f4334277
 Hu Chunming
提交3rdparty
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// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
#include "test_precomp.hpp"
namespace opencv_test { namespace {
void randomFillCategories(const string & filename, Mat & input)
{
Mat catMap;
Mat catCount;
std::vector<uchar> varTypes;
FileStorage fs(filename, FileStorage::READ);
FileNode root = fs.getFirstTopLevelNode();
root["cat_map"] >> catMap;
root["cat_count"] >> catCount;
root["var_type"] >> varTypes;
int offset = 0;
int countOffset = 0;
uint var = 0, varCount = (uint)varTypes.size();
for (; var < varCount; ++var)
{
if (varTypes[var] == ml::VAR_CATEGORICAL)
{
int size = catCount.at<int>(0, countOffset);
for (int row = 0; row < input.rows; ++row)
{
int randomChosenIndex = offset + ((uint)cv::theRNG()) % size;
int value = catMap.at<int>(0, randomChosenIndex);
input.at<float>(row, var) = (float)value;
}
offset += size;
++countOffset;
}
}
}
//==================================================================================================
typedef tuple<string, string> ML_Legacy_Param;
typedef testing::TestWithParam< ML_Legacy_Param > ML_Legacy_Params;
TEST_P(ML_Legacy_Params, legacy_load)
{
const string modelName = get<0>(GetParam());
const string dataName = get<1>(GetParam());
const string filename = findDataFile("legacy/" + modelName + "_" + dataName + ".xml");
const bool isTree = modelName == CV_BOOST || modelName == CV_DTREE || modelName == CV_RTREES;
Ptr<StatModel> model;
if (modelName == CV_BOOST)
model = Algorithm::load<Boost>(filename);
else if (modelName == CV_ANN)
model = Algorithm::load<ANN_MLP>(filename);
else if (modelName == CV_DTREE)
model = Algorithm::load<DTrees>(filename);
else if (modelName == CV_NBAYES)
model = Algorithm::load<NormalBayesClassifier>(filename);
else if (modelName == CV_SVM)
model = Algorithm::load<SVM>(filename);
else if (modelName == CV_RTREES)
model = Algorithm::load<RTrees>(filename);
else if (modelName == CV_SVMSGD)
model = Algorithm::load<SVMSGD>(filename);
ASSERT_TRUE(model);
Mat input = Mat(isTree ? 10 : 1, model->getVarCount(), CV_32F);
cv::theRNG().fill(input, RNG::UNIFORM, 0, 40);
if (isTree)
randomFillCategories(filename, input);
Mat output;
EXPECT_NO_THROW(model->predict(input, output, StatModel::RAW_OUTPUT | (isTree ? DTrees::PREDICT_SUM : 0)));
// just check if no internal assertions or errors thrown
}
ML_Legacy_Param param_list[] = {
ML_Legacy_Param(CV_ANN, "waveform"),
ML_Legacy_Param(CV_BOOST, "adult"),
ML_Legacy_Param(CV_BOOST, "1"),
ML_Legacy_Param(CV_BOOST, "2"),
ML_Legacy_Param(CV_BOOST, "3"),
ML_Legacy_Param(CV_DTREE, "abalone"),
ML_Legacy_Param(CV_DTREE, "mushroom"),
ML_Legacy_Param(CV_NBAYES, "waveform"),
ML_Legacy_Param(CV_SVM, "poletelecomm"),
ML_Legacy_Param(CV_SVM, "waveform"),
ML_Legacy_Param(CV_RTREES, "waveform"),
ML_Legacy_Param(CV_SVMSGD, "waveform"),
};
INSTANTIATE_TEST_CASE_P(/**/, ML_Legacy_Params, testing::ValuesIn(param_list));
/*TEST(ML_SVM, throw_exception_when_save_untrained_model)
{
Ptr<cv::ml::SVM> svm;
string filename = tempfile("svm.xml");
ASSERT_THROW(svm.save(filename.c_str()), Exception);
remove(filename.c_str());
}*/
}} // namespace
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