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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):
self.pose_estimation = Wholebody()
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)
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