--- /dev/null
+/*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;
+ }
+}
--- /dev/null
+/*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*/
+
+#include "precomp.hpp"
+#include "opencl_kernels.hpp"
+
+using namespace cv;
+using namespace cv::ocl;
+
+namespace
+{
+ const float HARRIS_K = 0.04f;
+ const int DESCRIPTOR_SIZE = 32;
+
+ const int bit_pattern_31_[256 * 4] =
+ {
+ 8,-3, 9,5/*mean (0), correlation (0)*/,
+ 4,2, 7,-12/*mean (1.12461e-05), correlation (0.0437584)*/,
+ -11,9, -8,2/*mean (3.37382e-05), correlation (0.0617409)*/,
+ 7,-12, 12,-13/*mean (5.62303e-05), correlation (0.0636977)*/,
+ 2,-13, 2,12/*mean (0.000134953), correlation (0.085099)*/,
+ 1,-7, 1,6/*mean (0.000528565), correlation (0.0857175)*/,
+ -2,-10, -2,-4/*mean (0.0188821), correlation (0.0985774)*/,
+ -13,-13, -11,-8/*mean (0.0363135), correlation (0.0899616)*/,
+ -13,-3, -12,-9/*mean (0.121806), correlation (0.099849)*/,
+ 10,4, 11,9/*mean (0.122065), correlation (0.093285)*/,
+ -13,-8, -8,-9/*mean (0.162787), correlation (0.0942748)*/,
+ -11,7, -9,12/*mean (0.21561), correlation (0.0974438)*/,
+ 7,7, 12,6/*mean (0.160583), correlation (0.130064)*/,
+ -4,-5, -3,0/*mean (0.228171), correlation (0.132998)*/,
+ -13,2, -12,-3/*mean (0.00997526), correlation (0.145926)*/,
+ -9,0, -7,5/*mean (0.198234), correlation (0.143636)*/,
+ 12,-6, 12,-1/*mean (0.0676226), correlation (0.16689)*/,
+ -3,6, -2,12/*mean (0.166847), correlation (0.171682)*/,
+ -6,-13, -4,-8/*mean (0.101215), correlation (0.179716)*/,
+ 11,-13, 12,-8/*mean (0.200641), correlation (0.192279)*/,
+ 4,7, 5,1/*mean (0.205106), correlation (0.186848)*/,
+ 5,-3, 10,-3/*mean (0.234908), correlation (0.192319)*/,
+ 3,-7, 6,12/*mean (0.0709964), correlation (0.210872)*/,
+ -8,-7, -6,-2/*mean (0.0939834), correlation (0.212589)*/,
+ -2,11, -1,-10/*mean (0.127778), correlation (0.20866)*/,
+ -13,12, -8,10/*mean (0.14783), correlation (0.206356)*/,
+ -7,3, -5,-3/*mean (0.182141), correlation (0.198942)*/,
+ -4,2, -3,7/*mean (0.188237), correlation (0.21384)*/,
+ -10,-12, -6,11/*mean (0.14865), correlation (0.23571)*/,
+ 5,-12, 6,-7/*mean (0.222312), correlation (0.23324)*/,
+ 5,-6, 7,-1/*mean (0.229082), correlation (0.23389)*/,
+ 1,0, 4,-5/*mean (0.241577), correlation (0.215286)*/,
+ 9,11, 11,-13/*mean (0.00338507), correlation (0.251373)*/,
+ 4,7, 4,12/*mean (0.131005), correlation (0.257622)*/,
+ 2,-1, 4,4/*mean (0.152755), correlation (0.255205)*/,
+ -4,-12, -2,7/*mean (0.182771), correlation (0.244867)*/,
+ -8,-5, -7,-10/*mean (0.186898), correlation (0.23901)*/,
+ 4,11, 9,12/*mean (0.226226), correlation (0.258255)*/,
+ 0,-8, 1,-13/*mean (0.0897886), correlation (0.274827)*/,
+ -13,-2, -8,2/*mean (0.148774), correlation (0.28065)*/,
+ -3,-2, -2,3/*mean (0.153048), correlation (0.283063)*/,
+ -6,9, -4,-9/*mean (0.169523), correlation (0.278248)*/,
+ 8,12, 10,7/*mean (0.225337), correlation (0.282851)*/,
+ 0,9, 1,3/*mean (0.226687), correlation (0.278734)*/,
+ 7,-5, 11,-10/*mean (0.00693882), correlation (0.305161)*/,
+ -13,-6, -11,0/*mean (0.0227283), correlation (0.300181)*/,
+ 10,7, 12,1/*mean (0.125517), correlation (0.31089)*/,
+ -6,-3, -6,12/*mean (0.131748), correlation (0.312779)*/,
+ 10,-9, 12,-4/*mean (0.144827), correlation (0.292797)*/,
+ -13,8, -8,-12/*mean (0.149202), correlation (0.308918)*/,
+ -13,0, -8,-4/*mean (0.160909), correlation (0.310013)*/,
+ 3,3, 7,8/*mean (0.177755), correlation (0.309394)*/,
+ 5,7, 10,-7/*mean (0.212337), correlation (0.310315)*/,
+ -1,7, 1,-12/*mean (0.214429), correlation (0.311933)*/,
+ 3,-10, 5,6/*mean (0.235807), correlation (0.313104)*/,
+ 2,-4, 3,-10/*mean (0.00494827), correlation (0.344948)*/,
+ -13,0, -13,5/*mean (0.0549145), correlation (0.344675)*/,
+ -13,-7, -12,12/*mean (0.103385), correlation (0.342715)*/,
+ -13,3, -11,8/*mean (0.134222), correlation (0.322922)*/,
+ -7,12, -4,7/*mean (0.153284), correlation (0.337061)*/,
+ 6,-10, 12,8/*mean (0.154881), correlation (0.329257)*/,
+ -9,-1, -7,-6/*mean (0.200967), correlation (0.33312)*/,
+ -2,-5, 0,12/*mean (0.201518), correlation (0.340635)*/,
+ -12,5, -7,5/*mean (0.207805), correlation (0.335631)*/,
+ 3,-10, 8,-13/*mean (0.224438), correlation (0.34504)*/,
+ -7,-7, -4,5/*mean (0.239361), correlation (0.338053)*/,
+ -3,-2, -1,-7/*mean (0.240744), correlation (0.344322)*/,
+ 2,9, 5,-11/*mean (0.242949), correlation (0.34145)*/,
+ -11,-13, -5,-13/*mean (0.244028), correlation (0.336861)*/,
+ -1,6, 0,-1/*mean (0.247571), correlation (0.343684)*/,
