public YDataFloat GetFloatData()
{
if (_floatData == null)
{
lock (_floatDataLock)
{
if (_floatData == null)
{
var floatArray = new float[Width, Height];
int height = Height;
unsafe
{
//for (int y = 0; y < Height; y++)
Parallel.For(0, height, (y) =>
{
fixed(byte *pData = Data)
{
fixed(float *pFloat = floatArray)
{
int *pDataInt = (int *)pData;
int ySrcOffset = (y * Stride) / sizeof(int);
int xMax = Width / 2;
int yDstOffset = y;
int x = 0;
while (x < xMax)
{
int color1, color2, color3, color4;
int color = pDataInt[ySrcOffset++];
color1 = (color & 0xFF);
color2 = (color >> 8) & 0xFF;
color3 = (color >> 16) & 0xFF;
color4 = (color >> 24) & 0xFF;
pFloat[yDstOffset] = color1;
yDstOffset += height;
pFloat[yDstOffset] = color2;
yDstOffset += height;
pFloat[yDstOffset] = color3;
yDstOffset += height;
pFloat[yDstOffset] = color4;
yDstOffset += height;
x += 2;
}
}
}
});
}
_floatData = new YDataFloat(floatArray, byte.MaxValue);
}
}
}
return(_floatData);
}
// -------------- Frame difference, for logo detection --------------
public static void CalculateLogoDifference(YDataFloat edgeMapAvg, YDataFloat logoDetectionFrame, float detectionFrameAverage, float detectionFrameMaximum, out float difference)
{
int width = edgeMapAvg.Width;
int height = edgeMapAvg.Height;
var detectionFrameMaximumOffset = (detectionFrameMaximum - detectionFrameAverage);
// Calculate absolute difference between values and apply power of important pixels from logoDetectionFrame
var total = 0.0f;
unsafe
{
fixed(float *pEdgeMapAvg = edgeMapAvg.Data, pLogoDetectionFrame = logoDetectionFrame.Data)
{
int size = width * height;
for (int i = 0; i < size; i++)
{
var detectionFrameOffset = Math.Abs(pLogoDetectionFrame[i] - detectionFrameAverage);
var edgeMapOffset = Math.Abs(pEdgeMapAvg[i] - detectionFrameAverage);
if (edgeMapOffset < detectionFrameOffset)
{
var power = detectionFrameOffset / detectionFrameMaximumOffset;
total += power * (detectionFrameOffset - edgeMapOffset) * 255.0f / detectionFrameMaximumOffset;
}
}
}
}
total /= (float)(width * height);
difference = total;
}
public static YDataFloat GenerateEdgeDetectedImage(YDataFloat imageData)
{
var width = imageData.Width;
var height = imageData.Height;
var edgeData = new float[width, height];
Parallel.For(0, height, (y) =>
{
for (int x = 0; x < width; x++)
{
if ((y < height - 1) && (x < width - 1))
{
edgeData[x, y] = imageData.Data[x + 1, y] + imageData.Data[x, y + 1] - imageData.Data[x, y] - imageData.Data[x, y];
edgeData[x, y] /= 2.0f;
edgeData[x, y] += (imageData.MaximumValue / 2.0f);
if (edgeData[x, y] < 0.0f)
{
edgeData[x, y] = 0.0f;
}
if (edgeData[x, y] > imageData.MaximumValue)
{
edgeData[x, y] = imageData.MaximumValue;
}
}
else
{
edgeData[x, y] = 127.5f; // byte.MaxValue / 2.0f;
}
}
});
return(new YDataFloat(edgeData, imageData.MaximumValue));
}
// -------------- Edge detection --------------
//"02.6656726 - Edge Detect CPU Single Threaded
//"01.1025473 - Edge Detect CPU Parallel
//"02.1731188 - Edge Detect Accel Multicore
//"00.7777913 - Edge Detect Accel DX9
public static YDataFloat GenerateEdgeDetectedImageSingleThreaded(YDataFloat imageData)
{
var width = imageData.Width;
var height = imageData.Height;
var edgeData = new float[width, height];
for (int y = 0; y < height; y++)
{
for (int x = 0; x < width; x++)
{
if ((y < height - 1) && (x < width - 1))
{
edgeData[x, y] = imageData.Data[x + 1, y] + imageData.Data[x, y + 1] - imageData.Data[x, y] - imageData.Data[x, y];
edgeData[x, y] /= 2.0f;
edgeData[x, y] += (imageData.MaximumValue / 2.0f);
if (edgeData[x, y] < 0.0f)
{
edgeData[x, y] = 0.0f;
}
if (edgeData[x, y] > imageData.MaximumValue)
{
edgeData[x, y] = imageData.MaximumValue;
}
}
else
{
edgeData[x, y] = 0.0f;
}
}
}
return(new YDataFloat(edgeData, imageData.MaximumValue));
}
public static void CalculateStandardDeviationCPU(YDataFloat imageData, float average, out float stdDev)
{
int width = imageData.Width;
int height = imageData.Height;
// Calculate standard deviation
stdDev = 0.0f;
float[] totalSlices = new float[height];
Parallel.For(0, height, (y) =>
{
unsafe
{
fixed(float *pFloat = imageData.Data)
{
int ySrcOffset = y;
float total = 0.0f;
for (int x = 0; x < width; x++)
{
var value = pFloat[ySrcOffset];
ySrcOffset += height;
value = (value - average) * (value - average);
total += value;
}
totalSlices[y] = total;
}
}
});
stdDev = totalSlices.AsParallel().Sum();
stdDev /= (float)(width * height);
stdDev = (float)Math.Sqrt(stdDev);
}
public static void CalculateAverageAndMaximumCPU(YDataFloat imageData, out float average, out float maximum)
