Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/alan-turing-institute/scivision
scivision: a framework for scientific image analysis
https://github.com/alan-turing-institute/scivision
computer-vision data-science hut23 hut23-1205 image-processing machine-learning scientific-research
Last synced: about 21 hours ago
JSON representation
scivision: a framework for scientific image analysis
- Host: GitHub
- URL: https://github.com/alan-turing-institute/scivision
- Owner: alan-turing-institute
- License: bsd-3-clause
- Created: 2021-05-13T11:12:52.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2024-10-30T04:26:55.000Z (15 days ago)
- Last Synced: 2024-10-30T06:23:44.706Z (15 days ago)
- Topics: computer-vision, data-science, hut23, hut23-1205, image-processing, machine-learning, scientific-research
- Language: JavaScript
- Homepage: https://sci.vision/
- Size: 32.3 MB
- Stars: 94
- Watchers: 10
- Forks: 40
- Open Issues: 68
-
Metadata Files:
- Readme: README.md
- Contributing: docs/contributing.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Citation: CITATION.cff
Awesome Lists containing this project
README
[](https://github.com/alan-turing-institute/scivision/actions/workflows/scivision.yml)
[](https://scivision.readthedocs.io/en/latest/?badge=latest)
[](https://pypi.org/project/scivision/)
[](#contributors)
[](https://github.com/alan-turing-institute/scivision/blob/main/LICENSE)
[](https://zenodo.org/doi/10.5281/zenodo.10792860)
If you are new to Scivision, start with the [website](https://sci.vision/).
The Scivision project is building:
- A **community** of computer vision practitioners in the sciences and humanities
([join the community on Slack](https://forms.office.com/e/cW28TK4aui))
- A **catalog** of community-curated computer vision [models](https://sci.vision/#/model-grid) and [datasets](https://sci.vision/#/datasource-grid) from the sciences and humanities
- A software **ecosystem of interoperable tools** and utilities for working with computer vision models and data, including:
- **Scivision.Py**, a Python package for conveniently downloading and using the computer vision models and datasets from Python ([Scivision on PyPI](https://pypi.org/project/scivision/))
- **[Pixelflow](https://github.com/alan-turing-institute/pixelflow)**, a tool for extracting information about the characteristics of objects in images
Example use cases for these tools can be found in the [**gallery of notebooks**](https://github.com/scivision-gallery) using Scivision models and datasets
The Scivision project was founded by [the Alan Turing Institute](https://www.turing.ac.uk/).
## Repository contents
This main [project repository on GitHub](https://github.com/alan-turing-institute/scivision) hosts
- development of the Python package (in the root directory)
- development of the website (in `frontend`)
- the documentation sources (in `docs`)
## Get involved
Submit a bug or feature request [here](https://github.com/alan-turing-institute/scivision/issues).
If you would like a link to a model or datasource to be listed in the catalog, such a contribution would be gratefully received. These can be submitted through the [Scivision website](https://sci.vision/#/contribute). See the [Contributing Guide](https://scivision.readthedocs.io/en/latest/contributing.html) for more details on how to format your model / data.
Pull requests for code changes are also welcome.
## Getting Started with Scivision.Py
A quick overview of using the Scivision.Py python package.
### Install Scivision.Py
```sh
$ pip install scivision
```
- [Full installation guide](https://scivision.readthedocs.io/en/latest/user_guide.html#installation)
### Load a Scivision model
```python
from scivision import load_pretrained_model
resnet18 = load_pretrained_model(
# The model URL
"https://github.com/alan-turing-institute/scivision_classifier",
# A Scivision model can contain several variants -- below we select the one to use
model_selection='resnet18',
# Allow the model and its dependencies to be installed if they are not already
# (including tensorflow in this example)
allow_install=True
)
```
We can give an image as input to the model. Any image data compatible with numpy (an 'Array_like') is accepted.
We can obtain some image data by loading a Scivision datasource.
- [More about Scivision models](https://scivision.readthedocs.io/en/latest/model_repository_template.html)
### Load a Scivision datasource
```python
from scivision import load_pretrained_model
dataset = load_dataset('https://github.com/alan-turing-institute/scivision-test-data')
# 'dataset' provides several named arrays. This datasource provides one named 'test_image':
# the keys can be looked up with `list(dataset)` (or by consulting the datasource documentation)
#
test_image = dataset['test_image'].read()
```
Optionally, inspect the image (with matplotlib, for example):
```python
import matplotlib.pyplot as plt
plt.imshow(test_image)
```
- [More about datasources](https://scivision.readthedocs.io/en/latest/data_repository_template.html)
### Run a Scivision model
```python
resnet18.predict(test_image)
```
Output: `koala : 99.78%`
### Query the model and datasource catalogs
```python
from scivision import default_catalog
# The datasource catalog as a Pandas dataframe
default_catalog.datasources.to_dataframe()
# Similarly for the model catalog
default_catalog.models.to_dataframe()
```
Output:
| | name | description | tasks | url | pkg_url | format | scivision_usable | pretrained | labels_required | institution | tags |
|---:|:---------|:---------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------|:----------------------------------------------------|:---------|:-------------------|:-------------|:------------------|:--------------------|:--------------------------------------------------------------------------------------------------------------------------------------|
| 0 | stardist | Single class object detection and segementation of star-convex polygons | (, ) | https://github.com/stardist/stardist | git+https://github.com/stardist/stardist.git@master | image | False | True | True | ('epfl',) | ('2D', '3D', 'optical-microscopy', 'xray', 'microtomography', 'cell-counting', 'plant-phenotyping', 'climate-change-and-agriculture') |
| 1 | PlantCV | Open-source image analysis software package targeted for plant phenotyping | (, , ) | https://github.com/danforthcenter/plantcv | git+https://github.com/danforthcenter/plantcv@main | image | False | True | True | ('danforthcenter',) | ('2D', 'hyperspectral', 'multispectral', 'near-infrared', 'infrared', 'plant-phenotyping', 'climate-change-and-agriculture') |
| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |
The catalogs are browsable online:
- [model catalog](https://sci.vision/#/model-grid)
- [datasource catalog](https://sci.vision/#/datasource-grid)
## Contributors
Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/docs/en/emoji-key)):
Aida Mehonic
📋 📖 🤔
Alan R Lowe
💻 🤔 📖 🚇 🔍
Alden Conner
🤔 📖 🎨 📋 📣 📆
Alejandro ©
💻 🤔 🎨 💡
Beatriz Costa Gomes
💻 🤔 📖 🎨 💡
Ben Evans
🤔
Ed Chalstrey
💻 🤔 📖 🚇
Eriol Fox
🤔 🎨
Evangeline Corcoran
💻 🤔 📖 🚇
Isabel Fenton
💻 🤔 📖 🚇
James Parkhurst
🤔 🔣 🔌
JamesAliScott
🤔 🔣
Kasra Hosseini
💻 🤔 📖 🚇
Martin Rogers
🔣 💡 💻 🤔
Miquel Massot
💻 🤔 📖 🔌
Robert Blackwell
🤔
Samuel Tonks
💻 🤔 📖 🚇
Scott Hosking
🔍 🤔
Seb Hickman
💡 📢
louisavz
🤔 📣 📝
nbarlowATI
🤔 📋 💡
ots22
💻 🤔 📖 🚇
pwochner
🤔 📋 💡
vimode
🤔 🎨 💻 ️️️️♿️
This project follows the [all-contributors](https://github.com/all-contributors/all-contributors) specification. Contributions of any kind welcome!