YOLO Segmentation Predictions to Labelme and Anylabeling-Compatible JSON

08 May 2024 in Blogs

yolosegment2labelme - a Python package that allows you to convert YOLO segmentation prediction results to LabelMe and anylabeling JSON format. This tool facilitates the annotation easy.

Reading_time: 5 min
Tags: [YOLO, ,Annotation, LabelMe, AnyLbaeling, ComputerVision, Json]

• Installing YOLO Segment to LabelMe Converter
• Installing YOLO Segment to LabelMe Converter
  • Prerequisites
  • Installation
    • Specifying a Version
  • Using a Virtual Environment
    • Creating a Virtual Environment with venv
  • Sample Usage
    • Customizing the Model and Confidence Score
    • Run
  • Sample Output in Anylabeling Annotation Tool
• Thanks for Reading!

Table of Contents:

• Setting your environment)
• Sample Usage
• Sample Output in Annotation tool

Installing YOLO Segment to LabelMe Converter

I’m excited to announce the release of a new Python package, yolosegment2labelme, which is now available on PyPI. This tool is specifically designed to streamline the conversion process of YOLO segmentation masks into a format that’s fully compatible with LabelMe and anylabeling, a widely recognized annotation tool for image segmentation tasks. In this guide, I’ll walk you through the installation process and provide you with a clear understanding of how to utilize this package effectively in your projects.

Installing YOLO Segment to LabelMe Converter

This guide will walk you through the process of installing the yolosegment2labelme package, a tool designed to convert YOLO segmentation masks into a format compatible with LabelMe, a popular annotation tool for image segmentation tasks.

The source code for yolosegment2labelme can be found on GitHub, where you can contribute, report issues, or explore the codebase.

Prerequisites

Before you begin, ensure you have the following prerequisites installed on your system:

• Python 3.x
• pip (Python package installer)

Installation

To install the yolosegment2labelme package, you can use the pip command. Open your terminal or command prompt and execute the following command:

python -m pip install yolosegment2labelme

If you’re using Python 3 and have both Python 2 and Python 3 installed, you might need to use python3 instead:

python3 -m pip install yolosegment2labelme

Specifying a Version

If you need a specific version of the package, you can specify it by appending ==<version> to the package name. For example, to install version 0.0.4, you would use:

python -m pip install yolosegment2labelme==0.0.4

Using a Virtual Environment

It’s recommended to use a virtual environment when installing Python packages to avoid conflicts between package versions. You can create a virtual environment using venv (included in Python 3.3 and later) or virtualenv.

Creating a Virtual Environment with venv

1. Create a virtual environment:

python3 -m venv myenv

1. Activate the virtual environment:

• On Windows:

myenv\Scripts\activate

• On macOS and Linux:

source myenv/bin/activate

1. With the virtual environment activated, install the yolosegment2labelme package:

python -m pip install yolosegment2labelme

Sample Usage

To convert segmentation masks from a directory of images using the default model (yolo8n-seg) and a default confidence score of 0.2, you can use the following command:

python yolosegment2labelme.py --images ../images

In this example, replace ../images with the path to the directory containing your YOLO segmentation mask images. The script will process all images in the specified directory, applying the default model and confidence score to generate LabelMe-compatible annotations.

Customizing the Model and Confidence Score

If you prefer to use a specific YOLO model or your custom fine-tuned model and adjust the confidence score for the predictions, you can do so by specifying the --model and --conf options:

python yolosegment2labelme.py --model yolov5n-seg.pt --images ../images --conf 0.3

In this command:

• --model yolov8n-seg.pt specifies the path to the YOLO model file you wish to use. Replace yolov8n-seg.pt with the path to your desired model file.
• --images ../images indicates the directory containing the images you want to process. Replace ../images with the actual path to your image directory.
• --conf 0.3 sets the confidence score threshold for the predictions. Adjust this value according to your needs; higher values require more confident predictions to be considered.