{
int width = imageData.Width;
int height = imageData.Height;
// Calculate average and maximum
average = 0.0f;
maximum = 0.0f;
var averageSlices = new float[height];
var maximumSlices = new float[height];
Parallel.For(0, height, (y) =>
{
float avg = 0.0f;
float max = 0.0f;
for (int x = 0; x < width; x++)
{
var value = imageData.Data[x, y];
avg += value;
if (max < value)
{
max = value;
}
}
averageSlices[y] = avg;
maximumSlices[y] = max;
});
average = averageSlices.AsParallel().Sum();
average /= (float)(width * height);
maximum = maximumSlices.AsParallel().Max();
}
public static YDataFloat NewData(int width, int height, float maximumValue, float defaultValue)
{
var result = new YDataFloat(new float[width, height], maximumValue);
unsafe
{
fixed(float *pData = result.Data)
{
int dataLength = result.Width * result.Height;
for (int i = 0; i < dataLength; i++)
{
pData[i] = defaultValue;
}
}
}
return(result);
}
// -------------- Advance sliding window of averages --------------
public static void AdvanceEdgeDetectionAverage(ref YDataFloat edgeMapAverage, YDataFloat edgeDataNew, YDataFloat edgeDataOld, int edgeMapAverageCount)
{
var width = edgeMapAverage.Width;
var height = edgeMapAverage.Height;
float edgeMapAverageCountFloat = edgeMapAverageCount;
unsafe
{
fixed(float *pAverage = edgeMapAverage.Data, pNew = edgeDataNew.Data, pOld = edgeDataOld.Data)
{
int size = width * height;
for (int i = 0; i < size; i++)
{
pAverage[i] += (pNew[i] - pOld[i]) / edgeMapAverageCountFloat;
}
}
}
}
public static YDataFloat GenerateLogoDifferenceMap(YDataFloat edgeMapAvg, YDataFloat logoDetectionFrame, float detectionFrameAverage, float detectionFrameMaximum)
{
int width = edgeMapAvg.Width;
int height = edgeMapAvg.Height;
var maximum = edgeMapAvg.MaximumValue;
var detectionFrameMaximumOffset = (detectionFrameMaximum - detectionFrameAverage);
// Calculate absolute difference between values and apply power of important pixels from logoDetectionFrame
var outputArray = new float[width, height];
Array.Clear(outputArray, 0, outputArray.Length);
unsafe
{
fixed(float *pEdgeMapAvg = edgeMapAvg.Data,
pLogoDetectionFrame = logoDetectionFrame.Data,
pOutput = outputArray)
{
int size = width * height;
for (int i = 0; i < size; i++)
{
var detectionFrameOffset = Math.Abs(pLogoDetectionFrame[i] - detectionFrameAverage);
var edgeMapOffset = Math.Abs(pEdgeMapAvg[i] - detectionFrameAverage);
if (edgeMapOffset < detectionFrameOffset)
{
var power = detectionFrameOffset / detectionFrameMaximumOffset;
pOutput[i] = power * (detectionFrameOffset - edgeMapOffset) * 255.0f / detectionFrameMaximumOffset;
}
else
{
pOutput[i] = 0.0f;
}
//pOutput[i] = byte.MaxValue * power;
}
}
}
return(new YDataFloat(outputArray, maximum));
}
public static void AdvanceEdgeDetectionAverageAccel(AcceleratorTarget acceleratorTarget, ref YDataFloat edgeMapAverage, YDataFloat edgeDataNew, YDataFloat edgeDataOld, int edgeMapAverageCount)
{
var target = GetAcceleratorTarget(acceleratorTarget);
var width = edgeMapAverage.Width;
var height = edgeMapAverage.Height;
float edgeMapAverageCountFloat = edgeMapAverageCount;
var fpAverage = new FloatParallelArray(edgeMapAverage.Data);
var fpNew = new FloatParallelArray(edgeDataNew.Data);
var fpOld = new FloatParallelArray(edgeDataOld.Data);
var fpAdjust = ParallelArrays.Subtract(fpNew, fpOld);
fpAdjust = ParallelArrays.Divide(fpAdjust, edgeMapAverageCountFloat);
var fpOutput = ParallelArrays.Subtract(fpAverage, fpAdjust);
var output = target.ToArray2D(fpOutput);
edgeMapAverage = new YDataFloat(output, edgeMapAverage.MaximumValue);
}
public static YDataFloat GenerateEdgeDetectedImageAccel(AcceleratorTarget acceleratorTarget, YDataFloat imageData)
{
var target = GetAcceleratorTarget(acceleratorTarget);
var width = imageData.Width;
var height = imageData.Height;
var fpInput = new FloatParallelArray(imageData.Data);
var fpInputX = ParallelArrays.Shift(fpInput, new int[] { 1, 0 });
var fpInputY = ParallelArrays.Shift(fpInput, new int[] { 0, 1 });
var fpDX = ParallelArrays.Subtract(fpInputX, fpInput);
var fpDY = ParallelArrays.Subtract(fpInputY, fpInput);
var fpTotals = ParallelArrays.Add(fpDX, fpDY);
var fpOutput = ParallelArrays.Divide(fpTotals, 2.0f);
fpOutput = ParallelArrays.Add(fpOutput, imageData.MaximumValue / 2.0f);
fpOutput = ParallelArrays.Max(fpOutput, 0.0f);
fpOutput = ParallelArrays.Min(fpOutput, imageData.MaximumValue);
var output = target.ToArray2D(fpOutput);
return(new YDataFloat(output, imageData.MaximumValue));
}
