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Zero
| # 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 torch | |
| import numpy as np | |
| from PIL import Image | |
| from ..util import HWC3, resize_image | |
| from . import util | |
| 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, det_config=None, det_ckpt=None, pose_config=None, pose_ckpt=None, device="cpu"): | |
| 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", **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 | |
| with torch.no_grad(): | |
| candidate, subset = self.pose_estimation(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] > 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) | |
| 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 | |