Added forecast visualizers

src/Bonsai.ML.Visualizers/Bonsai.ML.Visualizers.csproj

@@ -10,6 +10,8 @@
   <ItemGroup>
     <PackageReference Include="Bonsai.Core" Version="2.8.1" />
     <PackageReference Include="Bonsai.Design" Version="2.8.0" />
+    <PackageReference Include="Bonsai.Vision.Design" Version="2.8.1" />
+    <PackageReference Include="MathNet.Numerics" Version="5.0.0" />
     <PackageReference Include="OxyPlot.Core" Version="2.1.2" />
     <PackageReference Include="OxyPlot.WindowsForms" Version="2.1.2" />
   </ItemGroup>

src/Bonsai.ML.Visualizers/ForecastImageOverlay.cs

@@ -0,0 +1,91 @@
+using Bonsai.Design;
+using Bonsai.Vision.Design;
+using Bonsai;
+using Bonsai.ML.Visualizers;
+using Bonsai.ML.LinearDynamicalSystems.Kinematics;
+using System;
+using System.Collections.Generic;
+using OpenCV.Net;
+using MathNet.Numerics.LinearAlgebra;
+using OxyPlot;
+
+[assembly: TypeVisualizer(typeof(ForecastImageOverlay), Target = typeof(MashupSource<ImageMashupVisualizer, ForecastVisualizer>))]
+
+namespace Bonsai.ML.Visualizers
+{
+    /// <summary>
+    /// Provides a mashup visualizer to display the forecast of a Kalman Filter kinematics model overtime of an ImageMashupVisualizer.
+    /// </summary>
+    public class ForecastImageOverlay : DialogTypeVisualizer
+    {
+        private ImageMashupVisualizer visualizer;
+        private IplImage overlay;
+
+        /// <inheritdoc/>
+        public override void Show(object value)
+        {
+
+            var image = visualizer.VisualizerImage;
+            Size size = new Size(image.Width, image.Height);
+            IplDepth depth = image.Depth;
+            int channels = image.Channels;
+
+            overlay = new IplImage(size, depth, channels);
+            var alpha = 0.1;
+
+            Forecast forecast = (Forecast)value;
+            List<ForecastResult> forecastResults = forecast.ForecastResults;
+
+            for (int i = 0; i < forecastResults.Count; i++)
+            {
+                var forecastResult = forecastResults[i];
+                var kinematicState = forecastResult.KinematicState;
+
+                double xMean = kinematicState.Position.X.Mean;
+                double yMean = kinematicState.Position.Y.Mean;
+
+                Point center = new Point((int)Math.Round(xMean), (int)Math.Round(yMean));
+
+                double xVar = kinematicState.Position.X.Variance;
+                double yVar = kinematicState.Position.Y.Variance;
+                double xyCov = kinematicState.Position.Covariance;
+
+                var covariance = Matrix<double>.Build.DenseOfArray(new double[,] {
+                    { xVar, xyCov },
+                    { xyCov, yVar }
+                });
+
+                var evd = covariance.Evd();
+                var evals = evd.EigenValues.Real();
+                var evecs = evd.EigenVectors;
+
+                double angle = Math.Atan2(evecs[1, 0], evecs[0, 0]) * 180 / Math.PI;
+
+                Size axes = new Size
+                {
+                    Width = (int)(2 * Math.Sqrt(evals[0])),
+                    Height = (int)(2 * Math.Sqrt(evals[1]))
+                };
+
+                OxyColor color = OxyColors.Yellow;
+
+                CV.Ellipse(overlay, center, axes, angle, 0, 360, new Scalar(color.B, color.G, color.R, color.A), -1);
+            }
+
+            CV.AddWeighted(image, 1 - alpha, overlay, alpha, 1, image);
+            overlay.SetZero();
+        }
+
+        /// <inheritdoc/>
+        public override void Load(IServiceProvider provider)
+        {
+            visualizer = (ImageMashupVisualizer)provider.GetService(typeof(MashupVisualizer));
+        }
+
+        /// <inheritdoc/>
+        public override void Unload()
+        {
+            overlay.Dispose();
+        }
+    }
+}

