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Merge pull request #2480 from vpisarev:ocl_orb

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This commit is contained in:
Andrey Pavlenko 2014-03-14 19:04:18 +04:00 committed by OpenCV Buildbot
commit 6b434befc9
5 changed files with 989 additions and 310 deletions
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4
modules/features2d/perf/perf_orb.cpp
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@ -28,7 +28,7 @@ PERF_TEST_P(orb, detect, testing::Values(ORB_IMAGES))
TEST_CYCLE() detector(frame, mask, points);
sort(points.begin(), points.end(), comparators::KeypointGreater());
SANITY_CHECK_KEYPOINTS(points);
SANITY_CHECK_KEYPOINTS(points, 1e-5);
}
PERF_TEST_P(orb, extract, testing::Values(ORB_IMAGES))
@ -72,6 +72,6 @@ PERF_TEST_P(orb, full, testing::Values(ORB_IMAGES))
TEST_CYCLE() detector(frame, mask, points, descriptors, false);
perf::sort(points, descriptors);
SANITY_CHECK_KEYPOINTS(points);
SANITY_CHECK_KEYPOINTS(points, 1e-5);
SANITY_CHECK(descriptors);
}

98
modules/features2d/src/fast.cpp
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@ -43,6 +43,7 @@ The references are:
#include "precomp.hpp"
#include "fast_score.hpp"
#include "opencl_kernels.hpp"
#if defined _MSC_VER
# pragma warning( disable : 4127)
@ -249,8 +250,90 @@ void FAST_t(InputArray _img, std::vector<KeyPoint>& keypoints, int threshold, bo
}
}
template<typename pt>
struct cmp_pt
{
bool operator ()(const pt& a, const pt& b) const { return a.y < b.y || (a.y == b.y && a.x < b.x); }
};
static bool ocl_FAST( InputArray _img, std::vector<KeyPoint>& keypoints,
int threshold, bool nonmax_suppression, int maxKeypoints )
{
UMat img = _img.getUMat();
if( img.cols < 7 || img.rows < 7 )
return false;
size_t globalsize[] = { img.cols-6, img.rows-6 };
ocl::Kernel fastKptKernel("FAST_findKeypoints", ocl::features2d::fast_oclsrc);
if (fastKptKernel.empty())
return false;
UMat kp1(1, maxKeypoints*2+1, CV_32S);
UMat ucounter1(kp1, Rect(0,0,1,1));
ucounter1.setTo(Scalar::all(0));
if( !fastKptKernel.args(ocl::KernelArg::ReadOnly(img),
ocl::KernelArg::PtrReadWrite(kp1),
maxKeypoints, threshold).run(2, globalsize, 0, true))
return false;
Mat mcounter;
ucounter1.copyTo(mcounter);
int i, counter = mcounter.at<int>(0);
counter = std::min(counter, maxKeypoints);
keypoints.clear();
if( counter == 0 )
return true;
if( !nonmax_suppression )
{
Mat m;
kp1(Rect(0, 0, counter*2+1, 1)).copyTo(m);
const Point* pt = (const Point*)(m.ptr<int>() + 1);
for( i = 0; i < counter; i++ )
keypoints.push_back(KeyPoint((float)pt[i].x, (float)pt[i].y, 7.f, -1, 1.f));
}
else
{
UMat kp2(1, maxKeypoints*3+1, CV_32S);
UMat ucounter2 = kp2(Rect(0,0,1,1));
ucounter2.setTo(Scalar::all(0));
ocl::Kernel fastNMSKernel("FAST_nonmaxSupression", ocl::features2d::fast_oclsrc);
if (fastNMSKernel.empty())
return false;
size_t globalsize_nms[] = { counter };
if( !fastNMSKernel.args(ocl::KernelArg::PtrReadOnly(kp1),
ocl::KernelArg::PtrReadWrite(kp2),
ocl::KernelArg::ReadOnly(img),
counter, counter).run(1, globalsize_nms, 0, true))
return false;
Mat m2;
kp2(Rect(0, 0, counter*3+1, 1)).copyTo(m2);
Point3i* pt2 = (Point3i*)(m2.ptr<int>() + 1);
int newcounter = std::min(m2.at<int>(0), counter);
std::sort(pt2, pt2 + newcounter, cmp_pt<Point3i>());
for( i = 0; i < newcounter; i++ )
keypoints.push_back(KeyPoint((float)pt2[i].x, (float)pt2[i].y, 7.f, -1, (float)pt2[i].z));
}
return true;
}
void FAST(InputArray _img, std::vector<KeyPoint>& keypoints, int threshold, bool nonmax_suppression, int type)
{
if( ocl::useOpenCL() && _img.isUMat() && type == FastFeatureDetector::TYPE_9_16 &&
ocl_FAST(_img, keypoints, threshold, nonmax_suppression, 10000))
return;
switch(type) {
case FastFeatureDetector::TYPE_5_8:
FAST_t<8>(_img, keypoints, threshold, nonmax_suppression);
@ -268,6 +351,7 @@ void FAST(InputArray _img, std::vector<KeyPoint>& keypoints, int threshold, bool
}
}
void FAST(InputArray _img, std::vector<KeyPoint>& keypoints, int threshold, bool nonmax_suppression)
{
FAST(_img, keypoints, threshold, nonmax_suppression, FastFeatureDetector::TYPE_9_16);
@ -285,10 +369,16 @@ FastFeatureDetector::FastFeatureDetector( int _threshold, bool _nonmaxSuppressio
void FastFeatureDetector::detectImpl( InputArray _image, std::vector<KeyPoint>& keypoints, InputArray _mask ) const
{
Mat image = _image.getMat(), mask = _mask.getMat(), grayImage = image;
if( image.type() != CV_8U )
cvtColor( image, grayImage, COLOR_BGR2GRAY );
FAST( grayImage, keypoints, threshold, nonmaxSuppression, type );
Mat mask = _mask.getMat(), grayImage;
UMat ugrayImage;
_InputArray gray = _image;
if( _image.type() != CV_8U )
{
_OutputArray ogray = _image.isUMat() ? _OutputArray(ugrayImage) : _OutputArray(grayImage);
cvtColor( _image, ogray, COLOR_BGR2GRAY );
gray = ogray;
}
FAST( gray, keypoints, threshold, nonmaxSuppression, type );
KeyPointsFilter::runByPixelsMask( keypoints, mask );
}

162
modules/features2d/src/opencl/fast.cl Normal file
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@ -0,0 +1,162 @@
// OpenCL port of the FAST corner detector.
