// TODO: The order of data in the matrix is reverse of the channel index.
// m[11] => channel 0, etc.
protected override void Write(ONIContextTask ctx, Arr input)
{
var inputMatrix = input.GetMat();
// Check dims
var size = inputMatrix.Rows * inputMatrix.Cols;
if (size % AnalogInputDataFrame.NumberOfChannels != 0)
{
throw new IndexOutOfRangeException("Source must contain a multiple of 12 elements.");
}
if (DataType == AnalogDataType.S16 && inputMatrix.Depth == Depth.S16)
{
var data = new Mat(inputMatrix.Size, Depth.U16, 1);
CV.ConvertScale(inputMatrix, data, 1, 32768);
ctx.Write((uint)DeviceAddress.Address, data.Data, 2 * size);
}
else if (DataType == AnalogDataType.Volts && (inputMatrix.Depth == Depth.F32 || inputMatrix.Depth == Depth.F64))
{
var data = new Mat(inputMatrix.Size, Depth.U16, 1);
CV.ConvertScale(inputMatrix, data, 3276.75, 32768);
ctx.Write((uint)DeviceAddress.Address, data.Data, 2 * size);
}
else
{
throw new Bonsai.WorkflowRuntimeException("Source element depth must Depth.S16 when " +
"DataType is S16 and either Depth.F32 or Depth.F64 when Datatype is Volts.");
}
}
private Mat GetEphysDataF32(short[,] data)
{
var output = new Mat(NumberOfEphysChannels, NumberOfSamples, Depth.F32, 1);
using (var header = Mat.CreateMatHeader(data))
{
CV.ConvertScale(header, output, 0.195);
}
return(output);
}
protected override IObservable <IplImage> GetImageSource(IObservable <IObservable <object> > dataSource)
{
return(imageSource.Select(image =>
{
if (image.Depth != IplDepth.U8)
{
var normalizedImage = new IplImage(image.Size, IplDepth.U8, image.Channels);
var scale = byte.MaxValue / (double)ushort.MaxValue;
CV.ConvertScale(image, normalizedImage, scale);
image = normalizedImage;
}
return image;
}));
}
public override IObservable <IplImage> Process(IObservable <IplImage> source)
{
return(source.Select(input =>
{
double min, max;
Point minLoc, maxLoc;
var output = new IplImage(input.Size, IplDepth.F32, input.Channels);
CV.MinMaxLoc(input, out min, out max, out minLoc, out maxLoc);
var range = max - min;
var scale = range > 0 ? 1.0 / range : 0;
var shift = range > 0 ? -min : 0;
CV.ConvertScale(input, output, scale, shift);
return output;
}));
}
public override IObservable <Mat> Process(IObservable <Mat> source)
{
return(source.Select(input =>
{
var channels = Channels;
var output = new Mat(input.Size, input.Depth, input.Channels);
var reference = new Mat(1, input.Cols, input.Depth, input.Channels);
if (channels == null || channels.Length == 0)
{
if (input.Depth != Depth.F32)
{
var temp = new Mat(reference.Rows, reference.Cols, Depth.F32, reference.Channels);
CV.Reduce(input, temp, 0, ReduceOperation.Avg);
CV.Convert(temp, reference);
}
else
{
CV.Reduce(input, reference, 0, ReduceOperation.Avg);
}
}
else if (channels.Length == 1)
{
CV.Copy(input.GetRow(channels[0]), reference);
}
else
{
var sum = input.Depth != Depth.F32
? new Mat(reference.Rows, reference.Cols, Depth.F32, reference.Channels)
: reference;
sum.SetZero();
for (int i = 0; i < channels.Length; i++)
{
using (var referenceChannel = input.GetRow(channels[i]))
{
CV.Add(sum, referenceChannel, sum);
}
}
CV.ConvertScale(sum, reference, 1f / channels.Length);
}
CV.Repeat(reference, output);
CV.Sub(input, output, output);
return output;
}));
}
private Mat GetAuxiliaryData(ushort[,] data)
{
using (var header = Mat.CreateMatHeader(data))
{
if (AuxFormat == RHD2164Configuration.AuxDataFormat.Volts)
{
var output = new Mat(NumberOfAuxChannels, NumberOfSamples, Depth.F32, 1);
CV.ConvertScale(header, output, 0.0000374);
return(output);
}
else
{
var output = new Mat(NumberOfAuxChannels, NumberOfSamples, Depth.U16, 1);
CV.Convert(header, output);
return(output);
}
}
}
public override IObservable <IplImage> Process(IObservable <IplImage> source)
{
return(Observable.Defer(() =>
{
int averageCount = 0;
IplImage image = null;
IplImage difference = null;
IplImage background = null;
return source.Select(input =>
{
if (background == null || background.Size != input.Size)
{
averageCount = 0;
image = new IplImage(input.Size, IplDepth.F32, input.Channels);
difference = new IplImage(input.Size, IplDepth.F32, input.Channels);
background = new IplImage(input.Size, IplDepth.F32, input.Channels);
background.SetZero();
}
var output = new IplImage(input.Size, IplDepth.U8, input.Channels);
