Merge remote-tracking branch 'upstream/master' into svm_hog

modules/ml/doc/decision_trees.rst

@@ -318,4 +318,3 @@ decision tree.
 
 
 .. [Breiman84] Breiman, L., Friedman, J. Olshen, R. and Stone, C. (1984), *Classification and Regression Trees*, Wadsworth.
-

modules/ml/doc/k_nearest_neighbors.rst

@@ -11,6 +11,12 @@ CvKNearest
 
 The class implements K-Nearest Neighbors model as described in the beginning of this section.
 
+.. note::
+
+   * (Python) An example of digit recognition using KNearest can be found at opencv_source/samples/python2/digits.py
+   * (Python) An example of grid search digit recognition using KNearest can be found at opencv_source/samples/python2/digits_adjust.py
+   * (Python) An example of video digit recognition using KNearest can be found at opencv_source/samples/python2/digits_video.py
+
 CvKNearest::CvKNearest
 ----------------------
 Default and training constructors.
@@ -188,5 +194,3 @@ The sample below (currently using the obsolete ``CvMat`` structures) demonstrate
         cvReleaseMat( &trainData );
         return 0;
     }
-
-

modules/ml/doc/neural_networks.rst

@@ -278,4 +278,3 @@ Returns neurons weights of the particular layer.
 .. ocv:function:: double* CvANN_MLP::get_weights(int layer)
 
     :param layer: Index of the particular layer.
-

modules/ml/doc/statistical_models.rst

@@ -161,4 +161,3 @@ Predicts the response for a sample.
 The method is used to predict the response for a new sample. In case of a classification, the method returns the class label. In case of a regression, the method returns the output function value. The input sample must have as many components as the ``train_data`` passed to ``train`` contains. If the ``var_idx`` parameter is passed to ``train``, it is remembered and then is used to extract only the necessary components from the input sample in the method ``predict``.
 
 The suffix ``const`` means that prediction does not affect the internal model state, so the method can be safely called from within different threads.
-

modules/ml/doc/support_vector_machines.rst

@@ -158,6 +158,12 @@ CvSVM
 
 Support Vector Machines.
 
+.. note::
+
+   * (Python) An example of digit recognition using SVM can be found at opencv_source/samples/python2/digits.py
+   * (Python) An example of grid search digit recognition using SVM can be found at opencv_source/samples/python2/digits_adjust.py
+   * (Python) An example of video digit recognition using SVM can be found at opencv_source/samples/python2/digits_video.py
+
 CvSVM::CvSVM
 ------------
 Default and training constructors.

modules/ml/include/opencv2/ml.hpp

@@ -944,6 +944,8 @@ protected:
     CvDTreeNode* root;
     CvMat* var_importance;
     CvDTreeTrainData* data;
+    CvMat train_data_hdr, responses_hdr;
+    cv::Mat train_data_mat, responses_mat;
 
 public:
     int pruned_tree_idx;
@@ -1055,6 +1057,8 @@ protected:
     // array of the trees of the forest
     CvForestTree** trees;
     CvDTreeTrainData* data;
+    CvMat train_data_hdr, responses_hdr;
+    cv::Mat train_data_mat, responses_mat;
     int ntrees;
     int nclasses;
     double oob_error;
@@ -1270,6 +1274,8 @@ protected:
     virtual void initialize_weights(double (&p)[2]);
 
     CvDTreeTrainData* data;
+    CvMat train_data_hdr, responses_hdr;
+    cv::Mat train_data_mat, responses_mat;
     CvBoostParams params;
     CvSeq* weak;
 
@@ -2144,7 +2150,7 @@ typedef CvANN_MLP NeuralNet_MLP;
 typedef CvGBTreesParams GradientBoostingTreeParams;
 typedef CvGBTrees GradientBoostingTrees;
 