// Copyright (C) 2014, Itseez Inc. See the license at http://opencv.org
inline int cornerScore(__global const uchar* img, int step)
{
int k, tofs, v = img[0], a0 = 0, b0;
int d[16];
#define LOAD2(idx, ofs) \
tofs = ofs; d[idx] = (short)(v - img[tofs]); d[idx+8] = (short)(v - img[-tofs])
LOAD2(0, 3);
LOAD2(1, -step+3);
LOAD2(2, -step*2+2);
LOAD2(3, -step*3+1);
LOAD2(4, -step*3);
LOAD2(5, -step*3-1);
LOAD2(6, -step*2-2);
LOAD2(7, -step-3);
#pragma unroll
for( k = 0; k < 16; k += 2 )
{
int a = min((int)d[(k+1)&15], (int)d[(k+2)&15]);
a = min(a, (int)d[(k+3)&15]);
a = min(a, (int)d[(k+4)&15]);
a = min(a, (int)d[(k+5)&15]);
a = min(a, (int)d[(k+6)&15]);
a = min(a, (int)d[(k+7)&15]);
a = min(a, (int)d[(k+8)&15]);
a0 = max(a0, min(a, (int)d[k&15]));
a0 = max(a0, min(a, (int)d[(k+9)&15]));
}
b0 = -a0;
#pragma unroll
for( k = 0; k < 16; k += 2 )
{
int b = max((int)d[(k+1)&15], (int)d[(k+2)&15]);
b = max(b, (int)d[(k+3)&15]);
b = max(b, (int)d[(k+4)&15]);
b = max(b, (int)d[(k+5)&15]);
b = max(b, (int)d[(k+6)&15]);
b = max(b, (int)d[(k+7)&15]);
b = max(b, (int)d[(k+8)&15]);
b0 = min(b0, max(b, (int)d[k]));
b0 = min(b0, max(b, (int)d[(k+9)&15]));
}
return -b0-1;
}
__kernel
void FAST_findKeypoints(
__global const uchar * _img, int step, int img_offset,
int img_rows, int img_cols,
volatile __global int* kp_loc,
int max_keypoints, int threshold )
{
int j = get_global_id(0) + 3;
int i = get_global_id(1) + 3;
if (i < img_rows - 3 && j < img_cols - 3)
{
__global const uchar* img = _img + mad24(i, step, j + img_offset);
int v = img[0], t0 = v - threshold, t1 = v + threshold;
int k, tofs, v0, v1;
int m0 = 0, m1 = 0;
#define UPDATE_MASK(idx, ofs) \
tofs = ofs; v0 = img[tofs]; v1 = img[-tofs]; \
m0 |= ((v0 < t0) << idx) | ((v1 < t0) << (8 + idx)); \
m1 |= ((v0 > t1) << idx) | ((v1 > t1) << (8 + idx))
UPDATE_MASK(0, 3);
if( (m0 | m1) == 0 )
return;
UPDATE_MASK(2, -step*2+2);
UPDATE_MASK(4, -step*3);
UPDATE_MASK(6, -step*2-2);
#define EVEN_MASK (1+4+16+64)
if( ((m0 | (m0 >> 8)) & EVEN_MASK) != EVEN_MASK &&
((m1 | (m1 >> 8)) & EVEN_MASK) != EVEN_MASK )
return;
UPDATE_MASK(1, -step+3);
UPDATE_MASK(3, -step*3+1);
UPDATE_MASK(5, -step*3-1);
UPDATE_MASK(7, -step-3);
if( ((m0 | (m0 >> 8)) & 255) != 255 &&
((m1 | (m1 >> 8)) & 255) != 255 )
return;
m0 |= m0 << 16;
m1 |= m1 << 16;
#define CHECK0(i) ((m0 & (511 << i)) == (511 << i))
#define CHECK1(i) ((m1 & (511 << i)) == (511 << i))
if( CHECK0(0) + CHECK0(1) + CHECK0(2) + CHECK0(3) +
CHECK0(4) + CHECK0(5) + CHECK0(6) + CHECK0(7) +
CHECK0(8) + CHECK0(9) + CHECK0(10) + CHECK0(11) +
CHECK0(12) + CHECK0(13) + CHECK0(14) + CHECK0(15) +
CHECK1(0) + CHECK1(1) + CHECK1(2) + CHECK1(3) +
CHECK1(4) + CHECK1(5) + CHECK1(6) + CHECK1(7) +
CHECK1(8) + CHECK1(9) + CHECK1(10) + CHECK1(11) +
CHECK1(12) + CHECK1(13) + CHECK1(14) + CHECK1(15) == 0 )
return;
{
int idx = atomic_inc(kp_loc);
if( idx < max_keypoints )
{
kp_loc[1 + 2*idx] = j;
kp_loc[2 + 2*idx] = i;
}
}
}
}
///////////////////////////////////////////////////////////////////////////
// nonmaxSupression
__kernel
void FAST_nonmaxSupression(
__global const int* kp_in, volatile __global int* kp_out,
__global const uchar * _img, int step, int img_offset,
int rows, int cols, int counter, int max_keypoints)
{
const int idx = get_global_id(0);
if (idx < counter)
{
int x = kp_in[1 + 2*idx];
int y = kp_in[2 + 2*idx];
__global const uchar* img = _img + mad24(y, step, x + img_offset);
int s = cornerScore(img, step);
if( (x < 4 || s > cornerScore(img-1, step)) +
(y < 4 || s > cornerScore(img-step, step)) != 2 )
return;
if( (x >= cols - 4 || s > cornerScore(img+1, step)) +
(y >= rows - 4 || s > cornerScore(img+step, step)) +
(x < 4 || y < 4 || s > cornerScore(img-step-1, step)) +
(x >= cols - 4 || y < 4 || s > cornerScore(img-step+1, step)) +
(x < 4 || y >= rows - 4 || s > cornerScore(img+step-1, step)) +
(x >= cols - 4 || y >= rows - 4 || s > cornerScore(img+step+1, step)) == 6)
{
int new_idx = atomic_inc(kp_out);
if( new_idx < max_keypoints )
{
kp_out[1 + 3*new_idx] = x;
kp_out[2 + 3*new_idx] = y;
kp_out[3 + 3*new_idx] = s;
}
}
}
}

254
modules/features2d/src/opencl/orb.cl Normal file
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@ -0,0 +1,254 @@
// OpenCL port of the ORB feature detector and descriptor extractor
// Copyright (C) 2014, Itseez Inc. See the license at http://opencv.org
//
// The original code has been contributed by Peter Andreas Entschev, peter@entschev.com
#define LAYERINFO_SIZE 1
#define LAYERINFO_OFS 0
#define KEYPOINT_SIZE 3
#define ORIENTED_KEYPOINT_SIZE 4
#define KEYPOINT_X 0
#define KEYPOINT_Y 1
#define KEYPOINT_Z 2
#define KEYPOINT_ANGLE 3
/////////////////////////////////////////////////////////////
#ifdef ORB_RESPONSES
__kernel void
ORB_HarrisResponses(__global const uchar* imgbuf, int imgstep, int imgoffset0,
__global const int* layerinfo, __global const int* keypoints,
__global float* responses, int nkeypoints )
{
int idx = get_global_id(0);
if( idx < nkeypoints )
{
__global const int* kpt = keypoints + idx*KEYPOINT_SIZE;
__global const int* layer = layerinfo + kpt[KEYPOINT_Z]*LAYERINFO_SIZE;
__global const uchar* img = imgbuf + imgoffset0 + layer[LAYERINFO_OFS] +
(kpt[KEYPOINT_Y] - blockSize/2)*imgstep + (kpt[KEYPOINT_X] - blockSize/2);
