sface updates

README.md

@@ -63,7 +63,7 @@ df = DeepFace.find(img_path = "img1.jpg", db_path = "C:/workspace/my_db")
 Deepface is a **hybrid** face recognition package. It currently wraps many **state-of-the-art** face recognition models: [`VGG-Face`](https://sefiks.com/2018/08/06/deep-face-recognition-with-keras/) , [`Google FaceNet`](https://sefiks.com/2018/09/03/face-recognition-with-facenet-in-keras/), [`OpenFace`](https://sefiks.com/2019/07/21/face-recognition-with-openface-in-keras/), [`Facebook DeepFace`](https://sefiks.com/2020/02/17/face-recognition-with-facebook-deepface-in-keras/), [`DeepID`](https://sefiks.com/2020/06/16/face-recognition-with-deepid-in-keras/), [`ArcFace`](https://sefiks.com/2020/12/14/deep-face-recognition-with-arcface-in-keras-and-python/) and [`Dlib`](https://sefiks.com/2020/07/11/face-recognition-with-dlib-in-python/). The default configuration uses VGG-Face model.
 
 ```python
-models = ["VGG-Face", "Facenet", "Facenet512", "OpenFace", "DeepFace", "DeepID", "ArcFace", "Dlib"]
+models = ["VGG-Face", "Facenet", "Facenet512", "OpenFace", "DeepFace", "DeepID", "ArcFace", "Dlib", "SFace"]
 
 #face verification
 result = DeepFace.verify(img1_path = "img1.jpg", img2_path = "img2.jpg", model_name = models[1])
@@ -187,6 +187,15 @@ python api.py
 
 Face recognition, facial attribute analysis and vector representation functions are covered in the API. You are expected to call these functions as http post methods. Service endpoints will be `http://127.0.0.1:5000/verify` for face recognition, `http://127.0.0.1:5000/analyze` for facial attribute analysis, and `http://127.0.0.1:5000/represent` for vector representation. You should pass input images as base64 encoded string in this case. [Here](https://github.com/serengil/deepface/tree/master/api), you can find a postman project.
 
+**Command Line Interface**
+
+DeepFace comes with a command line interface as well. You are able to access its functions in command line in the command line as shown below. It expects the function name as 1st argument and functions arguments respectively.
+
+```shell
+deepface verify -img1_path tests/dataset/img1.jpg -img2_path tests/dataset/img2.jpg
+deepface analyze -img_path tests/dataset/img1.jpg
+```
+
 **Tech Stack** - [`Vlog`](https://youtu.be/R8fHsL7u3eE), [`Tutorial`](https://sefiks.com/2021/03/31/tech-stack-recommendations-for-face-recognition/)
 
 Face recognition models represent facial images as vector embeddings. The idea behind facial recognition is that vectors should be more similar for same person than different persons. The question is that where and how to store facial embeddings in a large scale system. Herein, deepface offers a represention function to find vector embeddings from facial images.

deepface/DeepFace.py

@@ -13,7 +13,7 @@ from tqdm import tqdm
 import pickle
 import fire
 
-from deepface.basemodels import VGGFace, OpenFace, Facenet, Facenet512, FbDeepFace, DeepID, DlibWrapper, ArcFace, Boosting, sface_opencv_wrapper
+from deepface.basemodels import VGGFace, OpenFace, Facenet, Facenet512, FbDeepFace, DeepID, DlibWrapper, ArcFace, Boosting, SFaceWrapper
 from deepface.extendedmodels import Age, Gender, Race, Emotion
 from deepface.commons import functions, realtime, distance as dst
 
@@ -47,7 +47,7 @@ def build_model(model_name):
 		'DeepID': DeepID.loadModel,
 		'Dlib': DlibWrapper.loadModel,
 		'ArcFace': ArcFace.loadModel,
-		'SFace': sface_opencv_wrapper.load_model,
+		'SFace': SFaceWrapper.load_model,
 		'Emotion': Emotion.loadModel,
 		'Age': Age.loadModel,
 		'Gender': Gender.loadModel,

requirements.txt

@@ -3,7 +3,7 @@ pandas>=0.23.4
 gdown>=3.10.1
 tqdm>=4.30.0
 Pillow>=5.2.0
-opencv-python>=4.5.0.34
+opencv-python>=4.5.5.64
 opencv-contrib-python>=4.3.0.36
 tensorflow>=1.9.0
 keras>=2.2.0

setup.py

@@ -24,5 +24,5 @@ setuptools.setup(
         ["deepface = deepface.DeepFace:cli"],
     },
     python_requires='>=3.5.5',
-    install_requires=["numpy>=1.14.0", "pandas>=0.23.4", "tqdm>=4.30.0", "gdown>=3.10.1", "Pillow>=5.2.0", "opencv-python>=3.4.4", "tensorflow>=1.9.0", "keras>=2.2.0", "Flask>=1.1.2", "mtcnn>=0.1.0", "retina-face>=0.0.1", "fire>=0.4.0"]
+    install_requires=["numpy>=1.14.0", "pandas>=0.23.4", "tqdm>=4.30.0", "gdown>=3.10.1", "Pillow>=5.2.0", "opencv-python>=4.5.5.64", "tensorflow>=1.9.0", "keras>=2.2.0", "Flask>=1.1.2", "mtcnn>=0.1.0", "retina-face>=0.0.1", "fire>=0.4.0"]
 )