How can I create a pixel labelled image for Semantic Segmentation?












1















I am following the Semantic Segmentation Examples tutorial by MathWorks.



I understand that I can load pixel labeled images



pxDir = fullfile(dataDir,'buildingPixelLabels');


Define the class names.



classNames = ["sky" "grass" "building" "sidewalk"];


Define the label ID for each class name.



pixelLabelID = [1 2 3 4];


and create a pixelLabelDatastore.



pxds = pixelLabelDatastore(pxDir,classNames,pixelLabelID);


But, how do I create a custom pixel labelled image where every pixel value represents the categorical label of that pixel?



I would then proceed by writing:



pxDir = fullfile(dataDir,'myCustomPixelLabels');


If I understood correctly, imageDatastore holds the actual image and not the pixel labels for that image.





EDIT:



On my system pxDir points to 'C:Program FilesMATLABR2017atoolboxvisionvisiondatabuildingPixelLabels'. Since I am on Matlab2017a so this example is not included by default, and I cannot compare or view the file to get a better understanding of what I need to do to reproduce this example.










share|improve this question




















  • 1





    Isn't that what the tutorial does just there after the last line you got from it?

    – Ander Biguri
    Nov 22 '18 at 10:29











  • Actually, this is what exactly happening at pxds = pixelLabelDatastore(pxDir,classNames,pixelLabelID);

    – Nouman Riaz Khan
    Nov 22 '18 at 13:19













  • @NoumanRiazKhan but pxDir loads the pixel labeled image, and that is what I'm asking about. How would one go about creating myCustomPixelLabels

    – Rrz0
    Nov 22 '18 at 13:42











  • @AnderBiguri so imds = imageDatastore(imDir); represents the image data where I need to label the pixels? How would this look like since imageDatastore is usually used to manage a collection of image files.

    – Rrz0
    Nov 22 '18 at 13:53











  • @AnderBiguri imageDatastore holds the actual image and not the pixel lables for that image.

    – Rrz0
    Nov 22 '18 at 14:31
















1















I am following the Semantic Segmentation Examples tutorial by MathWorks.



I understand that I can load pixel labeled images



pxDir = fullfile(dataDir,'buildingPixelLabels');


Define the class names.



classNames = ["sky" "grass" "building" "sidewalk"];


Define the label ID for each class name.



pixelLabelID = [1 2 3 4];


and create a pixelLabelDatastore.



pxds = pixelLabelDatastore(pxDir,classNames,pixelLabelID);


But, how do I create a custom pixel labelled image where every pixel value represents the categorical label of that pixel?



I would then proceed by writing:



pxDir = fullfile(dataDir,'myCustomPixelLabels');


If I understood correctly, imageDatastore holds the actual image and not the pixel labels for that image.





EDIT:



On my system pxDir points to 'C:Program FilesMATLABR2017atoolboxvisionvisiondatabuildingPixelLabels'. Since I am on Matlab2017a so this example is not included by default, and I cannot compare or view the file to get a better understanding of what I need to do to reproduce this example.










share|improve this question




















  • 1





    Isn't that what the tutorial does just there after the last line you got from it?

    – Ander Biguri
    Nov 22 '18 at 10:29











  • Actually, this is what exactly happening at pxds = pixelLabelDatastore(pxDir,classNames,pixelLabelID);

    – Nouman Riaz Khan
    Nov 22 '18 at 13:19













  • @NoumanRiazKhan but pxDir loads the pixel labeled image, and that is what I'm asking about. How would one go about creating myCustomPixelLabels

    – Rrz0
    Nov 22 '18 at 13:42











  • @AnderBiguri so imds = imageDatastore(imDir); represents the image data where I need to label the pixels? How would this look like since imageDatastore is usually used to manage a collection of image files.

    – Rrz0
    Nov 22 '18 at 13:53











  • @AnderBiguri imageDatastore holds the actual image and not the pixel lables for that image.

    – Rrz0
    Nov 22 '18 at 14:31














1












1








1








I am following the Semantic Segmentation Examples tutorial by MathWorks.