int i, j;
int a = 0, b = 0, c = 0;
for( i = 0; i < blockSize; i++, img += imgstep-blockSize )
{
for( j = 0; j < blockSize; j++, img++ )
{
int Ix = (img[1] - img[-1])*2 + img[-imgstep+1] - img[-imgstep-1] + img[imgstep+1] - img[imgstep-1];
int Iy = (img[imgstep] - img[-imgstep])*2 + img[imgstep-1] - img[-imgstep-1] + img[imgstep+1] - img[-imgstep+1];
a += Ix*Ix;
b += Iy*Iy;
c += Ix*Iy;
}
}
responses[idx] = ((float)a * b - (float)c * c - HARRIS_K * (float)(a + b) * (a + b))*scale_sq_sq;
}
}
#endif
/////////////////////////////////////////////////////////////
#ifdef ORB_ANGLES
#define _DBL_EPSILON 2.2204460492503131e-16f
#define atan2_p1 (0.9997878412794807f*57.29577951308232f)
#define atan2_p3 (-0.3258083974640975f*57.29577951308232f)
#define atan2_p5 (0.1555786518463281f*57.29577951308232f)
#define atan2_p7 (-0.04432655554792128f*57.29577951308232f)
inline float fastAtan2( float y, float x )
{
float ax = fabs(x), ay = fabs(y);
float a, c, c2;
if( ax >= ay )
{
c = ay/(ax + _DBL_EPSILON);
c2 = c*c;
a = (((atan2_p7*c2 + atan2_p5)*c2 + atan2_p3)*c2 + atan2_p1)*c;
}
else
{
c = ax/(ay + _DBL_EPSILON);
c2 = c*c;
a = 90.f - (((atan2_p7*c2 + atan2_p5)*c2 + atan2_p3)*c2 + atan2_p1)*c;
}
if( x < 0 )
a = 180.f - a;
if( y < 0 )
a = 360.f - a;
return a;
}
__kernel void
ORB_ICAngle(__global const uchar* imgbuf, int imgstep, int imgoffset0,
__global const int* layerinfo, __global const int* keypoints,
__global float* responses, const __global int* u_max,
int nkeypoints, int half_k )
{
int idx = get_global_id(0);
if( idx < nkeypoints )
{
__global const int* kpt = keypoints + idx*KEYPOINT_SIZE;
__global const int* layer = layerinfo + kpt[KEYPOINT_Z]*LAYERINFO_SIZE;
__global const uchar* center = imgbuf + imgoffset0 + layer[LAYERINFO_OFS] +
kpt[KEYPOINT_Y]*imgstep + kpt[KEYPOINT_X];
int u, v, m_01 = 0, m_10 = 0;
// Treat the center line differently, v=0
for( u = -half_k; u <= half_k; u++ )
m_10 += u * center[u];
// Go line by line in the circular patch
for( v = 1; v <= half_k; v++ )
{
// Proceed over the two lines
int v_sum = 0;
int d = u_max[v];
for( u = -d; u <= d; u++ )
{
int val_plus = center[u + v*imgstep], val_minus = center[u - v*imgstep];
v_sum += (val_plus - val_minus);
m_10 += u * (val_plus + val_minus);
}
m_01 += v * v_sum;
}
// we do not use OpenCL's atan2 intrinsic,
// because we want to get _exactly_ the same results as the CPU version
responses[idx] = fastAtan2((float)m_01, (float)m_10);
}
}
#endif
/////////////////////////////////////////////////////////////
#ifdef ORB_DESCRIPTORS
__kernel void
ORB_computeDescriptor(__global const uchar* imgbuf, int imgstep, int imgoffset0,
__global const int* layerinfo, __global const int* keypoints,
__global uchar* _desc, const __global int* pattern,
int nkeypoints, int dsize )
{
int idx = get_global_id(0);
if( idx < nkeypoints )
{
int i;
__global const int* kpt = keypoints + idx*ORIENTED_KEYPOINT_SIZE;
__global const int* layer = layerinfo + kpt[KEYPOINT_Z]*LAYERINFO_SIZE;
__global const uchar* center = imgbuf + imgoffset0 + layer[LAYERINFO_OFS] +
kpt[KEYPOINT_Y]*imgstep + kpt[KEYPOINT_X];
float angle = as_float(kpt[KEYPOINT_ANGLE]);
angle *= 0.01745329251994329547f;
float sina = sin(angle);
float cosa = cos(angle);
__global uchar* desc = _desc + idx*dsize;
#define GET_VALUE(idx) \
center[mad24(convert_int_rte(pattern[(idx)*2] * sina + pattern[(idx)*2+1] * cosa), imgstep, \
convert_int_rte(pattern[(idx)*2] * cosa - pattern[(idx)*2+1] * sina))]
for( i = 0; i < dsize; i++ )
{
int val;
#if WTA_K == 2
int t0, t1;
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;
pattern += 16*2;
#elif WTA_K == 3
int t0, t1, t2;
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;
pattern += 12*2;
#elif WTA_K == 4
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;
pattern += 16*2;
#else
#error "unknown/undefined WTA_K value; should be 2, 3 or 4"
#endif
desc[i] = (uchar)val;
}
}
}
#endif

557
modules/features2d/src/orb.cpp
View File

@ -35,6 +35,7 @@
/** Authors: Ethan Rublee, Vincent Rabaud, Gary Bradski */
#include "precomp.hpp"
#include "opencl_kernels.hpp"
#include <iterator>
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
@ -43,14 +44,86 @@ namespace cv
{
const float HARRIS_K = 0.04f;
const int DESCRIPTOR_SIZE = 32;
template<typename _Tp> inline void copyVectorToUMat(const std::vector<_Tp>& v, OutputArray um)
{
if(v.empty())
um.release();
Mat(1, (int)(v.size()*sizeof(v[0])), CV_8U, (void*)&v[0]).copyTo(um);
}
static bool
ocl_HarrisResponses(const UMat& imgbuf,
const UMat& layerinfo,
const UMat& keypoints,
UMat& responses,
int nkeypoints, int blockSize, float harris_k)
{
size_t globalSize[] = {nkeypoints};
float scale = 1.f/((1 << 2) * blockSize * 255.f);
float scale_sq_sq = scale * scale * scale * scale;
ocl::Kernel hr_ker("ORB_HarrisResponses", ocl::features2d::orb_oclsrc,
format("-D ORB_RESPONSES -D blockSize=%d -D scale_sq_sq=%.12ef -D HARRIS_K=%.12ff", blockSize, scale_sq_sq, harris_k));
if( hr_ker.empty() )
return false;
return hr_ker.args(ocl::KernelArg::ReadOnlyNoSize(imgbuf),
ocl::KernelArg::PtrReadOnly(layerinfo),
ocl::KernelArg::PtrReadOnly(keypoints),
