private void ExportInOutdoorHistToCsv(StreamWriter file, List <FileInfo> imagesOnDisk, ColorHistogramInOutDoorFeature inoutdoorFeature)
{
foreach (var image in imagesOnDisk)
{
using (Image <Bgr, Byte> tempImage = new Image <Bgr, Byte>(image.FullName))
{
inoutdoorFeature.Compute(tempImage);
var descriptor = string.Join(",", inoutdoorFeature.DescriptorArray);
file.WriteLine(descriptor);
}
}
}
/// <summary>
/// Find the pedestrian in the image
/// </summary>
/// <param name="image">The image</param>
/// <returns>The image with pedestrian highlighted.</returns>
public void Detect(Image <Bgr, Byte> image)
{
ColorHistogramInOutDoorFeature inoutdoorFeature = new ColorHistogramInOutDoorFeature();
inoutdoorFeature.Compute(image);
float response = _detector.Predict(inoutdoorFeature.Descriptors);
if (response > 0.0f)
{
_label = @"Outdoor";
}
else if (response < 0.0f)
{
_label = @"Indoor";
}
else
{
_label = @"";
}
}
private void trainIndoorOutdoorToolStripMenuItem_Click(object sender, EventArgs e)
{
this.folderBrowserDialog.RootFolder = System.Environment.SpecialFolder.MyComputer;
DialogResult dialogResult = this.folderBrowserDialog.ShowDialog();
if (dialogResult == DialogResult.OK)
{
string root = folderBrowserDialog.SelectedPath;
ColorHistogramInOutDoorFeature inoutdoorFeature = new ColorHistogramInOutDoorFeature();
DirectoryInfo trainPosDir = new DirectoryInfo(root + @"\Train\pos");
DirectoryInfo trainNegDir = new DirectoryInfo(root + @"\Train\neg");
DirectoryInfo testPosDir = new DirectoryInfo(root + @"\Test\pos");
DirectoryInfo testNegDir = new DirectoryInfo(root + @"\Test\neg");
var posImagesOnDisk = FileHelper.GetImages(trainPosDir);
var negImagesOnDisk = FileHelper.GetImages(trainNegDir);
int trainingCount = posImagesOnDisk.Count + negImagesOnDisk.Count;
int offset = 0;
int index = 0;
int dimension = 3 * 2 * 64;
Matrix <float> trainClasses = new Matrix <float>(trainingCount, 1);
float[,] flatDescriptors = new float[trainingCount, dimension];
foreach (var image in posImagesOnDisk)
{
using (Image <Bgr, Byte> tempImage = new Image <Bgr, Byte>(image.FullName))
{
inoutdoorFeature.Compute(tempImage);
var descriptor = inoutdoorFeature.Descriptors;
Buffer.BlockCopy(descriptor.ManagedArray, 0, flatDescriptors, offset, sizeof(float) * descriptor.ManagedArray.Length);
offset += sizeof(float) * descriptor.ManagedArray.Length;
trainClasses[index, 0] = 1.0f;
++index;
}
}
foreach (var image in negImagesOnDisk)
{
using (Image <Bgr, Byte> tempImage = new Image <Bgr, Byte>(image.FullName))
{
inoutdoorFeature.Compute(tempImage);
var descriptor = inoutdoorFeature.Descriptors;
Buffer.BlockCopy(descriptor.ManagedArray, 0, flatDescriptors, offset, sizeof(float) * descriptor.ManagedArray.Length);
offset += sizeof(float) * descriptor.ManagedArray.Length;
trainClasses[index, 0] = -1.0f;
++index;
}
}
Matrix <float> trainData = new Matrix <float>(flatDescriptors);
using (SVM model = new SVM())
{
SVMParams p = new SVMParams();
p.KernelType = Emgu.CV.ML.MlEnum.SVM_KERNEL_TYPE.LINEAR;
p.SVMType = Emgu.CV.ML.MlEnum.SVM_TYPE.C_SVC;
p.C = 1;
p.TermCrit = new MCvTermCriteria(1000, 0.01);
bool trained = model.TrainAuto(trainData, trainClasses, null, null, p.MCvSVMParams, 5);
model.Save(root + @"\inoutdoor.model");
}
}
}