public void Train(int imageWidth, int imageHeight, int[][] arrayBitmap, List<ImageSegmentElement> ImageSegmentData)
{
IterateInput UploadedFile = new IterateInput(imageWidth, imageHeight, arrayBitmap);
int pixelsToSkip = 5;
bool trainWithTheArray;
initializeLabeledSegmentBitmap(imageWidth, imageHeight);
for (int i = 0; i < ImageSegmentData.Count; i++)
{
for (int xOffset = 0; xOffset < ImageSegmentData[i].rect.Width; xOffset++)
{
for (int yOffset = 0; yOffset < ImageSegmentData[i].rect.Height; yOffset++)
{
HeuristicArray currentHArray = new HeuristicArray();
trainWithTheArray = currentHArray.DefineHeuristicArray(ImageSegmentData[i].rect.X + xOffset, ImageSegmentData[i].rect.Y + yOffset,
UploadedFile.SetOfIteratedBoards, imageWidth, imageHeight);
if (trainWithTheArray)
{
currentHArray.CommitArrayToTrainingData(ImageSegmentData[i].label, ImageSegmentData[i].rect);
labeledSegmentBitmap[ImageSegmentData[i].rect.X + xOffset][ImageSegmentData[i].rect.Y + yOffset] = i;
TestAlgorithm.labeledSegmentBitmap_test[ImageSegmentData[i].rect.X + xOffset][ImageSegmentData[i].rect.Y + yOffset] = i;
}
}
}
}
printLabeledSegementBitmap(imageWidth, imageHeight, pixelsToSkip);
UploadedFile.reset();
}
public void Test(int imageWidth, int imageHeight, int[][] arrayBitmap)
{
int pixelsToSkip = 5;
initializeLabeledPixelBitmap(imageWidth, imageHeight);
if (TrainingData.TrainingDataIndex.Count > 0) //make sure their is training data to test against
{
IterateInput UploadedFile = new IterateInput(imageWidth, imageHeight, arrayBitmap);
HeuristicArray currentHArray = new HeuristicArray();
for (int i = 0; i < imageWidth; i += pixelsToSkip)
{
for (int j = 0; j < imageHeight; j += pixelsToSkip)
{
currentHArray.DefineHeuristicArray(i, j, UploadedFile.SetOfIteratedBoards, imageWidth, imageHeight);
testArrayAgainstTrainingData(i, j, currentHArray.currentHeuristicArray);
}
}
printResultToFile(imageWidth, imageHeight, pixelsToSkip);
}
//Iterate through the entire bitmap (with a skip function)
//For each pixel define the heuristic array
//Test each heuristic array against the training Data
}
