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Merge branch 2.4

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pull/459/head
Andrey Kamaev 12 years ago
parent 4d785fff99 f608df9640
commit c527340cb6
33 changed files with 448 additions and 318 deletions
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  1. 71
      apps/traincascade/boost.cpp
  2. 20
      cmake/templates/OpenCV.mk.in
  3. 7
      modules/contrib/src/chamfermatching.cpp
  4. 20
      modules/core/src/matrix.cpp
  5. 10
      modules/core/src/stat.cpp
  6. 1
      modules/features2d/doc/common_interfaces_of_descriptor_extractors.rst
  7. 2
      modules/features2d/doc/common_interfaces_of_descriptor_matchers.rst
  8. 1
      modules/features2d/doc/common_interfaces_of_feature_detectors.rst
  9. 52
      modules/features2d/doc/feature_detection_and_description.rst
  10. 7
      modules/features2d/src/brisk.cpp
  11. 10
      modules/features2d/src/evaluation.cpp
  12. 2
      modules/gpu/src/cascadeclassifier.cpp
  13. 22
      modules/highgui/src/cap_dshow.cpp
  14. 42
      modules/imgproc/src/histogram.cpp
  15. 3
      modules/imgproc/src/pyramids.cpp
  16. 10
      modules/java/android_test/src/org/opencv/test/features2d/BruteForceDescriptorMatcherTest.java
  17. 10
      modules/java/android_test/src/org/opencv/test/features2d/BruteForceL1DescriptorMatcherTest.java
  18. 10
      modules/java/android_test/src/org/opencv/test/features2d/BruteForceSL2DescriptorMatcherTest.java
  19. 10
      modules/java/android_test/src/org/opencv/test/features2d/FlannBasedDescriptorMatcherTest.java
  20. 31
      modules/java/android_test/src/org/opencv/test/features2d/SURFDescriptorExtractorTest.java
  21. 4
      modules/java/android_test/src/org/opencv/test/features2d/SURFFeatureDetectorTest.java
  22. 8
      modules/ml/include/opencv2/ml/ml.hpp
  23. 14
      modules/ml/src/boost.cpp
  24. 58
      modules/ml/src/ertrees.cpp
  25. 9
      modules/ml/src/knearest.cpp
  26. 99
      modules/ml/src/tree.cpp
  27. 31
      modules/objdetect/src/haar.cpp
  28. 5
      modules/objdetect/src/matching.cpp
  29. 16
      modules/ocl/include/opencv2/ocl/ocl.hpp
  30. 1
      modules/ocl/src/arithm.cpp
  31. 170
      modules/ocl/src/initialization.cpp
  32. 8
      modules/ocl/src/precomp.hpp
  33. 2
      modules/ocl/src/pyrlk.cpp

71
apps/traincascade/boost.cpp
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@ -360,7 +360,7 @@ CvDTreeNode* CvCascadeBoostTrainData::subsample_data( const CvMat* _subsample_id
if (is_buf_16u)
{
unsigned short* udst_idx = (unsigned short*)(buf->data.s + root->buf_idx*buf->cols +
unsigned short* udst_idx = (unsigned short*)(buf->data.s + root->buf_idx*get_length_subbuf() +
vi*sample_count + data_root->offset);
for( int i = 0; i < num_valid; i++ )
{
@ -373,7 +373,7 @@ CvDTreeNode* CvCascadeBoostTrainData::subsample_data( const CvMat* _subsample_id
}
else
{
int* idst_idx = buf->data.i + root->buf_idx*buf->cols +
int* idst_idx = buf->data.i + root->buf_idx*get_length_subbuf() +
vi*sample_count + root->offset;
for( int i = 0; i < num_valid; i++ )
{
@ -390,14 +390,14 @@ CvDTreeNode* CvCascadeBoostTrainData::subsample_data( const CvMat* _subsample_id
const int* src_lbls = get_cv_labels(data_root, (int*)(uchar*)inn_buf);
if (is_buf_16u)
{
unsigned short* udst = (unsigned short*)(buf->data.s + root->buf_idx*buf->cols +
unsigned short* udst = (unsigned short*)(buf->data.s + root->buf_idx*get_length_subbuf() +
(workVarCount-1)*sample_count + root->offset);
for( int i = 0; i < count; i++ )
udst[i] = (unsigned short)src_lbls[sidx[i]];
}
else
{
int* idst = buf->data.i + root->buf_idx*buf->cols +
int* idst = buf->data.i + root->buf_idx*get_length_subbuf() +
(workVarCount-1)*sample_count + root->offset;
for( int i = 0; i < count; i++ )
idst[i] = src_lbls[sidx[i]];
@ -407,14 +407,14 @@ CvDTreeNode* CvCascadeBoostTrainData::subsample_data( const CvMat* _subsample_id
const int* sample_idx_src = get_sample_indices(data_root, (int*)(uchar*)inn_buf);
if (is_buf_16u)
{
unsigned short* sample_idx_dst = (unsigned short*)(buf->data.s + root->buf_idx*buf->cols +
unsigned short* sample_idx_dst = (unsigned short*)(buf->data.s + root->buf_idx*get_length_subbuf() +
workVarCount*sample_count + root->offset);
for( int i = 0; i < count; i++ )
sample_idx_dst[i] = (unsigned short)sample_idx_src[sidx[i]];
}
else
{
int* sample_idx_dst = buf->data.i + root->buf_idx*buf->cols +
int* sample_idx_dst = buf->data.i + root->buf_idx*get_length_subbuf() +
workVarCount*sample_count + root->offset;
for( int i = 0; i < count; i++ )
sample_idx_dst[i] = sample_idx_src[sidx[i]];
@ -489,6 +489,10 @@ void CvCascadeBoostTrainData::setData( const CvFeatureEvaluator* _featureEvaluat
int* idst = 0;
unsigned short* udst = 0;
uint64 effective_buf_size = 0;
int effective_buf_height = 0, effective_buf_width = 0;
clear();
shared = true;
have_labels = true;
@ -548,13 +552,28 @@ void CvCascadeBoostTrainData::setData( const CvFeatureEvaluator* _featureEvaluat
var_type->data.i[var_count] = cat_var_count;
var_type->data.i[var_count+1] = cat_var_count+1;
work_var_count = ( cat_var_count ? 0 : numPrecalcIdx ) + 1/*cv_lables*/;
buf_size = (work_var_count + 1) * sample_count/*sample_indices*/;
buf_count = 2;
buf_size = -1; // the member buf_size is obsolete
effective_buf_size = (uint64)(work_var_count + 1)*(uint64)sample_count * buf_count; // this is the total size of "CvMat buf" to be allocated
effective_buf_width = sample_count;
effective_buf_height = work_var_count+1;
if (effective_buf_width >= effective_buf_height)
effective_buf_height *= buf_count;
else
effective_buf_width *= buf_count;
if ((uint64)effective_buf_width * (uint64)effective_buf_height != effective_buf_size)
{
CV_Error(CV_StsBadArg, "The memory buffer cannot be allocated since its size exceeds integer fields limit");
}
if ( is_buf_16u )
buf = cvCreateMat( buf_count, buf_size, CV_16UC1 );
buf = cvCreateMat( effective_buf_height, effective_buf_width, CV_16UC1 );
else
buf = cvCreateMat( buf_count, buf_size, CV_32SC1 );
buf = cvCreateMat( effective_buf_height, effective_buf_width, CV_32SC1 );
cat_count = cvCreateMat( 1, cat_var_count + 1, CV_32SC1 );
@ -609,7 +628,7 @@ void CvCascadeBoostTrainData::setData( const CvFeatureEvaluator* _featureEvaluat
priors_mult = cvCloneMat( priors );
counts = cvCreateMat( 1, get_num_classes(), CV_32SC1 );
direction = cvCreateMat( 1, sample_count, CV_8UC1 );
split_buf = cvCreateMat( 1, sample_count, CV_32SC1 );
split_buf = cvCreateMat( 1, sample_count, CV_32SC1 );//TODO: make a pointer
}
void CvCascadeBoostTrainData::free_train_data()
@ -652,10 +671,10 @@ void CvCascadeBoostTrainData::get_ord_var_data( CvDTreeNode* n, int vi, float* o
if ( vi < numPrecalcIdx )
{
if( !is_buf_16u )
*sortedIndices = buf->data.i + n->buf_idx*buf->cols + vi*sample_count + n->offset;
*sortedIndices = buf->data.i + n->buf_idx*get_length_subbuf() + vi*sample_count + n->offset;
else
{
const unsigned short* shortIndices = (const unsigned short*)(buf->data.s + n->buf_idx*buf->cols +
const unsigned short* shortIndices = (const unsigned short*)(buf->data.s + n->buf_idx*get_length_subbuf() +
vi*sample_count + n->offset );
for( int i = 0; i < nodeSampleCount; i++ )
sortedIndicesBuf[i] = shortIndices[i];
@ -1027,6 +1046,7 @@ void CvCascadeBoostTree::split_node_data( CvDTreeNode* node )
int newBufIdx = data->get_child_buf_idx( node );
int workVarCount = data->get_work_var_count();
CvMat* buf = data->buf;
size_t length_buf_row = data->get_length_subbuf();
cv::AutoBuffer<uchar> inn_buf(n*(3*sizeof(int)+sizeof(float)));
int* tempBuf = (int*)(uchar*)inn_buf;
bool splitInputData;
@ -1070,7 +1090,7 @@ void CvCascadeBoostTree::split_node_data( CvDTreeNode* node )
if (data->is_buf_16u)
{
ushort *ldst, *rdst;
ldst = (ushort*)(buf->data.s + left->buf_idx*buf->cols +
ldst = (ushort*)(buf->data.s + left->buf_idx*length_buf_row +
vi*scount + left->offset);
rdst = (ushort*)(ldst + nl);
@ -1096,9 +1116,9 @@ void CvCascadeBoostTree::split_node_data( CvDTreeNode* node )
else
{
int *ldst, *rdst;
ldst = buf->data.i + left->buf_idx*buf->cols +
ldst = buf->data.i + left->buf_idx*length_buf_row +
vi*scount + left->offset;
rdst = buf->data.i + right->buf_idx*buf->cols +
rdst = buf->data.i + right->buf_idx*length_buf_row +
vi*scount + right->offset;
// split sorted
@ -1131,9 +1151,9 @@ void CvCascadeBoostTree::split_node_data( CvDTreeNode* node )
if (data->is_buf_16u)
{
unsigned short *ldst = (unsigned short *)(buf->data.s + left->buf_idx*buf->cols +
unsigned short *ldst = (unsigned short *)(buf->data.s + left->buf_idx*length_buf_row +
(workVarCount-1)*scount + left->offset);
unsigned short *rdst = (unsigned short *)(buf->data.s + right->buf_idx*buf->cols +
unsigned short *rdst = (unsigned short *)(buf->data.s + right->buf_idx*length_buf_row +
(workVarCount-1)*scount + right->offset);
for( int i = 0; i < n; i++ )
@ -1154,9 +1174,9 @@ void CvCascadeBoostTree::split_node_data( CvDTreeNode* node )
}
else
{
int *ldst = buf->data.i + left->buf_idx*buf->cols +
int *ldst = buf->data.i + left->buf_idx*length_buf_row +
(workVarCount-1)*scount + left->offset;
int *rdst = buf->data.i + right->buf_idx*buf->cols +
int *rdst = buf->data.i + right->buf_idx*length_buf_row +
(workVarCount-1)*scount + right->offset;
for( int i = 0; i < n; i++ )
@ -1184,9 +1204,9 @@ void CvCascadeBoostTree::split_node_data( CvDTreeNode* node )
if (data->is_buf_16u)
{
unsigned short* ldst = (unsigned short*)(buf->data.s + left->buf_idx*buf->cols +
unsigned short* ldst = (unsigned short*)(buf->data.s + left->buf_idx*length_buf_row +
workVarCount*scount + left->offset);
unsigned short* rdst = (unsigned short*)(buf->data.s + right->buf_idx*buf->cols +
unsigned short* rdst = (unsigned short*)(buf->data.s + right->buf_idx*length_buf_row +
workVarCount*scount + right->offset);
for (int i = 0; i < n; i++)
{
@ -1205,9 +1225,9 @@ void CvCascadeBoostTree::split_node_data( CvDTreeNode* node )
}
else
{
int* ldst = buf->data.i + left->buf_idx*buf->cols +
int* ldst = buf->data.i + left->buf_idx*length_buf_row +
workVarCount*scount + left->offset;
int* rdst = buf->data.i + right->buf_idx*buf->cols +
int* rdst = buf->data.i + right->buf_idx*length_buf_row +
workVarCount*scount + right->offset;
for (int i = 0; i < n; i++)
{
@ -1352,6 +1372,7 @@ void CvCascadeBoost::update_weights( CvBoostTree* tree )
sampleIdx = data->get_sample_indices( data->data_root, sampleIdxBuf );
}
CvMat* buf = data->buf;
size_t length_buf_row = data->get_length_subbuf();
if( !tree ) // before training the first tree, initialize weights and other parameters
{
int* classLabelsBuf = (int*)cur_inn_buf_pos; cur_inn_buf_pos = (uchar*)(classLabelsBuf + n);
@ -1375,7 +1396,7 @@ void CvCascadeBoost::update_weights( CvBoostTree* tree )
if (data->is_buf_16u)
{
unsigned short* labels = (unsigned short*)(buf->data.s + data->data_root->buf_idx*buf->cols +
unsigned short* labels = (unsigned short*)(buf->data.s + data->data_root->buf_idx*length_buf_row +
data->data_root->offset + (data->work_var_count-1)*data->sample_count);
for( int i = 0; i < n; i++ )
{
@ -1393,7 +1414,7 @@ void CvCascadeBoost::update_weights( CvBoostTree* tree )
}
else
{
int* labels = buf->data.i + data->data_root->buf_idx*buf->cols +
int* labels = buf->data.i + data->data_root->buf_idx*length_buf_row +
data->data_root->offset + (data->work_var_count-1)*data->sample_count;
for( int i = 0; i < n; i++ )

