Ejemplo n.º 1
0
 public static double DistanceTo(this Recognizer.Centroid first, ImageInfo info)
 {
     double distance = 0.0;
     for (int i = 0; i < ImageInfo.PropertiesCount; ++i)
     {
         distance += Math.Pow(first.PropertyValues[i] - info.GetPropertyByIndex(i), 2);
     }
     distance = Math.Sqrt(distance);
     return distance;
 }
Ejemplo n.º 2
0
        public static List<ImageInfo> CalculateStats(int[,] pic, Dictionary<int, List<Point>> pointsByLabel)
        {
            Func<double, double> sqrt = Math.Sqrt;
            Func<double, double> square = x => Math.Pow(x, 2);

            int height = pic.GetLength(0);
            int width = pic.GetLength(1);

            var images = new List<ImageInfo>();

            foreach (KeyValuePair<int, List<Point>> imagePoints in pointsByLabel)
            {
                var info = new ImageInfo();
                info.Label = imagePoints.Key;
                List<Point> points = imagePoints.Value;

                foreach (Point p in points)
                {
                    info.Area += 1;
                    if (p.X - 1 < 0 || p.X + 1 >= width || p.Y - 1 < 0 || p.Y + 1 >= height)
                    { // border
                        info.Perimeter += 1;
                    }
                    else if (pic[p.Y, p.X + 1] == -1 || pic[p.Y, p.X - 1] == -1 ||
                             pic[p.Y + 1, p.X] == -1 || pic[p.Y - 1, p.X] == -1)
                    {
                        info.Perimeter += 1;
                    }
                }

                Debug.WriteLine("Area: {0}", info.Area);
                Debug.WriteLine("Perimeter: {0}", info.Perimeter);
                info.Compactness = square(info.Perimeter) / info.Area;
                Debug.WriteLine("Compactness: {0}", info.Compactness);

                info.XAverage = points.Sum(p => p.X) / info.Area;
                info.YAverage = points.Sum(p => p.Y) / info.Area;

                Debug.WriteLine("XAverage: {0}", info.XAverage);
                Debug.WriteLine("YAverage: {0}", info.YAverage);

                info.M20 = 0;
                info.M11 = 0;
                info.M02 = 0;
                foreach (Point p in points)
                {
                    info.M20 += (p.X - info.XAverage) * (p.X - info.XAverage);
                    info.M11 += (p.X - info.XAverage) * (p.Y - info.YAverage);
                    info.M02 += (p.Y - info.YAverage) * (p.Y - info.YAverage);
                }

                info.Elongation = (info.M20 + info.M02 + sqrt(square(info.M20 - info.M02) + 4 * info.M11 * info.M11))
                    / (info.M20 + info.M02 - sqrt(square(info.M20 - info.M02) + 4 * info.M11 * info.M11));

                Debug.WriteLine("Elongation: {0}", info.Elongation);

                Debug.WriteLine("M11: " + info.M11);
                Debug.WriteLine("M20: " + info.M20);
                Debug.WriteLine("M02: " + info.M02);
                info.Orientation = 0.5 * Math.Atan2(2 * info.M11, info.M20 - info.M02);
                Debug.WriteLine("Orientation: " + info.Orientation);

                images.Add(info);
            }

            foreach (ImageInfo image in images)
            {
                if (double.IsInfinity(image.Elongation))
                {
                    Debugger.Break();
                    image.Elongation = images
                        .Where(im => !double.IsInfinity(im.Elongation))
                        .Max(im => im.Elongation);
                }
                else if (double.IsNaN(image.Elongation))
                {
                    Debugger.Break();
                }
            }

            return images;
        }
Ejemplo n.º 3
0
 private static int FindIndexOfNearestClusterMean(ImageInfo image, Centroid[] means)
 {
     Debug.WriteLine("Image's nearest mean:");
     int nearestMeanIndex = -1;
     double minDistance = double.MaxValue;
     for (int i = 0; i < means.Length; ++i)
     {
         double distanceToMean = means[i].DistanceTo(image);
         Debug.WriteLine("Distance to {0} = {1}", i, distanceToMean);
         if (distanceToMean < minDistance)
         {
             nearestMeanIndex = i;
             minDistance = distanceToMean;
         }
     }
     if (nearestMeanIndex == -1) Debugger.Break();
     Debug.WriteLine("Nearest mean index: {0}", nearestMeanIndex);
     return nearestMeanIndex;
 }