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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#if !defined CUDA_DISABLER
#include <thrust/device_ptr.h>
#include <thrust/transform.h>
#include "opencv2/core/cuda/common.hpp"
#include "opencv2/core/cuda/emulation.hpp"
#include "opencv2/core/cuda/vec_math.hpp"
#include "opencv2/core/cuda/functional.hpp"
#include "opencv2/opencv_modules.hpp"
#ifdef HAVE_OPENCV_CUDAARITHM
namespace cv { namespace cuda { namespace device
{
namespace ght
{
__device__ int g_counter;
template <typename T, int PIXELS_PER_THREAD>
__global__ void buildEdgePointList(const PtrStepSzb edges, const PtrStep<T> dx, const PtrStep<T> dy,
unsigned int* coordList, float* thetaList)
{
__shared__ unsigned int s_coordLists[4][32 * PIXELS_PER_THREAD];
__shared__ float s_thetaLists[4][32 * PIXELS_PER_THREAD];
__shared__ int s_sizes[4];
__shared__ int s_globStart[4];
const int x = blockIdx.x * blockDim.x * PIXELS_PER_THREAD + threadIdx.x;
const int y = blockIdx.y * blockDim.y + threadIdx.y;
if (threadIdx.x == 0)
s_sizes[threadIdx.y] = 0;
__syncthreads();
if (y < edges.rows)
{
// fill the queue
const uchar* edgesRow = edges.ptr(y);
const T* dxRow = dx.ptr(y);
const T* dyRow = dy.ptr(y);
for (int i = 0, xx = x; i < PIXELS_PER_THREAD && xx < edges.cols; ++i, xx += blockDim.x)
{
const T dxVal = dxRow[xx];
const T dyVal = dyRow[xx];
if (edgesRow[xx] && (dxVal != 0 || dyVal != 0))
{
const unsigned int coord = (y << 16) | xx;
float theta = ::atan2f(dyVal, dxVal);
if (theta < 0)
theta += 2.0f * CV_PI_F;
const int qidx = Emulation::smem::atomicAdd(&s_sizes[threadIdx.y], 1);
s_coordLists[threadIdx.y][qidx] = coord;
s_thetaLists[threadIdx.y][qidx] = theta;
}
}
}
__syncthreads();
// let one thread reserve the space required in the global list
if (threadIdx.x == 0 && threadIdx.y == 0)
{
// find how many items are stored in each list
int totalSize = 0;
for (int i = 0; i < blockDim.y; ++i)
{
s_globStart[i] = totalSize;
totalSize += s_sizes[i];
}
// calculate the offset in the global list
const int globalOffset = atomicAdd(&g_counter, totalSize);
for (int i = 0; i < blockDim.y; ++i)
s_globStart[i] += globalOffset;
}
__syncthreads();
// copy local queues to global queue
const int qsize = s_sizes[threadIdx.y];
int gidx = s_globStart[threadIdx.y] + threadIdx.x;
for(int i = threadIdx.x; i < qsize; i += blockDim.x, gidx += blockDim.x)
{
coordList[gidx] = s_coordLists[threadIdx.y][i];
thetaList[gidx] = s_thetaLists[threadIdx.y][i];
}
}
template <typename T>
int buildEdgePointList_gpu(PtrStepSzb edges, PtrStepSzb dx, PtrStepSzb dy, unsigned int* coordList, float* thetaList)
{
const int PIXELS_PER_THREAD = 8;
void* counterPtr;
cudaSafeCall( cudaGetSymbolAddress(&counterPtr, g_counter) );
cudaSafeCall( cudaMemset(counterPtr, 0, sizeof(int)) );
const dim3 block(32, 4);
const dim3 grid(divUp(edges.cols, block.x * PIXELS_PER_THREAD), divUp(edges.rows, block.y));
cudaSafeCall( cudaFuncSetCacheConfig(buildEdgePointList<T, PIXELS_PER_THREAD>, cudaFuncCachePreferShared) );