20
cmake/templates/OpenCV.mk.in
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@ -89,14 +89,20 @@ define add_opencv_camera_module
include $(PREBUILT_SHARED_LIBRARY)
endef
ifeq ($(OPENCV_INSTALL_MODULES),on)
$(foreach module,$(OPENCV_LIBS),$(eval $(call add_opencv_module,$(module))))
endif
$(foreach module,$(OPENCV_3RDPARTY_COMPONENTS),$(eval $(call add_opencv_3rdparty_component,$(module))))
$(foreach module,$(OPENCV_CAMERA_MODULES),$(eval $(call add_opencv_camera_module,$(module))))
ifeq ($(OPENCV_MK_ALREADY_INCLUDED),)
ifeq ($(OPENCV_INSTALL_MODULES),on)
$(foreach module,$(OPENCV_LIBS),$(eval $(call add_opencv_module,$(module))))
endif
$(foreach module,$(OPENCV_3RDPARTY_COMPONENTS),$(eval $(call add_opencv_3rdparty_component,$(module))))
$(foreach module,$(OPENCV_CAMERA_MODULES),$(eval $(call add_opencv_camera_module,$(module))))
ifneq ($(OPENCV_BASEDIR),)
OPENCV_LOCAL_C_INCLUDES += $(foreach mod, $(OPENCV_MODULES), $(OPENCV_BASEDIR)/modules/$(mod)/include)
endif
ifneq ($(OPENCV_BASEDIR),)
OPENCV_LOCAL_C_INCLUDES += $(foreach mod, $(OPENCV_MODULES), $(OPENCV_BASEDIR)/modules/$(mod)/include)
#turn off module installation to prevent their redefinition
OPENCV_MK_ALREADY_INCLUDED:=on
endif
ifeq ($(OPENCV_LOCAL_CFLAGS),)

7
modules/contrib/src/chamfermatching.cpp
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@ -622,7 +622,6 @@ void ChamferMatcher::Matching::followContour(Mat& templ_img, template_coords_t&
{
const int dir[][2] = { {-1,-1}, {-1,0}, {-1,1}, {0,1}, {1,1}, {1,0}, {1,-1}, {0,-1} };
coordinate_t next;
coordinate_t next_temp;
unsigned char ptr;
assert (direction==-1 || !coords.empty());
@ -931,15 +930,13 @@ void ChamferMatcher::Template::show() const
void ChamferMatcher::Matching::addTemplateFromImage(Mat& templ, float scale)
{
Template* cmt = new Template(templ, scale);
if(templates.size() > 0)
templates.clear();
templates.clear();
templates.push_back(cmt);
cmt->show();
}
void ChamferMatcher::Matching::addTemplate(Template& template_){
if(templates.size() > 0)
templates.clear();
templates.clear();
templates.push_back(&template_);
}
/**

20
modules/core/src/matrix.cpp
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@ -2507,7 +2507,7 @@ static void generateRandomCenter(const vector<Vec2f>& box, float* center, RNG& r
center[j] = ((float)rng*(1.f+margin*2.f)-margin)*(box[j][1] - box[j][0]) + box[j][0];
}
class KMeansPPDistanceComputer
class KMeansPPDistanceComputer : public ParallelLoopBody
{
public:
KMeansPPDistanceComputer( float *_tdist2,
@ -2523,10 +2523,10 @@ public:
step(_step),
stepci(_stepci) { }
void operator()( const cv::BlockedRange& range ) const
void operator()( const cv::Range& range ) const
{
const int begin = range.begin();
const int end = range.end();
const int begin = range.start;
const int end = range.end;
for ( int i = begin; i<end; i++ )
{
@ -2582,7 +2582,7 @@ static void generateCentersPP(const Mat& _data, Mat& _out_centers,
break;
int ci = i;
parallel_for(BlockedRange(0, N),
parallel_for_(Range(0, N),
KMeansPPDistanceComputer(tdist2, data, dist, dims, step, step*ci));
for( i = 0; i < N; i++ )
{
@ -2610,7 +2610,7 @@ static void generateCentersPP(const Mat& _data, Mat& _out_centers,
}
}
class KMeansDistanceComputer
class KMeansDistanceComputer : public ParallelLoopBody
{
public:
KMeansDistanceComputer( double *_distances,
@ -2624,10 +2624,10 @@ public:
{
}
void operator()( const BlockedRange& range ) const
void operator()( const Range& range ) const
{
const int begin = range.begin();
const int end = range.end();
const int begin = range.start;
const int end = range.end;
const int K = centers.rows;
const int dims = centers.cols;
@ -2884,7 +2884,7 @@ double cv::kmeans( InputArray _data, int K,
// assign labels
Mat dists(1, N, CV_64F);
double* dist = dists.ptr<double>(0);
parallel_for(BlockedRange(0, N),
parallel_for_(Range(0, N),
KMeansDistanceComputer(dist, labels, data, centers));
compactness = 0;
for( i = 0; i < N; i++ )

10
modules/core/src/stat.cpp
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@ -1726,7 +1726,7 @@ typedef void (*BatchDistFunc)(const uchar* src1, const uchar* src2, size_t step2
int nvecs, int len, uchar* dist, const uchar* mask);
struct BatchDistInvoker
struct BatchDistInvoker : public ParallelLoopBody
{
BatchDistInvoker( const Mat& _src1, const Mat& _src2,
Mat& _dist, Mat& _nidx, int _K,
@ -1743,12 +1743,12 @@ struct BatchDistInvoker
func = _func;
}
void operator()(const BlockedRange& range) const
void operator()(const Range& range) const
{
AutoBuffer<int> buf(src2->rows);
int* bufptr = buf;
for( int i = range.begin(); i < range.end(); i++ )
for( int i = range.start; i < range.end; i++ )
{
func(src1->ptr(i), src2->ptr(), src2->step, src2->rows, src2->cols,
K > 0 ? (uchar*)bufptr : dist->ptr(i), mask->data ? mask->ptr(i) : 0);
@ -1899,8 +1899,8 @@ void cv::batchDistance( InputArray _src1, InputArray _src2,
("The combination of type=%d, dtype=%d and normType=%d is not supported",
type, dtype, normType));
parallel_for(BlockedRange(0, src1.rows),
BatchDistInvoker(src1, src2, dist, nidx, K, mask, update, func));
parallel_for_(Range(0, src1.rows),
BatchDistInvoker(src1, src2, dist, nidx, K, mask, update, func));
}

1
modules/features2d/doc/common_interfaces_of_descriptor_extractors.rst
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@ -79,6 +79,7 @@ The current implementation supports the following types of a descriptor extracto
* ``"SIFT"`` -- :ocv:class:`SIFT`
* ``"SURF"`` -- :ocv:class:`SURF`
* ``"ORB"`` -- :ocv:class:`ORB`
* ``"BRISK"`` -- :ocv:class:`BRISK`
* ``"BRIEF"`` -- :ocv:class:`BriefDescriptorExtractor`
A combined format is also supported: descriptor extractor adapter name ( ``"Opponent"`` --

2
modules/features2d/doc/common_interfaces_of_descriptor_matchers.rst
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@ -269,7 +269,7 @@ Brute-force matcher constructor.
.. ocv:function:: BFMatcher::BFMatcher( int normType=NORM_L2, bool crossCheck=false )
:param normType: One of ``NORM_L1``, ``NORM_L2``, ``NORM_HAMMING``, ``NORM_HAMMING2``. ``L1`` and ``L2`` norms are preferable choices for SIFT and SURF descriptors, ``NORM_HAMMING`` should be used with ORB and BRIEF, ``NORM_HAMMING2`` should be used with ORB when ``WTA_K==3`` or ``4`` (see ORB::ORB constructor description).
:param normType: One of ``NORM_L1``, ``NORM_L2``, ``NORM_HAMMING``, ``NORM_HAMMING2``. ``L1`` and ``L2`` norms are preferable choices for SIFT and SURF descriptors, ``NORM_HAMMING`` should be used with ORB, BRISK and BRIEF, ``NORM_HAMMING2`` should be used with ORB when ``WTA_K==3`` or ``4`` (see ORB::ORB constructor description).
:param crossCheck: If it is false, this is will be default BFMatcher behaviour when it finds the k nearest neighbors for each query descriptor. If ``crossCheck==true``, then the ``knnMatch()`` method with ``k=1`` will only return pairs ``(i,j)`` such that for ``i-th`` query descriptor the ``j-th`` descriptor in the matcher's collection is the nearest and vice versa, i.e. the ``BFMathcher`` will only return consistent pairs. Such technique usually produces best results with minimal number of outliers when there are enough matches. This is alternative to the ratio test, used by D. Lowe in SIFT paper.