I understand that I can load pixel labeled images



pxDir = fullfile(dataDir,'buildingPixelLabels');


Define the class names.



classNames = ["sky" "grass" "building" "sidewalk"];


Define the label ID for each class name.



pixelLabelID = [1 2 3 4];


and create a pixelLabelDatastore.



pxds = pixelLabelDatastore(pxDir,classNames,pixelLabelID);


But, how do I create a custom pixel labelled image where every pixel value represents the categorical label of that pixel?



I would then proceed by writing:



pxDir = fullfile(dataDir,'myCustomPixelLabels');


If I understood correctly, imageDatastore holds the actual image and not the pixel labels for that image.





EDIT:



On my system pxDir points to 'C:Program FilesMATLABR2017atoolboxvisionvisiondatabuildingPixelLabels'. Since I am on Matlab2017a so this example is not included by default, and I cannot compare or view the file to get a better understanding of what I need to do to reproduce this example.










share|improve this question
















I am following the Semantic Segmentation Examples tutorial by MathWorks.



I understand that I can load pixel labeled images



pxDir = fullfile(dataDir,'buildingPixelLabels');


Define the class names.



classNames = ["sky" "grass" "building" "sidewalk"];


Define the label ID for each class name.



pixelLabelID = [1 2 3 4];


and create a pixelLabelDatastore.



pxds = pixelLabelDatastore(pxDir,classNames,pixelLabelID);


But, how do I create a custom pixel labelled image where every pixel value represents the categorical label of that pixel?



I would then proceed by writing:



pxDir = fullfile(dataDir,'myCustomPixelLabels');


If I understood correctly, imageDatastore holds the actual image and not the pixel labels for that image.





EDIT:



On my system pxDir points to 'C:Program FilesMATLABR2017atoolboxvisionvisiondatabuildingPixelLabels'. Since I am on Matlab2017a so this example is not included by default, and I cannot compare or view the file to get a better understanding of what I need to do to reproduce this example.







image matlab image-processing machine-learning computer-vision






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share|improve this question













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edited Nov 22 '18 at 19:06







Rrz0

















asked Nov 21 '18 at 22:10









Rrz0Rrz0

487518




487518








  • 1





    Isn't that what the tutorial does just there after the last line you got from it?

    – Ander Biguri
    Nov 22 '18 at 10:29











  • Actually, this is what exactly happening at pxds = pixelLabelDatastore(pxDir,classNames,pixelLabelID);

    – Nouman Riaz Khan
    Nov 22 '18 at 13:19













  • @NoumanRiazKhan but pxDir loads the pixel labeled image, and that is what I'm asking about. How would one go about creating myCustomPixelLabels

    – Rrz0
    Nov 22 '18 at 13:42











  • @AnderBiguri so imds = imageDatastore(imDir); represents the image data where I need to label the pixels? How would this look like since imageDatastore is usually used to manage a collection of image files.

    – Rrz0
    Nov 22 '18 at 13:53











  • @AnderBiguri imageDatastore holds the actual image and not the pixel lables for that image.

    – Rrz0
    Nov 22 '18 at 14:31














  • 1





    Isn't that what the tutorial does just there after the last line you got from it?

    – Ander Biguri
    Nov 22 '18 at 10:29











  • Actually, this is what exactly happening at pxds = pixelLabelDatastore(pxDir,classNames,pixelLabelID);

    – Nouman Riaz Khan
    Nov 22 '18 at 13:19













  • @NoumanRiazKhan but pxDir loads the pixel labeled image, and that is what I'm asking about. How would one go about creating myCustomPixelLabels

    – Rrz0
    Nov 22 '18 at 13:42











  • @AnderBiguri so imds = imageDatastore(imDir); represents the image data where I need to label the pixels? How would this look like since imageDatastore is usually used to manage a collection of image files.

    – Rrz0
    Nov 22 '18 at 13:53











  • @AnderBiguri imageDatastore holds the actual image and not the pixel lables for that image.

