unsafe private TensorFloat CustomTensorize(List <VideoFrame> frameList, List <float> mean, List <float> std, bool toRGB = false)
{
int temp_len = frameList.Count();
SoftwareBitmap softwareBitmap = frameList[0].SoftwareBitmap;
Int32 height = softwareBitmap.PixelHeight;
Int32 width = softwareBitmap.PixelWidth;
BitmapPixelFormat pixelFormat = softwareBitmap.BitmapPixelFormat;
Int32 channels = BitmapPixelFormat.Gray8 == pixelFormat ? 1 : 3;
List <Int64> shape = new List <Int64>()
{
1, temp_len, channels, height, width
}; // B,T,C,H,W
// The channels of image stored in buffer is in order of BGRA-BGRA-BGRA-BGRA.
// Then we transform it to the order of BBBBB....GGGGG....RRRR....AAAA(dropped)
TensorFloat tf = TensorFloat.Create(shape);
byte * pCPUTensorbyte;
float * pCPUTensor;
uint uCapacity;
// The channels of image stored in buffer is in order of BGRA-BGRA-BGRA-BGRA.
// Then we transform it to the order of BBBBB....GGGGG....RRRR....AAAA(dropped)
var tfr = tf.CreateReference();
var tfr2 = (IMemoryBufferByteAccess)tfr;
tfr2.GetBuffer(out pCPUTensorbyte, out uCapacity);
pCPUTensor = (float *)pCPUTensorbyte;
for (Int32 t = 0; t < temp_len; t += 1)
{
VideoFrame frame = frameList[t];
SoftwareBitmap softwareBitmap2 = frame.SoftwareBitmap;
// 1. Get the access to buffer of softwarebitmap
BitmapBuffer spBitmapBuffer = softwareBitmap2.LockBuffer(BitmapBufferAccessMode.Read);
IMemoryBufferReference reference = spBitmapBuffer.CreateReference();
byte *pData;
uint size;
((IMemoryBufferByteAccess)reference).GetBuffer(out pData, out size);
// 2. Transform the data in buffer to a vector of float
var offset = (height * width * channels) * t;
if (BitmapPixelFormat.Bgra8 == pixelFormat)
{
for (UInt32 i = 0; i < size; i += 4)
{
if (toRGB)
{
// suppose the model expects BGR image.
// index 0 is B, 1 is G, 2 is R, 3 is alpha(dropped).
UInt32 pixelInd = i / 4;
pCPUTensor[offset + (height * width * 0) + pixelInd] = (((float)pData[i + 2]) - mean[0]) / std[0];
pCPUTensor[offset + (height * width * 1) + pixelInd] = (((float)pData[i + 1]) - mean[1]) / std[1];
pCPUTensor[offset + (height * width * 2) + pixelInd] = (((float)pData[i + 0]) - mean[2]) / std[2];
}
else
{
// suppose the model expects BGR image.
// index 0 is B, 1 is G, 2 is R, 3 is alpha(dropped).
UInt32 pixelInd = i / 4;
pCPUTensor[offset + (height * width * 0) + pixelInd] = (((float)pData[i + 0]) - mean[0]) / std[0];
pCPUTensor[offset + (height * width * 1) + pixelInd] = (((float)pData[i + 1]) - mean[1]) / std[1];
pCPUTensor[offset + (height * width * 2) + pixelInd] = (((float)pData[i + 2]) - mean[2]) / std[2];
}
}
}
else if (BitmapPixelFormat.Rgba8 == pixelFormat)
{
for (UInt32 i = 0; i < size; i += 4)
{
// suppose the model expects BGR image.
// index 0 is B, 1 is G, 2 is R, 3 is alpha(dropped).
if (toRGB)
{
// suppose the model expects BGR image.
// index 0 is B, 1 is G, 2 is R, 3 is alpha(dropped).
UInt32 pixelInd = i / 4;
pCPUTensor[offset + (height * width * 0) + pixelInd] = (((float)pData[i + 0]) - mean[0]) / std[0];
pCPUTensor[offset + (height * width * 1) + pixelInd] = (((float)pData[i + 1]) - mean[1]) / std[1];
pCPUTensor[offset + (height * width * 2) + pixelInd] = (((float)pData[i + 2]) - mean[2]) / std[2];
}
else
{
UInt32 pixelInd = i / 4;
pCPUTensor[offset + (height * width * 0) + pixelInd] = (((float)pData[i + 2]) - mean[0]) / std[0];
pCPUTensor[offset + (height * width * 1) + pixelInd] = (((float)pData[i + 1]) - mean[1]) / std[1];
pCPUTensor[offset + (height * width * 2) + pixelInd] = (((float)pData[i + 0]) - mean[2]) / std[2];
}
}
}
else if (BitmapPixelFormat.Gray8 == pixelFormat)
{
for (UInt32 i = 0; i < size; i += 4)
{
// suppose the model expects BGR image.
// index 0 is B, 1 is G, 2 is R, 3 is alpha(dropped).
UInt32 pixelInd = i / 4;
float red = (float)pData[i + 2];
float green = (float)pData[i + 1];
float blue = (float)pData[i];
float gray = 0.2126f * red + 0.7152f * green + 0.0722f * blue;
pCPUTensor[offset + pixelInd] = gray;
}
}
}
// to prepend following error, copy to another instance and use it as model input.
// The tensor has outstanding memory buffer references that must be closed prior to evaluation!
TensorFloat ret = TensorFloat.CreateFromIterable(
tf.Shape,
tf.GetAsVectorView());
return(ret);
}