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import os |
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os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE" |
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import torch |
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import numpy as np |
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from . import util |
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from .body import Body |
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from .hand import Hand |
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from annotator.util import annotator_ckpts_path |
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body_model_path = "https://huggingface.co/lllyasviel/ControlNet/resolve/main/annotator/ckpts/body_pose_model.pth" |
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hand_model_path = "https://huggingface.co/lllyasviel/ControlNet/resolve/main/annotator/ckpts/hand_pose_model.pth" |
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class OpenposeDetector: |
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def __init__(self): |
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body_modelpath = os.path.join(annotator_ckpts_path, "body_pose_model.pth") |
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hand_modelpath = os.path.join(annotator_ckpts_path, "hand_pose_model.pth") |
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if not os.path.exists(hand_modelpath): |
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from basicsr.utils.download_util import load_file_from_url |
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load_file_from_url(body_model_path, model_dir=annotator_ckpts_path) |
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load_file_from_url(hand_model_path, model_dir=annotator_ckpts_path) |
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self.body_estimation = Body(body_modelpath) |
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self.hand_estimation = Hand(hand_modelpath) |
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def __call__(self, oriImg, hand=False): |
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oriImg = oriImg[:, :, ::-1].copy() |
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with torch.no_grad(): |
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candidate, subset = self.body_estimation(oriImg) |
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canvas = np.zeros_like(oriImg) |
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canvas = util.draw_bodypose(canvas, candidate, subset) |
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if hand: |
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hands_list = util.handDetect(candidate, subset, oriImg) |
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all_hand_peaks = [] |
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for x, y, w, is_left in hands_list: |
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peaks = self.hand_estimation(oriImg[y:y+w, x:x+w, :]) |
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peaks[:, 0] = np.where(peaks[:, 0] == 0, peaks[:, 0], peaks[:, 0] + x) |
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peaks[:, 1] = np.where(peaks[:, 1] == 0, peaks[:, 1], peaks[:, 1] + y) |
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all_hand_peaks.append(peaks) |
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canvas = util.draw_handpose(canvas, all_hand_peaks) |
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return canvas, dict(candidate=candidate.tolist(), subset=subset.tolist()) |
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