有没有关于在脚本中使用 "fit image" 函数（DM FitTools）的描述？
Are there any description about using "fit image" function (DM FitTools) in script?
我想在 DM 中集成 "Fit Image" plattlet 提供的功能（尤其是适合给定输入图像的 2D 多项式并减去它）编写脚本，以自动执行整个图像处理流程。
但是，我找不到任何有关如何操作的说明。
如果有人知道，或者有这方面的特定文档，我们将不胜感激。
The script functionality for fitting is not yet officially supported/documented.
但是，您可以使用以下示例来了解命令的工作原理：
命令
Boolean FitGaussian(Image* data, Image* errors, double* N, double* mu, double* sigma, double* chiSqr, double conv_cond)
ImageRef PlotGaussian(Image* data, double N, double mu, double sigma)
Boolean FitLorentzian(Image* data, Image* errors, double* I, double* x0, double* gamma, double* chiSqr, double conv_cond)
ImageRef PlotLorentzian(Image* data, double I, double x0, double gamma)
Boolean FitPolynomial(Image* data, Image* errors, Image* pars, Image* parsToFit, double* chiSqr, double conv_cond)
ImageRef PlotPolynomial(Image* data, Image* pars)
Boolean FitGaussian2D(Image* data, Image* errors, Image* pars, Image* parsToFit, double* chiSqr, double conv_cond)
ImageRef PlotGaussian2D(Image* data, Image* pars)
Boolean FitPolynomial2D(Image* data, Image* errors, Image* pars, Image* parsToFit, double* chiSqr, double conv_cond)
ImageRef PlotPolynomial2D(Image* data, Image* pars)
Boolean FitFormula(dm_string formulaStr, Image* data, Image* errors, Image* pars, Image* parsToFit, double* chiSqr, double conv_cond)
ImageRef PlotFormula(dm_string formulaStr, Image* data, Image* pars)
示例 1，一维公式拟合
// create the input image:
Image input := NewImage("formula test", 2, 100)
input = 500.5 - icol*11.1 + icol*icol*0.11
// add some random noise:
input += (random()-0.5)*sqrt(abs(input))
// create image with error data (not required)
Image errors := input.ImageClone()
errors = tert(input > 1, sqrt(input), 1)
// setup fit:
Image pars := NewImage("pars", 2, 3)
Image parsToFit := NewImage("pars to fit", 2, 3)
pars = 10; // starting values
parsToFit = 1;
Number chiSqr = 1e6
Number conv_cond = 0.00001
Result("\n starting pars = {")
Number xSize = pars.ImageGetDimensionSize(0)
Number i = 0
for (i = 0; i < xSize; i++)
{
Result(GetPixel(pars, i, 0))
if (i < (xSize-1)) Result(", ")
}
Result("}")
// fit:
String formulaStr = "p0 + p1*x + p2*x**2"
Number ok = FitFormula(formulaStr, input, errors, pars, parsToFit, chiSqr, conv_cond)
Result("\n results pars = {")
for (i = 0; i < xSize; i++)
{
Result(GetPixel(pars, i, 0))
if (i < (xSize-1)) Result(", ")
}
Result("}")
Result(", chiSqr ="+ chiSqr)
// plot results of fit:
Image plot := PlotFormula(formulaStr, input, pars)
// compare the plot and original data:
Image compare := NewImage("Compare Fit", 2, 100, 3)
compare[icol, 0] = input // original data
compare[icol, 1] = plot // fit function
compare[icol, 2] = input - plot // residuals
ImageDocument linePlotDoc = CreateImageDocument("Test Fitting")
ImageDisplay linePlotDsp = linePlotDoc.ImageDocumentAddImageDisplay(compare, 3)
linePlotDoc.ImageDocumentShow()
示例 2，二维高斯拟合
// $BACKGROUND$
// create data image
Image img := NewImage("Gaussian2D", 2, 200, 200)
Image true_pars := NewImage("Gaussian2D Pars", 2, 6)
// true parameters
true_pars[0,0] = 1000 // height of gaussian
true_pars[1,0] = 60 // center in x
true_pars[2,0] = 50 // width in x
true_pars[3,0] = 40 // center in y
true_pars[4,0] = 80 // width in y
true_pars[5,0] = 0.7 // rotation in radians
Image data := PlotGaussian2D(img, true_pars)
data += (gaussianrandom())*sqrt(abs(data)) //add noise
ShowImage(data)
Image errors := data.ImageClone()
errors = tert(abs(data) > 1, sqrt(abs(data)), 1)
