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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) 2013, OpenCV Foundation, 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.
//
// Authors:
// * Peter Andreas Entschev, peter@entschev.com
//
//M*/
#ifdef DOUBLE_SUPPORT
#ifdef cl_amd_fp64
#pragma OPENCL EXTENSION cl_amd_fp64:enable
#elif defined (cl_khr_fp64)
#pragma OPENCL EXTENSION cl_khr_fp64:enable
#endif
#define CV_PI M_PI
#else
#define CV_PI M_PI_F
#endif
#define X_ROW 0
#define Y_ROW 1
#define RESPONSE_ROW 2
#define ANGLE_ROW 3
#define OCTAVE_ROW 4
#define SIZE_ROW 5
#define ROWS_COUNT 6
#ifdef CPU
void reduce_32(volatile __local int* smem, volatile int* val, int tid)
{
#define op(A, B) (*A)+(B)
smem[tid] = *val;
barrier(CLK_LOCAL_MEM_FENCE);
for(int i = 16; i > 0; i >>= 1)
{
if(tid < i)
{
smem[tid] = *val = op(val, smem[tid + i]);
}
barrier(CLK_LOCAL_MEM_FENCE);
}
#undef op
}
#else
void reduce_32(volatile __local int* smem, volatile int* val, int tid)
{
#define op(A, B) (*A)+(B)
smem[tid] = *val;
barrier(CLK_LOCAL_MEM_FENCE);
#ifndef WAVE_SIZE
#define WAVE_SIZE 1
#endif
if (tid < 16)
{
smem[tid] = *val = op(val, smem[tid + 16]);
#if WAVE_SIZE < 16
}
barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 8)
{
#endif
smem[tid] = *val = op(val, smem[tid + 8]);
#if WAVE_SIZE < 8
}
barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 4)
{
#endif
smem[tid] = *val = op(val, smem[tid + 4]);
#if WAVE_SIZE < 4
}
barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 2)
{
#endif
smem[tid] = *val = op(val, smem[tid + 2]);
#if WAVE_SIZE < 2
}
barrier(CLK_LOCAL_MEM_FENCE);
if (tid < 1)
{
#endif
smem[tid] = *val = op(val, smem[tid + 1]);
}
#undef WAVE_SIZE
#undef op
}
#endif
////////////////////////////////////////////////////////////////////////////////////////////////////////
// HarrisResponses
__kernel
void HarrisResponses(__global const uchar* img,
__global float* keypoints,
const int npoints,
const int blockSize,
const float harris_k,
const int img_step,
const int keypoints_step)
{
__local int smem0[8 * 32];
__local int smem1[8 * 32];
__local int smem2[8 * 32];
const int ptidx = mad24(get_group_id(0), get_local_size(1), get_local_id(1));
if (ptidx < npoints)
{
const int pt_x = keypoints[mad24(keypoints_step, X_ROW, ptidx)];
const int pt_y = keypoints[mad24(keypoints_step, Y_ROW, ptidx)];
const int r = blockSize / 2;
const int x0 = pt_x - r;
const int y0 = pt_y - r;
int a = 0, b = 0, c = 0;
for (int ind = get_local_id(0); ind < blockSize * blockSize; ind += get_local_size(0))
{
const int i = ind / blockSize;
const int j = ind % blockSize;
int center = mad24(y0+i, img_step, x0+j);
int Ix = (img[center+1] - img[center-1]) * 2 +
(img[center-img_step+1] - img[center-img_step-1]) +
(img[center+img_step+1] - img[center+img_step-1]);
int Iy = (img[center+img_step] - img[center-img_step]) * 2 +
(img[center+img_step-1] - img[center-img_step-1]) +
(img[center+img_step+1] - img[center-img_step+1]);