1
modules/features2d/doc/common_interfaces_of_feature_detectors.rst
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@ -127,6 +127,7 @@ The following detector types are supported:
* ``"SIFT"`` -- :ocv:class:`SIFT` (nonfree module)
* ``"SURF"`` -- :ocv:class:`SURF` (nonfree module)
* ``"ORB"`` -- :ocv:class:`ORB`
* ``"BRISK"`` -- :ocv:class:`BRISK`
* ``"MSER"`` -- :ocv:class:`MSER`
* ``"GFTT"`` -- :ocv:class:`GoodFeaturesToTrackDetector`
* ``"HARRIS"`` -- :ocv:class:`GoodFeaturesToTrackDetector` with Harris detector enabled

52
modules/features2d/doc/feature_detection_and_description.rst
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@ -96,6 +96,58 @@ Finds keypoints in an image and computes their descriptors
:param useProvidedKeypoints: If it is true, then the method will use the provided vector of keypoints instead of detecting them.
BRISK
-----
.. ocv:class:: BRISK : public Feature2D
Class implementing the BRISK keypoint detector and descriptor extractor, described in [LCS11]_.
.. [LCS11] Stefan Leutenegger, Margarita Chli and Roland Siegwart: BRISK: Binary Robust Invariant Scalable Keypoints. ICCV 2011: 2548-2555.
BRISK::BRISK
------------
The BRISK constructor
.. ocv:function:: BRISK::BRISK(int thresh=30, int octaves=3, float patternScale=1.0f)
:param thresh: FAST/AGAST detection threshold score.
:param octaves: detection octaves. Use 0 to do single scale.
:param patternScale: apply this scale to the pattern used for sampling the neighbourhood of a keypoint.
BRISK::BRISK
------------
The BRISK constructor for a custom pattern
.. ocv:function:: BRISK::BRISK(std::vector<float> &radiusList, std::vector<int> &numberList, float dMax=5.85f, float dMin=8.2f, std::vector<int> indexChange=std::vector<int>())
:param radiusList: defines the radii (in pixels) where the samples around a keypoint are taken (for keypoint scale 1).
:param numberList: defines the number of sampling points on the sampling circle. Must be the same size as radiusList..
:param dMax: threshold for the short pairings used for descriptor formation (in pixels for keypoint scale 1).
:param dMin: threshold for the long pairings used for orientation determination (in pixels for keypoint scale 1).
:param indexChanges: index remapping of the bits.
BRISK::operator()
-----------------
Finds keypoints in an image and computes their descriptors
.. ocv:function:: void BRISK::operator()(InputArray image, InputArray mask, vector<KeyPoint>& keypoints, OutputArray descriptors, bool useProvidedKeypoints=false ) const
:param image: The input 8-bit grayscale image.
:param mask: The operation mask.
:param keypoints: The output vector of keypoints.
:param descriptors: The output descriptors. Pass ``cv::noArray()`` if you do not need it.
:param useProvidedKeypoints: If it is true, then the method will use the provided vector of keypoints instead of detecting them.
FREAK
-----
.. ocv:class:: FREAK : public DescriptorExtractor

7
modules/features2d/src/brisk.cpp
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@ -309,10 +309,9 @@ BRISK::generateKernel(std::vector<float> &radiusList, std::vector<int> &numberLi
{
indexChange.resize(points_ * (points_ - 1) / 2);
indSize = (unsigned int)indexChange.size();
}
for (unsigned int i = 0; i < indSize; i++)
{
indexChange[i] = i;
for (unsigned int i = 0; i < indSize; i++)
indexChange[i] = i;
}
const float dMin_sq = dMin_ * dMin_;
const float dMax_sq = dMax_ * dMax_;

10
modules/features2d/src/evaluation.cpp
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@ -241,7 +241,6 @@ static void filterEllipticKeyPointsByImageSize( vector<EllipticKeyPoint>& keypoi
struct IntersectAreaCounter
{
IntersectAreaCounter() : bua(0), bna(0) {}
IntersectAreaCounter( float _dr, int _minx,
int _miny, int _maxy,
const Point2f& _diff,
@ -257,6 +256,9 @@ struct IntersectAreaCounter
void operator()( const BlockedRange& range )
{
CV_Assert( miny < maxy );
CV_Assert( dr > FLT_EPSILON );
int temp_bua = bua, temp_bna = bna;
for( int i = range.begin(); i != range.end(); i++ )
{
@ -461,7 +463,7 @@ void cv::evaluateFeatureDetector( const Mat& img1, const Mat& img2, const Mat& H
keypoints2 = _keypoints2 != 0 ? _keypoints2 : &buf2;
if( (keypoints1->empty() || keypoints2->empty()) && fdetector.empty() )
CV_Error( CV_StsBadArg, "fdetector must be no empty when keypoints1 or keypoints2 is empty" );
CV_Error( CV_StsBadArg, "fdetector must not be empty when keypoints1 or keypoints2 is empty" );
if( keypoints1->empty() )
fdetector->detect( img1, *keypoints1 );
@ -572,10 +574,10 @@ void cv::evaluateGenericDescriptorMatcher( const Mat& img1, const Mat& img2, con
correctMatches1to2Mask = _correctMatches1to2Mask != 0 ? _correctMatches1to2Mask : &buf2;
if( keypoints1.empty() )
CV_Error( CV_StsBadArg, "keypoints1 must be no empty" );
CV_Error( CV_StsBadArg, "keypoints1 must not be empty" );
if( matches1to2->empty() && dmatcher.empty() )
CV_Error( CV_StsBadArg, "dmatch must be no empty when matches1to2 is empty" );
CV_Error( CV_StsBadArg, "dmatch must not be empty when matches1to2 is empty" );
bool computeKeypoints2ByPrj = keypoints2.empty();
if( computeKeypoints2ByPrj )

2
modules/gpu/src/cascadeclassifier.cpp
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@ -778,8 +778,6 @@ NCVStatus loadFromXML(const std::string &filename,
haar.bNeedsTiltedII = false;
Ncv32u curMaxTreeDepth;
std::vector<char> xmlFileCont;
std::vector<HaarClassifierNode128> h_TmpClassifierNotRootNodes;
haarStages.resize(0);
haarClassifierNodes.resize(0);

22
modules/highgui/src/cap_dshow.cpp
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@ -3120,6 +3120,7 @@ protected:
void init();
int index, width, height,fourcc;
int widthSet, heightSet;
IplImage* frame;
static videoInput VI;
};
@ -3138,6 +3139,7 @@ CvCaptureCAM_DShow::CvCaptureCAM_DShow()
index = -1;
frame = 0;
width = height = fourcc = -1;
widthSet = heightSet = -1;
CoInitialize(0);
}
@ -3155,7 +3157,7 @@ void CvCaptureCAM_DShow::close()
index = -1;
cvReleaseImage(&frame);
}
width = height = -1;
widthSet = heightSet = width = height = -1;
}
// Initialize camera input
@ -3282,9 +3284,12 @@ bool CvCaptureCAM_DShow::setProperty( int property_id, double value )
{
VI.stopDevice(index);
VI.setIdealFramerate(index,fps);
VI.setupDevice(index);
if (widthSet > 0 && heightSet > 0)
VI.setupDevice(index, widthSet, heightSet);
else
VI.setupDevice(index);
}
break;
return VI.isDeviceSetup(index);
}
@ -3299,8 +3304,15 @@ bool CvCaptureCAM_DShow::setProperty( int property_id, double value )
VI.setIdealFramerate(index, fps);
VI.setupDeviceFourcc(index, width, height, fourcc);
}
width = height = fourcc = -1;
return VI.isDeviceSetup(index);
bool success = VI.isDeviceSetup(index);
if (success)
{
widthSet = width;
heightSet = height;
width = height = fourcc = -1;
}
return success;
}
return true;
}

42
modules/imgproc/src/histogram.cpp
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@ -2592,39 +2592,36 @@ cvCompareHist( const CvHistogram* hist1,
CV_IMPL void
cvCopyHist( const CvHistogram* src, CvHistogram** _dst )
{
int eq = 0;
int is_sparse;
int i, dims1, dims2;
int size1[CV_MAX_DIM], size2[CV_MAX_DIM], total = 1;
float* ranges[CV_MAX_DIM];
float** thresh = 0;
CvHistogram* dst;
if( !_dst )
CV_Error( CV_StsNullPtr, "Destination double pointer is NULL" );
dst = *_dst;
CvHistogram* dst = *_dst;
if( !CV_IS_HIST(src) || (dst && !CV_IS_HIST(dst)) )
CV_Error( CV_StsBadArg, "Invalid histogram header[s]" );
is_sparse = CV_IS_SPARSE_MAT(src->bins);
dims1 = cvGetDims( src->bins, size1 );
for( i = 0; i < dims1; i++ )
total *= size1[i];
if( dst && is_sparse == CV_IS_SPARSE_MAT(dst->bins))
bool eq = false;
int size1[CV_MAX_DIM];
bool is_sparse = CV_IS_SPARSE_MAT(src->bins);
int dims1 = cvGetDims( src->bins, size1 );
if( dst && (is_sparse == CV_IS_SPARSE_MAT(dst->bins)))
{
dims2 = cvGetDims( dst->bins, size2 );
int size2[CV_MAX_DIM];
int dims2 = cvGetDims( dst->bins, size2 );
if( dims1 == dims2 )
{
int i;
for( i = 0; i < dims1; i++ )
{
if( size1[i] != size2[i] )
break;
}
eq = (i == dims1);
}
eq = i == dims1;
}
if( !eq )
@ -2636,14 +2633,21 @@ cvCopyHist( const CvHistogram* src, CvHistogram** _dst )
if( CV_HIST_HAS_RANGES( src ))
{
float* ranges[CV_MAX_DIM];
float** thresh = 0;
if( CV_IS_UNIFORM_HIST( src ))
{
for( i = 0; i < dims1; i++ )
for( int i = 0; i < dims1; i++ )
ranges[i] = (float*)src->thresh[i];
thresh = ranges;
}
else
{
thresh = src->thresh2;
}
cvSetHistBinRanges( dst, thresh, CV_IS_UNIFORM_HIST(src));
}