// starting parameters of fit
Image pars := NewImage("Gaussian2D Pars", 2, 6)
pars = 100
pars[0,0] = max(data) // estimate normalization from peak of data
pars[5,0] = 0 // 100 radians doesn't make sense
Image parsToFit := NewImage("tmp", 2, 6)
parsToFit = 1
Number chiSqr = 1e6
Number conv_cond = 0.00001
Result("\n starting pars = {")
Number xSize = pars.ImageGetDimensionSize(0)
Number i = 0
for (i = 0; i < xSize; i++)
{
Result(GetPixel(pars, i, 0))
if (i < (xSize-1)) Result(", ")
}
Result("}")
// fit
Number ok = FitGaussian2D(data, errors, pars, parsToFit, chiSqr, conv_cond)
if (chiSqr > 2)
ok = FitGaussian2D(data, errors, pars, parsToFit, chiSqr, conv_cond)
Image parDif = 100.0*(pars - true_pars)/true_pars
Result("\n results pars (% dif from true)= {")
for (i = 0; i < xSize; i++)
{
Result(GetPixel(parDif, i, 0))
if (i < (xSize-1)) Result(", ")
}
Result("}")
Result(", chiSqr ="+ chiSqr)
// show residuals
Image residuals := PlotGaussian2D(img, pars)
residuals = data - residuals
ShowImage(residuals)
示例 3，二维多项式拟合
// $BACKGROUND$
// The number of parameters are defined by the order,
// nPar = (order+1)*(order+2)/2. For example, a
// 3rd order poly will have (3+1)*(3+2)/2 = 10 parameters:
//
// x^0 x^1 x^2 x^3
// --------------------------------
// y^0 | p0 p1 p2 p3
// y^1 | p4 p5 p6 -- (the -- terms are higher)
// y^2 | p7 p8 -- -- (order so are ignored )
// y^3 | p9 -- -- --
//
//
// i.e. f(x,y|p) = p0 + p1*x + p2*x^2 + p3*x^3 + p4*y + p5*x*y
// + p6*x^2*y + p7*y^2 + p8*x*y^2 + p9*y^3
Number xImgSize = 512
Number yImgSize = 512 // create data image
Image img := NewImage("Poly2D", 2, xImgSize, yImgSize)
Image pars_true := NewImage("Poly2D Pars", 2, 3, 3)
// true parameters
pars_true[0,0] = 100
pars_true[1,0] = 60
pars_true[2,0] = -0.05
pars_true[0,1] = 70
pars_true[1,1] = 0.01
pars_true[0,2] = -0.1
Image data := PlotPolynomial2D(img, pars_true)
data += (gaussianrandom())*sqrt(abs(data)) //add noise
ShowImage(data)
Image errors := data.ImageClone()
errors = tert(abs(data) > 1, sqrt(abs(data)), 1)
// starting parameters of fit
Image pars := NewImage("Poly2D Pars", 2, 3, 3)
pars = 10
Image parsToFit := NewImage("tmp", 2, 3, 3)
parsToFit = 1
Number chiSqr = 1e6
Number conv_cond = 0.00001
Result("\n starting pars = {")
Number xSize = pars.ImageGetDimensionSize(0)
Number ySize = pars.ImageGetDimensionSize(1)
Number i, j
for (j = 0; j < ySize; j++)
{
if (j > 0) Result(", ")
Result("{")
for (i = 0; i < xSize; i++)
{
if (i > 0) Result(", ")
if ((i+j) > 2)
Result("-")
else
Result(GetPixel(pars, i, j))
}
Result("}")
}
Result("}")
// fit
Number startTicks = GetHighResTickCount()
Number ok = FitPolynomial2D(data, errors, pars, parsToFit, chiSqr, conv_cond)
Number endTicks = GetHighResTickCount()
Number secs = CalcHighResSecondsBetween(startTicks, endTicks)
Image parDif = 100*(pars - pars_true)/pars_true
Result("\n results pars (% diff from true) = {")
for (j = 0; j < ySize; j++)
{
if (j > 0) Result(", ")
Result("{")
for (i = 0; i < xSize; i++)
{
if (i > 0) Result(", ")
if ((i+j) > 2)
Result("-")
else
Result(GetPixel(parDif, i, j))
}
Result("}")
}
Result("}")
Result(", chiSqr = "+ chiSqr)
Result(", Fit Time (s) = " + secs)
// show residuals
Image residuals := PlotPolynomial2D(img, pars)
residuals = data - residuals
ShowImage(residuals)
我想在 DM 中集成 "Fit Image" plattlet 提供的功能（尤其是适合给定输入图像的 2D 多项式并减去它）编写脚本，以自动执行整个图像处理流程。 但是，我找不到任何有关如何操作的说明。
如果有人知道，或者有这方面的特定文档，我们将不胜感激。
The script functionality for fitting is not yet officially supported/documented.