Run

!yolosegment2labelme --model yolov8n-seg.pt --images ../images  --conf 0.3

image 1/10 /Users/aboniasojasingarayar/Documents/Github/yolosegment2labelme/yolosegment2labelme/../images/1.jpg: 448x640 1 bear, 118.4ms
image 2/10 /Users/aboniasojasingarayar/Documents/Github/yolosegment2labelme/yolosegment2labelme/../images/10.jpg: 448x640 1 cup, 1 laptop, 1 cell phone, 2 books, 93.8ms
image 3/10 /Users/aboniasojasingarayar/Documents/Github/yolosegment2labelme/yolosegment2labelme/../images/2.jpg: 640x448 1 dog, 83.3ms
image 4/10 /Users/aboniasojasingarayar/Documents/Github/yolosegment2labelme/yolosegment2labelme/../images/3.jpg: 480x640 4 chairs, 97.9ms
image 5/10 /Users/aboniasojasingarayar/Documents/Github/yolosegment2labelme/yolosegment2labelme/../images/4.jpg: 544x640 2 persons, 2 sports balls, 128.5ms
image 6/10 /Users/aboniasojasingarayar/Documents/Github/yolosegment2labelme/yolosegment2labelme/../images/5.jpg: 448x640 4 persons, 1 sports ball, 3 kites, 81.2ms
image 7/10 /Users/aboniasojasingarayar/Documents/Github/yolosegment2labelme/yolosegment2labelme/../images/6.jpg: 448x640 1 bird, 85.1ms
image 8/10 /Users/aboniasojasingarayar/Documents/Github/yolosegment2labelme/yolosegment2labelme/../images/7.jpg: 416x640 1 person, 1 dog, 84.8ms
image 9/10 /Users/aboniasojasingarayar/Documents/Github/yolosegment2labelme/yolosegment2labelme/../images/8.jpg: 448x640 11 persons, 5 cars, 4 traffic lights, 88.8ms
image 10/10 /Users/aboniasojasingarayar/Documents/Github/yolosegment2labelme/yolosegment2labelme/../images/9.jpg: 448x640 1 person, 79.8ms
Speed: 1.5ms preprocess, 94.1ms inference, 78.1ms postprocess per image at shape (1, 3, 448, 640)
Results saved to runs/segment/predict7
Polygon information saved for /Users/aboniasojasingarayar/Documents/Github/yolosegment2labelme/yolosegment2labelme/../images/1.jpg with label bear
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!python yolosegment2labelme.py --images ../images 

image 1/10 /Users/aboniasojasingarayar/Documents/Github/yolosegment2labelme/yolosegment2labelme/../images/1.jpg: 448x640 1 bear, 111.5ms
image 2/10 /Users/aboniasojasingarayar/Documents/Github/yolosegment2labelme/yolosegment2labelme/../images/10.jpg: 448x640 1 cup, 1 laptop, 1 cell phone, 5 books, 149.6ms
image 3/10 /Users/aboniasojasingarayar/Documents/Github/yolosegment2labelme/yolosegment2labelme/../images/2.jpg: 640x448 1 dog, 92.1ms
image 4/10 /Users/aboniasojasingarayar/Documents/Github/yolosegment2labelme/yolosegment2labelme/../images/3.jpg: 480x640 4 chairs, 101.8ms
image 5/10 /Users/aboniasojasingarayar/Documents/Github/yolosegment2labelme/yolosegment2labelme/../images/4.jpg: 544x640 2 persons, 2 sports balls, 119.7ms
image 6/10 /Users/aboniasojasingarayar/Documents/Github/yolosegment2labelme/yolosegment2labelme/../images/5.jpg: 448x640 6 persons, 1 sports ball, 3 kites, 91.8ms
image 7/10 /Users/aboniasojasingarayar/Documents/Github/yolosegment2labelme/yolosegment2labelme/../images/6.jpg: 448x640 1 bird, 83.4ms
image 8/10 /Users/aboniasojasingarayar/Documents/Github/yolosegment2labelme/yolosegment2labelme/../images/7.jpg: 416x640 1 person, 1 dog, 78.1ms
image 9/10 /Users/aboniasojasingarayar/Documents/Github/yolosegment2labelme/yolosegment2labelme/../images/8.jpg: 448x640 19 persons, 7 cars, 4 traffic lights, 85.0ms
image 10/10 /Users/aboniasojasingarayar/Documents/Github/yolosegment2labelme/yolosegment2labelme/../images/9.jpg: 448x640 1 person, 1 tv, 88.7ms
Speed: 1.1ms preprocess, 100.2ms inference, 72.5ms postprocess per image at shape (1, 3, 448, 640)
Results saved to runs/segment/predict4
Polygon information saved for /Users/aboniasojasingarayar/Documents/Github/yolosegment2labelme/yolosegment2labelme/../images/1.jpg with label bear
Polygon information saved for /Users/aboniasojasingarayar/Documents/Github/yolosegment2labelme/yolosegment2labelme/../images/10.jpg with label cup
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📦 Sample Notebook

Sample Output in Anylabeling Annotation Tool

Below are examples of image annotations created in json using yolosegment2labelme and viewed in the Anylabeling annotation tool:

Sample Image 1	Sample Image 2
Sample Annotation for Image 1	Sample Annotation for Image 2

Sample Image 3	Sample Image 4
Sample Annotation for Image 3	Sample Annotation for Image 4

By following these steps, you should now have the yolosegment2labelme package installed on your system, ready to convert YOLO segmentation masks into LabelMe-compatible formats.Hope this tool will be a helpful asset for researchers and developers working with image segmentation tasks, streamlining the process of preparing data for further analysis or model training.Stay tuned for our upcoming article! Until then, catch you in another exciting article!

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