3
modules/imgproc/src/pyramids.cpp
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@ -327,11 +327,10 @@ pyrUp_( const Mat& _src, Mat& _dst, int)
CV_Assert( std::abs(dsize.width - ssize.width*2) == dsize.width % 2 &&
std::abs(dsize.height - ssize.height*2) == dsize.height % 2);
int k, x, sy0 = -PU_SZ/2, sy = sy0, width0 = ssize.width - 1;
int k, x, sy0 = -PU_SZ/2, sy = sy0;
ssize.width *= cn;
dsize.width *= cn;
width0 *= cn;
for( x = 0; x < ssize.width; x++ )
dtab[x] = (x/cn)*2*cn + x % cn;

10
modules/java/android_test/src/org/opencv/test/features2d/BruteForceDescriptorMatcherTest.java
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@ -85,11 +85,11 @@ public class BruteForceDescriptorMatcherTest extends OpenCVTestCase {
matSize = 100;
truth = new DMatch[] {
new DMatch(0, 0, 0, 1.0496940f),
new DMatch(1, 0, 0, 1.0984558f),
new DMatch(2, 1, 0, 0.4945875f),
new DMatch(3, 1, 0, 0.48435235f),
new DMatch(4, 0, 0, 1.0836693f)
new DMatch(0, 0, 0, 0.6211397f),
new DMatch(1, 1, 0, 0.9177120f),
new DMatch(2, 1, 0, 0.3112163f),
new DMatch(3, 1, 0, 0.2925074f),
new DMatch(4, 1, 0, 0.9309178f)
};
}

10
modules/java/android_test/src/org/opencv/test/features2d/BruteForceL1DescriptorMatcherTest.java
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@ -85,11 +85,11 @@ public class BruteForceL1DescriptorMatcherTest extends OpenCVTestCase {
matSize = 100;
truth = new DMatch[] {
new DMatch(0, 1, 0, 6.9202340f),
new DMatch(1, 1, 0, 6.1675916f),
new DMatch(2, 1, 0, 2.6798590f),
new DMatch(3, 1, 0, 2.6545324f),
new DMatch(4, 0, 0, 6.1294870f)
new DMatch(0, 0, 0, 3.0975165f),
new DMatch(1, 1, 0, 3.5680308f),
new DMatch(2, 1, 0, 1.3722466f),
new DMatch(3, 1, 0, 1.3041023f),
new DMatch(4, 1, 0, 3.5970376f)
};
}

10
modules/java/android_test/src/org/opencv/test/features2d/BruteForceSL2DescriptorMatcherTest.java
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@ -90,11 +90,11 @@ public class BruteForceSL2DescriptorMatcherTest extends OpenCVTestCase {
matSize = 100;
truth = new DMatch[] {
new DMatch(0, 0, 0, 1.1018573f),
new DMatch(1, 0, 0, 1.2066052f),
new DMatch(2, 1, 0, 0.2446168f),
new DMatch(3, 1, 0, 0.23459719f),
new DMatch(4, 0, 0, 1.174339f)
new DMatch(0, 0, 0, 0.3858146f),
new DMatch(1, 1, 0, 0.8421953f),
new DMatch(2, 1, 0, 0.0968556f),
new DMatch(3, 1, 0, 0.0855606f),
new DMatch(4, 1, 0, 0.8666080f)
};
}

10
modules/java/android_test/src/org/opencv/test/features2d/FlannBasedDescriptorMatcherTest.java
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@ -158,11 +158,11 @@ public class FlannBasedDescriptorMatcherTest extends OpenCVTestCase {
matcher = DescriptorMatcher.create(DescriptorMatcher.FLANNBASED);
matSize = 100;
truth = new DMatch[] {
new DMatch(0, 0, 0, 1.049694f),
new DMatch(1, 0, 0, 1.0984558f),
new DMatch(2, 1, 0, 0.4945875f),
new DMatch(3, 1, 0, 0.48435235f),
new DMatch(4, 0, 0, 1.0836693f)
new DMatch(0, 0, 0, 0.6211397f),
new DMatch(1, 1, 0, 0.9177120f),
new DMatch(2, 1, 0, 0.3112163f),
new DMatch(3, 1, 0, 0.2925075f),
new DMatch(4, 1, 0, 0.9309179f)
};
}

31
modules/java/android_test/src/org/opencv/test/features2d/SURFDescriptorExtractorTest.java
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@ -27,7 +27,12 @@ public class SURFDescriptorExtractorTest extends OpenCVTestCase {
@Override
protected void setUp() throws Exception {
super.setUp();
extractor = DescriptorExtractor.create(DescriptorExtractor.SURF);
String filename = OpenCVTestRunner.getTempFileName("yml");
writeFile(filename, "%YAML:1.0\nextended: 1\nhessianThreshold: 100.\nnOctaveLayers: 2\nnOctaves: 4\nupright: 0");
extractor.read(filename);
matSize = 100;
}
@ -46,19 +51,19 @@ public class SURFDescriptorExtractorTest extends OpenCVTestCase {
Mat truth = new Mat(1, 128, CvType.CV_32FC1) {
{
put(0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0.045382127, 0.075976953, -0.031969212, 0.035002094, 0.012224297,
0.012286193, -0.0088025155, 0.0088025155, 0.00017225844, 0.00017225844, 0, 0, 8.2743405e-05,
8.2743405e-05, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8.2743405e-05, 8.2743405e-05, -0.00017225844,
0.00017225844, 0, 0, 0.31723264, 0.42715758, -0.19872268, 0.23621935, 0.033304065, 0.033918764,
-0.021780485, 0.021780485, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.0088025145,
0.0088025145, 0.012224296, 0.012286192, -0.045382123, 0.075976953, 0.031969212, 0.035002094,
0.10047197, 0.21463872, -0.0012294546, 0.18176091, -0.075555265, 0.35627601, 0.01270232,
0.20058797, -0.037658721, 0.037658721, 0.064850949, 0.064850949, -0.27688536, 0.44229308,
0.14888979, 0.14888979, -0.0031531656, 0.0031531656, 0.0068481555, 0.0072466261, -0.034193151,
0.040314503, 0.01108359, 0.023398584, -0.00071876607, 0.00071876607, -0.0031819802,
0.0031819802, 0, 0, -0.0013680183, 0.0013680183, 0.034193147, 0.040314503, -0.01108359,
0.023398584, 0.006848156, 0.0072466265, -0.0031531656, 0.0031531656, 0, 0, 0, 0, 0, 0, 0, 0,
-0.0013680183, 0.0013680183, 0, 0, 0.00071876607, 0.00071876607, 0.0031819802, 0.0031819802
0, 0, 0, 0, 0, 0, 0, 0, 0.058821894, 0.058821894, -0.045962855, 0.046261817, 0.0085156476,
0.0085754395, -0.0064509804, 0.0064509804, 0.00044069235, 0.00044069235, 0, 0, 0.00025723741,
0.00025723741, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.00025723741, 0.00025723741, -0.00044069235,
0.00044069235, 0, 0, 0.36278215, 0.36278215, -0.24688604, 0.26173124, 0.052068226, 0.052662034,
-0.032815345, 0.032815345, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.0064523756,
0.0064523756, 0.0082002236, 0.0088908644, -0.059001274, 0.059001274, 0.045789491, 0.04648013,
0.11961588, 0.22789426, -0.01322381, 0.18291828, -0.14042182, 0.23973691, 0.073782086, 0.23769434,
-0.027880307, 0.027880307, 0.049587864, 0.049587864, -0.33991757, 0.33991757, 0.21437603, 0.21437603,
-0.0020763327, 0.0020763327, 0.006245892, 0.006245892, -0.04067041, 0.04067041, 0.019361559,
0.019361559, 0, 0, -0.0035977389, 0.0035977389, 0, 0, -0.00099993451, 0.00099993451, 0.040670406,
0.040670406, -0.019361559, 0.019361559, 0.006245892, 0.006245892, -0.0020763327, 0.0020763327,
-0.00034532088, 0.00034532088, 0, 0, 0, 0, 0.00034532088, 0.00034532088, -0.00099993451,
0.00099993451, 0, 0, 0, 0, 0.0035977389, 0.0035977389
);
}
};

4
modules/java/android_test/src/org/opencv/test/features2d/SURFFeatureDetectorTest.java
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@ -150,7 +150,7 @@ public class SURFFeatureDetectorTest extends OpenCVTestCase {
detector.write(filename);
String truth = "<?xml version=\"1.0\"?>\n<opencv_storage>\n<name>Feature2D.SURF</name>\n<extended>1</extended>\n<hessianThreshold>100.</hessianThreshold>\n<nOctaveLayers>2</nOctaveLayers>\n<nOctaves>4</nOctaves>\n<upright>0</upright>\n</opencv_storage>\n";
String truth = "<?xml version=\"1.0\"?>\n<opencv_storage>\n<name>Feature2D.SURF</name>\n<extended>0</extended>\n<hessianThreshold>100.</hessianThreshold>\n<nOctaveLayers>3</nOctaveLayers>\n<nOctaves>4</nOctaves>\n<upright>0</upright>\n</opencv_storage>\n";
assertEquals(truth, readFile(filename));
}
@ -159,7 +159,7 @@ public class SURFFeatureDetectorTest extends OpenCVTestCase {
detector.write(filename);
String truth = "%YAML:1.0\nname: \"Feature2D.SURF\"\nextended: 1\nhessianThreshold: 100.\nnOctaveLayers: 2\nnOctaves: 4\nupright: 0\n";
String truth = "%YAML:1.0\nname: \"Feature2D.SURF\"\nextended: 0\nhessianThreshold: 100.\nnOctaveLayers: 3\nnOctaves: 4\nupright: 0\n";
assertEquals(truth, readFile(filename));
}

8
modules/ml/include/opencv2/ml/ml.hpp
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@ -796,7 +796,7 @@ struct CV_EXPORTS CvDTreeTrainData
const CvMat* responses;
CvMat* responses_copy; // used in Boosting
int buf_count, buf_size;
int buf_count, buf_size; // buf_size is obsolete, please do not use it, use expression ((int64)buf->rows * (int64)buf->cols / buf_count) instead
bool shared;
int is_buf_16u;
@ -806,6 +806,12 @@ struct CV_EXPORTS CvDTreeTrainData
CvMat* counts;
CvMat* buf;
inline size_t get_length_subbuf() const
{
size_t res = (size_t)(work_var_count + 1) * (size_t)sample_count;
return res;
}
CvMat* direction;
CvMat* split_buf;

