| import os |
| os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE" |
|
|
| import torch |
| import numpy as np |
| from . import util |
| from .body import Body |
| from .hand import Hand |
|
|
| from huggingface_hub import hf_hub_url, cached_download |
| REPO_ID = "lllyasviel/ControlNet" |
| body_estimation = Body(cached_download(hf_hub_url(REPO_ID, 'annotator/ckpts/body_pose_model.pth'))) |
| hand_estimation = Hand(cached_download(hf_hub_url(REPO_ID,'annotator/ckpts/hand_pose_model.pth'))) |
|
|
|
|
| def apply_openpose(oriImg, hand=False): |
| oriImg = oriImg[:, :, ::-1].copy() |
| with torch.no_grad(): |
| candidate, subset = body_estimation(oriImg) |
| canvas = np.zeros_like(oriImg) |
| canvas = util.draw_bodypose(canvas, candidate, subset) |
| if hand: |
| hands_list = util.handDetect(candidate, subset, oriImg) |
| all_hand_peaks = [] |
| for x, y, w, is_left in hands_list: |
| peaks = hand_estimation(oriImg[y:y+w, x:x+w, :]) |
| peaks[:, 0] = np.where(peaks[:, 0] == 0, peaks[:, 0], peaks[:, 0] + x) |
| peaks[:, 1] = np.where(peaks[:, 1] == 0, peaks[:, 1], peaks[:, 1] + y) |
| all_hand_peaks.append(peaks) |
| canvas = util.draw_handpose(canvas, all_hand_peaks) |
| return canvas, dict(candidate=candidate.tolist(), subset=subset.tolist()) |
|
|