ocl::KernelArg::PtrWriteOnly(responses),
nkeypoints).run(1, globalSize, 0, true);
}
static bool
ocl_ICAngles(const UMat& imgbuf, const UMat& layerinfo,
const UMat& keypoints, UMat& responses,
const UMat& umax, int nkeypoints, int half_k)
{
size_t globalSize[] = {nkeypoints};
ocl::Kernel icangle_ker("ORB_ICAngle", ocl::features2d::orb_oclsrc, "-D ORB_ANGLES");
if( icangle_ker.empty() )
return false;
return icangle_ker.args(ocl::KernelArg::ReadOnlyNoSize(imgbuf),
ocl::KernelArg::PtrReadOnly(layerinfo),
ocl::KernelArg::PtrReadOnly(keypoints),
ocl::KernelArg::PtrWriteOnly(responses),
ocl::KernelArg::PtrReadOnly(umax),
nkeypoints, half_k).run(1, globalSize, 0, true);
}
static bool
ocl_computeOrbDescriptors(const UMat& imgbuf, const UMat& layerInfo,
const UMat& keypoints, UMat& desc, const UMat& pattern,
int nkeypoints, int dsize, int WTA_K)
{
size_t globalSize[] = {nkeypoints};
ocl::Kernel desc_ker("ORB_computeDescriptor", ocl::features2d::orb_oclsrc,
format("-D ORB_DESCRIPTORS -D WTA_K=%d", WTA_K));
if( desc_ker.empty() )
return false;
return desc_ker.args(ocl::KernelArg::ReadOnlyNoSize(imgbuf),
ocl::KernelArg::PtrReadOnly(layerInfo),
ocl::KernelArg::PtrReadOnly(keypoints),
ocl::KernelArg::PtrWriteOnly(desc),
ocl::KernelArg::PtrReadOnly(pattern),
nkeypoints, dsize).run(1, globalSize, 0, true);
}
/**
* Function that computes the Harris responses in a
* blockSize x blockSize patch at given points in an image
* blockSize x blockSize patch at given points in the image
*/
static void
HarrisResponses(const Mat& img, std::vector<KeyPoint>& pts, int blockSize, float harris_k)
HarrisResponses(const Mat& img, const std::vector<Rect>& layerinfo,
std::vector<KeyPoint>& pts, int blockSize, float harris_k)
{
CV_Assert( img.type() == CV_8UC1 && blockSize*blockSize <= 2048 );
@ -60,8 +133,7 @@ HarrisResponses(const Mat& img, std::vector<KeyPoint>& pts, int blockSize, float
int step = (int)(img.step/img.elemSize1());
int r = blockSize/2;
float scale = (1 << 2) * blockSize * 255.0f;
scale = 1.0f / scale;
float scale = 1.f/((1 << 2) * blockSize * 255.f);
float scale_sq_sq = scale * scale * scale * scale;
AutoBuffer<int> ofsbuf(blockSize*blockSize);
@ -72,10 +144,11 @@ HarrisResponses(const Mat& img, std::vector<KeyPoint>& pts, int blockSize, float
for( ptidx = 0; ptidx < ptsize; ptidx++ )
{
int x0 = cvRound(pts[ptidx].pt.x - r);
int y0 = cvRound(pts[ptidx].pt.y - r);
int x0 = cvRound(pts[ptidx].pt.x);
int y0 = cvRound(pts[ptidx].pt.y);
int z = pts[ptidx].octave;
const uchar* ptr0 = ptr00 + y0*step + x0;
const uchar* ptr0 = ptr00 + (y0 - r + layerinfo[z].y)*step + x0 - r + layerinfo[z].x;
int a = 0, b = 0, c = 0;
for( int k = 0; k < blockSize*blockSize; k++ )
@ -94,19 +167,24 @@ HarrisResponses(const Mat& img, std::vector<KeyPoint>& pts, int blockSize, float
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
static float IC_Angle(const Mat& image, const int half_k, Point2f pt,
const std::vector<int> & u_max)
static void ICAngles(const Mat& img, const std::vector<Rect>& layerinfo,
std::vector<KeyPoint>& pts, const std::vector<int> & u_max, int half_k)
{
int m_01 = 0, m_10 = 0;
int step = (int)img.step1();
size_t ptidx, ptsize = pts.size();
const uchar* center = &image.at<uchar> (cvRound(pt.y), cvRound(pt.x));
for( ptidx = 0; ptidx < ptsize; ptidx++ )
{
const Rect& layer = layerinfo[pts[ptidx].octave];
const uchar* center = &img.at<uchar>(cvRound(pts[ptidx].pt.y) + layer.y, cvRound(pts[ptidx].pt.x) + layer.x);
int m_01 = 0, m_10 = 0;
// Treat the center line differently, v=0
for (int u = -half_k; u <= half_k; ++u)
m_10 += u * center[u];
// Go line by line in the circular patch
int step = (int)image.step1();
for (int v = 1; v <= half_k; ++v)
{
// Proceed over the two lines
@ -121,25 +199,37 @@ static float IC_Angle(const Mat& image, const int half_k, Point2f pt,
m_01 += v * v_sum;
}
return fastAtan2((float)m_01, (float)m_10);
pts[ptidx].angle = fastAtan2((float)m_01, (float)m_10);
}
}
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
static void computeOrbDescriptor(const KeyPoint& kpt,
const Mat& img, const Point* pattern,
uchar* desc, int dsize, int WTA_K)
static void
computeOrbDescriptors( const Mat& imagePyramid, const std::vector<Rect>& layerInfo,
const std::vector<float>& layerScale, std::vector<KeyPoint>& keypoints,
Mat& descriptors, const std::vector<Point>& _pattern, int dsize, int WTA_K )
{
int step = (int)imagePyramid.step;
int j, i, nkeypoints = (int)keypoints.size();
for( j = 0; j < nkeypoints; j++ )
{
const KeyPoint& kpt = keypoints[j];
const Rect& layer = layerInfo[kpt.octave];
float scale = 1.f/layerScale[kpt.octave];
float angle = kpt.angle;
//angle = cvFloor(angle/12)*12.f;
angle *= (float)(CV_PI/180.f);
float a = (float)cos(angle), b = (float)sin(angle);
const uchar* center = &img.at<uchar>(cvRound(kpt.pt.y), cvRound(kpt.pt.x));
int step = (int)img.step;
const uchar* center = &imagePyramid.at<uchar>(cvRound(kpt.pt.y*scale) + layer.y,
cvRound(kpt.pt.x*scale) + layer.x);
float x, y;
int ix, iy;
const Point* pattern = &_pattern[0];
uchar* desc = descriptors.ptr<uchar>(j);
#if 1
#define GET_VALUE(idx) \
(x = pattern[idx].x*a - pattern[idx].y*b, \
