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batched inputs in representation
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galthran-wq
parent
72e82f0605
commit
0ef420bc10
@ -18,7 +18,7 @@ class FacialRecognition(ABC):
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input_shape: Tuple[int, int]
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output_shape: int
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def forward(self, img: np.ndarray) -> List[float]:
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def forward(self, img: np.ndarray) -> Union[List[float], List[List[float]]]:
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if not isinstance(self.model, Model):
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raise ValueError(
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"You must overwrite forward method if it is not a keras model,"
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@ -26,4 +26,10 @@ class FacialRecognition(ABC):
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)
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# model.predict causes memory issue when it is called in a for loop
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# embedding = model.predict(img, verbose=0)[0].tolist()
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return self.model(img, training=False).numpy()[0].tolist()
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if img.shape == 4 and img.shape[0] == 1:
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img = img[0]
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embeddings = self.model(img, training=False).numpy()
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if embeddings.shape[0] == 1:
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return embeddings[0].tolist()
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else:
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return embeddings.tolist()
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@ -11,7 +11,7 @@ from deepface.models.FacialRecognition import FacialRecognition
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def represent(
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img_path: Union[str, np.ndarray],
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img_path: Union[str, np.ndarray, List[Union[str, np.ndarray]]],
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model_name: str = "VGG-Face",
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enforce_detection: bool = True,
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detector_backend: str = "opencv",
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@ -25,9 +25,9 @@ def represent(
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Represent facial images as multi-dimensional vector embeddings.
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Args:
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img_path (str or np.ndarray): The exact path to the image, a numpy array in BGR format,
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or a base64 encoded image. If the source image contains multiple faces, the result will
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include information for each detected face.
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img_path (str, np.ndarray, or list): The exact path to the image, a numpy array in BGR format,
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a base64 encoded image, or a list of these. If the source image contains multiple faces,
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the result will include information for each detected face.
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model_name (str): Model for face recognition. Options: VGG-Face, Facenet, Facenet512,
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OpenFace, DeepFace, DeepID, Dlib, ArcFace, SFace and GhostFaceNet
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@ -70,70 +70,92 @@ def represent(
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task="facial_recognition", model_name=model_name
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)
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# ---------------------------------
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# we have run pre-process in verification. so, this can be skipped if it is coming from verify.
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target_size = model.input_shape
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if detector_backend != "skip":
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img_objs = detection.extract_faces(
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img_path=img_path,
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detector_backend=detector_backend,
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grayscale=False,
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enforce_detection=enforce_detection,
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align=align,
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expand_percentage=expand_percentage,
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anti_spoofing=anti_spoofing,
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max_faces=max_faces,
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)
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else: # skip
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# Try load. If load error, will raise exception internal
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img, _ = image_utils.load_image(img_path)
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# Handle list of image paths or 4D numpy array
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if isinstance(img_path, list):
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images = img_path
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elif isinstance(img_path, np.ndarray) and img_path.ndim == 4:
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images = [img_path[i] for i in range(img_path.shape[0])]
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else:
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images = [img_path]
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if len(img.shape) != 3:
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raise ValueError(f"Input img must be 3 dimensional but it is {img.shape}")
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batch_images = []
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batch_regions = []
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batch_confidences = []
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# make dummy region and confidence to keep compatibility with `extract_faces`
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img_objs = [
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{
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"face": img,
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"facial_area": {"x": 0, "y": 0, "w": img.shape[0], "h": img.shape[1]},
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"confidence": 0,
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}
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]
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# ---------------------------------
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for single_img_path in images:
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# ---------------------------------
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# we have run pre-process in verification. so, this can be skipped if it is coming from verify.
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target_size = model.input_shape
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if detector_backend != "skip":
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img_objs = detection.extract_faces(
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img_path=single_img_path,
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detector_backend=detector_backend,
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grayscale=False,
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enforce_detection=enforce_detection,
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align=align,
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expand_percentage=expand_percentage,
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anti_spoofing=anti_spoofing,
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max_faces=max_faces,
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)
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else: # skip
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# Try load. If load error, will raise exception internal
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img, _ = image_utils.load_image(single_img_path)
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if max_faces is not None and max_faces < len(img_objs):
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# sort as largest facial areas come first
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img_objs = sorted(
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img_objs,
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key=lambda img_obj: img_obj["facial_area"]["w"] * img_obj["facial_area"]["h"],
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reverse=True,
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)
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# discard rest of the items
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img_objs = img_objs[0:max_faces]
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if len(img.shape) != 3:
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raise ValueError(f"Input img must be 3 dimensional but it is {img.shape}")
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for img_obj in img_objs:
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if anti_spoofing is True and img_obj.get("is_real", True) is False:
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raise ValueError("Spoof detected in the given image.")
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img = img_obj["face"]
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# make dummy region and confidence to keep compatibility with `extract_faces`
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img_objs = [
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{
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"face": img,
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"facial_area": {"x": 0, "y": 0, "w": img.shape[0], "h": img.shape[1]},
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"confidence": 0,
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}
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]
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# ---------------------------------
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# bgr to rgb
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img = img[:, :, ::-1]
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if max_faces is not None and max_faces < len(img_objs):
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# sort as largest facial areas come first
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img_objs = sorted(
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img_objs,
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key=lambda img_obj: img_obj["facial_area"]["w"] * img_obj["facial_area"]["h"],
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reverse=True,
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)
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# discard rest of the items
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img_objs = img_objs[0:max_faces]
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region = img_obj["facial_area"]
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confidence = img_obj["confidence"]
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for img_obj in img_objs:
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if anti_spoofing is True and img_obj.get("is_real", True) is False:
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raise ValueError("Spoof detected in the given image.")
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img = img_obj["face"]
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# resize to expected shape of ml model
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img = preprocessing.resize_image(
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img=img,
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# thanks to DeepId (!)
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target_size=(target_size[1], target_size[0]),
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)
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# bgr to rgb
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img = img[:, :, ::-1]
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# custom normalization
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img = preprocessing.normalize_input(img=img, normalization=normalization)
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region = img_obj["facial_area"]
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confidence = img_obj["confidence"]
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embedding = model.forward(img)
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# resize to expected shape of ml model
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img = preprocessing.resize_image(
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img=img,
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# thanks to DeepId (!)
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target_size=(target_size[1], target_size[0]),
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)
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# custom normalization
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img = preprocessing.normalize_input(img=img, normalization=normalization)
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batch_images.append(img)
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batch_regions.append(region)
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batch_confidences.append(confidence)
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# Convert list of images to a numpy array for batch processing
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batch_images = np.concat(batch_images)
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# Forward pass through the model for the entire batch
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embeddings = model.forward(batch_images)
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for embedding, region, confidence in zip(embeddings, batch_regions, batch_confidences):
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resp_objs.append(
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{
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"embedding": embedding,
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