14
modules/ml/src/boost.cpp
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@ -1135,13 +1135,13 @@ CvBoost::update_weights( CvBoostTree* tree )
int *sample_idx_buf;
const int* sample_idx = 0;
cv::AutoBuffer<uchar> inn_buf;
size_t _buf_size = (params.boost_type == LOGIT) || (params.boost_type == GENTLE) ? data->sample_count*sizeof(int) : 0;
size_t _buf_size = (params.boost_type == LOGIT) || (params.boost_type == GENTLE) ? (size_t)(data->sample_count)*sizeof(int) : 0;
if( !tree )
_buf_size += n*sizeof(int);
else
{
if( have_subsample )
_buf_size += data->buf->cols*(sizeof(float)+sizeof(uchar));
_buf_size += data->get_length_subbuf()*(sizeof(float)+sizeof(uchar));
}
inn_buf.allocate(_buf_size);
uchar* cur_buf_pos = (uchar*)inn_buf;
@ -1156,6 +1156,7 @@ CvBoost::update_weights( CvBoostTree* tree )
sample_idx = data->get_sample_indices( data->data_root, sample_idx_buf );
}
CvMat* dtree_data_buf = data->buf;
size_t length_buf_row = data->get_length_subbuf();
if( !tree ) // before training the first tree, initialize weights and other parameters
{
int* class_labels_buf = (int*)cur_buf_pos;
@ -1195,7 +1196,7 @@ CvBoost::update_weights( CvBoostTree* tree )
if (data->is_buf_16u)
{
unsigned short* labels = (unsigned short*)(dtree_data_buf->data.s + data->data_root->buf_idx*dtree_data_buf->cols +
unsigned short* labels = (unsigned short*)(dtree_data_buf->data.s + data->data_root->buf_idx*length_buf_row +
data->data_root->offset + (data->work_var_count-1)*data->sample_count);
for( i = 0; i < n; i++ )
{
@ -1213,7 +1214,7 @@ CvBoost::update_weights( CvBoostTree* tree )
}
else
{
int* labels = dtree_data_buf->data.i + data->data_root->buf_idx*dtree_data_buf->cols +
int* labels = dtree_data_buf->data.i + data->data_root->buf_idx*length_buf_row +
data->data_root->offset + (data->work_var_count-1)*data->sample_count;
for( i = 0; i < n; i++ )
@ -1260,9 +1261,10 @@ CvBoost::update_weights( CvBoostTree* tree )
if( have_subsample )
{
float* values = (float*)cur_buf_pos;
cur_buf_pos = (uchar*)(values + data->buf->cols);
cur_buf_pos = (uchar*)(values + data->get_length_subbuf());
uchar* missing = cur_buf_pos;
cur_buf_pos = missing + data->buf->step;
cur_buf_pos = missing + data->get_length_subbuf() * (size_t)CV_ELEM_SIZE(data->buf->type);
CvMat _sample, _mask;
// invert the subsample mask

58
modules/ml/src/ertrees.cpp
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@ -80,6 +80,9 @@ void CvERTreeTrainData::set_data( const CvMat* _train_data, int _tflag,
char err[100];
const int *sidx = 0, *vidx = 0;
uint64 effective_buf_size = 0;
int effective_buf_height = 0, effective_buf_width = 0;
if ( _params.use_surrogates )
CV_ERROR(CV_StsBadArg, "CvERTrees do not support surrogate splits");
@ -179,18 +182,34 @@ void CvERTreeTrainData::set_data( const CvMat* _train_data, int _tflag,
have_labels = cv_n > 0 || (ord_var_count == 1 && cat_var_count == 0) || _add_labels;
work_var_count = cat_var_count + (is_classifier ? 1 : 0) + (have_labels ? 1 : 0);
buf_size = (work_var_count + 1)*sample_count;
shared = _shared;
buf_count = shared ? 2 : 1;
buf_size = -1; // the member buf_size is obsolete
effective_buf_size = (uint64)(work_var_count + 1)*(uint64)sample_count * buf_count; // this is the total size of "CvMat buf" to be allocated
effective_buf_width = sample_count;
effective_buf_height = work_var_count+1;
if (effective_buf_width >= effective_buf_height)
effective_buf_height *= buf_count;
else
effective_buf_width *= buf_count;
if ((uint64)effective_buf_width * (uint64)effective_buf_height != effective_buf_size)
{
CV_Error(CV_StsBadArg, "The memory buffer cannot be allocated since its size exceeds integer fields limit");
}
if ( is_buf_16u )
{
CV_CALL( buf = cvCreateMat( buf_count, buf_size, CV_16UC1 ));
CV_CALL( buf = cvCreateMat( effective_buf_height, effective_buf_width, CV_16UC1 ));
CV_CALL( pair16u32s_ptr = (CvPair16u32s*)cvAlloc( sample_count*sizeof(pair16u32s_ptr[0]) ));
}
else
{
CV_CALL( buf = cvCreateMat( buf_count, buf_size, CV_32SC1 ));
CV_CALL( buf = cvCreateMat( effective_buf_height, effective_buf_width, CV_32SC1 ));
CV_CALL( int_ptr = (int**)cvAlloc( sample_count*sizeof(int_ptr[0]) ));
}
@ -293,13 +312,13 @@ void CvERTreeTrainData::set_data( const CvMat* _train_data, int _tflag,
for( i = 0; i < sample_count; i++ )
{
int val = INT_MAX, si = sidx ? sidx[i] : i;
if( !mask || !mask[si*m_step] )
if( !mask || !mask[(size_t)si*m_step] )
{
if( idata )
val = idata[si*step];
val = idata[(size_t)si*step];
else
{
float t = fdata[si*step];
float t = fdata[(size_t)si*step];
val = cvRound(t);
if( val != t )
{
@ -405,12 +424,12 @@ void CvERTreeTrainData::set_data( const CvMat* _train_data, int _tflag,
{
float val = ord_nan;
int si = sidx ? sidx[i] : i;
if( !mask || !mask[si*m_step] )
if( !mask || !mask[(size_t)si*m_step] )
{
if( idata )
val = (float)idata[si*step];
val = (float)idata[(size_t)si*step];
else
val = fdata[si*step];
val = fdata[(size_t)si*step];
if( fabs(val) >= ord_nan )
{
@ -578,9 +597,9 @@ const int* CvERTreeTrainData::get_cat_var_data( CvDTreeNode* n, int vi, int* cat
int ci = get_var_type( vi);
const int* cat_values = 0;
if( !is_buf_16u )
cat_values = buf->data.i + n->buf_idx*buf->cols + ci*sample_count + n->offset;
cat_values = buf->data.i + n->buf_idx*get_length_subbuf() + ci*sample_count + n->offset;
else {
const unsigned short* short_values = (const unsigned short*)(buf->data.s + n->buf_idx*buf->cols +
const unsigned short* short_values = (const unsigned short*)(buf->data.s + n->buf_idx*get_length_subbuf() +
ci*sample_count + n->offset);
for( int i = 0; i < n->sample_count; i++ )
cat_values_buf[i] = short_values[i];
@ -1333,6 +1352,7 @@ void CvForestERTree::split_node_data( CvDTreeNode* node )
CvDTreeNode *left = 0, *right = 0;
int new_buf_idx = data->get_child_buf_idx( node );
CvMat* buf = data->buf;
size_t length_buf_row = data->get_length_subbuf();
cv::AutoBuffer<int> temp_buf(n);
complete_node_dir(node);
@ -1385,9 +1405,9 @@ void CvForestERTree::split_node_data( CvDTreeNode* node )
if (data->is_buf_16u)
{
unsigned short *ldst = (unsigned short *)(buf->data.s + left->buf_idx*buf->cols +
unsigned short *ldst = (unsigned short *)(buf->data.s + left->buf_idx*length_buf_row +
ci*scount + left->offset);
unsigned short *rdst = (unsigned short *)(buf->data.s + right->buf_idx*buf->cols +
unsigned short *rdst = (unsigned short *)(buf->data.s + right->buf_idx*length_buf_row +
ci*scount + right->offset);
for( i = 0; i < n; i++ )
@ -1415,9 +1435,9 @@ void CvForestERTree::split_node_data( CvDTreeNode* node )
}
else
{
int *ldst = buf->data.i + left->buf_idx*buf->cols +
int *ldst = buf->data.i + left->buf_idx*length_buf_row +
ci*scount + left->offset;
int *rdst = buf->data.i + right->buf_idx*buf->cols +
int *rdst = buf->data.i + right->buf_idx*length_buf_row +
ci*scount + right->offset;
for( i = 0; i < n; i++ )
@ -1460,9 +1480,9 @@ void CvForestERTree::split_node_data( CvDTreeNode* node )
if (data->is_buf_16u)
{
unsigned short* ldst = (unsigned short*)(buf->data.s + left->buf_idx*buf->cols +
unsigned short* ldst = (unsigned short*)(buf->data.s + left->buf_idx*length_buf_row +
pos*scount + left->offset);
unsigned short* rdst = (unsigned short*)(buf->data.s + right->buf_idx*buf->cols +
unsigned short* rdst = (unsigned short*)(buf->data.s + right->buf_idx*length_buf_row +
pos*scount + right->offset);
for (i = 0; i < n; i++)
@ -1483,9 +1503,9 @@ void CvForestERTree::split_node_data( CvDTreeNode* node )
}
else
{
int* ldst = buf->data.i + left->buf_idx*buf->cols +
int* ldst = buf->data.i + left->buf_idx*length_buf_row +
pos*scount + left->offset;
int* rdst = buf->data.i + right->buf_idx*buf->cols +
int* rdst = buf->data.i + right->buf_idx*length_buf_row +
pos*scount + right->offset;
for (i = 0; i < n; i++)
{

9
modules/ml/src/knearest.cpp
Unescape View File

@ -100,9 +100,9 @@ bool CvKNearest::train( const CvMat* _train_data, const CvMat* _responses,
__BEGIN__;
CvVectors* _samples;
float** _data;
int _count, _dims, _dims_all, _rsize;
CvVectors* _samples = 0;
float** _data = 0;
int _count = 0, _dims = 0, _dims_all = 0, _rsize = 0;
if( !_update_base )
clear();
@ -114,6 +114,9 @@ bool CvKNearest::train( const CvMat* _train_data, const CvMat* _responses,
_responses, CV_VAR_ORDERED, 0, _sample_idx, true, (const float***)&_data,
&_count, &_dims, &_dims_all, &responses, 0, 0 ));
if( !responses )
CV_ERROR( CV_StsNoMem, "Could not allocate memory for responses" );
if( _update_base && _dims != var_count )
CV_ERROR( CV_StsBadArg, "The newly added data have different dimensionality" );

