public BackpropNeuralNetwork Clone()
{
var result = new BackpropNeuralNetwork();
var ms = new MemoryStream();
Save(ms);
ms.Seek(0, SeekOrigin.Begin);
result.Open(ms);
return result;
}
public BackpropNeuralNetwork Clone()
{
var result = new BackpropNeuralNetwork();
var ms = new MemoryStream();
Save(ms);
ms.Seek(0, SeekOrigin.Begin);
result.Open(ms);
return(result);
}
public static void ExportResult(Dataset testData, BackpropNeuralNetwork net)
{
var reader = new StreamReader(Path.Combine(dataPath, "IdLookupTable.csv"));
var writer = new StreamWriter(Path.Combine(dataPath, "result.csv"));
var featureNames = new List<string>()
{
"left_eye_center_x", "left_eye_center_y",
"right_eye_center_x", "right_eye_center_y",
"left_eye_inner_corner_x", "left_eye_inner_corner_y",
"left_eye_outer_corner_x", "left_eye_outer_corner_y",
"right_eye_inner_corner_x", "right_eye_inner_corner_y",
"right_eye_outer_corner_x", "right_eye_outer_corner_y",
"left_eyebrow_inner_end_x", "left_eyebrow_inner_end_y",
"left_eyebrow_outer_end_x", "left_eyebrow_outer_end_y",
"right_eyebrow_inner_end_x", "right_eyebrow_inner_end_y",
"right_eyebrow_outer_end_x", "right_eyebrow_outer_end_y",
"nose_tip_x", "nose_tip_y",
"mouth_left_corner_x", "mouth_left_corner_y",
"mouth_right_corner_x", "mouth_right_corner_y",
"mouth_center_top_lip_x", "mouth_center_top_lip_y",
"mouth_center_bottom_lip_x", "mouth_center_bottom_lip_y"
};
var featureLookup = featureNames.Select((f, i) => new { key = f, value = i }).ToDictionary(t => t.key, t => t.value);
Console.WriteLine();
var image = -1;
var p = default(float[]);
var s = reader.ReadLine();
writer.WriteLine("RowId,Location");
int row = 1;
while((s = reader.ReadLine()) != null)
{
var v = s.Split(',');
var img = int.Parse(v[1]);
if (img != image)
{
image = img;
p = net.Predict(testData.input[image-1]);
}
var idx = featureLookup[v[2]];
var value = Tools.Clamp(p[idx] * origImgSize, 0, origImgSize);
var rl = row + "," + value;
row++;
writer.WriteLine(rl);
Console.Write(".");
}
Console.WriteLine();
reader.Dispose();
writer.Dispose();
}
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;
}
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);
}
