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'''
* Copyright (c) 2023 Salesforce, Inc.
* All rights reserved.
* SPDX-License-Identifier: Apache License 2.0
* For full license text, see LICENSE.txt file in the repo root or http://www.apache.org/licenses/
* By Can Qin
* Modified from ControlNet repo: https://github.com/lllyasviel/ControlNet
* Copyright (c) 2023 Lvmin Zhang and Maneesh Agrawala
'''
# 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
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 annotator.util import annotator_ckpts_path
body_model_path = "https://huggingface.co/lllyasviel/ControlNet/resolve/main/annotator/ckpts/body_pose_model.pth"
hand_model_path = "https://huggingface.co/lllyasviel/ControlNet/resolve/main/annotator/ckpts/hand_pose_model.pth"
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")
if not os.path.exists(hand_modelpath):
from basicsr.utils.download_util import load_file_from_url
load_file_from_url(body_model_path, model_dir=annotator_ckpts_path)
load_file_from_url(hand_model_path, model_dir=annotator_ckpts_path)
self.body_estimation = Body(body_modelpath)
self.hand_estimation = Hand(hand_modelpath)
def __call__(self, oriImg, hand=False):
oriImg = oriImg[:, :, ::-1].copy()
with torch.no_grad():
candidate, subset = self.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 = self.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())
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