99
modules/ml/src/tree.cpp
Unescape View File

@ -50,7 +50,8 @@ static const int block_size_delta = 1 << 10;
CvDTreeTrainData::CvDTreeTrainData()
{
var_idx = var_type = cat_count = cat_ofs = cat_map =
priors = priors_mult = counts = buf = direction = split_buf = responses_copy = 0;
priors = priors_mult = counts = direction = split_buf = responses_copy = 0;
buf = 0;
tree_storage = temp_storage = 0;
clear();
@ -64,7 +65,8 @@ CvDTreeTrainData::CvDTreeTrainData( const CvMat* _train_data, int _tflag,
bool _shared, bool _add_labels )
{
var_idx = var_type = cat_count = cat_ofs = cat_map =
priors = priors_mult = counts = buf = direction = split_buf = responses_copy = 0;
priors = priors_mult = counts = direction = split_buf = responses_copy = 0;
buf = 0;
tree_storage = temp_storage = 0;
@ -157,6 +159,9 @@ void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag,
char err[100];
const int *sidx = 0, *vidx = 0;
uint64 effective_buf_size = 0;
int effective_buf_height = 0, effective_buf_width = 0;
if( _update_data && data_root )
{
data = new CvDTreeTrainData( _train_data, _tflag, _responses, _var_idx,
@ -285,18 +290,35 @@ void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag,
work_var_count = var_count + (is_classifier ? 1 : 0) // for responses class_labels
+ (have_labels ? 1 : 0); // for cv_labels
buf_size = (work_var_count + 1 /*for sample_indices*/) * sample_count;
shared = _shared;
buf_count = shared ? 2 : 1;
buf_size = -1; // the member buf_size is obsolete
effective_buf_size = (uint64)(work_var_count + 1)*(uint64)sample_count * buf_count; // this is the total size of "CvMat buf" to be allocated
effective_buf_width = sample_count;
effective_buf_height = work_var_count+1;
if (effective_buf_width >= effective_buf_height)
effective_buf_height *= buf_count;
else
effective_buf_width *= buf_count;
if ((uint64)effective_buf_width * (uint64)effective_buf_height != effective_buf_size)
{
CV_Error(CV_StsBadArg, "The memory buffer cannot be allocated since its size exceeds integer fields limit");
}
if ( is_buf_16u )
{
CV_CALL( buf = cvCreateMat( buf_count, buf_size, CV_16UC1 ));
CV_CALL( buf = cvCreateMat( effective_buf_height, effective_buf_width, CV_16UC1 ));
CV_CALL( pair16u32s_ptr = (CvPair16u32s*)cvAlloc( sample_count*sizeof(pair16u32s_ptr[0]) ));
}
else
{
CV_CALL( buf = cvCreateMat( buf_count, buf_size, CV_32SC1 ));
CV_CALL( buf = cvCreateMat( effective_buf_height, effective_buf_width, CV_32SC1 ));
CV_CALL( int_ptr = (int**)cvAlloc( sample_count*sizeof(int_ptr[0]) ));
}
@ -356,7 +378,7 @@ void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag,
{
int ci;
const uchar* mask = 0;
int m_step = 0, step;
int64 m_step = 0, step;
const int* idata = 0;
const float* fdata = 0;
int num_valid = 0;
@ -399,13 +421,13 @@ void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag,
for( i = 0; i < sample_count; i++ )
{
int val = INT_MAX, si = sidx ? sidx[i] : i;
if( !mask || !mask[si*m_step] )
if( !mask || !mask[(size_t)si*m_step] )
{
if( idata )
val = idata[si*step];
val = idata[(size_t)si*step];
else
{
float t = fdata[si*step];
float t = fdata[(size_t)si*step];
val = cvRound(t);
if( fabs(t - val) > FLT_EPSILON )
{
@ -515,12 +537,12 @@ void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag,
{
float val = ord_nan;
int si = sidx ? sidx[i] : i;
if( !mask || !mask[si*m_step] )
if( !mask || !mask[(size_t)si*m_step] )
{
if( idata )
val = (float)idata[si*step];
val = (float)idata[(size_t)si*step];
else
val = fdata[si*step];
val = fdata[(size_t)si*step];
if( fabs(val) >= ord_nan )
{
@ -532,7 +554,7 @@ void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag,
}
if (is_buf_16u)
udst[i] = (unsigned short)i;
udst[i] = (unsigned short)i; // TODO: memory corruption may be here
else
idst[i] = i;
_fdst[i] = val;
@ -751,7 +773,7 @@ CvDTreeNode* CvDTreeTrainData::subsample_data( const CvMat* _subsample_idx )
if (is_buf_16u)
{
unsigned short* udst = (unsigned short*)(buf->data.s + root->buf_idx*buf->cols +
unsigned short* udst = (unsigned short*)(buf->data.s + root->buf_idx*get_length_subbuf() +
vi*sample_count + root->offset);
for( i = 0; i < count; i++ )
{
@ -762,7 +784,7 @@ CvDTreeNode* CvDTreeTrainData::subsample_data( const CvMat* _subsample_idx )
}
else
{
int* idst = buf->data.i + root->buf_idx*buf->cols +
int* idst = buf->data.i + root->buf_idx*get_length_subbuf() +
vi*sample_count + root->offset;
for( i = 0; i < count; i++ )
{
@ -788,7 +810,7 @@ CvDTreeNode* CvDTreeTrainData::subsample_data( const CvMat* _subsample_idx )
if (is_buf_16u)
{
unsigned short* udst_idx = (unsigned short*)(buf->data.s + root->buf_idx*buf->cols +
unsigned short* udst_idx = (unsigned short*)(buf->data.s + root->buf_idx*get_length_subbuf() +
vi*sample_count + data_root->offset);
for( i = 0; i < num_valid; i++ )
{
@ -812,7 +834,7 @@ CvDTreeNode* CvDTreeTrainData::subsample_data( const CvMat* _subsample_idx )
}
else
{
int* idst_idx = buf->data.i + root->buf_idx*buf->cols +
int* idst_idx = buf->data.i + root->buf_idx*get_length_subbuf() +
vi*sample_count + root->offset;
for( i = 0; i < num_valid; i++ )
{
@ -840,14 +862,14 @@ CvDTreeNode* CvDTreeTrainData::subsample_data( const CvMat* _subsample_idx )
const int* sample_idx_src = get_sample_indices(data_root, (int*)(uchar*)inn_buf);
if (is_buf_16u)
{
unsigned short* sample_idx_dst = (unsigned short*)(buf->data.s + root->buf_idx*buf->cols +
unsigned short* sample_idx_dst = (unsigned short*)(buf->data.s + root->buf_idx*get_length_subbuf() +
workVarCount*sample_count + root->offset);
for (i = 0; i < count; i++)
sample_idx_dst[i] = (unsigned short)sample_idx_src[sidx[i]];
}
else
{
int* sample_idx_dst = buf->data.i + root->buf_idx*buf->cols +
int* sample_idx_dst = buf->data.i + root->buf_idx*get_length_subbuf() +
workVarCount*sample_count + root->offset;
for (i = 0; i < count; i++)
sample_idx_dst[i] = sample_idx_src[sidx[i]];
@ -1158,10 +1180,10 @@ void CvDTreeTrainData::get_ord_var_data( CvDTreeNode* n, int vi, float* ord_valu
const int* sample_indices = get_sample_indices(n, sample_indices_buf);
if( !is_buf_16u )
*sorted_indices = buf->data.i + n->buf_idx*buf->cols +
*sorted_indices = buf->data.i + n->buf_idx*get_length_subbuf() +
vi*sample_count + n->offset;
else {
const unsigned short* short_indices = (const unsigned short*)(buf->data.s + n->buf_idx*buf->cols +
const unsigned short* short_indices = (const unsigned short*)(buf->data.s + n->buf_idx*get_length_subbuf() +
vi*sample_count + n->offset );
for( int i = 0; i < node_sample_count; i++ )
sorted_indices_buf[i] = short_indices[i];
@ -1232,10 +1254,10 @@ const int* CvDTreeTrainData::get_cat_var_data( CvDTreeNode* n, int vi, int* cat_
{
const int* cat_values = 0;
if( !is_buf_16u )
cat_values = buf->data.i + n->buf_idx*buf->cols +
cat_values = buf->data.i + n->buf_idx*get_length_subbuf() +
vi*sample_count + n->offset;
else {
const unsigned short* short_values = (const unsigned short*)(buf->data.s + n->buf_idx*buf->cols +
const unsigned short* short_values = (const unsigned short*)(buf->data.s + n->buf_idx*get_length_subbuf() +
vi*sample_count + n->offset);
for( int i = 0; i < n->sample_count; i++ )
cat_values_buf[i] = short_values[i];
@ -3004,6 +3026,7 @@ void CvDTree::split_node_data( CvDTreeNode* node )
int new_buf_idx = data->get_child_buf_idx( node );
int work_var_count = data->get_work_var_count();
CvMat* buf = data->buf;
size_t length_buf_row = data->get_length_subbuf();
cv::AutoBuffer<uchar> inn_buf(n*(3*sizeof(int) + sizeof(float)));
int* temp_buf = (int*)(uchar*)inn_buf;
@ -3049,7 +3072,7 @@ void CvDTree::split_node_data( CvDTreeNode* node )
{
unsigned short *ldst, *rdst, *ldst0, *rdst0;
//unsigned short tl, tr;
ldst0 = ldst = (unsigned short*)(buf->data.s + left->buf_idx*buf->cols +
ldst0 = ldst = (unsigned short*)(buf->data.s + left->buf_idx*length_buf_row +
vi*scount + left->offset);
rdst0 = rdst = (unsigned short*)(ldst + nl);
@ -3095,9 +3118,9 @@ void CvDTree::split_node_data( CvDTreeNode* node )
else
{
int *ldst0, *ldst, *rdst0, *rdst;
ldst0 = ldst = buf->data.i + left->buf_idx*buf->cols +
ldst0 = ldst = buf->data.i + left->buf_idx*length_buf_row +
vi*scount + left->offset;
rdst0 = rdst = buf->data.i + right->buf_idx*buf->cols +
rdst0 = rdst = buf->data.i + right->buf_idx*length_buf_row +
vi*scount + right->offset;
// split sorted
@ -3158,9 +3181,9 @@ void CvDTree::split_node_data( CvDTreeNode* node )
if (data->is_buf_16u)
{
unsigned short *ldst = (unsigned short *)(buf->data.s + left->buf_idx*buf->cols +
unsigned short *ldst = (unsigned short *)(buf->data.s + left->buf_idx*length_buf_row +
vi*scount + left->offset);
unsigned short *rdst = (unsigned short *)(buf->data.s + right->buf_idx*buf->cols +
unsigned short *rdst = (unsigned short *)(buf->data.s + right->buf_idx*length_buf_row +
vi*scount + right->offset);
for( i = 0; i < n; i++ )
@ -3188,9 +3211,9 @@ void CvDTree::split_node_data( CvDTreeNode* node )
}
else
{
int *ldst = buf->data.i + left->buf_idx*buf->cols +
int *ldst = buf->data.i + left->buf_idx*length_buf_row +
vi*scount + left->offset;
int *rdst = buf->data.i + right->buf_idx*buf->cols +
int *rdst = buf->data.i + right->buf_idx*length_buf_row +
vi*scount + right->offset;
for( i = 0; i < n; i++ )
@ -3230,9 +3253,9 @@ void CvDTree::split_node_data( CvDTreeNode* node )
int pos = data->get_work_var_count();
if (data->is_buf_16u)
{
unsigned short* ldst = (unsigned short*)(buf->data.s + left->buf_idx*buf->cols +
unsigned short* ldst = (unsigned short*)(buf->data.s + left->buf_idx*length_buf_row +
pos*scount + left->offset);
unsigned short* rdst = (unsigned short*)(buf->data.s + right->buf_idx*buf->cols +
unsigned short* rdst = (unsigned short*)(buf->data.s + right->buf_idx*length_buf_row +
pos*scount + right->offset);
for (i = 0; i < n; i++)
{
@ -3252,9 +3275,9 @@ void CvDTree::split_node_data( CvDTreeNode* node )
}
else
{
int* ldst = buf->data.i + left->buf_idx*buf->cols +
int* ldst = buf->data.i + left->buf_idx*length_buf_row +
pos*scount + left->offset;
int* rdst = buf->data.i + right->buf_idx*buf->cols +
int* rdst = buf->data.i + right->buf_idx*length_buf_row +
pos*scount + right->offset;
for (i = 0; i < n; i++)
{
@ -3310,7 +3333,7 @@ float CvDTree::calc_error( CvMLData* _data, int type, vector<float> *resp )
float r = (float)predict( &sample, missing ? &miss : 0 )->value;
if( pred_resp )
pred_resp[i] = r;
int d = fabs((double)r - response->data.fl[si*r_step]) <= FLT_EPSILON ? 0 : 1;
int d = fabs((double)r - response->data.fl[(size_t)si*r_step]) <= FLT_EPSILON ? 0 : 1;
err += d;
}
err = sample_count ? err / (float)sample_count * 100 : -FLT_MAX;
@ -3327,7 +3350,7 @@ float CvDTree::calc_error( CvMLData* _data, int type, vector<float> *resp )
float r = (float)predict( &sample, missing ? &miss : 0 )->value;
if( pred_resp )
pred_resp[i] = r;
float d = r - response->data.fl[si*r_step];
float d = r - response->data.fl[(size_t)si*r_step];
err += d*d;
}
err = sample_count ? err / (float)sample_count : -FLT_MAX;
@ -3633,8 +3656,8 @@ CvDTreeNode* CvDTree::predict( const CvMat* _sample,
int vi = split->var_idx;
int ci = vtype[vi];
i = vidx ? vidx[vi] : vi;
float val = sample[i*step];
if( m && m[i*mstep] )
float val = sample[(size_t)i*step];
if( m && m[(size_t)i*mstep] )
continue;
if( ci < 0 ) // ordered
dir = val <= split->ord.c ? -1 : 1;