src/Bonsai.ML.Visualizers/ForecastPlotOverlay.cs

@@ -0,0 +1,116 @@
+using Bonsai.Design;
+using Bonsai;
+using Bonsai.ML.Visualizers;
+using Bonsai.ML.LinearDynamicalSystems;
+using Bonsai.ML.LinearDynamicalSystems.Kinematics;
+using System;
+using System.Collections.Generic;
+using OxyPlot.Series;
+using OxyPlot;
+
+[assembly: TypeVisualizer(typeof(ForecastPlotOverlay), Target = typeof(MashupSource<KinematicStateVisualizer, ForecastVisualizer>))]
+
+namespace Bonsai.ML.Visualizers
+{
+    /// <summary>
+    /// Provides a mashup visualizer to display the forecast of a Kalman Filter kinematics model overtime of a KinematicStateVisualizer.
+    /// </summary>
+    public class ForecastPlotOverlay : DialogTypeVisualizer
+    {
+        private List<LineSeries> lineSeriesList = new();
+
+        private List<AreaSeries> areaSeriesList = new();
+
+        private KinematicStateVisualizer visualizer;
+
+        /// <inheritdoc/>
+        public override void Show(object value)
+        {
+            var time = DateTime.Now;
+            Forecast forecast = (Forecast)value;
+            var componentVisualizers = visualizer.ComponentVisualizers;
+
+            for (int i = 0; i < componentVisualizers.Count; i++)
+            {
+                var plot = componentVisualizers[i].Plot;
+                var lineSeries = lineSeriesList[i];
+                var areaSeries = areaSeriesList[i];
+
+                plot.ResetLineSeries(lineSeries);
+                plot.ResetAreaSeries(areaSeries);
+
+                DateTime forecastTime = time;
+
+                for (int j = 0; j < forecast.ForecastResults.Count; j++)
+                {
+                    var forecastResult = forecast.ForecastResults[j];
+                    var kinematicState = forecastResult.KinematicState;
+                    forecastTime = time + forecastResult.Timestep;
+
+                    StateComponent[] stateComponents = new StateComponent[] {kinematicState.Position.X, kinematicState.Position.Y, kinematicState.Velocity.X, kinematicState.Velocity.Y, kinematicState.Acceleration.X, kinematicState.Acceleration.Y};
+
+                    AddStateComponentDataToSeries(plot, stateComponents[i], lineSeries, areaSeries, forecastTime);                    
+
+                }
+
+                plot.SetAxes(minTime: forecastTime.AddSeconds(-plot.Capacity), maxTime: forecastTime);
+            }
+        }
+
+        private void AddStateComponentDataToSeries(TimeSeriesOxyPlotBase plot, StateComponent stateComponent, LineSeries lineSeries, AreaSeries areaSeries, DateTime time)
+        {
+            double mean = stateComponent.Mean;
+            double variance = stateComponent.Variance;
+
+            plot.AddToLineSeries(
+                lineSeries: lineSeries,
+                time: time,
+                value: mean
+            );
+
+            plot.AddToAreaSeries(
+                areaSeries: areaSeries,
+                time: time,
+                value1: mean + variance,
+                value2: mean - variance
+            );
+        }
+
+        /// <inheritdoc/>
+        public override void Load(IServiceProvider provider)
+        {
+            if (lineSeriesList.Count > 0)
+            {
+                lineSeriesList.Clear();
+                lineSeriesList = new();
+            }
+
+            if (areaSeriesList.Count > 0)
+            {
+                areaSeriesList.Clear();
+                areaSeriesList = new();
+            }
+
+            var service = provider.GetService(typeof(MashupVisualizer));
+            visualizer = (KinematicStateVisualizer)service;
+            var componentVisualizers = visualizer.ComponentVisualizers;
+
+            for (int i = 0; i < componentVisualizers.Count; i++)
+            {
+                var lineSeries = componentVisualizers[i].Plot.AddNewLineSeries($"Forecast {visualizer.Labels[i]} Mean", color: OxyColors.Yellow);
+                var areaSeries = componentVisualizers[i].Plot.AddNewAreaSeries($"Forecast {visualizer.Labels[i]} Variance", color: OxyColors.Yellow, opacity: 50);
+
+                componentVisualizers[i].Plot.ResetLineSeries(lineSeries);
+                componentVisualizers[i].Plot.ResetAreaSeries(areaSeries);
+
+                lineSeriesList.Add(lineSeries);
+                areaSeriesList.Add(areaSeries);
+            }
+        }
+
+        /// <inheritdoc/>
+        public override void Unload()
+        {
+        }
+    }
+}

