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libopencv-dev / usr / local / lib / libopencv_features2d.so.3.0.0
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"@NE$@_&@î)@k+@P.@Â/1@=4@97@ ý:@ýÁ>@oØB@±PG@?L@r¿Q@¡øW@O#_@xg@èq@³)@â@±@?UCN2cv4MSEREN2cv9MSER_ImplEN2cv6detail12PtrOwnerImplINS_9MSER_ImplENS_14DefaultDeleterIS2_EEEE4»½×Ùß|Û=ðx@ _ B/home/yi/dextro_devops/debpkging/opencv/opencv-3.0.0/modules/features2d/src/feature2d.cppdetectAndComputecomputedetectN2cv9Feature2DE/home/yi/dextro_devops/debpkging/opencv/opencv-3.0.0/modules/features2d/src/draw.cppdrawMatches_prepareImgAndDrawKeypointsdrawMatches_drawKeypointdrawKeypointsA4C/home/yi/dextro_devops/debpkging/opencv/opencv-3.0.0/modules/features2d/src/fast_score.cppmakeOffsetsÿÿÿÿþÿÿÿýÿÿÿýÿÿÿÿÿÿÿýÿÿÿþÿÿÿþÿÿÿýÿÿÿÿÿÿÿýÿÿÿýÿÿÿþÿÿÿÿÿÿÿÿÿÿÿþÿÿÿþÿÿÿÿÿÿÿþÿÿÿþÿÿÿÿÿÿÿþÿÿÿþÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿüüüüüüüüèèèèèèèèFAST_findKeypointsFAST_nonmaxSupressionN2cv19FastFeatureDetectorEN2cv24FastFeatureDetector_ImplEN2cv6detail12PtrOwnerImplINS_24FastFeatureDetector_ImplENS_14DefaultDeleterIS2_EEEE/home/yi/dextro_devops/debpkging/opencv/opencv-3.0.0/modules/features2d/src/agast_score.cppmakeAgastOffsetsýÿÿÿýÿÿÿÿÿÿÿþÿÿÿþÿÿÿÿÿÿÿýÿÿÿýÿÿÿýÿÿÿþÿÿÿÿÿÿÿÿÿÿÿþÿÿÿýÿÿÿýÿÿÿþÿÿÿÿÿÿÿÿÿÿÿþÿÿÿýÿÿÿþÿÿÿÿÿÿÿÿÿÿÿþÿÿÿþÿÿÿþÿÿÿÿÿÿÿÿÿÿÿþÿÿÿþÿÿÿþÿÿÿÿÿÿÿÿÿÿÿþÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿdescriptordescriptor_channelsdescriptor_sizethresholdoctavessublevelsdiffusivity/home/yi/dextro_devops/debpkging/opencv/opencv-3.0.0/modules/features2d/src/akaze.cppoperator<<operator<<detectAndComputeN2cv5AKAZEEN2cv10AKAZE_ImplEN2cv6detail12PtrOwnerImplINS_10AKAZE_ImplENS_14DefaultDeleterIS2_EEEEp?ð>N2cv20AgastFeatureDetectorEN2cv25AgastFeatureDetector_ImplEN2cv6detail12PtrOwnerImplINS_25AgastFeatureDetector_ImplENS_14DefaultDeleterIS2_EEEE_/home/yi/dextro_devops/debpkging/opencv/opencv-3.0.0/modules/features2d/src/keypoint.cpprunByKeypointSizeN2cv12GFTTDetectorEN2cv17GFTTDetector_ImplEN2cv6detail12PtrOwnerImplINS_17GFTTDetector_ImplENS_14DefaultDeleterIS2_EEEE/home/yi/dextro_devops/debpkging/opencv/opencv-3.0.0/modules/features2d/src/matchers.cppNo element name has been given-D T=%s -D TN=%s -D kercn=%d %s -D DIST_TYPE=%d -D BLOCK_SIZE=%d -D MAX_DESC_LEN=%d-D T=%s -D TN=%s -D kercn=%d %s -D DIST_TYPE=%d -D BLOCK_SIZE=%dtrainDescCollection.size() == 0_queryDescriptors.type() == trainDescType(int64)imgCount*IMGIDX_ONE < INT_MAX_descriptors.isUMat() || _descriptors.isUMatVector() || _descriptors.isMat() || _descriptors.isMatVector()indexParamsip.type() == FileNode::SEQtypevaluesearchParamssp.type() == FileNode::SEQmasks.size() == imageCountFlannBasedBruteForceBruteForce-SL2BruteForce-L1BruteForce-HammingBruteForce-HammingLUTBruteForce-Hamming(2)Unknown matcher name[typename]imageCount > 0dim > 0-D T_FLOATBruteForceMatch_MatchBruteForceMatch_knnMatchBruteForceMatch_RadiusMatchvector::_M_range_insertàçýÿàçýÿàçýÿàçýÿàçýÿçýÿ èýÿ(èýÿåýÿåýÿåýÿåýÿåýÿåýÿåýÿðèýÿåýÿåýÿåýÿåýÿåýÿåýÿåýÿPåýÿÀéýÿÀéýÿÀéýÿÀéýÿÀéýÿpéýÿêýÿêýÿ éýÿ éýÿ éýÿ éýÿ éýÿ éýÿ éýÿÐêýÿ éýÿ éýÿ éýÿ éýÿ éýÿ éýÿ éýÿàèýÿ¸6þÿ6þÿH6þÿ6þÿØ5þÿ 5þÿp5þÿ°1þÿð6þÿð6þÿð6þÿð6þÿð6þÿð6þÿð6þÿØ5þÿð6þÿð6þÿð6þÿð6þÿð6þÿð6þÿð6þÿØ5þÿ9þÿà8þÿ¨8þÿ88þÿ8þÿp8þÿÐ7þÿð6þÿP9þÿP9þÿP9þÿP9þÿP9þÿP9þÿP9þÿ8þÿP9þÿP9þÿP9þÿP9þÿP9þÿP9þÿP9þÿ8þÿcloneoperator<<operator<<operator<<operator<<operator<<operator<<operator<<readtrainFlannBasedMatchercreateradiusMatchImplknnMatchImplradiusMatchknnMatchcheckMasksconvertMatchesaddgetLocalIdxgetDescriptorgetDescriptorsetN2cv17DescriptorMatcher20DescriptorCollectionEN2cv17DescriptorMatcherEN2cv9BFMatcherEN2cv17FlannBasedMatcherEN2cv6detail12PtrOwnerImplINS_5flann17KDTreeIndexParamsENS_14DefaultDeleterIS3_EEEEN2cv6detail12PtrOwnerImplINS_5flann12SearchParamsENS_14DefaultDeleterIS3_EEEEN2cv6detail12PtrOwnerImplINS_9BFMatcherENS_14DefaultDeleterIS2_EEEEN2cv6detail12PtrOwnerImplINS_17FlannBasedMatcherENS_14DefaultDeleterIS2_EEEEN2cv6detail12PtrOwnerImplINS_5flann5IndexENS_14DefaultDeleterIS3_EEEEN2cv6detail12PtrOwnerImplINS_5flann11IndexParamsENS_14DefaultDeleterIS3_EEEEð¿ð¿/home/yi/dextro_devops/debpkging/opencv/opencv-3.0.0/modules/features2d/src/evaluation.cppcomputeRecallPrecisionCurveoperator()evaluateFeatureDetectorcalcProjectionconvertðAHB?ÿBÿÿÿÿÿÿïÿÿÿÿÿÿïextendedupright/home/yi/dextro_devops/debpkging/opencv/opencv-3.0.0/modules/features2d/src/kaze.cppoperator<<operator<<detectAndComputeN2cv4KAZEEN2cv9KAZE_ImplEN2cv6detail12PtrOwnerImplINS_9KAZE_ImplENS_14DefaultDeleterIS2_EEEE/home/yi/dextro_devops/debpkging/opencv/opencv-3.0.0/modules/features2d/src/bagofwords.cppcomputeclusteraddN2cv10BOWTrainerEN2cv16BOWKMeansTrainerEN2cv25BOWImgDescriptorExtractorE#pragma OPENCL EXTENSION cl_khr_global_int32_base_atomics:enable
#define MAX_FLOAT 3.40282e+038f
#ifndef T
#define T float
#endif
#ifndef BLOCK_SIZE
#define BLOCK_SIZE 16
#endif
#ifndef MAX_DESC_LEN
#define MAX_DESC_LEN 64
#endif
#define BLOCK_SIZE_ODD          (BLOCK_SIZE + 1)
#ifndef SHARED_MEM_SZ
#  if (BLOCK_SIZE < MAX_DESC_LEN)
#    define SHARED_MEM_SZ      (kercn * (BLOCK_SIZE * MAX_DESC_LEN + BLOCK_SIZE * BLOCK_SIZE))
#  else
#    define SHARED_MEM_SZ      (kercn * 2 * BLOCK_SIZE_ODD * BLOCK_SIZE)
#  endif
#endif
#ifndef DIST_TYPE
#define DIST_TYPE 2
#endif
#if (DIST_TYPE == 2)
#   ifdef T_FLOAT
typedef float result_type;
#       if (8 == kercn)
typedef float8 value_type;
#           define DIST(x, y) {value_type d = fabs((x) - (y)); result += d.s0 + d.s1 + d.s2 + d.s3 + d.s4 + d.s5 + d.s6 + d.s7;}
#       elif (4 == kercn)
typedef float4 value_type;
#           define DIST(x, y) {value_type d = fabs((x) - (y)); result += d.s0 + d.s1 + d.s2 + d.s3;}
#       else
typedef float value_type;
#           define DIST(x, y) result += fabs((x) - (y))
#       endif
#   else
typedef int result_type;
#       if (8 == kercn)
typedef int8 value_type;
