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