+ 5,-3, 5,2/*mean (0.000697256), correlation (0.357265)*/,
+ -4,-13, -4,12/*mean (0.00213675), correlation (0.373827)*/,
+ -9,-6, -9,6/*mean (0.0126856), correlation (0.373938)*/,
+ -12,-10, -8,-4/*mean (0.0152497), correlation (0.364237)*/,
+ 10,2, 12,-3/*mean (0.0299933), correlation (0.345292)*/,
+ 7,12, 12,12/*mean (0.0307242), correlation (0.366299)*/,
+ -7,-13, -6,5/*mean (0.0534975), correlation (0.368357)*/,
+ -4,9, -3,4/*mean (0.099865), correlation (0.372276)*/,
+ 7,-1, 12,2/*mean (0.117083), correlation (0.364529)*/,
+ -7,6, -5,1/*mean (0.126125), correlation (0.369606)*/,
+ -13,11, -12,5/*mean (0.130364), correlation (0.358502)*/,
+ -3,7, -2,-6/*mean (0.131691), correlation (0.375531)*/,
+ 7,-8, 12,-7/*mean (0.160166), correlation (0.379508)*/,
+ -13,-7, -11,-12/*mean (0.167848), correlation (0.353343)*/,
+ 1,-3, 12,12/*mean (0.183378), correlation (0.371916)*/,
+ 2,-6, 3,0/*mean (0.228711), correlation (0.371761)*/,
+ -4,3, -2,-13/*mean (0.247211), correlation (0.364063)*/,
+ -1,-13, 1,9/*mean (0.249325), correlation (0.378139)*/,
+ 7,1, 8,-6/*mean (0.000652272), correlation (0.411682)*/,
+ 1,-1, 3,12/*mean (0.00248538), correlation (0.392988)*/,
+ 9,1, 12,6/*mean (0.0206815), correlation (0.386106)*/,
+ -1,-9, -1,3/*mean (0.0364485), correlation (0.410752)*/,
+ -13,-13, -10,5/*mean (0.0376068), correlation (0.398374)*/,
+ 7,7, 10,12/*mean (0.0424202), correlation (0.405663)*/,
+ 12,-5, 12,9/*mean (0.0942645), correlation (0.410422)*/,
+ 6,3, 7,11/*mean (0.1074), correlation (0.413224)*/,
+ 5,-13, 6,10/*mean (0.109256), correlation (0.408646)*/,
+ 2,-12, 2,3/*mean (0.131691), correlation (0.416076)*/,
+ 3,8, 4,-6/*mean (0.165081), correlation (0.417569)*/,
+ 2,6, 12,-13/*mean (0.171874), correlation (0.408471)*/,
+ 9,-12, 10,3/*mean (0.175146), correlation (0.41296)*/,
+ -8,4, -7,9/*mean (0.183682), correlation (0.402956)*/,
+ -11,12, -4,-6/*mean (0.184672), correlation (0.416125)*/,
+ 1,12, 2,-8/*mean (0.191487), correlation (0.386696)*/,
+ 6,-9, 7,-4/*mean (0.192668), correlation (0.394771)*/,
+ 2,3, 3,-2/*mean (0.200157), correlation (0.408303)*/,
+ 6,3, 11,0/*mean (0.204588), correlation (0.411762)*/,
+ 3,-3, 8,-8/*mean (0.205904), correlation (0.416294)*/,
+ 7,8, 9,3/*mean (0.213237), correlation (0.409306)*/,
+ -11,-5, -6,-4/*mean (0.243444), correlation (0.395069)*/,
+ -10,11, -5,10/*mean (0.247672), correlation (0.413392)*/,
+ -5,-8, -3,12/*mean (0.24774), correlation (0.411416)*/,
+ -10,5, -9,0/*mean (0.00213675), correlation (0.454003)*/,
+ 8,-1, 12,-6/*mean (0.0293635), correlation (0.455368)*/,
+ 4,-6, 6,-11/*mean (0.0404971), correlation (0.457393)*/,
+ -10,12, -8,7/*mean (0.0481107), correlation (0.448364)*/,
+ 4,-2, 6,7/*mean (0.050641), correlation (0.455019)*/,
+ -2,0, -2,12/*mean (0.0525978), correlation (0.44338)*/,
+ -5,-8, -5,2/*mean (0.0629667), correlation (0.457096)*/,
+ 7,-6, 10,12/*mean (0.0653846), correlation (0.445623)*/,
+ -9,-13, -8,-8/*mean (0.0858749), correlation (0.449789)*/,
+ -5,-13, -5,-2/*mean (0.122402), correlation (0.450201)*/,
+ 8,-8, 9,-13/*mean (0.125416), correlation (0.453224)*/,
+ -9,-11, -9,0/*mean (0.130128), correlation (0.458724)*/,
+ 1,-8, 1,-2/*mean (0.132467), correlation (0.440133)*/,
+ 7,-4, 9,1/*mean (0.132692), correlation (0.454)*/,
+ -2,1, -1,-4/*mean (0.135695), correlation (0.455739)*/,
+ 11,-6, 12,-11/*mean (0.142904), correlation (0.446114)*/,
+ -12,-9, -6,4/*mean (0.146165), correlation (0.451473)*/,
+ 3,7, 7,12/*mean (0.147627), correlation (0.456643)*/,
+ 5,5, 10,8/*mean (0.152901), correlation (0.455036)*/,
+ 0,-4, 2,8/*mean (0.167083), correlation (0.459315)*/,
+ -9,12, -5,-13/*mean (0.173234), correlation (0.454706)*/,
+ 0,7, 2,12/*mean (0.18312), correlation (0.433855)*/,
+ -1,2, 1,7/*mean (0.185504), correlation (0.443838)*/,
+ 5,11, 7,-9/*mean (0.185706), correlation (0.451123)*/,
+ 3,5, 6,-8/*mean (0.188968), correlation (0.455808)*/,
+ -13,-4, -8,9/*mean (0.191667), correlation (0.459128)*/,
+ -5,9, -3,-3/*mean (0.193196), correlation (0.458364)*/,
+ -4,-7, -3,-12/*mean (0.196536), correlation (0.455782)*/,
+ 6,5, 8,0/*mean (0.1972), correlation (0.450481)*/,
+ -7,6, -6,12/*mean (0.199438), correlation (0.458156)*/,
+ -13,6, -5,-2/*mean (0.211224), correlation (0.449548)*/,
+ 1,-10, 3,10/*mean (0.211718), correlation (0.440606)*/,
+ 4,1, 8,-4/*mean (0.213034), correlation (0.443177)*/,
+ -2,-2, 2,-13/*mean (0.234334), correlation (0.455304)*/,
