IMUL(blockIdx.x, blockDim.x) + threadIdx.x;
if(idx >= num) return;
int kidx = idx / 10, vidx = idx - IMUL(kidx , 10);
float4 v = tex1Dfetch(texDataF4, kidx);
float sz = fabs(v.z * 3.0f);
///////////////////////
float s, c; __sincosf(v.w, &s, &c);
///////////////////////
float dx = vidx == 0? 0 : ((vidx <= 4 || vidx >= 9)? sz : -sz);
float dy = vidx <= 1? 0 : ((vidx <= 2 || vidx >= 7)? -sz : sz);
float4 pos;
pos.x = v.x + c * dx - s * dy;
pos.y = v.y + c * dy + s * dx;
pos.z = 0; pos.w = 1.0f;
d_result[idx] = pos;
}
void ProgramCU::DisplayKeyBox(CuTexImage* ftex, CuTexImage* out)
{
int len = ftex->GetImgWidth();
int block_width = 32;
dim3 grid((len * 10 + block_width -1) / block_width);
dim3 block(block_width);
ftex->BindTexture(texDataF4);
DisplayKeyBox_Kernel<<<grid, block>>>((float4*) out->_cuData, len * 10);
}
///////////////////////////////////////////////////////////////////
inline void CuTexImage:: BindTexture(textureReference& texRef)
{
cudaBindTexture(NULL, &texRef, _cuData, &texRef.channelDesc, _numBytes);
}
inline void CuTexImage::BindTexture2D(textureReference& texRef)
{
#if defined(SIFTGPU_ENABLE_LINEAR_TEX2D)
cudaBindTexture2D(0, &texRef, _cuData, &texRef.channelDesc, _imgWidth, _imgHeight, _imgWidth* _numChannel* sizeof(float));
#else
cudaChannelFormatDesc desc;
cudaGetChannelDesc(&desc, _cuData2D);
cudaBindTextureToArray(&texRef, _cuData2D, &desc);
#endif
}
int ProgramCU::CheckCudaDevice(int device)
{
int count = 0, device_used;
if(cudaGetDeviceCount(&count) != cudaSuccess || count <= 0)
{
ProgramCU::CheckErrorCUDA("CheckCudaDevice");
return 0;
}else if(count == 1)
{
cudaDeviceProp deviceProp;
if ( cudaGetDeviceProperties(&deviceProp, 0) != cudaSuccess ||
(deviceProp.major == 9999 && deviceProp.minor == 9999))
{
fprintf(stderr, "CheckCudaDevice: no device supporting CUDA.\n");
return 0;
}else
{
GlobalUtil::_MemCapGPU = deviceProp.totalGlobalMem / 1024;
GlobalUtil::_texMaxDimGL = 32768;
if(GlobalUtil::_verbose)
fprintf(stdout, "NOTE: changing maximum texture dimension to %d\n", GlobalUtil::_texMaxDimGL);
}
}
if(device >0 && device < count)
{
cudaSetDevice(device);
CheckErrorCUDA("cudaSetDevice\n");
}
cudaGetDevice(&device_used);
if(device != device_used)
fprintf(stderr, "\nERROR: Cannot set device to %d\n"
"\nWARNING: Use # %d device instead (out of %d)\n", device, device_used, count);
return 1;
}
////////////////////////////////////////////////////////////////////////////////////////
// siftmatch funtions
//////////////////////////////////////////////////////////////////////////////////////////
#define MULT_TBLOCK_DIMX 128
#define MULT_TBLOCK_DIMY 1
#define MULT_BLOCK_DIMX (MULT_TBLOCK_DIMX)
#define MULT_BLOCK_DIMY (8 * MULT_TBLOCK_DIMY)
texture<uint4, 1, cudaReadModeElementType> texDes1;
texture<uint4, 1, cudaReadModeElementType> texDes2;
void __global__ MultiplyDescriptor_Kernel(int* d_result, int num1, int num2, int3* d_temp)
{
int idx01 = (blockIdx.y * MULT_BLOCK_DIMY), idx02 = (blockIdx.x * MULT_BLOCK_DIMX);
int idx1 = idx01 + threadIdx.y, idx2 = idx02 + threadIdx.x;
__shared__ int data1[17 * 2 * MULT_BLOCK_DIMY];
int read_idx1 = idx01 * 8 + threadIdx.x, read_idx2 = idx2 * 8;
int col4 = threadIdx.x & 0x3, row4 = threadIdx.x >> 2;
int cache_idx1 = IMUL(row4, 17) + (col4 << 2);
