public PostScoreAction GetTrackInOut(BlockedImage image, int InLabel)
{
if (image.Label == InLabel)
{
return(TrackRelevantInBlocks);
}
else
{
return(TrackRelevantOutBlocks);
}
}
static float GetScore(BlockedImage image, Kernel kernel, PostScoreAction action = null)
{
var blocks = image.Blocks;
float score = float.MaxValue;
for (int i = 0; i < blocks.Length; i++)
{
var block = blocks[i];
float error = kernel.Score(block, action);
score = Math.Min(score, error);
}
return(score);
}
static int ClassifyImage(BlockedImage image, List <List <Tuple <AffineKernel, float> > > kernel_lists)
{
var blocks = image.Blocks;
float[] hits = new float[NumLabels];
for (int label = 0; label < NumLabels; label++)
{
var kernel_list = kernel_lists[label];
foreach (var kernel in kernel_list)
{
float score = GetScore(image, kernel.Item1);
if (score < kernel.Item1.ClassificationCutoff)
{
//hits[i]++;
hits[label] += (float)Math.Log(kernel.Item2);
}
}
}
int argmax = -1; float max = 0;
for (int label = 0; label < NumLabels; label++)
{
if (argmax < 0 || hits[label] > max)
{
argmax = label;
max = hits[label];
}
}
Console.WriteLine($"Hits {s(hits)}");
Console.WriteLine($" Classify as {argmax}, actually a {image.Label}");
return(argmax);
}
public static BlockedImage[] GetBlocks(LabeledImage[] inputs, int Rows, int Width, int Height, int Channels,
int BlockDim, bool AddMirrors = false)
{
int BlockSize = BlockDim * BlockDim * Channels;
// Unpack blocks.
int num_blocks = (Width - (BlockDim - 1)) * (Height - (BlockDim - 1));
if (AddMirrors)
{
num_blocks *= 8;
}
var blocked_images = new BlockedImage[Rows];
for (int row = 0; row < Rows; row++)
{
var image = blocked_images[row] = new BlockedImage(inputs[row]);
var pixels = image.Pixels;
var image_blocks = image.Blocks = new float[num_blocks][];
int block_index = 0;
for (int mirror = 0; mirror < (AddMirrors ? 8 : 1); mirror++)
{
for (int x = 0; x < Width - (BlockDim - 1); x++)
{
for (int y = 0; y < Height - (BlockDim - 1); y++)
{
var block = image_blocks[block_index] = new float[BlockSize];
block_index++;
int i = 0;
for (int _x = 0; _x < BlockDim; _x++)
{
for (int _y = 0; _y < BlockDim; _y++)
{
for (int _c = 0; _c < Channels; _c++)
{
if (mirror == 0)
{
block[i++] = (float)pixels[x + _x, y + _y, _c];
}
else if (mirror == 1)
{
block[i++] = (float)pixels[x + BlockDim - 1 - _x, y + _y, _c];
}
else if (mirror == 2)
{
block[i++] = (float)pixels[x + _x, y + BlockDim - 1 - _y, _c];
}
else if (mirror == 3)
{
block[i++] = (float)pixels[x + BlockDim - 1 - _x, y + BlockDim - 1 - _y, _c];
}
else if (mirror == 4)
{
block[i++] = (float)pixels[y + _y, x + _x, _c];
}
else if (mirror == 5)
{
block[i++] = (float)pixels[y + BlockDim - 1 - _y, x + _x, _c];
}
else if (mirror == 6)
{
block[i++] = (float)pixels[y + _y, x + BlockDim - 1 - _x, _c];
}
else if (mirror == 7)
{
block[i++] = (float)pixels[y + BlockDim - 1 - _y, x + BlockDim - 1 - _x, _c];
}
}
}
}
}
}
}
Debug.Assert(block_index == blocked_images[row].Blocks.Length);
}
return(blocked_images);
}
