Python-based Free Annotator for Images: LabelMe Review

In this video, we review the Free Image annotation tool called LabelMe
Github: github.com/wke...
Commands for Installation:
conda create --name=labelme python=3.6
conda activate labelme
pip install labelme
Commands for Visualization and conversion from json to dataset:
github.com/wke...

[@RohanPaul-AI]

Very informative and well presented. Thanks.

[@SanjayKumar-gv2mo]

Hello I am phase ing probelm in labelimg like I want to annotate the images like polygons tools but in my labelimg only show the rectangle not show any others tool like points polygon's please give me solution

[@epictv8845]

Any opportunity for image annotator away from these Companies such as remotask

[@shruti274]

I have Created 4 labels list in labelme. Each label is in different colors. me because when I output All dataset . The same types of labels that have different colors are found in different output Images. Is there any way to do the same types of polygon labels that have the same colors in others images?

[@AIwithSohini]

Hello and thanks for the query. Yes it is true such that images that contain a chair can be annotated in different colors in two images based on the number of objects in them. However, when you create the masks for each class label, the labels will remain consistent. The coloring is just a way of checking each objects are assigned a different value and are not hard coded for specific values. So when you generate binary masks for each object type, that will stay consistent. Hope this helps! Stay tuned!

[@thijslangius8884]

Thanks for the tutorial. Is there a way to automate the labeling process, once I've labeled a few objects?

[@AIwithSohini]

Thanks for the response. You can check out the V7 video that allows for automated object detection models to auto detect ROIs th-cam.com/video/TAYuwd6N18s/w-d-xo.html. The only way to automate labelling once you have annotated a few images is to train/fine tune an object detection model (like YOLO) and generate automated estimations that can then be corrected by the humans. V7 is a great solution and it is free for academic use. Good luck and stay tuned!

[@RezaAlamsyah-he5ku]

Is v7 free for annotate more than 20.000 data?

[@RezaAlamsyah-he5ku]

​@@AIwithSohiniis v7 free for labelling more than 20.000 data?

[@AIwithSohini]

Hello @@RezaAlamsyah-he5ku, Thanks for the question. I know V7 was free to academic and research purposes, but I am sure there is limitation on data usage. Once you sign up, you will be prompted how to extend your usage base. Hope this helps and good luck!

[@bdai625]

Nice video ,Thanks for sharing ,,,But would you tell us how to get remote data annotator job ? thanks

[@AIwithSohini]

Hello there. That’s a great question. I know that several data annotation companies outsource the actual annotation work. I have seen ads in fiverr and upwork. I don’t endorse any companies so just google for data annotation jobs and apply directly. Good luck and stay tuned.

[@tejagana]

Nice tutorial! But, could you please give me an idea on labelling the concentric circles? like I have objects around another object that is already labelled (like a water formation in and around a ring) Thank you!

[@AIwithSohini]

Hello and thanks for the great question. The answer is yes, as long as you annotate each region with a different class label you can annotate overlapping and concentric regions. So in case of rain droplets your class could be drop 1 vs drop 2 etc. hope this helps.

[@shahriaralom4547]

Thank you mama, but I can rename class names and marge different dataset classes and turn them into a single dataset?

[@AIwithSohini]

Hello and thanks for the great question. Yes you can of course merge datasets and labels but you will have to code that your self. Just iterate over the number of regions of interest and renumber the region ID or pixel value. Good luck.

[@yogaaditya2797]

Hello, i have many data images, and want to convert all of them(json file) to dataset with not just one by one?, can we do that?

[@AIwithSohini]

Hello, there are several json to image converters thy you can use such as products.groupdocs.app/conversion/json-to-jpg

[@yogaaditya2797]

@@AIwithSohini to gray image for semantic segmentation dataset actually

[@AIwithSohini]

@@yogaaditya2797 Hello there, so typically writing your own python script here will be most useful since the outcome depends on the json format. These could be helpful github.com/topics/json-to-png. Good luck and stay tuned!

[@teddyquindayjr3999]

do you know how to install on arch base distro? thank you

[@AIwithSohini]

Thanks for the question. Installing labelme should be the same process. If this does not work please report it to the owners of the GitHub codebase. Link on description

[@iceman1125]

Can you use it to annotate converted dicom image data ?

[@SalaarArifKhan]

Is there a way to resize the images after annotating in LabelMe ??

[@AIwithSohini]

Hello there. Typically the cv2 library in python can do a good job for all resizing and pixel normalization tasks

[@SalaarArifKhan]

@@AIwithSohini thanks but its not just the resizing of the images but also the annotations too. How do i transform the annotations so they work on resized images?

[@AIwithSohini]

@@SalaarArifKhan Hey there. So the keras package, Imagedatagenerator can take care of resizing images and annotations together. Here is the code github.com/sohiniroych/U-net-for-Multi-class-semantic-segmentation/blob/main/Google%20Colab%20Code/code/U-net_binary_DB1.ipynb and checkout my "debugging unet" video for description. Good luck and stay tuned.

[@SalaarArifKhan]

@@AIwithSohini thank you so much!

[@kendalberibadat9355]

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