Using image data to create a 3D Histogram

I have a black and white .tiff file of a beam spot:

Beam profile

I am trying to characterize the source in a Monte Carlo program, to do this I want to convert the image into a 3D Histogram with controllable bin size.

Haven't been able to figure out how to convert the image data to match Histogram3D input parameters, closest I've gotten so far are 3D plots using:

ListPlot3D[Reverse@ImageData@RemoveAlphaChannel@ColorConvert[beam1, "Grayscale"], AxesLabel -> {"x", "y", "intensity"}, PlotRange -> All]

Any pointers on how to go about this? Thanks!

asked Nov 20 '18 at 18:31

avocado_gradient

405

add a comment |

I have a black and white .tiff file of a beam spot:

Beam profile

I am trying to characterize the source in a Monte Carlo program, to do this I want to convert the image into a 3D Histogram with controllable bin size.

Haven't been able to figure out how to convert the image data to match Histogram3D input parameters, closest I've gotten so far are 3D plots using:

ListPlot3D[Reverse@ImageData@RemoveAlphaChannel@ColorConvert[beam1, "Grayscale"], AxesLabel -> {"x", "y", "intensity"}, PlotRange -> All]

Any pointers on how to go about this? Thanks!

asked Nov 20 '18 at 18:31

avocado_gradient

405

add a comment |

I have a black and white .tiff file of a beam spot:

Beam profile

I am trying to characterize the source in a Monte Carlo program, to do this I want to convert the image into a 3D Histogram with controllable bin size.

Haven't been able to figure out how to convert the image data to match Histogram3D input parameters, closest I've gotten so far are 3D plots using:

ListPlot3D[Reverse@ImageData@RemoveAlphaChannel@ColorConvert[beam1, "Grayscale"], AxesLabel -> {"x", "y", "intensity"}, PlotRange -> All]

Any pointers on how to go about this? Thanks!

asked Nov 20 '18 at 18:31

avocado_gradient

405

I have a black and white .tiff file of a beam spot:

Beam profile

I am trying to characterize the source in a Monte Carlo program, to do this I want to convert the image into a 3D Histogram with controllable bin size.

Haven't been able to figure out how to convert the image data to match Histogram3D input parameters, closest I've gotten so far are 3D plots using:

ListPlot3D[Reverse@ImageData@RemoveAlphaChannel@ColorConvert[beam1, "Grayscale"], AxesLabel -> {"x", "y", "intensity"}, PlotRange -> All]

Any pointers on how to go about this? Thanks!

plotting image-processing histograms

asked Nov 20 '18 at 18:31

avocado_gradient

405

asked Nov 20 '18 at 18:31

avocado_gradient

405

asked Nov 20 '18 at 18:31

avocado_gradient

405

asked Nov 20 '18 at 18:31

avocado_gradient

405

asked Nov 20 '18 at 18:31

avocado_gradient

405

add a comment |

2 Answers
2

active

oldest

votes

Method 1: `Histogram3D`

Import and clean the image:

i = Import["https://i.stack.imgur.com/uGWn6.jpg"];

i = RemoveAlphaChannel@ColorConvert[i, "Grayscale"]

Histogram3D looks at the number of points in the bins, but that is uniform for an image as indexes of pixels are uniform. So I guess besides that you want data weighted by pixel values:

dataW=

WeightedData[

    Flatten[Array[List,Reverse@ImageDimensions[i]],1],

    Flatten[ImageData[i],1]

]

Now you can bin in a custom way:

Histogram3D[dataW, {50, 60}, ColorFunction -> "Rainbow"]

enter image description here

With a little bit of style you can get it looking quite any way you want:

Histogram3D[dataW,{30,35},

    ChartElementFunction->

    ChartElementDataFunction["ProfileCube","Profile"->2.,"TaperRatio"->0.6],

    ColorFunction->"Rainbow",ChartStyle->Opacity[.5],PlotTheme->"Marketing"]

enter image description here

Method 2: `ListPlot3D`

You could achieve a similar result differently, in a bit hack-ish way. Define data as:

data = ImageData[ImageResize[i, {30, 30}]];

so ImageResize spec 30x30 gives effective number of bins in X and Y directions. Resizing an image is effectively binning (in your specific case).

