Merge pull request #962 from serengil/feat-task-2101-interfaces

cosmetic changes about interfaces

deepface/basemodels/ArcFace.py

@@ -1,5 +1,7 @@
+from typing import List
 import os
 import gdown
+import numpy as np
 from deepface.commons import functions
 from deepface.commons.logger import Logger
 from deepface.models.FacialRecognition import FacialRecognition
@@ -43,7 +45,7 @@
     )
 
 # pylint: disable=too-few-public-methods
-class ArcFace(FacialRecognition):
+class ArcFaceClient(FacialRecognition):
     """
     ArcFace model class
     """
@@ -52,6 +54,18 @@ def __init__(self):
         self.model = load_model()
         self.model_name = "ArcFace"
 
+    def find_embeddings(self, img: np.ndarray) -> List[float]:
+        """
+        find embeddings with ArcFace model
+        Args:
+            img (np.ndarray): pre-loaded image in BGR
+        Returns
+            embeddings (list): multi-dimensional vector
+        """
+        # model.predict causes memory issue when it is called in a for loop
+        # embedding = model.predict(img, verbose=0)[0].tolist()
+        return self.model(img, training=False).numpy()[0].tolist()
+
 
 def load_model(
     url="https://github.com/serengil/deepface_models/releases/download/v1.0/arcface_weights.h5",

deepface/basemodels/DeepID.py

@@ -1,5 +1,7 @@
+from typing import List
 import os
 import gdown
+import numpy as np
 from deepface.commons import functions
 from deepface.commons.logger import Logger
 from deepface.models.FacialRecognition import FacialRecognition
@@ -39,7 +41,7 @@
 # -------------------------------------
 
 # pylint: disable=too-few-public-methods
-class DeepId(FacialRecognition):
+class DeepIdClient(FacialRecognition):
     """
     DeepId model class
     """
@@ -48,6 +50,18 @@ def __init__(self):
         self.model = load_model()
         self.model_name = "DeepId"
 
+    def find_embeddings(self, img: np.ndarray) -> List[float]:
+        """
+        find embeddings with DeepId model
+        Args:
+            img (np.ndarray): pre-loaded image in BGR
+        Returns
+            embeddings (list): multi-dimensional vector
+        """
+        # model.predict causes memory issue when it is called in a for loop
+        # embedding = model.predict(img, verbose=0)[0].tolist()
+        return self.model(img, training=False).numpy()[0].tolist()
+
 
 def load_model(
     url="https://github.com/serengil/deepface_models/releases/download/v1.0/deepid_keras_weights.h5",

deepface/basemodels/DlibResNet.py → deepface/basemodels/Dlib.py

@@ -1,3 +1,4 @@
+from typing import List
 import os
 import bz2
 import gdown
@@ -11,7 +12,7 @@
 # pylint: disable=too-few-public-methods
 
 
-class Dlib(FacialRecognition):
+class DlibClient(FacialRecognition):
     """
     Dlib model class
     """
@@ -20,15 +21,33 @@ def __init__(self):
         self.model = DlibResNet()
         self.model_name = "Dlib"
 
-    def find_embeddings(self, img: np.ndarray) -> list:
+    def find_embeddings(self, img: np.ndarray) -> List[float]:
         """
-        Custom find embeddings function of Dlib different than FacialRecognition's one
+        find embeddings with Dlib model - different than regular models
         Args:
-            img (np.ndarray)
-        Retunrs:
-            embeddings (list)
+            img (np.ndarray): pre-loaded image in BGR
+        Returns
+            embeddings (list): multi-dimensional vector
         """
-        return self.model.predict(img)[0].tolist()
+        # return self.model.predict(img)[0].tolist()
+
+        # extract_faces returns 4 dimensional images
+        if len(img.shape) == 4:
+            img = img[0]
+
+        # bgr to rgb
+        img = img[:, :, ::-1]  # bgr to rgb
+
+        # img is in scale of [0, 1] but expected [0, 255]
+        if img.max() <= 1:
+            img = img * 255
+
+        img = img.astype(np.uint8)
+
+        img_representation = self.model.model.compute_face_descriptor(img)
+        img_representation = np.array(img_representation)
+        img_representation = np.expand_dims(img_representation, axis=0)
+        return img_representation[0].tolist()
 
 
 class DlibResNet:
@@ -69,38 +88,12 @@ def __init__(self):
 
