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https://github.com/docsaidlab/docsaidkit
Developer's Toolbox
https://github.com/docsaidlab/docsaidkit
opencv python pytorch pytorch-lightning
Last synced: 9 days ago
JSON representation
Developer's Toolbox
- Host: GitHub
- URL: https://github.com/docsaidlab/docsaidkit
- Owner: DocsaidLab
- License: apache-2.0
- Created: 2023-07-09T07:24:37.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-10-12T07:34:00.000Z (about 1 month ago)
- Last Synced: 2024-10-21T10:36:54.031Z (26 days ago)
- Topics: opencv, python, pytorch, pytorch-lightning
- Language: Python
- Homepage:
- Size: 1.35 MB
- Stars: 3
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
README
[English](./README.md) | **[中文](./README_tw.md)**
# DocsaidKit
## Introduction
This project is a toolbox for image processing and deep learning, primarily consisting of the following components:
- **Vision**: Functions related to computer vision, such as image and video processing.
- **Structures**: Modules for handling structured data, such as BoundingBox and Polygon.
- **ONNXEngine**: Provides ONNX inference capabilities, supporting ONNX format models.
- **Torch**: Related to PyTorch, including neural network architectures, optimizers, etc.
- **Utils**: Miscellaneous utilities that do not fit into other categories.
- **Tests**: Test files for verifying the functionality of various functions.
## Documentation
For installation and usage instructions, please refer to the [**DocsaidKit Documents**](https://docsaid.org/en/docs/docsaidkit).
Here, you will find all the detailed information about this project.
## Installation
Before installing DocsaidKit, ensure your system meets the following requirements:
### Python Version
- Ensure Python 3.8 or higher is installed on your system.
### Dependencies
Install the required dependencies based on your operating system.
- **Ubuntu**
Open the terminal and run the following commands to install dependencies:
```bash
sudo apt install libturbojpeg exiftool ffmpeg libheif-dev
```
- **MacOS**
Use brew to install dependencies:
```bash
brew install jpeg-turbo exiftool ffmpeg libheif
```
### pdf2image Dependencies
pdf2image is a Python module for converting PDF documents into images.
Follow these instructions to install it based on your operating system:
- For detailed installation instructions, refer to the [**pdf2image**](https://github.com/Belval/pdf2image) project page.
- MacOS: Mac users need to install poppler. Install it via Brew:
```bash
brew install poppler
```
- Linux: Most Linux distributions come with `pdftoppm` and `pdftocairo` pre-installed.
If not, install poppler-utils via your package manager:
```bash
sudo apt install poppler-utils
```
### Installation via git clone
1. Clone the repository:
```bash
git clone https://github.com/DocsaidLab/DocsaidKit.git
```
2. Install the wheel package:
```bash
pip install wheel
```
3. Build the wheel file:
```bash
cd DocsaidKit
python setup.py bdist_wheel
```
4. Install the built wheel package:
```bash
pip install dist/docsaidkit-*-py3-none-any.whl
```
To install the version that supports PyTorch:
```bash
pip install "dist/docsaidKit-${version}-none-any.whl[torch]"
```
### Installation via Docker (Recommended)
Install via Docker to ensure environment consistency.
Use the following commands:
```bash
cd DocsaidKit
bash docker/build.bash
```
Once completed, run your commands within Docker:
```bash
docker run -v ${PWD}:/code -it docsaid_training_base_image your_scripts.py
```
For the specifics of the build file, refer to: [**Dockerfile**](https://github.com/DocsaidLab/DocsaidKit/blob/main/docker/Dockerfile)
## Testing
To ensure the stability and accuracy of DocsaidKit, we use `pytest` for unit testing.
Users can run the tests themselves to verify the accuracy of the functionalities they are using.
To run the tests:
```bash
python -m pytest tests
```