openpose.py

# Copyright (c) 2023, Teriks
#
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import typing

import PIL.Image
import cv2
import numpy

import dgenerate.extras.controlnet_aux as _cna
import dgenerate.extras.controlnet_aux.open_pose as _cna_open_pose
import dgenerate.extras.controlnet_aux.util as _cna_util
import dgenerate.image as _image
import dgenerate.imageprocessors.imageprocessor as _imageprocessor
import dgenerate.textprocessing as _textprocessing
import dgenerate.types as _types


class OpenPoseProcessor(_imageprocessor.ImageProcessor):
    """
    Generate an OpenPose rigging from the input image (of a human/humanoid) for use with a ControlNet.

    "include-body" is a boolean value indicating if a body rigging should be generated.

    "include-hand" is a boolean value indicating if a detailed hand/finger rigging should be generated.

    "include-face" is a boolean value indicating if a detailed face rigging should be generated.

    The argument "detect-resolution" is the resolution the image is resized to internal to the processor before
    detection is run on it. It should be a single dimension for example: "detect-resolution=512" or the X/Y dimensions
    seperated by an "x" character, like so: "detect-resolution=1024x512". If you do not specify this argument,
    the detector runs on the input image at its full resolution. After processing the image will be resized to
    whatever you have requested dgenerate resize it to via --output-size or --resize/--align in the case of the
    image-process sub-command, if you have not requested any resizing the output will be resized back to the original
    size of the input image.

    The argument "detect-aspect" determines if the image resize requested by "detect-resolution" before
    detection runs is aspect correct, this defaults to true.

    The argument "detect-align" determines the pixel alignment of the image resize requested by
    "detect-resolution", it defaults to 1 indicating no requested alignment.

    The "pre-resize" argument determines if the processing occurs before or after dgenerate resizes the image.
    This defaults to False, meaning the image is processed after dgenerate is done resizing it.
    """

    NAMES = ['openpose']

    def __init__(self,
                 include_body: bool = True,
                 include_hand: bool = False,
                 include_face: bool = False,
                 detect_resolution: typing.Optional[str] = None,
                 detect_aspect: bool = True,
                 detect_align: int = 1,
                 pre_resize: bool = False,
                 **kwargs):
        """

        :param include_body: generate a body rig?
        :param include_hand: include detailed hand rigging?
        :param include_face: include detailed face rigging?
        :param pre_resize: process the image before it is resized, or after? default is after (False)
        :param detect_resolution: the input image is resized to this dimension before being processed,
            providing ``None`` indicates it is not to be resized.  If there is no resize requested during
            the processing action via ``resize_resolution`` it will be resized back to its original size.
        :param detect_aspect: if the input image is resized by ``detect_resolution`` or ``detect_align``
            before processing, will it be an aspect correct resize?
        :param detect_align: the input image is forcefully aligned to this amount of pixels
            before being processed.
        :param pre_resize: process the image before it is resized, or after? default is ``False`` (after).
        :param kwargs: forwarded to base class
        """

        super().__init__(**kwargs)

        if detect_align < 1:
            raise self.argument_error('Argument "detect-align" may not be less than 1.')

        self._include_body = include_body
        self._include_hand = include_hand
        self._include_face = include_face
        self._pre_resize = pre_resize
        self._detect_align = detect_align
        self._detect_aspect = detect_aspect

        if detect_resolution is not None:
            try:
                self._detect_resolution = _textprocessing.parse_image_size(detect_resolution)
            except ValueError:
                raise self.argument_error('Could not parse the "detect-resolution" argument as an image dimension.')
        else:
            self._detect_resolution = None

        self._openpose = _cna.OpenposeDetector.from_pretrained('lllyasviel/Annotators')
        self.register_module(self._openpose)

    def __str__(self):
        args = [
            ('include_body', self._include_body),
            ('include_hand', self._include_hand),
            ('include_face', self._include_face),
            ('include_face', self._include_face),
            ('detect_resolution', self._detect_resolution),
            ('detect_aspect', self._detect_aspect),
            ('detect_align', self._detect_align),
            ('pre_resize', self._pre_resize)
        ]
        return f'{self.__class__.__name__}({", ".join(f"{k}={v}" for k, v in args)})'

    def _process(self, image, resize_resolution, return_to_original_size=False):
        original_size = image.size

        with image:
            resized = _image.resize_image(
                image,
                self._detect_resolution,
                aspect_correct=self._detect_aspect,
                align=self._detect_align
            )

        image = resized

        input_image = _cna_util.HWC3(numpy.array(image, dtype=numpy.uint8))

        height, width = input_image.shape[:2]

        poses = self._openpose.detect_poses(input_image,
                                            self._include_hand,
                                            self._include_face)

        canvas = _cna_open_pose.draw_poses(poses, height, width,
                                           draw_body=self._include_body,
                                           draw_hand=self._include_hand,
                                           draw_face=self._include_face)

        detected_map = _cna_util.HWC3(canvas)

        if resize_resolution is not None:
            detected_map = cv2.resize(detected_map, resize_resolution, interpolation=cv2.INTER_LINEAR)
        elif self._detect_resolution is not None and return_to_original_size:
            detected_map = cv2.resize(detected_map, original_size, interpolation=cv2.INTER_LINEAR)

        return PIL.Image.fromarray(detected_map)

    def impl_pre_resize(self, image: PIL.Image.Image, resize_resolution: _types.OptionalSize):
        """
        Pre resize, OpenPose rig generation may or may not occur here depending
        on the boolean value of the processor argument "pre-resize"


        :param image: image to process
        :param resize_resolution: purely informational, is unused by this processor
        :return: possibly an OpenPose rig image, or the input image
        """
        if self._pre_resize:
            return self._process(image, resize_resolution, return_to_original_size=True)
        return image

    def impl_post_resize(self, image: PIL.Image.Image):
        """
        Post resize, OpenPose rig generation may or may not occur here depending
        on the boolean value of the processor argument "pre-resize"

        :param image: image to process
        :return: possibly an OpenPose rig image, or the input image
        """
        if not self._pre_resize:
            return self._process(image, None)
        return image


__all__ = _types.module_all()