但是，您可以使用以下示例来了解命令的工作原理：
命令
Boolean FitGaussian(Image* data, Image* errors, double* N, double* mu, double* sigma, double* chiSqr, double conv_cond)
ImageRef PlotGaussian(Image* data, double N, double mu, double sigma)
Boolean FitLorentzian(Image* data, Image* errors, double* I, double* x0, double* gamma, double* chiSqr, double conv_cond)
ImageRef PlotLorentzian(Image* data, double I, double x0, double gamma)
Boolean FitPolynomial(Image* data, Image* errors, Image* pars, Image* parsToFit, double* chiSqr, double conv_cond)
ImageRef PlotPolynomial(Image* data, Image* pars)
Boolean FitGaussian2D(Image* data, Image* errors, Image* pars, Image* parsToFit, double* chiSqr, double conv_cond)
ImageRef PlotGaussian2D(Image* data, Image* pars)
Boolean FitPolynomial2D(Image* data, Image* errors, Image* pars, Image* parsToFit, double* chiSqr, double conv_cond)
ImageRef PlotPolynomial2D(Image* data, Image* pars)
Boolean FitFormula(dm_string formulaStr, Image* data, Image* errors, Image* pars, Image* parsToFit, double* chiSqr, double conv_cond)
ImageRef PlotFormula(dm_string formulaStr, Image* data, Image* pars)
示例 1，一维公式拟合
// create the input image:
Image input := NewImage("formula test", 2, 100)
input = 500.5 - icol*11.1 + icol*icol*0.11
// add some random noise:
input += (random()-0.5)*sqrt(abs(input))
// create image with error data (not required)
Image errors := input.ImageClone()
errors = tert(input > 1, sqrt(input), 1)
// setup fit:
Image pars := NewImage("pars", 2, 3)
Image parsToFit := NewImage("pars to fit", 2, 3)
pars = 10; // starting values
parsToFit = 1;
Number chiSqr = 1e6
Number conv_cond = 0.00001
Result("\n starting pars = {")
Number xSize = pars.ImageGetDimensionSize(0)
Number i = 0
for (i = 0; i < xSize; i++)
{
Result(GetPixel(pars, i, 0))
if (i < (xSize-1)) Result(", ")
}
Result("}")
// fit:
String formulaStr = "p0 + p1*x + p2*x**2"
Number ok = FitFormula(formulaStr, input, errors, pars, parsToFit, chiSqr, conv_cond)
Result("\n results pars = {")
for (i = 0; i < xSize; i++)
{
Result(GetPixel(pars, i, 0))
if (i < (xSize-1)) Result(", ")
}
Result("}")
Result(", chiSqr ="+ chiSqr)
// plot results of fit:
Image plot := PlotFormula(formulaStr, input, pars)
// compare the plot and original data:
Image compare := NewImage("Compare Fit", 2, 100, 3)
compare[icol, 0] = input // original data
compare[icol, 1] = plot // fit function
compare[icol, 2] = input - plot // residuals
ImageDocument linePlotDoc = CreateImageDocument("Test Fitting")
ImageDisplay linePlotDsp = linePlotDoc.ImageDocumentAddImageDisplay(compare, 3)
linePlotDoc.ImageDocumentShow()
示例 2，二维高斯拟合
// $BACKGROUND$
// create data image
Image img := NewImage("Gaussian2D", 2, 200, 200)
Image true_pars := NewImage("Gaussian2D Pars", 2, 6)
// true parameters
true_pars[0,0] = 1000 // height of gaussian
true_pars[1,0] = 60 // center in x
true_pars[2,0] = 50 // width in x
true_pars[3,0] = 40 // center in y
true_pars[4,0] = 80 // width in y
true_pars[5,0] = 0.7 // rotation in radians
Image data := PlotGaussian2D(img, true_pars)
data += (gaussianrandom())*sqrt(abs(data)) //add noise
ShowImage(data)
Image errors := data.ImageClone()
errors = tert(abs(data) > 1, sqrt(abs(data)), 1)
// starting parameters of fit
