Batching on .represent to improve performance and utilize GPU in full

deepface/DeepFace.py

@@ -2,7 +2,7 @@
 import os
 import warnings
 import logging
-from typing import Any, Dict, IO, List, Union, Optional
+from typing import Any, Dict, IO, List, Union, Optional, Sequence
 
 # this has to be set before importing tensorflow
 os.environ["TF_USE_LEGACY_KERAS"] = "1"
@@ -373,7 +373,7 @@ def find(
 
 
 def represent(
-    img_path: Union[str, np.ndarray, IO[bytes]],
+    img_path: Union[str, np.ndarray, IO[bytes], Sequence[Union[str, np.ndarray, IO[bytes]]]],
     model_name: str = "VGG-Face",
     enforce_detection: bool = True,
     detector_backend: str = "opencv",
@@ -387,10 +387,13 @@ def represent(
     Represent facial images as multi-dimensional vector embeddings.
 
     Args:
-        img_path (str or np.ndarray or IO[bytes]): The exact path to the image, a numpy array
+        img_path (str, np.ndarray, IO[bytes], or Sequence[Union[str, np.ndarray, IO[bytes]]]): 
+            The exact path to the image, a numpy array
             in BGR format, a file object that supports at least `.read` and is opened in binary
             mode, or a base64 encoded image. If the source image contains multiple faces,
-            the result will include information for each detected face.
+            the result will include information for each detected face. If a sequence is provided,
+            each element should be a string or numpy array representing an image, and the function
+            will process images in batch.
 
         model_name (str): Model for face recognition. Options: VGG-Face, Facenet, Facenet512,
             OpenFace, DeepFace, DeepID, Dlib, ArcFace, SFace and GhostFaceNet

deepface/models/FacialRecognition.py

@@ -18,12 +18,17 @@ class FacialRecognition(ABC):
     input_shape: Tuple[int, int]
     output_shape: int
 
-    def forward(self, img: np.ndarray) -> List[float]:
+    def forward(self, img: np.ndarray) -> Union[List[float], List[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()
+        if img.shape == 4 and img.shape[0] == 1:
+            img = img[0]
+        embeddings = self.model(img, training=False).numpy()
+        if embeddings.shape[0] == 1:
+            return embeddings[0].tolist()
+        return embeddings.tolist()

deepface/models/facial_recognition/Dlib.py

@@ -1,5 +1,5 @@
 # built-in dependencies
-from typing import List
+from typing import List, Union
 
 # 3rd party dependencies
 import numpy as np
@@ -26,35 +26,34 @@ def __init__(self):
         self.input_shape = (150, 150)
         self.output_shape = 128
 
-    def forward(self, img: np.ndarray) -> List[float]:
+    def forward(self, img: np.ndarray) -> Union[List[float], List[List[float]]]:
         """
         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
+            img (np.ndarray): pre-loaded image(s) in BGR
         Returns
-            embeddings (list): multi-dimensional vector
+            embeddings (list of lists or list of floats): multi-dimensional vectors
         """
-        # return self.model.predict(img)[0].tolist()
-
-        # extract_faces returns 4 dimensional images
-        if len(img.shape) == 4:
-            img = img[0]
+        # Handle single image case
+        if len(img.shape) == 3:
+            img = np.expand_dims(img, axis=0)
 
         # bgr to rgb
-        img = img[:, :, ::-1]  # 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()
+        embeddings = self.model.model.compute_face_descriptor(img)
+        embeddings = [np.array(embedding).tolist() for embedding in embeddings]
+        if len(embeddings) == 1:
+            return embeddings[0]
+        return embeddings
 
 
 class DlibResNet:

deepface/models/facial_recognition/SFace.py

@@ -1,5 +1,5 @@
 # built-in dependencies
-from typing import Any, List
+from typing import Any, List, Union
 
 # 3rd party dependencies
 import numpy as np
@@ -27,7 +27,7 @@ def __init__(self):
         self.input_shape = (112, 112)
         self.output_shape = 128
 
-    def forward(self, img: np.ndarray) -> List[float]:
+    def forward(self, img: np.ndarray) -> Union[List[float], List[List[float]]]:
         """
         Find embeddings with SFace model
             This model necessitates the override of the forward method
@@ -37,14 +37,17 @@ def forward(self, img: np.ndarray) -> List[float]:
         Returns
             embeddings (list): multi-dimensional vector
         """
-        # return self.model.predict(img)[0].tolist()
+        input_blob = (img * 255).astype(np.uint8)
 
