/** Runs inference and returns the classification results. */
public List <Recognition> recognizeImage(Bitmap bitmap)
{
// Log this method so that it can be analyzed with systrace.
Trace.BeginSection("recognizeImage");
Trace.BeginSection("preprocessBitmap");
convertBitmapToByteBuffer(bitmap);
Trace.EndSection();
// Run the inference call.
Trace.BeginSection("runInference");
long startTime = SystemClock.UptimeMillis();
runInference();
long endTime = SystemClock.UptimeMillis();
Trace.EndSection();
//LOGGER.v("Timecost to run model inference: " + (endTime - startTime));
// Find the best classifications.
Queue <Recognition> pq = new Queue <Recognition>(3);
//new PriorityQueue<Recognition>(
// 3,
//new Comparator<Recognition>()
//{
// // override
// public int compare(Recognition lhs, Recognition rhs)
// {
// // Intentionally reversed to put high confidence at the head of the queue.
// return Float.compare(rhs.getConfidence(), lhs.getConfidence());
// }
//});
for (int i = 0; i < labels.Count; ++i)
{
pq.Enqueue(
new Recognition(
"" + i,
labels.Count > i ? labels[i] : "unknown",
getNormalizedProbability(i),
null));
}
var recognitions = new List <Recognition>();
int recognitionsSize = Math.Min(pq.Count, MAX_RESULTS);
for (int i = 0; i < recognitionsSize; ++i)
{
recognitions.Add(pq.Dequeue());
}
Trace.EndSection();
return(recognitions);
}
