public static double MeanSquaredError(Image left, Image right)
{
GuardImages(left, right);
ImagePipeline
.FromImage(left)
.Raw()
.ToBuffer(out var leftBuffer);
ImagePipeline
.FromImage(right)
.Raw()
.ToBuffer(out var rightBuffer);
if (leftBuffer == null || rightBuffer == null)
{
throw new SharpSharpException("Failed to load buffers.");
}
var sumSquared = 0;
for (var y = 0; y < left.Height; y++)
{
for (var x = 0; x < left.Width; x++)
{
var p1 = leftBuffer[y * left.Height + x];
var p2 = rightBuffer[y * left.Height + x];
var error = p2 - p1;
sumSquared += error * error;
}
}
return(sumSquared / (double)(left.Height * left.Width));
}
public static string Fingerprint(Image image)
{
Guard.NotNull(image, nameof(image));
ImagePipeline
.FromImage(image)
.ToBuffer(out var buffer);
return(Convert.ToString((long)DifferenceHash.HashLong(buffer), 2));
}
public static double PeakSignalToNoiseRatio(Image left, Image right)
{
GuardImages(left, right);
ImagePipeline
.FromImage(left)
.Raw()
.ToBuffer(out var leftBuffer);
ImagePipeline
.FromImage(right)
.Raw()
.ToBuffer(out var rightBuffer);
if (leftBuffer == null || rightBuffer == null)
{
throw new SharpSharpException("Failed to load buffers.");
}
double se = 0;
for (var y = 0; y < left.Height; y++)
{
for (var x = 0; x < left.Width; x++)
{
for (var channel = 0; channel < left.Bands; channel++)
{
double e = leftBuffer[y * left.Height + x + channel] - rightBuffer[y * left.Height + x + channel];
se += e * e;
}
}
}
var mse = se / (left.Width * left.Height * left.Bands);
return(10 * Math.Log10(255 * 255 / mse));
}
public static void returns_pipeline() => ImagePipeline.FromImage(Image).ShouldNotBeNull();
public static double StructuralSimilarity(Image left, Image right)
{
GuardImages(left, right);
ImagePipeline
.FromImage(left)
.Raw()
.ToBuffer(out var leftBuffer);
ImagePipeline
.FromImage(right)
.Raw()
.ToBuffer(out var rightBuffer);
if (leftBuffer == null || rightBuffer == null)
{
throw new SharpSharpException("Failed to load buffers.");
}
var k1 = 0.01;
var k2 = 0.03;
var c1 = k1 * 255 * (k1 * 255);
var c2 = k2 * 255 * (k2 * 255);
var c3 = c2 / 2;
double valSum1 = 0;
double valSum2 = 0;
for (var y = 0; y < left.Height; y++)
{
for (var x = 0; x < left.Width; x++)
{
for (var channel = 0; channel < left.Bands; channel++)
{
valSum1 += leftBuffer[y * left.Height + x + channel];
valSum2 += rightBuffer[y * left.Height + x + channel];
}
}
}
var miu1 = valSum1 / (left.Width * left.Height * left.Bands);
var miu2 = valSum2 / (left.Width * left.Height * left.Bands);
double varSum1 = 0;
double varSum2 = 0;
double covSum = 0;
for (var y = 0; y < left.Height; y++)
{
for (var x = 0; x < left.Width; x++)
{
for (var channel = 0; channel < left.Bands; channel++)
{
var e1 = leftBuffer[y * left.Height + x + channel] - miu1;
var e2 = rightBuffer[y * left.Height + x + channel] - miu2;
varSum1 += e1 * e1;
varSum2 += e2 * e2;
covSum += e1 * e2;
}
}
}
var sigma1 = Math.Sqrt(varSum1 / (left.Width * left.Height * left.Bands - 1));
var sigma2 = Math.Sqrt(varSum2 / (left.Width * left.Height * left.Bands - 1));
var sigma12 = covSum / (left.Width * left.Height * left.Bands - 1);
var l = (2 * miu1 * miu2 + c1) / (miu1 * miu1 + miu2 * miu2 + c1);
var c = (2 * sigma1 * sigma2 + c2) / (sigma1 * sigma1 + sigma2 * sigma2 + c2);
var s = (sigma12 + c3) / (sigma1 * sigma2 + c3);
return(l * c * s);
}