-        # revert the image to original format and preprocess using the model
-        input_blob = (img[0] * 255).astype(np.uint8)
+        embeddings = []
+        for i in range(input_blob.shape[0]):
+            embedding = self.model.model.feature(input_blob[i])
+            embeddings.append(embedding)
+        embeddings = np.concatenate(embeddings, axis=0)
 
-        embeddings = self.model.model.feature(input_blob)
-
-        return embeddings[0].tolist()
+        if embeddings.shape[0] == 1:
+            return embeddings[0].tolist()
+        return embeddings.tolist()
 
 
 def load_model(

deepface/models/facial_recognition/VGGFace.py

@@ -57,8 +57,7 @@ def __init__(self):
     def forward(self, img: np.ndarray) -> List[float]:
         """
         Generates embeddings using the VGG-Face model.
-            This method incorporates an additional normalization layer,
-            necessitating the override of the forward method.
+            This method incorporates an additional normalization layer.
 
         Args:
             img (np.ndarray): pre-loaded image in BGR
@@ -70,8 +69,14 @@ def forward(self, img: np.ndarray) -> List[float]:
 
         # 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()
-        embedding = verification.l2_normalize(embedding)
+        embedding = super().forward(img)
+        if (
+            isinstance(embedding, list) and
+            isinstance(embedding[0], list)
+        ):
+            embedding = verification.l2_normalize(embedding, axis=1)
+        else:
+            embedding = verification.l2_normalize(embedding)
         return embedding.tolist()
 
 

deepface/modules/representation.py

@@ -1,5 +1,5 @@
 # built-in dependencies
-from typing import Any, Dict, List, Union, Optional
+from typing import Any, Dict, List, Union, Optional, Sequence, IO
 
 # 3rd party dependencies
 import numpy as np
@@ -11,7 +11,7 @@
 
 
 def represent(
-    img_path: Union[str, np.ndarray],
+    img_path: Union[str, IO[bytes], np.ndarray, Sequence[Union[str, np.ndarray, IO[bytes]]]],
     model_name: str = "VGG-Face",
     enforce_detection: bool = True,
     detector_backend: str = "opencv",
@@ -25,9 +25,11 @@ def represent(
     Represent facial images as multi-dimensional vector embeddings.
 
     Args:
-        img_path (str or np.ndarray): The exact path to the image, a numpy array in BGR format,
-            or a base64 encoded image. If the source image contains multiple faces, the result will
-            include information for each detected face.
+        img_path (str, np.ndarray, or Sequence[Union[str, np.ndarray]]): 
+            The exact path to the image, a numpy array in BGR format,
+            a base64 encoded image, or a sequence of these.
+            If the source image contains multiple faces,
+            the result will include information for each detected face.
 
         model_name (str): Model for face recognition. Options: VGG-Face, Facenet, Facenet512,
             OpenFace, DeepFace, DeepID, Dlib, ArcFace, SFace and GhostFaceNet
@@ -70,70 +72,95 @@ def represent(
         task="facial_recognition", model_name=model_name
     )
 