buildEdgePointList<T, PIXELS_PER_THREAD><<<grid, block>>>(edges, (PtrStepSz<T>) dx, (PtrStepSz<T>) dy, coordList, thetaList);
cudaSafeCall( cudaGetLastError() );
cudaSafeCall( cudaDeviceSynchronize() );
int totalCount;
cudaSafeCall( cudaMemcpy(&totalCount, counterPtr, sizeof(int), cudaMemcpyDeviceToHost) );
return totalCount;
}
template int buildEdgePointList_gpu<short>(PtrStepSzb edges, PtrStepSzb dx, PtrStepSzb dy, unsigned int* coordList, float* thetaList);
template int buildEdgePointList_gpu<int>(PtrStepSzb edges, PtrStepSzb dx, PtrStepSzb dy, unsigned int* coordList, float* thetaList);
template int buildEdgePointList_gpu<float>(PtrStepSzb edges, PtrStepSzb dx, PtrStepSzb dy, unsigned int* coordList, float* thetaList);
__global__ void buildRTable(const unsigned int* coordList, const float* thetaList, const int pointsCount,
PtrStep<short2> r_table, int* r_sizes, int maxSize,
const short2 templCenter, const float thetaScale)
{
const int tid = blockIdx.x * blockDim.x + threadIdx.x;
if (tid >= pointsCount)
return;
const unsigned int coord = coordList[tid];
short2 p;
p.x = (coord & 0xFFFF);
p.y = (coord >> 16) & 0xFFFF;
const float theta = thetaList[tid];
const int n = __float2int_rn(theta * thetaScale);
const int ind = ::atomicAdd(r_sizes + n, 1);
if (ind < maxSize)
r_table(n, ind) = saturate_cast<short2>(p - templCenter);
}
void buildRTable_gpu(const unsigned int* coordList, const float* thetaList, int pointsCount,
PtrStepSz<short2> r_table, int* r_sizes,
short2 templCenter, int levels)
{
const dim3 block(256);
const dim3 grid(divUp(pointsCount, block.x));
const float thetaScale = levels / (2.0f * CV_PI_F);
buildRTable<<<grid, block>>>(coordList, thetaList, pointsCount, r_table, r_sizes, r_table.cols, templCenter, thetaScale);
cudaSafeCall( cudaGetLastError() );
cudaSafeCall( cudaDeviceSynchronize() );
}
////////////////////////////////////////////////////////////////////////
// Ballard_Pos
__global__ void Ballard_Pos_calcHist(const unsigned int* coordList, const float* thetaList, const int pointsCount,
const PtrStep<short2> r_table, const int* r_sizes,
PtrStepSzi hist,
const float idp, const float thetaScale)
{
const int tid = blockIdx.x * blockDim.x + threadIdx.x;
if (tid >= pointsCount)
return;
const unsigned int coord = coordList[tid];
short2 p;
p.x = (coord & 0xFFFF);
p.y = (coord >> 16) & 0xFFFF;
const float theta = thetaList[tid];
const int n = __float2int_rn(theta * thetaScale);
const short2* r_row = r_table.ptr(n);
const int r_row_size = r_sizes[n];
for (int j = 0; j < r_row_size; ++j)
{
short2 c = saturate_cast<short2>(p - r_row[j]);
c.x = __float2int_rn(c.x * idp);
c.y = __float2int_rn(c.y * idp);
if (c.x >= 0 && c.x < hist.cols - 2 && c.y >= 0 && c.y < hist.rows - 2)
::atomicAdd(hist.ptr(c.y + 1) + c.x + 1, 1);
}
}
void Ballard_Pos_calcHist_gpu(const unsigned int* coordList, const float* thetaList, int pointsCount,
PtrStepSz<short2> r_table, const int* r_sizes,
PtrStepSzi hist,
float dp, int levels)
{
const dim3 block(256);
