private static FloatPair get_rnn_vid_data(Network net, string[] files, int n, int batch, int steps)
{
int b;
Image outIm = Network.get_network_image(net);
int outputSize = outIm.W * outIm.H * outIm.C;
Console.Write($"%d %d %d\n", outIm.W, outIm.H, outIm.C);
float[] feats = new float[net.Batch * batch * outputSize];
for (b = 0; b < batch; ++b)
{
int inputSize = net.W * net.H * net.C;
float[] input = new float[inputSize * net.Batch];
string filename = files[Utils.Rand.Next() % n];
using (VideoCapture cap = new VideoCapture(filename))
{
int frames = (int)cap.GetCaptureProperty(CapProp.FrameCount);
int index = Utils.Rand.Next() % (frames - steps - 2);
if (frames < (steps + 4))
{
--b;
continue;
}
Console.Write($"frames: %d, index: %d\n", frames, index);
cap.SetCaptureProperty(CapProp.PosFrames, index);
int i;
for (i = 0; i < net.Batch; ++i)
{
using (Mat src = cap.QueryFrame())
{
Image im = new Image(src);
LoadArgs.rgbgr_image(im);
Image re = LoadArgs.resize_image(im, net.W, net.H);
Array.Copy(re.Data, 0, input, i * inputSize, inputSize);
}
}
float[] output = Network.network_predict(net, input);
for (i = 0; i < net.Batch; ++i)
{
Array.Copy(output, i * outputSize, feats, (b + i * batch) * outputSize, outputSize);
}
}
}
FloatPair p = new FloatPair();
p.X = feats;
p.Y = new float[feats.Length - outputSize * batch];
Array.Copy(feats, outputSize * batch, p.Y, 0, p.Y.Length);
return(p);
}
