private FakeImageObject GetRandomFromObjectList()
{
FakeImageObject result = new FakeImageObject();
if (hitClosesImagesList == null)
{
hitClosesImagesList = new int[objectList.Count];
}
if (hitClosesImagesList.Where(x => x == 1).Count() == objectList.Count)
{
result.SetPropertiesFromImageObject(objectList.First());
return(result);
}
int imageIndex;
do
{
imageIndex = random.Next(0, objectList.Count - 1);
} while (hitClosesImagesList[imageIndex] != 0);
hitClosesImagesList[imageIndex] = 1;
result.SetPropertiesFromImageObject(objectList.ToArray()[imageIndex]);
return(result);
}
private FakeImageObject GetClosestObjectFromObjectList(FakeImageObject fakeImage)
{
FakeImageObject result = new FakeImageObject();
if (hitClosesImagesList == null)
{
hitClosesImagesList = new int[objectList.Count];
}
ImageObject[] classes = objectList.ToArray();
double[] distances = new double[objectList.Count];
for (int i = 0; i < objectList.Count; i++)
{
distances[i] =
Math.Abs(areaCoefficient * (classes[i].Area - fakeImage.Area)) +
Math.Abs(densityCoefficient * (classes[i].Density - fakeImage.Density)) +
Math.Abs(elongationCoefficient * (classes[i].Elongation - fakeImage.Elongation)) +
Math.Abs(perimeterCoefficient * (classes[i].Perimeter - fakeImage.Perimeter)) +
Math.Abs(massCenterCoefficient * Math.Sqrt(
(classes[i].MassCenter.X - fakeImage.MassCenter.X) * (classes[i].MassCenter.X - fakeImage.MassCenter.X) +
(classes[i].MassCenter.Y - fakeImage.MassCenter.Y) * (classes[i].MassCenter.Y - fakeImage.MassCenter.Y))
);
}
int index = 0;
double minRange = distances[0];
for (int i = 0; i < objectList.Count; i++)
{
if (hitClosesImagesList[i] == 0 && distances[i] <= minRange)
{
minRange = distances[i];
index = i;
}
}
result.SetPropertiesFromImageObject(classes[index]);
hitClosesImagesList[index] = 1;
return(result);
}
public void SetObjectsToGroups()
{
FakeImageObject[] mediansArray = new FakeImageObject[kMedians];
hitClosesImagesList = null;
for (int i = 0; i < kMedians; i++)
{
mediansArray[i] = GetRandomFromObjectList();
}
hitClosesImagesList = null;
List <ImageObject>[] imageClasses = new List <ImageObject> [kMedians];
for (int i = 0; i < kMedians; i++)
{
imageClasses[i] = new List <ImageObject>();
}
foreach (ImageObject imageObject in objectList)
{
imageClasses[GetClassIndex(mediansArray, imageObject)].Add(imageObject);
}
bool flag = true;
while (flag)
{
flag = false;
mediansArray = new FakeImageObject[kMedians];
hitClosesImagesList = null;
for (int i = 0; i < kMedians; i++)
{
mediansArray[i] = GetClosestObjectFromObjectList(GetAverageFromImageList(imageClasses[i]));
}
hitClosesImagesList = null;
List <ImageObject>[] tempImageClasses = new List <ImageObject> [kMedians];
for (int i = 0; i < kMedians; i++)
{
tempImageClasses[i] = new List <ImageObject>();
}
foreach (List <ImageObject> objectList1 in imageClasses)
{
foreach (ImageObject imageObject in objectList1)
{
tempImageClasses[GetClassIndex(mediansArray, imageObject)].Add(imageObject);
}
}
for (int i = 0; i < kMedians; i++)
{
if (tempImageClasses[i].Equals(imageClasses[i]))
{
flag = true;
}
}
imageClasses = tempImageClasses;
}
foreach (List <ImageObject> imageObjectList in imageClasses)
{
foreach (ImageObject imageObject in imageObjectList)
{
foreach (Point point in imageObject.Points)
{
groupMap[point.X, point.Y] = Array.IndexOf(imageClasses, imageObjectList) + 2;
}
}
}
}
private FakeImageObject GetRandomFromObjectList()
{
FakeImageObject result = new FakeImageObject();
if (hitClosesImagesList == null)
{
hitClosesImagesList = new int[objectList.Count];
}
if (hitClosesImagesList.Where(x => x == 1).Count() == objectList.Count)
{
result.SetPropertiesFromImageObject(objectList.First());
return result;
}
int imageIndex;
do
{
imageIndex = random.Next(0, objectList.Count - 1);