    – Rrz0
    Nov 22 '18 at 14:31








1




1





Isn't that what the tutorial does just there after the last line you got from it?

– Ander Biguri
Nov 22 '18 at 10:29





Isn't that what the tutorial does just there after the last line you got from it?

– Ander Biguri
Nov 22 '18 at 10:29













Actually, this is what exactly happening at pxds = pixelLabelDatastore(pxDir,classNames,pixelLabelID);

– Nouman Riaz Khan
Nov 22 '18 at 13:19







Actually, this is what exactly happening at pxds = pixelLabelDatastore(pxDir,classNames,pixelLabelID);

– Nouman Riaz Khan
Nov 22 '18 at 13:19















@NoumanRiazKhan but pxDir loads the pixel labeled image, and that is what I'm asking about. How would one go about creating myCustomPixelLabels

– Rrz0
Nov 22 '18 at 13:42





@NoumanRiazKhan but pxDir loads the pixel labeled image, and that is what I'm asking about. How would one go about creating myCustomPixelLabels

– Rrz0
Nov 22 '18 at 13:42













@AnderBiguri so imds = imageDatastore(imDir); represents the image data where I need to label the pixels? How would this look like since imageDatastore is usually used to manage a collection of image files.

– Rrz0
Nov 22 '18 at 13:53





@AnderBiguri so imds = imageDatastore(imDir); represents the image data where I need to label the pixels? How would this look like since imageDatastore is usually used to manage a collection of image files.

– Rrz0
Nov 22 '18 at 13:53













@AnderBiguri imageDatastore holds the actual image and not the pixel lables for that image.

– Rrz0
Nov 22 '18 at 14:31





@AnderBiguri imageDatastore holds the actual image and not the pixel lables for that image.

– Rrz0
Nov 22 '18 at 14:31












1 Answer
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The answer can be found here.



Matlab 2017a



Go to the APPS tab, and search for Image Labeler, or Training Image Labeler



Click on Add Images to add your training images.



Click on Add ROI Labels to add class names for the regions of interest.



Proceed to select the regions of interest manually from the uploaded images.



Once areas are selected, the data can be exported to workspace as a Ground Truth object.






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    The answer can be found here.



    Matlab 2017a



    Go to the APPS tab, and search for Image Labeler, or Training Image Labeler



    Click on Add Images to add your training images.



    Click on Add ROI Labels to add class names for the regions of interest.



    Proceed to select the regions of interest manually from the uploaded images.



    Once areas are selected, the data can be exported to workspace as a Ground Truth object.






    share|improve this answer






























      1














      The answer can be found here.



      Matlab 2017a



      Go to the APPS tab, and search for Image Labeler, or Training Image Labeler



      Click on Add Images to add your training images.



      Click on Add ROI Labels to add class names for the regions of interest.



      Proceed to select the regions of interest manually from the uploaded images.



      Once areas are selected, the data can be exported to workspace as a Ground Truth object.






      share|improve this answer




























        1












        1








        1







        The answer can be found here.



        Matlab 2017a



        Go to the APPS tab, and search for Image Labeler, or Training Image Labeler



        Click on Add Images to add your training images.



        Click on Add ROI Labels to add class names for the regions of interest.



        Proceed to select the regions of interest manually from the uploaded images.



        Once areas are selected, the data can be exported to workspace as a Ground Truth object.






        share|improve this answer















        The answer can be found here.



        Matlab 2017a



        Go to the APPS tab, and search for Image Labeler, or Training Image Labeler



        Click on Add Images to add your training images.



        Click on Add ROI Labels to add class names for the regions of interest.



        Proceed to select the regions of interest manually from the uploaded images.



        Once areas are selected, the data can be exported to workspace as a Ground Truth object.







        share|improve this answer














        share|improve this answer



        share|improve this answer








        edited Nov 29 '18 at 7:26

























        answered Nov 22 '18 at 19:42









        Rrz0Rrz0

        487518




        487518






























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