///////////////////////////////////////////////////////////////
//Load feature descriptors
///////////////////////////////////////////////////////////////
#if MULT_BLOCK_DIMY == 16
uint4 v = tex1Dfetch(texDes1, read_idx1);
data1[cache_idx1] = v.x; data1[cache_idx1+1] = v.y;
data1[cache_idx1+2] = v.z; data1[cache_idx1+3] = v.w;
#elif MULT_BLOCK_DIMY == 8
if(threadIdx.x < 64)
{
uint4 v = tex1Dfetch(texDes1, read_idx1);
data1[cache_idx1] = v.x; data1[cache_idx1+1] = v.y;
data1[cache_idx1+2] = v.z; data1[cache_idx1+3] = v.w;
}
#else
#error
#endif
__syncthreads();
///
if(idx2 >= num2) return;
///////////////////////////////////////////////////////////////////////////
//compare descriptors
int results[MULT_BLOCK_DIMY];
#pragma unroll
for(int i = 0; i < MULT_BLOCK_DIMY; ++i) results[i] = 0;
#pragma unroll
for(int i = 0; i < 8; ++i)
{
uint4 v = tex1Dfetch(texDes2, read_idx2 + i);
unsigned char* p2 = (unsigned char*)(&v);
#pragma unroll
for(int k = 0; k < MULT_BLOCK_DIMY; ++k)
{
unsigned char* p1 = (unsigned char*) (data1 + k * 34 + i * 4 + (i/4));
results[k] += ( IMUL(p1[0], p2[0]) + IMUL(p1[1], p2[1])
+ IMUL(p1[2], p2[2]) + IMUL(p1[3], p2[3])
+ IMUL(p1[4], p2[4]) + IMUL(p1[5], p2[5])
+ IMUL(p1[6], p2[6]) + IMUL(p1[7], p2[7])
+ IMUL(p1[8], p2[8]) + IMUL(p1[9], p2[9])
+ IMUL(p1[10], p2[10]) + IMUL(p1[11], p2[11])
+ IMUL(p1[12], p2[12]) + IMUL(p1[13], p2[13])
+ IMUL(p1[14], p2[14]) + IMUL(p1[15], p2[15]));
}
}
int dst_idx = IMUL(idx1, num2) + idx2;
if(d_temp)
{
int3 cmp_result = make_int3(0, -1, 0);
#pragma unroll
for(int i = 0; i < MULT_BLOCK_DIMY; ++i)
{
if(idx1 + i < num1)
{
cmp_result = results[i] > cmp_result.x?
make_int3(results[i], idx1 + i, cmp_result.x) :
make_int3(cmp_result.x, cmp_result.y, max(cmp_result.z, results[i]));
d_result[dst_idx + IMUL(i, num2)] = results[i];
}
}
d_temp[ IMUL(blockIdx.y, num2) + idx2] = cmp_result;
}else
{
#pragma unroll
for(int i = 0; i < MULT_BLOCK_DIMY; ++i)
{
if(idx1 + i < num1) d_result[dst_idx + IMUL(i, num2)] = results[i];
}
}
}
void ProgramCU::MultiplyDescriptor(CuTexImage* des1, CuTexImage* des2, CuTexImage* texDot, CuTexImage* texCRT)
{
int num1 = des1->GetImgWidth() / 8;
int num2 = des2->GetImgWidth() / 8;
dim3 grid( (num2 + MULT_BLOCK_DIMX - 1)/ MULT_BLOCK_DIMX,
(num1 + MULT_BLOCK_DIMY - 1)/MULT_BLOCK_DIMY);
dim3 block(MULT_TBLOCK_DIMX, MULT_TBLOCK_DIMY);
texDot->InitTexture( num2,num1);
if(texCRT) texCRT->InitTexture(num2, (num1 + MULT_BLOCK_DIMY - 1)/MULT_BLOCK_DIMY, 32);
des1->BindTexture(texDes1);
des2->BindTexture(texDes2);
MultiplyDescriptor_Kernel<<<grid, block>>>((int*)texDot->_cuData, num1, num2,
(texCRT? (int3*)texCRT->_cuData : NULL));
ProgramCU::CheckErrorCUDA("MultiplyDescriptor");
}
texture<float, 1, cudaReadModeElementType> texLoc1;
texture<float2, 1, cudaReadModeElementType> texLoc2;
struct Matrix33{float mat[3][3];};
void __global__ MultiplyDescriptorG_Kernel(int* d_result, int num1, int num2, int3* d_temp,
Matrix33 H, float hdistmax, Matrix33 F, float fdistmax)
{
int idx01 = (blockIdx.y * MULT_BLOCK_DIMY);
int idx02 = (blockIdx.x * MULT_BLOCK_DIMX);
int idx1 = idx01 + threadIdx.y;
int idx2 = idx02 + threadIdx.x;
__shared__ int data1[17 * 2 * MULT_BLOCK_DIMY];
__shared__ float loc1[MULT_BLOCK_DIMY * 2];
int read_idx1 = idx01 * 8 + threadIdx.x ;
int read_idx2 = idx2 * 8;
