04-30-2016، 12:59 PM
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کسی سورس کد ssim را در سی شارپ داره؟
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کسی سورس کد ssim را در سی شارپ داره؟
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امتیاز موضوع:
Title: کد ssim در سی شارپ
حالت موضوعی |
05-01-2016، 05:27 PM
درود
لطفا عکس نمایتون رو تغییر بدید !
کد الگوریتم ssim برای پردازش تصاویر :
[code] // Compute Structural Similarity Index (SSIM) image quality metrics
// See http://www.ece.uwaterloo.ca/~z70wang/research/ssim/
// C# implementation Copyright Chris Lomont 2009-2011
// Send fixes and comments to WWW.LOMONT.ORG
/* History:
* June 2011
* 0.91 - Fix to Bitmap creation to prevent locking file handles
*
* Sept 2009
* 0.9 - Initial Release
*
*
*/
using System;
using System.Drawing;
namespace Lomont
{
class SSIM
{
/// <summary>
/// The version of the SSIM code
/// </summary>
internal static string Version { get { return "0.91";} }
/// <summary>
/// Compute from two linear ushort grayscale images with given size and bitdepth
/// </summary>
/// <param name="img1">Image 1 data</param>
/// <param name="img2">Image 2 data</param>
/// <param name="w">width</param>
/// <param name="h">height</param>
/// <param name="depth">Bit depth (1-16)</param>
/// <returns></returns>
internal double Index(ushort[] img1, ushort[] img2, int w, int h, int depth)
{
L = (1 << depth) - 1;
return ComputeSSIM(ConvertLinear(img1, w, h), ConvertLinear(img2, w, h));
}
/// <summary>
/// Take two System.Drawing.Bitmaps
/// </summary>
/// <param name="img1"></param>
/// <param name="img2"></param>
/// <returns></returns>
internal double Index(Bitmap img1, Bitmap img2)
{
L = 255; // todo - this assumes 8 bit, but color conversion later is always 8 bit, so ok?
return ComputeSSIM(ConvertBitmap(img1), ConvertBitmap(img2));
}
/// <summary>
/// Take two filenames
/// </summary>
/// <param name="filename1"></param>
/// <param name="filename2"></param>
/// <returns></returns>
internal double Index(string filename1, string filename2)
{
using (var b1 = new Bitmap(filename1))
using (var b2 = new Bitmap(filename2))
return Index(b1,b2);
}
#region Implementation
#region Locals
// default settings, names from paper
internal double K1 = 0.01, K2 = 0.03;
internal double L = 255;
readonly Grid window = Gaussian(11, 1.5);
#endregion
/// <summary>
/// Compute the SSIM index of two same sized Grids
/// </summary>
/// <param name="img1">The first Grid</param>
/// <param name="img2">The second Grid</param>
/// <returns>SSIM index</returns>
double ComputeSSIM(Grid img1, Grid img2)
{
// uses notation from paper
// automatic downsampling
int f = (int)Math.Max(1, Math.Round(Math.Min(img1.width,img1.height) / 256.0));
if (f > 1)
{ // downsampling by f
// use a simple low-pass ....... and subsample by f
img1 = SubSample(img1, f);
img2 = SubSample(img2, f);
}
// normalize window - todo - do in window set {}
double scale = 1.0/window.Total;
Grid.Op((i, j) => window[i, j] * scale, window);
// image statistics
var mu1 = .......(img1, window);
var mu2 = .......(img2, window);
var mu1mu2 = mu1 * mu2;
var mu1SQ = mu1 * mu1;
var mu2SQ = mu2 * mu2;
var sigma12 = .......(img1 * img2, window) - mu1mu2;
var sigma1SQ = .......(img1 * img1, window) - mu1SQ;
var sigma2SQ = .......(img2 * img2, window) - mu2SQ;
// constants from the paper
double C1 = K1 * L; C1 *= C1;
double C2 = K2 * L; C2 *= C2;
Grid ssim_map = null;
if ((C1 > 0) && (C2 > 0))
{
ssim_map = Grid.Op((i, j) =>
(2 * mu1mu2[i, j] + C1) * (2 * sigma12[i, j] + C2) /
(mu1SQ[i, j] + mu2SQ[i, j] + C1) / (sigma1SQ[i, j] + sigma2SQ[i, j] + C2),
new Grid(mu1mu2.width, mu1mu2.height));
}
else
{
var num1 = Linear(2, mu1mu2, C1);
var num2 = Linear(2, sigma12, C2);
var den1 = Linear(1, mu1SQ + mu2SQ, C1);
var den2 = Linear(1, sigma1SQ + sigma2SQ, C2);
var den = den1 * den2; // total denominator
ssim_map = new Grid(mu1.width, mu1.height);
for (int i = 0; i < ssim_map.width; ++i)
for (int j = 0; j < ssim_map.height; ++j)
