ml: add checks of empty train data

modules/ml/src/ann_mlp.cpp

@@ -920,6 +920,7 @@ public:
 
     bool train( const Ptr<TrainData>& trainData, int flags ) CV_OVERRIDE
     {
+        CV_Assert(!trainData.empty());
         const int MAX_ITER = 1000;
         const double DEFAULT_EPSILON = FLT_EPSILON;
 
@@ -955,6 +956,7 @@ public:
     }
     int train_anneal(const Ptr<TrainData>& trainData)
     {
+        CV_Assert(!trainData.empty());
         SimulatedAnnealingANN_MLP s(*this, trainData);
         trained = true; // Enable call to CalcError
         int iter = simulatedAnnealingSolver(s, params.initialT, params.finalT, params.coolingRatio, params.itePerStep, NULL, params.rEnergy);

modules/ml/src/boost.cpp

@@ -88,6 +88,7 @@ public:
 
     void startTraining( const Ptr<TrainData>& trainData, int flags ) CV_OVERRIDE
     {
+        CV_Assert(!trainData.empty());
         DTreesImpl::startTraining(trainData, flags);
         sumResult.assign(w->sidx.size(), 0.);
 
@@ -184,6 +185,7 @@ public:
 
     bool train( const Ptr<TrainData>& trainData, int flags ) CV_OVERRIDE
     {
+        CV_Assert(!trainData.empty());
         startTraining(trainData, flags);
         int treeidx, ntrees = bparams.weakCount >= 0 ? bparams.weakCount : 10000;
         vector<int> sidx = w->sidx;
@@ -482,6 +484,7 @@ public:
 
     bool train( const Ptr<TrainData>& trainData, int flags ) CV_OVERRIDE
     {
+        CV_Assert(!trainData.empty());
         return impl.train(trainData, flags);
     }
 

modules/ml/src/em.cpp

@@ -112,6 +112,7 @@ public:
 
     bool train(const Ptr<TrainData>& data, int) CV_OVERRIDE
     {
+        CV_Assert(!data.empty());
         Mat samples = data->getTrainSamples(), labels;
         return trainEM(samples, labels, noArray(), noArray());
     }

modules/ml/src/inner_functions.cpp

@@ -59,9 +59,10 @@ bool StatModel::empty() const { return !isTrained(); }
 
 int StatModel::getVarCount() const { return 0; }
 
-bool StatModel::train( const Ptr<TrainData>&, int )
+bool StatModel::train(const Ptr<TrainData>& trainData, int )
 {
     CV_TRACE_FUNCTION();
+    CV_Assert(!trainData.empty());
     CV_Error(CV_StsNotImplemented, "");
     return false;
 }
@@ -69,6 +70,7 @@ bool StatModel::train( const Ptr<TrainData>&, int )
 bool StatModel::train( InputArray samples, int layout, InputArray responses )
 {
     CV_TRACE_FUNCTION();
+    CV_Assert(!samples.empty());
     return train(TrainData::create(samples, layout, responses));
 }
 
@@ -134,6 +136,7 @@ public:
 float StatModel::calcError(const Ptr<TrainData>& data, bool testerr, OutputArray _resp) const
 {
     CV_TRACE_FUNCTION_SKIP_NESTED();
+    CV_Assert(!data.empty());
     Mat samples = data->getSamples();
     Mat sidx = testerr ? data->getTestSampleIdx() : data->getTrainSampleIdx();
     Mat weights = testerr ? data->getTestSampleWeights() : data->getTrainSampleWeights();

modules/ml/src/knearest.cpp

@@ -73,6 +73,7 @@ public:
 
     bool train( const Ptr<TrainData>& data, int flags )
     {
+        CV_Assert(!data.empty());
         Mat new_samples = data->getTrainSamples(ROW_SAMPLE);
         Mat new_responses;
         data->getTrainResponses().convertTo(new_responses, CV_32F);
@@ -494,6 +495,7 @@ public:
 