int col4 = threadIdx.x & 0x3, row4 = threadIdx.x >> 2;
int cache_idx1 = IMUL(row4, 17) + (col4 << 2);
#if MULT_BLOCK_DIMY == 16
uint4 v = tex1Dfetch(texDes1, read_idx1);
data1[cache_idx1] = v.x;
data1[cache_idx1+1] = v.y;
data1[cache_idx1+2] = v.z;
data1[cache_idx1+3] = v.w;
#elif MULT_BLOCK_DIMY == 8
if(threadIdx.x < 64)
{
uint4 v = tex1Dfetch(texDes1, read_idx1);
data1[cache_idx1] = v.x;
data1[cache_idx1+1] = v.y;
data1[cache_idx1+2] = v.z;
data1[cache_idx1+3] = v.w;
}
#else
#error
#endif
__syncthreads();
if(threadIdx.x < MULT_BLOCK_DIMY * 2)
{
loc1[threadIdx.x] = tex1Dfetch(texLoc1, 2 * idx01 + threadIdx.x);
}
__syncthreads();
if(idx2 >= num2) return;
int results[MULT_BLOCK_DIMY];
/////////////////////////////////////////////////////////////////////////////////////////////
//geometric verification
/////////////////////////////////////////////////////////////////////////////////////////////
int good_count = 0;
float2 loc2 = tex1Dfetch(texLoc2, idx2);
#pragma unroll
for(int i = 0; i < MULT_BLOCK_DIMY; ++i)
{
if(idx1 + i < num1)
{
float* loci = loc1 + i * 2;
float locx = loci[0], locy = loci[1];
//homography
float x[3], diff[2];
x[0] = H.mat[0][0] * locx + H.mat[0][1] * locy + H.mat[0][2];
x[1] = H.mat[1][0] * locx + H.mat[1][1] * locy + H.mat[1][2];
x[2] = H.mat[2][0] * locx + H.mat[2][1] * locy + H.mat[2][2];
diff[0] = fabs(FDIV(x[0], x[2]) - loc2.x);
diff[1] = fabs(FDIV(x[1], x[2]) - loc2.y);
if(diff[0] < hdistmax && diff[1] < hdistmax)
{
//check fundamental matrix
float fx1[3], ftx2[3], x2fx1, se;
fx1[0] = F.mat[0][0] * locx + F.mat[0][1] * locy + F.mat[0][2];
fx1[1] = F.mat[1][0] * locx + F.mat[1][1] * locy + F.mat[1][2];
fx1[2] = F.mat[2][0] * locx + F.mat[2][1] * locy + F.mat[2][2];
ftx2[0] = F.mat[0][0] * loc2.x + F.mat[1][0] * loc2.y + F.mat[2][0];
ftx2[1] = F.mat[0][1] * loc2.x + F.mat[1][1] * loc2.y + F.mat[2][1];
//ftx2[2] = F.mat[0][2] * loc2.x + F.mat[1][2] * loc2.y + F.mat[2][2];
x2fx1 = loc2.x * fx1[0] + loc2.y * fx1[1] + fx1[2];
se = FDIV(x2fx1 * x2fx1, fx1[0] * fx1[0] + fx1[1] * fx1[1] + ftx2[0] * ftx2[0] + ftx2[1] * ftx2[1]);
results[i] = se < fdistmax? 0: -262144;
}else
{
results[i] = -262144;
}
}else
{
results[i] = -262144;
}
good_count += (results[i] >=0);
}
/////////////////////////////////////////////////////////////////////////////////////////////
///compare feature descriptors anyway
/////////////////////////////////////////////////////////////////////////////////////////////
if(good_count > 0)
{
#pragma unroll
for(int i = 0; i < 8; ++i)
{
uint4 v = tex1Dfetch(texDes2, read_idx2 + i);
unsigned char* p2 = (unsigned char*)(&v);
#pragma unroll
for(int k = 0; k < MULT_BLOCK_DIMY; ++k)
{
unsigned char* p1 = (unsigned char*) (data1 + k * 34 + i * 4 + (i/4));
results[k] += ( IMUL(p1[0], p2[0]) + IMUL(p1[1], p2[1])
+ IMUL(p1[2], p2[2]) + IMUL(p1[3], p2[3])
+ IMUL(p1[4], p2[4]) + IMUL(p1[5], p2[5])
+ IMUL(p1[6], p2[6]) + IMUL(p1[7], p2[7])
+ IMUL(p1[8], p2[8]) + IMUL(p1[9], p2[9])
+ IMUL(p1[10], p2[10]) + IMUL(p1[11], p2[11])
+ IMUL(p1[12], p2[12]) + IMUL(p1[13], p2[13])
+ IMUL(p1[14], p2[14]) + IMUL(p1[15], p2[15]));
}
}
}
int dst_idx = IMUL(idx1, num2) + idx2;
if(d_temp)
{
int3 cmp_result = make_int3(0, -1, 0);
#pragma unroll
for(int i= 0; i < MULT_BLOCK_DIMY; ++i)
{
if(idx1 + i < num1)
{
cmp_result = results[i] > cmp_result.x?
