public void ComputeTest()
{
byte[,] gradient = new byte[255, 255];
for (int i = 0; i < 255; i++)
for (int j = 0; j < 255; j++)
gradient[i, j] = (byte)j;
UnmanagedImage output;
new MatrixToImage().Convert(gradient, out output);
LocalBinaryPattern lbp = new LocalBinaryPattern();
List<double[]> result = lbp.ProcessImage(output);
int[,] actualPatterns = lbp.Patterns;
Assert.AreEqual(255, actualPatterns.GetLength(0));
Assert.AreEqual(255, actualPatterns.GetLength(1));
for (int i = 0; i < 255; i++)
{
for (int j = 0; j < 255; j++)
{
if (j == 0 || i == 0 || i == 254 || j == 254)
{
Assert.AreEqual(0, actualPatterns[i, j]);
}
else
{
Assert.AreEqual(7, actualPatterns[i, j]);
}
}
}
Assert.AreEqual(196, result.Count);
}
public void CloneTest()
{
LocalBinaryPattern original = new LocalBinaryPattern();
LocalBinaryPattern target = (LocalBinaryPattern)original.Clone();
UnmanagedImage output = createGradient();
List <double[]> result = target.ProcessImage(output);
int[,] actualPatterns = target.Patterns;
Assert.AreEqual(255, actualPatterns.GetLength(0));
Assert.AreEqual(255, actualPatterns.GetLength(1));
for (int i = 0; i < 255; i++)
{
for (int j = 0; j < 255; j++)
{
if (j == 0 || i == 0 || i == 254 || j == 254)
{
Assert.AreEqual(0, actualPatterns[i, j]);
}
else
{
Assert.AreEqual(7, actualPatterns[i, j]);
}
}
}
Assert.AreEqual(196, result.Count);
}
public void ComputeTest()
{
byte[,] gradient = new byte[255, 255];
for (int i = 0; i < 255; i++)
{
for (int j = 0; j < 255; j++)
{
gradient[i, j] = (byte)j;
}
}
UnmanagedImage output;
new MatrixToImage().Convert(gradient, out output);
LocalBinaryPattern lbp = new LocalBinaryPattern();
List <double[]> result = lbp.ProcessImage(output);
int[,] actualPatterns = lbp.Patterns;
Assert.AreEqual(255, actualPatterns.GetLength(0));
Assert.AreEqual(255, actualPatterns.GetLength(1));
for (int i = 0; i < 255; i++)
{
for (int j = 0; j < 255; j++)
{
if (j == 0 || i == 0 || i == 254 || j == 254)
{
Assert.AreEqual(0, actualPatterns[i, j]);
}
else
{
Assert.AreEqual(7, actualPatterns[i, j]);
}
}
}
Assert.AreEqual(196, result.Count);
}
public void doc_test()
{
string localPath = TestContext.CurrentContext.TestDirectory;
#region doc_apply
// Let's load an example image, such as Lena,
// from a standard dataset of example images:
var images = new TestImages(path: localPath);
Bitmap lena = images["lena.bmp"];
// Create a new Local Binary Pattern with default values:
var lbp = new LocalBinaryPattern(blockSize: 3, cellSize: 6);
// Use it to extract descriptors from the Lena image:
List <double[]> descriptors = lbp.ProcessImage(lena);
// Now those descriptors can be used to represent the image itself, such
// as for example, in the Bag-of-Visual-Words approach for classification.
#endregion
Assert.AreEqual(784, descriptors.Count);
double sum = descriptors.Sum(x => x.Sum());
Assert.AreEqual(6094.543992693033, sum, 1e-10);
}
public void bulkkprocess()
{
string[] file = Directory.GetFiles(@"C:\Users\kishor\Desktop\New folder (5)\cat\New folder", "*.png");
string orgImagePath = @"C:\Users\kishor\Desktop\New folder (5)\cat\17_cat.png";// file[0];
Bitmap lena = (Bitmap)Accord.Imaging.Image.FromFile(orgImagePath);
// Create a new Local Binary Pattern with default values:
var lbp = new LocalBinaryPattern(blockSize: 3, cellSize: 6);
// Use it to extract descriptors from the Lena image:
List <double[]> descriptors = lbp.ProcessImage(lena);
var a = lbp.Histograms;
// System.Console.Write("\n" + descriptors.Count+" v ");
for (int i = 0; i < file.Length; i++)
{
var lbp2 = new LocalBinaryPattern(blockSize: 3, cellSize: 6);
System.Console.Write("\"" + file[i] + "\"");
string dupImagePath = file[i];
Bitmap lena2 = (Bitmap)Accord.Imaging.Image.FromFile(dupImagePath);
List <double[]> descriptors2 = lbp2.ProcessImage(lena2);
lena2.Dispose();
var b = lbp2.Histograms;
// var percentage = getAbsoluteDifferencea(descriptors[0], descriptors2[0]);
// System.Console.Write("," + percentage);
var percentage = getAbsoluteDifference(a, b);
System.Console.Write("," + percentage);
percentage = getAbsoluteDifferenceec(a, b);
System.Console.Write("," + percentage);
// System.Drawing.Image img1 = System.Drawing.Image.FromFile(orgImagePath);
// System.Drawing.Image img2 = System.Drawing.Image.FromFile(dupImagePath);
// var percentage = Bhattacharyya.BhattacharyyaDifference(img1, img2);
// percentage = percentage * 100;
// percentage = Math.Round(percentage, 3);
//MagickImage orgImage = new MagickImage(orgImagePath);
//MagickImage dupImage = new MagickImage(dupImagePath);
//percentage = orgImage.Compare(dupImage, ErrorMetric.Absolute);
