68 lines
2.0 KiB
Python
68 lines
2.0 KiB
Python
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# Openpose
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# Original from CMU https://github.com/CMU-Perceptual-Computing-Lab/openpose
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# 2nd Edited by https://github.com/Hzzone/pytorch-openpose
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# 3rd Edited by ControlNet
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# 4th Edited by ControlNet (added face and correct hands)
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import os
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os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
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import torch
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import numpy as np
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from . import util
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from .wholebody import Wholebody
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def draw_pose(pose, H, W):
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bodies = pose['bodies']
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faces = pose['faces']
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hands = pose['hands']
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candidate = bodies['candidate']
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subset = bodies['subset']
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canvas = np.zeros(shape=(H, W, 3), dtype=np.uint8)
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canvas = util.draw_bodypose(canvas, candidate, subset)
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canvas = util.draw_handpose(canvas, hands)
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canvas = util.draw_facepose(canvas, faces)
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return canvas
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class DWposeDetector:
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def __init__(self, model_root, device):
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self.pose_estimation = Wholebody(model_root, device)
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def __call__(self, oriImg):
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oriImg = oriImg.copy()
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H, W, C = oriImg.shape
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with torch.no_grad():
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candidate, subset = self.pose_estimation(oriImg)
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nums, keys, locs = candidate.shape
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candidate[..., 0] /= float(W)
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candidate[..., 1] /= float(H)
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body = candidate[:,:18].copy()
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body = body.reshape(nums*18, locs)
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ori_score = subset[:,:18].copy()
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score = subset[:,:18].copy()
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for i in range(len(score)):
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for j in range(len(score[i])):
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if score[i][j] > 0.3:
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score[i][j] = int(18*i+j)
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else:
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score[i][j] = -1
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un_visible = subset<0.3
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candidate[un_visible] = -1
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foot = candidate[:,18:24]
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faces = candidate[:,24:92]
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hands = candidate[:,92:113]
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hands = np.vstack([hands, candidate[:,113:]])
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bodies = dict(candidate=body, subset=score)
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pose = dict(bodies=bodies, hands=hands, faces=faces)
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return draw_pose(pose, H, W), body, ori_score, candidate
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