ListPlot3D[data,PlotRange->{{5,25},{10,25},All},InterpolationOrder->0,

ColorFunction->"Rainbow",Filling->Bottom,Mesh->None]

enter image description here

edited Nov 20 '18 at 20:56

answered Nov 20 '18 at 19:59

Vitaliy Kaurov

57k6158278

add a comment |

Add another data processing method

im1=RemoveAlphaChannel[

        ColorConvert[Import["https://i.stack.imgur.com/uGWn6.jpg"], 

         "Grayscale"]];

    data = ImageData[im1];

    dim = Dimensions[data];

    A = Flatten[

       Table[{i, j}, {i, dim[[2]]}, {j, dim[[1]]}, {k, 

         PixelValue[im1, {i, j}, "Byte"]}], 2];



Histogram3D[A, {{.5}, {2}}, ColorFunction -> Hue]

fig1

answered Nov 20 '18 at 21:17

Alex Trounev

6,1201419

add a comment |

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2 Answers
2

active

oldest

votes

2 Answers
2

active

oldest

votes

Method 1: `Histogram3D`

Import and clean the image:

i = Import["https://i.stack.imgur.com/uGWn6.jpg"];

i = RemoveAlphaChannel@ColorConvert[i, "Grayscale"]

Histogram3D looks at the number of points in the bins, but that is uniform for an image as indexes of pixels are uniform. So I guess besides that you want data weighted by pixel values:

dataW=

WeightedData[

    Flatten[Array[List,Reverse@ImageDimensions[i]],1],

    Flatten[ImageData[i],1]

]

Now you can bin in a custom way:

Histogram3D[dataW, {50, 60}, ColorFunction -> "Rainbow"]

enter image description here

With a little bit of style you can get it looking quite any way you want:

Histogram3D[dataW,{30,35},

    ChartElementFunction->

    ChartElementDataFunction["ProfileCube","Profile"->2.,"TaperRatio"->0.6],

    ColorFunction->"Rainbow",ChartStyle->Opacity[.5],PlotTheme->"Marketing"]

enter image description here

Method 2: `ListPlot3D`

You could achieve a similar result differently, in a bit hack-ish way. Define data as:

data = ImageData[ImageResize[i, {30, 30}]];

so ImageResize spec 30x30 gives effective number of bins in X and Y directions. Resizing an image is effectively binning (in your specific case).

ListPlot3D[data,PlotRange->{{5,25},{10,25},All},InterpolationOrder->0,

ColorFunction->"Rainbow",Filling->Bottom,Mesh->None]

enter image description here

edited Nov 20 '18 at 20:56

answered Nov 20 '18 at 19:59

Vitaliy Kaurov

57k6158278

add a comment |

Method 1: `Histogram3D`

Import and clean the image:

i = Import["https://i.stack.imgur.com/uGWn6.jpg"];

i = RemoveAlphaChannel@ColorConvert[i, "Grayscale"]

Histogram3D looks at the number of points in the bins, but that is uniform for an image as indexes of pixels are uniform. So I guess besides that you want data weighted by pixel values:

dataW=

WeightedData[

    Flatten[Array[List,Reverse@ImageDimensions[i]],1],

    Flatten[ImageData[i],1]

]

Now you can bin in a custom way:

Histogram3D[dataW, {50, 60}, ColorFunction -> "Rainbow"]

enter image description here

With a little bit of style you can get it looking quite any way you want:

Histogram3D[dataW,{30,35},

    ChartElementFunction->

    ChartElementDataFunction["ProfileCube","Profile"->2.,"TaperRatio"->0.6],

    ColorFunction->"Rainbow",ChartStyle->Opacity[.5],PlotTheme->"Marketing"]

enter image description here

Method 2: `ListPlot3D`

You could achieve a similar result differently, in a bit hack-ish way. Define data as:

data = ImageData[ImageResize[i, {30, 30}]];

so ImageResize spec 30x30 gives effective number of bins in X and Y directions. Resizing an image is effectively binning (in your specific case).

ListPlot3D[data,PlotRange->{{5,25},{10,25},All},InterpolationOrder->0,

ColorFunction->"Rainbow",Filling->Bottom,Mesh->None]

enter image description here

edited Nov 20 '18 at 20:56

answered Nov 20 '18 at 19:59

Vitaliy Kaurov

57k6158278

add a comment |

Method 1: `Histogram3D`

Import and clean the image:

i = Import["https://i.stack.imgur.com/uGWn6.jpg"];

i = RemoveAlphaChannel@ColorConvert[i, "Grayscale"]

Histogram3D looks at the number of points in the bins, but that is uniform for an image as indexes of pixels are uniform. So I guess besides that you want data weighted by pixel values:

dataW=

WeightedData[

    Flatten[Array[List,Reverse@ImageDimensions[i]],1],

    Flatten[ImageData[i],1]

]

Now you can bin in a custom way:

Histogram3D[dataW, {50, 60}, ColorFunction -> "Rainbow"]

enter image description here

With a little bit of style you can get it looking quite any way you want:

Histogram3D[dataW,{30,35},

    ChartElementFunction->

    ChartElementDataFunction["ProfileCube","Profile"->2.,"TaperRatio"->0.6],

    ColorFunction->"Rainbow",ChartStyle->Opacity[.5],PlotTheme->"Marketing"]

enter image description here

Method 2: `ListPlot3D`

You could achieve a similar result differently, in a bit hack-ish way. Define data as:

data = ImageData[ImageResize[i, {30, 30}]];

so ImageResize spec 30x30 gives effective number of bins in X and Y directions. Resizing an image is effectively binning (in your specific case).

