Metadata-Version: 2.1
Name: deepface
Version: 0.0.1
Summary: Deep Face Recognition Framework
Home-page: https://github.com/serengil/deepface
Author: Sefik Ilkin Serengil
Author-email: serengil@gmail.com
License: UNKNOWN
Description: # deepface
        
        **deepface** is a lightweight facial recognition framework. You can verify faces with just a few lines of codes.
        
        ```python
        from deepface import DeepFace
        result = DeepFace.verify("img1.jpg", "img2.jpg")
        ```
        
        # Face recognition models
        
        Face recognition can be handled by different models. Currently, [`VGG-Face`](https://sefiks.com/2018/08/06/deep-face-recognition-with-keras/) , [`Facenet`](https://sefiks.com/2018/09/03/face-recognition-with-facenet-in-keras/) and [`OpenFace`](https://sefiks.com/2019/07/21/face-recognition-with-openface-in-keras/) models are supported in deepface. The default configuration verifies faces with **VGG-Face** model. You can set the base model while verification as illustared below. Accuracy and speed show difference based on the performing model.
        
        ```python
        vggface_result = DeepFace.verify("img1.jpg", "img2.jpg")
        #vggface_result = DeepFace.verify("img1.jpg", "img2.jpg", model_name = "VGG-Face")
        facenet_result = DeepFace.verify("img1.jpg", "img2.jpg", model_name = "Facenet")
        openface_result = DeepFace.verify("img1.jpg", "img2.jpg", model_name = "OpenFace")
        ```
        
        # Similarity
        
        These models actually find the vector embeddings of faces. Decision of verification is based on the distance between vectors. Distance could be found by different metrics such as [`Cosine Similarity`](https://sefiks.com/2018/08/13/cosine-similarity-in-machine-learning/), Euclidean Distance and L2 form. The default configuration finds the **cosine similarity**. You can alternatively set the similarity metric while verification as demostratred below.
        
        ```python
        result = DeepFace.verify("img1.jpg", "img2.jpg", model_name = "VGG-Face", distance_metric = "cosine")
        result = DeepFace.verify("img1.jpg", "img2.jpg", model_name = "VGG-Face", distance_metric = "euclidean")
        result = DeepFace.verify("img1.jpg", "img2.jpg", model_name = "VGG-Face", distance_metric = "euclidean_l2")
        ```
        
        # Verification
        
        Verification function returns a tuple including boolean verification result, distance between two faces and max threshold to identify. 
        
        ```
        (True, 0.281734, 0.30)
        ```
        
        You can just check the verification result to decide that two images are same person or not. Thresholds for distance metrics are already tuned in the framework for face recognition models and distance metrics.
        
        ```python
        verified = result[0] #returns True if images are same person's face
        ```
        
        Instead of using pre-tuned threshold values, you can alternatively check the distance by yourself.
        
        ```python
        distance = result[1] #the less the better
        
        threshold = 0.30 #threshold for VGG-Face and Cosine Similarity
        
        if distance < threshold:
        	return True
        else:
        	return False
        ```
        
        # Installation
        
        The easiest way to install deepface is to download it from PyPI.
        
        ```
        pip install deepface
        ```
        
        Alternatively, you can directly download the source code from this repository. GitHub repo might be newer than the PyPI version.
        
        ```
        git clone https://github.com/serengil/chefboost.git
        cd chefboost
        pip install -e .
        ```
        
        Initial tests are run for Python 3.5.5 on Windows 10 but this is an OS-independent framework. Even though pip handles to install dependent libraries, the framework basically needs the following dependencies. You might need the following library requirements if you install the source code from github.
        
        ```
        pip install numpy==1.14.0
        pip install matplotlib==2.2.2
        pip install gdown==3.10.1
        pip install opencv-python==3.4.4
        pip install tensorflow==1.9.0
        pip install keras==2.2.0
        ```
        
        # Support
        
        There are many ways to support a project - starring⭐️ the GitHub repos is just one.
        
        # Licence
        
        Chefboost is licensed under the MIT License - see [`LICENSE`](https://github.com/serengil/deepface/blob/master/LICENSE) for more details.
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.5.5
Description-Content-Type: text/markdown