     bool train( const Ptr<TrainData>& data, int flags ) CV_OVERRIDE
     {
+        CV_Assert(!data.empty());
         return impl->train(data, flags);
     }
 

modules/ml/src/lr.cpp

@@ -142,12 +142,10 @@ Ptr<LogisticRegression> LogisticRegression::load(const String& filepath, const S
 bool LogisticRegressionImpl::train(const Ptr<TrainData>& trainData, int)
 {
     CV_TRACE_FUNCTION_SKIP_NESTED();
+    CV_Assert(!trainData.empty());
+
     // return value
     bool ok = false;
-
-    if (trainData.empty()) {
-        return false;
-    }
     clear();
     Mat _data_i = trainData->getSamples();
     Mat _labels_i = trainData->getResponses();

modules/ml/src/nbayes.cpp

@@ -54,6 +54,7 @@ public:
 
     bool train( const Ptr<TrainData>& trainData, int flags ) CV_OVERRIDE
     {
+        CV_Assert(!trainData.empty());
         const float min_variation = FLT_EPSILON;
         Mat responses = trainData->getNormCatResponses();
         Mat __cls_labels = trainData->getClassLabels();

modules/ml/src/rtrees.cpp

@@ -111,6 +111,7 @@ public:
     void startTraining( const Ptr<TrainData>& trainData, int flags ) CV_OVERRIDE
     {
         CV_TRACE_FUNCTION();
+        CV_Assert(!trainData.empty());
         DTreesImpl::startTraining(trainData, flags);
         int nvars = w->data->getNVars();
         int i, m = rparams.nactiveVars > 0 ? rparams.nactiveVars : cvRound(std::sqrt((double)nvars));
@@ -133,6 +134,7 @@ public:
     bool train( const Ptr<TrainData>& trainData, int flags ) CV_OVERRIDE
     {
         CV_TRACE_FUNCTION();
+        CV_Assert(!trainData.empty());
         startTraining(trainData, flags);
         int treeidx, ntrees = (rparams.termCrit.type & TermCriteria::COUNT) != 0 ?
             rparams.termCrit.maxCount : 10000;
@@ -463,6 +465,7 @@ public:
     bool train( const Ptr<TrainData>& trainData, int flags ) CV_OVERRIDE
     {
         CV_TRACE_FUNCTION();
+        CV_Assert(!trainData.empty());
         if (impl.getCVFolds() != 0)
             CV_Error(Error::StsBadArg, "Cross validation for RTrees is not implemented");
         return impl.train(trainData, flags);

modules/ml/src/svm.cpp

@@ -1613,6 +1613,7 @@ public:
 
     bool train( const Ptr<TrainData>& data, int ) CV_OVERRIDE
     {
+        CV_Assert(!data.empty());
         clear();
 
         checkParams();
@@ -1739,6 +1740,7 @@ public:
                     ParamGrid nu_grid, ParamGrid coef_grid, ParamGrid degree_grid,
                     bool balanced ) CV_OVERRIDE
     {
+        CV_Assert(!data.empty());
         checkParams();
 
         int svmType = params.svmType;

modules/ml/src/svmsgd.cpp

@@ -230,6 +230,7 @@ float SVMSGDImpl::calcShift(InputArray _samples, InputArray _responses) const
 
 bool SVMSGDImpl::train(const Ptr<TrainData>& data, int)
 {
+    CV_Assert(!data.empty());
     clear();
     CV_Assert( isClassifier() );   //toDo: consider
 

modules/ml/src/tree.cpp

@@ -98,6 +98,7 @@ DTrees::Split::Split()
 
 DTreesImpl::WorkData::WorkData(const Ptr<TrainData>& _data)
 {
+    CV_Assert(!_data.empty());
     data = _data;
     vector<int> subsampleIdx;
     Mat sidx0 = _data->getTrainSampleIdx();
@@ -136,6 +137,7 @@ void DTreesImpl::clear()
 
 void DTreesImpl::startTraining( const Ptr<TrainData>& data, int )
 {
+    CV_Assert(!data.empty());
     clear();
     w = makePtr<WorkData>(data);
 
