Source code for dabble.bbox_to_3d_loc

# 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
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""
Estimates the 3D coordinates of an object given a 2D bounding box.
"""

from typing import Any, Dict

import numpy as np

from peekingduck.pipeline.nodes.abstract_node import AbstractNode


[docs]class Node(AbstractNode): """Uses 2D bounding boxes information to estimate 3D location. Inputs: |bboxes_data| Outputs: |obj_3D_locs_data| Configs: focal_length (:obj:`float`): **default = 1.14**. |br| Approximate focal length of webcam used, in metres. Example on measuring focal length can be found `here <https://learnopencv.com /approximate-focal-length-for-webcams-and-cell-phone-cameras/>`_. height_factor (:obj:`float`): **default = 2.5**. |br| A factor used to estimate real-world distance from pixels, based on average human height in metres. The value varies across different camera set-ups, and calibration may be required. Please refer to the :ref:`Social Distancing use case <use_case_social_distancing_using_object_detection>` for more information. """ def __init__(self, config: Dict[str, Any] = None, **kwargs: Any) -> None: super().__init__(config, node_path=__name__, **kwargs) def run(self, inputs: Dict[str, Any]) -> Dict[str, Any]: """Converts 2D bounding boxes into 3D locations.""" locations = [] for bbox in inputs["bboxes"]: # Subtraction is to make the camera the origin of the coordinate system center_2d = ((bbox[0:2] + bbox[2:4]) * 0.5) - np.array([0.5, 0.5]) bbox_height = bbox[3] - bbox[1] z_coord = (self.focal_length * self.height_factor) / bbox_height x_coord = (center_2d[0] * self.height_factor) / bbox_height y_coord = (center_2d[1] * self.height_factor) / bbox_height point = np.array([x_coord, y_coord, z_coord]) locations.append(point) outputs = {"obj_3D_locs": locations} return outputs def _get_config_types(self) -> Dict[str, Any]: """Returns dictionary mapping the node's config keys to respective types.""" return {"focal_length": float, "height_factor": float}