sface class

README.md

@@ -190,7 +190,7 @@ Face recognition, facial attribute analysis and vector representation functions
 
 **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 function arguments respectively.
+DeepFace comes with a command line interface as well. You are able to access its functions in command line as shown below. The command deepface expects the function name as 1st argument and function arguments thereafter.
 
 ```shell
 deepface verify -img1_path tests/dataset/img1.jpg -img2_path tests/dataset/img2.jpg

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, SFaceWrapper
+from deepface.basemodels import VGGFace, OpenFace, Facenet, Facenet512, FbDeepFace, DeepID, DlibWrapper, ArcFace, SFace, Boosting
 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': SFaceWrapper.load_model,
+		'SFace': SFace.load_model,
 		'Emotion': Emotion.loadModel,
 		'Age': Age.loadModel,
 		'Gender': Gender.loadModel,

deepface/basemodels/SFace.py

@@ -0,0 +1,48 @@
+import os
+import numpy as np
+import cv2 as cv
+import gdown
+
+from deepface.commons import functions
+
+class _Layer:
+    input_shape = (None, 112, 112, 3)
+    output_shape = (None, 1, 128)
+
+class SFaceModel:
+
+    def __init__(self, model_path):
+
+        self.model = cv.FaceRecognizerSF.create(
+            model = model_path,
+            config = "",
+            backend_id = 0,
+            target_id = 0)
+
+        self.layers = [_Layer()]
+
+    def predict(self, image):
+        # Preprocess
+        input_blob = (image[0] * 255).astype(np.uint8)  # revert the image to original format and preprocess using the model
+
+        # Forward
+        embeddings = self.model.feature(input_blob)
+        
+        return embeddings
+
+
+def load_model(url = "https://github.com/opencv/opencv_zoo/raw/master/models/face_recognition_sface/face_recognition_sface_2021dec.onnx"):
+
+    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...")
+
+        gdown.download(url, file_name, quiet=False)
+
+    model = SFaceModel(model_path = file_name)
+
+    return model

deepface/basemodels/SFaceWrapper.py

@@ -1,53 +0,0 @@
-import os
-
-import numpy as np
-import cv2 as cv
-import gdown
-
-from deepface.commons import functions
-
-class _Layer:
-    input_shape = (None, 112, 112, 3)
-    output_shape = (None, 1, 128)
-
-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(url = "https://github.com/opencv/opencv_zoo/raw/master/models/face_recognition_sface/face_recognition_sface_2021dec.onnx", *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