#           define DIST(x, y) {value_type d = abs((x) - (y)); result += d.s0 + d.s1 + d.s2 + d.s3 + d.s4 + d.s5 + d.s6 + d.s7;}
#       elif (4 == kercn)
typedef int4 value_type;
#           define DIST(x, y) {value_type d = abs((x) - (y)); result += d.s0 + d.s1 + d.s2 + d.s3;}
#       else
typedef int  value_type;
#           define DIST(x, y) result += abs((x) - (y))
#       endif
#   endif
#   define DIST_RES(x) (x)
#elif (DIST_TYPE == 4)
typedef float result_type;
#   if (8 == kercn)
typedef float8 value_type;
#       define DIST(x, y)   {value_type d = ((x) - (y)); result += dot(d.s0123, d.s0123) + dot(d.s4567, d.s4567);}
#   elif (4 == kercn)
typedef float4      value_type;
#       define DIST(x, y)   {value_type d = ((x) - (y)); result += dot(d, d);}
#   else
typedef float       value_type;
#       define DIST(x, y)   {value_type d = ((x) - (y)); result = mad(d, d, result);}
#   endif
#   define DIST_RES(x) sqrt(x)
#elif (DIST_TYPE == 6)
#   if (8 == kercn)
typedef int8 value_type;
#   elif (4 == kercn)
typedef int4 value_type;
#   else
typedef int value_type;
#   endif
typedef int result_type;
#   define DIST(x, y) result += popcount( (x) ^ (y) )
#   define DIST_RES(x) (x)
#endif
inline result_type reduce_block(
__local value_type *s_query,
__local value_type *s_train,
int lidx,
int lidy
)
{
result_type result = 0;
#pragma unroll
for (int j = 0 ; j < BLOCK_SIZE ; j++)
{
DIST(s_query[lidy * BLOCK_SIZE_ODD + j], s_train[j * BLOCK_SIZE_ODD + lidx]);
}
return DIST_RES(result);
}
inline result_type reduce_block_match(
__local value_type *s_query,
__local value_type *s_train,
int lidx,
int lidy
)
{
result_type result = 0;
#pragma unroll
for (int j = 0 ; j < BLOCK_SIZE ; j++)
{
DIST(s_query[lidy * BLOCK_SIZE_ODD + j], s_train[j * BLOCK_SIZE_ODD + lidx]);
}
return result;
}
inline result_type reduce_multi_block(
__local value_type *s_query,
__local value_type *s_train,
int block_index,
int lidx,
int lidy
)
{
result_type result = 0;
#pragma unroll
for (int j = 0 ; j < BLOCK_SIZE ; j++)
{
DIST(s_query[lidy * MAX_DESC_LEN + block_index * BLOCK_SIZE + j], s_train[j * BLOCK_SIZE + lidx]);
}
return result;
}
__kernel void BruteForceMatch_Match(
__global T *query,
__global T *train,
__global int *bestTrainIdx,
__global float *bestDistance,
int query_rows,
int query_cols,
int train_rows,
int train_cols,
int step
)
{
const int lidx = get_local_id(0);
const int lidy = get_local_id(1);
const int groupidx = get_group_id(0);
const int queryIdx = mad24(BLOCK_SIZE, groupidx, lidy);
const int queryOffset = min(queryIdx, query_rows - 1) * step;
__global TN *query_vec = (__global TN *)(query + queryOffset);
query_cols /= kercn;
__local float sharebuffer[SHARED_MEM_SZ];
__local value_type *s_query = (__local value_type *)sharebuffer;
#if 0 < MAX_DESC_LEN
__local value_type *s_train = (__local value_type *)sharebuffer + BLOCK_SIZE * MAX_DESC_LEN;
#pragma unroll
for (int i = 0; i < MAX_DESC_LEN / BLOCK_SIZE; i++)
{
const int loadx = mad24(BLOCK_SIZE, i, lidx);