+ 2,-12, 12,12/*mean (0.235684), correlation (0.443436)*/,
+ -2,-13, 0,-6/*mean (0.237674), correlation (0.452525)*/,
+ 4,1, 9,3/*mean (0.23962), correlation (0.444824)*/,
+ -6,-10, -3,-5/*mean (0.248459), correlation (0.439621)*/,
+ -3,-13, -1,1/*mean (0.249505), correlation (0.456666)*/,
+ 7,5, 12,-11/*mean (0.00119208), correlation (0.495466)*/,
+ 4,-2, 5,-7/*mean (0.00372245), correlation (0.484214)*/,
+ -13,9, -9,-5/*mean (0.00741116), correlation (0.499854)*/,
+ 7,1, 8,6/*mean (0.0208952), correlation (0.499773)*/,
+ 7,-8, 7,6/*mean (0.0220085), correlation (0.501609)*/,
+ -7,-4, -7,1/*mean (0.0233806), correlation (0.496568)*/,
+ -8,11, -7,-8/*mean (0.0236505), correlation (0.489719)*/,
+ -13,6, -12,-8/*mean (0.0268781), correlation (0.503487)*/,
+ 2,4, 3,9/*mean (0.0323324), correlation (0.501938)*/,
+ 10,-5, 12,3/*mean (0.0399235), correlation (0.494029)*/,
+ -6,-5, -6,7/*mean (0.0420153), correlation (0.486579)*/,
+ 8,-3, 9,-8/*mean (0.0548021), correlation (0.484237)*/,
+ 2,-12, 2,8/*mean (0.0616622), correlation (0.496642)*/,
+ -11,-2, -10,3/*mean (0.0627755), correlation (0.498563)*/,
+ -12,-13, -7,-9/*mean (0.0829622), correlation (0.495491)*/,
+ -11,0, -10,-5/*mean (0.0843342), correlation (0.487146)*/,
+ 5,-3, 11,8/*mean (0.0929937), correlation (0.502315)*/,
+ -2,-13, -1,12/*mean (0.113327), correlation (0.48941)*/,
+ -1,-8, 0,9/*mean (0.132119), correlation (0.467268)*/,
+ -13,-11, -12,-5/*mean (0.136269), correlation (0.498771)*/,
+ -10,-2, -10,11/*mean (0.142173), correlation (0.498714)*/,
+ -3,9, -2,-13/*mean (0.144141), correlation (0.491973)*/,
+ 2,-3, 3,2/*mean (0.14892), correlation (0.500782)*/,
+ -9,-13, -4,0/*mean (0.150371), correlation (0.498211)*/,
+ -4,6, -3,-10/*mean (0.152159), correlation (0.495547)*/,
+ -4,12, -2,-7/*mean (0.156152), correlation (0.496925)*/,
+ -6,-11, -4,9/*mean (0.15749), correlation (0.499222)*/,
+ 6,-3, 6,11/*mean (0.159211), correlation (0.503821)*/,
+ -13,11, -5,5/*mean (0.162427), correlation (0.501907)*/,
+ 11,11, 12,6/*mean (0.16652), correlation (0.497632)*/,
+ 7,-5, 12,-2/*mean (0.169141), correlation (0.484474)*/,
+ -1,12, 0,7/*mean (0.169456), correlation (0.495339)*/,
+ -4,-8, -3,-2/*mean (0.171457), correlation (0.487251)*/,
+ -7,1, -6,7/*mean (0.175), correlation (0.500024)*/,
+ -13,-12, -8,-13/*mean (0.175866), correlation (0.497523)*/,
+ -7,-2, -6,-8/*mean (0.178273), correlation (0.501854)*/,
+ -8,5, -6,-9/*mean (0.181107), correlation (0.494888)*/,
+ -5,-1, -4,5/*mean (0.190227), correlation (0.482557)*/,
+ -13,7, -8,10/*mean (0.196739), correlation (0.496503)*/,
+ 1,5, 5,-13/*mean (0.19973), correlation (0.499759)*/,
+ 1,0, 10,-13/*mean (0.204465), correlation (0.49873)*/,
+ 9,12, 10,-1/*mean (0.209334), correlation (0.49063)*/,
+ 5,-8, 10,-9/*mean (0.211134), correlation (0.503011)*/,
+ -1,11, 1,-13/*mean (0.212), correlation (0.499414)*/,
+ -9,-3, -6,2/*mean (0.212168), correlation (0.480739)*/,
+ -1,-10, 1,12/*mean (0.212731), correlation (0.502523)*/,
+ -13,1, -8,-10/*mean (0.21327), correlation (0.489786)*/,
+ 8,-11, 10,-6/*mean (0.214159), correlation (0.488246)*/,
+ 2,-13, 3,-6/*mean (0.216993), correlation (0.50287)*/,
+ 7,-13, 12,-9/*mean (0.223639), correlation (0.470502)*/,
+ -10,-10, -5,-7/*mean (0.224089), correlation (0.500852)*/,
+ -10,-8, -8,-13/*mean (0.228666), correlation (0.502629)*/,
+ 4,-6, 8,5/*mean (0.22906), correlation (0.498305)*/,
+ 3,12, 8,-13/*mean (0.233378), correlation (0.503825)*/,
+ -4,2, -3,-3/*mean (0.234323), correlation (0.476692)*/,
+ 5,-13, 10,-12/*mean (0.236392), correlation (0.475462)*/,
+ 4,-13, 5,-1/*mean (0.236842), correlation (0.504132)*/,
+ -9,9, -4,3/*mean (0.236977), correlation (0.497739)*/,
+ 0,3, 3,-9/*mean (0.24314), correlation (0.499398)*/,
+ -12,1, -6,1/*mean (0.243297), correlation (0.489447)*/,
+ 3,2, 4,-8/*mean (0.00155196), correlation (0.553496)*/,
+ -10,-10, -10,9/*mean (0.00239541), correlation (0.54297)*/,
+ 8,-13, 12,12/*mean (0.0034413), correlation (0.544361)*/,
+ -8,-12, -6,-5/*mean (0.003565), correlation (0.551225)*/,
+ 2,2, 3,7/*mean (0.00835583), correlation (0.55285)*/,
+ 10,6, 11,-8/*mean (0.00885065), correlation (0.540913)*/,
+ 6,8, 8,-12/*mean (0.0101552), correlation (0.551085)*/,
+ -7,10, -6,5/*mean (0.0102227), correlation (0.533635)*/,
+ -3,-9, -3,9/*mean (0.0110211), correlation (0.543121)*/,
+ -1,-13, -1,5/*mean (0.0113473), correlation (0.550173)*/,
+ -3,-7, -3,4/*mean (0.0140913), correlation (0.554774)*/,
+ -8,-2, -8,3/*mean (0.017049), correlation (0.55461)*/,
