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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 | |
| os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE" | |
| import torch | |
| import numpy as np | |
| from . import util | |
| from .wholebody import Wholebody | |
| def draw_pose(pose, H, W): | |
| 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) | |
| canvas = util.draw_bodypose(canvas, candidate, subset) | |
| canvas = util.draw_handpose(canvas, hands) | |
| canvas = util.draw_facepose(canvas, faces) | |
| return canvas | |
| class DWposeDetector: | |
| def __init__(self, device): | |
| self.pose_estimation = Wholebody(device) | |
| def __call__(self, oriImg): | |
| oriImg = oriImg.copy() | |
| H, W, C = oriImg.shape | |
| with torch.no_grad(): | |
| candidate, subset = self.pose_estimation(oriImg) | |
| nums, keys, locs = candidate.shape | |
| candidate[..., 0] /= float(W) | |
| candidate[..., 1] /= float(H) | |
| body = candidate[:,:18].copy() | |
| body = body.reshape(nums*18, locs) | |
| score = subset[:,:18] | |
| for i in range(len(score)): | |
| for j in range(len(score[i])): | |
| if score[i][j] > 0.3: | |
| score[i][j] = int(18*i+j) | |
| else: | |
| score[i][j] = -1 | |
| un_visible = subset<0.3 | |
| candidate[un_visible] = -1 | |
| foot = candidate[:,18:24] | |
| faces = candidate[:,24:92] | |
| hands = candidate[:,92:113] | |
| hands = np.vstack([hands, candidate[:,113:]]) | |
| bodies = dict(candidate=body, subset=score) | |
| pose = dict(bodies=bodies, hands=hands, faces=faces) | |
| return draw_pose(pose, H, W) | |