31
modules/objdetect/src/haar.cpp
Unescape View File

@ -1935,20 +1935,14 @@ icvLoadCascadeCART( const char** input_cascade, int n, CvSize orig_window_size )
CV_IMPL CvHaarClassifierCascade*
cvLoadHaarClassifierCascade( const char* directory, CvSize orig_window_size )
{
const char** input_cascade = 0;
CvHaarClassifierCascade *cascade = 0;
int i, n;
const char* slash;
char name[_MAX_PATH];
int size = 0;
char* ptr = 0;
if( !directory )
CV_Error( CV_StsNullPtr, "Null path is passed" );
n = (int)strlen(directory)-1;
slash = directory[n] == '\\' || directory[n] == '/' ? "" : "/";
char name[_MAX_PATH];
int n = (int)strlen(directory)-1;
const char* slash = directory[n] == '\\' || directory[n] == '/' ? "" : "/";
int size = 0;
/* try to read the classifier from directory */
for( n = 0; ; n++ )
@ -1969,10 +1963,14 @@ cvLoadHaarClassifierCascade( const char* directory, CvSize orig_window_size )
CV_Error( CV_StsBadArg, "Invalid path" );
size += (n+1)*sizeof(char*);
input_cascade = (const char**)cvAlloc( size );
ptr = (char*)(input_cascade + n + 1);
for( i = 0; i < n; i++ )
const char** input_cascade = (const char**)cvAlloc( size );
if( !input_cascade )
CV_Error( CV_StsNoMem, "Could not allocate memory for input_cascade" );
char* ptr = (char*)(input_cascade + n + 1);
for( int i = 0; i < n; i++ )
{
sprintf( name, "%s/%d/AdaBoostCARTHaarClassifier.txt", directory, i );
FILE* f = fopen( name, "rb" );
@ -1990,7 +1988,8 @@ cvLoadHaarClassifierCascade( const char* directory, CvSize orig_window_size )
}
input_cascade[n] = 0;
cascade = icvLoadCascadeCART( input_cascade, n, orig_window_size );
CvHaarClassifierCascade* cascade = icvLoadCascadeCART( input_cascade, n, orig_window_size );
if( input_cascade )
cvFree( &input_cascade );

5
modules/objdetect/src/matching.cpp
Unescape View File

@ -1396,7 +1396,7 @@ static int createSchedule(const CvLSVMFeaturePyramid *H, const CvLSVMFilterObjec
const int n, const int bx, const int by,
const int threadsNum, int *kLevels, int **processingLevels)
{
int rootFilterDim, sumPartFiltersDim, i, numLevels, dbx, dby, numDotProducts;
int rootFilterDim, sumPartFiltersDim, i, numLevels, dbx, dby;
int j, minValue, argMin, lambda, maxValue, k;
int *dotProd, *weights, *disp;
if (H == NULL || all_F == NULL)
@ -1420,8 +1420,6 @@ static int createSchedule(const CvLSVMFeaturePyramid *H, const CvLSVMFilterObjec
// of feature map with part filter
dbx = 2 * bx;
dby = 2 * by;
// Total number of dot products for all levels
numDotProducts = 0;
lambda = LAMBDA;
for (i = 0; i < numLevels; i++)
{
@ -1429,7 +1427,6 @@ static int createSchedule(const CvLSVMFeaturePyramid *H, const CvLSVMFilterObjec
H->pyramid[i + lambda]->sizeY * rootFilterDim +
(H->pyramid[i]->sizeX + dbx) *
(H->pyramid[i]->sizeY + dby) * sumPartFiltersDim;
numDotProducts += dotProd[i];
}
// Allocation memory for saving dot product number performed by each thread
weights = (int *)malloc(sizeof(int) * threadsNum);

16
modules/ocl/include/opencv2/ocl/ocl.hpp
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@ -84,20 +84,26 @@ namespace cv
//this function may be obsoleted
//CV_EXPORTS cl_device_id getDevice();
//the function must be called before any other cv::ocl::functions, it initialize ocl runtime
//each Info relates to an OpenCL platform
//there is one or more devices in each platform, each one has a separate name
CV_EXPORTS int getDevice(std::vector<Info> &oclinfo, int devicetype = CVCL_DEVICE_TYPE_GPU);
//set device you want to use, optional function after getDevice be called
//the devnum is the index of the selected device in DeviceName vector of INfo
CV_EXPORTS void setDevice(Info &oclinfo, int devnum = 0);
//this function is not ready yet
//CV_EXPORTS void getComputeCapability(cl_device_id device, int &major, int &minor);
//optional function, if you want save opencl binary kernel to the file, set its path
CV_EXPORTS void setBinpath(const char *path);
//The two functions below are used to get opencl runtime so that opencv can interactive with
//other opencl program
//The two functions below enable other opencl program to use ocl module's cl_context and cl_command_queue
CV_EXPORTS void* getoclContext();
CV_EXPORTS void* getoclCommandQueue();
//this function enable ocl module to use customized cl_context and cl_command_queue
//getDevice also need to be called before this function
CV_EXPORTS void setDeviceEx(Info &oclinfo, void *ctx, void *qu, int devnum = 0);
//////////////////////////////// Error handling ////////////////////////
CV_EXPORTS void error(const char *error_string, const char *file, const int line, const char *func);

1
modules/ocl/src/arithm.cpp
Unescape View File

@ -2205,7 +2205,6 @@ void cv::ocl::transpose(const oclMat &src, oclMat &dst)
CV_Assert(src.type() == CV_8UC1 || src.type() == CV_8UC3 || src.type() == CV_8UC4 || src.type() == CV_8SC3 || src.type() == CV_8SC4 ||
src.type() == CV_16UC2 || src.type() == CV_16SC2 || src.type() == CV_32SC1 || src.type() == CV_32FC1);
stringstream idxstr;
oclMat emptyMat;
if( src.data == dst.data && dst.cols == dst.rows )

