napari-apple
Detection of apple based on YOLOv4-tiny model
This napari plugin was generated with Cookiecutter using @napari's cookiecutter-napari-plugin template.
Installation
Before you can operate the module, you must install the napari-apple module.
Instruction for napari-module
You can install napari-apple via pip:
pip install napari-apple
To install latest development version :
pip install git+https://github.com/hereariim/napari-apple.git
How does it works
Here, user drop its images in the napari windows. The plugin shows two widgets :
- Image detection
- Export data
In Image detection, user select the interesting layer to detect apple. The "Run" button run the inference detection based on Yolov4-tiny model. At the end, the result is displayed on screen. User can correct freely the detection by removing or adding box in image.
In Export data, user export select the interesting shape layer and RGB image. A button "Save to csv" save bounding box coordinate in Yolo way into a text file.
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-apple" is free and open source software
Issues
If you encounter any problems, please file an issue along with a detailed description.
1 maintainer
Metadata
1 maintainer
Release files for napari-apple 0.0.8
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
|---|---|---|---|---|
| napari_apple-0.0.8.tar.gz | 22.0 MB | Without provenance | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| napari_apple-0.0.8-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 43.9 MB
Release files / napari_apple-0.0.8.tar.gz
| Download URL | napari_apple-0.0.8.tar.gz |
|---|---|
| Size | 22.0 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
c0bd460bf229077bbb9ad22a2768ac0c45799196391800f5a4301ae9cd85d624
|
|
BLAKE2b-256 checksum How to use checksums |
8e8d1facfc79b7910ad5cf8d3218665f896d37948fb28af4e017d54d45a2e092
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/5.0.0 CPython/3.8.19
|
Release files / napari_apple-0.0.8-py3-none-any.whl
| Download URL | napari_apple-0.0.8-py3-none-any.whl |
|---|---|
| Size | 22.0 MB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
f8224685b96e9f5587941403b2af1249678baaaad42abb8a11af17dac689fed8
|
|
BLAKE2b-256 checksum How to use checksums |
4c4c791cf991dfadf2899e071ca2e5c2b53bdd6dd65b3ce85a9bd9577f96fbea
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/5.0.0 CPython/3.8.19
|
Security for napari-apple 0.0.8
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
No provenance found
None of the files in this release were uploaded with provenance metadata.
A note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