ListPlot3D[data,PlotRange->{{5,25},{10,25},All},InterpolationOrder->0,

ColorFunction->"Rainbow",Filling->Bottom,Mesh->None]

enter image description here

edited Nov 20 '18 at 20:56

answered Nov 20 '18 at 19:59

Vitaliy Kaurov

57k6158278

Method 1: `Histogram3D`

Import and clean the image:

i = Import["https://i.stack.imgur.com/uGWn6.jpg"];

i = RemoveAlphaChannel@ColorConvert[i, "Grayscale"]

Histogram3D looks at the number of points in the bins, but that is uniform for an image as indexes of pixels are uniform. So I guess besides that you want data weighted by pixel values:

dataW=

WeightedData[

    Flatten[Array[List,Reverse@ImageDimensions[i]],1],

    Flatten[ImageData[i],1]

]

Now you can bin in a custom way:

Histogram3D[dataW, {50, 60}, ColorFunction -> "Rainbow"]

enter image description here

With a little bit of style you can get it looking quite any way you want:

Histogram3D[dataW,{30,35},

    ChartElementFunction->

    ChartElementDataFunction["ProfileCube","Profile"->2.,"TaperRatio"->0.6],

    ColorFunction->"Rainbow",ChartStyle->Opacity[.5],PlotTheme->"Marketing"]

enter image description here

Method 2: `ListPlot3D`

You could achieve a similar result differently, in a bit hack-ish way. Define data as:

data = ImageData[ImageResize[i, {30, 30}]];

so ImageResize spec 30x30 gives effective number of bins in X and Y directions. Resizing an image is effectively binning (in your specific case).

ListPlot3D[data,PlotRange->{{5,25},{10,25},All},InterpolationOrder->0,

ColorFunction->"Rainbow",Filling->Bottom,Mesh->None]

enter image description here

edited Nov 20 '18 at 20:56

answered Nov 20 '18 at 19:59

Vitaliy Kaurov

57k6158278

edited Nov 20 '18 at 20:56

answered Nov 20 '18 at 19:59

Vitaliy Kaurov

57k6158278

answered Nov 20 '18 at 19:59

Vitaliy Kaurov

57k6158278

answered Nov 20 '18 at 19:59

Vitaliy Kaurov

57k6158278

add a comment |

Add another data processing method

im1=RemoveAlphaChannel[

        ColorConvert[Import["https://i.stack.imgur.com/uGWn6.jpg"], 

         "Grayscale"]];

    data = ImageData[im1];

    dim = Dimensions[data];

    A = Flatten[

       Table[{i, j}, {i, dim[[2]]}, {j, dim[[1]]}, {k, 

         PixelValue[im1, {i, j}, "Byte"]}], 2];



Histogram3D[A, {{.5}, {2}}, ColorFunction -> Hue]

fig1

answered Nov 20 '18 at 21:17

Alex Trounev

6,1201419

add a comment |

Add another data processing method

im1=RemoveAlphaChannel[

        ColorConvert[Import["https://i.stack.imgur.com/uGWn6.jpg"], 

         "Grayscale"]];

    data = ImageData[im1];

    dim = Dimensions[data];

    A = Flatten[

       Table[{i, j}, {i, dim[[2]]}, {j, dim[[1]]}, {k, 

         PixelValue[im1, {i, j}, "Byte"]}], 2];



Histogram3D[A, {{.5}, {2}}, ColorFunction -> Hue]

fig1

answered Nov 20 '18 at 21:17

Alex Trounev

6,1201419

add a comment |

Add another data processing method

im1=RemoveAlphaChannel[

        ColorConvert[Import["https://i.stack.imgur.com/uGWn6.jpg"], 

         "Grayscale"]];

    data = ImageData[im1];

    dim = Dimensions[data];

    A = Flatten[

       Table[{i, j}, {i, dim[[2]]}, {j, dim[[1]]}, {k, 

         PixelValue[im1, {i, j}, "Byte"]}], 2];



Histogram3D[A, {{.5}, {2}}, ColorFunction -> Hue]

fig1

answered Nov 20 '18 at 21:17

Alex Trounev

6,1201419

Add another data processing method

im1=RemoveAlphaChannel[

        ColorConvert[Import["https://i.stack.imgur.com/uGWn6.jpg"], 

         "Grayscale"]];

    data = ImageData[im1];

    dim = Dimensions[data];

    A = Flatten[

       Table[{i, j}, {i, dim[[2]]}, {j, dim[[1]]}, {k, 

         PixelValue[im1, {i, j}, "Byte"]}], 2];



Histogram3D[A, {{.5}, {2}}, ColorFunction -> Hue]

fig1

answered Nov 20 '18 at 21:17

Alex Trounev

6,1201419

answered Nov 20 '18 at 21:17

Alex Trounev

6,1201419

answered Nov 20 '18 at 21:17

Alex Trounev

6,1201419

answered Nov 20 '18 at 21:17

Alex Trounev

6,1201419

add a comment |

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