{
ssim_map[i, j] = 1;
if (den[i, j] > 0)
ssim_map[i, j] = num1[i, j] * num2[i, j] / (den1[i, j] * den2[i, j]);
else if ((den1[i, j] != 0) && (den2[i, j] == 0))
ssim_map[i, j] = num1[i, j] / den1[i, j];
}
}
// average all values
return ssim_map.Total / (ssim_map.width * ssim_map.height);
} // ComputeSSIM
#region Grid
/// <summary>
/// Hold a grid of doubles as an array with appropriate operators
/// </summary>
class Grid
{
double[,] data;
internal int width, height;
internal Grid(int w, int h)
{
data = new double[w, h];
width = w;
height = h;
}
/// <summary>
/// Indexer to read the i,j item
/// </summary>
/// <param name="i"></param>
/// <param name="j"></param>
/// <returns></returns>
internal double this[int i, int j]
{
get { return data[i, j]; }
set { data[i, j] = value; }
}
/// <summary>
/// Get the summed value from the Grid
/// </summary>
internal double Total
{
get
{
double s = 0;
foreach (var d in data) s += d;
return s;
}
}
/// <summary>
/// componentwise addition of Grids
/// </summary>
/// <param name="a"></param>
/// <param name="b"></param>
/// <returns></returns>
static public Grid operator+(Grid a, Grid b)
{
return Op((i,j)=>a[i,j]+b[i,j],new Grid(a.width,a.height));
}
/// <summary>
/// componentwise subtraction of Grids
/// </summary>
/// <param name="a"></param>
/// <param name="b"></param>
/// <returns></returns>
static public Grid operator-(Grid a, Grid b)
{
return Op((i, j) => a[i, j] - b[i, j], new Grid(a.width, a.height));
}
/// <summary>
/// componentwise multiplication of Grids
/// </summary>
/// <param name="a"></param>
/// <param name="b"></param>
/// <returns></returns>
static public Grid operator*(Grid a, Grid b)
{
return Op((i, j) => a[i, j] * b[i, j], new Grid(a.width, a.height));
}
/// <summary>
/// componentwise division of Grids
/// </summary>
/// <param name="a"></param>
/// <param name="b"></param>
/// <returns></returns>
static public Grid operator/(Grid a, Grid b)
{
return Op((i, j) => a[i, j] / b[i, j], new Grid(a.width, a.height));
}
/// <summary>
/// Generic function maps (i,j) onto the given grid
/// </summary>
/// <param name="f"></param>
/// <param name="a"></param>
/// <returns></returns>
static internal Grid Op(Func<int,int,double> f,Grid g)
{
int w = g.width, h = g.height;
for (int i = 0; i < w; ++i)
for (int j = 0; j < h; ++j)
g[i, j] = f(i,j);
return g;
}
} // class Grid
#endregion //Grid
/// <summary>
/// Create a gaussian window of the given size and standard deviation
/// </summary>
/// <param name="size">Odd number</param>
/// <param name="sigma">Gaussian std deviation</param>
/// <returns></returns>
static Grid Gaussian(int size, double sigma)
{
var ....... = new Grid(size, size);
double s2 = sigma * sigma, c = (size-1)/2.0, dx, dy;
Grid.Op((i, j) =>
{
dx = i - c;
dy = j - c;
return Math.Exp(-(dx * dx + dy * dy) / (2 * s2));
},
.......);
var scale = 1.0/........Total;
Grid.Op((i, j) => .......[i, j] * scale, .......);
return .......;
}
/// <summary>
/// subsample a grid by step size, averaging each box into the result value
/// </summary>
/// <returns></returns>
static Grid SubSample(Grid img, int skip)
{
int w = img.width;
int h = img.height;
double scale = 1.0 / (skip * skip);
var ans = new Grid(w / skip, h / skip);
for (int i = 0; i < w-skip ; i+=skip)
for (int j = 0; j < h-skip ; j+=skip)
{
double sum = 0;
for (int x = i; x < i + skip; ++x)
for (int y = j; y < j+ skip; ++y)
sum += img[x, y];
ans[i/skip, j/skip] = sum * scale;
}
return ans;
}
/// <summary>
/// Apply ......., return only center part.
/// C = .......(A,B) should be same as matlab filter2( ,'valid')
/// </summary>
/// <returns></returns>
static Grid .......(Grid a, Grid b)
{
#if false
int ax = a.width, ay = a.height;
int bx = b.width, by = b.height;
int bcx = (bx + 1) / 2, bcy = (by + 1) / 2; // center position
var c = new Grid(ax - bx + 1, ay - by + 1);
for (int i = bx - bcx + 1; i < ax - bx; ++i)
for (int j = by - bcy + 1; j < ay - by;
لطفا عکس نمایتون رو تغییر بدید !
