Esempio n. 1
0
        public static Dataset Create()
        {
            Dataset result = new Dataset();
            Bitmap  bmp    = new Bitmap(width, height);

            StringFormat stringFormat = new StringFormat();

            stringFormat.Alignment     = StringAlignment.Center;
            stringFormat.LineAlignment = StringAlignment.Center;
            float size = 8;

            Rectangle rect = new Rectangle(0, 0, width, height);


            using (Graphics g = Graphics.FromImage(bmp))
            {
                g.TextRenderingHint = TextRenderingHint.ClearTypeGridFit;
                for (int x = 0; x < 100; ++x)
                {
                    // var fonts = fontsToTrain.Shuffle().ToList();
                    //  foreach (var f in fonts)
                    {
                        var ai = Enumerable.Range(0, alphabet.Length).Shuffle().ToList();
                        // for (int i = 0; i < alphabet.Length; ++i)
                        foreach (var i in ai)
                        {
                            var f = fontsToTrain[Tools.rnd.Next(fontsToTrain.Length)];
                            size = 9.0f + (float)Tools.rnd.NextDouble() * 4.0f;
                            using (Font font = new Font(f, size))
                            {
                                var a = alphabet[i];
                                g.Clear(Color.Black);
                                g.TextRenderingHint  = TextRenderingHint.AntiAlias;
                                g.CompositingQuality = System.Drawing.Drawing2D.CompositingQuality.HighQuality;
                                g.SmoothingMode      = System.Drawing.Drawing2D.SmoothingMode.HighQuality;
                                g.InterpolationMode  = System.Drawing.Drawing2D.InterpolationMode.HighQualityBicubic;
                                g.TranslateTransform(width / 2, height / 2);
                                g.RotateTransform(((float)Tools.rnd.NextDouble() - 0.5f) * 45);
                                g.TranslateTransform(-width / 2, -height / 2);
                                var dx = ((float)Tools.rnd.NextDouble() - 0.5f) * 2.0f;
                                var dy = ((float)Tools.rnd.NextDouble() - 0.5f) * 2.0f;
                                g.TranslateTransform(dx, dy);
                                g.DrawString(a, font, Brushes.White, rect, stringFormat);

                                g.ResetTransform();
                                var inp  = Tools.bmp_to_float(bmp);
                                var outp = new float[alphabet.Length];
                                outp[i] = 1.0f;
                                result.AddPair(inp, outp);
                            }
                        }
                    }
                }
            }
            result.VisualizeInput  = visualizeInput;
            result.VisualizeOutput = visualizeOutput;


            return(result);
        }
Esempio n. 2
0
        public static Dataset Create()
        {
            Dataset result = new Dataset();
            Bitmap bmp = new Bitmap(width, height);

            StringFormat stringFormat = new StringFormat();
            stringFormat.Alignment = StringAlignment.Center;
            stringFormat.LineAlignment = StringAlignment.Center;
            float size = 8;

            Rectangle rect = new Rectangle(0, 0, width, height);

            using (Graphics g = Graphics.FromImage(bmp))
            {
                g.TextRenderingHint = TextRenderingHint.ClearTypeGridFit;
                for (int x = 0; x < 100; ++x)
                {
                    // var fonts = fontsToTrain.Shuffle().ToList();
                   //  foreach (var f in fonts)
                    {
                        var ai = Enumerable.Range(0, alphabet.Length).Shuffle().ToList();
                        // for (int i = 0; i < alphabet.Length; ++i)
                        foreach (var i in ai)
                        {
                            var f = fontsToTrain[Tools.rnd.Next(fontsToTrain.Length)];
                            size = 9.0f + (float)Tools.rnd.NextDouble() * 4.0f;
                            using (Font font = new Font(f, size))
                            {
                                var a = alphabet[i];
                                g.Clear(Color.Black);
                                g.TextRenderingHint = TextRenderingHint.AntiAlias;
                                g.CompositingQuality = System.Drawing.Drawing2D.CompositingQuality.HighQuality;
                                g.SmoothingMode = System.Drawing.Drawing2D.SmoothingMode.HighQuality;
                                g.InterpolationMode = System.Drawing.Drawing2D.InterpolationMode.HighQualityBicubic;
                                g.TranslateTransform(width / 2, height / 2);
                                g.RotateTransform(((float)Tools.rnd.NextDouble()-0.5f) * 45);
                                g.TranslateTransform(-width / 2, -height / 2);
                                var dx = ((float)Tools.rnd.NextDouble() - 0.5f) * 2.0f;
                                var dy = ((float)Tools.rnd.NextDouble() - 0.5f) * 2.0f;
                                g.TranslateTransform(dx, dy);
                                g.DrawString(a, font, Brushes.White, rect, stringFormat);

                                g.ResetTransform();
                                var inp = Tools.bmp_to_float(bmp);
                                var outp = new float[alphabet.Length];
                                outp[i] = 1.0f;
                                result.AddPair(inp, outp);
                            }
                        }
                    }
                }
            }
            result.VisualizeInput = visualizeInput;
            result.VisualizeOutput = visualizeOutput;

            return result;
        }
Esempio n. 3
0
 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);
 }
Esempio n. 4
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        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);
        }
Esempio n. 5
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        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);
        }