public bool LoadTrainData(Stream stream, out ConfigurationFieldClassifier config, out string trainPath)
{
try
{
var model = ModelFieldCLassify.Load(stream);
model.Ksvm.Position = 0;
m_ksvm = MulticlassSupportVectorMachine.Load(model.Ksvm);
model.Bow.Position = 0;
// m_bow = BagOfVisualWords.Load<CornerFeaturePointEx>(model.Bow);
BinaryFormatter f = new BinaryFormatter();
model.TrainImageFeatureVectors.Position = 0;
m_trainImageFeatureVectors = f.Deserialize(model.TrainImageFeatureVectors) as double[][];
model.ClassIdClassNameMap.Position = 0;
m_classIdClassNameMap = f.Deserialize(model.ClassIdClassNameMap) as Dictionary <int, string>;
config = model.Configuration;
trainPath = model.TrainPath;
m_pageThreshold = model.PageThreshold;
}
catch
{
Reset();
config = null;
trainPath = string.Empty;
return(false);
}
return(true);
}
public static ConfigurationFieldClassifier GetSetupData(Stream stream, out string trainPath)
{
var model = ModelFieldCLassify.Load(stream);
trainPath = model.TrainPath;
return(model.Configuration);
}
// Create bag-of-words (BoW) with the given number of words
// private BagOfVisualWords<CornerFeaturePointEx> m_bow = null;
#endregion
#region Public methods
public void SaveTrainData(Stream stream, ConfigurationFieldClassifier config, string trainPath, double pageThreshold)
{
var model = new ModelFieldCLassify();
model.PageThreshold = pageThreshold;
// m_bow.Save(model.Bow);
m_ksvm.Save(model.Ksvm);
BinaryFormatter f = new BinaryFormatter();
f.Serialize(model.TrainImageFeatureVectors, m_trainImageFeatureVectors);
f.Serialize(model.ClassIdClassNameMap, m_classIdClassNameMap);
model.Configuration = config;
model.TrainPath = trainPath;
ModelFieldCLassify.Save(model, stream);
}
// Create bag-of-words (BoW) with the given number of words
// private BagOfVisualWords<CornerFeaturePointEx> m_bow = null;
#endregion
#region Public methods
public void SaveTrainData(Stream stream, ConfigurationFieldClassifier config, string trainPath, double pageThreshold, IEnumerable <IFieldFeature> features)
{
var model = new ModelFieldCLassify();
model.PageThreshold = pageThreshold;
// m_bow.Save(model.Bow);
(m_machine as MulticlassSupportVectorMachine).Save(model.Ksvm);
//BinaryFormatter f = new BinaryFormatter();
//f.Serialize(model.TrainImageFeatureVectors, m_trainImageFeatureVectors);
//f.Serialize(model.ClassIdClassNameMap, m_classIdClassNameMap);
model.Configuration = config;
model.TrainPath = trainPath;
model.FeaturesScale = features.ToDictionary(x => x.Name, y => y.FieldScale);
ModelFieldCLassify.Save(model, stream);
}
public bool LoadTrainData(Stream stream, out ConfigurationFieldClassifier config, out string trainPath, IEnumerable <IFieldFeature> features)
{
try
{
var model = ModelFieldCLassify.Load(stream);
model.Ksvm.Position = 0;
m_machine = MulticlassSupportVectorMachine.Load(model.Ksvm);
// model.Bow.Position = 0;
// m_bow = BagOfVisualWords.Load<CornerFeaturePointEx>(model.Bow);
//BinaryFormatter f = new BinaryFormatter();
//model.TrainImageFeatureVectors.Position = 0;
//m_trainImageFeatureVectors = f.Deserialize(model.TrainImageFeatureVectors) as double[][];
//model.ClassIdClassNameMap.Position = 0;
//m_classIdClassNameMap = f.Deserialize(model.ClassIdClassNameMap) as Dictionary<int, string>;
config = model.Configuration;
trainPath = model.TrainPath;
m_pageThreshold = model.PageThreshold;
if (model.FeaturesScale != null && model.FeaturesScale.Count > 0)
{
foreach (var featue in features)
{
double scale;
if (model.FeaturesScale.TryGetValue(featue.Name, out scale) == true)
{
featue.FieldScale = scale;
}
}
}
}
catch
{
Reset();
config = null;
trainPath = string.Empty;
return(false);
}
return(true);
}