170
modules/ocl/src/initialization.cpp
Unescape View File

@ -12,11 +12,13 @@
//
// Copyright (C) 2010-2012, Institute Of Software Chinese Academy Of Science, all rights reserved.
// Copyright (C) 2010-2012, Advanced Micro Devices, Inc., all rights reserved.
// Copyright (C) 2010-2012, Multicoreware, Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// @Authors
// Guoping Long, longguoping@gmail.com
// Niko Li, newlife20080214@gmail.com
// Niko Li, newlife20080214@gmail.com
// Yao Wang, bitwangyaoyao@gmail.com
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
@ -292,23 +294,12 @@ namespace cv
}
return devcienums;
}
void setDevice(Info &oclinfo, int devnum)
{
CV_Assert(devnum >= 0);
cl_int status = 0;
cl_context_properties cps[3] =
{
CL_CONTEXT_PLATFORM, (cl_context_properties)(oclinfo.impl->oclplatform), 0
};
oclinfo.impl->devnum = devnum;
oclinfo.impl->oclcontext = clCreateContext(cps, 1, &oclinfo.impl->devices[devnum], NULL, NULL, &status);
openCLVerifyCall(status);
//create the command queue using the first device of the list
oclinfo.impl->clCmdQueue = clCreateCommandQueue(oclinfo.impl->oclcontext, oclinfo.impl->devices[devnum],
CL_QUEUE_PROFILING_ENABLE, &status);
openCLVerifyCall(status);
static void fillClcontext(Info &oclinfo)
{
//get device information
size_t devnum = oclinfo.impl->devnum;
openCLSafeCall(clGetDeviceInfo(oclinfo.impl->devices[devnum], CL_DEVICE_MAX_WORK_GROUP_SIZE,
sizeof(size_t), (void *)&oclinfo.impl->maxWorkGroupSize, NULL));
openCLSafeCall(clGetDeviceInfo(oclinfo.impl->devices[devnum], CL_DEVICE_MAX_WORK_ITEM_DIMENSIONS,
@ -338,7 +329,41 @@ namespace cv
oclinfo.impl -> double_support = 1;
}
Context::setContext(oclinfo);
}
void setDevice(Info &oclinfo, int devnum)
{
CV_Assert(devnum >= 0);
cl_int status = 0;
cl_context_properties cps[3] =
{
CL_CONTEXT_PLATFORM, (cl_context_properties)(oclinfo.impl->oclplatform), 0
};
oclinfo.impl->devnum = devnum;
oclinfo.impl->oclcontext = clCreateContext(cps, 1, &oclinfo.impl->devices[devnum], NULL, NULL, &status);
openCLVerifyCall(status);
//create the command queue using the first device of the list
oclinfo.impl->clCmdQueue = clCreateCommandQueue(oclinfo.impl->oclcontext, oclinfo.impl->devices[devnum],
CL_QUEUE_PROFILING_ENABLE, &status);
openCLVerifyCall(status);
fillClcontext(oclinfo);
}
void setDeviceEx(Info &oclinfo, void *ctx, void *q, int devnum)
{
CV_Assert(devnum >= 0);
oclinfo.impl->devnum = devnum;
if(ctx && q)
{
oclinfo.impl->oclcontext = (cl_context)ctx;
oclinfo.impl->clCmdQueue = (cl_command_queue)q;
clRetainContext((cl_context)ctx);
clRetainCommandQueue((cl_command_queue)q);
fillClcontext(oclinfo);
}
}
void *getoclContext()
{
@ -440,87 +465,35 @@ namespace cv
Context *clcxt = Context::getContext();
clcxt->impl->Binpath = path;
}
int savetofile(const Context *clcxt, cl_program &program, const char *fileName)
{
//cl_int status;
size_t numDevices = 1;
cl_device_id *devices = clcxt->impl->devices;
//figure out the sizes of each of the binaries.
size_t *binarySizes = (size_t *)malloc( sizeof(size_t) * numDevices );
int savetofile(const Context*, cl_program &program, const char *fileName)
{
size_t binarySize;
openCLSafeCall(clGetProgramInfo(program,
CL_PROGRAM_BINARY_SIZES,
sizeof(size_t) * numDevices,
binarySizes, NULL));
size_t i = 0;
//copy over all of the generated binaries.
char **binaries = (char **)malloc( sizeof(char *) * numDevices );
if(binaries == NULL)
CL_PROGRAM_BINARY_SIZES,
sizeof(size_t),
&binarySize, NULL));
char* binary = (char*)malloc(binarySize);
if(binary == NULL)
{
CV_Error(CV_StsNoMem, "Failed to allocate host memory.(binaries)\r\n");
}
for(i = 0; i < numDevices; i++)
{
if(binarySizes[i] != 0)
{
binaries[i] = (char *)malloc( sizeof(char) * binarySizes[i]);
if(binaries[i] == NULL)
{
CV_Error(CV_StsNoMem, "Failed to allocate host memory.(binaries[i])\r\n");
}
}
else
{
binaries[i] = NULL;
}
CV_Error(CV_StsNoMem, "Failed to allocate host memory.");
}
openCLSafeCall(clGetProgramInfo(program,
CL_PROGRAM_BINARIES,
sizeof(char *) * numDevices,
binaries,
NULL));
CL_PROGRAM_BINARIES,
sizeof(char *),
&binary,
NULL));
//dump out each binary into its own separate file.
for(i = 0; i < numDevices; i++)
FILE *fp = fopen(fileName, "wb+");
if(fp != NULL)
{
if(binarySizes[i] != 0)
{
char deviceName[1024];
openCLSafeCall(clGetDeviceInfo(devices[i],
CL_DEVICE_NAME,
sizeof(deviceName),
deviceName,
NULL));
printf( "%s binary kernel: %s\n", deviceName, fileName);
FILE *fp = fopen(fileName, "wb+");
if(fp == NULL)
{
char *temp = NULL;
sprintf(temp, "Failed to load kernel file : %s\r\n", fileName);
CV_Error(CV_GpuApiCallError, temp);
}
else
{
fwrite(binaries[i], binarySizes[i], 1, fp);
free(binaries[i]);
fclose(fp);
}
}
else
{
printf("Skipping %s since there is no binary data to write!\n",
fileName);
}
fwrite(binary, binarySize, 1, fp);
free(binary);
fclose(fp);
}
free(binarySizes);
free(binaries);
return 1;
}
cl_kernel openCLGetKernelFromSource(const Context *clCxt, const char **source, string kernelName,
const char *build_options)
{
@ -572,7 +545,7 @@ namespace cv
program = clCreateProgramWithSource(
clCxt->impl->clContext, 1, source, NULL, &status);
openCLVerifyCall(status);
status = clBuildProgram(program, 1, &(clCxt->impl->devices[0]), all_build_options, NULL, NULL);
status = clBuildProgram(program, 1, &(clCxt->impl->devices), all_build_options, NULL, NULL);
if(status == CL_SUCCESS && clCxt->impl->Binpath.size())
savetofile(clCxt, program, filename.c_str());
}
@ -587,13 +560,14 @@ namespace cv
cl_int status = 0;
program = clCreateProgramWithBinary(clCxt->impl->clContext,
1,
&(clCxt->impl->devices[0]),
&(clCxt->impl->devices),
(const size_t *)&binarySize,
(const unsigned char **)&binary,
NULL,
&status);
openCLVerifyCall(status);
status = clBuildProgram(program, 1, &(clCxt->impl->devices[0]), all_build_options, NULL, NULL);
status = clBuildProgram(program, 1, &(clCxt->impl->devices), all_build_options, NULL, NULL);
delete[] binary;
}
if(status != CL_SUCCESS)
@ -604,14 +578,14 @@ namespace cv
char *buildLog = NULL;
size_t buildLogSize = 0;
logStatus = clGetProgramBuildInfo(program,
clCxt->impl->devices[0], CL_PROGRAM_BUILD_LOG, buildLogSize,
clCxt->impl->devices, CL_PROGRAM_BUILD_LOG, buildLogSize,
buildLog, &buildLogSize);
if(logStatus != CL_SUCCESS)
cout << "Failed to build the program and get the build info." << endl;
buildLog = new char[buildLogSize];
CV_DbgAssert(!!buildLog);
memset(buildLog, 0, buildLogSize);
openCLSafeCall(clGetProgramBuildInfo(program, clCxt->impl->devices[0],
openCLSafeCall(clGetProgramBuildInfo(program, clCxt->impl->devices,
CL_PROGRAM_BUILD_LOG, buildLogSize, buildLog, NULL));
cout << "\n\t\t\tBUILD LOG\n";
cout << buildLog << endl;
@ -633,7 +607,7 @@ namespace cv
void openCLVerifyKernel(const Context *clCxt, cl_kernel kernel, size_t *localThreads)
{
size_t kernelWorkGroupSize;
openCLSafeCall(clGetKernelWorkGroupInfo(kernel, clCxt->impl->devices[0],
openCLSafeCall(clGetKernelWorkGroupInfo(kernel, clCxt->impl->devices,
CL_KERNEL_WORK_GROUP_SIZE, sizeof(size_t), &kernelWorkGroupSize, 0));
CV_Assert( (localThreads[0] <= clCxt->impl->maxWorkItemSizes[0]) &&
(localThreads[1] <= clCxt->impl->maxWorkItemSizes[1]) &&
@ -795,15 +769,16 @@ namespace cv
Context *clcxt = getContext();
clcxt->impl->clContext = oclinfo.impl->oclcontext;
clcxt->impl->clCmdQueue = oclinfo.impl->clCmdQueue;
clcxt->impl->devices = &oclinfo.impl->devices[oclinfo.impl->devnum];
clcxt->impl->devices = oclinfo.impl->devices[oclinfo.impl->devnum];
clcxt->impl->devName = oclinfo.impl->devName[oclinfo.impl->devnum];
clcxt->impl->maxDimensions = oclinfo.impl->maxDimensions;
clcxt->impl->maxWorkGroupSize = oclinfo.impl->maxWorkGroupSize;
clcxt->impl->maxWorkItemSizes = oclinfo.impl->maxWorkItemSizes;
for(size_t i=0; i<clcxt->impl->maxDimensions && i<4; i++)
clcxt->impl->maxWorkItemSizes[i] = oclinfo.impl->maxWorkItemSizes[i];
clcxt->impl->maxComputeUnits = oclinfo.impl->maxComputeUnits;
clcxt->impl->double_support = oclinfo.impl->double_support;
//extra options to recognize compiler options
clcxt->impl->extra_options = oclinfo.impl->extra_options;
memcpy(clcxt->impl->extra_options, oclinfo.impl->extra_options, 512);
}
Context::Context()
{
@ -814,11 +789,12 @@ namespace cv
impl->devices = NULL;
impl->maxDimensions = 0;
impl->maxWorkGroupSize = 0;
impl->maxWorkItemSizes = NULL;
for(int i=0; i<4; i++)
impl->maxWorkItemSizes[i] = 0;
impl->maxComputeUnits = 0;
impl->double_support = 0;
//extra options to recognize vendor specific fp64 extensions
impl->extra_options = NULL;
memset(impl->extra_options, 0, 512);
programCache = ProgramCache::getProgramCache();
}

8
modules/ocl/src/precomp.hpp
Unescape View File

@ -12,10 +12,12 @@
//
// Copyright (C) 2010-2012, Institute Of Software Chinese Academy Of Science, all rights reserved.
// Copyright (C) 2010-2012, Advanced Micro Devices, Inc., all rights reserved.
// Copyright (C) 2010-2012, Multicoreware, Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// @Authors
// Guoping Long, longguoping@gmail.com
// Yao Wang, bitwangyaoyao@gmail.com
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
@ -131,15 +133,15 @@ namespace cv
//Information of the OpenCL context
cl_context clContext;
cl_command_queue clCmdQueue;
cl_device_id *devices;
cl_device_id devices;
string devName;
cl_uint maxDimensions;
size_t maxWorkGroupSize;
size_t *maxWorkItemSizes;
size_t maxWorkItemSizes[4];
cl_uint maxComputeUnits;
int double_support;
//extra options to recognize vendor specific fp64 extensions
char *extra_options;
char extra_options[512];
string Binpath;
};
}

2
modules/ocl/src/pyrlk.cpp
Unescape View File

@ -742,7 +742,7 @@ static void lkSparse_run(oclMat &I, oclMat &J,
Context *clCxt = I.clCxt;
char platform[256] = {0};
cl_platform_id pid;
clGetDeviceInfo(*clCxt->impl->devices, CL_DEVICE_PLATFORM, sizeof(pid), &pid, NULL);
clGetDeviceInfo(clCxt->impl->devices, CL_DEVICE_PLATFORM, sizeof(pid), &pid, NULL);
clGetPlatformInfo(pid, CL_PLATFORM_NAME, 256, platform, NULL);
std::string namestr = platform;
bool isImageSupported = true;

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