| Name | spannotation JSON |
| Version |
0.1.15
JSON |
| download |
| home_page | https://github.com/sof-danny/spannotation |
| Summary | 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. |
| upload_time | 2024-02-24 18:47:53 |
| maintainer | |
| docs_url | None |
| author | Samuel O. Folorunsho |
| requires_python | >=3.6 |
| license | |
| keywords |
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
|
## spannotation
## Description of software
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.
## Key applications of this package include:
1. Row Crop Management: In agricultural settings, such as cornfields, the package can segment row crops, identifying drivable paths for farm machinery and ensuring efficient navigation through the fields.
2. Off-Road Navigation: For off-road scenarios, like dirt tracks, the tool can demarcate navigable paths, assisting in the planning and navigation of off-road vehicles.
3. On-Road Navigation: In typical urban or rural roads, the package can be used to distinguish the actual road (drivable region) from its surroundings (non-drivable regions, including shoulders and adjacent land), aiding in basic navigation tasks.
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](https://github.com/sof-danny/spannotation) page.
Please check the package download statistics:
[](https://pepy.tech/project/spannotation)
[](https://pepy.tech/project/spannotation)
[](https://pepy.tech/project/spannotation)
## Time comparison with dense annotation
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**
## Installation
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 ```
## Usage on Python code Editor
Here's a quick example of how to use spannotation if you are running from a typical code editor like jupyter, VSCode, etc:
# Step 1: Install spannotation
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 ```
# Step 2: Import the Package
In a new cell, import the MaskGenerator class from the package:
``` from Spannotation import MaskGenerator ```
# Step 3: Initialize the Generator
Create an instance of the MaskGenerator:
``` generator = MaskGenerator() ```
# Step 4: Process Images
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.**
## Usage on Command line / Terminal
### Installation
``` 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.
### Process a Single Image
- To process a single image and generate a mask:
1. Run the command: `python3 -m Spannotation.cli`
2. Choose option `1` for a single image.
3. Enter the full path to your image.
4. Enter the full path where the mask should be saved.
### Process Multiple Images in a Folder
- To process multiple images in a folder:
1. Run the command: `python3 -m Spannotation.cli`
2. Choose option `2` for a folder.
3. Enter the full path to your folder containing images.
4. Enter the full path where the masks should be saved.
## Important Notice for GUI Window
When processing images, spannotation uses OpenCV to open a GUI window for point selection. Please note:
- After the first use, the OpenCV window might not automatically come to the foreground for subsequent image processing.
- If the image window does not appear in front, please manually click on the window from your taskbar or window manager.
- This behavior can vary based on your operating system and its window management settings.
## Uninstalling spannotation
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.
## Examples
### Example 1: Row-Crop Segmentation
### Example 2: Off-Road Navigation
## Performing deep learning training using generated mask
Training performance of 1030 images/masks used to train a U-Net model:
Sample semantic segmentation from the model trained with the data:
## Contributing
Contributions to spannotation are welcome! reach out to [Folorunsho Samuel](mailto:folorunshosamuel001@gmail.com) or help resolve the pending [issues]([https://opensource.org/license/mit](https://github.com/sof-danny/spannotation/issues))
## License
spannotation is released under the [MIT License](https://opensource.org/license/mit).
