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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 cv2
import numpy as np
from PIL import Image
from modules.control.util import HWC3, resize_image
from .draw import draw_bodypose, draw_handpose, draw_facepose
checked_ok = False
def check_dependencies():
global checked_ok # pylint: disable=global-statement
from installer import installed, install, log
packages = [('openmim', 'openmim'), ('mmengine', 'mmengine'), ('mmcv', 'mmcv'), ('mmpose', 'mmpose'), ('mmdet', 'mmdet')]
for pkg in packages:
if not installed(pkg[1], reload=True, quiet=True):
install(pkg[0], pkg[1], ignore=False)
try:
import mmcv # pylint: disable=unused-import
checked_ok = True
return True
except Exception as e:
log.error(f'DWPose: {e}')
return False
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 = draw_bodypose(canvas, candidate, subset)
canvas = draw_handpose(canvas, hands)
canvas = draw_facepose(canvas, faces)
return canvas
class DWposeDetector:
def __init__(self, det_config=None, det_ckpt=None, pose_config=None, pose_ckpt=None, device="cpu"):
if not checked_ok:
if not check_dependencies():
return
from .wholebody import Wholebody
self.pose_estimation = Wholebody(det_config, det_ckpt, pose_config, pose_ckpt, device)
def to(self, device):
self.pose_estimation.to(device)
return self
def __call__(self, input_image, detect_resolution=512, image_resolution=512, output_type="pil", min_confidence=0.3, **kwargs):
input_image = cv2.cvtColor(np.array(input_image, dtype=np.uint8), cv2.COLOR_RGB2BGR)
input_image = HWC3(input_image)
input_image = resize_image(input_image, detect_resolution)
H, W, _C = input_image.shape
candidate, subset = self.pose_estimation(input_image)
if candidate is None:
return Image.fromarray(input_image)
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] > min_confidence:
score[i][j] = int(18*i+j)
else:
score[i][j] = -1
un_visible = subset < min_confidence
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)
detected_map = draw_pose(pose, H, W)
detected_map = HWC3(detected_map)
img = resize_image(input_image, image_resolution)
H, W, _C = img.shape
detected_map = cv2.resize(detected_map, (W, H), interpolation=cv2.INTER_LINEAR)
if output_type == "pil":
detected_map = Image.fromarray(detected_map)
return detected_map
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