} while (hitClosesImagesList[imageIndex] != 0);
hitClosesImagesList[imageIndex] = 1;
result.SetPropertiesFromImageObject(objectList.ToArray()[imageIndex]);
return result;
}
private FakeImageObject GetClosestObjectFromObjectList(FakeImageObject fakeImage)
{
FakeImageObject result = new FakeImageObject();
if (hitClosesImagesList == null)
{
hitClosesImagesList = new int[objectList.Count];
}
ImageObject[] classes = objectList.ToArray();
double[] distances = new double[objectList.Count];
for (int i = 0; i < objectList.Count; i++)
{
distances[i] =
Math.Abs(areaCoefficient * (classes[i].Area - fakeImage.Area)) +
Math.Abs(densityCoefficient * (classes[i].Density - fakeImage.Density)) +
Math.Abs(elongationCoefficient * (classes[i].Elongation - fakeImage.Elongation)) +
Math.Abs(perimeterCoefficient * (classes[i].Perimeter - fakeImage.Perimeter)) +
Math.Abs(massCenterCoefficient * Math.Sqrt(
(classes[i].MassCenter.X - fakeImage.MassCenter.X) * (classes[i].MassCenter.X - fakeImage.MassCenter.X) +
(classes[i].MassCenter.Y - fakeImage.MassCenter.Y) * (classes[i].MassCenter.Y - fakeImage.MassCenter.Y))
);
}
int index = 0;
double minRange = distances[0];
for (int i = 0; i < objectList.Count; i++)
{
if (hitClosesImagesList[i] == 0 && distances[i] <= minRange)
{
minRange = distances[i];
index = i;
}
}
result.SetPropertiesFromImageObject(classes[index]);
hitClosesImagesList[index] = 1;
return result;
}
private int GetClassIndex(FakeImageObject[] classes, ImageObject imageObject)
{
double[] distances = new double[classes.Count()];
for (int i = 0; i < classes.Count(); i++)
{
distances[i] =
Math.Abs(areaCoefficient * (classes[i].Area - imageObject.Area)) +
Math.Abs(densityCoefficient * (classes[i].Density - imageObject.Density)) +
Math.Abs(elongationCoefficient * (classes[i].Elongation - imageObject.Elongation)) +
Math.Abs(perimeterCoefficient * (classes[i].Perimeter - imageObject.Perimeter)) +
Math.Abs(massCenterCoefficient * Math.Sqrt(
(classes[i].MassCenter.X - imageObject.MassCenter.X) * (classes[i].MassCenter.X - imageObject.MassCenter.X) +
(classes[i].MassCenter.Y - imageObject.MassCenter.Y) * (classes[i].MassCenter.Y - imageObject.MassCenter.Y))
);
}
return Array.IndexOf(distances, distances.Min());
}
public void SetObjectsToGroups()
{
FakeImageObject[] mediansArray = new FakeImageObject[kMedians];
hitClosesImagesList = null;
for (int i = 0; i < kMedians; i++)
{
mediansArray[i] = GetRandomFromObjectList();
}
hitClosesImagesList = null;
List<ImageObject>[] imageClasses = new List<ImageObject>[kMedians];
for (int i = 0; i < kMedians; i++)
{
imageClasses[i] = new List<ImageObject>();
}
foreach (ImageObject imageObject in objectList)
{
imageClasses[GetClassIndex(mediansArray, imageObject)].Add(imageObject);
}
bool flag = true;
while (flag)
{
flag = false;
mediansArray = new FakeImageObject[kMedians];
hitClosesImagesList = null;
for (int i = 0; i < kMedians; i++)
{
mediansArray[i] = GetClosestObjectFromObjectList(GetAverageFromImageList(imageClasses[i]));
}
hitClosesImagesList = null;
List<ImageObject>[] tempImageClasses = new List<ImageObject>[kMedians];
for (int i = 0; i < kMedians; i++)
{
tempImageClasses[i] = new List<ImageObject>();
}
foreach (List<ImageObject> objectList1 in imageClasses)
{
foreach (ImageObject imageObject in objectList1)
{
tempImageClasses[GetClassIndex(mediansArray, imageObject)].Add(imageObject);
}
}
for (int i = 0; i < kMedians; i++)
{
if (tempImageClasses[i].Equals(imageClasses[i]))
{
flag = true;
}
}
imageClasses = tempImageClasses;
}
foreach (List<ImageObject> imageObjectList in imageClasses)
{
foreach (ImageObject imageObject in imageObjectList)
{
foreach (Point point in imageObject.Points)
{
groupMap[point.X, point.Y] = Array.IndexOf(imageClasses, imageObjectList) + 2;
}
}
}
}