s_query[mad24(MAX_DESC_LEN, lidy, loadx)] = loadx < query_cols ? query_vec[loadx] : 0;
}
#else
__local value_type *s_train = (__local value_type *)sharebuffer + BLOCK_SIZE_ODD * BLOCK_SIZE;
const int s_query_i = mad24(BLOCK_SIZE_ODD, lidy, lidx);
const int s_train_i = mad24(BLOCK_SIZE_ODD, lidx, lidy);
#endif
float myBestDistance = MAX_FLOAT;
int myBestTrainIdx = -1;
for (int t = 0, endt = (train_rows + BLOCK_SIZE - 1) / BLOCK_SIZE; t < endt; t++)
{
result_type result = 0;
const int trainOffset = min(mad24(BLOCK_SIZE, t, lidy), train_rows - 1) * step;
__global TN *train_vec = (__global TN *)(train + trainOffset);
#if 0 < MAX_DESC_LEN
#pragma unroll
for (int i = 0; i < MAX_DESC_LEN / BLOCK_SIZE; i++)
{
const int loadx = mad24(BLOCK_SIZE, i, lidx);
s_train[mad24(BLOCK_SIZE, lidx, lidy)] = loadx < train_cols ? train_vec[loadx] : 0;
barrier(CLK_LOCAL_MEM_FENCE);
result += reduce_multi_block(s_query, s_train, i, lidx, lidy);
barrier(CLK_LOCAL_MEM_FENCE);
}
#else
for (int i = 0, endq = (query_cols + BLOCK_SIZE - 1) / BLOCK_SIZE; i < endq; i++)
{
const int loadx = mad24(i, BLOCK_SIZE, lidx);
if (loadx < query_cols)
{
s_query[s_query_i] = query_vec[loadx];
s_train[s_train_i] = train_vec[loadx];
}
else
{
s_query[s_query_i] = 0;
s_train[s_train_i] = 0;
}
barrier(CLK_LOCAL_MEM_FENCE);
result += reduce_block_match(s_query, s_train, lidx, lidy);
barrier(CLK_LOCAL_MEM_FENCE);
}
#endif
result = DIST_RES(result);
const int trainIdx = mad24(BLOCK_SIZE, t, lidx);
if (queryIdx < query_rows && trainIdx < train_rows && result < myBestDistance )
{
myBestDistance = result;
myBestTrainIdx = trainIdx;
}
}
barrier(CLK_LOCAL_MEM_FENCE);
__local float *s_distance = (__local float *)sharebuffer;
__local int *s_trainIdx = (__local int *)(sharebuffer + BLOCK_SIZE_ODD * BLOCK_SIZE);
s_distance += lidy * BLOCK_SIZE_ODD;
s_trainIdx += lidy * BLOCK_SIZE_ODD;
s_distance[lidx] = myBestDistance;
s_trainIdx[lidx] = myBestTrainIdx;
barrier(CLK_LOCAL_MEM_FENCE);
#pragma unroll
for (int k = 0 ; k < BLOCK_SIZE; k++)
{
if (myBestDistance > s_distance[k])
{
myBestDistance = s_distance[k];
myBestTrainIdx = s_trainIdx[k];
}
}
if (queryIdx < query_rows && lidx == 0)
{
bestTrainIdx[queryIdx] = myBestTrainIdx;
bestDistance[queryIdx] = myBestDistance;
}
}
__kernel void BruteForceMatch_RadiusMatch(
__global T *query,
__global T *train,
float maxDistance,
__global int *bestTrainIdx,
__global float *bestDistance,
__global int *nMatches,
int query_rows,
int query_cols,
int train_rows,
int train_cols,
int bestTrainIdx_cols,
int step,
int ostep
)
{
const int lidx = get_local_id(0);
const int lidy = get_local_id(1);
const int groupidx = get_group_id(0);
const int groupidy = get_group_id(1);
const int queryIdx = mad24(BLOCK_SIZE, groupidy, lidy);
const int queryOffset = min(queryIdx, query_rows - 1) * step;
__global TN *query_vec = (__global TN *)(query + queryOffset);
const int trainIdx = mad24(BLOCK_SIZE, groupidx, lidx);