کد الگوریتم ssim برای پردازش تصاویر :
[code] // Compute Structural Similarity Index (SSIM) image quality metrics
// See http://www.ece.uwaterloo.ca/~z70wang/research/ssim/
// C# implementation Copyright Chris Lomont 2009-2011
// Send fixes and comments to WWW.LOMONT.ORG
/* History:
* June 2011
* 0.91 - Fix to Bitmap creation to prevent locking file handles
*
* Sept 2009
* 0.9 - Initial Release
*
*
*/
using System;
using System.Drawing;
namespace Lomont
{
class SSIM
{
/// <summary>
/// The version of the SSIM code
/// </summary>
internal static string Version { get { return "0.91";} }
/// <summary>
/// Compute from two linear ushort grayscale images with given size and bitdepth
/// </summary>
/// <param name="img1">Image 1 data</param>
/// <param name="img2">Image 2 data</param>
/// <param name="w">width</param>
/// <param name="h">height</param>
/// <param name="depth">Bit depth (1-16)</param>
/// <returns></returns>
internal double Index(ushort[] img1, ushort[] img2, int w, int h, int depth)
{
L = (1 << depth) - 1;
return ComputeSSIM(ConvertLinear(img1, w, h), ConvertLinear(img2, w, h));
}
/// <summary>
/// Take two System.Drawing.Bitmaps
/// </summary>
/// <param name="img1"></param>
/// <param name="img2"></param>
/// <returns></returns>
internal double Index(Bitmap img1, Bitmap img2)
{
L = 255; // todo - this assumes 8 bit, but color conversion later is always 8 bit, so ok?
return ComputeSSIM(ConvertBitmap(img1), ConvertBitmap(img2));
}
/// <summary>
/// Take two filenames
/// </summary>
/// <param name="filename1"></param>
/// <param name="filename2"></param>
/// <returns></returns>
internal double Index(string filename1, string filename2)
{
using (var b1 = new Bitmap(filename1))
using (var b2 = new Bitmap(filename2))
return Index(b1,b2);
}
#region Implementation
#region Locals
// default settings, names from paper
internal double K1 = 0.01, K2 = 0.03;
internal double L = 255;
readonly Grid window = Gaussian(11, 1.5);
#endregion
/// <summary>
/// Compute the SSIM index of two same sized Grids
/// </summary>
/// <param name="img1">The first Grid</param>
/// <param name="img2">The second Grid</param>
/// <returns>SSIM index</returns>
double ComputeSSIM(Grid img1, Grid img2)
{
// uses notation from paper
// automatic downsampling
int f = (int)Math.Max(1, Math.Round(Math.Min(img1.width,img1.height) / 256.0));
if (f > 1)
{ // downsampling by f
// use a simple low-pass ....... and subsample by f
img1 = SubSample(img1, f);
img2 = SubSample(img2, f);
}
// normalize window - todo - do in window set {}
double scale = 1.0/window.Total;
Grid.Op((i, j) => window[i, j] * scale, window);
// image statistics
var mu1 = .......(img1, window);
var mu2 = .......(img2, window);
var mu1mu2 = mu1 * mu2;
var mu1SQ = mu1 * mu1;
var mu2SQ = mu2 * mu2;
var sigma12 = .......(img1 * img2, window) - mu1mu2;
var sigma1SQ = .......(img1 * img1, window) - mu1SQ;
var sigma2SQ = .......(img2 * img2, window) - mu2SQ;
// constants from the paper
double C1 = K1 * L; C1 *= C1;
double C2 = K2 * L; C2 *= C2;
Grid ssim_map = null;
if ((C1 > 0) && (C2 > 0))
{
ssim_map = Grid.Op((i, j) =>
(2 * mu1mu2[i, j] + C1) * (2 * sigma12[i, j] + C2) /
(mu1SQ[i, j] + mu2SQ[i, j] + C1) / (sigma1SQ[i, j] + sigma2SQ[i, j] + C2),
new Grid(mu1mu2.width, mu1mu2.height));
}
else
{
var num1 = Linear(2, mu1mu2, C1);
var num2 = Linear(2, sigma12, C2);
var den1 = Linear(1, mu1SQ + mu2SQ, C1);
var den2 = Linear(1, sigma1SQ + sigma2SQ, C2);
var den = den1 * den2; // total denominator
ssim_map = new Grid(mu1.width, mu1.height);
for (int i = 0; i < ssim_map.width; ++i)
for (int j = 0; j < ssim_map.height; ++j)
{
ssim_map[i, j] = 1;
if (den[i, j] > 0)
ssim_map[i, j] = num1[i, j] * num2[i, j] / (den1[i, j] * den2[i, j]);
else if ((den1[i, j] != 0) && (den2[i, j] == 0))
ssim_map[i, j] = num1[i, j] / den1[i, j];