-    # ---------------------------------
-    # we have run pre-process in verification. so, this can be skipped if it is coming from verify.
-    target_size = model.input_shape
-    if detector_backend != "skip":
-        img_objs = detection.extract_faces(
-            img_path=img_path,
-            detector_backend=detector_backend,
-            grayscale=False,
-            enforce_detection=enforce_detection,
-            align=align,
-            expand_percentage=expand_percentage,
-            anti_spoofing=anti_spoofing,
-            max_faces=max_faces,
-        )
-    else:  # skip
-        # Try load. If load error, will raise exception internal
-        img, _ = image_utils.load_image(img_path)
-
-        if len(img.shape) != 3:
-            raise ValueError(f"Input img must be 3 dimensional but it is {img.shape}")
-
-        # make dummy region and confidence to keep compatibility with `extract_faces`
-        img_objs = [
-            {
-                "face": img,
-                "facial_area": {"x": 0, "y": 0, "w": img.shape[0], "h": img.shape[1]},
-                "confidence": 0,
-            }
-        ]
-    # ---------------------------------
-
-    if max_faces is not None and max_faces < len(img_objs):
-        # sort as largest facial areas come first
-        img_objs = sorted(
-            img_objs,
-            key=lambda img_obj: img_obj["facial_area"]["w"] * img_obj["facial_area"]["h"],
-            reverse=True,
-        )
-        # discard rest of the items
-        img_objs = img_objs[0:max_faces]
-
-    for img_obj in img_objs:
-        if anti_spoofing is True and img_obj.get("is_real", True) is False:
-            raise ValueError("Spoof detected in the given image.")
-        img = img_obj["face"]
-
-        # bgr to rgb
-        img = img[:, :, ::-1]
-
-        region = img_obj["facial_area"]
-        confidence = img_obj["confidence"]
-
-        # resize to expected shape of ml model
-        img = preprocessing.resize_image(
-            img=img,
-            # thanks to DeepId (!)
-            target_size=(target_size[1], target_size[0]),
-        )
-
-        # custom normalization
-        img = preprocessing.normalize_input(img=img, normalization=normalization)
-
-        embedding = model.forward(img)
-
+    # Handle list of image paths or 4D numpy array
+    if isinstance(img_path, list):
+        images = img_path
+    elif isinstance(img_path, np.ndarray) and img_path.ndim == 4:
+        images = [img_path[i] for i in range(img_path.shape[0])]
+    else:
+        images = [img_path]
+
+    batch_images = []
+    batch_regions = []
+    batch_confidences = []
+
+    for single_img_path in images:
+        # ---------------------------------
+        # we have run pre-process in verification.
+        # so, this can be skipped if it is coming from verify.
+        target_size = model.input_shape
+        if detector_backend != "skip":
+            img_objs = detection.extract_faces(
+                img_path=single_img_path,
+                detector_backend=detector_backend,
+                grayscale=False,
+                enforce_detection=enforce_detection,
+                align=align,
+                expand_percentage=expand_percentage,
+                anti_spoofing=anti_spoofing,
+                max_faces=max_faces,
+            )
+        else:  # skip
+            # Try load. If load error, will raise exception internal
+            img, _ = image_utils.load_image(single_img_path)
+
+            if len(img.shape) != 3:
+                raise ValueError(f"Input img must be 3 dimensional but it is {img.shape}")
+
+            # make dummy region and confidence to keep compatibility with `extract_faces`
+            img_objs = [
+                {
+                    "face": img,
+                    "facial_area": {"x": 0, "y": 0, "w": img.shape[0], "h": img.shape[1]},
+                    "confidence": 0,
+                }
+            ]
+        # ---------------------------------
+
+        if max_faces is not None and max_faces < len(img_objs):
+            # sort as largest facial areas come first
+            img_objs = sorted(
+                img_objs,
+                key=lambda img_obj: img_obj["facial_area"]["w"] * img_obj["facial_area"]["h"],
+                reverse=True,
+            )
+            # discard rest of the items
+            img_objs = img_objs[0:max_faces]
+
+        for img_obj in img_objs:
+            if anti_spoofing is True and img_obj.get("is_real", True) is False:
+                raise ValueError("Spoof detected in the given image.")
+            img = img_obj["face"]
+
+            # bgr to rgb
+            img = img[:, :, ::-1]
+
+            region = img_obj["facial_area"]
+            confidence = img_obj["confidence"]
+
+            # resize to expected shape of ml model
+            img = preprocessing.resize_image(
+                img=img,
+                # thanks to DeepId (!)
+                target_size=(target_size[1], target_size[0]),
+            )
+
+            # custom normalization
+            img = preprocessing.normalize_input(img=img, normalization=normalization)
+
+            batch_images.append(img)
+            batch_regions.append(region)
+            batch_confidences.append(confidence)
+
+    # Convert list of images to a numpy array for batch processing
+    batch_images = np.concatenate(batch_images, axis=0)
+
+    # Forward pass through the model for the entire batch
+    embeddings = model.forward(batch_images)
+    if len(batch_images) == 1:
+        embeddings = [embeddings]
+
+    for embedding, region, confidence in zip(embeddings, batch_regions, batch_confidences):
         resp_objs.append(
             {
                 "embedding": embedding,