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FeaturesExtraction

FeaturesExtractor

Source code in Agent/modules/emotion_detection/features_extraction.py
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class FeaturesExtractor:
    def __init__(self) -> None:
        self.padding_mode = "zeros"
        self.padding_location = "back"

    @staticmethod
    def get_audio_embedding(audios: List[str]) -> torch.Tensor:
        """Extracts and returns average audio features from a list of audio files."""
        features = []
        for audio_path in audios:
            y, sr = librosa.load(audio_path)
            hop_length = 512
            f0 = librosa.feature.zero_crossing_rate(y, hop_length=hop_length).T
            mfcc = librosa.feature.mfcc(y=y, sr=sr, hop_length=hop_length, htk=True).T
            cqt = librosa.feature.chroma_cqt(y=y, sr=sr, hop_length=hop_length).T
            temp_feature = np.concatenate([f0, mfcc, cqt], axis=-1)
            features.append(temp_feature)
        feature = np.mean(np.concatenate(features), axis=0).reshape(1, -1)
        # get them into tensor
        feature = torch.tensor(feature).float()
        return feature

    def get_images_tensor(self, images: List[np.ndarray]) -> torch.Tensor:
        """Extracts features from a list of images using a specified model."""
        model_name = "OpenFace"
        image_features = [
            self.represent(image, model_name=model_name)[0]["embedding"]
            for image in images
        ]
        return torch.tensor(image_features)

    def represent(
        self,
        img,
        model_name: str = "VGG-Face",
        enforce_detection: bool = False,
        detector_backend: str = "opencv",
        align: bool = True,
        expand_percentage: int = 0,
        normalization: str = "base",
    ) -> List[Dict[str, Any]]:
        resp_objs = []

        model: FacialRecognition = modeling.build_model(model_name)

        # ---------------------------------
        # we have run pre-process in verification. so, this can be skipped if it is coming from verifying.
        target_size = model.input_shape
        if detector_backend != "skip":
            img_objs = self.extract_faces(
                img,
                target_size=(target_size[1], target_size[0]),
                detector_backend=detector_backend,
                grayscale=False,
                enforce_detection=enforce_detection,
                align=align,
                expand_percentage=expand_percentage,
            )
        else:  # skip
            # --------------------------------
            if len(img.shape) == 4:
                img = img[0]  # e.g. (1, 224, 224, 3) to (224, 224, 3)
            if len(img.shape) == 3:
                img = cv2.resize(img, target_size)
                img = np.expand_dims(img, axis=0)
                # When called from verifying, this is already normalized. But needed when user given.
                if img.max() > 1:
                    img = (img.astype(np.float32) / 255.0).astype(np.float32)
            # --------------------------------
            # make dummy region and confidence to keep compatibility with `extract_faces`
            img_objs = [
                {
                    "face": img,
                    "facial_area": {
                        "x": 0,
                        "y": 0,
                        "w": img.shape[1],
                        "h": img.shape[2],
                    },
                    "confidence": 0,
                }
            ]
        # ---------------------------------

        for img_obj in img_objs:
            img = img_obj["face"]
            region = img_obj["facial_area"]
            confidence = img_obj["confidence"]
            # custom normalization
            img = preprocessing.normalize_input(img=img, normalization=normalization)

            embedding = model.find_embeddings(img)

            resp_obj = {
                "embedding": embedding,
                "facial_area": region,
                "face_confidence": confidence,
            }
            resp_objs.append(resp_obj)

        return resp_objs

    logger = Logger(module="deepface/modules/detection.py")

    @staticmethod
    def extract_faces(
        img,
        target_size: Optional[Tuple[int, int]] = (224, 224),
        detector_backend: str = "opencv",
        enforce_detection: bool = True,
        align: bool = False,
        expand_percentage: int = 0.2,
        grayscale: bool = False,
        human_readable=False,
    ) -> List[Dict[str, Any]]:
        resp_objs = []
        base_region = FacialAreaRegion(
            x=0, y=0, w=img.shape[1], h=img.shape[0], confidence=0
        )

        if detector_backend == "skip":
            face_objs = [DetectedFace(img=img, facial_area=base_region, confidence=0)]
        else:
            face_objs = DetectorWrapper.detect_faces(
                detector_backend=detector_backend,
                img=img,
                align=align,
                expand_percentage=expand_percentage,
            )
        # logger.info(f"Detected {len(face_objs)} faces.")
        # in case of no face found
        if len(face_objs) == 0 and enforce_detection is True:
            raise ValueError(
                "Face could not be detected. Please confirm that the picture is a face photo "
                "or consider to set enforce_detection param to False."
            )

        if len(face_objs) == 0 and enforce_detection is False:
            face_objs = [DetectedFace(img=img, facial_area=base_region, confidence=0)]

        for face_obj in face_objs:
            current_img = face_obj.img
            current_region = face_obj.facial_area

            if current_img.shape[0] == 0 or current_img.shape[1] == 0:
                continue

            if grayscale is True:
                current_img = cv2.cvtColor(current_img, cv2.COLOR_BGR2GRAY)

