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