         # ---------------------
 
-        model = dlib.face_recognition_model_v1(weight_file)
-        self.__model = model
+        self.model = dlib.face_recognition_model_v1(weight_file)
 
         # ---------------------
 
         # return None  # classes must return None
 
-    def predict(self, img_aligned: np.ndarray) -> np.ndarray:
-
-        # functions.detectFace returns 4 dimensional images
-        if len(img_aligned.shape) == 4:
-            img_aligned = img_aligned[0]
-
-        # functions.detectFace returns bgr images
-        img_aligned = img_aligned[:, :, ::-1]  # bgr to rgb
-
-        # deepface.detectFace returns an array in scale of [0, 1]
-        # but dlib expects in scale of [0, 255]
-        if img_aligned.max() <= 1:
-            img_aligned = img_aligned * 255
-
-        img_aligned = img_aligned.astype(np.uint8)
-
-        model = self.__model
-
-        img_representation = model.compute_face_descriptor(img_aligned)
-
-        img_representation = np.array(img_representation)
-        img_representation = np.expand_dims(img_representation, axis=0)
-
-        return img_representation
-
 
 class DlibMetaData:
     def __init__(self):

deepface/basemodels/Facenet.py

@@ -1,5 +1,7 @@
+from typing import List
 import os
 import gdown
+import numpy as np
 from deepface.commons import functions
 from deepface.commons.logger import Logger
 from deepface.models.FacialRecognition import FacialRecognition
@@ -43,7 +45,7 @@
 # --------------------------------
 
 # pylint: disable=too-few-public-methods
-class FaceNet128d(FacialRecognition):
+class FaceNet128dClient(FacialRecognition):
     """
     FaceNet-128d model class
     """
@@ -52,8 +54,20 @@ def __init__(self):
         self.model = load_facenet128d_model()
         self.model_name = "FaceNet-128d"
 
+    def find_embeddings(self, img: np.ndarray) -> List[float]:
+        """
+        find embeddings with FaceNet-128d model
+        Args:
+            img (np.ndarray): pre-loaded image in BGR
+        Returns
+            embeddings (list): multi-dimensional vector
+        """
+        # model.predict causes memory issue when it is called in a for loop
+        # embedding = model.predict(img, verbose=0)[0].tolist()
+        return self.model(img, training=False).numpy()[0].tolist()
 
-class FaceNet512d(FacialRecognition):
+
+class FaceNet512dClient(FacialRecognition):
     """
     FaceNet-1512d model class
     """
@@ -62,6 +76,18 @@ def __init__(self):
         self.model = load_facenet512d_model()
         self.model_name = "FaceNet-512d"
 
+    def find_embeddings(self, img: np.ndarray) -> List[float]:
+        """
+        find embeddings with FaceNet-512d model
+        Args:
+            img (np.ndarray): pre-loaded image in BGR
+        Returns
+            embeddings (list): multi-dimensional vector
+        """
+        # model.predict causes memory issue when it is called in a for loop
+        # embedding = model.predict(img, verbose=0)[0].tolist()
+        return self.model(img, training=False).numpy()[0].tolist()
+
 
 def scaling(x, scale):
     return x * scale

deepface/basemodels/FbDeepFace.py

@@ -1,6 +1,8 @@
+from typing import List
 import os
 import zipfile
 import gdown
+import numpy as np
 from deepface.commons import functions
 from deepface.commons.logger import Logger
 from deepface.models.FacialRecognition import FacialRecognition
@@ -36,7 +38,7 @@
 
 # -------------------------------------
 # pylint: disable=line-too-long, too-few-public-methods
-class DeepFace(FacialRecognition):
+class DeepFaceClient(FacialRecognition):
     """
     Fb's DeepFace model class
     """
@@ -45,6 +47,18 @@ def __init__(self):
         self.model = load_model()
         self.model_name = "DeepFace"
 