@ -159,7 +249,7 @@ static void computeOrbDescriptor(const KeyPoint& kpt,
if( WTA_K == 2 )
{
for (int i = 0; i < dsize; ++i, pattern += 16)
for (i = 0; i < dsize; ++i, pattern += 16)
{
int t0, t1, val;
t0 = GET_VALUE(0); t1 = GET_VALUE(1);
@ -184,7 +274,7 @@ static void computeOrbDescriptor(const KeyPoint& kpt,
}
else if( WTA_K == 3 )
{
for (int i = 0; i < dsize; ++i, pattern += 12)
for (i = 0; i < dsize; ++i, pattern += 12)
{
int t0, t1, t2, val;
t0 = GET_VALUE(0); t1 = GET_VALUE(1); t2 = GET_VALUE(2);
@ -204,7 +294,7 @@ static void computeOrbDescriptor(const KeyPoint& kpt,
}
else if( WTA_K == 4 )
{
for (int i = 0; i < dsize; ++i, pattern += 16)
for (i = 0; i < dsize; ++i, pattern += 16)
{
int t0, t1, t2, t3, u, v, k, val;
t0 = GET_VALUE(0); t1 = GET_VALUE(1);
@ -244,9 +334,9 @@ static void computeOrbDescriptor(const KeyPoint& kpt,
}
else
CV_Error( Error::StsBadSize, "Wrong WTA_K. It can be only 2, 3 or 4." );
#undef GET_VALUE
}
}
static void initializeOrbPattern( const Point* pattern0, std::vector<Point>& pattern, int ntuples, int tupleSize, int poolSize )
@ -591,21 +681,37 @@ void ORB::operator()(InputArray image, InputArray mask, std::vector<KeyPoint>& k
}
/** Compute the ORB keypoint orientations
* @param image the image to compute the features and descriptors on
* @param integral_image the integral image of the iamge (can be empty, but the computation will be slower)
* @param scale the scale at which we compute the orientation
* @param keypoints the resulting keypoints
*/
static void computeOrientation(const Mat& image, std::vector<KeyPoint>& keypoints,
int halfPatchSize, const std::vector<int>& umax)
static void uploadORBKeypoints(const std::vector<KeyPoint>& src, std::vector<Vec3i>& buf, OutputArray dst)
{
// Process each keypoint
for (std::vector<KeyPoint>::iterator keypoint = keypoints.begin(),
keypointEnd = keypoints.end(); keypoint != keypointEnd; ++keypoint)
{
keypoint->angle = IC_Angle(image, halfPatchSize, keypoint->pt, umax);
size_t i, n = src.size();
buf.resize(std::max(buf.size(), n));
for( i = 0; i < n; i++ )
buf[i] = Vec3i(cvRound(src[i].pt.x), cvRound(src[i].pt.y), src[i].octave);
copyVectorToUMat(buf, dst);
}
typedef union if32_t
{
int i;
float f;
}
if32_t;
static void uploadORBKeypoints(const std::vector<KeyPoint>& src,
const std::vector<float>& layerScale,
std::vector<Vec4i>& buf, OutputArray dst)
{
size_t i, n = src.size();
buf.resize(std::max(buf.size(), n));
for( i = 0; i < n; i++ )
{
int z = src[i].octave;
float scale = 1.f/layerScale[z];
if32_t angle;
angle.f = src[i].angle;
buf[i] = Vec4i(cvRound(src[i].pt.x*scale), cvRound(src[i].pt.y*scale), z, angle.i);
}
copyVectorToUMat(buf, dst);
}
@ -614,13 +720,18 @@ static void computeOrientation(const Mat& image, std::vector<KeyPoint>& keypoint
* @param mask_pyramid the masks to apply at every level
* @param keypoints the resulting keypoints, clustered per level
*/
static void computeKeyPoints(const std::vector<Mat>& imagePyramid,
const std::vector<Mat>& maskPyramid,
std::vector<std::vector<KeyPoint> >& allKeypoints,
int nfeatures, int firstLevel, double scaleFactor,
int edgeThreshold, int patchSize, int scoreType )
static void computeKeyPoints(const Mat& imagePyramid,
const UMat& uimagePyramid,
const Mat& maskPyramid,
const std::vector<Rect>& layerInfo,
const UMat& ulayerInfo,
const std::vector<float>& layerScale,
std::vector<KeyPoint>& allKeypoints,
int nfeatures, double scaleFactor,
int edgeThreshold, int patchSize, int scoreType,
bool useOCL )
{
int nlevels = (int)imagePyramid.size();
int i, nkeypoints, level, nlevels = (int)layerInfo.size();
std::vector<int> nfeaturesPerLevel(nlevels);
// fill the extractors and descriptors for the corresponding scales
@ -628,7 +739,7 @@ static void computeKeyPoints(const std::vector<Mat>& imagePyramid,
float ndesiredFeaturesPerScale = nfeatures*(1 - factor)/(1 - (float)std::pow((double)factor, (double)nlevels));
int sumFeatures = 0;
for( int level = 0; level < nlevels-1; level++ )
for( level = 0; level < nlevels-1; level++ )
{
nfeaturesPerLevel[level] = cvRound(ndesiredFeaturesPerScale);
sumFeatures += nfeaturesPerLevel[level];
@ -657,66 +768,116 @@ static void computeKeyPoints(const std::vector<Mat>& imagePyramid,
++v0;
}
allKeypoints.resize(nlevels);
allKeypoints.clear();
std::vector<KeyPoint> keypoints;
std::vector<int> counters(nlevels);
keypoints.reserve(nfeaturesPerLevel[0]*2);
for (int level = 0; level < nlevels; ++level)
for( level = 0; level < nlevels; level++ )
{
int featuresNum = nfeaturesPerLevel[level];
allKeypoints[level].reserve(featuresNum*2);
std::vector<KeyPoint> & keypoints = allKeypoints[level];
Mat img = imagePyramid(layerInfo[level]);
Mat mask = maskPyramid.empty() ? Mat() : maskPyramid(layerInfo[level]);
// Detect FAST features, 20 is a good threshold
FastFeatureDetector fd(20, true);
fd.detect(imagePyramid[level], keypoints, maskPyramid[level]);
fd.detect(img, keypoints, mask);
// Remove keypoints very close to the border
KeyPointsFilter::runByImageBorder(keypoints, imagePyramid[level].size(), edgeThreshold);
KeyPointsFilter::runByImageBorder(keypoints, img.size(), edgeThreshold);
// Keep more points than necessary as FAST does not give amazing corners
