public static IList <int> PairwiseFeatures(Document document, Mention m1, Mention m2, Dictionaries dictionaries, bool isConll)
{
string speaker1 = m1.headWord.Get(typeof(CoreAnnotations.SpeakerAnnotation));
string speaker2 = m2.headWord.Get(typeof(CoreAnnotations.SpeakerAnnotation));
IList <int> features = new List <int>();
features.Add(isConll ? (speaker1.Equals(speaker2) ? 1 : 0) : 0);
features.Add(isConll ? (CorefRules.AntecedentIsMentionSpeaker(document, m2, m1, dictionaries) ? 1 : 0) : 0);
features.Add(isConll ? (CorefRules.AntecedentIsMentionSpeaker(document, m1, m2, dictionaries) ? 1 : 0) : 0);
features.Add(m1.HeadsAgree(m2) ? 1 : 0);
features.Add(m1.ToString().Trim().ToLower().Equals(m2.ToString().Trim().ToLower()) ? 1 : 0);
features.Add(FeatureExtractor.RelaxedStringMatch(m1, m2) ? 1 : 0);
return(features);
}
public bool IsFace(Bitmap image)
{
if (image == null)
{
throw new ArgumentNullException(nameof(image));
}
using (var windowedImageForFeatureExtraction = image.ExtractImageSectionAndResize(new Rectangle(new Point(0, 0), image.Size), new Size(_sampleWidth, _sampleHeight)))
{
return(_svm.Decide(
FeatureExtractor.GetFor(windowedImageForFeatureExtraction, _blockSize, optionalHogPreviewImagePath: null, normaliser: _normaliser).ToArray()
));
}
}
private void LoadSignatures()
{
Svc2004Loader loader = new Svc2004Loader(@"Databases\Online\SVC2004\Task2.zip", true);
Signers = new List <Signer>(loader.EnumerateSigners());
foreach (var signer in Signers)
{
for (int i = 0; i < signer.Signatures.Count; i++)
{
FeatureExtractor featureExtractor = new FeatureExtractor(signer.Signatures[i]);
signer.Signatures[i] = featureExtractor.GetAllDerivedSVC2004Features();
}
}
}
/// <inheritdoc/>
public void Transform(Signature signature)
{
var extractor = new FeatureExtractor(signature);
extractor.GetAllDerivedSVC2004Features();
}
public static int Main(string[] args)
{
DataSets sets = new DataSets();
DataSet dataset = sets.LoadMall();
string datasetPath = dataset.Path;
Image <Bgr, byte> background = new Image <Bgr, byte>(Path.Combine(datasetPath, "derived images", "background.png"));
int width = 320; // dataset.DefaultWidth;
int height = 240; // dataset.DefaultHeight;
//IPerspectiveCorrector linearPerspectiveCorrector = new LinearPerspectiveCorrector(75 / 480.0, 53.0, 408 / 480.0, 143.0);
int N = 8; //dataset.DefaultGridN;
int M = 8; // dataset.DefaultGridM;
FeatureExtractor extractor = new FeatureExtractor(new IdlePerspectiveCorrector(), N, M, width, height, background);
IDataNormalizer dataNormalizer = new MeanAndVarianceDataNormalizer();
IMultiRegression regression = new MultiRidgeRegression(1e-3);//100, 1e-5);
CrowdCountingRegression countingRegression = new CrowdCountingRegression(extractor, dataNormalizer, regression);
//countingRegression.CachePath = Path.Combine(dataset.Path, "cache");
//string experimentFile = Path.Combine(datasetPath,"analysis","experiments_nullcorrection.txt");
Stopwatch sw = new Stopwatch();
sw.Start();
countingRegression.Train(dataset.TrainPlusValidationInput, dataset.TrainPlusValidationOutput);
Console.WriteLine("Training time (800 frames): {0} ms", sw.ElapsedMilliseconds);
//PeopleCountingVideoCreator.CreateVideo(countingRegression, N, M, width, height, dataset.TestInput, dataset.TestOutput);
MathN::Matrix <double> shouldBe = PeoplePositions.GridQuantize(dataset.TestOutput, N, M, 640, 480);
sw.Restart();
MathN::Matrix <double> predictions2 = countingRegression.Predict(dataset.TestInput);
sw.Stop();
Console.WriteLine("Parallelized prediction time (1200 frames): {0} ms", sw.ElapsedMilliseconds);
File.WriteAllText("D:\\groundtruth.txt", shouldBe.ToMatrixString(5000, 5000), Encoding.UTF8);
sw.Restart();
double[] results = Evaluator.Evaluate(predictions2, shouldBe, new SquaredErrorCalculator(), new AbsoluteErrorCalculator(), new RelativeDeviationErrorCalculator());
