public string[] AnalyzePicture(ArtImage artImage)
{
string[] arrayOfEachColumnCellValue = new string[WorkingCellCount];
//method that compares first metric
Dictionary<string, double> firstMetricValues = testDimensionAspect(artImage);
//method for second metric
colorSAD.determineBitFrequency(artImage);
Dictionary<string, double> secondMetricValues = testColor(artImage);
//method for obtaining the mean column values
Dictionary<string, double> averageColumnValues = getColumnAverages(firstMetricValues, secondMetricValues);
//fills the string array for the fields
int i = 0;
foreach (string artistName in allArtists.Keys)
{
arrayOfEachColumnCellValue[i] = artistName;
string temp1 = "" + firstMetricValues[artistName] + ".0000";
arrayOfEachColumnCellValue[i + 5] = temp1.Substring(0, 6);
string temp2 = "" + secondMetricValues[artistName] + ".0000";
arrayOfEachColumnCellValue[i + 10] = temp2.Substring(0, 6);
string temp4 = "" + averageColumnValues[artistName] + ".0000";
arrayOfEachColumnCellValue[i + 15] = temp4.Substring(0, 6);
i++;
}
arrayOfEachColumnCellValue = SortBySimilarity(arrayOfEachColumnCellValue);
return arrayOfEachColumnCellValue;
}
public void SetTextBox(String[] results, ArtImage image)
{
InputImage = image;
List<string> artistNames = new List<string>();
double[] averageValues = new double[5];
for (int i = 0; i < 5; i++)
{
averageValues[i] = Convert.ToDouble(results[(results.Length - 5) + i]);
}
artistNames.Add(results[4]);
for (int i = 0; i < 4; i++)
{
if (averageValues[4] - averageValues[i] < 5)
{
artistNames.Add(results[i]);
}
}
string resultString;
if (artistNames.Count > 1)
{
resultString = "The results are inconclusive. The artist could be ";
for (int j = 0; j < artistNames.Count - 1; j++)
{
resultString += artistNames[j] + ", ";
}
resultString += "or " + artistNames[artistNames.Count - 1];
}
else
{
resultString = "The Artist of this image is " + artistNames[0];
}
ArtistResultBox.Text = resultString;
}
//checks every pixel in an artImage, and counts each time a specific color occurs
public void determineBitFrequency(ArtImage artImage)
{
BitmapImage bitmap = artImage.getBitmapImage();
byte[] pixels = ConvertBitmapImageToByteArray(bitmap);
int stride = bitmap.PixelWidth * 4;
int currentR = 0;
int currentG = 0;
int currentB = 0;
int currentA = 0;
for (int x = 0; x < bitmap.Width; x++)
{
for (int y = 0; y < bitmap.Height; y++)
{
int index = (y * stride) + (4 * x);
if (index < pixels.Length)
{
currentR = roundNumber(pixels[index]) > 250 ? 255 : roundNumber(pixels[index]);
currentG = roundNumber(pixels[index + 1]) > 250 ? 255 : roundNumber(pixels[index + 1]);
currentB = roundNumber(pixels[index + 2]) > 250 ? 255 : roundNumber(pixels[index + 2]);
currentA = roundNumber(pixels[index + 3]) > 250 ? 255 : roundNumber(pixels[index + 3]);
Color currentColor = Color.FromArgb((byte)currentA, (byte)currentR, (byte)currentG, (byte)currentB);
artImage.countColor(currentColor);
}
}
}
}
private void button2_Click(object sender, RoutedEventArgs e)
{
if (ImagePreview.Source != null)
{
ArtImage artImage = new ArtImage((BitmapImage)ImagePreview.Source, fileName);
string[] results = artdentifier.AnalyzePicture(artImage);
ResultBox.Text = "";
ArtistHeader.Text = "Artist Names";
Column1Header.Text = "Dimensions";
Column2Header.Text = "Colors";
Column3Header.Text = "Average";
for (int i = 0; i < results.Length; i++)
{
artistChanceValues[i / COUNT_OF_TOP_ARTISTS_SHOWN, i % (COUNT_OF_TOP_ARTISTS_SHOWN)] = results[i];
}
for (int j = 0; j < COUNT_OF_TOP_ARTISTS_SHOWN; j++)
{
for (int k = 0; k < METRICS_MESURED_ON; k++)
{
resultGrid[k, j].Text = artistChanceValues[k, j];
}
}
DetermineArtist(results, artImage);
}
else
{
ResultBox.Text = "You must submit an image";
}
}
public void AutomaticBox(String[] results, ArtImage image)
{
InputImage = image;
List<string> artistNames = new List<string>();
double[] averageValues = new double[5];
for (int i = 0; i < 5; i++)
{
averageValues[i] = Convert.ToDouble(results[(results.Length - 5) + i]);
}
artistNames.Add(results[4]);
for (int i = 0; i < 4; i++)
{