-template<> CV_EXPORTS void Ptr<CvDTreeSplit>::delete_obj();
+template<> CV_EXPORTS void DefaultDeleter<CvDTreeSplit>::operator ()(CvDTreeSplit* obj) const;
 
 CV_EXPORTS bool initModule_ml(void);
 }

modules/ml/include/opencv2/ml/ml.hpp

@@ -45,4 +45,4 @@
 #error this is a compatibility header which should not be used inside the OpenCV library
 #endif
 
-#include "opencv2/ml.hpp"
+#include "opencv2/ml.hpp"

modules/ml/src/boost.cpp

@@ -2122,9 +2122,14 @@ CvBoost::train( const Mat& _train_data, int _tflag,
                const Mat& _missing_mask,
                CvBoostParams _params, bool _update )
 {
-    CvMat tdata = _train_data, responses = _responses, vidx = _var_idx,
-        sidx = _sample_idx, vtype = _var_type, mmask = _missing_mask;
-    return train(&tdata, _tflag, &responses, vidx.data.ptr ? &vidx : 0,
+    train_data_hdr = _train_data;
+    train_data_mat = _train_data;
+    responses_hdr = _responses;
+    responses_mat = _responses;
+
+    CvMat vidx = _var_idx, sidx = _sample_idx, vtype = _var_type, mmask = _missing_mask;
+
+    return train(&train_data_hdr, _tflag, &responses_hdr, vidx.data.ptr ? &vidx : 0,
           sidx.data.ptr ? &sidx : 0, vtype.data.ptr ? &vtype : 0,
           mmask.data.ptr ? &mmask : 0, _params, _update);
 }

modules/ml/src/ertrees.cpp

@@ -1844,12 +1844,16 @@ bool CvERTrees::train( const Mat& _train_data, int _tflag,
                       const Mat& _sample_idx, const Mat& _var_type,
                       const Mat& _missing_mask, CvRTParams params )
 {
-    CvMat tdata = _train_data, responses = _responses, vidx = _var_idx,
-    sidx = _sample_idx, vtype = _var_type, mmask = _missing_mask;
-    return train(&tdata, _tflag, &responses, vidx.data.ptr ? &vidx : 0,
+    train_data_hdr = _train_data;
+    train_data_mat = _train_data;
+    responses_hdr = _responses;
+    responses_mat = _responses;
+
+    CvMat vidx = _var_idx, sidx = _sample_idx, vtype = _var_type, mmask = _missing_mask;
+
+    return train(&train_data_hdr, _tflag, &responses_hdr, vidx.data.ptr ? &vidx : 0,
                  sidx.data.ptr ? &sidx : 0, vtype.data.ptr ? &vtype : 0,
                  mmask.data.ptr ? &mmask : 0, params);
 }
 
 // End of file.
-

modules/ml/src/knearest.cpp

@@ -116,7 +116,7 @@ bool CvKNearest::train( const CvMat* _train_data, const CvMat* _responses,
 
     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" );
 
@@ -480,4 +480,3 @@ float CvKNearest::find_nearest( const cv::Mat& _samples, int k, CV_OUT cv::Mat&
 }
 
 /* End of file */
-

modules/ml/src/ml_init.cpp

@@ -56,7 +56,7 @@ CV_INIT_ALGORITHM(EM, "StatModel.EM",
 
 bool initModule_ml(void)
 {
-    Ptr<Algorithm> em = createEM_hidden();
+    Ptr<Algorithm> em = createEM_ptr_hidden();
     return em->info() != 0;
 }
 

modules/ml/src/nbayes.cpp

@@ -210,6 +210,8 @@ bool CvNormalBayesClassifier::train( const CvMat* _train_data, const CvMat* _res
                 prod_data[c2] += train_vec[c2]*val1;
         }
     }
+    cvReleaseMat( &responses );
+    responses = 0;
 
     /* calculate avg, covariance matrix, c */
     for( cls = 0; cls < nclasses; cls++ )
@@ -623,4 +625,3 @@ float CvNormalBayesClassifier::predict( const Mat& _samples, Mat* _results ) con
 }
 
