annotator/openpose/__init__.py

# Openpose
# Original from CMU https://github.com/CMU-Perceptual-Computing-Lab/openpose
# 2nd Edited by https://github.com/Hzzone/pytorch-openpose
# 3rd Edited by ControlNet
# 4th Edited by ControlNet (added face and correct hands)
# 5th Edited by ControlNet (Improved JSON serialization/deserialization, and lots of bug fixs)
# This preprocessor is licensed by CMU for non-commercial use only.


import os
os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"

import json
import torch
import numpy as np
from . import util
from .body import Body, BodyResult, Keypoint
from .hand import Hand
from .face import Face
from .types import PoseResult, HandResult, FaceResult
from modules import devices
from annotator.annotator_path import models_path

from typing import Tuple, List, Callable, Union, Optional

body_model_path = "https://huggingface.co/lllyasviel/Annotators/resolve/main/body_pose_model.pth"
hand_model_path = "https://huggingface.co/lllyasviel/Annotators/resolve/main/hand_pose_model.pth"
face_model_path = "https://huggingface.co/lllyasviel/Annotators/resolve/main/facenet.pth"

remote_onnx_det = "https://huggingface.co/yzd-v/DWPose/resolve/main/yolox_l.onnx"
remote_onnx_pose = "https://huggingface.co/yzd-v/DWPose/resolve/main/dw-ll_ucoco_384.onnx"


def draw_poses(poses: List[PoseResult], H, W, draw_body=True, draw_hand=True, draw_face=True):
    """
    Draw the detected poses on an empty canvas.

    Args:
        poses (List[PoseResult]): A list of PoseResult objects containing the detected poses.
        H (int): The height of the canvas.
        W (int): The width of the canvas.
        draw_body (bool, optional): Whether to draw body keypoints. Defaults to True.
        draw_hand (bool, optional): Whether to draw hand keypoints. Defaults to True.
        draw_face (bool, optional): Whether to draw face keypoints. Defaults to True.

    Returns:
        numpy.ndarray: A 3D numpy array representing the canvas with the drawn poses.
    """
    canvas = np.zeros(shape=(H, W, 3), dtype=np.uint8)

    for pose in poses:
        if draw_body:
            canvas = util.draw_bodypose(canvas, pose.body.keypoints)

        if draw_hand:
            canvas = util.draw_handpose(canvas, pose.left_hand)
            canvas = util.draw_handpose(canvas, pose.right_hand)

        if draw_face:
            canvas = util.draw_facepose(canvas, pose.face)

    return canvas


def decode_json_as_poses(json_string: str, normalize_coords: bool = False) -> Tuple[List[PoseResult], int, int]:
    """ Decode the json_string complying with the openpose JSON output format
    to poses that controlnet recognizes.
    https://github.com/CMU-Perceptual-Computing-Lab/openpose/blob/master/doc/02_output.md

    Args:
        json_string: The json string to decode.
        normalize_coords: Whether to normalize coordinates of each keypoint by canvas height/width.
                          `draw_pose` only accepts normalized keypoints. Set this param to True if
                          the input coords are not normalized.
    
    Returns:
        poses
        canvas_height
        canvas_width                      
    """
    pose_json = json.loads(json_string)
    height = pose_json['canvas_height']
    width = pose_json['canvas_width']

    def chunks(lst, n):
        """Yield successive n-sized chunks from lst."""
        for i in range(0, len(lst), n):
            yield lst[i:i + n]
    
    def decompress_keypoints(numbers: Optional[List[float]]) -> Optional[List[Optional[Keypoint]]]:
        if not numbers:
            return None
        
        assert len(numbers) % 3 == 0

        def create_keypoint(x, y, c):
            if c < 1.0:
                return None
            keypoint = Keypoint(x, y)
            return keypoint

        return [
            create_keypoint(x, y, c)
            for x, y, c in chunks(numbers, n=3)
        ]
    
    return (
        [
            PoseResult(
                body=BodyResult(keypoints=decompress_keypoints(pose.get('pose_keypoints_2d'))),
                left_hand=decompress_keypoints(pose.get('hand_left_keypoints_2d')),
                right_hand=decompress_keypoints(pose.get('hand_right_keypoints_2d')),
                face=decompress_keypoints(pose.get('face_keypoints_2d'))
            )
            for pose in pose_json['people']
        ],
        height,
        width,
    )


