Merge pull request #474 from rodrigo2019/master

Opencv SFace implementation

deepface/DeepFace.py

@@ -11,10 +11,9 @@ import numpy as np
 import pandas as pd
 from tqdm import tqdm
 import pickle
-
 import fire
 
-from deepface.basemodels import VGGFace, OpenFace, Facenet, Facenet512, FbDeepFace, DeepID, DlibWrapper, ArcFace, Boosting
+from deepface.basemodels import VGGFace, OpenFace, Facenet, Facenet512, FbDeepFace, DeepID, DlibWrapper, ArcFace, Boosting, sface_opencv_wrapper
 from deepface.extendedmodels import Age, Gender, Race, Emotion
 from deepface.commons import functions, realtime, distance as dst
 
@@ -48,6 +47,7 @@ def build_model(model_name):
 		'DeepID': DeepID.loadModel,
 		'Dlib': DlibWrapper.loadModel,
 		'ArcFace': ArcFace.loadModel,
+		'SFace': sface_opencv_wrapper.load_model,
 		'Emotion': Emotion.loadModel,
 		'Age': Age.loadModel,
 		'Gender': Gender.loadModel,

deepface/basemodels/sface_opencv_wrapper.py

@@ -0,0 +1,56 @@
+import os
+
+import numpy as np
+import cv2 as cv
+import gdown
+
+from deepface.commons import functions
+
+_url = "https://github.com/opencv/opencv_zoo/raw/master/models/face_recognition_sface/face_recognition_sface_2021dec.onnx"
+
+
+class _Layer:
+    input_shape = (None, 112, 112, 3)
+
+
+class SFace:
+    def __init__(self, model_path, backend_id=0, target_id=0):
+        self._modelPath = model_path
+        self._backendId = backend_id
+        self._targetId = target_id
+        self._model = cv.FaceRecognizerSF.create(
+            model=self._modelPath,
+            config="",
+            backend_id=self._backendId,
+            target_id=self._targetId)
+
+        self.layers = [_Layer()]
+
+    def _preprocess(self, image, bbox):
+        if bbox is None:
+            return image
+        else:
+            return self._model.alignCrop(image, bbox)
+
+    def predict(self, image, bbox=None, **kwargs):
+        # Preprocess
+        image = (image[0] * 255).astype(np.uint8)  # revert the image to original format and preprocess using the model
+        input_blob = self._preprocess(image, bbox)
+
+        # Forward
+        features = self._model.feature(input_blob)
+        return features
+
+
+def load_model(*args, **kwargs):
+    home = functions.get_deepface_home()
+
+    file_name = home + '/.deepface/weights/face_recognition_sface_2021dec.onnx'
+    if not os.path.isfile(file_name):
+        print("sface weights will be downloaded...")
+
+        output = file_name
+        gdown.download(_url, output, quiet=False)
+
+    model = SFace(file_name, 0, 0)
+    return model

requirements.txt

@@ -3,7 +3,7 @@ pandas>=0.23.4
 gdown>=3.10.1
 tqdm>=4.30.0
 Pillow>=5.2.0
-opencv-python>=4.2.0.34
+opencv-python>=4.5.0.34
 opencv-contrib-python>=4.3.0.36
 tensorflow>=1.9.0
 keras>=2.2.0