src/Bonsai.ML.Visualizers/ForecastVisualizer.cs

@@ -0,0 +1,133 @@
+using System;
+using System.Windows.Forms;
+using System.Collections.Generic;
+using Bonsai;
+using Bonsai.Design;
+using Bonsai.ML.Visualizers;
+using Bonsai.ML.LinearDynamicalSystems.Kinematics;
+using OxyPlot;
+using System.Reactive;
+using System.Linq;
+using System.Reactive.Linq;
+
+[assembly: TypeVisualizer(typeof(ForecastVisualizer), Target = typeof(Forecast))]
+
+namespace Bonsai.ML.Visualizers
+{
+    /// <summary>
+    /// Provides a type visualizer to display the forecast of a Kalman Filter kinematics model.
+    /// </summary>
+    public class ForecastVisualizer : BufferedVisualizer
+    {
+
+        private int rowCount = 3;
+        private int columnCount = 2;
+        private string[] labels = new string[] { 
+            "Forecast Position X", 
+            "Forecast Position Y", 
+            "Forecast Velocity X", 
+            "Forecast Velocity Y", 
+            "Forecast Acceleration X", 
+            "Forecast Acceleration Y" 
+        };
+
+        private List<StateComponentVisualizer> componentVisualizers = new();
+        private TableLayoutPanel container;
+
+        /// <inheritdoc/>
+        public override void Load(IServiceProvider provider)
+        {
+            container = new TableLayoutPanel
+            {
+                ColumnCount = columnCount,
+                RowCount = rowCount,
+                Dock = DockStyle.Fill
+            };
+
+            for (int i = 0; i < container.RowCount; i++)
+            {
+                container.RowStyles.Add(new RowStyle(SizeType.Percent, 100f / rowCount));
+            }
+
+            for (int i = 0; i < container.ColumnCount; i++)
+            {
+                container.ColumnStyles.Add(new ColumnStyle(SizeType.Percent, 100f / columnCount));
+            }
+
+            for (int i = 0 ; i < rowCount; i++)
+            {
+                for (int j = 0; j < columnCount; j++)
+                {
+                    var StateComponentVisualizer = new StateComponentVisualizer() {
+                        Label = labels[i * columnCount + j],
+                        LineSeriesColor = OxyColors.Yellow,
+                        AreaSeriesColor = OxyColors.Yellow
+                    };
+                    StateComponentVisualizer.Load(provider);
+                    container.Controls.Add(StateComponentVisualizer.Plot, j, i);
+                    componentVisualizers.Add(StateComponentVisualizer);
+                }
+            }
+
+            var visualizerService = (IDialogTypeVisualizerService)provider.GetService(typeof(IDialogTypeVisualizerService));
+
+            if (visualizerService != null)
+            {
+                visualizerService.AddControl(container);
+            }
+        }
+
+        /// <inheritdoc/>
+        public override void Show(object value)
+        {
+        }
+
+        /// <inheritdoc/>
+        protected override void ShowBuffer(IList<Timestamped<object>> values)
+        {
+            if (values.Count == 0) return;
+            var latestForecast = values.Last();
+            var timestamp = latestForecast.Timestamp;
+            var forecast = (Forecast)latestForecast.Value;
+            var futureTime = timestamp;
+
+            List<Timestamped<object>> positionX = new();
+            List<Timestamped<object>> positionY = new();
+            List<Timestamped<object>> velocityX = new();
+            List<Timestamped<object>> velocityY = new();
+            List<Timestamped<object>> accelerationX = new();
+            List<Timestamped<object>> accelerationY = new();
+
+            foreach (var forecastResult in forecast.ForecastResults)
+            {
+                futureTime = timestamp + forecastResult.Timestep;
+                positionX.Add(new Timestamped<object>(forecastResult.KinematicState.Position.X, futureTime));
+                positionY.Add(new Timestamped<object>(forecastResult.KinematicState.Position.Y, futureTime));
+                velocityX.Add(new Timestamped<object>(forecastResult.KinematicState.Velocity.X, futureTime));
+                velocityY.Add(new Timestamped<object>(forecastResult.KinematicState.Velocity.Y, futureTime));
+                accelerationX.Add(new Timestamped<object>(forecastResult.KinematicState.Acceleration.X, futureTime));
+                accelerationY.Add(new Timestamped<object>(forecastResult.KinematicState.Acceleration.Y, futureTime));
+            }
+
+            var dataList = new List<List<Timestamped<object>>>() { positionX, positionY, velocityX, velocityY, accelerationX, accelerationY };
+
+            var zippedData = dataList.Zip(componentVisualizers, (data, visualizer) => new { Data = data, Visualizer = visualizer });
+
+            foreach (var item in zippedData)
+            {
+                item.Visualizer.Plot.ResetLineSeries(item.Visualizer.LineSeries);
+                item.Visualizer.Plot.ResetAreaSeries(item.Visualizer.AreaSeries);
+                item.Visualizer.ShowDataBuffer(item.Data);
+                item.Visualizer.Plot.SetAxes(minTime: timestamp.DateTime, maxTime: futureTime.DateTime);
+            }
+        }
+
+        /// <inheritdoc/>
+        public override void Unload()
+        {
+            foreach (var componentVisualizer in componentVisualizers) componentVisualizer.Unload();
+            if (componentVisualizers.Count > 0) componentVisualizers.Clear();
+            if (!container.IsDisposed) container.Dispose();
+        }
+    }
+}