+ 4,2, 12,12/*mean (0.01778), correlation (0.546921)*/,
+ 2,-5, 3,11/*mean (0.0224022), correlation (0.549667)*/,
+ 6,-9, 11,-13/*mean (0.029161), correlation (0.546295)*/,
+ 3,-1, 7,12/*mean (0.0303081), correlation (0.548599)*/,
+ 11,-1, 12,4/*mean (0.0355151), correlation (0.523943)*/,
+ -3,0, -3,6/*mean (0.0417904), correlation (0.543395)*/,
+ 4,-11, 4,12/*mean (0.0487292), correlation (0.542818)*/,
+ 2,-4, 2,1/*mean (0.0575124), correlation (0.554888)*/,
+ -10,-6, -8,1/*mean (0.0594242), correlation (0.544026)*/,
+ -13,7, -11,1/*mean (0.0597391), correlation (0.550524)*/,
+ -13,12, -11,-13/*mean (0.0608974), correlation (0.55383)*/,
+ 6,0, 11,-13/*mean (0.065126), correlation (0.552006)*/,
+ 0,-1, 1,4/*mean (0.074224), correlation (0.546372)*/,
+ -13,3, -9,-2/*mean (0.0808592), correlation (0.554875)*/,
+ -9,8, -6,-3/*mean (0.0883378), correlation (0.551178)*/,
+ -13,-6, -8,-2/*mean (0.0901035), correlation (0.548446)*/,
+ 5,-9, 8,10/*mean (0.0949843), correlation (0.554694)*/,
+ 2,7, 3,-9/*mean (0.0994152), correlation (0.550979)*/,
+ -1,-6, -1,-1/*mean (0.10045), correlation (0.552714)*/,
+ 9,5, 11,-2/*mean (0.100686), correlation (0.552594)*/,
+ 11,-3, 12,-8/*mean (0.101091), correlation (0.532394)*/,
+ 3,0, 3,5/*mean (0.101147), correlation (0.525576)*/,
+ -1,4, 0,10/*mean (0.105263), correlation (0.531498)*/,
+ 3,-6, 4,5/*mean (0.110785), correlation (0.540491)*/,
+ -13,0, -10,5/*mean (0.112798), correlation (0.536582)*/,
+ 5,8, 12,11/*mean (0.114181), correlation (0.555793)*/,
+ 8,9, 9,-6/*mean (0.117431), correlation (0.553763)*/,
+ 7,-4, 8,-12/*mean (0.118522), correlation (0.553452)*/,
+ -10,4, -10,9/*mean (0.12094), correlation (0.554785)*/,
+ 7,3, 12,4/*mean (0.122582), correlation (0.555825)*/,
+ 9,-7, 10,-2/*mean (0.124978), correlation (0.549846)*/,
+ 7,0, 12,-2/*mean (0.127002), correlation (0.537452)*/,
+ -1,-6, 0,-11/*mean (0.127148), correlation (0.547401)*/
+ };
+
+ void initializeOrbPattern(const Point* pattern0, Mat& pattern, int ntuples, int tupleSize, int poolSize)
+ {
+ RNG rng(0x12345678);
+
+ pattern.create(2, ntuples * tupleSize, CV_32SC1);
+ pattern.setTo(Scalar::all(0));
+
+ int* pattern_x_ptr = pattern.ptr<int>(0);
+ int* pattern_y_ptr = pattern.ptr<int>(1);
+
+ for (int i = 0; i < ntuples; i++)
+ {
+ for (int k = 0; k < tupleSize; k++)
+ {
+ for(;;)
+ {
+ int idx = rng.uniform(0, poolSize);
+ Point pt = pattern0[idx];
+
+ int k1;
+ for (k1 = 0; k1 < k; k1++)
+ if (pattern_x_ptr[tupleSize * i + k1] == pt.x && pattern_y_ptr[tupleSize * i + k1] == pt.y)
+ break;
+
+ if (k1 == k)
+ {
+ pattern_x_ptr[tupleSize * i + k] = pt.x;
+ pattern_y_ptr[tupleSize * i + k] = pt.y;
+ break;
+ }
+ }
+ }
+ }
+ }
+
+ void makeRandomPattern(int patchSize, Point* pattern, int npoints)
+ {
+ // we always start with a fixed seed,
+ // to make patterns the same on each run
+ RNG rng(0x34985739);
+
+ for (int i = 0; i < npoints; i++)
+ {
+ pattern[i].x = rng.uniform(-patchSize / 2, patchSize / 2 + 1);
+ pattern[i].y = rng.uniform(-patchSize / 2, patchSize / 2 + 1);
+ }
+ }
+}
+
+cv::ocl::ORB_OCL::ORB_OCL(int nFeatures, float scaleFactor, int nLevels, int edgeThreshold, int firstLevel, int WTA_K, int scoreType, int patchSize) :
+ nFeatures_(nFeatures), scaleFactor_(scaleFactor), nLevels_(nLevels), edgeThreshold_(edgeThreshold), firstLevel_(firstLevel), WTA_K_(WTA_K),
+ scoreType_(scoreType), patchSize_(patchSize),
+ fastDetector_(DEFAULT_FAST_THRESHOLD)
+{
+ CV_Assert(patchSize_ >= 2);
+
+ // fill the extractors and descriptors for the corresponding scales
+ float factor = 1.0f / scaleFactor_;
+ float n_desired_features_per_scale = nFeatures_ * (1.0f - factor) / (1.0f - std::pow(factor, nLevels_));
+
+ n_features_per_level_.resize(nLevels_);
+ size_t sum_n_features = 0;
+ for (int level = 0; level < nLevels_ - 1; ++level)
+ {
+ n_features_per_level_[level] = cvRound(n_desired_features_per_scale);
+ sum_n_features += n_features_per_level_[level];
+ n_desired_features_per_scale *= factor;
+ }
+ n_features_per_level_[nLevels_ - 1] = nFeatures - sum_n_features;
+
+ // pre-compute the end of a row in a circular patch
+ int half_patch_size = patchSize_ / 2;
+ std::vector<int> u_max(half_patch_size + 2);
+ for (int v = 0; v <= half_patch_size * std::sqrt(2.f) / 2 + 1; ++v)
+ u_max[v] = cvRound(std::sqrt(static_cast<float>(half_patch_size * half_patch_size - v * v)));
+
+ // Make sure we are symmetric
+ for (int v = half_patch_size, v_0 = 0; v >= half_patch_size * std::sqrt(2.f) / 2; --v)
