// TODO : remove dependencies with feautre extraction and svm classification.
private void SetSVM()
{
//Get feautures from model
featureExtractor = new TFFeatureExtraction("input_1", "block_15_project/convolution", 224, 224, 127.5f, 127.5f, DLModel, LabelsFile, 180, 0.01f);
// set and load svm model
svm_model = new SVMClassification();
svm_model.SetModelParameters("SVM_Weights", "mu", "sigma");
svmClassifier = svm_model;
}
// Start is called before the first frame update
void Start()
{
TFSharpClassification classifier = new TFSharpClassification("input_1", "Logits/Softmax", 224, 224, 127.5f, 127.5f, model, labelsFile, angle, 0.05f);
var tex = Resources.Load <Texture2D>("Textures/Masoutis/" + image_file);
//var tex = Resources.Load<Texture2D>("Textures/" + image_file);
//Texture2D tex = new Texture2D(img.texture.width, img.texture.height);
//tex = img.texture as Texture2D;
RawImage img2 = GameObject.Find("img2").GetComponent <RawImage>();
img2.texture = tex;;
//var oute = classifier.FetchOutput(tex);
//foreach (KeyValuePair<string, float> value in oute)
//{
// Debug.Log("class :" + value.Key + ": " + value.Value);
//}
//print("===============================");
//IModelPrediction classifier_3 = new TFClassification("input_1", "Logits/Softmax", 224, 224, 127.5f, 127.5f, model, labelsFile, angle, 0.01f);
//var output = classifier_3.FetchOutput<IList, Texture2D>(tex);
//foreach (KeyValuePair<string, float> value in output)
//{
// Debug.Log("class :" + value.Key + ": " + value.Value);
//}
IModelPrediction classifier_4 = new TFFeatureExtraction("input_1", "block_15_project/convolution", 224, 224, 127.5f, 127.5f, model, labelsFile, angle, 0.01f);
var output = classifier_4.FetchOutput <List <float>, Texture2D>(tex);
double[] output_array = GenericUtils.ConvertToDouble(output.ToArray());
SVMClassification obj = new SVMClassification();
obj.SetModelParameters("SVM_Weights", "mu", "sigma");
var norm_fv = obj.NormalizeElements(output_array, obj.muData, obj.sigmaData);
List <double> norm_fv_list = new List <double>(norm_fv);
IModelPrediction svm = obj;
var probs = svm.FetchOutput <List <float>, List <double> >(norm_fv_list);
foreach (var item in probs)
{
Debug.Log(item);
}
}
void Start()
{
SVMClassification obj = new SVMClassification();
IModelPrediction svm = obj;
}
