Module facetorch.analyzer.detector.core

Classes

class FaceDetector (downloader: BaseDownloader,
device: torch.device,
preprocessor: BaseDetPreProcessor,
postprocessor: BaseDetPostProcessor,
native_model_class: str | None = None,
compile_model: bool = False,
compile_options: dict | None = None)

Expand source code

class FaceDetector(BaseModel):
    @Timer(
        "FaceDetector.__init__", "{name}: {milliseconds:.2f} ms", logger=logger.debug
    )
    def __init__(
        self,
        downloader: BaseDownloader,
        device: torch.device,
        preprocessor: BaseDetPreProcessor,
        postprocessor: BaseDetPostProcessor,
        native_model_class: Optional[str] = None,
        compile_model: bool = False,
        compile_options: Optional[dict] = None,
    ):
        """FaceDetector is a wrapper around a neural network model that is trained to detect faces.

        Args:
            downloader (BaseDownloader): Downloader that downloads the model.
            device (torch.device): Torch device cpu or cuda for the model.
            preprocessor (BaseDetPreProcessor): Preprocessor that runs before the model.
            postprocessor (BaseDetPostProcessor): Postprocessor that runs after the model.
            native_model_class (Optional[str]): Fully qualified native model class.
            compile_model (bool): If True, compile the loaded model. Default: False.
            compile_options (Optional[dict]): Keyword arguments forwarded to
                ``torch.compile``. Default: None.
        """
        super().__init__(
            downloader,
            device,
            native_model_class=native_model_class,
            compile_model=compile_model,
            compile_options=compile_options,
        )

        self.preprocessor = preprocessor
        self.postprocessor = postprocessor

    @Timer("FaceDetector.run", "{name}: {milliseconds:.2f} ms", logger=logger.debug)
    def run(self, data: ImageData) -> ImageData:
        """Detect all faces in the image.

        Args:
            ImageData: ImageData object containing the image tensor with values between 0 - 255 and shape (batch_size, channels, height, width).

        Returns:
            ImageData: Image data object with Detection tensors and detected Face objects.
        """
        preserves_input = (
            getattr(self.preprocessor, "preserves_input_tensor", False) is True
        )
        raw_tensor = data.tensor if preserves_input else data.tensor.clone()
        img_h, img_w = raw_tensor.shape[-2:]
        data = self.preprocessor.run(data)
        detector_scale = getattr(
            data, "_facetorch_detector_coordinate_scale", (1.0, 1.0)
        )
        if hasattr(data, "_facetorch_detector_coordinate_scale"):
            delattr(data, "_facetorch_detector_coordinate_scale")
        scale_x, scale_y = detector_scale
        logits = self.inference(data.tensor)
        data = self.postprocessor.run(data, logits)
        self._scale_detection_geometry(data, scale_x, scale_y)

        data.tensor = raw_tensor
        data.set_dims()

        extract_faces = getattr(self.postprocessor, "extract_faces", None)
        if callable(extract_faces):
            data.faces = []
            data = extract_faces(data)
        else:
            data.faces = self._restore_custom_faces(
                data,
                img_w,
                img_h,
                scale_x=scale_x,
                scale_y=scale_y,
            )
        self._clamp_detection_geometry(data, img_w, img_h)

        return data

    @staticmethod
    def _scale_detection_geometry(
        data: ImageData, scale_x: float, scale_y: float
    ) -> None:
        """Map detector-image coordinates back to the source image."""
        if scale_x == 1.0 and scale_y == 1.0:
            return
        if data.det.dets.numel() > 0 and data.det.dets.ndim == 2:
            dets = data.det.dets.clone()
            dets[:, 0].mul_(scale_x)
            dets[:, 2].mul_(scale_x)
            dets[:, 1].mul_(scale_y)
            dets[:, 3].mul_(scale_y)
            data.det.dets = dets
        if data.det.boxes.numel() > 0 and data.det.boxes.ndim == 2:
            boxes = data.det.boxes.clone()
            boxes[:, 0].mul_(scale_x)
            boxes[:, 2].mul_(scale_x)
            boxes[:, 1].mul_(scale_y)
            boxes[:, 3].mul_(scale_y)
            data.det.boxes = boxes
        if data.det.landmarks.numel() > 0 and data.det.landmarks.ndim == 2:
            landmarks = data.det.landmarks.clone()
            landmarks[:, 0::2].mul_(scale_x)
            landmarks[:, 1::2].mul_(scale_y)
            data.det.landmarks = landmarks