+    def find_embeddings(self, img: np.ndarray) -> List[float]:
+        """
+        find embeddings with OpenFace model
+        Args:
+            img (np.ndarray): pre-loaded image in BGR
+        Returns
+            embeddings (list): multi-dimensional vector
+        """
+        # model.predict causes memory issue when it is called in a for loop
+        # embedding = model.predict(img, verbose=0)[0].tolist()
+        return self.model(img, training=False).numpy()[0].tolist()
+
 
 def load_model(
     url="https://github.com/swghosh/DeepFace/releases/download/weights-vggface2-2d-aligned/VGGFace2_DeepFace_weights_val-0.9034.h5.zip",

deepface/basemodels/OpenFace.py

@@ -1,6 +1,8 @@
+from typing import List
 import os
 import gdown
 import tensorflow as tf
+import numpy as np
 from deepface.commons import functions
 from deepface.commons.logger import Logger
 from deepface.models.FacialRecognition import FacialRecognition
@@ -26,7 +28,7 @@
 # ---------------------------------------
 
 # pylint: disable=too-few-public-methods
-class OpenFace(FacialRecognition):
+class OpenFaceClient(FacialRecognition):
     """
     OpenFace model class
     """
@@ -35,6 +37,18 @@ def __init__(self):
         self.model = load_model()
         self.model_name = "OpenFace"
 
+    def find_embeddings(self, img: np.ndarray) -> List[float]:
+        """
+        find embeddings with OpenFace model
+        Args:
+            img (np.ndarray): pre-loaded image in BGR
+        Returns
+            embeddings (list): multi-dimensional vector
+        """
+        # model.predict causes memory issue when it is called in a for loop
+        # embedding = model.predict(img, verbose=0)[0].tolist()
+        return self.model(img, training=False).numpy()[0].tolist()
+
 
 def load_model(
     url="https://github.com/serengil/deepface_models/releases/download/v1.0/openface_weights.h5",

deepface/basemodels/SFace.py

@@ -1,5 +1,5 @@
 import os
-from typing import Any
+from typing import Any, List
 
 import numpy as np
 import cv2 as cv
@@ -14,7 +14,7 @@
 # pylint: disable=line-too-long, too-few-public-methods
 
 
-class SFace(FacialRecognition):
+class SFaceClient(FacialRecognition):
     """
     SFace model class
     """
@@ -23,15 +23,22 @@ def __init__(self):
         self.model = load_model()
         self.model_name = "SFace"
 
-    def find_embeddings(self, img: np.ndarray) -> list:
+    def find_embeddings(self, img: np.ndarray) -> List[float]:
         """
-        Custom find embeddings function of SFace different than FacialRecognition's one
+        find embeddings with SFace model - different than regular models
         Args:
-            img (np.ndarray)
-        Retunrs:
-            embeddings (list)
+            img (np.ndarray): pre-loaded image in BGR
+        Returns
+            embeddings (list): multi-dimensional vector
         """
-        return self.model.predict(img)[0].tolist()
+        # return self.model.predict(img)[0].tolist()
+
+        # revert the image to original format and preprocess using the model
+        input_blob = (img[0] * 255).astype(np.uint8)
+
+        embeddings = self.model.model.feature(input_blob)
+
+        return embeddings[0].tolist()
 
 
 def load_model(
@@ -74,17 +81,6 @@ def __init__(self, model_path):
 
         self.layers = [_Layer()]
 
-    def predict(self, image: np.ndarray) -> np.ndarray:
-        # 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
-
 
 class _Layer:
     input_shape = (None, 112, 112, 3)

deepface/basemodels/VGGFace.py

@@ -1,5 +1,7 @@
+from typing import List
 import os
 import gdown
+import numpy as np
 from deepface.commons import functions
 from deepface.commons.logger import Logger
 from deepface.models.FacialRecognition import FacialRecognition
@@ -37,7 +39,7 @@
 # ---------------------------------------
 
 # pylint: disable=too-few-public-methods
-class VggFace(FacialRecognition):
+class VggFaceClient(FacialRecognition):
     """
     VGG-Face model class
     """
@@ -46,6 +48,18 @@ def __init__(self):
         self.model = load_model()
         self.model_name = "VGG-Face"
 
+    def find_embeddings(self, img: np.ndarray) -> List[float]:
+        """
+        find embeddings with VGG-Face model
+        Args:
+            img (np.ndarray): pre-loaded image in BGR
+        Returns
+            embeddings (list): multi-dimensional vector
+        """
+        # model.predict causes memory issue when it is called in a for loop
+        # embedding = model.predict(img, verbose=0)[0].tolist()
+        return self.model(img, training=False).numpy()[0].tolist()
+
 
 def base_model() -> Sequential:
     """