const dim3 grid(divUp(pointsCount, block.x));
const float idp = 1.0f / dp;
const float thetaScale = levels / (2.0f * CV_PI_F);
Ballard_Pos_calcHist<<<grid, block>>>(coordList, thetaList, pointsCount, r_table, r_sizes, hist, idp, thetaScale);
cudaSafeCall( cudaGetLastError() );
cudaSafeCall( cudaDeviceSynchronize() );
}
__global__ void Ballard_Pos_findPosInHist(const PtrStepSzi hist, float4* out, int3* votes,
const int maxSize, const float dp, const int threshold)
{
const int x = blockIdx.x * blockDim.x + threadIdx.x;
const int y = blockIdx.y * blockDim.y + threadIdx.y;
if (x >= hist.cols - 2 || y >= hist.rows - 2)
return;
const int curVotes = hist(y + 1, x + 1);
if (curVotes > threshold &&
curVotes > hist(y + 1, x) &&
curVotes >= hist(y + 1, x + 2) &&
curVotes > hist(y, x + 1) &&
curVotes >= hist(y + 2, x + 1))
{
const int ind = ::atomicAdd(&g_counter, 1);
if (ind < maxSize)
{
out[ind] = make_float4(x * dp, y * dp, 1.0f, 0.0f);
votes[ind] = make_int3(curVotes, 0, 0);
}
}
}
int Ballard_Pos_findPosInHist_gpu(PtrStepSzi hist, float4* out, int3* votes, int maxSize, float dp, int threshold)
{
void* counterPtr;
cudaSafeCall( cudaGetSymbolAddress(&counterPtr, g_counter) );
cudaSafeCall( cudaMemset(counterPtr, 0, sizeof(int)) );
const dim3 block(32, 8);
const dim3 grid(divUp(hist.cols - 2, block.x), divUp(hist.rows - 2, block.y));
cudaSafeCall( cudaFuncSetCacheConfig(Ballard_Pos_findPosInHist, cudaFuncCachePreferL1) );
Ballard_Pos_findPosInHist<<<grid, block>>>(hist, out, votes, maxSize, dp, threshold);
cudaSafeCall( cudaGetLastError() );
cudaSafeCall( cudaDeviceSynchronize() );
int totalCount;
cudaSafeCall( cudaMemcpy(&totalCount, counterPtr, sizeof(int), cudaMemcpyDeviceToHost) );
totalCount = ::min(totalCount, maxSize);
return totalCount;
}
////////////////////////////////////////////////////////////////////////
// Guil_Full
struct FeatureTable
{
uchar* p1_pos_data;
size_t p1_pos_step;
uchar* p1_theta_data;
size_t p1_theta_step;
uchar* p2_pos_data;
size_t p2_pos_step;
uchar* d12_data;
size_t d12_step;
uchar* r1_data;
size_t r1_step;
uchar* r2_data;
size_t r2_step;
};
__constant__ FeatureTable c_templFeatures;
__constant__ FeatureTable c_imageFeatures;
void Guil_Full_setTemplFeatures(PtrStepb p1_pos, PtrStepb p1_theta, PtrStepb p2_pos, PtrStepb d12, PtrStepb r1, PtrStepb r2)
{
FeatureTable tbl;
tbl.p1_pos_data = p1_pos.data;
tbl.p1_pos_step = p1_pos.step;
tbl.p1_theta_data = p1_theta.data;
tbl.p1_theta_step = p1_theta.step;
tbl.p2_pos_data = p2_pos.data;
tbl.p2_pos_step = p2_pos.step;
tbl.d12_data = d12.data;
tbl.d12_step = d12.step;
tbl.r1_data = r1.data;
tbl.r1_step = r1.step;
tbl.r2_data = r2.data;
tbl.r2_step = r2.step;
cudaSafeCall( cudaMemcpyToSymbol(c_templFeatures, &tbl, sizeof(FeatureTable)) );
}
void Guil_Full_setImageFeatures(PtrStepb p1_pos, PtrStepb p1_theta, PtrStepb p2_pos, PtrStepb d12, PtrStepb r1, PtrStepb r2)
{
FeatureTable tbl;
tbl.p1_pos_data = p1_pos.data;
tbl.p1_pos_step = p1_pos.step;
tbl.p1_theta_data = p1_theta.data;
tbl.p1_theta_step = p1_theta.step;