//System.Console.Write("," + percentage);
//percentage = orgImage.Compare(dupImage, ErrorMetric.Fuzz);
//System.Console.Write("," + percentage);
//percentage = orgImage.Compare(dupImage, ErrorMetric.MeanAbsolute);
//System.Console.Write("," + percentage);
//percentage = orgImage.Compare(dupImage, ErrorMetric.MeanErrorPerPixel);
//System.Console.Write("," + percentage);
//percentage = orgImage.Compare(dupImage, ErrorMetric.MeanSquared);
//System.Console.Write("," + percentage);
//percentage = orgImage.Compare(dupImage, ErrorMetric.NormalizedCrossCorrelation);
//System.Console.Write("," + percentage);
//percentage = orgImage.Compare(dupImage, ErrorMetric.PeakAbsolute);
//System.Console.Write("," + percentage);
//percentage = orgImage.Compare(dupImage, ErrorMetric.PeakSignalToNoiseRatio);
//System.Console.Write("," + percentage);
//percentage = orgImage.Compare(dupImage, ErrorMetric.PerceptualHash);
//System.Console.Write("," + percentage);
//percentage = orgImage.Compare(dupImage, ErrorMetric.RootMeanSquared);
//System.Console.Write("," + percentage);
//percentage = orgImage.Compare(dupImage, ErrorMetric.Undefined);
// System.Console.Write("," + percentage);
System.Console.Write("\n");
}
}
public rettype runalgo(string s)
{
string dupImagePath = s;
Bitmap org0 = (Bitmap)Accord.Imaging.Image.FromFile(dupImagePath);
// Bitmap org1 = org0.Clone(System.Drawing.Imaging.PixelFormat.Format24bppRgb);
if (greyscaleenable)
{
Accord.Imaging.Filters.GrayscaleBT709 gr = new Accord.Imaging.Filters.GrayscaleBT709();
org0 = gr.Apply(org0);
}
Bitmap org1 = org0.Clone(System.Drawing.Imaging.PixelFormat.Format24bppRgb);
Accord.Imaging.Filters.Difference filter = new Accord.Imaging.Filters.Difference(org1);
Bitmap noiserem = org1.Clone(System.Drawing.Imaging.PixelFormat.Format24bppRgb);
foreach (string filterid in algorithm)
{
int filterno = getfilter(filterid);
if (filterno == 2)
{
// Console.WriteLine("AdaptiveSmoothing");
Accord.Imaging.Filters.AdaptiveSmoothing noisefilter = new Accord.Imaging.Filters.AdaptiveSmoothing();
noiserem = noisefilter.Apply(noiserem);
}
else if (filterno == 4)
{
// Console.WriteLine("AdditiveNoise");
Accord.Imaging.Filters.AdditiveNoise noisefilter = new Accord.Imaging.Filters.AdditiveNoise();
noiserem = noisefilter.Apply(noiserem);
}
else if (filterno == 3)
{
// Console.WriteLine("BilateralSmoothing");
Accord.Imaging.Filters.BilateralSmoothing noisefilter = new Accord.Imaging.Filters.BilateralSmoothing();
noiserem = noisefilter.Apply(noiserem);
}
if (filterno == 5)
{
//Bitmap gra = new Bitmap(noiserem.Width, noiserem.Height, PixelFormat.Format8bppIndexed);
// Bitmap gra = new bnoiserem.Clone(System.Drawing.Imaging.PixelFormat.form);
// Console.WriteLine("AdaptiveSmoothing");
Accord.Imaging.Filters.Erosion noisefilter = new Accord.Imaging.Filters.Erosion();
noiserem = noisefilter.Apply(noiserem);
}
else if (filterno == 6)
{
// Console.WriteLine("AdditiveNoise");
Accord.Imaging.Filters.Pixellate noisefilter = new Accord.Imaging.Filters.Pixellate();
noiserem = noisefilter.Apply(noiserem);
}
else if (filterno == 7)
{
// Console.WriteLine("BilateralSmoothing");
Accord.Imaging.Filters.GaussianBlur noisefilter = new Accord.Imaging.Filters.GaussianBlur();
noiserem = noisefilter.Apply(noiserem);
}
else if (filterno == 8)
{
// Console.WriteLine("BilateralSmoothing");
Accord.Imaging.Filters.GaussianSharpen noisefilter = new Accord.Imaging.Filters.GaussianSharpen();
noiserem = noisefilter.Apply(noiserem);
}
else
{
///donothing
}
}
// apply the filter
Bitmap resultImage = filter.Apply(noiserem);
double mean = 0.0;// histogram.Mean; // mean red value
Accord.Statistics.Visualizations.Histogram histogram = null;;
if (greyscaleenable == false)
{
Accord.Imaging.ImageStatistics statistics = new Accord.Imaging.ImageStatistics(resultImage);
histogram = statistics.Red;
mean = histogram.Mean; // mean red value
}
else
{
Accord.Imaging.Filters.GrayscaleBT709 gr = new Accord.Imaging.Filters.GrayscaleBT709();
resultImage = gr.Apply(resultImage);
Accord.Imaging.ImageStatistics statistics = new Accord.Imaging.ImageStatistics(resultImage);
histogram = statistics.Gray;
mean = histogram.Mean; // mean red value
}
var lbp = new LocalBinaryPattern(blockSize: 3, cellSize: 6);
// Use it to extract descriptors from the Lena image:
var descriptors = lbp.ProcessImage(resultImage);
double av = 0.0;
foreach (var d in descriptors)
{
av = av + d.Average();
}
av = av / descriptors.Count;
rettype ret = new rettype();
ret.histogram = histogram;
ret.lbpavg = av;
ret.histoavg = mean;
return(ret);
}