def encode_poses_as_json(poses: List[PoseResult], canvas_height: int, canvas_width: int) -> dict:
    """ Encode the pose as a JSON compatible dict following openpose JSON output format:
    https://github.com/CMU-Perceptual-Computing-Lab/openpose/blob/master/doc/02_output.md
    """
    def compress_keypoints(keypoints: Union[List[Keypoint], None]) -> Union[List[float], None]:
        if not keypoints:
            return None
        
        return [
            value
            for keypoint in keypoints
            for value in (
                [float(keypoint.x), float(keypoint.y), 1.0]
                if keypoint is not None
                else [0.0, 0.0, 0.0]
            )
        ]

    return {
        'people': [
            {
                'pose_keypoints_2d': compress_keypoints(pose.body.keypoints),
                "face_keypoints_2d": compress_keypoints(pose.face),
                "hand_left_keypoints_2d": compress_keypoints(pose.left_hand),
                "hand_right_keypoints_2d":compress_keypoints(pose.right_hand),
            }
            for pose in poses
        ],
        'canvas_height': canvas_height,
        'canvas_width': canvas_width,
    }

class OpenposeDetector:
    """
    A class for detecting human poses in images using the Openpose model.

    Attributes:
        model_dir (str): Path to the directory where the pose models are stored.
    """
    model_dir = os.path.join(models_path, "openpose")

    def __init__(self):
        self.device = devices.get_device_for("controlnet")
        self.body_estimation = None
        self.hand_estimation = None
        self.face_estimation = None

        self.dw_pose_estimation = None

    def load_model(self):
        """
        Load the Openpose body, hand, and face models.
        """
        body_modelpath = os.path.join(self.model_dir, "body_pose_model.pth")
        hand_modelpath = os.path.join(self.model_dir, "hand_pose_model.pth")
        face_modelpath = os.path.join(self.model_dir, "facenet.pth")

        if not os.path.exists(body_modelpath):
            from basicsr.utils.download_util import load_file_from_url
            load_file_from_url(body_model_path, model_dir=self.model_dir)

        if not os.path.exists(hand_modelpath):
            from basicsr.utils.download_util import load_file_from_url
            load_file_from_url(hand_model_path, model_dir=self.model_dir)

        if not os.path.exists(face_modelpath):
            from basicsr.utils.download_util import load_file_from_url
            load_file_from_url(face_model_path, model_dir=self.model_dir)

        self.body_estimation = Body(body_modelpath)
        self.hand_estimation = Hand(hand_modelpath)
        self.face_estimation = Face(face_modelpath)
    
    def load_dw_model(self):
        from .wholebody import Wholebody # DW Pose

        def load_model(filename: str, remote_url: str):
            local_path = os.path.join(self.model_dir, filename)
            if not os.path.exists(local_path):
                from basicsr.utils.download_util import load_file_from_url
                load_file_from_url(remote_url, model_dir=self.model_dir)
            return local_path

        onnx_det = load_model("yolox_l.onnx", remote_onnx_det)
        onnx_pose  = load_model("dw-ll_ucoco_384.onnx", remote_onnx_pose)
        self.dw_pose_estimation = Wholebody(onnx_det, onnx_pose)

    def unload_model(self):
        """
        Unload the Openpose models by moving them to the CPU.
        Note: DW Pose models always run on CPU, so no need to `unload` them.
        """
        if self.body_estimation is not None:
            self.body_estimation.model.to("cpu")
            self.hand_estimation.model.to("cpu")
            self.face_estimation.model.to("cpu")

    def detect_hands(self, body: BodyResult, oriImg) -> Tuple[Union[HandResult, None], Union[HandResult, None]]:
        left_hand = None
        right_hand = None
        H, W, _ = oriImg.shape
        for x, y, w, is_left in util.handDetect(body, oriImg):
            peaks = self.hand_estimation(oriImg[y:y+w, x:x+w, :]).astype(np.float32)
            if peaks.ndim == 2 and peaks.shape[1] == 2:
                peaks[:, 0] = np.where(peaks[:, 0] < 1e-6, -1, peaks[:, 0] + x) / float(W)
                peaks[:, 1] = np.where(peaks[:, 1] < 1e-6, -1, peaks[:, 1] + y) / float(H)
                
                hand_result = [
                    Keypoint(x=peak[0], y=peak[1])
                    for peak in peaks
                ]

                if is_left:
                    left_hand = hand_result
                else:
                    right_hand = hand_result