 /* End of file. */
-

modules/ml/src/precomp.cpp

@@ -1,45 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#include "precomp.hpp"
-
-/* End of file. */

modules/ml/src/precomp.hpp

@@ -41,7 +41,6 @@
 #ifndef __OPENCV_PRECOMP_H__
 #define __OPENCV_PRECOMP_H__
 
-
 #include "opencv2/core.hpp"
 #include "opencv2/ml.hpp"
 #include "opencv2/core/core_c.h"

modules/ml/src/rtrees.cpp

@@ -126,7 +126,7 @@ void ForestTreeBestSplitFinder::operator()(const BlockedRange& range)
         }
 
         if( res && bestSplit->quality < split->quality )
-                memcpy( (CvDTreeSplit*)bestSplit, (CvDTreeSplit*)split, splitSize );
+            memcpy( bestSplit.get(), split.get(), splitSize );
     }
 }
 }
@@ -839,9 +839,14 @@ bool CvRTrees::train( const Mat& _train_data, int _tflag,
                      const Mat& _sample_idx, const Mat& _var_type,
                      const Mat& _missing_mask, CvRTParams _params )
 {
-    CvMat tdata = _train_data, responses = _responses, vidx = _var_idx,
-    sidx = _sample_idx, vtype = _var_type, mmask = _missing_mask;
-    return train(&tdata, _tflag, &responses, vidx.data.ptr ? &vidx : 0,
+    train_data_hdr = _train_data;
+    train_data_mat = _train_data;
+    responses_hdr = _responses;
+    responses_mat = _responses;
+
+    CvMat vidx = _var_idx, sidx = _sample_idx, vtype = _var_type, mmask = _missing_mask;
+
+    return train(&train_data_hdr, _tflag, &responses_hdr, vidx.data.ptr ? &vidx : 0,
                  sidx.data.ptr ? &sidx : 0, vtype.data.ptr ? &vtype : 0,
                  mmask.data.ptr ? &mmask : 0, _params);
 }

modules/ml/src/svm.cpp

@@ -1391,6 +1391,8 @@ bool CvSVM::do_train( int svm_type, int sample_count, int var_count, const float
         for( i = 0; i < sample_count; i++ )
             sv_count += fabs(alpha[i]) > 0;
 
+        CV_Assert(sv_count != 0);
+
         sv_total = df->sv_count = sv_count;
         CV_CALL( df->alpha = (double*)cvMemStorageAlloc( storage, sv_count*sizeof(df->alpha[0])) );
         CV_CALL( sv = (float**)cvMemStorageAlloc( storage, sv_count*sizeof(sv[0])));
@@ -2997,4 +2999,3 @@ cvTrainSVM_CrossValidation( const CvMat* train_data, int tflag,
 #endif
 
 /* End of file. */
-

modules/ml/src/tree.cpp

@@ -1594,9 +1594,14 @@ bool CvDTree::train( const Mat& _train_data, int _tflag,
                     const Mat& _sample_idx, const Mat& _var_type,
                     const Mat& _missing_mask, CvDTreeParams _params )
 {
-    CvMat tdata = _train_data, responses = _responses, vidx=_var_idx,
-        sidx=_sample_idx, vtype=_var_type, mmask=_missing_mask;
-    return train(&tdata, _tflag, &responses, vidx.data.ptr ? &vidx : 0, sidx.data.ptr ? &sidx : 0,
+    train_data_hdr = _train_data;
+    train_data_mat = _train_data;
+    responses_hdr = _responses;
+    responses_mat = _responses;
+
+    CvMat vidx=_var_idx, sidx=_sample_idx, vtype=_var_type, mmask=_missing_mask;
+
+    return train(&train_data_hdr, _tflag, &responses_hdr, vidx.data.ptr ? &vidx : 0, sidx.data.ptr ? &sidx : 0,
                  vtype.data.ptr ? &vtype : 0, mmask.data.ptr ? &mmask : 0, _params);
 }
 
