private void InferenceEmotion(int shotsTaken, NDArray img_buffer_)
{
var imgArr = ReadTensorFromDetected(img_buffer_, img_size: 60);
ConfigProto config = new ConfigProto();
GPUOptions gpuConfig = new GPUOptions();
gpuConfig.AllowGrowth = true;
gpuConfig.PerProcessGpuMemoryFraction = 0.3;
config.GpuOptions = gpuConfig;
using (var sess = tf.Session(emotionGraph, config))
{
Tensor tensorClasses = emotionGraph.OperationByName("Identity");
Tensor imgTensor = emotionGraph.OperationByName("x");
Tensor[] outTensorArr = new Tensor[] { tensorClasses };
var results = sess.run(outTensorArr, new FeedItem(imgTensor, imgArr));
var emotions = results[0].ToArray <float>();
//var records = new List<object>
//{
// new { Frame = shotsTaken, Results = results[0] },
//};
//csv.WriteRecord(new { Frame = shotsTaken, Results = results[0] });
//csv.Flush();
var record = new CSVRecord();
record.Neutral = (int)(Math.Round(emotions[0], 2) * 100);
record.Happy = (int)(Math.Round(emotions[1], 2) * 100);
record.Sad = (int)(Math.Round(emotions[2], 2) * 100);
record.Angry = (int)(Math.Round(emotions[3], 2) * 100);
record.Surprised = (int)(Math.Round(emotions[4], 2) * 100);
record.Date = DateTime.Now.ToString("yyyy-MM-dd HH:mm:ss");
using (var stream = File.Open("output.csv", FileMode.Append))
using (var writer = new StreamWriter(stream))
using (var csv = new CsvWriter(writer, CultureInfo.InvariantCulture))
{
// Don't write the header again.
csv.Configuration.HasHeaderRecord = false;
csv.WriteRecord <CSVRecord>(record);
csv.NextRecord();
}
Console.WriteLine($"Results: {results[0].ToString()}");
//PreProcessEmotion(img_buffer, results[0]);
}
}
private NDArray ReadTensorFromDetected(NDArray img_buffer_, int img_size = 60)
{
var graph = tf.Graph().as_default();
ConfigProto config = new ConfigProto();
GPUOptions gpuConfig = new GPUOptions();
gpuConfig.AllowGrowth = true;
gpuConfig.PerProcessGpuMemoryFraction = 0.3;
config.GpuOptions = gpuConfig;
var t3 = tf.constant(img_buffer_, dtype: TF_DataType.TF_UINT8);
//var inp = tf.reshape(t3, (height, width, 3));
var casted = tf.cast(t3, tf.float32);
var dims_expander = tf.expand_dims(casted, 0);
var resize = tf.constant(new int[] { img_size, img_size });
var bilinear = tf.image.resize_bilinear(dims_expander, resize);
using (var sess = tf.Session(graph, config))
return(sess.run(bilinear));
}
private NDArray InferenceDetector(NDArray img_buffer_)
{
var imgArr = ReadTensorFromImageFile(img_buffer_);
ConfigProto config = new ConfigProto();
GPUOptions gpuConfig = new GPUOptions();
gpuConfig.AllowGrowth = true;
gpuConfig.PerProcessGpuMemoryFraction = 0.3;
config.GpuOptions = gpuConfig;
using (var sess = tf.Session(detectorGraph, config))
{
Tensor tensorClasses = detectorGraph.OperationByName("Identity");
Tensor imgTensor = detectorGraph.OperationByName("x");
Tensor[] outTensorArr = new Tensor[] { tensorClasses };
var results = sess.run(outTensorArr, new FeedItem(imgTensor, imgArr));
//Console.WriteLine($"Results: {results[0].ToString()}");
return(PreProcessEmotion(img_buffer_, results[0]));
}
}
