# 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)