public static IEnumerable <PhotoData> Connect(ImageFolder f, List <MovementPoint> pointsList, int timeStepMilliseconds)
{
var pointsDates = pointsList.Select(p => p.Date);
var firstPhotoDate = pointsDates.Min();
var lastPhotoDate = pointsDates.Max();
List <Photo> photos = new List <Photo>();
for (int i = 0; i < f.GetCount(); i++)
{
photos.Add(new Photo {
Number = i, FileName = f.GetImg(i)
});
}
return(photos.Select(photo =>
{
var photoDateTime = firstPhotoDate.AddMilliseconds(photo.Number * timeStepMilliseconds);
var maxTimeRangeDiff = timeStepMilliseconds / 2;
var closestPointInRange = pointsList
.Where(point => Math.Abs((point.Date - photoDateTime).TotalMilliseconds) < maxTimeRangeDiff)
.OrderBy(point => Math.Abs((point.Date - photoDateTime).TotalMilliseconds))
.FirstOrDefault();
if (closestPointInRange == null)
{
return null;
}
return new PhotoData
{
MovementPoint = closestPointInRange,
Photo = photo
};
})
.Where(pd => pd != null));
}
public string[] RecognizeAll(ImageFolder imf)
{
int count = imf.GetCount();
string[] result = new string[count];
for (int i = 0; i < count; i++)
{
var image = new Image <Gray, byte>(imf.GetImg(i)).Bytes;
if (image.Length != 32 * 32)
{
result[0] = "wrong image size";
return(result);
}
if (net.Layers.Count == 0)
{
this.CreateCNN();
LoadCNN(path);
}
var dataShape = new Shape(32, 32, 1, 1);
var data = new double[dataShape.TotalLength];
var dataVolume = BuilderInstance.Volume.From(data, dataShape);
var j = 0;
for (var y = 0; y < net.Layers[0].InputHeight; y++)
{
for (var x = 0; x < net.Layers[0].InputWidth; x++)
{
dataVolume.Set(x, y, 0, 0, image[j++]);
}
}
net.Forward(dataVolume);
var prediction = net.GetPrediction();
result[i] = GetClassNameFromNumber(prediction[0]);
}
return(result);
}