if (averageValues[4] - averageValues[i] < 5)
{
artistNames.Add(results[i]);
}
}
if (!(artistNames.Count > 1))
{
artDistinguisher.AddArtImage(artistNames[0], InputImage);
}
}
public double getDimensionRatio(ArtImage artImage)
{
double widthHeightRatio = (double)artImage.Width / artImage.Height;
return widthHeightRatio;
}
public double calculateAccuracy()
{
double accuracy = 0.0;
int imageCount = 0;
int correctGuesses = 0;
string dir = @"c:\Users\Jeff\Documents\GitHub\Capstone\Documentation\Misc\Z_TestImages\";
string[] filePaths = Directory.GetFiles(dir);
foreach (string str in filePaths)
{
if (isAnImageType(str))
{
Uri artistLocation = new Uri(str);
try
{
BitmapImage myBitmapImage = BitmapImageFromURI.GetBitmapImage(artistLocation);
ArtImage ai = new ArtImage(myBitmapImage, str);
Console.WriteLine(ai.KnownArtistName);
string[] results = AnalyzePicture(ai);
string[,] artistChanceValues = new string[4, 5];
for (int i = 0; i < results.Length; i++)
{
artistChanceValues[i / 5, i % 5] = results[i];
}
if (ai.KnownArtistName.Contains(artistChanceValues[0, 4]))
{
correctGuesses++;
}
imageCount++;
}
catch (Exception e)
{
Console.WriteLine("error with " + str);
}
}
}
accuracy = ((double)correctGuesses) / ((double)imageCount);
return accuracy;
}
public void AddArtImage(string artistName, ArtImage artImage)
{
allArtists[artistName].Add(artImage);
}
private Dictionary<string, double> testDimensionAspect(ArtImage artImage)
{
Dictionary<string, double> DimensionalCheckRatios = new Dictionary<string, double>();
double dimensionalRatio = dimensionAnalyzer.getDimensionRatio(artImage);
foreach (List<ArtImage> lists in allArtists.Values)
{
double dimensionalSimilarity = 0.00;
int i = 1;
foreach (ArtImage a in lists)
{
double currentImageRatio = dimensionAnalyzer.getDimensionRatio(a);
double tempSimilarity = 1 - Math.Abs(dimensionalRatio - currentImageRatio);
if (tempSimilarity == 1)
{
dimensionalSimilarity = 1;
break;
}
else
{
if (tempSimilarity - (dimensionalSimilarity / i) > -20)
{
dimensionalSimilarity = dimensionalSimilarity + tempSimilarity;
i++;
}
}
}
foreach (KeyValuePair<String, List<ArtImage>> kvp in allArtists)
{
if (kvp.Value.Equals(lists))
{
dimensionalSimilarity = dimensionalSimilarity == 1 ? 1 : (dimensionalSimilarity / i);
DimensionalCheckRatios.Add(kvp.Key, dimensionalSimilarity * 100);
}
}
}
return DimensionalCheckRatios;
}
private Dictionary<string, double> testColor(ArtImage artImage)
{
Dictionary<string, double> ColorResultReturns = new Dictionary<string, double>();
Dictionary<string, double> redColorResults = compareImageRed(artImage);
Dictionary<string, double> greenColorResults = compareImageGreen(artImage);
Dictionary<string, double> blueColorResults = compareImageBlue(artImage);
foreach (string artistName in allArtists.Keys)
{
double meanColorValues = (redColorResults[artistName] + greenColorResults[artistName]
+ blueColorResults[artistName]) / 3;
ColorResultReturns.Add(artistName, meanColorValues);
}
return ColorResultReturns;
}
private Dictionary<string, double> compareImageRed(ArtImage artImage)
{
Dictionary<string, double> redCheckRatios = new Dictionary<string, double>();
byte inputRed = artImage.getMostFrequentRed();
foreach (List<ArtImage> lists in allArtists.Values)
{
double redSimilarity = 0.00;
int i = 1;
foreach (ArtImage a in lists)
{
byte redValue = a.getMostFrequentRed();
double redRatio = ((double)Math.Abs(redValue - inputRed)) / 255;
double tempSimilarity = 1 - redRatio;
if (tempSimilarity == 1)
{
redSimilarity = 1;
break;
}
else
{
redSimilarity = redSimilarity + tempSimilarity;
i++;
}
}
foreach (KeyValuePair<String, List<ArtImage>> kvp in allArtists)
{
if (kvp.Value.Equals(lists))
{
redSimilarity = redSimilarity == 1 ? 1 : (redSimilarity / i);
redCheckRatios.Add(kvp.Key, redSimilarity * 100);
}
}
}
return redCheckRatios;
}
private void DetermineArtist(String[] results, ArtImage image)
{
if ((bool)CheckBox1.IsChecked)
{
ResultWindow resultWindow = new ResultWindow(artdentifier);
resultWindow.SetTextBox(results, image);
resultWindow.ShowDialog();
}
else if ((bool)CheckBox2.IsChecked)
{
}
}
