semvec/pypi-packages huggingface dataset:
unveil-python 0.4.0
Implementation of UNVEIL
Keywords: tractography, viewer

# UNVEIL
[![PyPI](https://img.shields.io/pypi/v/unveil-python?label=pypi%20package)](https://pypi.org/project/unveil-python/)
[![PyPI - Downloads](https://img.shields.io/pypi/dm/unveil-python)](https://pypi.org/project/unveil-python/)
![GitHub repo size](https://img.shields.io/github/repo-size/DelinteNicolas/unveil)


A simple tractography viewer implemented in python.

<img width="1297" height="757" alt="image" src="https://github.com/user-attachments/assets/28807e18-17fb-485b-a8ce-2179cdef796b" />


The GUI is launched with the following python command in a python console:

```
import unveil.__main__
```
or with
```
python -c "import unveil.__main__"
```
from a terminal with a python environment activated.
circuitpython-nrf24l01 2.2.0
CircuitPython driver library for the nRF24L01 transceiver
Requires Python: >=3.9
Keywords: blinka, circuitpython, nrf24l01, nrf24l01+, raspberrypi, driver, radio, transceiver


.. image:: https://readthedocs.org/projects/circuitpython-nrf24l01/badge/?version=stable
    :target: https://circuitpython-nrf24l01.readthedocs.io/en/stable/
    :alt: Documentation Status

.. image:: https://github.com/2bndy5/CircuitPython_nRF24L01/workflows/Build%20CI/badge.svg
    :target: https://github.com/2bndy5/CircuitPython_nRF24L01/actions?query=workflow%3A%22Build+CI%22
    :alt: Build Status

.. image:: https://img.shields.io/pypi/v/circuitpython-nrf24l01.svg
    :target: https://pypi.python.org/pypi/circuitpython-nrf24l01
    :alt: latest version on PyPI

.. image:: https://static.pepy.tech/personalized-badge/circuitpython-nrf24l01?period=total&units=international_system&left_color=grey&right_color=blue&left_text=PyPi%20Downloads
    :target: https://pepy.tech/project/circuitpython-nrf24l01
    :alt: Total PyPI downloads

.. image:: https://codecov.io/gh/nRF24/CircuitPython_nRF24L01/branch/master/graph/badge.svg?token=0CKIC3TVB6
    :target: https://codecov.io/gh/nRF24/CircuitPython_nRF24L01
    :alt: Code Coverage

Read The Docs
=============

Documentation for this library is hosted at https://circuitpython-nrf24l01.rtfd.io/

About this Library
==================

This is a Circuitpython driver library for the nRF24L01 transceiver.

Originally this code was a Micropython module written by Damien P. George
& Peter Hinch which can still be found `here
<https://github.com/micropython/micropython-lib/tree/master/micropython/drivers/radio/nrf24l01>`_

The Micropython source has since been rewritten to expose all the nRF24L01's
features and for Circuitpython compatible devices (including linux-based
SoC computers like the Raspberry Pi).
Modified by Brendan Doherty & Rhys Thomas.

* Authors: Damien P. George, Peter Hinch, Rhys Thomas, Brendan Doherty
autodistill-sam3 0.1.0
Segment Anything 3 model for use with Autodistill.
Requires Python: >=3.7

<div align="center">
  <p>
    <a align="center" href="" target="_blank">
      <img
        width="850"
        src="https://media.roboflow.com/open-source/autodistill/autodistill-banner.png"
      >
    </a>
  </p>
</div>

# Autodistill Segment Anything 3 Module

This repository contains the code supporting the Segment Anything 3 base model for use with [Autodistill](https://github.com/autodistill/autodistill).

Segment Anything 3 (SAM3), developed by Meta Research, is a state-of-the-art zero-shot image segmentation model. You can prompt SAM3 with image regions and open text prompts.

Autodistill currently supports using text prompts to auto-label images for use in fine-tuning smaller vision models, such as an RF-DETR object detection model.

The Autodistill SAM 3 package only works on a GPU.

Read the full [Autodistill documentation](https://autodistill.github.io/autodistill/).

Read the [SAM3 Autodistill documentation](https://autodistill.github.io/autodistill/base_models/sam3/).

## Installation

To use SAM3 with Autodistill, you need to install the following dependency:


```bash
pip3 install autodistill-sam3
```

## Quickstart

```python
from autodistill_sam3 import SegmentAnything3
from autodistill.detection import CaptionOntology
from autodistill.helpers import load_image

import supervision as sv

# define an ontology to map class names to our SAM 3 prompt
# the ontology dictionary has the format {caption: class}
# where caption is the prompt sent to the base model, and class is the label that will
# be saved for that caption in the generated annotations
# then, load the model
base_model = SegmentAnything3(
    ontology=CaptionOntology(
        {
            "fruit": "fruit",
            "leaf": "leaf"
        }
    )
)

# run inference on a single iamge
detections = base_model.predict("image.jpg")

image = load_image("image.jpg", return_format="cv2")

# visualise results
label_annotator = sv.LabelAnnotator(text_position=sv.Position.CENTER)
mask_annotator = sv.MaskAnnotator()
annotated_frame = mask_annotator.annotate(
    scene=image.copy(),
    detections=detections
)
annotated_frame = label_annotator.annotate(
    scene=annotated_frame,
    detections=detections,
    labels=[base_model.ontology.classes()[class_id] for class_id in detections.class_id]
)
sv.plot_image(annotated_frame)

# label a folder of images
base_model.label("./images_to_label", extension=".jpeg")
```

## License

SAM 3 is licensed under a custom SAM license. [See the SAM 3 license in the official facebookresearch/sam3 repository](https://github.com/facebookresearch/sam3/blob/main/LICENSE).

## 🏆 Contributing

We love your input! Please see the core Autodistill [contributing guide](https://github.com/autodistill/autodistill/blob/main/CONTRIBUTING.md) to get started. Thank you 🙏 to all our contributors!