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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 .face import Face |
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from annotator.util import annotator_ckpts_path |
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body_model_path = "https://huggingface.co./lllyasviel/Annotators/resolve/main/body_pose_model.pth" |
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hand_model_path = "https://huggingface.co./lllyasviel/Annotators/resolve/main/hand_pose_model.pth" |
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face_model_path = "https://huggingface.co./lllyasviel/Annotators/resolve/main/facenet.pth" |
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def draw_pose(pose, H, W, draw_body=True, draw_hand=True, draw_face=True): |
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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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if draw_body: |
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canvas = util.draw_bodypose(canvas, candidate, subset) |
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if draw_hand: |
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canvas = util.draw_handpose(canvas, hands) |
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if draw_face: |
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canvas = util.draw_facepose(canvas, faces) |
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return canvas |
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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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face_modelpath = os.path.join(annotator_ckpts_path, "facenet.pth") |
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if not os.path.exists(body_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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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(hand_model_path, model_dir=annotator_ckpts_path) |
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if not os.path.exists(face_modelpath): |
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from basicsr.utils.download_util import load_file_from_url |
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load_file_from_url(face_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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self.face_estimation = Face(face_modelpath) |
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def __call__(self, oriImg, hand_and_face=False, return_is_index=False): |
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oriImg = oriImg[:, :, ::-1].copy() |
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H, W, C = oriImg.shape |
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with torch.no_grad(): |
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candidate, subset = self.body_estimation(oriImg) |
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hands = [] |
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faces = [] |
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if hand_and_face: |
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hands_list = util.handDetect(candidate, subset, oriImg) |
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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, :]).astype(np.float32) |
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if peaks.ndim == 2 and peaks.shape[1] == 2: |
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peaks[:, 0] = np.where(peaks[:, 0] < 1e-6, -1, peaks[:, 0] + x) / float(W) |
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peaks[:, 1] = np.where(peaks[:, 1] < 1e-6, -1, peaks[:, 1] + y) / float(H) |
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hands.append(peaks.tolist()) |
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faces_list = util.faceDetect(candidate, subset, oriImg) |
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for x, y, w in faces_list: |
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heatmaps = self.face_estimation(oriImg[y:y+w, x:x+w, :]) |
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peaks = self.face_estimation.compute_peaks_from_heatmaps(heatmaps).astype(np.float32) |
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if peaks.ndim == 2 and peaks.shape[1] == 2: |
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peaks[:, 0] = np.where(peaks[:, 0] < 1e-6, -1, peaks[:, 0] + x) / float(W) |
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peaks[:, 1] = np.where(peaks[:, 1] < 1e-6, -1, peaks[:, 1] + y) / float(H) |
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faces.append(peaks.tolist()) |
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if candidate.ndim == 2 and candidate.shape[1] == 4: |
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candidate = candidate[:, :2] |
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candidate[:, 0] /= float(W) |
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candidate[:, 1] /= float(H) |
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bodies = dict(candidate=candidate.tolist(), subset=subset.tolist()) |
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pose = dict(bodies=bodies, hands=hands, faces=faces) |
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if return_is_index: |
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return pose |
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else: |
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return draw_pose(pose, H, W) |
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