# Copyright 2022 AI Singapore
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
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"""Converts bounding boxes to a single point of reference."""
from typing import Any, Dict, Tuple
from peekingduck.pipeline.nodes.abstract_node import AbstractNode
[docs]class Node(AbstractNode):
"""Converts bounding boxes to a single point which is the bottom midpoint
of the bounding box.
This node is primarily used for zone counting. The bottom midpoint is an
unambiguous way of telling whether an object is in the zone specified, as
the bottom midpoint usually corresponds to the point where the object is
located.
Inputs:
|img_data|
|bboxes_data|
Outputs:
|btm_midpoint_data|
Configs:
None.
"""
def __init__(self, config: Dict[str, Any] = None, **kwargs: Any) -> None:
super().__init__(config, node_path=__name__, **kwargs)
self.img_size = None
def run(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
"""Converts bounding boxes to a single point of reference for use in
zone analytics.
"""
# get xy midpoint of each bbox (x1, y1, x2, y2)
# This is calculated as x is (x1-x2)/2 and y is y2
bboxes = inputs["bboxes"]
frame = inputs["img"]
self.img_size = (frame.shape[1], frame.shape[0]) # type:ignore
return {
"btm_midpoint": [
self._xy_on_img(((bbox[0] + bbox[2]) / 2), bbox[3]) for bbox in bboxes
]
}
def _xy_on_img(self, pt_x: float, pt_y: float) -> Tuple[int, int]:
"""Return the int x y points of the midpoint on the original image"""
assert self.img_size is not None
return (int(pt_x * self.img_size[0]), int(pt_y * self.img_size[1]))