if (averageCount < BackgroundFrames)
{
averageCount++;
output.SetZero();
CV.Acc(input, background);
if (averageCount == BackgroundFrames)
{
CV.ConvertScale(background, background, 1.0 / averageCount, 0);
}
}
else
{
CV.Convert(input, image);
switch (SubtractionMethod)
{
case SubtractionMethod.Bright:
CV.Sub(image, background, difference);
break;
case SubtractionMethod.Dark:
CV.Sub(background, image, difference);
break;
case SubtractionMethod.Absolute:
default:
CV.AbsDiff(image, background, difference);
break;
}
if (AdaptationRate > 0)
{
CV.RunningAvg(image, background, AdaptationRate);
}
CV.Threshold(difference, output, ThresholdValue, 255, ThresholdType);
}
return output;
});
}));
}
public override IObservable <Mat> Process(IObservable <Mat> source)
{
return(Observable.Create <Mat>(observer =>
{
var carry = 0;
var index = 0;
var offset = 0;
var lottery = 0;
var scaleFactor = 0.0;
var currentFactor = 0;
var buffer = default(Mat);
var carryBuffer = default(Mat);
var downsampling = Downsampling;
var random = downsampling == DownsamplingMethod.Dithering ? new Random() : null;
var reduceOp = (ReduceOperation)(downsampling - DownsamplingMethod.Sum);
if (reduceOp == ReduceOperation.Avg)
{
reduceOp = ReduceOperation.Sum;
}
var downsample = downsampling == DownsamplingMethod.LowPass ? filter.Process(source) : source;
return downsample.Subscribe(input =>
{
try
{
var bufferLength = BufferLength;
if (bufferLength == 0)
{
bufferLength = input.Cols;
}
if (buffer == null || buffer.Rows != input.Rows || currentFactor != factor)
{
index = 0;
currentFactor = factor;
if (downsampling >= DownsamplingMethod.Sum)
{
carry = currentFactor;
carryBuffer = new Mat(input.Rows, 1, input.Depth, input.Channels);
if (downsampling == DownsamplingMethod.Avg)
{
scaleFactor = 1.0 / currentFactor;
}
else
{
scaleFactor = 0;
}
}
else if (random != null)
{
lottery = random.Next(currentFactor);
offset = lottery;
}
else
{
offset = 0;
}
buffer = CreateBuffer(bufferLength, input);
}
while (offset < input.Cols)
{
// Process decimation data on this buffer
Rect outputRect;
if (downsampling > DownsamplingMethod.LowPass)
{
outputRect = new Rect(index, 0, 1, input.Rows);
}
else
{
var samples = input.Cols - offset;
var whole = samples / currentFactor;
outputRect = new Rect(index, 0, Math.Min(buffer.Cols - index, whole), input.Rows);
}
if (downsampling >= DownsamplingMethod.Sum)
{
// Reduce decimate
var inputSamples = Math.Min(input.Cols - offset, carry);
var inputRect = new Rect(offset, 0, inputSamples, input.Rows);
using (var inputBuffer = input.GetSubRect(inputRect))
using (var outputBuffer = buffer.GetCol(index))
{
if (carry < currentFactor)
{
CV.Reduce(inputBuffer, carryBuffer, 1, reduceOp);
switch (reduceOp)
{
case ReduceOperation.Sum:
CV.Add(outputBuffer, carryBuffer, outputBuffer);
break;
case ReduceOperation.Max:
CV.Max(outputBuffer, carryBuffer, outputBuffer);
break;
case ReduceOperation.Min:
CV.Min(outputBuffer, carryBuffer, outputBuffer);
break;
}
}
else
{
CV.Reduce(inputBuffer, outputBuffer, 1, reduceOp);
}
offset += inputRect.Width;
carry -= inputSamples;
if (carry <= 0)
{
index++;
carry = currentFactor;
if (scaleFactor > 0)
{
CV.ConvertScale(outputBuffer, outputBuffer, scaleFactor);
}
}
}
}
else if (outputRect.Width > 1)
{
// Block decimate
var inputRect = new Rect(offset, 0, outputRect.Width * currentFactor, input.Rows);
using (var inputBuffer = input.GetSubRect(inputRect))
using (var outputBuffer = buffer.GetSubRect(outputRect))
{
CV.Resize(inputBuffer, outputBuffer, SubPixelInterpolation.NearestNeighbor);
}
index += outputRect.Width;
offset += inputRect.Width;
}
else
{
// Decimate single time point
using (var inputBuffer = input.GetCol(offset))
using (var outputBuffer = buffer.GetCol(index))
{
CV.Copy(inputBuffer, outputBuffer);
}
index++;
if (random != null)
{
offset += currentFactor - lottery;
lottery = random.Next(currentFactor);
offset += lottery;
}
else
{
offset += currentFactor;
}
}
if (index >= buffer.Cols)
{
index = 0;
observer.OnNext(buffer);
buffer = CreateBuffer(bufferLength, input);
}
}
offset -= input.Cols;
}
catch (Exception ex)
{
observer.OnError(ex);
}
},
observer.OnError,
() =>
{
// Emit pending buffer
if (index > 0)
{
observer.OnNext(buffer.GetCols(0, index));
}
buffer = null;
observer.OnCompleted();
});
}));
}