a += Ix * Ix;
b += Iy * Iy;
c += Ix * Iy;
}
__local int* srow0 = smem0 + get_local_id(1) * get_local_size(0);
__local int* srow1 = smem1 + get_local_id(1) * get_local_size(0);
__local int* srow2 = smem2 + get_local_id(1) * get_local_size(0);
reduce_32(srow0, &a, get_local_id(0));
reduce_32(srow1, &b, get_local_id(0));
reduce_32(srow2, &c, get_local_id(0));
if (get_local_id(0) == 0)
{
float scale = (1 << 2) * blockSize * 255.0f;
scale = 1.0f / scale;
const float scale_sq_sq = scale * scale * scale * scale;
float response = ((float)a * b - (float)c * c - harris_k * ((float)a + b) * ((float)a + b)) * scale_sq_sq;
keypoints[mad24(keypoints_step, RESPONSE_ROW, ptidx)] = response;
}
}
}
////////////////////////////////////////////////////////////////////////////////////////////////////////
// IC_Angle
__kernel
void IC_Angle(__global const uchar* img,
__global float* keypoints_,
__global const int* u_max,
const int npoints,
const int half_k,
const int img_step,
const int keypoints_step)
{
__local int smem0[8 * 32];
__local int smem1[8 * 32];
__local int* srow0 = smem0 + get_local_id(1) * get_local_size(0);
__local int* srow1 = smem1 + get_local_id(1) * get_local_size(0);
const int ptidx = mad24(get_group_id(0), get_local_size(1), get_local_id(1));
if (ptidx < npoints)
{
int m_01 = 0, m_10 = 0;
const int pt_x = keypoints_[mad24(keypoints_step, X_ROW, ptidx)];
const int pt_y = keypoints_[mad24(keypoints_step, Y_ROW, ptidx)];
// Treat the center line differently, v=0
for (int u = get_local_id(0) - half_k; u <= half_k; u += get_local_size(0))
m_10 += u * img[mad24(pt_y, img_step, pt_x+u)];
reduce_32(srow0, &m_10, get_local_id(0));
for (int v = 1; v <= half_k; ++v)
{
// Proceed over the two lines
int v_sum = 0;
int m_sum = 0;
const int d = u_max[v];
for (int u = get_local_id(0) - d; u <= d; u += get_local_size(0))
{
int val_plus = img[mad24(pt_y+v, img_step, pt_x+u)];
int val_minus = img[mad24(pt_y-v, img_step, pt_x+u)];
v_sum += (val_plus - val_minus);
m_sum += u * (val_plus + val_minus);
}
reduce_32(srow0, &v_sum, get_local_id(0));
reduce_32(srow1, &m_sum, get_local_id(0));
m_10 += m_sum;
m_01 += v * v_sum;
}
if (get_local_id(0) == 0)
{
float kp_dir = atan2((float)m_01, (float)m_10);
kp_dir += (kp_dir < 0) * (2.0f * CV_PI);
kp_dir *= 180.0f / CV_PI;
keypoints_[mad24(keypoints_step, ANGLE_ROW, ptidx)] = kp_dir;
}
}
}
////////////////////////////////////////////////////////////////////////////////////////////////////////
// computeOrbDescriptor
#define GET_VALUE(idx) \
img[mad24(loc.y + (int)round(pattern[idx] * sina + pattern[pattern_step+idx] * cosa), img_step, \
loc.x + (int)round(pattern[idx] * cosa - pattern[pattern_step+idx] * sina))]
int calcOrbDescriptor_2(__global const uchar* img,
__global const int* pattern,
const int2 loc,
const float sina,
const float cosa,
const int i,
const int img_step,
const int pattern_step)
{
pattern += 16 * i;
int t0, t1, val;
t0 = GET_VALUE(0); t1 = GET_VALUE(1);
