Merge pull request #1156 from serengil/feat-task-2803-resize-moved-to-representation

Feat task 2803 moving find embedding methods to super if not require additional logic

deepface/basemodels/ArcFace.py

@@ -1,7 +1,5 @@
-from typing import List
 import os
 import gdown
-import numpy as np
 from deepface.commons import package_utils, folder_utils
 from deepface.commons.logger import Logger
 from deepface.models.FacialRecognition import FacialRecognition
@@ -56,18 +54,6 @@ class ArcFaceClient(FacialRecognition):
         self.input_shape = (112, 112)
         self.output_shape = 512
 
-    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

@@ -1,7 +1,5 @@
-from typing import List
 import os
 import gdown
-import numpy as np
 from deepface.commons import package_utils, folder_utils
 from deepface.commons.logger import Logger
 from deepface.models.FacialRecognition import FacialRecognition
@@ -52,18 +50,6 @@ class DeepIdClient(FacialRecognition):
         self.input_shape = (47, 55)
         self.output_shape = 160
 
-    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/Dlib.py

@@ -23,9 +23,11 @@ class DlibClient(FacialRecognition):
         self.input_shape = (150, 150)
         self.output_shape = 128
 
-    def find_embeddings(self, img: np.ndarray) -> List[float]:
+    def forward(self, img: np.ndarray) -> List[float]:
         """
-        find embeddings with Dlib model - different than regular models
+        Find embeddings with Dlib model.
+            This model necessitates the override of the forward method
+            because it is not a keras model.
         Args:
             img (np.ndarray): pre-loaded image in BGR
         Returns

deepface/basemodels/Facenet.py

@@ -1,7 +1,5 @@
-from typing import List
 import os
 import gdown
-import numpy as np
 from deepface.commons import package_utils, folder_utils
 from deepface.commons.logger import Logger
 from deepface.models.FacialRecognition import FacialRecognition
@@ -56,18 +54,6 @@ class FaceNet128dClient(FacialRecognition):
         self.input_shape = (160, 160)
         self.output_shape = 128
 
-    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 FaceNet512dClient(FacialRecognition):
     """
@@ -80,18 +66,6 @@ class FaceNet512dClient(FacialRecognition):
         self.input_shape = (160, 160)
         self.output_shape = 512
 
-    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

@@ -1,8 +1,6 @@
-from typing import List
 import os
 import zipfile
 import gdown
-import numpy as np
 from deepface.commons import package_utils, folder_utils
 from deepface.commons.logger import Logger
 from deepface.models.FacialRecognition import FacialRecognition
@@ -56,18 +54,6 @@ class DeepFaceClient(FacialRecognition):
         self.input_shape = (152, 152)
         self.output_shape = 4096
 
-    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/GhostFaceNet.py

@@ -1,10 +1,8 @@
 # built-in dependencies
 import os
-from typing import List
 
 # 3rd party dependencies
 import gdown
-import numpy as np
 import tensorflow as tf
 
 # project dependencies
@@ -72,11 +70,6 @@ class GhostFaceNetClient(FacialRecognition):
         self.output_shape = 512
         self.model = load_model()
 
-    def find_embeddings(self, img: np.ndarray) -> List[float]:
-        # 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():
     model = GhostFaceNetV1()

deepface/basemodels/OpenFace.py

@@ -1,8 +1,6 @@
-from typing import List
 import os
 import gdown
 import tensorflow as tf
-import numpy as np
 from deepface.commons import package_utils, folder_utils
 from deepface.commons.logger import Logger
 from deepface.models.FacialRecognition import FacialRecognition
@@ -39,18 +37,6 @@ class OpenFaceClient(FacialRecognition):
         self.input_shape = (96, 96)
         self.output_shape = 128
 
-    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

@@ -25,9 +25,11 @@ class SFaceClient(FacialRecognition):
         self.input_shape = (112, 112)
         self.output_shape = 128
 
-    def find_embeddings(self, img: np.ndarray) -> List[float]:
+    def forward(self, img: np.ndarray) -> List[float]:
         """
-        find embeddings with SFace model - different than regular models
+        Find embeddings with SFace model
+            This model necessitates the override of the forward method
+            because it is not a keras model.
         Args:
             img (np.ndarray): pre-loaded image in BGR
         Returns

deepface/basemodels/VGGFace.py

@@ -47,9 +47,12 @@ class VggFaceClient(FacialRecognition):
         self.input_shape = (224, 224)
         self.output_shape = 4096
 
-    def find_embeddings(self, img: np.ndarray) -> List[float]:
+    def forward(self, img: np.ndarray) -> List[float]:
         """
-        find embeddings with VGG-Face model
+        Generates embeddings using the VGG-Face model.
+            This method incorporates an additional normalization layer,
+            necessitating the override of the forward method.
+
         Args:
             img (np.ndarray): pre-loaded image in BGR
         Returns
@@ -57,6 +60,7 @@ class VggFaceClient(FacialRecognition):
         """
         # model.predict causes memory issue when it is called in a for loop
         # embedding = model.predict(img, verbose=0)[0].tolist()
+
         # having normalization layer in descriptor troubles for some gpu users (e.g. issue 957, 966)
         # instead we are now calculating it with traditional way not with keras backend
         embedding = self.model(img, training=False).numpy()[0].tolist()

deepface/models/FacialRecognition.py

@@ -1,4 +1,4 @@
-from abc import ABC, abstractmethod
+from abc import ABC
 from typing import Any, Union, List, Tuple
 import numpy as np
 from deepface.commons import package_utils
@@ -18,7 +18,12 @@ class FacialRecognition(ABC):
     input_shape: Tuple[int, int]
     output_shape: int
 
-
-    @abstractmethod
-    def find_embeddings(self, img: np.ndarray) -> List[float]:
-        pass
+    def forward(self, img: np.ndarray) -> List[float]:
+        if not isinstance(self.model, Model):
+            raise ValueError(
+                "You must overwrite forward method if it is not a keras model,"
+                f"but {self.model_name} not overwritten!"
+            )
+        # 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()

deepface/modules/representation.py

@@ -104,7 +104,7 @@ def represent(
         # custom normalization
         img = preprocessing.normalize_input(img=img, normalization=normalization)
 
-        embedding = model.find_embeddings(img)
+        embedding = model.forward(img)
 
         resp_obj = {}
         resp_obj["embedding"] = embedding

tests/face-recognition-how.py

@@ -23,11 +23,11 @@ logger.info(f"target_size: {target_size}")
 
 img1 = DeepFace.extract_faces(img_path="dataset/img1.jpg", target_size=target_size)[0]["face"]
 img1 = np.expand_dims(img1, axis=0)  # to (1, 224, 224, 3)
-img1_representation = model.find_embeddings(img1)
+img1_representation = model.forward(img1)
 
 img2 = DeepFace.extract_faces(img_path="dataset/img3.jpg", target_size=target_size)[0]["face"]
 img2 = np.expand_dims(img2, axis=0)
-img2_representation = model.find_embeddings(img2)
+img2_representation = model.forward(img2)
 
 img1_representation = np.array(img1_representation)
 img2_representation = np.array(img2_representation)