@@ -1877,7 +1882,7 @@ double CvDTree::calc_node_dir( CvDTreeNode* node )
 namespace cv
 {
 
-template<> CV_EXPORTS void Ptr<CvDTreeSplit>::delete_obj()
+template<> CV_EXPORTS void DefaultDeleter<CvDTreeSplit>::operator ()(CvDTreeSplit* obj) const
 {
     fastFree(obj);
 }
@@ -1888,12 +1893,12 @@ DTreeBestSplitFinder::DTreeBestSplitFinder( CvDTree* _tree, CvDTreeNode* _node)
     node = _node;
     splitSize = tree->get_data()->split_heap->elem_size;
 
-    bestSplit = (CvDTreeSplit*)fastMalloc(splitSize);
-    memset((CvDTreeSplit*)bestSplit, 0, splitSize);
+    bestSplit.reset((CvDTreeSplit*)fastMalloc(splitSize));
+    memset(bestSplit.get(), 0, splitSize);
     bestSplit->quality = -1;
     bestSplit->condensed_idx = INT_MIN;
-    split = (CvDTreeSplit*)fastMalloc(splitSize);
-    memset((CvDTreeSplit*)split, 0, splitSize);
+    split.reset((CvDTreeSplit*)fastMalloc(splitSize));
+    memset(split.get(), 0, splitSize);
     //haveSplit = false;
 }
 
@@ -1903,10 +1908,10 @@ DTreeBestSplitFinder::DTreeBestSplitFinder( const DTreeBestSplitFinder& finder,
     node = finder.node;
     splitSize = tree->get_data()->split_heap->elem_size;
 
-    bestSplit = (CvDTreeSplit*)fastMalloc(splitSize);
-    memcpy((CvDTreeSplit*)(bestSplit), (const CvDTreeSplit*)finder.bestSplit, splitSize);
-    split = (CvDTreeSplit*)fastMalloc(splitSize);
-    memset((CvDTreeSplit*)split, 0, splitSize);
+    bestSplit.reset((CvDTreeSplit*)fastMalloc(splitSize));
+    memcpy(bestSplit.get(), finder.bestSplit.get(), splitSize);
+    split.reset((CvDTreeSplit*)fastMalloc(splitSize));
+    memset(split.get(), 0, splitSize);
 }
 
 void DTreeBestSplitFinder::operator()(const BlockedRange& range)
@@ -1939,14 +1944,14 @@ void DTreeBestSplitFinder::operator()(const BlockedRange& range)
         }
 
         if( res && bestSplit->quality < split->quality )
-                memcpy( (CvDTreeSplit*)bestSplit, (CvDTreeSplit*)split, splitSize );
+                memcpy( bestSplit.get(), split.get(), splitSize );
     }
 }
 
 void DTreeBestSplitFinder::join( DTreeBestSplitFinder& rhs )
 {
     if( bestSplit->quality < rhs.bestSplit->quality )
-        memcpy( (CvDTreeSplit*)bestSplit, (CvDTreeSplit*)rhs.bestSplit, splitSize );
+        memcpy( bestSplit.get(), rhs.bestSplit.get(), splitSize );
 }
 }
 

modules/ml/test/test_emknearestkmeans.cpp

@@ -678,4 +678,3 @@ TEST(ML_KNearest, accuracy) { CV_KNearestTest test; test.safe_run(); }
 TEST(ML_EM, accuracy) { CV_EMTest test; test.safe_run(); }
 TEST(ML_EM, save_load) { CV_EMTest_SaveLoad test; test.safe_run(); }
 TEST(ML_EM, classification) { CV_EMTest_Classification test; test.safe_run(); }
-

modules/ml/test/test_precomp.cpp

@@ -1 +0,0 @@
-#include "test_precomp.hpp"