+ {
+ while (u_max[v_0] == u_max[v_0 + 1])
+ ++v_0;
+ u_max[v] = v_0;
+ ++v_0;
+ }
+ CV_Assert(u_max.size() < 32);
+ //cv::cuda::device::orb::loadUMax(&u_max[0], static_cast<int>(u_max.size()));
+ uMax_ = oclMat(1, u_max.size(), CV_32SC1, &u_max[0]);
+
+ // Calc pattern
+ const int npoints = 512;
+ Point pattern_buf[npoints];
+ const Point* pattern0 = (const Point*)bit_pattern_31_;
+ if (patchSize_ != 31)
+ {
+ pattern0 = pattern_buf;
+ makeRandomPattern(patchSize_, pattern_buf, npoints);
+ }
+
+ CV_Assert(WTA_K_ == 2 || WTA_K_ == 3 || WTA_K_ == 4);
+
+ Mat h_pattern;
+
+ if (WTA_K_ == 2)
+ {
+ h_pattern.create(2, npoints, CV_32SC1);
+
+ int* pattern_x_ptr = h_pattern.ptr<int>(0);
+ int* pattern_y_ptr = h_pattern.ptr<int>(1);
+
+ for (int i = 0; i < npoints; ++i)
+ {
+ pattern_x_ptr[i] = pattern0[i].x;
+ pattern_y_ptr[i] = pattern0[i].y;
+ }
+ }
+ else
+ {
+ int ntuples = descriptorSize() * 4;
+ initializeOrbPattern(pattern0, h_pattern, ntuples, WTA_K_, npoints);
+ }
+
+ pattern_.upload(h_pattern);
+
+ //blurFilter = ocl::createGaussianFilter(CV_8UC1, -1, Size(7, 7), 2, 2, BORDER_REFLECT_101);
+ blurFilter = ocl::createGaussianFilter_GPU(CV_8UC1, Size(7, 7), 2, 2, BORDER_REFLECT_101);
+
+ blurForDescriptor = true;
+}
+
+namespace
+{
+ inline float getScale(float scaleFactor, int firstLevel, int level)
+ {
+ return pow(scaleFactor, level - firstLevel);
+ }
+}
+
+void cv::ocl::ORB_OCL::buildScalePyramids(const oclMat& image, const oclMat& mask)
+{
+ CV_Assert(image.type() == CV_8UC1);
+ CV_Assert(mask.empty() || (mask.type() == CV_8UC1 && mask.size() == image.size()));
+
+ imagePyr_.resize(nLevels_);
+ maskPyr_.resize(nLevels_);
+
+ for (int level = 0; level < nLevels_; ++level)
+ {
+ float scale = 1.0f / getScale(scaleFactor_, firstLevel_, level);
+
+ Size sz(cvRound(image.cols * scale), cvRound(image.rows * scale));
+
+ ensureSizeIsEnough(sz, image.type(), imagePyr_[level]);
+ ensureSizeIsEnough(sz, CV_8UC1, maskPyr_[level]);
+ maskPyr_[level].setTo(Scalar::all(255));
+
+ // Compute the resized image
+ if (level != firstLevel_)
+ {
+ if (level < firstLevel_)
+ {
+ ocl::resize(image, imagePyr_[level], sz, 0, 0, INTER_LINEAR);
+
+ if (!mask.empty())
+ ocl::resize(mask, maskPyr_[level], sz, 0, 0, INTER_LINEAR);
+ }
+ else
+ {
+ ocl::resize(imagePyr_[level - 1], imagePyr_[level], sz, 0, 0, INTER_LINEAR);
+
+ if (!mask.empty())
+ {
+ ocl::resize(maskPyr_[level - 1], maskPyr_[level], sz, 0, 0, INTER_LINEAR);
+ ocl::threshold(maskPyr_[level], maskPyr_[level], 254, 0, THRESH_TOZERO);
+ }
+ }
+ }
+ else
+ {
+ image.copyTo(imagePyr_[level]);
+
+ if (!mask.empty())
+ mask.copyTo(maskPyr_[level]);
+ }
+
+ // Filter keypoints by image border
+ ensureSizeIsEnough(sz, CV_8UC1, buf_);
+ buf_.setTo(Scalar::all(0));
+ Rect inner(edgeThreshold_, edgeThreshold_, sz.width - 2 * edgeThreshold_, sz.height - 2 * edgeThreshold_);
+ buf_(inner).setTo(Scalar::all(255));
+
+ ocl::bitwise_and(maskPyr_[level], buf_, maskPyr_[level]);
+ }
+}
+
+static void HarrisResponses_OCL(const oclMat& img, oclMat& keypoints, const int npoints, int blockSize, float harris_k)
+{
+ size_t localThreads[3] = {32, 8, 1};
+ size_t globalThreads[3] = {divUp(npoints, localThreads[1]) * localThreads[1] * localThreads[0],
+ 1,
+ 1};
+
+ Context *clCxt = Context::getContext();
+ String kernelName = "HarrisResponses";
+ std::vector< std::pair<size_t, const void *> > args;
+
+ int imgStep = img.step / img.elemSize();
+ int keypointsStep = keypoints.step / keypoints.elemSize();
+
+ args.push_back( std::make_pair( sizeof(cl_mem), (void *)&img.data));
+ args.push_back( std::make_pair( sizeof(cl_mem), (void *)&keypoints.data));
+ args.push_back( std::make_pair( sizeof(cl_int), (void *)&npoints));
+ args.push_back( std::make_pair( sizeof(cl_int), (void *)&blockSize));
+ args.push_back( std::make_pair( sizeof(cl_float), (void *)&harris_k));
+ args.push_back( std::make_pair( sizeof(cl_int), (void *)&imgStep));
+ args.push_back( std::make_pair( sizeof(cl_int), (void *)&keypointsStep));
+
+ bool is_cpu = isCpuDevice();
+ if (is_cpu)
+ openCLExecuteKernel(clCxt, &orb, kernelName, globalThreads, localThreads, args, -1, -1, (char*)"-D CPU");
+ else
+ {
+ cl_kernel kernel = openCLGetKernelFromSource(Context::getContext(), &orb, kernelName);
+ int wave_size = (int)queryWaveFrontSize(kernel);
+ openCLSafeCall(clReleaseKernel(kernel));
+
+ std::string opt = format("-D WAVE_SIZE=%d", wave_size);
