public void LookupFalseTest()
{
GroundTruth.CreateGroundTruthLookup();
string[] imageArray = GroundTruth.GetImageKeys();
bool isHealthy = GroundTruth.Lookup("DSC00041.JPG");
Assert.IsFalse(isHealthy);
}
public void GetImageKeysTest()
{
GroundTruth.CreateGroundTruthLookup();
string[] imageArray = GroundTruth.GetImageKeys();
int numEntries = imageArray.Length;
int numPictures = 1787;
Assert.IsTrue(numEntries == numPictures);
}
public void CreateGroundTruth(Prediction prediction, string label, string source)
{
var groundTruth = new GroundTruth();
groundTruth.Prediction = prediction;
groundTruth.Label = label;
groundTruth.Source = source;
var request = new GroundTruthRequest();
request.GroundTruth = groundTruth;
var response = this.client.CreateGroundTruth(request);
}
// Attempt AI Prediction:
public JsonResult OnGetGroundTruth()
{
// 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(GroundTruth.Lookup(imageURL)));
}
/// <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");
}
public void CreateGroundTruth(Prediction prediction, string label, string source)
{
var groundTruth = new GroundTruth
{
Prediction = prediction,
Label = label,
Source = source
};
var request = new GroundTruthRequest
{
GroundTruth = groundTruth
};
this.client.CreateGroundTruth(request);
}
public async Task <bool> CreateGroundTruthAsync(Prediction prediction, string label, string source)
{
var groundTruth = new GroundTruth
{
Prediction = prediction,
Label = label,
Source = source
};
var request = new GroundTruthRequest
{
GroundTruth = groundTruth
};
await this.client.CreateGroundTruthAsync(request);
return(true);
}
public void getNumTrueAndFalse()
{
int numTrue = 0;
int numFalse = 0;
GroundTruth.CreateGroundTruthLookup();
string[] imageArray = GroundTruth.GetImageKeys();
int numImages = imageArray.Length;
for (int i = 0; i < imageArray.Length; i++)
{
if (GroundTruth.Lookup((imageArray[i]).ToString()) == true)
{
numTrue++;
}
else
{
numFalse++;
}
}
Assert.IsTrue((numFalse + numTrue) == numImages);
}
internal void AddPrediction(TSolution truth, TSolution prediction)
{
GroundTruth.Add(truth);
Predicted.Add(prediction);
}
// Attempt Getting Image Keys:
public JsonResult OnGetImageKeys()
{
// Attempt to return the response:
return(new JsonResult(GroundTruth.GetImageKeys()));
}
public static SortedDictionary <String, List <GroundTruth> > buildDataFromFile(string pathFile, string pathDataset)
{
string[] data = File.ReadAllLines(pathFile);
string[] txt = null;
string imgName = null;
string dbName = new DirectoryInfo(pathDataset).Name;
GroundTruth gt = null;
SortedDictionary <String, List <GroundTruth> > groundTruth = new SortedDictionary <string, List <GroundTruth> >();
MenuService menuService = new MenuService();
for (int i = 0; i < data.Length; i++)
{
if (data[i].CompareTo("") == 0)
{
continue;
}
txt = data[i].Split(FileUtils._token);
if (txt.Length == 1)
{
imgName = txt[0].Trim();
i++;
while (i < data.Length && data[i].CompareTo("") != 0)
{
txt = data[i].Split(FileUtils._token);
gt = new GroundTruth();
gt._sing._x = int.Parse(txt[0]);
gt._sing._y = int.Parse(txt[1]);
gt._sing._type = Singularity.stringToSingType(txt[2].Trim());
gt._datasetName = dbName;
gt._imageName = imgName;
string[] file = imgName.Split('.');
string rectImgName = string.Format("{0}_{1}_{2}_{3}.{4}", file[0], gt._sing._type.ToString(), gt._sing._x, gt._sing._y, file[1]);
string p = Path.Combine(pathDataset, gt._sing._type.ToString(), rectImgName);
if (File.Exists(p))
{
gt._sing._image = new Bitmap(p);
}
else
{
p = p = Path.Combine(pathDataset, imgName);
Bitmap b = new Bitmap(p);
Rectangle rect = GraphicsUtils.getRectFromSing(gt._sing, b.Width, b.Height);
gt._sing._image = b.Clone(rect, b.PixelFormat);
}
menuService.addGroundTruth(groundTruth, imgName, gt);
i++;
}
}
}
return(groundTruth);
}
public void addGroundTruth(SortedDictionary <String, List <GroundTruth> > map, string nameImage, GroundTruth g)
{
List <GroundTruth> l;
if (!map.TryGetValue(nameImage, out l))
{
map.Add(nameImage, new List <GroundTruth>());
}
map[nameImage].Add(g);
}