make_int3(results[i], idx1 + i, cmp_result.x) :
make_int3(cmp_result.x, cmp_result.y, max(cmp_result.z, results[i]));
d_result[dst_idx + IMUL(i, num2)] = max(results[i], 0);
}else
{
break;
}
}
d_temp[ IMUL(blockIdx.y, num2) + idx2] = cmp_result;
}else
{
#pragma unroll
for(int i = 0; i < MULT_BLOCK_DIMY; ++i)
{
if(idx1 + i < num1) d_result[dst_idx + IMUL(i, num2)] = max(results[i], 0);
else break;
}
}
}
void ProgramCU::MultiplyDescriptorG(CuTexImage* des1, CuTexImage* des2,
CuTexImage* loc1, CuTexImage* loc2, CuTexImage* texDot, CuTexImage* texCRT,
float H[3][3], float hdistmax, float F[3][3], float fdistmax)
{
int num1 = des1->GetImgWidth() / 8;
int num2 = des2->GetImgWidth() / 8;
Matrix33 MatF, MatH;
//copy the matrix
memcpy(MatF.mat, F, 9 * sizeof(float));
memcpy(MatH.mat, H, 9 * sizeof(float));
//thread blocks
dim3 grid( (num2 + MULT_BLOCK_DIMX - 1)/ MULT_BLOCK_DIMX,
(num1 + MULT_BLOCK_DIMY - 1)/MULT_BLOCK_DIMY);
dim3 block(MULT_TBLOCK_DIMX, MULT_TBLOCK_DIMY);
//intermediate results
texDot->InitTexture( num2,num1);
if(texCRT) texCRT->InitTexture( num2, (num1 + MULT_BLOCK_DIMY - 1)/MULT_BLOCK_DIMY, 3);
loc1->BindTexture(texLoc1);
loc2->BindTexture(texLoc2);
des1->BindTexture(texDes1);
des2->BindTexture(texDes2);
MultiplyDescriptorG_Kernel<<<grid, block>>>((int*)texDot->_cuData, num1, num2,
(texCRT? (int3*)texCRT->_cuData : NULL),
MatH, hdistmax, MatF, fdistmax);
}
texture<int, 1, cudaReadModeElementType> texDOT;
#define ROWMATCH_BLOCK_WIDTH 32
#define ROWMATCH_BLOCK_HEIGHT 1
void __global__ RowMatch_Kernel(int*d_dot, int* d_result, int num2, float distmax, float ratiomax)
{
#if ROWMATCH_BLOCK_HEIGHT == 1
__shared__ int dotmax[ROWMATCH_BLOCK_WIDTH];
__shared__ int dotnxt[ROWMATCH_BLOCK_WIDTH];
__shared__ int dotidx[ROWMATCH_BLOCK_WIDTH];
int row = blockIdx.y;
#else
__shared__ int x_dotmax[ROWMATCH_BLOCK_HEIGHT][ROWMATCH_BLOCK_WIDTH];
__shared__ int x_dotnxt[ROWMATCH_BLOCK_HEIGHT][ROWMATCH_BLOCK_WIDTH];
__shared__ int x_dotidx[ROWMATCH_BLOCK_HEIGHT][ROWMATCH_BLOCK_WIDTH];
int* dotmax = x_dotmax[threadIdx.y];
int* dotnxt = x_dotnxt[threadIdx.y];
int* dotidx = x_dotidx[threadIdx.y];
int row = IMUL(blockIdx.y, ROWMATCH_BLOCK_HEIGHT) + threadIdx.y;
#endif
int base_address = IMUL(row , num2);
int t_dotmax = 0, t_dotnxt = 0, t_dotidx = -1;
for(int i = 0; i < num2; i += ROWMATCH_BLOCK_WIDTH)
{
if(threadIdx.x + i < num2)
{
int v = tex1Dfetch(texDOT, base_address + threadIdx.x + i);//d_dot[base_address + threadIdx.x + i];//
bool test = v > t_dotmax;
t_dotnxt = test? t_dotmax : max(t_dotnxt, v);
t_dotidx = test? (threadIdx.x + i) : t_dotidx;