## Project details
Please check the gitup page : [Github](https://github.com/sof-danny/spannotation)
## Contact Info
Please reach out via:
[LinkedIn](https://www.linkedin.com/in/samuelofolorunsho/)
[Email](mailto:folorunshosamuel001@gmail.com)
## Citation
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}
}
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"description": "## spannotation\n\n## Description of software\nThe 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.\n\n## Key applications of this package include:\n\n1. Row Crop Management: In agricultural settings, such as cornfields, the package can segment row crops, identifying drivable paths for farm machinery and ensuring efficient navigation through the fields.\n2. Off-Road Navigation: For off-road scenarios, like dirt tracks, the tool can demarcate navigable paths, assisting in the planning and navigation of off-road vehicles.\n3. On-Road Navigation: In typical urban or rural roads, the package can be used to distinguish the actual road (drivable region) from its surroundings (non-drivable regions, including shoulders and adjacent land), aiding in basic navigation tasks.\n\nThis 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](https://github.com/sof-danny/spannotation) page. \n\nPlease check the package download statistics:\n\n[](https://pepy.tech/project/spannotation)\n\n[](https://pepy.tech/project/spannotation)\n\n[](https://pepy.tech/project/spannotation)\n\n## Time comparison with dense annotation \n\nSpannotation saved average of **30 seconds** on one annotation of an average crop row or dirt road. \n\n**spannotation: 6.03 seconds** \n\n**Regular annotation: 40.39 seconds** \n\n## Installation\nTo install SPAnnotation, simply use pip: \n\n``` pip install spannotation ```\n\nthen to ensure the latest version is installed: \n\n``` pip install --upgrade spannotation ```\n\nor \n\nIf you want to install a specific version:\n\n``` pip install spannotation==0.1.11 ```\n\n\n## Usage on Python code Editor\nHere's a quick example of how to use spannotation if you are running from a typical code editor like jupyter, VSCode, etc:\n\n\n# Step 1: Install spannotation\nFirst, install the package using pip. Run the following command in a cell in your code editor:\n``` pip install spannotation ```\n\n \nthen to ensure the latest version is installed:\n\n``` pip install --upgrade spannotation ```\n\nor \n\nIf you want to install a specific version:\n\n``` pip install spannotation==0.1.11 ```\n\n\n\n# Step 2: Import the Package\nIn a new cell, import the MaskGenerator class from the package:\n\n``` from Spannotation import MaskGenerator ```\n\n# Step 3: Initialize the Generator\nCreate an instance of the MaskGenerator:\n``` generator = MaskGenerator() ```\n\n# Step 4: Process Images\nNow, you can use the generator to process an image or a folder of images.\n\nTo process a single image:\n\n``` generator.process_image('path/to/your/image.jpg', 'path/to/save/mask') ```\n\nTo process all images in a folder:\n\n``` generator.process_folder('path/to/your/images', 'path/to/save/masks') ```\n\nReplace 'path/to/your/image.jpg', 'path/to/your/images', and 'path/to/save/masks' with the actual paths on your system.**\n\n\n## Usage on Command line / Terminal\n### Installation\n``` pip install spannotation ```\n\nthen to ensure the latest version is installed:\n\n``` pip install --upgrade spannotation ```\n\nor \n\nIf you want to install a specific version:\n\n``` pip install spannotation==0.1.9 ```\n\n\n\nAfter installing, you can use Spannotation to process images through the command line.\n\n### Process a Single Image\n- To process a single image and generate a mask:\n 1. Run the command: `python3 -m Spannotation.cli`\n 2. Choose option `1` for a single image.\n 3. Enter the full path to your image.\n 4. Enter the full path where the mask should be saved.\n\n### Process Multiple Images in a Folder\n\n- To process multiple images in a folder:\n 1. Run the command: `python3 -m Spannotation.cli`\n 2. Choose option `2` for a folder.\n 3. Enter the full path to your folder containing images.\n 4. Enter the full path where the masks should be saved.\n\n## Important Notice for GUI Window\n\nWhen processing images, spannotation uses OpenCV to open a GUI window for point selection. Please note:\n\n- After the first use, the OpenCV window might not automatically come to the foreground for subsequent image processing.\n- If the image window does not appear in front, please manually click on the window from your taskbar or window manager.\n- This behavior can vary based on your operating system and its window management settings.\n\n\n## Uninstalling spannotation\nWe hate to see you go but if you have to uninstalll for any reasons. Please use:\n\n``` pip uninstall spannotation ```\n\nThis will remove the spannotation package from your Python environment.\n\n\n## Examples \n### Example 1: Row-Crop Segmentation\n\n\n\n\n### Example 2: Off-Road Navigation\n\n\n\n\n## Performing deep learning training using generated mask \n\nTraining performance of 1030 images/masks used to train a U-Net model:\n\n\n\nSample semantic segmentation from the model trained with the data:\n\n\n\n\n## Contributing\nContributions to spannotation are welcome! reach out to [Folorunsho Samuel](mailto:folorunshosamuel001@gmail.com) or help resolve the pending [issues]([https://opensource.org/license/mit](https://github.com/sof-danny/spannotation/issues))\n\n\n## License\nspannotation is released under the [MIT License](https://opensource.org/license/mit).\n\n## Project details\nPlease check the gitup page : [Github](https://github.com/sof-danny/spannotation)\n\n\n## Contact Info\nPlease reach out via: \n\n[LinkedIn](https://www.linkedin.com/in/samuelofolorunsho/)\n\n[Email](mailto:folorunshosamuel001@gmail.com)\n\n\n## Citation\n\nIf you use spannotation in your research or project, please consider citing it. 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