+ openCLExecuteKernel(Context::getContext(), &orb, kernelName, globalThreads, localThreads, args, -1, -1, opt.c_str());
+ }
+}
+
+static void IC_Angle_OCL(const oclMat& image, oclMat& keypoints, const oclMat& uMax, int npoints, int half_k)
+{
+ size_t localThreads[3] = {32, 8, 1};
+ size_t globalThreads[3] = {divUp(npoints, localThreads[1]) * localThreads[1] * localThreads[0],
+ 1,
+ 1};
+
+ Context *clCxt = Context::getContext();
+ String kernelName = "IC_Angle";
+ std::vector< std::pair<size_t, const void *> > args;
+
+ int imageStep = image.step / image.elemSize();
+ int keypointsStep = keypoints.step / keypoints.elemSize();
+
+ args.push_back( std::make_pair( sizeof(cl_mem), (void *)&image.data));
+ args.push_back( std::make_pair( sizeof(cl_mem), (void *)&keypoints.data));
+ args.push_back( std::make_pair( sizeof(cl_mem), (void *)&uMax.data));
+ args.push_back( std::make_pair( sizeof(cl_int), (void *)&npoints));
+ args.push_back( std::make_pair( sizeof(cl_int), (void *)&half_k));
+ args.push_back( std::make_pair( sizeof(cl_int), (void *)&imageStep));
+ args.push_back( std::make_pair( sizeof(cl_int), (void *)&keypointsStep));
+
+ bool is_cpu = isCpuDevice();
+ if (is_cpu)
+ openCLExecuteKernel(clCxt, &orb, kernelName, globalThreads, localThreads, args, -1, -1, (char*)"-D CPU");
+ else
+ {
+ cl_kernel kernel = openCLGetKernelFromSource(Context::getContext(), &orb, kernelName);
+ int wave_size = (int)queryWaveFrontSize(kernel);
+ openCLSafeCall(clReleaseKernel(kernel));
+
+ std::string opt = format("-D WAVE_SIZE=%d", wave_size);
+ openCLExecuteKernel(Context::getContext(), &orb, kernelName, globalThreads, localThreads, args, -1, -1, opt.c_str());
+ }
+}
+
+static void convertRowsToChannels_OCL(const oclMat& keypointsIn, oclMat& keypointsOut, int npoints)
+{
+ size_t localThreads[3] = {256, 1, 1};
+ size_t globalThreads[3] = {divUp(npoints, localThreads[0]) * localThreads[0],
+ 1,
+ 1};
+
+ Context *clCxt = Context::getContext();
+ String kernelName = "convertRowsToChannels";
+ std::vector< std::pair<size_t, const void *> > args;
+
+ int keypointsInStep = keypointsIn.step / keypointsIn.elemSize();
+ int keypointsOutStep = keypointsOut.step / keypointsOut.elemSize();
+
+ args.push_back( std::make_pair( sizeof(cl_mem), (void *)&keypointsIn.data));
+ args.push_back( std::make_pair( sizeof(cl_mem), (void *)&keypointsOut.data));
+ args.push_back( std::make_pair( sizeof(cl_int), (void *)&npoints));
+ args.push_back( std::make_pair( sizeof(cl_int), (void *)&keypointsInStep));
+ args.push_back( std::make_pair( sizeof(cl_int), (void *)&keypointsOutStep));
+
+ openCLExecuteKernel(clCxt, &orb, kernelName, globalThreads, localThreads, args, -1, -1);
+}
+
+static void convertChannelsToRows_OCL(const oclMat& keypointsPos, const oclMat& keypointsResp,
+ oclMat& keypointsOut, int npoints)
+{
+ size_t localThreads[3] = {256, 1, 1};
+ size_t globalThreads[3] = {divUp(npoints, localThreads[0]) * localThreads[0],
+ 1,
+ 1};
+
+ Context *clCxt = Context::getContext();
+ String kernelName = "convertChannelsToRows";
+ std::vector< std::pair<size_t, const void *> > args;
+
+ int keypointsPosStep = keypointsPos.step / keypointsResp.elemSize();
+ int keypointsRespStep = keypointsResp.step / keypointsResp.elemSize();
+ int keypointsOutStep = keypointsOut.step / keypointsOut.elemSize();
+
+ args.push_back( std::make_pair( sizeof(cl_mem), (void *)&keypointsPos.data));
+ args.push_back( std::make_pair( sizeof(cl_mem), (void *)&keypointsResp.data));
+ args.push_back( std::make_pair( sizeof(cl_mem), (void *)&keypointsOut.data));
+ args.push_back( std::make_pair( sizeof(cl_int), (void *)&npoints));
+ args.push_back( std::make_pair( sizeof(cl_int), (void *)&keypointsPosStep));
+ args.push_back( std::make_pair( sizeof(cl_int), (void *)&keypointsRespStep));
+ args.push_back( std::make_pair( sizeof(cl_int), (void *)&keypointsOutStep));
+
+ openCLExecuteKernel(clCxt, &orb, kernelName, globalThreads, localThreads, args, -1, -1);
+}
+
+void cv::ocl::ORB_OCL::computeKeyPointsPyramid()
+{
+ int half_patch_size = patchSize_ / 2;
+
+ keyPointsPyr_.resize(nLevels_);
+ keyPointsCount_.resize(nLevels_);
+
+ for (int level = 0; level < nLevels_; ++level)
+ {
+ keyPointsCount_[level] = fastDetector_.calcKeyPointsLocation(imagePyr_[level], maskPyr_[level]);
+
+ if (keyPointsCount_[level] == 0)
+ continue;
+
+ keyPointsCount_[level] = fastDetector_.getKeyPoints(keyPointsPyr_[level]);
+
+ if (keyPointsCount_[level] == 0)
+ continue;