@@ -223,6 +225,7 @@ void DTreesImpl::endTraining()
 
 bool DTreesImpl::train( const Ptr<TrainData>& trainData, int flags )
 {
+    CV_Assert(!trainData.empty());
     startTraining(trainData, flags);
     bool ok = addTree( w->sidx ) >= 0;
     w.release();

modules/ml/test/test_lr.cpp

@@ -94,11 +94,7 @@ void CV_LRTest::run( int /*start_from*/ )
     // initialize variables from the popular Iris Dataset
     string dataFileName = ts->get_data_path() + "iris.data";
     Ptr<TrainData> tdata = TrainData::loadFromCSV(dataFileName, 0);
-
-    if (tdata.empty()) {
-        ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_TEST_DATA);
-        return;
-    }
+    ASSERT_FALSE(tdata.empty()) << "Could not find test data file : " << dataFileName;
 
     // run LR classifier train classifier
     Ptr<LogisticRegression> p = LogisticRegression::create();
@@ -156,6 +152,7 @@ void CV_LRTest_SaveLoad::run( int /*start_from*/ )
     // initialize variables from the popular Iris Dataset
     string dataFileName = ts->get_data_path() + "iris.data";
     Ptr<TrainData> tdata = TrainData::loadFromCSV(dataFileName, 0);
+    ASSERT_FALSE(tdata.empty()) << "Could not find test data file : " << dataFileName;
 
     Mat responses1, responses2;
     Mat learnt_mat1, learnt_mat2;

modules/ml/test/test_mltests2.cpp

@@ -105,6 +105,7 @@ int str_to_ann_activation_function(String& str)
 void ann_check_data( Ptr<TrainData> _data )
 {
     CV_TRACE_FUNCTION();
+    CV_Assert(!_data.empty());
     Mat values = _data->getSamples();
     Mat var_idx = _data->getVarIdx();
     int nvars = (int)var_idx.total();
@@ -118,6 +119,7 @@ void ann_check_data( Ptr<TrainData> _data )
 Mat ann_get_new_responses( Ptr<TrainData> _data, map<int, int>& cls_map )
 {
     CV_TRACE_FUNCTION();
+    CV_Assert(!_data.empty());
     Mat train_sidx = _data->getTrainSampleIdx();
     int* train_sidx_ptr = train_sidx.ptr<int>();
     Mat responses = _data->getResponses();
@@ -150,6 +152,8 @@ Mat ann_get_new_responses( Ptr<TrainData> _data, map<int, int>& cls_map )
 float ann_calc_error( Ptr<StatModel> ann, Ptr<TrainData> _data, map<int, int>& cls_map, int type, vector<float> *resp_labels )
 {
     CV_TRACE_FUNCTION();
+    CV_Assert(!ann.empty());
+    CV_Assert(!_data.empty());
     float err = 0;
     Mat samples = _data->getSamples();
     Mat responses = _data->getResponses();
@@ -264,13 +268,15 @@ TEST_P(ML_ANN_METHOD, Test)
     String dataname = folder + "waveform" + '_' + methodName;
 
     Ptr<TrainData> tdata2 = TrainData::loadFromCSV(original_path, 0);
+    ASSERT_FALSE(tdata2.empty()) << "Could not find test data file : " << original_path;
+
     Mat samples = tdata2->getSamples()(Range(0, N), Range::all());
     Mat responses(N, 3, CV_32FC1, Scalar(0));
     for (int i = 0; i < N; i++)
         responses.at<float>(i, static_cast<int>(tdata2->getResponses().at<float>(i, 0))) = 1;
     Ptr<TrainData> tdata = TrainData::create(samples, ml::ROW_SAMPLE, responses);
+    ASSERT_FALSE(tdata.empty());
 
-    ASSERT_FALSE(tdata.empty()) << "Could not find test data file : " << original_path;
     RNG& rng = theRNG();
     rng.state = 0;
     tdata->setTrainTestSplitRatio(0.8);