public static Dataset LoadResultData()
{
Dataset result = new Dataset();
var reader = new StreamReader(Path.Combine(dataPath, "test.csv"));
// ignore first line
var line = reader.ReadLine();
while ((line = reader.ReadLine()) != null)
{
var ls = line.Split(',');
int pos = 0;
var output = default(float[]);
var pic = ls.Last().Split(' ');
var img = new float[inputSize];
for (int j = 0; j < inputSize; ++j)
{
img[j] = float.Parse(pic[j]) / 255.0f;
}
var input = default(float[]);
using (var bmp = Tools.float_to_bmp(img, origImgSize, origImgSize))
{
using (var filtered = applyFilters(bmp))
{
input = Tools.bmp_to_float(filtered);
}
}
result.AddPair(input, output);
}
return result;
}
private static void addData(float[] input, float[] output, Dataset data)
{
Monitor.Enter(data);
if (data.input.Count % 1000 == 0)
Console.WriteLine("{0}", data.input.Count);
data.AddPair(input, output);
Monitor.Exit(data);
}
public static Dataset LoadTrainData()
{
Dataset result = new Dataset();
var net = new PragmaLearn.Learner.BackpropNeuralNetwork();
// net.Open("network.dat");
var reader = new StreamReader(Path.Combine(dataPath, "training.csv"));
// ignore first line
var line = reader.ReadLine();
while ((line = reader.ReadLine()) != null)
{
var ls = line.Split(',');
bool skipped = false;
var keypoints = new double[outputSize];
for (int i = 0; i < outputSize; ++i)
{
if (string.IsNullOrEmpty(ls[i]))
{
Console.Write(".");
keypoints[i] = -1;
// output[i] = Tools.rnd.NextDouble();
skipped = true;
// break;
}
else
{
keypoints[i] = double.Parse(ls[i]);
}
}
//if (skipped)
// continue;
var pic = ls.Last().Split(' ');
var img = new float[inputSize];
for (int j = 0; j < inputSize; ++j)
{
img[j] = float.Parse(pic[j]) / 255.0f;
}
//if (skipped)
//{
// using (var bmp = Tools.double_to_bmp(img, origImgSize, origImgSize))
// {
// using (var filtered = applyFilters(bmp))
// {
// var input = Tools.bmp_to_double(filtered);
// var p = net.Predict(input);
// for (int i = 0; i < keypoints.Length; ++i)
// {
// if (keypoints[i] == -1)
// keypoints[i] = p[i] * origImgSize;
// }
// }
// }
//}
for (int x = 0; x < 20; ++x)
{
var points = new PointF[keypoints.Length / 2];
for (int i = 0; i < keypoints.Length; i += 2)
{
points[i / 2] = new PointF((float)keypoints[i], (float)keypoints[i + 1]);
}
var input = default(float[]);
using (var bmp = Tools.float_to_bmp(img, origImgSize, origImgSize))
{
if (x != 0)
{
using (var transformed = transformRandom(bmp, points))
{
using (var filtered = applyFilters(transformed))
{
input = Tools.bmp_to_float(filtered);
}
}
}
else
{
using (var filtered = applyFilters(bmp))
{
input = Tools.bmp_to_float(filtered);
}
}
}
var output = new float[keypoints.Length];
for (int i = 0; i < points.Length; i++)
{
output[2 * i] = points[i].X != -1 ? points[i].X / origImgSize : -1;
output[2 * i + 1] = points[i].Y != -1 ? points[i].Y / origImgSize : -1;
}
result.AddPair(input, output);
}
//if (skipped)
//{
// var p = net.Predict(input);
// for (int i = 0; i < output.Length; ++i)
// {
// if (output[i] == -1)
// output[i] = p[i];
// }
//}
}
reader.Dispose();
return result;
}