            # resize and padding
            if target_size is not None:
                factor_0 = target_size[0] / current_img.shape[0]
                factor_1 = target_size[1] / current_img.shape[1]
                factor = min(factor_0, factor_1)

                dsize = (
                    int(current_img.shape[1] * factor),
                    int(current_img.shape[0] * factor),
                )
                current_img = cv2.resize(current_img, dsize)

                diff_0 = target_size[0] - current_img.shape[0]
                diff_1 = target_size[1] - current_img.shape[1]
                if grayscale is False:
                    # Put the base image in the middle of the padded image
                    current_img = np.pad(
                        current_img,
                        (
                            (diff_0 // 2, diff_0 - diff_0 // 2),
                            (diff_1 // 2, diff_1 - diff_1 // 2),
                            (0, 0),
                        ),
                        "constant",
                    )
                else:
                    current_img = np.pad(
                        current_img,
                        (
                            (diff_0 // 2, diff_0 - diff_0 // 2),
                            (diff_1 // 2, diff_1 - diff_1 // 2),
                        ),
                        "constant",
                    )

                # double check: if target image is not still the same size with target.
                if current_img.shape[0:2] != target_size:
                    current_img = cv2.resize(current_img, target_size)

            # normalizing the image pixels
            # what this line doing? must?
            img_pixels = image.img_to_array(current_img)
            img_pixels = np.expand_dims(img_pixels, axis=0)
            img_pixels /= 255  # normalize input in [0, 1]
            # discard expanded dimension
            if human_readable is True and len(img_pixels.shape) == 4:
                img_pixels = img_pixels[0]

            resp_objs.append(
                {
                    "face": (
                        img_pixels[:, :, ::-1] if human_readable is True else img_pixels
                    ),
                    "facial_area": {
                        "x": int(current_region.x),
                        "y": int(current_region.y),
                        "w": int(current_region.w),
                        "h": int(current_region.h),
                        "left_eye": current_region.left_eye,
                        "right_eye": current_region.right_eye,
                    },
                    "confidence": round(current_region.confidence, 2),
                }
            )

        if len(resp_objs) == 0 and enforce_detection is True:
            raise ValueError(
                "Exception while extracting faces from ...."
                "Consider to set enforce_detection arg to False."
            )

        return resp_objs

    @staticmethod
    def align_face(
        img: np.ndarray,
        left_eye: Union[list, tuple],
        right_eye: Union[list, tuple],
    ) -> Tuple[np.ndarray, float]:
        # if eye could not be detected for the given image, return the image itself
        if left_eye is None or right_eye is None:
            return img, 0

        # sometimes unexpectedly detected images come with nil dimensions
        if img.shape[0] == 0 or img.shape[1] == 0:
            return img, 0

        angle = float(
            np.degrees(
                np.arctan2(right_eye[1] - left_eye[1], right_eye[0] - left_eye[0])
            )
        )
        img = np.array(Image.fromarray(img).rotate(angle))
        return img, angle

get_audio_embedding(audios) staticmethod

Extracts and returns average audio features from a list of audio files.

Source code in Agent/modules/emotion_detection/features_extraction.py
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@staticmethod
def get_audio_embedding(audios: List[str]) -> torch.Tensor:
    """Extracts and returns average audio features from a list of audio files."""
    features = []
    for audio_path in audios:
        y, sr = librosa.load(audio_path)
        hop_length = 512
        f0 = librosa.feature.zero_crossing_rate(y, hop_length=hop_length).T
        mfcc = librosa.feature.mfcc(y=y, sr=sr, hop_length=hop_length, htk=True).T
        cqt = librosa.feature.chroma_cqt(y=y, sr=sr, hop_length=hop_length).T
        temp_feature = np.concatenate([f0, mfcc, cqt], axis=-1)
        features.append(temp_feature)
    feature = np.mean(np.concatenate(features), axis=0).reshape(1, -1)
    # get them into tensor
    feature = torch.tensor(feature).float()
    return feature

get_images_tensor(images)

Extracts features from a list of images using a specified model.

Source code in Agent/modules/emotion_detection/features_extraction.py
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def get_images_tensor(self, images: List[np.ndarray]) -> torch.Tensor:
    """Extracts features from a list of images using a specified model."""
    model_name = "OpenFace"
    image_features = [
        self.represent(image, model_name=model_name)[0]["embedding"]
        for image in images
    ]
    return torch.tensor(image_features)