<filename>peekingduck/pipeline/nodes/heuristic/bbox_to_btm_midpoint.py
# Copyright 2021 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.

"""
Converts bounding boxes to a single point of reference
"""

from typing import Dict, Any, Tuple
from peekingduck.pipeline.nodes.node import AbstractNode


class Node(AbstractNode):
    """This node 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
    at which the object is located.

    Inputs:
        |img|


        |bboxes|

    Outputs:
        |btm_midpoint|

    Configs:
        None

    """

    def __init__(self, config: Dict[str, Any]) -> None:
        super().__init__(config, node_path=__name__)
        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]))

<filename>peekingduck/pipeline/nodes/heuristic/bbox_to_btm_midpoint.py
# Copyright 2021 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.

"""
Converts bounding boxes to a single point of reference
"""

from typing import Dict, Any, Tuple
from peekingduck.pipeline.nodes.node import AbstractNode


class Node(AbstractNode):
    """This node 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
    at which the object is located.

    Inputs:
        |img|


        |bboxes|

    Outputs:
        |btm_midpoint|

    Configs:
        None

    """

    def __init__(self, config: Dict[str, Any]) -> None:
        super().__init__(config, node_path=__name__)
        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]))

# Copyright 2021 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. Converts bounding boxes to a single point of reference This node 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     at which the object is located.      Inputs:         |img|           |bboxes|      Outputs:         |btm_midpoint|      Configs:         None 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 # type:ignore Return the int x y points of the midpoint on the original image