KeyPointsFilter::retainBest(keypoints, scoreType == ORB::HARRIS_SCORE ? 2 * featuresNum : featuresNum);
nkeypoints = (int)keypoints.size();
counters[level] = nkeypoints;
float sf = layerScale[level];
for( i = 0; i < nkeypoints; i++ )
{
keypoints[i].octave = level;
keypoints[i].size = patchSize*sf;
}
std::copy(keypoints.begin(), keypoints.end(), std::back_inserter(allKeypoints));
}
std::vector<Vec3i> ukeypoints_buf;
nkeypoints = (int)allKeypoints.size();
Mat responses;
UMat ukeypoints, uresponses(1, nkeypoints, CV_32F);
// Select best features using the Harris cornerness (better scoring than FAST)
if( scoreType == ORB::HARRIS_SCORE )
{
// Keep more points than necessary as FAST does not give amazing corners
KeyPointsFilter::retainBest(keypoints, 2 * featuresNum);
// Compute the Harris cornerness (better scoring than FAST)
HarrisResponses(imagePyramid[level], keypoints, 7, HARRIS_K);
if( useOCL )
{
uploadORBKeypoints(allKeypoints, ukeypoints_buf, ukeypoints);
useOCL = ocl_HarrisResponses( uimagePyramid, ulayerInfo, ukeypoints,
uresponses, nkeypoints, 7, HARRIS_K );
if( useOCL )
{
uresponses.copyTo(responses);
for( i = 0; i < nkeypoints; i++ )
allKeypoints[i].response = responses.at<float>(i);
}
}
//cull to the final desired level, using the new Harris scores or the original FAST scores.
if( !useOCL )
HarrisResponses(imagePyramid, layerInfo, allKeypoints, 7, HARRIS_K);
std::vector<KeyPoint> newAllKeypoints;
newAllKeypoints.reserve(nfeaturesPerLevel[0]*nlevels);
int offset = 0;
for( level = 0; level < nlevels; level++ )
{
int featuresNum = nfeaturesPerLevel[level];
nkeypoints = counters[level];
keypoints.resize(nkeypoints);
std::copy(allKeypoints.begin() + offset,
allKeypoints.begin() + offset + nkeypoints,
keypoints.begin());
offset += nkeypoints;
//cull to the final desired level, using the new Harris scores.
KeyPointsFilter::retainBest(keypoints, featuresNum);
float sf = getScale(level, firstLevel, scaleFactor);
std::copy(keypoints.begin(), keypoints.end(), std::back_inserter(newAllKeypoints));
}
std::swap(allKeypoints, newAllKeypoints);
}
// Set the level of the coordinates
for (std::vector<KeyPoint>::iterator keypoint = keypoints.begin(),
keypointEnd = keypoints.end(); keypoint != keypointEnd; ++keypoint)
nkeypoints = (int)allKeypoints.size();
if( useOCL )
{
keypoint->octave = level;
keypoint->size = patchSize*sf;
}
UMat uumax;
if( useOCL )
copyVectorToUMat(umax, uumax);
computeOrientation(imagePyramid[level], keypoints, halfPatchSize, umax);
}
}
uploadORBKeypoints(allKeypoints, ukeypoints_buf, ukeypoints);
useOCL = ocl_ICAngles(uimagePyramid, ulayerInfo, ukeypoints, uresponses, uumax,
nkeypoints, halfPatchSize);
/** Compute the ORB decriptors
* @param image the image to compute the features and descriptors on
* @param integral_image the integral image of the image (can be empty, but the computation will be slower)
* @param level the scale at which we compute the orientation
* @param keypoints the keypoints to use
* @param descriptors the resulting descriptors
*/
static void computeDescriptors(const Mat& image, std::vector<KeyPoint>& keypoints, Mat& descriptors,
const std::vector<Point>& pattern, int dsize, int WTA_K)
if( useOCL )
{
//convert to grayscale if more than one color
CV_Assert(image.type() == CV_8UC1);
//create the descriptor mat, keypoints.size() rows, BYTES cols
descriptors = Mat::zeros((int)keypoints.size(), dsize, CV_8UC1);
uresponses.copyTo(responses);
for( i = 0; i < nkeypoints; i++ )
allKeypoints[i].angle = responses.at<float>(i);
}
}
for (size_t i = 0; i < keypoints.size(); i++)
computeOrbDescriptor(keypoints[i], image, &pattern[0], descriptors.ptr((int)i), dsize, WTA_K);
if( !useOCL )
{
ICAngles(imagePyramid, layerInfo, allKeypoints, umax, halfPatchSize);
}
for( i = 0; i < nkeypoints; i++ )
{
float scale = layerScale[allKeypoints[i].octave];
allKeypoints[i].pt *= scale;
}
}
@ -728,7 +889,7 @@ static void computeDescriptors(const Mat& image, std::vector<KeyPoint>& keypoint
* @param do_keypoints if true, the keypoints are computed, otherwise used as an input
* @param do_descriptors if true, also computes the descriptors
*/
void ORB::operator()( InputArray _image, InputArray _mask, std::vector<KeyPoint>& _keypoints,
void ORB::operator()( InputArray _image, InputArray _mask, std::vector<KeyPoint>& keypoints,
OutputArray _descriptors, bool useProvidedKeypoints ) const
{
CV_Assert(patchSize >= 2);
@ -744,11 +905,14 @@ void ORB::operator()( InputArray _image, InputArray _mask, std::vector<KeyPoint>
int halfPatchSize = patchSize / 2;
int border = std::max(edgeThreshold, std::max(halfPatchSize, HARRIS_BLOCK_SIZE/2))+1;
bool useOCL = ocl::useOpenCL();
Mat image = _image.getMat(), mask = _mask.getMat();
if( image.type() != CV_8UC1 )
cvtColor(_image, image, COLOR_BGR2GRAY);
int levelsNum = this->nlevels;
int i, level, nLevels = this->nlevels, nkeypoints = (int)keypoints.size();
bool sortedByLevel = true;
if( !do_keypoints )
{
@ -761,129 +925,145 @@ void ORB::operator()( InputArray _image, InputArray _mask, std::vector<KeyPoint>
//
// In short, ultimately the descriptor should
// ignore octave parameter and deal only with the keypoint size.
