[GreenLab] - Libraries
1. Description
This library is built for specific tasks
- Face Recognition Project
- Face Detection
- Extract Face Embedding
- Search Face in database
- License Plate Recognition Project
- Crowd Counting Project
- Density Map-based method
- Person Detection-based method
- Imutils
- Modify the source code of https://github.com/jrosebr1/imutils for our purpose
Documentation: https://leviethung2103.github.io/
Pypi Project: https://pypi.org/project/greenlab-library/
2. Table of Contents
- Setup guide
- Usage
- Examples
- APIs
- References
3. Setup guide
3.1 System requirements
- Python>=3.6
- CUDA==10.1
- Tensorflow-GPU>=2.1.0
- Keras==2.3.1
3.2 Installation
Create a new virtual environment, you can choose virtualenv or anaconda.
Virtualenv
python3 -m venv venv
source venv/bin/activate
Anaconda
# create environment
conda create --name face_recog_test python=3.7
conda activate face_recog_test
Install packages and dependencies
# install dependencies for cpu
pip install -r configs/requirements/cpu_base.txt
# or install dependencies for gpu
pip install -r configs/requirements/gpu_base.txt
# install face recognition library
pip install --upgrade greenlab-library
4. Usage
4.1 Face Recognition Library
Documentation: https://leviethung2103.github.io/pkg/face_recognition.html
Getting started code: https://github.com/leviethung2103/face-recognition-baseline
4.2 License Plate Library
Documentation: https://leviethung2103.github.io/pkg/license_recog.html
Getting started code: https://github.com/leviethung2103/license-plate-baseline
4.3 Crowd Counting Library
Documentation: Getting started code: https://gitlab.com/greenlabs/crowdcounting
5. APIs
6. References
7. Maintainers
Author: Hung Le Viet
Last Update: July 20, 2020
Updates
[Jul 9]Initial version0.1.1[Jul 13]Make the models can run on GPU/CPU. Integratedrcnnlibrary.[Jul 20]Version0.1.3: Update crowd counting into library. Restructure for requirements file.[Nov 15]Fix bugnd.waitAll()when inference
1 maintainer
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