# yunet Python API: `depthai_nodes.node.parsers.yunet` ## Classes ### YuNetParser Parser class for parsing the output of the YuNet face detection model. > **Note** > Emits `dai.ImgDetections` messages. dai.ImgDetections message containing bounding boxes, labels, confidence scores, and keypoints of detected faces. #### Methods ##### init ```python def __init__(conf_threshold: float = 0.8, iou_threshold: float = 0.3, max_det: int = 5000, input_size: tuple[int, int] = None, loc_output_layer_name: str = None, conf_output_layer_name: str = None, iou_output_layer_name: str = None): ``` Initializes the parser node. Parameters * `conf_threshold` (`float`): Confidence score threshold for detected faces. * `iou_threshold` (`float`): Non-maximum suppression threshold. * `max_det` (`int`): Maximum number of detections to keep. * `input_size` (`tuple[int, int]`): Input size of the model (width, height). * `loc_output_layer_name` (`str`): Output layer name for the location predictions. * `conf_output_layer_name` (`str`): Output layer name for the confidence predictions. * `iou_output_layer_name` (`str`): Output layer name for the IoU predictions. ##### build ```python def build(head_config: dict[str, Any]) -> YuNetParser: ``` Configures the parser. Parameters * `head_config` (`dict[str, Any]`): The head configuration for the parser. Returns * `YuNetParser`: The parser object with the head configuration set. ##### compute ```python def compute(*, input_size: tuple[int, int], loc: np.ndarray, conf: np.ndarray, iou: np.ndarray, conf_threshold: float, iou_threshold: float, max_det: int, anchors: np.ndarray, label_names: list[str] | None = None, nms_fn: Callable[..., np.ndarray], top_left_wh_to_xywh_fn: Callable[[np.ndarray], np.ndarray]) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, list[str] | None]: ``` Compute parser results from extracted tensors without sending messages. > **Note** > Uses [depthai_nodes.node.parsers.utils.yunet.compute_yunet_detections](https://docs.luxonis.com/software-v3/ai-inference/inference/depthai-nodes/depthai-nodes-api-reference/node/parsers/utils/yunet.md); see that helper for tensor layout and validation details. Parameters * `input_size` (`tuple[int, int]`): Model input size as `(width, height)`. * `loc` (`np.ndarray`): Per-anchor box and five-landmark offsets. * `conf` (`np.ndarray`): Per-anchor class-confidence tensor. * `iou` (`np.ndarray`): Per-anchor IoU confidence tensor. * `conf_threshold` (`float`): Minimum detection confidence used to filter candidates. * `iou_threshold` (`float`): Intersection-over-union threshold for non-maximum suppression. * `max_det` (`int`): Maximum number of detection candidates to retain or consider during suppression. * `anchors` (`np.ndarray`): Precomputed anchor coordinates used to decode model predictions. * `label_names` (`list[str] | None`): Optional class-name lookup indexed by predicted class ID. * `nms_fn` (`Callable[..., np.ndarray]`): Suppression callable accepting boxes, scores, confidence/IoU thresholds, and `max_det`; returns retained indexes. * `top_left_wh_to_xywh_fn` (`Callable[[np.ndarray], np.ndarray]`): Callable converting top-left XY/width/height boxes to center-XY/width/height. Returns * `tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, list[str] | None]`: Normalized center-XY/width/height boxes, five normalized XY landmarks per face, scores, zero-valued class IDs, and optional class names. No candidates produces empty arrays. ##### emit ```python def emit(output: dai.NNData, bboxes: np.ndarray, keypoints: np.ndarray, scores: np.ndarray, labels: np.ndarray, label_names: list[str] | None): ``` Create a `dai.ImgDetections` message and send it on `out`. Copies source timestamps and sequence number, and carries the source image transformation when present. Parameters * `output` (`dai.NNData`): Neural network output carrying tensors and source timestamps, sequence number, and optional image transformation. * `bboxes` (`np.ndarray`): Normalized center-XY/width/height boxes returned by `compute()`. * `keypoints` (`np.ndarray`): Normalized keypoint coordinates returned by `compute()`. * `scores` (`np.ndarray`): Confidence scores corresponding to the computed payload. * `labels` (`np.ndarray`): Integer class IDs corresponding to the boxes. * `label_names` (`list[str] | None`): Optional class names corresponding to the detections. ##### extract ```python def extract(output: dai.NNData) -> tuple[np.ndarray, np.ndarray, np.ndarray]: ``` Select and dequantize the model tensors needed for parsing. Parameters * `output` (`dai.NNData`): Neural network output carrying tensors and source timestamps, sequence number, and optional image transformation. Returns * `tuple[np.ndarray, np.ndarray, np.ndarray]`: The localization, class-confidence, and IoU tensors, in that order. Missing configured names are inferred from unique `loc`, `conf`, and `iou` prefixes. Raises * `ValueError`: If a configured layer is absent or inferred layer prefixes are missing or ambiguous. ##### run ```python def run(self): ``` Read queued network outputs, parse them, and emit results while running. The pipeline invokes this processing loop. It exits when the input queue closes or the node stops. ##### setInputSize ```python def setInputSize(input_size: tuple[int, int]): ``` Sets the input size of the model. Parameters * `input_size` (`tuple[int, int]`): Input size of the model (width, height). ##### setOutputLayerConf ```python def setOutputLayerConf(conf_output_layer_name: str): ``` Sets the name of the output layer containing the confidence predictions. Parameters * `conf_output_layer_name` (`str`): Output layer name for the conf tensor. ##### setOutputLayerIou ```python def setOutputLayerIou(iou_output_layer_name: str): ``` Sets the name of the output layer containing the IoU predictions. Parameters * `iou_output_layer_name` (`str`): Output layer name for the IoU tensor. ##### setOutputLayerLoc ```python def setOutputLayerLoc(loc_output_layer_name: str): ``` Sets the name of the output layer containing the location predictions. Parameters * `loc_output_layer_name` (`str`): Output layer name for the loc tensor. #### Attributes ##### conf_output_layer_name Name of the output layer containing the confidence predictions. ##### conf_threshold Confidence score threshold for detected faces. ##### input_shape ##### input_size Input size (width, height). ##### iou_output_layer_name Name of the output layer containing the IoU predictions. ##### iou_threshold Non-maximum suppression threshold. ##### label_names ##### layout ##### loc_output_layer_name Name of the output layer containing the location predictions. ##### max_det Maximum number of detections to keep.