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| # 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) | |
| import os | |
| import pdb | |
| 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 .face import Face | |
| from annotator.util import annotator_ckpts_path | |
| 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" | |
| def draw_pose(pose, H, W, draw_body=True, draw_hand=True, draw_face=True): | |
| bodies = pose['bodies'] | |
| faces = pose['faces'] | |
| hands = pose['hands'] | |
| candidate = bodies['candidate'] | |
| subset = bodies['subset'] | |
| canvas = np.zeros(shape=(H, W, 3), dtype=np.uint8) | |
| if draw_body: | |
| canvas = util.draw_bodypose(canvas, candidate, subset) | |
| if draw_hand: | |
| canvas = util.draw_handpose(canvas, hands) | |
| if draw_face: | |
| canvas = util.draw_facepose(canvas, faces) | |
| return canvas | |
| 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") | |
| # face_modelpath = os.path.join(annotator_ckpts_path, "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=annotator_ckpts_path) | |
| # 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=annotator_ckpts_path) | |
| # 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=annotator_ckpts_path) | |
| self.body_estimation = Body(body_modelpath) | |
| # self.hand_estimation = Hand(hand_modelpath) | |
| # self.face_estimation = Face(face_modelpath) | |
| def __call__(self, oriImg, hand_and_face=False, return_is_index=False): | |
| oriImg = oriImg[:, :, ::-1].copy() | |
| H, W, C = oriImg.shape | |
| with torch.no_grad(): | |
| candidate, subset = self.body_estimation(oriImg) | |
| hands = [] | |
| faces = [] | |
| if hand_and_face: | |
| # Hand | |
| hands_list = util.handDetect(candidate, subset, oriImg) | |
| for x, y, w, is_left in hands_list: | |
| 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) | |
| hands.append(peaks.tolist()) | |
| # Face | |
| faces_list = util.faceDetect(candidate, subset, oriImg) | |
| for x, y, w in faces_list: | |
| 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) | |
| faces.append(peaks.tolist()) | |
| if candidate.ndim == 2 and candidate.shape[1] == 4: | |
| candidate = candidate[:, :2] | |
| candidate[:, 0] /= float(W) | |
| candidate[:, 1] /= float(H) | |
| bodies = dict(candidate=candidate.tolist(), subset=subset.tolist()) | |
| pose = dict(bodies=bodies, hands=hands, faces=faces) | |
| if return_is_index: | |
| return pose | |
| else: | |
| return pose, draw_pose(pose, H, W) | |