https://github.com/jss95/araviq6
Python package for NDArray-based processing of QVideoFrame with Qt6.
https://github.com/jss95/araviq6
image-processing numpy pyqt6 pyside6 python qt6
Last synced: 8 months ago
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Python package for NDArray-based processing of QVideoFrame with Qt6.
- Host: GitHub
- URL: https://github.com/jss95/araviq6
- Owner: JSS95
- License: mit
- Created: 2022-06-04T18:09:26.000Z (about 4 years ago)
- Default Branch: master
- Last Pushed: 2023-09-26T06:45:56.000Z (over 2 years ago)
- Last Synced: 2024-10-12T01:21:34.258Z (over 1 year ago)
- Topics: image-processing, numpy, pyqt6, pyside6, python, qt6
- Language: Python
- Homepage: https://araviq6.readthedocs.io/
- Size: 1.24 MB
- Stars: 9
- Watchers: 1
- Forks: 2
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# AraViQ6 - NDArray-based Video processing with Qt6
[](https://badge.fury.io/py/AraViQ6)
[](https://pypi.org/project/araviq6/)
[](https://github.com/JSS95/araviq6/actions/workflows/ci.yml)
[](https://araviq6.readthedocs.io/en/latest/?badge=latest)
[](https://github.com/JSS95/araviq6/blob/master/LICENSE)
AraViQ6 is a Python package which integrates NDArray-based image processing with video pipeline of Qt6.
It provides:
- QVideoFrame processor based on array processing
- Converters between NDArray and QVideoFrame
- Convenience classes and widgets for array displaying
The following Qt bindings are supported:
- [PySide6](https://pypi.org/project/PySide6/)
- [PyQt6](https://pypi.org/project/PyQt6/)
# How to use
There are two ways to use AraViQ6; using QVideoFrame-based pipeline, or using NDArray-based pipeline.
## Frame-based pipeline
Frame-based pipeline is a high-level approach that works well with Qt Multimedia scheme.
QVideoFrame pipeline with AraViQ6
Frame-based pipeline consists of:
1. `VideoFrameWorker`
2. `VideoFrameProcessor`
QVideoFrame comes from and goes to Qt6's `QVideoSink`. AraViQ6's
`VideoFrameWorker` converts QVideoFrame to numpy array, performs processing, and sends the results to downstream in both QVideoFrame and NDArray. User may subclass the worker to define own processing.
`VideoFrameProcessor` wraps the worker and provides API around it.
Worker is mulithreaded in the processor.
## Array-based pipeline
Array-based pipeline is a low-level approach.
It can be useful when third-party package provides video frame in numpy array format.
NDArray pipeline with AraViQ6
Array-based pipeline consists of:
1. `FrameToArrayConverter`
2. `ArrayWorker`
3. `ArrayProcessor`
4. `ArrayToFrameConverter`
`FrameToArrayConverter` and `ArrayToFrameConverter` performs conversion between frame pipeline and array pipeline.
To retain the metadata (e.g., timestamp) of QVideoFrame, these classes includes the original frame for the array.
`ArrayWorker` performs processing on incoming array and sends the result to downstream in NDArray. User may subclass the worker to define own processing.
`ArrayProcessor` wraps the worker and provides API around it.
Worker is mulithreaded in the processor.
## Convenicence classes
AraViQ6 also provides various convenience classes to make building the pipeline easier.
The following classes help setting array pipeline with the video source and the display.
- `NDArrayVideoPlayer`
- `NDArrayMediaCaptureSession`
- `NDArrayLabel`
The following classes are plug-and-play widgets where user can process the video with minimal boilerplate.
- `PlayerProcessWidget`
- `CameraProcessWidget`
# Examples
Use cases are provided in [examples](https://github.com/JSS95/araviq6/tree/master/doc/source/examples) directory.
They can be found in documentation as well.
# Installation
Before you install, be careful for other Qt-dependent packages installed in your environment.
For example, non-headless OpenCV-Python modifies the Qt dependency thus can make other Qt bindings unavailable.
`araviq6` can be installed using `pip`.
```
$ pip install araviq6
```
# Documentation
AraViQ6 is documented with [Sphinx](https://pypi.org/project/Sphinx/).
Documentation can be found on Read the Docs:
> https://araviq6.readthedocs.io/
If you want to build the document yourself, clone the source code and install with `[doc]` option.
Go to `doc` directory and build the document.
```
$ pip install araviq6[doc]
$ cd doc
$ make html
```
Document will be generated in `build/html` directory. Open `index.html` to see the central page.