levelsNum = 0;
for( size_t i = 0; i < _keypoints.size(); i++ )
levelsNum = std::max(levelsNum, std::max(_keypoints[i].octave, 0));
levelsNum++;
nLevels = 0;
for( i = 0; i < nkeypoints; i++ )
{
level = keypoints[i].octave;
CV_Assert(level >= 0);
if( i > 0 && level < keypoints[i-1].octave )
sortedByLevel = false;
nLevels = std::max(nLevels, level);
}
nLevels++;
}
// Pre-compute the scale pyramids
std::vector<Mat> imagePyramid(levelsNum), maskPyramid(levelsNum);
for (int level = 0; level < levelsNum; ++level)
std::vector<Rect> layerInfo(nLevels);
std::vector<int> layerOfs(nLevels);
std::vector<float> layerScale(nLevels);
Mat imagePyramid, maskPyramid;
UMat uimagePyramid, ulayerInfo;
int level_dy = image.rows + border*2;
Point level_ofs(0,0);
Size bufSize((image.cols + border*2 + 15) & -16, 0);
for( level = 0; level < nLevels; level++ )
{
float scale = 1/getScale(level, firstLevel, scaleFactor);
Size sz(cvRound(image.cols*scale), cvRound(image.rows*scale));
float scale = getScale(level, firstLevel, scaleFactor);
layerScale[level] = scale;
Size sz(cvRound(image.cols/scale), cvRound(image.rows/scale));
Size wholeSize(sz.width + border*2, sz.height + border*2);
Mat temp(wholeSize, image.type()), masktemp;
imagePyramid[level] = temp(Rect(border, border, sz.width, sz.height));
if( level_ofs.x + wholeSize.width > bufSize.width )
{
level_ofs = Point(0, level_ofs.y + level_dy);
level_dy = wholeSize.height;
}
Rect linfo(level_ofs.x + border, level_ofs.y + border, sz.width, sz.height);
layerInfo[level] = linfo;
layerOfs[level] = linfo.y*bufSize.width + linfo.x;
level_ofs.x += wholeSize.width;
}
bufSize.height = level_ofs.y + level_dy;
imagePyramid.create(bufSize, CV_8U);
if( !mask.empty() )
maskPyramid.create(bufSize, CV_8U);
Mat prevImg = image, prevMask = mask;
// Pre-compute the scale pyramids
for (level = 0; level < nLevels; ++level)
{
Rect linfo = layerInfo[level];
Size sz(linfo.width, linfo.height);
Size wholeSize(sz.width + border*2, sz.height + border*2);
Rect wholeLinfo = Rect(linfo.x - border, linfo.y - border, wholeSize.width, wholeSize.height);
Mat extImg = imagePyramid(wholeLinfo), extMask;
Mat currImg = extImg(Rect(border, border, sz.width, sz.height)), currMask;
if( !mask.empty() )
{
masktemp = Mat(wholeSize, mask.type());
maskPyramid[level] = masktemp(Rect(border, border, sz.width, sz.height));
extMask = maskPyramid(wholeLinfo);
currMask = extMask(Rect(border, border, sz.width, sz.height));
}
// Compute the resized image
if( level != firstLevel )
{
if( level < firstLevel )
{
resize(image, imagePyramid[level], sz, 0, 0, INTER_LINEAR);
if (!mask.empty())
resize(mask, maskPyramid[level], sz, 0, 0, INTER_LINEAR);
}
else
{
resize(imagePyramid[level-1], imagePyramid[level], sz, 0, 0, INTER_LINEAR);
resize(prevImg, currImg, sz, 0, 0, INTER_LINEAR);
if( !mask.empty() )
{
resize(maskPyramid[level-1], maskPyramid[level], sz, 0, 0, INTER_LINEAR);
threshold(maskPyramid[level], maskPyramid[level], 254, 0, THRESH_TOZERO);
}
resize(prevMask, currMask, sz, 0, 0, INTER_LINEAR);
if( level > firstLevel )
threshold(currMask, currMask, 254, 0, THRESH_TOZERO);
}
copyMakeBorder(imagePyramid[level], temp, border, border, border, border,
copyMakeBorder(currImg, extImg, border, border, border, border,
BORDER_REFLECT_101+BORDER_ISOLATED);
if (!mask.empty())
copyMakeBorder(maskPyramid[level], masktemp, border, border, border, border,
copyMakeBorder(currMask, extMask, border, border, border, border,
BORDER_CONSTANT+BORDER_ISOLATED);
}
else
{
copyMakeBorder(image, temp, border, border, border, border,
copyMakeBorder(image, extImg, border, border, border, border,
BORDER_REFLECT_101);
if( !mask.empty() )
copyMakeBorder(mask, masktemp, border, border, border, border,
copyMakeBorder(mask, extMask, border, border, border, border,
BORDER_CONSTANT+BORDER_ISOLATED);
}
prevImg = currImg;
prevMask = currMask;
}
// Pre-compute the keypoints (we keep the best over all scales, so this has to be done beforehand
std::vector < std::vector<KeyPoint> > allKeypoints;
if( useOCL )
copyVectorToUMat(layerOfs, ulayerInfo);
if( do_keypoints )
{
if( useOCL )
imagePyramid.copyTo(uimagePyramid);
// Get keypoints, those will be far enough from the border that no check will be required for the descriptor
computeKeyPoints(imagePyramid, maskPyramid, allKeypoints,
nfeatures, firstLevel, scaleFactor,
edgeThreshold, patchSize, scoreType);
// make sure we have the right number of keypoints keypoints
/*std::vector<KeyPoint> temp;