}
}
// average all values
return ssim_map.Total / (ssim_map.width * ssim_map.height);
} // ComputeSSIM
#region Grid
/// <summary>
/// Hold a grid of doubles as an array with appropriate operators
/// </summary>
class Grid
{
double[,] data;
internal int width, height;
internal Grid(int w, int h)
{
data = new double[w, h];
width = w;
height = h;
}
/// <summary>
/// Indexer to read the i,j item
/// </summary>
/// <param name="i"></param>
/// <param name="j"></param>
/// <returns></returns>
internal double this[int i, int j]
{
get { return data[i, j]; }
set { data[i, j] = value; }
}
/// <summary>
/// Get the summed value from the Grid
/// </summary>
internal double Total
{
get
{
double s = 0;
foreach (var d in data) s += d;
return s;
}
}
/// <summary>
/// componentwise addition of Grids
/// </summary>
/// <param name="a"></param>
/// <param name="b"></param>
/// <returns></returns>
static public Grid operator+(Grid a, Grid b)
{
return Op((i,j)=>a[i,j]+b[i,j],new Grid(a.width,a.height));
}
/// <summary>
/// componentwise subtraction of Grids
/// </summary>
/// <param name="a"></param>
/// <param name="b"></param>
/// <returns></returns>
static public Grid operator-(Grid a, Grid b)
{
return Op((i, j) => a[i, j] - b[i, j], new Grid(a.width, a.height));
}
/// <summary>
/// componentwise multiplication of Grids
/// </summary>
/// <param name="a"></param>
/// <param name="b"></param>
/// <returns></returns>
static public Grid operator*(Grid a, Grid b)
{
return Op((i, j) => a[i, j] * b[i, j], new Grid(a.width, a.height));
}
/// <summary>
/// componentwise division of Grids
/// </summary>
/// <param name="a"></param>
/// <param name="b"></param>
/// <returns></returns>
static public Grid operator/(Grid a, Grid b)
{
return Op((i, j) => a[i, j] / b[i, j], new Grid(a.width, a.height));
}
/// <summary>
/// Generic function maps (i,j) onto the given grid
/// </summary>
/// <param name="f"></param>
/// <param name="a"></param>
/// <returns></returns>
static internal Grid Op(Func<int,int,double> f,Grid g)
{
int w = g.width, h = g.height;
for (int i = 0; i < w; ++i)
for (int j = 0; j < h; ++j)
g[i, j] = f(i,j);
return g;
}
} // class Grid
#endregion //Grid
/// <summary>
/// Create a gaussian window of the given size and standard deviation
/// </summary>
/// <param name="size">Odd number</param>
/// <param name="sigma">Gaussian std deviation</param>
/// <returns></returns>
static Grid Gaussian(int size, double sigma)
{
var ....... = new Grid(size, size);
double s2 = sigma * sigma, c = (size-1)/2.0, dx, dy;
Grid.Op((i, j) =>
{
dx = i - c;
dy = j - c;
return Math.Exp(-(dx * dx + dy * dy) / (2 * s2));
},
.......);
var scale = 1.0/........Total;
Grid.Op((i, j) => .......[i, j] * scale, .......);
return .......;
}
/// <summary>
/// subsample a grid by step size, averaging each box into the result value
/// </summary>
/// <returns></returns>
static Grid SubSample(Grid img, int skip)
{
int w = img.width;
int h = img.height;
double scale = 1.0 / (skip * skip);
var ans = new Grid(w / skip, h / skip);
for (int i = 0; i < w-skip ; i+=skip)
for (int j = 0; j < h-skip ; j+=skip)
{
double sum = 0;
for (int x = i; x < i + skip; ++x)
for (int y = j; y < j+ skip; ++y)
sum += img[x, y];
ans[i/skip, j/skip] = sum * scale;
}
return ans;
}
/// <summary>
/// Apply ......., return only center part.
/// C = .......(A,B) should be same as matlab filter2( ,'valid')
/// </summary>
/// <returns></returns>
static Grid .......(Grid a, Grid b)
{
#if false
int ax = a.width, ay = a.height;
int bx = b.width, by = b.height;
int bcx = (bx + 1) / 2, bcy = (by + 1) / 2; // center position
var c = new Grid(ax - bx + 1, ay - by + 1);
for (int i = bx - bcx + 1; i < ax - bx; ++i)
for (int j = by - bcy + 1; j < ay - by;
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