protected IEnumerable <ImageNetData> PredictDataUsingModel(string testLocation,
string imagesFolder,
string labelsLocation,
PredictionEngine <ImageNetData, ImageNetPrediction> model)
{
ConsoleWriteHeader("Classificate images");
Console.WriteLine($"Images folder: {imagesFolder}");
Console.WriteLine($"Training file: {testLocation}");
Console.WriteLine($"Labels file: {labelsLocation}");
var labels = ModelHelpers.ReadLabels(labelsLocation);
var testData = ImageNetData.ReadFromCsv(testLocation, imagesFolder);
foreach (var sample in testData)
{
var probs = model.Predict(sample).PredictedLabels;
var imageData = new ImageNetDataProbability()
{
ImagePath = sample.ImagePath,
Label = sample.Label
};
(imageData.PredictedLabel, imageData.Probability) = GetBestLabel(labels, probs);
imageData.ConsoleWrite();
yield return(imageData);
}
}
protected IEnumerable <ImageNetData> PredictDataUsingModel(List <ImageNetData> imageData,
string labelsLocation,
PredictionEngine <ImageNetData, ImageNetPrediction> model)
{
ConsoleWriteHeader("Classify images");
Console.WriteLine($"Images folder: {imagesFolder}");
Console.WriteLine($"Labels file: {labelsLocation}");
var labels = ModelHelpers.ReadLabels(labelsLocation);
foreach (var sample in imageData)
{
var probs = model.Predict(sample).PredictedLabels;
var result = new ImageNetDataProbability()
{
ImagePath = sample.ImagePath,
};
(result.PredictedLabel, result.Probability) = GetBestLabel(labels, probs);
if (result.Probability > 0.4f)
{
result.ConsoleWrite();
yield return(result);
}
}
}
public static void ConsoleWrite(this ImageNetDataProbability self)
{
var defaultForeground = Console.ForegroundColor;
var labelColor = ConsoleColor.Magenta;
var probColor = ConsoleColor.Blue;
var exactLabel = ConsoleColor.Green;
var failLabel = ConsoleColor.Red;
Console.Write("ImagePath: ");
Console.ForegroundColor = labelColor;
Console.Write($"{Path.GetFileName(self.ImagePath)}");
Console.ForegroundColor = defaultForeground;
Console.Write(" labeled as ");
Console.ForegroundColor = labelColor;
Console.Write(self.Label);
Console.ForegroundColor = defaultForeground;
Console.Write(" predicted as ");
if (self.Label.Equals(self.PredictedLabel))
{
Console.ForegroundColor = exactLabel;
Console.Write($"{self.PredictedLabel}");
}
else
{
Console.ForegroundColor = failLabel;
Console.Write($"{self.PredictedLabel}");
}
Console.ForegroundColor = defaultForeground;
Console.Write(" with probability ");
Console.ForegroundColor = probColor;
Console.Write(self.Probability);
Console.ForegroundColor = defaultForeground;
Console.WriteLine("");
}
protected IEnumerable <ImageNetData> PredictDataUsingModel(string testLocation, string imagesFolder, PredictionModel <ImageNetData, ImageNetPrediction> model)
{
ConsoleWriteHeader("Classificate images");
Console.WriteLine($"Images folder: {imagesFolder}");
Console.WriteLine($"Training file: {testLocation}");
Console.WriteLine(" ");
model.TryGetScoreLabelNames(out string[] labels);
var testData = ImageNetData.ReadFromCsv(testLocation, imagesFolder).ToList();
// add an extra image to "really" test the model
testData = testData.Concat(new[] { new ImageNetData()
{
ImagePath = Path.Combine(imagesFolder, "teddy5.jpg")
} }).ToList();
foreach (var sample in testData)
{
var probs = model.Predict(sample).PredictedLabels;
var imageData = new ImageNetDataProbability()
{
ImagePath = sample.ImagePath,
};
(imageData.Label, imageData.Probability) = GetLabel(labels, probs);
imageData.ConsoleWriteLine();
yield return(imageData);
}
}
protected string PredictDataUsingModel(string testLocation,
string imagesFolder,
string labelsLocation,
PredictionEngine <ImageNetData, ImageNetPrediction> model)
{
Console.WriteLine($"Images folder: {imagesFolder}");
Console.WriteLine($"Training file: {testLocation}");
Console.WriteLine($"Labels file: {labelsLocation}");
var labels = ModelHelpers.ReadLabels(labelsLocation);
var testData = ImageNetData.ReadFromCsv(testLocation, imagesFolder);
List <ImageNetDataProbability> result = new List <ImageNetDataProbability>();
foreach (var sample in testData)
{
var probs = model.Predict(sample);
var imageData = new ImageNetDataProbability()
{
ImagePath = sample.ImagePath,
Label = sample.Label
