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"""🔲 Scalable and efficient object detection."""
from typing import Any, Dict, List, Optional, Union
import cv2
import numpy as np
from peekingduck.pipeline.nodes.abstract_node import AbstractNode
from peekingduck.pipeline.nodes.model.efficientdet_d04 import efficientdet_model
[docs]class Node(AbstractNode):
"""Initializes an EfficientDet model to detect bounding boxes from an image.
The EfficientDet node is capable of detecting objects from 80 categories.
The table of categories can be found
:ref:`here <general-object-detection-ids>`.
EfficientDet node has five levels of compound coefficient (0 - 4). A higher
compound coefficient will scale up all dimensions of the backbone network
width, depth, input resolution, feature network, and box/class prediction
at the same time, which results in better performance but slower inference
time. The default compound coefficient is 0 and can be changed to other
values.
Inputs:
|img_data|
Outputs:
|bboxes_data|
|bbox_labels_data|
|bbox_scores_data|
Configs:
model_type (:obj:`int`): **{0, 1, 2, 3, 4}, default = 0**. |br|
Defines the compound coefficient for EfficientDet.
score_threshold (:obj:`float`): **[0, 1], default = 0.3**.
Bounding boxes with confidence score below the threshold will be
discarded.
detect (:obj:`List[Union[int, str]]`): **default = [0]**. |br|
List of object class names or IDs to be detected. To detect all classes,
refer to the :ref:`tech note <general-object-detection-ids>`.
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.
References:
EfficientDet: Scalable and Efficient Object Detection:
https://arxiv.org/abs/1911.09070
Code adapted from https://github.com/xuannianz/EfficientDet.
"""
def __init__(self, config: Dict[str, Any] = None, **kwargs: Any) -> None:
super().__init__(config, node_path=__name__, **kwargs)
self.model = efficientdet_model.EfficientDetModel(self.config)
def run(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
"""Takes an image as input and returns bboxes of objects specified
in config.
"""
image = cv2.cvtColor(inputs["img"], cv2.COLOR_BGR2RGB)
bboxes, labels, scores = self.model.predict(image)
bboxes = np.clip(bboxes, 0, 1)
outputs = {"bboxes": bboxes, "bbox_labels": labels, "bbox_scores": scores}
return outputs
def _get_config_types(self) -> Dict[str, Any]:
"""Returns dictionary mapping the node's config keys to respective types."""
return {
"detect": List[Union[int, str]],
"model_type": int,
"score_threshold": float,
"weights_parent_dir": Optional[str],
}