Plugin to correct manually pixel wrongly predicted on image by annotation
Project description
napari-pixel-correction
Plugin to correct manually pixel wrongly predicted on image by annotation
This napari plugin was generated with Cookiecutter using @napari's cookiecutter-napari-plugin template.
This plugin allows you to manually correct the images of the apple tree flowers by annotation. Below, a piece of an image shows the predicted pixels (in brown). A pixel in brown is assigned to the flower class. We can see that the brown colour does not necessarily cover a flower in this image.
Installation
You can install napari-pixel-correction
via pip:
pip install napari-pixel-correction
To install latest development version :
pip install git+https://github.com/hereariim/napari-pixel-correction.git
How does it work
First, you need a compressed file (in .zip format) were you have all your images. For a compressed file named as input.zip
, the compressed file should be built like :
.
└── input.zip
└── repository
├── image
│ ├── im_1.JPG
│ ├── im_2.JPG
│ ├── im_3.JPG
│ ...
│ └── im_n.JPG
│
└── mask
├── im_1_mask.JPG
├── im_2_mask.JPG
├── im_3_mask.JPG
...
└── im_n_mask.JPG
In repository, each image folder should have two elements : image in RGB and the segmented mask in binary image (where no-flower class is 0 and flower class is 255)
Contributing
Contributions are very welcome. Tests can be run with tox, please ensure the coverage at least stays the same before you submit a pull request.
License
Distributed under the terms of the BSD-3 license, "napari-pixel-correction" is free and open source software
Issues
If you encounter any problems, please file an issue along with a detailed description.
Project details
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Source Distribution
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Hashes for napari-pixel-correction-0.1.4.tar.gz
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Hashes for napari_pixel_correction-0.1.4-py3-none-any.whl
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