const int trainOffset = min(mad24(BLOCK_SIZE, groupidx, lidy), train_rows - 1) * step;
__global TN *train_vec = (__global TN *)(train + trainOffset);
query_cols /= kercn;
__local float sharebuffer[SHARED_MEM_SZ];
__local value_type *s_query = (__local value_type *)sharebuffer;
__local value_type *s_train = (__local value_type *)sharebuffer + BLOCK_SIZE_ODD * BLOCK_SIZE;
result_type result = 0;
const int s_query_i = mad24(BLOCK_SIZE_ODD, lidy, lidx);
const int s_train_i = mad24(BLOCK_SIZE_ODD, lidx, lidy);
for (int i = 0 ; i < (query_cols + BLOCK_SIZE - 1) / BLOCK_SIZE ; ++i)
{
const int loadx = mad24(BLOCK_SIZE, i, lidx);
if (loadx < query_cols)
{
s_query[s_query_i] = query_vec[loadx];
s_train[s_train_i] = train_vec[loadx];
}
else
{
s_query[s_query_i] = 0;
s_train[s_train_i] = 0;
}
barrier(CLK_LOCAL_MEM_FENCE);
result += reduce_block(s_query, s_train, lidx, lidy);
barrier(CLK_LOCAL_MEM_FENCE);
}
if (queryIdx < query_rows && trainIdx < train_rows && convert_float(result) < maxDistance)
{
int ind = atom_inc(nMatches + queryIdx);
if(ind < bestTrainIdx_cols)
{
bestTrainIdx[mad24(queryIdx, ostep, ind)] = trainIdx;
bestDistance[mad24(queryIdx, ostep, ind)] = result;
}
}
}
__kernel void BruteForceMatch_knnMatch(
__global T *query,
__global T *train,
__global int2 *bestTrainIdx,
__global float2 *bestDistance,
int query_rows,
int query_cols,
int train_rows,
int train_cols,
int step
)
{
const int lidx = get_local_id(0);
const int lidy = get_local_id(1);
const int groupidx = get_group_id(0);
const int queryIdx = mad24(BLOCK_SIZE, groupidx, lidy);
const int queryOffset = min(queryIdx, query_rows - 1) * step;
__global TN *query_vec = (__global TN *)(query + queryOffset);
query_cols /= kercn;
__local float sharebuffer[SHARED_MEM_SZ];
__local value_type *s_query = (__local value_type *)sharebuffer;
#if 0 < MAX_DESC_LEN
__local value_type *s_train = (__local value_type *)sharebuffer + BLOCK_SIZE * MAX_DESC_LEN;
#pragma unroll
for (int i = 0 ;  i <  MAX_DESC_LEN / BLOCK_SIZE; i ++)
{
int loadx = mad24(BLOCK_SIZE, i, lidx);
s_query[mad24(MAX_DESC_LEN, lidy, loadx)] = loadx < query_cols ? query_vec[loadx] : 0;
}
#else
__local value_type *s_train = (__local value_type *)sharebuffer + BLOCK_SIZE_ODD * BLOCK_SIZE;
const int s_query_i = mad24(BLOCK_SIZE_ODD, lidy, lidx);
const int s_train_i = mad24(BLOCK_SIZE_ODD, lidx, lidy);
#endif
float myBestDistance1 = MAX_FLOAT;
float myBestDistance2 = MAX_FLOAT;
int myBestTrainIdx1 = -1;
int myBestTrainIdx2 = -1;
for (int t = 0, endt = (train_rows + BLOCK_SIZE - 1) / BLOCK_SIZE; t < endt ; t++)
{
result_type result = 0;
int trainOffset = min(mad24(BLOCK_SIZE, t, lidy), train_rows - 1) * step;
__global TN *train_vec = (__global TN *)(train + trainOffset);
#if 0 < MAX_DESC_LEN
#pragma unroll
for (int i = 0 ; i < MAX_DESC_LEN / BLOCK_SIZE ; i++)
{
const int loadx = mad24(BLOCK_SIZE, i, lidx);
s_train[mad24(BLOCK_SIZE, lidx, lidy)] = loadx < train_cols ? train_vec[loadx] : 0;
