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This Python package offers a simple yet powerful tool for image segmentation, particularly useful for navigation and path detection tasks in various environments. Utilizing minimal user input, it allows for the efficient segmentation of images into 'drivable' and 'non-drivable' regions, making it an ideal solution for a range of applications from agriculture to autonomous navigation.
Readme
The package introduces a novel approach to image annotation and segmentation, requiring users to select just three key points on an image. These points define a triangular area representing the region of interest, which the algorithm then uses to generate a binary mask. This mask distinctly categorizes the image into two classes: drivable and non-drivable regions.
This tool is particularly useful for researchers and practitioners in autonomous vehicle navigation, agricultural robotics, and geographic information systems (GIS), where accurate and efficient image segmentation is critical.Please read more about the project on the Github page.
Please check the package download statistics:
Spannotation saved average of 30 seconds on one annotation of an average crop row or dirt road.
spannotation: 6.03 seconds
Regular annotation: 40.39 seconds
To install SPAnnotation, simply use pip:
pip install spannotation
then to ensure the latest version is installed:
pip install --upgrade spannotation
or
If you want to install a specific version:
pip install spannotation==0.1.11
Here's a quick example of how to use spannotation if you are running from a typical code editor like jupyter, VSCode, etc:
First, install the package using pip. Run the following command in a cell in your code editor:
pip install spannotation
then to ensure the latest version is installed:
pip install --upgrade spannotation
or
If you want to install a specific version:
pip install spannotation==0.1.11
In a new cell, import the MaskGenerator class from the package:
from Spannotation import MaskGenerator
Create an instance of the MaskGenerator:
generator = MaskGenerator()
Now, you can use the generator to process an image or a folder of images.
To process a single image:
generator.process_image('path/to/your/image.jpg', 'path/to/save/mask')
To process all images in a folder:
generator.process_folder('path/to/your/images', 'path/to/save/masks')
Replace 'path/to/your/image.jpg', 'path/to/your/images', and 'path/to/save/masks' with the actual paths on your system.**
pip install spannotation
then to ensure the latest version is installed:
pip install --upgrade spannotation
or
If you want to install a specific version:
pip install spannotation==0.1.9
After installing, you can use Spannotation to process images through the command line.
python3 -m Spannotation.cli
1
for a single image.python3 -m Spannotation.cli
2
for a folder.When processing images, spannotation uses OpenCV to open a GUI window for point selection. Please note:
We hate to see you go but if you have to uninstalll for any reasons. Please use:
pip uninstall spannotation
This will remove the spannotation package from your Python environment.
Training performance of 1030 images/masks used to train a U-Net model:
Sample semantic segmentation from the model trained with the data:
Contributions to spannotation are welcome! reach out to Folorunsho Samuel or help resolve the pending issues
spannotation is released under the MIT License.
Please check the gitup page : Github
Please reach out via:
If you use spannotation in your research or project, please consider citing it. Here is an example citation format you can use:
@misc{spannotation, author = {Samuel Folorunsho}, title = {spannotation: Efficient Image Segmentation for Navigation Tasks}, year = {2024}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {https://github.com/sof-danny/spannotation} }
FAQs
This Python package offers a simple yet powerful tool for image segmentation, particularly useful for navigation and path detection tasks in various environments. Utilizing minimal user input, it allows for the efficient segmentation of images into 'drivable' and 'non-drivable' regions, making it an ideal solution for a range of applications from agriculture to autonomous navigation.
We found that spannotation demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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