Giter VIP home page Giter VIP logo

Comments (20)

mosolab avatar mosolab commented on June 6, 2024 2

@alexheat, yes, you are right. I had 0.1.44.
Just checked 0.1.45 and it works and looks great!
Thanks for support!

from pylabel.

alexheat avatar alexheat commented on June 6, 2024 1

@mosolab, I need to find another package that can visualize these kinds of annotations. But in the meantime you can use the site that sudheer recomended, https://app.roboflow.com/. You can upload the annotations and images and you will see a visualization for each image like this.
image

from pylabel.

alexheat avatar alexheat commented on June 6, 2024 1

from pylabel.

Robotatron avatar Robotatron commented on June 6, 2024 1

you are awesome, thanks!

from pylabel.

alexheat avatar alexheat commented on June 6, 2024

Hi @Robotatron I want to look into this. Would you be able to help? Can you share any resources on the Yolo5 segmentation with segmentation samples in other formats like Coco that would make sense to transform into Yolo?

from pylabel.

mosolab avatar mosolab commented on June 6, 2024

Hi! Thanks for tool.
I’m interested too and could provide you examples.
Please let me know how to share it.

from pylabel.

alexheat avatar alexheat commented on June 6, 2024

from pylabel.

Robotatron avatar Robotatron commented on June 6, 2024

@alexheat

Hi @Robotatron I want to look into this. Would you be able to help? Can you share any resources on the Yolo5 segmentation with segmentation samples in other formats like Coco that would make sense to transform into Yolo?

Sure. Here is an official YOLO5 repo to convert COCO segmentation to YOLO5 format (works with YOLO7 as well) https://github.com/ultralytics/JSON2YOLO/blob/master/general_json2yolo.py

If you'd like to test training a YOLO5 for segmentation it's exactly the same as for object detection but using train.py from their "segment" folder from the repo: https://github.com/ultralytics/yolov5/tree/master/segment

Let me know if there is other information I could help with

from pylabel.

alexheat avatar alexheat commented on June 6, 2024

@Robotatron , @mosolab , I have written a proposal for how I will implement the conversion of segmentation conversion. If you have time please take a look and let me know if you have any feedback #72

from pylabel.

alexheat avatar alexheat commented on June 6, 2024

I have release version .44 with support for converting Coco segmentation annotations to Yolo segmentation format. You can see a demonstration in this notebook https://github.com/pylabel-project/samples/blob/main/coco2yolosegmentation.ipynb

from pylabel.

sudheer-palaparambil avatar sudheer-palaparambil commented on June 6, 2024

Thanks @alexheat for your immediate response and time. I downloaded the latest pylabel 0.1.44 and tried with some Coco segmentation annotations (done in makesense.ai). I got some broken annotations in some images. Attached both actual image and PyLabel generated image.
Actual
PyLabel generated

from pylabel.

alexheat avatar alexheat commented on June 6, 2024

Thank you @sudheer-palaparambil , can you explain what you mean by broken images? I do not see what is broken. Do you mean the dotted lines? I don't see how those could have been added by PyLabel because it is only touching the annotations.

Can you provide the steps that you are doing to get those lines and the dataset like you did last time? Also, what tool are you using the rendering the images?

from pylabel.

sudheer-palaparambil avatar sudheer-palaparambil commented on June 6, 2024

Hi @alexheat,

I mean that gap in the bottom annotation line.
I executed the following source.

<---------- Beginning
from pylabel import importer
import os

os.makedirs("data", exist_ok=True)
#Specify path to the coco.json file
path_to_annotations = "data/project-22124.json"
#Specify the path to the images (if they are in a different folder than the annotations)
path_to_images = "images/"

#Import the dataset into the pylable schema
dataset = importer.ImportCoco(path_to_annotations, path_to_images=path_to_images, name="Segmentation")
dataset.df.head(5)

dataset.path_to_annotations = "data/yolo"
dataset.export.ExportToYoloV5(segmentation=True)[1]
<----------- End

I copied the labels generated from training\labels folder and uploaded it along with the images to roboflow. The above images are rendered in roboflow. For annotation I used makesense.ai

The first images in the previous mail is rendred when I uploaded images and Coco-JSON generated by makesense.ai

Thank you

from pylabel.

alexheat avatar alexheat commented on June 6, 2024

I see it now thank you. Can you share the original annotations as well? I want to investigate what is causing the gap by looking at the actual annotation coordinates

from pylabel.

sudheer-palaparambil avatar sudheer-palaparambil commented on June 6, 2024

Herewith attaching the dataset containing original annotations, PyLabel generated annotations and rendered images as well.
Dataset.zip

from pylabel.

mosolab avatar mosolab commented on June 6, 2024

@alexheat also on this now. I could convert it but would like also to visualise labelling.
Could converted labelling be visualised with pylabel?

from pylabel.

alexheat avatar alexheat commented on June 6, 2024

Thank you @sudheer-palaparambil , I have released v45 that fixes the issue. The issue was an extra space at the end of each line. I am not sure it it really broke the annotations or just the visual on roboflow but the issue is fixed. Thank you for your help to test it.

from pylabel.

mosolab avatar mosolab commented on June 6, 2024

@alexheat here is my example. looks like not full connected
Screenshot 2023-01-18 at 20 41 04

from pylabel.

sudheer-palaparambil avatar sudheer-palaparambil commented on June 6, 2024

@alexheat, sorry for the delayed response. It is working pefectly now. Thanks, keep the good work.

from pylabel.

alexheat avatar alexheat commented on June 6, 2024

Thank you everyone

from pylabel.

Related Issues (20)

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google ❤️ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.