t_dotmax = test? v: t_dotmax;
}
__syncthreads();
}
dotmax[threadIdx.x] = t_dotmax;
dotnxt[threadIdx.x] = t_dotnxt;
dotidx[threadIdx.x] = t_dotidx;
__syncthreads();
#pragma unroll
for(int step = ROWMATCH_BLOCK_WIDTH/2; step >0; step /= 2)
{
if(threadIdx.x < step)
{
int v1 = dotmax[threadIdx.x], v2 = dotmax[threadIdx.x + step];
bool test = v2 > v1;
dotnxt[threadIdx.x] = test? max(v1, dotnxt[threadIdx.x + step]) :max(dotnxt[threadIdx.x], v2);
dotidx[threadIdx.x] = test? dotidx[threadIdx.x + step] : dotidx[threadIdx.x];
dotmax[threadIdx.x] = test? v2 : v1;
}
__syncthreads();
}
if(threadIdx.x == 0)
{
float dist = acos(min(dotmax[0] * 0.000003814697265625f, 1.0));
float distn = acos(min(dotnxt[0] * 0.000003814697265625f, 1.0));
//float ratio = dist / distn;
d_result[row] = (dist < distmax) && (dist < distn * ratiomax) ? dotidx[0] : -1;//? : -1;
}
}
void ProgramCU::GetRowMatch(CuTexImage* texDot, CuTexImage* texMatch, float distmax, float ratiomax)
{
int num1 = texDot->GetImgHeight();
int num2 = texDot->GetImgWidth();
dim3 grid(1, num1/ROWMATCH_BLOCK_HEIGHT);
dim3 block(ROWMATCH_BLOCK_WIDTH, ROWMATCH_BLOCK_HEIGHT);
texDot->BindTexture(texDOT);
RowMatch_Kernel<<<grid, block>>>((int*)texDot->_cuData,
(int*)texMatch->_cuData, num2, distmax, ratiomax);
}
#define COLMATCH_BLOCK_WIDTH 32
//texture<int3, 1, cudaReadModeElementType> texCT;
void __global__ ColMatch_Kernel(int3*d_crt, int* d_result, int height, int num2, float distmax, float ratiomax)
{
int col = COLMATCH_BLOCK_WIDTH * blockIdx.x + threadIdx.x;
if(col >= num2) return;
int3 result = d_crt[col];//tex1Dfetch(texCT, col);
int read_idx = col + num2;
for(int i = 1; i < height; ++i, read_idx += num2)
{
int3 temp = d_crt[read_idx];//tex1Dfetch(texCT, read_idx);
result = result.x < temp.x?
make_int3(temp.x, temp.y, max(result.x, temp.z)) :
make_int3(result.x, result.y, max(result.z, temp.x));
}
float dist = acos(min(result.x * 0.000003814697265625f, 1.0));
float distn = acos(min(result.z * 0.000003814697265625f, 1.0));
//float ratio = dist / distn;
d_result[col] = (dist < distmax) && (dist < distn * ratiomax) ? result.y : -1;//? : -1;
}
void ProgramCU::GetColMatch(CuTexImage* texCRT, CuTexImage* texMatch, float distmax, float ratiomax)
{
int height = texCRT->GetImgHeight();
int num2 = texCRT->GetImgWidth();
//texCRT->BindTexture(texCT);
dim3 grid((num2 + COLMATCH_BLOCK_WIDTH -1) / COLMATCH_BLOCK_WIDTH);
dim3 block(COLMATCH_BLOCK_WIDTH);
ColMatch_Kernel<<<grid, block>>>((int3*)texCRT->_cuData, (int*) texMatch->_cuData, height, num2, distmax, ratiomax);
}
#endif