tbl.p2_pos_data = p2_pos.data;
tbl.p2_pos_step = p2_pos.step;
tbl.d12_data = d12.data;
tbl.d12_step = d12.step;
tbl.r1_data = r1.data;
tbl.r1_step = r1.step;
tbl.r2_data = r2.data;
tbl.r2_step = r2.step;
cudaSafeCall( cudaMemcpyToSymbol(c_imageFeatures, &tbl, sizeof(FeatureTable)) );
}
struct TemplFeatureTable
{
static __device__ float2* p1_pos(int n)
{
return (float2*)(c_templFeatures.p1_pos_data + n * c_templFeatures.p1_pos_step);
}
static __device__ float* p1_theta(int n)
{
return (float*)(c_templFeatures.p1_theta_data + n * c_templFeatures.p1_theta_step);
}
static __device__ float2* p2_pos(int n)
{
return (float2*)(c_templFeatures.p2_pos_data + n * c_templFeatures.p2_pos_step);
}
static __device__ float* d12(int n)
{
return (float*)(c_templFeatures.d12_data + n * c_templFeatures.d12_step);
}
static __device__ float2* r1(int n)
{
return (float2*)(c_templFeatures.r1_data + n * c_templFeatures.r1_step);
}
static __device__ float2* r2(int n)
{
return (float2*)(c_templFeatures.r2_data + n * c_templFeatures.r2_step);
}
};
struct ImageFeatureTable
{
static __device__ float2* p1_pos(int n)
{
return (float2*)(c_imageFeatures.p1_pos_data + n * c_imageFeatures.p1_pos_step);
}
static __device__ float* p1_theta(int n)
{
return (float*)(c_imageFeatures.p1_theta_data + n * c_imageFeatures.p1_theta_step);
}
static __device__ float2* p2_pos(int n)
{
return (float2*)(c_imageFeatures.p2_pos_data + n * c_imageFeatures.p2_pos_step);
}
static __device__ float* d12(int n)
{
return (float*)(c_imageFeatures.d12_data + n * c_imageFeatures.d12_step);
}
static __device__ float2* r1(int n)
{
return (float2*)(c_imageFeatures.r1_data + n * c_imageFeatures.r1_step);
}
static __device__ float2* r2(int n)
{
return (float2*)(c_imageFeatures.r2_data + n * c_imageFeatures.r2_step);
}
};
__device__ float clampAngle(float a)
{
float res = a;
while (res > 2.0f * CV_PI_F)
res -= 2.0f * CV_PI_F;
while (res < 0.0f)
res += 2.0f * CV_PI_F;
return res;
}
__device__ bool angleEq(float a, float b, float eps)
{
return (::fabs(clampAngle(a - b)) <= eps);
}
template <class FT, bool isTempl>
__global__ void Guil_Full_buildFeatureList(const unsigned int* coordList, const float* thetaList, const int pointsCount,
int* sizes, const int maxSize,
const float xi, const float angleEpsilon, const float alphaScale,
const float2 center, const float maxDist)
{
const float p1_theta = thetaList[blockIdx.x];
const unsigned int coord1 = coordList[blockIdx.x];
float2 p1_pos;
p1_pos.x = (coord1 & 0xFFFF);
p1_pos.y = (coord1 >> 16) & 0xFFFF;
for (int i = threadIdx.x; i < pointsCount; i += blockDim.x)
{
const float p2_theta = thetaList[i];
const unsigned int coord2 = coordList[i];
float2 p2_pos;
p2_pos.x = (coord2 & 0xFFFF);
p2_pos.y = (coord2 >> 16) & 0xFFFF;
if (angleEq(p1_theta - p2_theta, xi, angleEpsilon))
{
const float2 d = p1_pos - p2_pos;
float alpha12 = clampAngle(::atan2(d.y, d.x) - p1_theta);
float d12 = ::sqrtf(d.x * d.x + d.y * d.y);
if (d12 > maxDist)
continue;
float2 r1 = p1_pos - center;
