/// <summary>
/// for estimation
/// </summary>
/// <param name="svm_path"></param>
public VehicleSpeedEstimator(System.Drawing.Size hog_training_img_size, string svm_path)
{
try {
anomaly = new AnomalySpeedDetector();
AllowErrorRate = 1.5f;
SurgingSpeedMin = 0.45f;
SurgingCountLimit = 7;
classifier = OpenCvSharp.ML.SVM.Load(svm_path);
hog = new HOGDescriptor(new OpenCvSharp.Size(hog_training_img_size.Width, hog_training_img_size.Height), new OpenCvSharp.Size(16, 16),
new OpenCvSharp.Size(8, 8), new OpenCvSharp.Size(8, 8));
var support_vectors = classifier.GetSupportVectors();
var vectors = new float[support_vectors.Width * support_vectors.Height];
Marshal.Copy(support_vectors.Data, vectors, 0, support_vectors.Width * support_vectors.Height);
hog.SetSVMDetector(vectors);
}
catch (Exception) {
classifier = OpenCvSharp.ML.SVM.Create();
}
}
/// <summary>
/// for hog training
/// </summary>
/// <param name="positive_path"></param>
/// <param name="negative_path"></param>
/// <param name="svm_save_path"></param>
/// <returns></returns>
public bool Training(string root_path, string svm_save_path)
{
try {
var computes = new List <float[]>();
var response = new List <int>();
var color_folders = Directory.GetDirectories(root_path);
if (color_folders.Length == 0)
{
return(false);
}
foreach (var color in color_folders)
{
var positive_path = string.Format(color + "\\" + "positive");
var negative_path = string.Format(color + "\\" + "negative");
var positives = Directory.GetFiles(positive_path);
var negatives = Directory.GetFiles(negative_path);
foreach (var file in positives)
{
computes.Add(hog.Compute(Cv2.ImRead(file, ImreadModes.GrayScale).Resize(hog.WinSize)));
response.Add(1);
}
foreach (var file in negatives)
{
computes.Add(hog.Compute(Cv2.ImRead(file, ImreadModes.GrayScale).Resize(hog.WinSize)));
response.Add(-1);
}
}
var respon_mat = new Mat(response.Count, 1, MatType.CV_32SC1, response.ToArray());
var samples = new float[computes[0].Length * computes.Count];
var sample_mat = new Mat(computes.Count, computes[0].Length, MatType.CV_32FC1, samples);
for (int idx = 0; idx < computes[0].Length * computes.Count; idx += computes[0].Length)
{
for (int col = 0; col < computes[0].Length; col++)
{
samples[idx + col] = computes[idx / computes[0].Length][col];
}
}
classifier.KernelType = OpenCvSharp.ML.SVM.KernelTypes.Linear;
classifier.Type = OpenCvSharp.ML.SVM.Types.EpsSvr;
classifier.P = 0.1;
classifier.Nu = 0.1;
classifier.C = 0.01;
classifier.Gamma = 0.1;
classifier.Train(sample_mat, OpenCvSharp.ML.SampleTypes.RowSample, respon_mat);
classifier.Save(svm_save_path);
var support_vectors = classifier.GetSupportVectors();
var vectors = new float[support_vectors.Width * support_vectors.Height];
Marshal.Copy(support_vectors.Data, vectors, 0, support_vectors.Width * support_vectors.Height);
hog.SetSVMDetector(vectors);
return(true);
}
catch (Exception e) {
Trace.WriteLine(e.StackTrace + e.Message);
return(false);
}
}