public void Init()
{
gm = new GradientArrayMaker(0.5d);
iws = new ImageWindowScheme(2, 2, 256, 256, 4);
hm50 = new HogsMaker(gm, 50);
avg50 = new MomentFeature(hm50, iws, 0);
second50 = new MomentFeature(hm50, iws, 2);
}
public static ImageData FromImage(Bitmap img, ImageWindowScheme iws, FeatureSet feature_set)
{
SiteFeatureSet[,] sitefeatures = new SiteFeatureSet[iws.XSites, iws.YSites];
for(int x = 0; x < iws.XSites; x++) for(int y = 0; y < iws.YSites; y++)
{
sitefeatures[x,y] = new SiteFeatureSet(feature_set.ApplyToBitmap(img, x, y), feature_set);
}
return new ImageData(sitefeatures, feature_set.Length);
}
public void Init()
{
gm = new GradientArrayMaker(0.5d);
iws = new ImageWindowScheme(2, 2, 256, 256, 4);
hm50 = new HogsMaker(gm, 50);
pf50 = new PeaksFeature(hm50, iws);
hm2 = new HogsMaker(gm, 2);
pf2 = new PeaksFeature(hm2, iws);
}
public void Init()
{
gm = new GradientArrayMaker(0.5d);
iws = new ImageWindowScheme(2, 2, 256, 256, 4);
hm50 = new HogsMaker(gm, 50);
raf50 = new RightAngleFeature(hm50, iws, 2);
hm2 = new HogsMaker(gm, 2);
raf2 = new RightAngleFeature(hm2, iws, 1);
}
public static void Main(string[] args)
{
ResourceManager.Builder resources_builder = new ResourceManager.Builder();
//Set paths here
ResourceManager resources = resources_builder.Build();
ImageWindowScheme iws = new ImageWindowScheme(16, 16, 256, 256, 3);
GradientArrayMaker gm = new GradientArrayMaker(0.5);
HogsMaker hm = new HogsMaker(gm, 50);
FeatureSet.Builder feature_set_builder = new FeatureSet.Builder();
feature_set_builder.AddFeature(new MomentFeature(hm, iws, 2));
FeatureSet feature_set = feature_set_builder.Build();
ImageData.Factory idf = new ImageData.Factory(iws.XSites, iws.YSites, feature_set);
Console.WriteLine("Importing ImageDatas:");
List<ImageData> images = resources.EachTrainingImage((Bitmap bmp) => {
return idf.FromImage(bmp);
});
Console.WriteLine("Importing Classifications:");
List<Classification> classifications = resources.EachTrainingCSV((StreamReader csv) => {
return Classification.FromLabeling(csv, iws.XSites, iws.YSites);
});
ModelFactory.Builder mfb = new ModelFactory.Builder(images, classifications);
//Set hyperparameters here
ModelFactory model_factory = mfb.Build();
Model mfm = model_factory.PseudoLikelihoodTrain();
Console.WriteLine("Model converged! Estimating image ...");
string imagename = 192.ToString("D3"); //I still like 192
ImageData input = resources.UsingTrainingBitmap(imagename+".jpg", bmp => idf.FromImage(bmp));
Classification out_classed; //See what I did there?
string inference_algorithm = "logistic";
if(inference_algorithm == "logistic")
{
Console.WriteLine("Inferring with Logistic classifier...");
out_classed = mfm.LogisticInfer(input);
}
else if (inference_algorithm == "map")
{
Console.WriteLine("Inferring with MAP classifier...");
out_classed = mfm.MaximumAPosterioriInfer(input);
}
else
{
Console.WriteLine("Inferring with ICM classifier...");
out_classed = mfm.ICMInfer(input);
}
resources.UsingOutputCSV("192.txt", (sw) => {
sw.Write(out_classed.ToString());
});
}
public void Init()
{
iws = new ImageWindowScheme(16, 16, 32, 33, 2);
}
public void Init()
{
iws = new ImageWindowScheme(2, 2, 256, 256, 4);
a50 = new AverageRGBFeature(iws);
}