float2 r2 = p2_pos - center;
const int n = __float2int_rn(alpha12 * alphaScale);
const int ind = ::atomicAdd(sizes + n, 1);
if (ind < maxSize)
{
if (!isTempl)
{
FT::p1_pos(n)[ind] = p1_pos;
FT::p2_pos(n)[ind] = p2_pos;
}
FT::p1_theta(n)[ind] = p1_theta;
FT::d12(n)[ind] = d12;
if (isTempl)
{
FT::r1(n)[ind] = r1;
FT::r2(n)[ind] = r2;
}
}
}
}
}
template <class FT, bool isTempl>
void Guil_Full_buildFeatureList_caller(const unsigned int* coordList, const float* thetaList, int pointsCount,
int* sizes, int maxSize,
float xi, float angleEpsilon, int levels,
float2 center, float maxDist)
{
const dim3 block(256);
const dim3 grid(pointsCount);
const float alphaScale = levels / (2.0f * CV_PI_F);
Guil_Full_buildFeatureList<FT, isTempl><<<grid, block>>>(coordList, thetaList, pointsCount,
sizes, maxSize,
xi * (CV_PI_F / 180.0f), angleEpsilon * (CV_PI_F / 180.0f), alphaScale,
center, maxDist);
cudaSafeCall( cudaGetLastError() );
cudaSafeCall( cudaDeviceSynchronize() );
thrust::device_ptr<int> sizesPtr(sizes);
thrust::transform(sizesPtr, sizesPtr + levels + 1, sizesPtr, device::bind2nd(device::minimum<int>(), maxSize));
}
void Guil_Full_buildTemplFeatureList_gpu(const unsigned int* coordList, const float* thetaList, int pointsCount,
int* sizes, int maxSize,
float xi, float angleEpsilon, int levels,
float2 center, float maxDist)
{
Guil_Full_buildFeatureList_caller<TemplFeatureTable, true>(coordList, thetaList, pointsCount,
sizes, maxSize,
xi, angleEpsilon, levels,
center, maxDist);
}
void Guil_Full_buildImageFeatureList_gpu(const unsigned int* coordList, const float* thetaList, int pointsCount,
int* sizes, int maxSize,
float xi, float angleEpsilon, int levels,
float2 center, float maxDist)
{
Guil_Full_buildFeatureList_caller<ImageFeatureTable, false>(coordList, thetaList, pointsCount,
sizes, maxSize,
xi, angleEpsilon, levels,
center, maxDist);
}
__global__ void Guil_Full_calcOHist(const int* templSizes, const int* imageSizes, int* OHist,
const float minAngle, const float maxAngle, const float iAngleStep, const int angleRange)
{
extern __shared__ int s_OHist[];
for (int i = threadIdx.x; i <= angleRange; i += blockDim.x)
s_OHist[i] = 0;
__syncthreads();
const int tIdx = blockIdx.x;
const int level = blockIdx.y;
const int tSize = templSizes[level];
if (tIdx < tSize)
{
const int imSize = imageSizes[level];
const float t_p1_theta = TemplFeatureTable::p1_theta(level)[tIdx];
for (int i = threadIdx.x; i < imSize; i += blockDim.x)
{
const float im_p1_theta = ImageFeatureTable::p1_theta(level)[i];
const float angle = clampAngle(im_p1_theta - t_p1_theta);
if (angle >= minAngle && angle <= maxAngle)
{
const int n = __float2int_rn((angle - minAngle) * iAngleStep);
Emulation::smem::atomicAdd(&s_OHist[n], 1);
}
}
}
__syncthreads();
for (int i = threadIdx.x; i <= angleRange; i += blockDim.x)
::atomicAdd(OHist + i, s_OHist[i]);
}
void Guil_Full_calcOHist_gpu(const int* templSizes, const int* imageSizes, int* OHist,
float minAngle, float maxAngle, float angleStep, int angleRange,
int levels, int tMaxSize)
