Metadata-Version: 2.1
Name: arcface
Version: 0.0.5
Summary: ArcFace face recognition implementation in Tensorflow Light.
Home-page: https://github.com/mobilesec/arcface-tensorflowlight
Author: Philipp Hofer
Author-email: philipp.hofer@ins.jku.at
License: European Union Public Licence 1.2 (EUPL 1.2)
Description: # ArcFace face recognition
        Implementation of the [ArcFace face recognition algorithm](https://openaccess.thecvf.com/content_CVPR_2019/html/Deng_ArcFace_Additive_Angular_Margin_Loss_for_Deep_Face_Recognition_CVPR_2019_paper.htm). It includes a pre-trained model based on [ResNet50](https://arxiv.org/abs/1512.03385).
        
        The code is based on [peteryuX's](https://github.com/peteryuX/arcface-tf2) implementation. Instead of using full Tensorflow for the inference, the model has been converted to a Tensorflow lite model using `tf.lite.TFLiteConverter` which increased the speed of the inference by a factor of ~2.27.
        
        ## Installation
        You can install the package through pip:
        ```
        pip install arcface
        ```
        
        ## Quick start
        
        The following example illustrates the ease of use of this package:
        ```python
        >>> from arcface import ArcFace
        >>> face_rec = ArcFace.ArcFace()
        >>> emb1 = face_rec.calc_emb("~/Downloads/test.jpg")
        >>> print(emb1)
        array([-1.70827676e-02, -2.69084200e-02, -5.85994311e-02,  3.33652040e-03,
                9.58345132e-04,  1.21807214e-02, -6.81217164e-02, -1.33364811e-03,
               -2.12905575e-02,  1.67165045e-02,  3.52908894e-02, -5.26051633e-02,
        	   ...
               -2.11241804e-02,  2.22553015e-02, -5.71946353e-02, -2.33468022e-02],
              dtype=float32)
        >>> emb2 = face_rec.calc_emb("~/Downloads/test2.jpg")
        >>> face_rec.get_distance_embeddings(emb1, emb2)
        0.78542
        ```
        You can feed the `calc_emb` function either a single image or an array of images. Furthermore, you can supply the image as (absolute or relative) path, or an cv2-image. To make it more clear, hear are the four possibilities:
        
        1. (Absolute or relative) path to a single image: `face_rec.calc_emb("test.jpg")`
        2. Array of images: `face_rec.calc_emb(["test1.jpg", "test2.png"])`
        3. Single cv2-image: `face_rec.calc_emb(cv2.imread("test.png"))`
        4. Array of cv2-images: `face_rec.calc_emb([cv2.imread("test1.jpg"), cv2.imread("test2.png")])`
        
        The face recognition tool returns (an array of) 512-d embedding(s) as a numpy array.
        
        > Notice! This package does neither perform face detection nor face alignment! It assumes that the images are already pre-processsed!
        
        ## Benchmark
        
        | Model | Backbone | Framework | LFW Accuracy | Speed [ms/embedding] * |
        |----------|------|------|-----|-----|
        | [ArcFace paper](https://openaccess.thecvf.com/content_CVPR_2019/html/Deng_ArcFace_Additive_Angular_Margin_Loss_for_Deep_Face_Recognition_CVPR_2019_paper.htm) | R100     | MXNet        | 99.82        | -       |
        | [ArcFace TF2](https://github.com/peteryuX/arcface-tf2)   | R50      | Tensorflow 2 | 99.35 | 102 |
        | **This repository** | **R50** | **Tensorflow Lite** | **96.87** | **45** |
        
        \* executed on a CPU: Intel i7-10510U
        
        ## License
        
        Licensed under the EUPL, Version 1.2 or – as soon they will be approved by the European Commission - subsequent versions of the EUPL (the "Licence"). You may not use this work except in compliance with the Licence.
        
        **License**: [European Union Public License v1.2](https://joinup.ec.europa.eu/software/page/eupl)
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: European Union Public Licence 1.2 (EUPL 1.2)
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