barrier(CLK_LOCAL_MEM_FENCE);
result += reduce_multi_block(s_query, s_train, i, lidx, lidy);
barrier(CLK_LOCAL_MEM_FENCE);
}
#else
for (int i = 0, endq = (query_cols + BLOCK_SIZE -1) / BLOCK_SIZE; i < endq ; i++)
{
const int loadx = mad24(BLOCK_SIZE, i, lidx);
if (loadx < query_cols)
{
s_query[s_query_i] = query_vec[loadx];
s_train[s_train_i] = train_vec[loadx];
}
else
{
s_query[s_query_i] = 0;
s_train[s_train_i] = 0;
}
barrier(CLK_LOCAL_MEM_FENCE);
result += reduce_block_match(s_query, s_train, lidx, lidy);
barrier(CLK_LOCAL_MEM_FENCE);
}
#endif
result = DIST_RES(result);
const int trainIdx = mad24(BLOCK_SIZE, t, lidx);
if (queryIdx < query_rows && trainIdx < train_rows)
{
if (result < myBestDistance1)
{
myBestDistance2 = myBestDistance1;
myBestTrainIdx2 = myBestTrainIdx1;
myBestDistance1 = result;
myBestTrainIdx1 = trainIdx;
}
else if (result < myBestDistance2)
{
myBestDistance2 = result;
myBestTrainIdx2 = trainIdx;
}
}
}
barrier(CLK_LOCAL_MEM_FENCE);
__local float *s_distance = (__local float *)sharebuffer;
__local int *s_trainIdx = (__local int *)(sharebuffer + BLOCK_SIZE_ODD * BLOCK_SIZE);
s_distance += lidy * BLOCK_SIZE_ODD;
s_trainIdx += lidy * BLOCK_SIZE_ODD;
s_distance[lidx] = myBestDistance1;
s_trainIdx[lidx] = myBestTrainIdx1;
float bestDistance1 = MAX_FLOAT;
float bestDistance2 = MAX_FLOAT;
int bestTrainIdx1 = -1;
int bestTrainIdx2 = -1;
barrier(CLK_LOCAL_MEM_FENCE);
if (lidx == 0)
{
for (int i = 0 ; i < BLOCK_SIZE ; i++)
{
float val = s_distance[i];
if (val < bestDistance1)
{
bestDistance2 = bestDistance1;
bestTrainIdx2 = bestTrainIdx1;
bestDistance1 = val;
bestTrainIdx1 = s_trainIdx[i];
}
else if (val < bestDistance2)
{
bestDistance2 = val;
bestTrainIdx2 = s_trainIdx[i];
}
}
}
barrier(CLK_LOCAL_MEM_FENCE);
s_distance[lidx] = myBestDistance2;
s_trainIdx[lidx] = myBestTrainIdx2;
barrier(CLK_LOCAL_MEM_FENCE);
if (lidx == 0)
{
for (int i = 0 ; i < BLOCK_SIZE ; i++)
{
float val = s_distance[i];
if (val < bestDistance2)
{
bestDistance2 = val;
bestTrainIdx2 = s_trainIdx[i];
}
}
}
myBestDistance1 = bestDistance1;
myBestDistance2 = bestDistance2;
myBestTrainIdx1 = bestTrainIdx1;
myBestTrainIdx2 = bestTrainIdx2;
if (queryIdx < query_rows && lidx == 0)
{
bestTrainIdx[queryIdx] = (int2)(myBestTrainIdx1, myBestTrainIdx2);
bestDistance[queryIdx] = (float2)(myBestDistance1, myBestDistance2);
}
}
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;
}
}
}
}
__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;
}
}
}
}
#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;
for( u = -half_k; u <= half_k; u++ )
m_10 += u * center[u];
for( v = 1; v <= half_k; v++ )
{
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;
}
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 cosa;
float sina = sincos(angle, &cosa);
__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;
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
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