private async void predict_predict_Click(object sender, RoutedEventArgs e)
{
List <float> predictions = new List <float>();
int deviceId = int.Parse(train_device.Text);
string s = $"Count: {predictData.Count()}\n";
var task = Task.Run(() =>
{
MLModel model = new MLModel($"{deviceId}.mlm");
for (int i = 0; i < predictData.Count; i++)
{
var prediction = model.Predict(predictData[i]);
predictions.Add(prediction);
s += $"{predictData[i].Timestamp.ToString("HH")}: {predictData[i].Volume:f1} -> {prediction:f1}" +
$" ({((predictData[i].Volume != 0) ? 100 - predictData[i].Volume / prediction * 100:0):f2}%)\n";
}
});
await Process(task);
DrawChild(predictions, "Prediction");
Log(s);
}
private async void train_test_Click(object sender, RoutedEventArgs e)
{
List <float> predictions = new List <float>();
MLModel.Metrics metrics = null;
var task = Task.Run(() =>
{
for (int i = 0; i < trainData.Count; i++)
{
var prediction = _mlModel.Predict(trainData[i]);
predictions.Add(prediction);
}
metrics = _mlModel.Validate();
});
await Process(task);
DrawChild(predictions, "Prediction");
Log($"ML model metrics:\n" +
$"Mean absolute error: {metrics.MeanAbsoluteError:f2}\n" +
$"Mean squared error: {metrics.MeanSquaredError:f2}");
}
public async Task PredictAll()
{
_applicationStatusManager.ChangeCurrentAppStatus(Enums.Status.Working, "");
try
{
Status = "starting ml model...";
//load config
var confDir = Path.Join(Environment.GetFolderPath(Environment.SpecialFolder.LocalApplicationData), "lacmus");
var configPath = Path.Join(confDir, "appConfig.json");
_appConfig = await AppConfig.Create(configPath);
var config = _appConfig.MlModelConfig;
using (var model = new MLModel(config))
{
await model.Init();
var count = 0;
var objectCount = 0;
Status = "processing...";
foreach (var photoViewModel in _photos.Items)
{
try
{
photoViewModel.Annotation.Objects = await model.Predict(photoViewModel);
photoViewModel.BoundBoxes = photoViewModel.GetBoundingBoxes();
if (photoViewModel.BoundBoxes.Any())
{
photoViewModel.Photo.Attribute = Attribute.WithObject;
photoViewModel.IsHasObject = true;
}
objectCount += photoViewModel.BoundBoxes.Count();
count++;
PredictProgress = (double)count / _photos.Items.Count() * 100;
PredictTextProgress = $"{Convert.ToInt32(PredictProgress)} %";
_applicationStatusManager.ChangeCurrentAppStatus(Enums.Status.Working, $"Working | {(int)((double) count / _photos.Items.Count() * 100)} %, [{count} of {_photos.Items.Count()}]");
Console.WriteLine($"\tProgress: {(double) count / _photos.Items.Count() * 100} %");
}
catch (Exception e)
{
Log.Error(e, $"Unable to process file {photoViewModel.Path}. Slipped.");
}
}
Status = "stopping ml model...";
await model.Stop();
PredictTextProgress = $"predict {_photos.Count} photos.";
Log.Information($"Successfully predict {_photos.Count} photos. Find {objectCount} objects.");
}
}
catch (Exception e)
{
Status = "error.";
Log.Error(e, "Unable to get prediction.");
}
_applicationStatusManager.ChangeCurrentAppStatus(Enums.Status.Ready, "");
}
public async Task GetPrediction(float[] image)
{
var digit = MinstDigit.FromList(image, false);
var prediction = ml.Predict(digit);
prediction.Label = prediction.Score.Select((value, index) => new { Value = value, Index = index }).Aggregate((a, b) => (a.Value > b.Value) ? a : b).Index;
await Clients.Caller.SendAsync("receivePrediction", image, prediction);
await Clients.All.SendAsync("receiveBackground", image, prediction);
}
// Attempt AI Prediction:
public JsonResult OnGetAIPrediction()
{
// Get the query string:
var queryStringObject = Request.QueryString;
string queryString = queryStringObject.ToString();
// Parse the query string and extract parameters:
string[] queryParams = queryString.Split("&");
string imageURL = queryParams[1].Replace("imageURL=", "");
// Attempt to return the response:
return(new JsonResult(MLModel.Predict(imageURL).Prediction));
}
/// <summary>
/// This is the starting point of the server program. Here we assign the app a configuration and initialize Machine Learning engines.
/// </summary>
/// <param name="configuration">An IConfiguration object</param>
public Startup(IConfiguration configuration)
{
Configuration = configuration;
// Set Firebase URL:
RestfulDBConnection.FIREBASE_URL = Configuration["FirebaseURL"];
// Initialize Machine Learning Components:
MLModel.CreatePredictionEngine();
GroundTruth.CreateGroundTruthLookup();
// Get the Machine Learning Warmed Up:
MLModel.Predict("https://firebasestorage.googleapis.com/v0/b/agricultureai-15ce0.appspot.com/o/DSC00025.JPG?alt=media");
}