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"""🔲 Multi-task Cascaded Convolutional Networks for face detection. Works best
with unmasked faces.
"""
from typing import Any, Dict, List, Optional
import numpy as np
from peekingduck.pipeline.nodes.abstract_node import AbstractNode
from peekingduck.pipeline.nodes.model.mtcnnv1 import mtcnn_model
[docs]class Node(AbstractNode):
"""Initializes and uses the MTCNN model to infer bboxes from an image
frame.
The MTCNN node is a single-class model capable of detecting human faces. To
a certain extent, it is also capable of detecting bounding boxes around
faces with face masks (e.g. surgical masks).
Inputs:
|img_data|
Outputs:
|bboxes_data|
|bbox_scores_data|
|bbox_labels_data|
Configs:
weights_parent_dir (:obj:`Optional[str]`): **default = null**. |br|
Change the parent directory where weights will be stored by
replacing ``null`` with an absolute path to the desired directory.
min_size (:obj:`int`): **default = 40**. |br|
Minimum height and width of face in pixels to be detected.
scale_factor (:obj:`float`): **[0, 1], default = 0.709**. |br|
Scale factor to create the image pyramid. A larger scale factor
produces more accurate detections at the expense of inference
speed.
network_thresholds (:obj:`List[float]`):
**[0, 1], default = [0.6, 0.7, 0.7]**. |br|
Threshold values for the Proposal Network (P-Net), Refine Network
(R-Net) and Output Network (O-Net) in the MTCNN model.
Calibration is performed at each stage in which bounding boxes with
confidence scores less than the specified threshold are discarded.
score_threshold (:obj:`float`): **[0, 1], default = 0.7**. |br|
Bounding boxes with confidence scores less than the specified
threshold in the final output are discarded.
References:
Joint Face Detection and Alignment using Multi-task Cascaded
Convolutional Networks:
https://arxiv.org/ftp/arxiv/papers/1604/1604.02878.pdf
Model weights trained by https://github.com/blaueck/tf-mtcnn
.. versionchanged:: 1.2.0 |br|
``mtcnn_min_size`` is renamed to ``min_size``. |br|
``mtcnn_factor`` is renamed to ``scale_factor``. |br|
``mtcnn_thresholds`` is renamed to ``network_thresholds``. |br|
``mtcnn_score`` is renamed to ``score_threshold``.
"""
def __init__(self, config: Dict[str, Any] = None, **kwargs: Any) -> None:
super().__init__(config, node_path=__name__, **kwargs)
self.model = mtcnn_model.MTCNNModel(self.config)
def run(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
"""Reads the image input and returns the bboxes, scores and labels of
faces detected.
Args:
inputs (dict): Dictionary of inputs with key "img".
Returns:
outputs (dict): Outputs in dictionary format with keys "bboxes",
"bbox_scores", and "bbox_labels".
"""
bboxes, bbox_scores, _ = self.model.predict(inputs["img"])
bbox_labels = np.array(["face"] * len(bboxes))
bboxes = np.clip(bboxes, 0, 1)
return {
"bboxes": bboxes,
"bbox_labels": bbox_labels,
"bbox_scores": bbox_scores,
}
def _get_config_types(self) -> Dict[str, Any]:
"""Returns dictionary mapping the node's config keys to respective types."""
return {
"min_size": int,
"network_thresholds": List[float],
"scale_factor": float,
"score_threshold": float,
"weights_parent_dir": Optional[str],
}