{
const dim3 block(256);
const dim3 grid(tMaxSize, levels + 1);
minAngle *= (CV_PI_F / 180.0f);
maxAngle *= (CV_PI_F / 180.0f);
angleStep *= (CV_PI_F / 180.0f);
const size_t smemSize = (angleRange + 1) * sizeof(float);
Guil_Full_calcOHist<<<grid, block, smemSize>>>(templSizes, imageSizes, OHist,
minAngle, maxAngle, 1.0f / angleStep, angleRange);
cudaSafeCall( cudaGetLastError() );
cudaSafeCall( cudaDeviceSynchronize() );
}
__global__ void Guil_Full_calcSHist(const int* templSizes, const int* imageSizes, int* SHist,
const float angle, const float angleEpsilon,
const float minScale, const float maxScale, const float iScaleStep, const int scaleRange)
{
extern __shared__ int s_SHist[];
for (int i = threadIdx.x; i <= scaleRange; i += blockDim.x)
s_SHist[i] = 0;
__syncthreads();
const int tIdx = blockIdx.x;
const int level = blockIdx.y;
const int tSize = templSizes[level];
if (tIdx < tSize)
{
const int imSize = imageSizes[level];
const float t_p1_theta = TemplFeatureTable::p1_theta(level)[tIdx] + angle;
const float t_d12 = TemplFeatureTable::d12(level)[tIdx] + angle;
for (int i = threadIdx.x; i < imSize; i += blockDim.x)
{
const float im_p1_theta = ImageFeatureTable::p1_theta(level)[i];
const float im_d12 = ImageFeatureTable::d12(level)[i];
if (angleEq(im_p1_theta, t_p1_theta, angleEpsilon))
{
const float scale = im_d12 / t_d12;
if (scale >= minScale && scale <= maxScale)
{
const int s = __float2int_rn((scale - minScale) * iScaleStep);
Emulation::smem::atomicAdd(&s_SHist[s], 1);
}
}
}
}
__syncthreads();
for (int i = threadIdx.x; i <= scaleRange; i += blockDim.x)
::atomicAdd(SHist + i, s_SHist[i]);
}
void Guil_Full_calcSHist_gpu(const int* templSizes, const int* imageSizes, int* SHist,
float angle, float angleEpsilon,
float minScale, float maxScale, float iScaleStep, int scaleRange,
int levels, int tMaxSize)
{
const dim3 block(256);
const dim3 grid(tMaxSize, levels + 1);
angle *= (CV_PI_F / 180.0f);
angleEpsilon *= (CV_PI_F / 180.0f);
const size_t smemSize = (scaleRange + 1) * sizeof(float);
Guil_Full_calcSHist<<<grid, block, smemSize>>>(templSizes, imageSizes, SHist,
angle, angleEpsilon,
minScale, maxScale, iScaleStep, scaleRange);
cudaSafeCall( cudaGetLastError() );
cudaSafeCall( cudaDeviceSynchronize() );
}
__global__ void Guil_Full_calcPHist(const int* templSizes, const int* imageSizes, PtrStepSzi PHist,
const float angle, const float sinVal, const float cosVal, const float angleEpsilon, const float scale,
const float idp)
{
const int tIdx = blockIdx.x;
const int level = blockIdx.y;
const int tSize = templSizes[level];
if (tIdx < tSize)
{
const int imSize = imageSizes[level];
const float t_p1_theta = TemplFeatureTable::p1_theta(level)[tIdx] + angle;
float2 r1 = TemplFeatureTable::r1(level)[tIdx];
float2 r2 = TemplFeatureTable::r2(level)[tIdx];
r1 = r1 * scale;
r2 = r2 * scale;
r1 = make_float2(cosVal * r1.x - sinVal * r1.y, sinVal * r1.x + cosVal * r1.y);
r2 = make_float2(cosVal * r2.x - sinVal * r2.y, sinVal * r2.x + cosVal * r2.y);