val = t0 < t1;
t0 = GET_VALUE(2); t1 = GET_VALUE(3);
val |= (t0 < t1) << 1;
t0 = GET_VALUE(4); t1 = GET_VALUE(5);
val |= (t0 < t1) << 2;
t0 = GET_VALUE(6); t1 = GET_VALUE(7);
val |= (t0 < t1) << 3;
t0 = GET_VALUE(8); t1 = GET_VALUE(9);
val |= (t0 < t1) << 4;
t0 = GET_VALUE(10); t1 = GET_VALUE(11);
val |= (t0 < t1) << 5;
t0 = GET_VALUE(12); t1 = GET_VALUE(13);
val |= (t0 < t1) << 6;
t0 = GET_VALUE(14); t1 = GET_VALUE(15);
val |= (t0 < t1) << 7;
return val;
}
int calcOrbDescriptor_3(__global const uchar* img,
__global const int* pattern,
const int2 loc,
const float sina,
const float cosa,
const int i,
const int img_step,
const int pattern_step)
{
pattern += 12 * i;
int t0, t1, t2, val;
t0 = GET_VALUE(0); t1 = GET_VALUE(1); t2 = GET_VALUE(2);
val = t2 > t1 ? (t2 > t0 ? 2 : 0) : (t1 > t0);
t0 = GET_VALUE(3); t1 = GET_VALUE(4); t2 = GET_VALUE(5);
val |= (t2 > t1 ? (t2 > t0 ? 2 : 0) : (t1 > t0)) << 2;
t0 = GET_VALUE(6); t1 = GET_VALUE(7); t2 = GET_VALUE(8);
val |= (t2 > t1 ? (t2 > t0 ? 2 : 0) : (t1 > t0)) << 4;
t0 = GET_VALUE(9); t1 = GET_VALUE(10); t2 = GET_VALUE(11);
val |= (t2 > t1 ? (t2 > t0 ? 2 : 0) : (t1 > t0)) << 6;
return val;
}
int calcOrbDescriptor_4(__global const uchar* img,
__global const int* pattern,
const int2 loc,
const float sina,
const float cosa,
const int i,
const int img_step,
const int pattern_step)
{
pattern += 16 * i;
int t0, t1, t2, t3, k, val;
int a, b;
t0 = GET_VALUE(0); t1 = GET_VALUE(1);
t2 = GET_VALUE(2); t3 = GET_VALUE(3);
a = 0, b = 2;
if( t1 > t0 ) t0 = t1, a = 1;
if( t3 > t2 ) t2 = t3, b = 3;
k = t0 > t2 ? a : b;
val = k;
t0 = GET_VALUE(4); t1 = GET_VALUE(5);
t2 = GET_VALUE(6); t3 = GET_VALUE(7);
a = 0, b = 2;
if( t1 > t0 ) t0 = t1, a = 1;
if( t3 > t2 ) t2 = t3, b = 3;
k = t0 > t2 ? a : b;
val |= k << 2;
t0 = GET_VALUE(8); t1 = GET_VALUE(9);
t2 = GET_VALUE(10); t3 = GET_VALUE(11);
a = 0, b = 2;
if( t1 > t0 ) t0 = t1, a = 1;
if( t3 > t2 ) t2 = t3, b = 3;
k = t0 > t2 ? a : b;
val |= k << 4;
t0 = GET_VALUE(12); t1 = GET_VALUE(13);
t2 = GET_VALUE(14); t3 = GET_VALUE(15);
a = 0, b = 2;
if( t1 > t0 ) t0 = t1, a = 1;
if( t3 > t2 ) t2 = t3, b = 3;
k = t0 > t2 ? a : b;
val |= k << 6;
return val;
}
#undef GET_VALUE
__kernel
void computeOrbDescriptor(__global const uchar* img,
__global const float* keypoints,
__global const int* pattern,
__global uchar* desc,
const int npoints,
const int dsize,
const int WTA_K,
const int offset,
const int img_step,
const int keypoints_step,
const int pattern_step,
const int desc_step)
{
const int descidx = mad24(get_group_id(0), get_local_size(0), get_local_id(0));
const int ptidx = mad24(get_group_id(1), get_local_size(1), get_local_id(1));
if (ptidx < npoints && descidx < dsize)
{
int2 loc = {(int)keypoints[mad24(keypoints_step, X_ROW, ptidx)],
(int)keypoints[mad24(keypoints_step, Y_ROW, ptidx)]};
float angle = keypoints[mad24(keypoints_step, ANGLE_ROW, ptidx)];
angle *= (float)(CV_PI / 180.f);
float sina = sin(angle);
