Metadata-Version: 2.1
Name: greenlab-library
Version: 0.1.1
Summary: HungLV package
Home-page: https://gitlab.com/greenlabs/greenlab_libraries
Author: Hung LV
Author-email: hunglv@greenglobal.vn
License: MIT
Description: # [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**
        
        **Documentation**: 
        
        ## 2. Table of Contents
        
        * Setup guide
        * Usage
        * Examples
        * APIs
        * References
        
        ## 3. Setup guide
        
        ### 3.1 System requirements
        
        * Python>=3.6
        * CUDA==10.0
        * MXNet
        * Tensorflow
        * Keras
        
        ### 3.2 Installation
        
        Make sure `conda` is installed.
        
        **Note that**: If you are using GPU, you need to change the version of cuda in `requirements.txt` file.
        
        *`mxnet-cu101`* means the package is built with CUDA/cuDNN and the CUDA version is 10.1.
        
        ```
        # create environment 
        conda create --name face_recog_test python=3.6
        conda activate face_recog_test
        
        # install dependencies for cpu 
        pip install -r requirements-cpu.txt
        # or install dependencies for gpu 
        pip install -r requirements-gpu.txt
        
        # install face recognition library
        pip install --upgrade green-face-recognition
        ```
        
        ## 4. Usage
        
        Prepare the config file as `yaml` type. Take a look [this example](https://github.com/leviethung2103/face-recognition-baseline/blob/master/configs/server_api.yaml).
        
        **Show the list of models**
        
        ```python
        from face_recognition import models
        models.show_avai_models()
        ```
        
        **Load the models**
        
        ```python
        retina_model = models.build_model('retina-r50',config_path)
        arcface = models.build_model('arc-face',config_path)
        ```
        
        **Make the prediction**
        
        ```python
        # get faces and landmarks
        retina_model.detect_fast(img,img.shape,0.8,[1],do_flip=False)
        ```
        
        ## 5. Examples
        
        [Face Recognition - Getting Started](https://github.com/leviethung2103/face-recognition-baseline)
        
        ## 6. APIs
        
        ## 7. References
        
        [1. RetinaFaceModel](https://github.com/deepinsight/insightface/tree/master/RetinaFace)
        
        ## Maintainers
        
        **Author:** Hung Le Viet
        
        **Last Update:** July 14, 2020
        
        ## Updates
        
        * `[Jul 9]` Initial version
        * `[Jul 13]` Make the models can run on GPU/CPU. Integrated `rcnn` library. 
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
