import os os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE" import torch import numpy as np from . import util from .body import Body from .hand import Hand from annotator.util import annotator_ckpts_path body_model_path = "https://huggingface.co./lllyasviel/ControlNet/resolve/main/annotator/ckpts/body_pose_model.pth" hand_model_path = "https://huggingface.co./lllyasviel/ControlNet/resolve/main/annotator/ckpts/hand_pose_model.pth" class OpenposeDetector: def __init__(self): body_modelpath = os.path.join(annotator_ckpts_path, "body_pose_model.pth") hand_modelpath = os.path.join(annotator_ckpts_path, "hand_pose_model.pth") if not os.path.exists(hand_modelpath): from basicsr.utils.download_util import load_file_from_url load_file_from_url(body_model_path, model_dir=annotator_ckpts_path) load_file_from_url(hand_model_path, model_dir=annotator_ckpts_path) self.body_estimation = Body(body_modelpath) self.hand_estimation = Hand(hand_modelpath) def __call__(self, oriImg, hand=False): oriImg = oriImg[:, :, ::-1].copy() with torch.no_grad(): candidate, subset = self.body_estimation(oriImg) canvas = np.zeros_like(oriImg) canvas = util.draw_bodypose(canvas, candidate, subset) if hand: hands_list = util.handDetect(candidate, subset, oriImg) all_hand_peaks = [] for x, y, w, is_left in hands_list: peaks = self.hand_estimation(oriImg[y:y+w, x:x+w, :]) peaks[:, 0] = np.where(peaks[:, 0] == 0, peaks[:, 0], peaks[:, 0] + x) peaks[:, 1] = np.where(peaks[:, 1] == 0, peaks[:, 1], peaks[:, 1] + y) all_hand_peaks.append(peaks) canvas = util.draw_handpose(canvas, all_hand_peaks) return canvas, dict(candidate=candidate.tolist(), subset=subset.tolist())