public static string Execute(string imagePath)
{
var img = Cv2.ImRead(imagePath);
List <string> result = new List <string>();
List <KeyValuePair <string, double> > featureScores = new List <KeyValuePair <string, double> >();
var features = IndexImages.GetFeatures();
OpenCvSharp.AKAZE kaze = OpenCvSharp.AKAZE.Create();
KeyPoint[] keyPoints;
Mat desc = new Mat();
kaze.DetectAndCompute(img, null, out keyPoints, desc);
foreach (var feature in features)
{
var train = new Mat(feature.Rows, feature.Cols, feature.ImgType, feature.ImgData);
var matches = GetMatches(desc, train);
var score = GetScore(matches);
if (score == double.MaxValue)
{
continue;
}
featureScores.Add(new KeyValuePair <string, double>(feature.Category, score));
}
return(featureScores.Count > 0 ? featureScores.OrderBy(x => (x.Value)).ToList()[0].Key : "Unknown");
}
static void Main(string[] args)
{
string command = "";
string folder = "";
if (args.Length == 0)
{
Console.WriteLine("Use command train or predict along with path to folder as second args.");
}
if (args.Length == 1)
{
Console.WriteLine("Missing path as second argument");
}
command = args[0].ToLower();
folder = args[1];
DateTime start = DateTime.Now;
switch (command)
{
case "train":
IndexImages.Execute(folder);
Console.WriteLine("Indexing completed.");
Console.WriteLine("Time taken (in Mins): " + (DateTime.Now - start).TotalMinutes);
Console.ReadLine();
break;
case "predict":
string prediction = Predictor.Execute(folder);
Console.WriteLine("Prediction: " + prediction);
Console.WriteLine("Time taken (in sec): " + (DateTime.Now - start).TotalSeconds);
Console.ReadLine();
break;
case "test":
int match = 0;
int total = 0;
PredictCategory(folder, "cat", ref match, ref total);
PredictCategory(folder, "dog", ref match, ref total);
Console.WriteLine("Accuracy score: " + (match == 0 ? 0 : (match * 100) / total));
Console.WriteLine("Time taken (in sec): " + (DateTime.Now - start).TotalSeconds);
Console.ReadLine();
break;
default:
break;
}
}