        return left_hand, right_hand

    def detect_face(self, body: BodyResult, oriImg) -> Union[FaceResult, None]:
        face = util.faceDetect(body, oriImg)
        if face is None:
            return None
        
        x, y, w = face
        H, W, _ = oriImg.shape
        heatmaps = self.face_estimation(oriImg[y:y+w, x:x+w, :])
        peaks = self.face_estimation.compute_peaks_from_heatmaps(heatmaps).astype(np.float32)
        if peaks.ndim == 2 and peaks.shape[1] == 2:
            peaks[:, 0] = np.where(peaks[:, 0] < 1e-6, -1, peaks[:, 0] + x) / float(W)
            peaks[:, 1] = np.where(peaks[:, 1] < 1e-6, -1, peaks[:, 1] + y) / float(H)
            return [
                Keypoint(x=peak[0], y=peak[1])
                for peak in peaks
            ]
        
        return None

    def detect_poses(self, oriImg, include_hand=False, include_face=False) -> List[PoseResult]:
        """
        Detect poses in the given image.
            Args:
                oriImg (numpy.ndarray): The input image for pose detection.
                include_hand (bool, optional): Whether to include hand detection. Defaults to False.
                include_face (bool, optional): Whether to include face detection. Defaults to False.

        Returns:
            List[PoseResult]: A list of PoseResult objects containing the detected poses.
        """
        if self.body_estimation is None:
            self.load_model()
            
        self.body_estimation.model.to(self.device)
        self.hand_estimation.model.to(self.device)
        self.face_estimation.model.to(self.device)

        self.body_estimation.cn_device = self.device
        self.hand_estimation.cn_device = self.device
        self.face_estimation.cn_device = self.device

        oriImg = oriImg[:, :, ::-1].copy()
        H, W, C = oriImg.shape
        with torch.no_grad():
            candidate, subset = self.body_estimation(oriImg)
            bodies = self.body_estimation.format_body_result(candidate, subset)

            results = []
            for body in bodies:
                left_hand, right_hand, face = (None,) * 3
                if include_hand:
                    left_hand, right_hand = self.detect_hands(body, oriImg)
                if include_face:
                    face = self.detect_face(body, oriImg)
                
                results.append(PoseResult(BodyResult(
                    keypoints=[
                        Keypoint(
                            x=keypoint.x / float(W),
                            y=keypoint.y / float(H)
                        ) if keypoint is not None else None
                        for keypoint in body.keypoints
                    ], 
                    total_score=body.total_score,
                    total_parts=body.total_parts
                ), left_hand, right_hand, face))
            
            return results
    
    def detect_poses_dw(self, oriImg) -> List[PoseResult]:
        """
        Detect poses in the given image using DW Pose:
        https://github.com/IDEA-Research/DWPose

        Args:
            oriImg (numpy.ndarray): The input image for pose detection.

        Returns:
            List[PoseResult]: A list of PoseResult objects containing the detected poses.
        """
        from .wholebody import Wholebody # DW Pose

        self.load_dw_model()

        with torch.no_grad():
            keypoints_info = self.dw_pose_estimation(oriImg.copy())
            return Wholebody.format_result(keypoints_info)

    def __call__(
            self, oriImg, include_body=True, include_hand=False, include_face=False, 
            use_dw_pose=False, json_pose_callback: Callable[[str], None] = None,
        ):
        """
        Detect and draw poses in the given image.

        Args:
            oriImg (numpy.ndarray): The input image for pose detection and drawing.
            include_body (bool, optional): Whether to include body keypoints. Defaults to True.
            include_hand (bool, optional): Whether to include hand keypoints. Defaults to False.
            include_face (bool, optional): Whether to include face keypoints. Defaults to False.
            use_dw_pose (bool, optional): Whether to use DW pose detection algorithm. Defaults to False.
            json_pose_callback (Callable, optional): A callback that accepts the pose JSON string.

        Returns:
            numpy.ndarray: The image with detected and drawn poses.
        """
        H, W, _ = oriImg.shape

        if use_dw_pose:
            poses = self.detect_poses_dw(oriImg)
        else:
            poses = self.detect_poses(oriImg, include_hand, include_face)

        if json_pose_callback:
            json_pose_callback(encode_poses_as_json(poses, H, W))
        return draw_poses(poses, H, W, draw_body=include_body, draw_hand=include_hand, draw_face=include_face)