};
(imageData.PredictedLabel, imageData.Probability) = ModelHelpers.GetBestLabel(labels, probs.PredictedLabels);
result.Add(imageData);
}
return(result.OrderByDescending(x => x.Probability).FirstOrDefault().PredictedLabel);
}
protected IEnumerable <ImageNetData> PredictDataUsingModel(string testLocation,
string imagesFolder,
string labelsLocation,
PredictionEngine <ImageNetData, ImageNetPrediction> model)
{
ConsoleWriteHeader("Classificate images");
Console.WriteLine($"Images folder: {imagesFolder}");
Console.WriteLine($"Training file: {testLocation}");
Console.WriteLine($"Labels file: {labelsLocation}");
double acc = 0;
double tp = 0;
double fp = 0;
double fn = 0;
var labels = ModelHelpers.ReadLabels(labelsLocation);
var testData = ImageNetData.ReadFromCsv(testLocation, imagesFolder);
var count = 0;
Console.ForegroundColor = ConsoleColor.Red;
foreach (var sample in testData)
{
var probs = model.Predict(sample).PredictedLabels;
var imageData = new ImageNetDataProbability()
{
ImagePath = sample.ImagePath,
Label = sample.Label
};
(imageData.PredictedLabel, imageData.Probability) = GetBestLabel(labels, probs);
//imageData.ConsoleWrite();
acc += imageData.Label == imageData.PredictedLabel ? 1 : 0;
tp += imageData.PredictedLabel == imageData.Label && imageData.Label == labels[1] ? 1 : 0;
fp += imageData.Label == labels[0] && imageData.PredictedLabel == labels[1] ? 1 : 0;
fn += imageData.Label == labels[1] && imageData.PredictedLabel == labels[0] ? 1 : 0;
count++;
if (count % 100 == 0)
{
Console.WriteLine($"{count} images have been processed");
}
yield return(imageData);
}
acc = (acc / testData.ToList().Count()) * 100;
double prec = tp / (tp + fp);
double rec = tp / (tp + fn);
double f1 = 2 * (prec * rec) / (prec + rec);
Console.WriteLine($"Accuracy: {acc}%, precision: {prec}, recall: {rec}, f1 score: {f1}");
}
protected ImageNetData PredictDataUsingModel(ImageNetData testData,
string labelsLocation,
PredictionEngine <ImageNetData, ImageNetPrediction> model)
{
var labels = ModelHelpers.ReadLabels(labelsLocation);
var probs = model.Predict(testData).PredictedLabels;
var imageData = new ImageNetDataProbability()
{
ImagePath = testData.ImagePath,
Label = testData.Label
};
(imageData.PredictedLabel, imageData.Probability) = GetBestLabel(labels, probs);
return(imageData);
}
public static void ConsoleWrite(this ImageNetDataProbability self)
{
var defaultForeground = Console.ForegroundColor;
var labelColor = ConsoleColor.Magenta;
var probColor = ConsoleColor.Blue;
Console.Write("ImagePath: ");
Console.ForegroundColor = labelColor;
Console.Write($"{Path.GetFileName(self.ImagePath)}");
Console.ForegroundColor = defaultForeground;
Console.Write(" identified as as ");
Console.Write($"{self.PredictedLabel}");
Console.ForegroundColor = defaultForeground;
Console.Write(" with probability ");
Console.ForegroundColor = probColor;
Console.Write(self.Probability);
Console.ForegroundColor = defaultForeground;
Console.WriteLine("");
}
protected IEnumerable <ImageNetDataProbability> PredictImageUsingModel(string testLocation,
string imageFile,
string labelsLocation,
PredictionEngine <ImageNetData, ImageNetPrediction> model)
{
var labels = ModelHelpers.ReadLabels(labelsLocation);
var testData = new ImageNetData {
ImagePath = imageFile
};
var probs = model.Predict(testData).PredictedLabels;
var imageData = new ImageNetDataProbability()
{
ImagePath = testData.ImagePath,
Label = testData.Label
};
(imageData.PredictedLabel, imageData.Probability) = ModelHelpers.GetBestLabel(labels, probs);
yield return(imageData);
}
public ImageNetDataProbability PredictSingleImageDataUsingModel(
string imagePath,
string labelsLocation,
PredictionEngine <ImageNetData, ImageNetPrediction> model)
{
Debug.WriteLine($"Images folder: {imagePath}");
Debug.WriteLine($"Labels folder: {labelsLocation}");
var labels = ReadLabels(labelsLocation);
var testData = new ImageNetData {
ImagePath = imagePath
};
var probs = model.Predict(testData).PredictedLabels;
var imageData = new ImageNetDataProbability()
{
ImagePath = testData.ImagePath,
Label = string.Empty
};
(imageData.PredictedLabel, imageData.Probability) = GetBestLabel(labels, probs);
return(imageData);
}