+
+ int n_features = static_cast<int>(n_features_per_level_[level]);
+
+ if (scoreType_ == ORB::HARRIS_SCORE)
+ {
+ int featuresToIncrease = 2 * n_features - keyPointsPyr_[level].cols;
+ if (featuresToIncrease < 0) featuresToIncrease = 0;
+
+ // Keeps more points than necessary as FAST does not give amazing corners
+ // and expands rows in the keypoint matrix to store angle, octave and size
+ copyMakeBorder(keyPointsPyr_[level], keyPointsPyr_[level],
+ 0, ROWS_COUNT-keyPointsPyr_[level].rows,
+ 0, featuresToIncrease,
+ BORDER_CONSTANT, 0.f);
+
+ // Compute the Harris cornerness (better scoring than FAST)
+ HarrisResponses_OCL(imagePyr_[level], keyPointsPyr_[level], keyPointsCount_[level], 7, HARRIS_K);
+ }
+ else
+ {
+ // Expands rows in the keypoint matrix to store angle, octave and size
+ copyMakeBorder(keyPointsPyr_[level], keyPointsPyr_[level],
+ 0, ROWS_COUNT-keyPointsPyr_[level].rows,
+ 0, 0,
+ BORDER_CONSTANT, 0.f);
+ }
+
+
+ // To use sortByKey the keypoint locations have to be reorganized as one row and two channels,
+ // leaving the keys (responses) as a one row, one channel matrix.
+ // TODO: change this when sortByRow is implemented.
+ oclMat keypointsResp, keypointsPos(1,keyPointsCount_[level],CV_32FC2);
+ keyPointsPyr_[level].row(RESPONSE_ROW).colRange(0,keyPointsCount_[level]).copyTo(keypointsResp);
+
+ convertRowsToChannels_OCL(keyPointsPyr_[level].rowRange(0,2), keypointsPos, keyPointsCount_[level]);
+ ocl::sortByKey(keypointsResp, keypointsPos, SORT_MERGE, true);
+
+ keyPointsCount_[level] = std::min(n_features,keyPointsCount_[level]);
+
+ // The data is then reorganized back to one channel, three rows (X_ROW, Y_ROW, RESPONSE_ROW)
+ convertChannelsToRows_OCL(keypointsPos, keypointsResp, keyPointsPyr_[level], keyPointsCount_[level]);
+
+ // Compute orientation
+ IC_Angle_OCL(imagePyr_[level], keyPointsPyr_[level], uMax_, keyPointsCount_[level], half_patch_size);
+ }
+}
+
+static void computeOrbDescriptor_OCL(const oclMat& img, const oclMat& keypoints, const oclMat& pattern,
+ oclMat& desc, const int npoints, const int dsize, const int WTA_K,
+ const int offset)
+{
+ size_t localThreads[3] = {32, 8, 1};
+ size_t globalThreads[3] = {divUp(dsize, localThreads[0]) * localThreads[0],
+ divUp(npoints, localThreads[1]) * localThreads[1],
+ 1};
+
+ Context *clCxt = Context::getContext();
+ String kernelName = "computeOrbDescriptor";
+ std::vector< std::pair<size_t, const void *> > args;
+
+ int imgStep = img.step / img.elemSize();
+ int keypointsStep = keypoints.step / keypoints.elemSize();
+ int patternStep = pattern.step / pattern.elemSize();
+ int descStep = desc.step / desc.elemSize();
+
+ args.push_back( std::make_pair( sizeof(cl_mem), (void *)&img.data));
+ args.push_back( std::make_pair( sizeof(cl_mem), (void *)&keypoints.data));
+ args.push_back( std::make_pair( sizeof(cl_mem), (void *)&pattern.data));
+ args.push_back( std::make_pair( sizeof(cl_mem), (void *)&desc.data));
+ args.push_back( std::make_pair( sizeof(cl_int), (void *)&npoints));
+ args.push_back( std::make_pair( sizeof(cl_int), (void *)&dsize));
+ args.push_back( std::make_pair( sizeof(cl_int), (void *)&WTA_K));
+ args.push_back( std::make_pair( sizeof(cl_int), (void *)&offset));
+ args.push_back( std::make_pair( sizeof(cl_int), (void *)&imgStep));
+ args.push_back( std::make_pair( sizeof(cl_int), (void *)&keypointsStep));
+ args.push_back( std::make_pair( sizeof(cl_int), (void *)&patternStep));
+ args.push_back( std::make_pair( sizeof(cl_int), (void *)&descStep));
+
+ openCLExecuteKernel(clCxt, &orb, kernelName, globalThreads, localThreads, args, -1, -1);
+}
+
+void cv::ocl::ORB_OCL::computeDescriptors(oclMat& descriptors)
+{
+ int nAllkeypoints = 0;
+
+ for (int level = 0; level < nLevels_; ++level)
+ nAllkeypoints += keyPointsCount_[level];
+
+ if (nAllkeypoints == 0)
+ {
+ descriptors.release();
+ return;
+ }
+
+ ensureSizeIsEnough(nAllkeypoints, descriptorSize(), CV_8UC1, descriptors);
+
+ int offset = 0;
+
+ for (int level = 0; level < nLevels_; ++level)
+ {
+ if (keyPointsCount_[level] == 0)
+ continue;
+
+ if (blurForDescriptor)
+ {
+ // preprocess the resized image
+ ensureSizeIsEnough(imagePyr_[level].size(), imagePyr_[level].type(), buf_);
+ blurFilter->apply(imagePyr_[level], buf_);
+ }
+
+ computeOrbDescriptor_OCL(blurForDescriptor ? buf_ : imagePyr_[level], keyPointsPyr_[level],
+ pattern_, descriptors, keyPointsCount_[level], descriptorSize(), WTA_K_, offset);
+
