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); }