dev(narugo): add docs

from imgutils.pose import dwpose_estimate, op18_visualize

from plot import image_plot

def _detect(img, **kwargs):

    return op18_visualize(img, dwpose_estimate(img), **kwargs)

if __name__ == '__main__':

    image_plot(

        ('dwpose/lie.jpg', 'lie (original)'),

        (_detect('dwpose/lie.jpg'), 'lie (keypoints)'),

        ('dwpose/squat.jpg', 'squat (original)'),

        (_detect('dwpose/squat.jpg'), 'squat (keypoints)'),

        columns=2,

        figsize=(9, 9),

        autocensor=False,

    )

"""

Overview:

    Utilities to detect human keypoints in anime images.

    .. image:: pose_demo.plot.py.svg

        :align: center

"""

from .dwpose import dwpose_estimate

from .format import OP18KeyPointSet, OP18_BODY_MAX, OP18_BODY_MIN, OP18_FACE_MAX, OP18_FACE_MIN, \

    OP18_LEFT_FOOT_MAX, OP18_LEFT_FOOT_MIN, OP18_LEFT_HAND_MAX, OP18_LEFT_HAND_MIN, \

"""

Overview:

    Detect human keypoints in anime images.

    The model is from `https://github.com/IDEA-Research/DWPose <https://github.com/IDEA-Research/DWPose>`_.

    .. image:: dwpose_demo.plot.py.svg

        :align: center

    This is an overall benchmark of all the keypoint detect models:

    .. image:: dwpose_benchmark.plot.py.svg

        :align: center

"""

import warnings

from functools import lru_cache

from typing import Tuple, List

    """

    Performs inference on the RTMPose model and returns keypoints and scores.

    Args:

        image (ImageTyping): Input image.

        auto_detect: Auto detect person with :func:`imgutils.detect.detect_person`.

        out_bboxes (List[Tuple[int, int, int, int]], optional): Bounding boxes.

        person_detect_cfgs: Config arguments for :func:`imgutils.detect.detect_person`.

    Returns:

        List of mapping of different parts, including ``all``, ``head``, ``body``, ``foot``, ``hand1`` and ``hand2``.

    :param image: Input image.

    :type image: ImageTyping

    :param auto_detect: Auto detect person with :func:`imgutils.detect.person.detect_person`.

    :type auto_detect: bool

    :param out_bboxes: Bounding boxes.

    :type out_bboxes: Optional[List[Tuple[int, int, int, int]]]

    :param person_detect_cfgs: Config arguments for :func:`imgutils.detect.person.detect_person`.

    :type person_detect_cfgs: Optional[Dict]

    :return: List of mapping of different parts, including ``all``, ``head``, ``body``, ``foot``, ``hand1`` and ``hand2``.

    :rtype: List[OP18KeyPointSet]

    Examples:

        >>> from imgutils.data import load_image

        >>> from imgutils.pose import dwpose_estimate, op18_visualize

        >>>

        >>> image = load_image('dwpose/squat.jpg')

        >>> keypoints = dwpose_estimate(image)

        >>> keypoints

        [<imgutils.pose.format.OP18KeyPointSet object at 0x7f5ca933f3d0>]

        >>>

        >>> from matplotlib import pyplot as plt

        >>> plt.imshow(op18_visualize(image, keypoints))

        <matplotlib.image.AxesImage object at 0x7f5c98069790>

        >>> plt.show()

        .. note::

            Function :func:`imgutils.pose.visual.op18_visualize` can be used to visualize this result.

    """

    session = _open_dwpose_model()

    h, w = session.get_inputs()[0].shape[-2:]

Jinja2~=3.0.0

sphinx~=3.2.0

sphinx_rtd_theme~=0.4.3

sphinx>=3.2.0

sphinx_rtd_theme>=0.4.3

enum_tools~=0.9.0

sphinx-toolbox

plantumlcli>=0.0.2