sw.Stop();
Console.WriteLine("Eval time: {0} ms", sw.ElapsedMilliseconds);
string resultString = String.Format("{0} ; {1}", countingRegression.ToString(), String.Join(" ; ", results));
Console.WriteLine(resultString);
MathN::Matrix <double> shouldBeTrain = PeoplePositions.GridQuantize(dataset.TrainOutput, N, M, 640, 480);
Console.Write(shouldBeTrain.Row(0));
//MathN::Matrix<double> predictions = countingRegression.Predict(dataset.ValidationInput);
//countingRegression.Train(new List<string>(dataset.TrainInput.Concat(dataset.ValidationInput)),
// new List<IList<PointF>>(dataset.TrainOutput.Concat(dataset.ValidationOutput)));
//MathN::Matrix<double> predictions = countingRegression.Predict(dataset.TestInput);
//MathN::Matrix<double> shouldBe = PeoplePositions.GridQuantize(dataset.ValidationOutput, N, M, 640, 480);
//double[] results = Evaluator.Evaluate(predictions, shouldBe, new SquaredErrorCalculator(), new AbsoluteErrorCalculator(), new RelativeDeviationErrorCalculator());
//string resultString = String.Format("{0} ; {1}", countingRegression.ToString(), String.Join(" ; ", results));
//object locker = new object();
//IMultiRegression regression = new SVMRegression(1, 1e-3);
//CrowdCountingRegression countingRegression = new CrowdCountingRegression(extractor, dataNormalizer, regression);
//countingRegression.CachePath = Path.Combine(dataset.Path, "cache");
////lock (locker)
////{
//// if (File.ReadLines(experimentFile).FirstOrDefault((string s) => s.StartsWith(countingRegression.ToString() + " ;")) != null)
//// {
//// ; //already did this experiment
//// }
////}
//Console.WriteLine(countingRegression.ToString());
//countingRegression.Train(dataset.TrainInput, dataset.TrainOutput);
//MathN::Matrix<double> predictions = countingRegression.Predict(dataset.ValidationInput);
////countingRegression.Train(new List<string>(dataset.TrainInput.Concat(dataset.ValidationInput)),
//// new List<IList<PointF>>(dataset.TrainOutput.Concat(dataset.ValidationOutput)));
////MathN::Matrix<double> predictions = countingRegression.Predict(dataset.TestInput);
//MathN::Matrix<double> shouldBe = PeoplePositions.GridQuantize(dataset.ValidationOutput, N, M, 640, 480);
//double[] results = Evaluator.Evaluate(predictions, shouldBe, new SquaredErrorCalculator(), new AbsoluteErrorCalculator(), new RelativeDeviationErrorCalculator());
//string resultString = String.Format("{0} ; {1}", countingRegression.ToString(), String.Join(" ; ", results));
//lock (locker)
//{
// File.AppendAllLines(experimentFile, new string[] { resultString });
//}
//// }
////});
return(0);
}
public CrowdCountingRegression(FeatureExtractor featureExtractor, IDataNormalizer dataNormalizer, IMultiRegression regression)
{
this.featureExtractor = featureExtractor;
this.dataNormalizer = dataNormalizer;
this.innerRegression = regression;
}
/// <summary>
/// Gets the topics important in this document
/// </summary>
/// <param name="comparisonDocuments">The comparison documents.</param>
/// <param name="featureCount">The feature count.</param>
/// <param name="featureExtractionType">Type of the feature extraction.</param>
/// <returns>The features/topics important in this document.</returns>
public string[] GetFeatures(Document[] comparisonDocuments, int featureCount, FeatureExtractionType featureExtractionType)
{
return(FeatureExtractor.Extract(this, comparisonDocuments, featureCount, featureExtractionType));
}
internal FeatureImageSize(FeatureExtractor featureExtractor)
{
parent = featureExtractor;
}
internal FeatureMargin(FeatureExtractor extractor)
{
parent = extractor;
}
public virtual double[] ExtractFeatureVector(string doc, out bool isEmpty)
{
return(FeatureExtractor.ExtractFeatureVector(doc, out isEmpty));
}