Image pars := NewImage("Gaussian2D Pars", 2, 6)
pars = 100
pars[0,0] = max(data) // estimate normalization from peak of data
pars[5,0] = 0 // 100 radians doesn't make sense
Image parsToFit := NewImage("tmp", 2, 6)
parsToFit = 1
Number chiSqr = 1e6
Number conv_cond = 0.00001
Result("\n starting pars = {")
Number xSize = pars.ImageGetDimensionSize(0)
Number i = 0
for (i = 0; i < xSize; i++)
{
Result(GetPixel(pars, i, 0))
if (i < (xSize-1)) Result(", ")
}
Result("}")
// fit
Number ok = FitGaussian2D(data, errors, pars, parsToFit, chiSqr, conv_cond)
if (chiSqr > 2)
ok = FitGaussian2D(data, errors, pars, parsToFit, chiSqr, conv_cond)
Image parDif = 100.0*(pars - true_pars)/true_pars
Result("\n results pars (% dif from true)= {")
for (i = 0; i < xSize; i++)
{
Result(GetPixel(parDif, i, 0))
if (i < (xSize-1)) Result(", ")
}
Result("}")
Result(", chiSqr ="+ chiSqr)
// show residuals
Image residuals := PlotGaussian2D(img, pars)
residuals = data - residuals
ShowImage(residuals)
示例 3，二维多项式拟合
// $BACKGROUND$
// The number of parameters are defined by the order,
// nPar = (order+1)*(order+2)/2. For example, a
// 3rd order poly will have (3+1)*(3+2)/2 = 10 parameters:
//
// x^0 x^1 x^2 x^3
// --------------------------------
// y^0 | p0 p1 p2 p3
// y^1 | p4 p5 p6 -- (the -- terms are higher)
// y^2 | p7 p8 -- -- (order so are ignored )
// y^3 | p9 -- -- --
//
//
// i.e. f(x,y|p) = p0 + p1*x + p2*x^2 + p3*x^3 + p4*y + p5*x*y
// + p6*x^2*y + p7*y^2 + p8*x*y^2 + p9*y^3
Number xImgSize = 512
Number yImgSize = 512 // create data image
Image img := NewImage("Poly2D", 2, xImgSize, yImgSize)
Image pars_true := NewImage("Poly2D Pars", 2, 3, 3)
// true parameters
pars_true[0,0] = 100
pars_true[1,0] = 60
pars_true[2,0] = -0.05
pars_true[0,1] = 70
pars_true[1,1] = 0.01
pars_true[0,2] = -0.1
Image data := PlotPolynomial2D(img, pars_true)
data += (gaussianrandom())*sqrt(abs(data)) //add noise
ShowImage(data)
Image errors := data.ImageClone()
errors = tert(abs(data) > 1, sqrt(abs(data)), 1)
// starting parameters of fit
Image pars := NewImage("Poly2D Pars", 2, 3, 3)
pars = 10
Image parsToFit := NewImage("tmp", 2, 3, 3)
parsToFit = 1
Number chiSqr = 1e6
Number conv_cond = 0.00001
Result("\n starting pars = {")
Number xSize = pars.ImageGetDimensionSize(0)
Number ySize = pars.ImageGetDimensionSize(1)
Number i, j
for (j = 0; j < ySize; j++)
{
if (j > 0) Result(", ")
Result("{")
for (i = 0; i < xSize; i++)
{
if (i > 0) Result(", ")
if ((i+j) > 2)
Result("-")
else
Result(GetPixel(pars, i, j))
}
Result("}")
}
Result("}")
// fit
Number startTicks = GetHighResTickCount()
Number ok = FitPolynomial2D(data, errors, pars, parsToFit, chiSqr, conv_cond)
Number endTicks = GetHighResTickCount()
Number secs = CalcHighResSecondsBetween(startTicks, endTicks)
Image parDif = 100*(pars - pars_true)/pars_true
Result("\n results pars (% diff from true) = {")
for (j = 0; j < ySize; j++)
{
if (j > 0) Result(", ")
Result("{")
for (i = 0; i < xSize; i++)
{
if (i > 0) Result(", ")
if ((i+j) > 2)
Result("-")
else
Result(GetPixel(parDif, i, j))
}
Result("}")
}
Result("}")
Result(", chiSqr = "+ chiSqr)
Result(", Fit Time (s) = " + secs)
// show residuals
Image residuals := PlotPolynomial2D(img, pars)
residuals = data - residuals
ShowImage(residuals)