for (int i = threadIdx.x; i < imSize; i += blockDim.x)
{
const float im_p1_theta = ImageFeatureTable::p1_theta(level)[i];
const float2 im_p1_pos = ImageFeatureTable::p1_pos(level)[i];
const float2 im_p2_pos = ImageFeatureTable::p2_pos(level)[i];
if (angleEq(im_p1_theta, t_p1_theta, angleEpsilon))
{
float2 c1, c2;
c1 = im_p1_pos - r1;
c1 = c1 * idp;
c2 = im_p2_pos - r2;
c2 = c2 * idp;
if (::fabs(c1.x - c2.x) > 1 || ::fabs(c1.y - c2.y) > 1)
continue;
if (c1.y >= 0 && c1.y < PHist.rows - 2 && c1.x >= 0 && c1.x < PHist.cols - 2)
::atomicAdd(PHist.ptr(__float2int_rn(c1.y) + 1) + __float2int_rn(c1.x) + 1, 1);
}
}
}
}
void Guil_Full_calcPHist_gpu(const int* templSizes, const int* imageSizes, PtrStepSzi PHist,
float angle, float angleEpsilon, float scale,
float dp,
int levels, int tMaxSize)
{
const dim3 block(256);
const dim3 grid(tMaxSize, levels + 1);
angle *= (CV_PI_F / 180.0f);
angleEpsilon *= (CV_PI_F / 180.0f);
const float sinVal = ::sinf(angle);
const float cosVal = ::cosf(angle);
cudaSafeCall( cudaFuncSetCacheConfig(Guil_Full_calcPHist, cudaFuncCachePreferL1) );
Guil_Full_calcPHist<<<grid, block>>>(templSizes, imageSizes, PHist,
angle, sinVal, cosVal, angleEpsilon, scale,
1.0f / dp);
cudaSafeCall( cudaGetLastError() );
cudaSafeCall( cudaDeviceSynchronize() );
}
__global__ void Guil_Full_findPosInHist(const PtrStepSzi hist, float4* out, int3* votes, const int maxSize,
const float angle, const int angleVotes, const float scale, const int scaleVotes,
const float dp, const int threshold)
{
const int x = blockIdx.x * blockDim.x + threadIdx.x;
const int y = blockIdx.y * blockDim.y + threadIdx.y;
if (x >= hist.cols - 2 || y >= hist.rows - 2)
return;
const int curVotes = hist(y + 1, x + 1);
if (curVotes > threshold &&
curVotes > hist(y + 1, x) &&
curVotes >= hist(y + 1, x + 2) &&
curVotes > hist(y, x + 1) &&
curVotes >= hist(y + 2, x + 1))
{
const int ind = ::atomicAdd(&g_counter, 1);
if (ind < maxSize)
{
out[ind] = make_float4(x * dp, y * dp, scale, angle);
votes[ind] = make_int3(curVotes, scaleVotes, angleVotes);
}
}
}
int Guil_Full_findPosInHist_gpu(PtrStepSzi hist, float4* out, int3* votes, int curSize, int maxSize,
float angle, int angleVotes, float scale, int scaleVotes,
float dp, int threshold)
{
void* counterPtr;
cudaSafeCall( cudaGetSymbolAddress(&counterPtr, g_counter) );
cudaSafeCall( cudaMemcpy(counterPtr, &curSize, sizeof(int), cudaMemcpyHostToDevice) );
const dim3 block(32, 8);
const dim3 grid(divUp(hist.cols - 2, block.x), divUp(hist.rows - 2, block.y));
cudaSafeCall( cudaFuncSetCacheConfig(Guil_Full_findPosInHist, cudaFuncCachePreferL1) );
Guil_Full_findPosInHist<<<grid, block>>>(hist, out, votes, maxSize,
angle, angleVotes, scale, scaleVotes,
dp, threshold);
cudaSafeCall( cudaGetLastError() );
cudaSafeCall( cudaDeviceSynchronize() );
int totalCount;
cudaSafeCall( cudaMemcpy(&totalCount, counterPtr, sizeof(int), cudaMemcpyDeviceToHost) );
totalCount = ::min(totalCount, maxSize);
return totalCount;
}
}
}}}
#endif // HAVE_OPENCV_CUDAARITHM
#endif /* CUDA_DISABLER */