    @staticmethod
    def _clamp_detection_geometry(
        data: ImageData, image_width: int, image_height: int
    ) -> None:
        """Keep all public detector geometry in original-image coordinates."""
        if data.det.dets.numel() > 0 and data.det.dets.ndim == 2:
            data.det.dets[:, 0].clamp_(0, image_width)
            data.det.dets[:, 2].clamp_(0, image_width)
            data.det.dets[:, 1].clamp_(0, image_height)
            data.det.dets[:, 3].clamp_(0, image_height)
        if data.det.boxes.numel() > 0 and data.det.boxes.ndim == 2:
            data.det.boxes[:, 0].clamp_(0, image_width)
            data.det.boxes[:, 2].clamp_(0, image_width)
            data.det.boxes[:, 1].clamp_(0, image_height)
            data.det.boxes[:, 3].clamp_(0, image_height)
        if data.det.landmarks.numel() > 0 and data.det.landmarks.ndim == 2:
            data.det.landmarks[:, 0::2].clamp_(0, image_width)
            data.det.landmarks[:, 1::2].clamp_(0, image_height)

    @staticmethod
    def _restore_custom_faces(
        data: ImageData,
        image_width: int,
        image_height: int,
        *,
        scale_x: float = 1.0,
        scale_y: float = 1.0,
    ) -> list:
        """Validate and recrop faces produced directly by a custom postprocessor."""
        restored = []
        image_area = image_width * image_height
        for face in data.faces:
            loc = Location(
                x1=round(face.loc.x1 * scale_x),
                y1=round(face.loc.y1 * scale_y),
                x2=round(face.loc.x2 * scale_x),
                y2=round(face.loc.y2 * scale_y),
            )
            loc.clamp(image_width, image_height)
            face_tensor = data.tensor[0, :, loc.y1 : loc.y2, loc.x1 : loc.x2]
            if face_tensor.numel() == 0:
                continue
            dims = Dimensions(
                height=int(face_tensor.shape[-2]),
                width=int(face_tensor.shape[-1]),
            )
            restored.append(
                Face(
                    indx=len(restored),
                    loc=loc,
                    dims=dims,
                    tensor=face_tensor,
                    ratio=(dims.height * dims.width) / image_area,
                    preds=face.preds,
                )
            )
        return restored

FaceDetector is a wrapper around a neural network model that is trained to detect faces.

Args

-----=

downloader : BaseDownloader

Downloader that downloads the model.

device : torch.device

Torch device cpu or cuda for the model.

preprocessor : BaseDetPreProcessor

Preprocessor that runs before the model.

postprocessor : BaseDetPostProcessor

Postprocessor that runs after the model.

native_model_class : Optional[str]

Fully qualified native model class.

compile_model : bool

If True, compile the loaded model. Default: False.

compile_options : Optional[dict]

Keyword arguments forwarded to torch.compile. Default: None.

Ancestors

• BaseModel

Methods

def run(self,
data: ImageData) ‑> ImageData

Expand source code

@Timer("FaceDetector.run", "{name}: {milliseconds:.2f} ms", logger=logger.debug)
def run(self, data: ImageData) -> ImageData:
    """Detect all faces in the image.

    Args:
        ImageData: ImageData object containing the image tensor with values between 0 - 255 and shape (batch_size, channels, height, width).

    Returns:
        ImageData: Image data object with Detection tensors and detected Face objects.
    """
    preserves_input = (
        getattr(self.preprocessor, "preserves_input_tensor", False) is True
    )
    raw_tensor = data.tensor if preserves_input else data.tensor.clone()
    img_h, img_w = raw_tensor.shape[-2:]
    data = self.preprocessor.run(data)
    detector_scale = getattr(
        data, "_facetorch_detector_coordinate_scale", (1.0, 1.0)
    )
    if hasattr(data, "_facetorch_detector_coordinate_scale"):
        delattr(data, "_facetorch_detector_coordinate_scale")
    scale_x, scale_y = detector_scale
    logits = self.inference(data.tensor)
    data = self.postprocessor.run(data, logits)
    self._scale_detection_geometry(data, scale_x, scale_y)

    data.tensor = raw_tensor
    data.set_dims()

    extract_faces = getattr(self.postprocessor, "extract_faces", None)
    if callable(extract_faces):
        data.faces = []
        data = extract_faces(data)
    else:
        data.faces = self._restore_custom_faces(
            data,
            img_w,
            img_h,
            scale_x=scale_x,
            scale_y=scale_y,
        )
    self._clamp_detection_geometry(data, img_w, img_h)

    return data

Detect all faces in the image.

Args

-----=

ImageData

ImageData object containing the image tensor with values between 0 - 255 and shape (batch_size, channels, height, width).

Returns

-----=

ImageData

Image data object with Detection tensors and detected Face objects.

Inherited members

• BaseModel:
  • inference
  • load_model