+ offset += keyPointsCount_[level];
+ }
+}
+
+static void mergeLocation_OCL(const oclMat& keypointsIn, oclMat& keypointsOut, const int npoints,
+ const int offset, const float scale, const int octave, const float size)
+{
+ size_t localThreads[3] = {256, 1, 1};
+ size_t globalThreads[3] = {divUp(npoints, localThreads[0]) * localThreads[0],
+ 1,
+ 1};
+
+ Context *clCxt = Context::getContext();
+ String kernelName = "mergeLocation";
+ std::vector< std::pair<size_t, const void *> > args;
+
+ int keypointsInStep = keypointsIn.step / keypointsIn.elemSize();
+ int keypointsOutStep = keypointsOut.step / keypointsOut.elemSize();
+
+ args.push_back( std::make_pair( sizeof(cl_mem), (void *)&keypointsIn.data));
+ args.push_back( std::make_pair( sizeof(cl_mem), (void *)&keypointsOut.data));
+ args.push_back( std::make_pair( sizeof(cl_int), (void *)&npoints));
+ args.push_back( std::make_pair( sizeof(cl_int), (void *)&offset));
+ args.push_back( std::make_pair( sizeof(cl_float), (void *)&scale));
+ args.push_back( std::make_pair( sizeof(cl_int), (void *)&octave));
+ args.push_back( std::make_pair( sizeof(cl_float), (void *)&size));
+ args.push_back( std::make_pair( sizeof(cl_int), (void *)&keypointsInStep));
+ args.push_back( std::make_pair( sizeof(cl_int), (void *)&keypointsOutStep));
+
+ openCLExecuteKernel(clCxt, &orb, kernelName, globalThreads, localThreads, args, -1, -1);
+}
+
+void cv::ocl::ORB_OCL::mergeKeyPoints(oclMat& keypoints)
+{
+ int nAllkeypoints = 0;
+
+ for (int level = 0; level < nLevels_; ++level)
+ nAllkeypoints += keyPointsCount_[level];
+
+ if (nAllkeypoints == 0)
+ {
+ keypoints.release();
+ return;
+ }
+
+ ensureSizeIsEnough(ROWS_COUNT, nAllkeypoints, CV_32FC1, keypoints);
+
+ int offset = 0;
+
+ for (int level = 0; level < nLevels_; ++level)
+ {
+ if (keyPointsCount_[level] == 0)
+ continue;
+
+ float sf = getScale(scaleFactor_, firstLevel_, level);
+
+ float locScale = level != firstLevel_ ? sf : 1.0f;
+ float size = patchSize_ * sf;
+
+ mergeLocation_OCL(keyPointsPyr_[level], keypoints, keyPointsCount_[level], offset, locScale, level, size);
+
+ offset += keyPointsCount_[level];
+ }
+}
+
+void cv::ocl::ORB_OCL::downloadKeyPoints(const oclMat &d_keypoints, std::vector<KeyPoint>& keypoints)
+{
+ if (d_keypoints.empty())
+ {
+ keypoints.clear();
+ return;
+ }
+
+ Mat h_keypoints(d_keypoints);
+
+ convertKeyPoints(h_keypoints, keypoints);
+}
+
+void cv::ocl::ORB_OCL::convertKeyPoints(const Mat &d_keypoints, std::vector<KeyPoint>& keypoints)
+{
+ if (d_keypoints.empty())
+ {
+ keypoints.clear();
+ return;
+ }
+
+ CV_Assert(d_keypoints.type() == CV_32FC1 && d_keypoints.rows == ROWS_COUNT);
+
+ const float* x_ptr = d_keypoints.ptr<float>(X_ROW);
+ const float* y_ptr = d_keypoints.ptr<float>(Y_ROW);
+ const float* response_ptr = d_keypoints.ptr<float>(RESPONSE_ROW);
+ const float* angle_ptr = d_keypoints.ptr<float>(ANGLE_ROW);
+ const float* octave_ptr = d_keypoints.ptr<float>(OCTAVE_ROW);
+ const float* size_ptr = d_keypoints.ptr<float>(SIZE_ROW);
+
+ keypoints.resize(d_keypoints.cols);
+
+ for (int i = 0; i < d_keypoints.cols; ++i)
+ {
+ KeyPoint kp;
+
+ kp.pt.x = x_ptr[i];
+ kp.pt.y = y_ptr[i];
+ kp.response = response_ptr[i];
+ kp.angle = angle_ptr[i];
+ kp.octave = static_cast<int>(octave_ptr[i]);
+ kp.size = size_ptr[i];
+
+ keypoints[i] = kp;
+ }
+}
+
+void cv::ocl::ORB_OCL::operator()(const oclMat& image, const oclMat& mask, oclMat& keypoints)
+{
+ buildScalePyramids(image, mask);
+ computeKeyPointsPyramid();
+ mergeKeyPoints(keypoints);
+}
+
+void cv::ocl::ORB_OCL::operator()(const oclMat& image, const oclMat& mask, oclMat& keypoints, oclMat& descriptors)
+{
+ buildScalePyramids(image, mask);
+ computeKeyPointsPyramid();
+ computeDescriptors(descriptors);
+ mergeKeyPoints(keypoints);
+}
+
+void cv::ocl::ORB_OCL::operator()(const oclMat& image, const oclMat& mask, std::vector<KeyPoint>& keypoints)
+{
+ (*this)(image, mask, d_keypoints_);
+ downloadKeyPoints(d_keypoints_, keypoints);
+}
+
+void cv::ocl::ORB_OCL::operator()(const oclMat& image, const oclMat& mask, std::vector<KeyPoint>& keypoints, oclMat& descriptors)
+{
+ (*this)(image, mask, d_keypoints_, descriptors);
+ downloadKeyPoints(d_keypoints_, keypoints);
+}
+
+void cv::ocl::ORB_OCL::release()
+{
+ imagePyr_.clear();
+ maskPyr_.clear();
+
+ buf_.release();
+
+ keyPointsPyr_.clear();
+
+ fastDetector_.release();
+
+ d_keypoints_.release();
+
+ uMax_.release();
+}
projects / profile / ivi / opencv.git / commitdiff