for (int level = 0; level < n_levels; ++level)
{
std::vector<KeyPoint>& keypoints = all_keypoints[level];
temp.insert(temp.end(), keypoints.begin(), keypoints.end());
keypoints.clear();
}
KeyPoint::retainBest(temp, n_features_);
for (std::vector<KeyPoint>::iterator keypoint = temp.begin(),
keypoint_end = temp.end(); keypoint != keypoint_end; ++keypoint)
all_keypoints[keypoint->octave].push_back(*keypoint);*/
computeKeyPoints(imagePyramid, uimagePyramid, maskPyramid,
layerInfo, ulayerInfo, layerScale, keypoints,
nfeatures, scaleFactor, edgeThreshold, patchSize, scoreType, useOCL);
}
else
{
// Remove keypoints very close to the border
KeyPointsFilter::runByImageBorder(_keypoints, image.size(), edgeThreshold);
KeyPointsFilter::runByImageBorder(keypoints, image.size(), edgeThreshold);
// Cluster the input keypoints depending on the level they were computed at
allKeypoints.resize(levelsNum);
for (std::vector<KeyPoint>::iterator keypoint = _keypoints.begin(),
keypointEnd = _keypoints.end(); keypoint != keypointEnd; ++keypoint)
allKeypoints[keypoint->octave].push_back(*keypoint);
// Make sure we rescale the coordinates
for (int level = 0; level < levelsNum; ++level)
if( !sortedByLevel )
{
if (level == firstLevel)
continue;
std::vector<KeyPoint> & keypoints = allKeypoints[level];
float scale = 1/getScale(level, firstLevel, scaleFactor);
for (std::vector<KeyPoint>::iterator keypoint = keypoints.begin(),
keypointEnd = keypoints.end(); keypoint != keypointEnd; ++keypoint)
keypoint->pt *= scale;
std::vector<std::vector<KeyPoint> > allKeypoints(nLevels);
nkeypoints = (int)keypoints.size();
for( i = 0; i < nkeypoints; i++ )
{
level = keypoints[i].octave;
CV_Assert(0 <= level);
allKeypoints[level].push_back(keypoints[i]);
}
keypoints.clear();
for( level = 0; level < nLevels; level++ )
std::copy(allKeypoints[level].begin(), allKeypoints[level].end(), std::back_inserter(keypoints));
}
}
Mat descriptors;
std::vector<Point> pattern;
if( do_descriptors )
{
int nkeypoints = 0;
for (int level = 0; level < levelsNum; ++level)
nkeypoints += (int)allKeypoints[level].size();
int dsize = descriptorSize();
nkeypoints = (int)keypoints.size();
if( nkeypoints == 0 )
_descriptors.release();
else
{
_descriptors.create(nkeypoints, descriptorSize(), CV_8U);
descriptors = _descriptors.getMat();
_descriptors.release();
return;
}
_descriptors.create(nkeypoints, dsize, CV_8U);
std::vector<Point> pattern;
const int npoints = 512;
Point patternbuf[npoints];
const Point* pattern0 = (const Point*)bit_pattern_31_;
@ -903,43 +1083,36 @@ void ORB::operator()( InputArray _image, InputArray _mask, std::vector<KeyPoint>
int ntuples = descriptorSize()*4;
initializeOrbPattern(pattern0, pattern, ntuples, WTA_K, npoints);
}
}
_keypoints.clear();
int offset = 0;
for (int level = 0; level < levelsNum; ++level)
for( level = 0; level < nLevels; level++ )
{
// Get the features and compute their orientation
std::vector<KeyPoint>& keypoints = allKeypoints[level];
int nkeypoints = (int)keypoints.size();
// Compute the descriptors
if (do_descriptors)
{
Mat desc;
if (!descriptors.empty())
{
desc = descriptors.rowRange(offset, offset + nkeypoints);
}
offset += nkeypoints;
// preprocess the resized image
Mat& workingMat = imagePyramid[level];
Mat workingMat = imagePyramid(layerInfo[level]);
//boxFilter(working_mat, working_mat, working_mat.depth(), Size(5,5), Point(-1,-1), true, BORDER_REFLECT_101);
GaussianBlur(workingMat, workingMat, Size(7, 7), 2, 2, BORDER_REFLECT_101);
computeDescriptors(workingMat, keypoints, desc, pattern, descriptorSize(), WTA_K);
}
// Copy to the output data
if (level != firstLevel)
if( useOCL )
{
float scale = getScale(level, firstLevel, scaleFactor);
for (std::vector<KeyPoint>::iterator keypoint = keypoints.begin(),
keypointEnd = keypoints.end(); keypoint != keypointEnd; ++keypoint)
keypoint->pt *= scale;
imagePyramid.copyTo(uimagePyramid);
std::vector<Vec4i> kptbuf;
UMat ukeypoints, upattern;
copyVectorToUMat(pattern, upattern);
uploadORBKeypoints(keypoints, layerScale, kptbuf, ukeypoints);
UMat udescriptors = _descriptors.getUMat();
useOCL = ocl_computeOrbDescriptors(uimagePyramid, ulayerInfo,
ukeypoints, udescriptors, upattern,
nkeypoints, dsize, WTA_K);
}
if( !useOCL )
{
Mat descriptors = _descriptors.getMat();
computeOrbDescriptors(imagePyramid, layerInfo, layerScale,
keypoints, descriptors, pattern, dsize, WTA_K);
}
// And add the keypoints to the output
_keypoints.insert(_keypoints.end(), keypoints.begin(), keypoints.end());
}
}