float cosa = cos(angle);
if (WTA_K == 2)
desc[mad24(ptidx+offset, desc_step, descidx)] = calcOrbDescriptor_2(img, pattern, loc, sina, cosa, descidx, img_step, pattern_step);
else if (WTA_K == 3)
desc[mad24(ptidx+offset, desc_step, descidx)] = calcOrbDescriptor_3(img, pattern, loc, sina, cosa, descidx, img_step, pattern_step);
else if (WTA_K == 4)
desc[mad24(ptidx+offset, desc_step, descidx)] = calcOrbDescriptor_4(img, pattern, loc, sina, cosa, descidx, img_step, pattern_step);
}
}
////////////////////////////////////////////////////////////////////////////////////////////////////////
// mergeLocation
__kernel
void mergeLocation(__global const float* keypoints_in,
__global float* keypoints_out,
const int npoints,
const int offset,
const float scale,
const int octave,
const float size,
const int keypoints_in_step,
const int keypoints_out_step)
{
//const int ptidx = blockIdx.x * blockDim.x + threadIdx.x;
const int ptidx = mad24(get_group_id(0), get_local_size(0), get_local_id(0));
if (ptidx < npoints)
{
float pt_x = keypoints_in[mad24(keypoints_in_step, X_ROW, ptidx)] * scale;
float pt_y = keypoints_in[mad24(keypoints_in_step, Y_ROW, ptidx)] * scale;
float response = keypoints_in[mad24(keypoints_in_step, RESPONSE_ROW, ptidx)];
float angle = keypoints_in[mad24(keypoints_in_step, ANGLE_ROW, ptidx)];
keypoints_out[mad24(keypoints_out_step, X_ROW, ptidx+offset)] = pt_x;
keypoints_out[mad24(keypoints_out_step, Y_ROW, ptidx+offset)] = pt_y;
keypoints_out[mad24(keypoints_out_step, RESPONSE_ROW, ptidx+offset)] = response;
keypoints_out[mad24(keypoints_out_step, ANGLE_ROW, ptidx+offset)] = angle;
keypoints_out[mad24(keypoints_out_step, OCTAVE_ROW, ptidx+offset)] = (float)octave;
keypoints_out[mad24(keypoints_out_step, SIZE_ROW, ptidx+offset)] = size;
}
}
__kernel
void convertRowsToChannels(__global const float* keypoints_in,
__global float* keypoints_out,
const int npoints,
const int keypoints_in_step,
const int keypoints_out_step)
{
const int ptidx = mad24(get_group_id(0), get_local_size(0), get_local_id(0));
if (ptidx < npoints)
{
const int pt_x = keypoints_in[mad24(keypoints_in_step, X_ROW, ptidx)];
const int pt_y = keypoints_in[mad24(keypoints_in_step, Y_ROW, ptidx)];
keypoints_out[ptidx*2] = pt_x;
keypoints_out[ptidx*2+1] = pt_y;
}
}
__kernel
void convertChannelsToRows(__global const float* keypoints_pos,
__global const float* keypoints_resp,
__global float* keypoints_out,
const int npoints,
const int keypoints_pos_step,
const int keypoints_resp_step,
const int keypoints_out_step)
{
const int ptidx = mad24(get_group_id(0), get_local_size(0), get_local_id(0));
if (ptidx < npoints)
{
const float pt_x = keypoints_pos[ptidx*2];
const float pt_y = keypoints_pos[ptidx*2+1];
const float resp = keypoints_resp[ptidx];
keypoints_out[mad24(keypoints_out_step, X_ROW, ptidx)] = pt_x;
keypoints_out[mad24(keypoints_out_step, Y_ROW, ptidx)] = pt_y;
keypoints_out[mad24(keypoints_out_step, RESPONSE_ROW, ptidx)] = resp;
}
}