body_pose_model.pth + dw-ll_ucoco_384.onnx + facenet.pth + hand_pose_model.pth + rtmpose-m_simcc-ap10k_pt-aic-coco_210e-256x25.onnx + yolox_l.onnx
import cv2import matplotlib.pyplot as pltimport copyimport numpy as npimport torchfrom src import modelfrom src import utilfrom src.body import Bodyfrom src.hand import Handbody_estimation = Body('model/body_pose_model.pth')hand_estimation = Hand('model/hand_pose_model.pth')print(f"Torch ...
body_estimation = Body('model/body_pose_model.pth') hand_estimation = Hand('model/hand_pose_model.pth')print(f"Torch device:{torch.cuda.get_device_name()}") cap = cv2.VideoCapture("D:/3code/6pytorch/opencv_demo/12_open_pose/11.mp4") cap.set(3,640) cap.set(4,480) indices =1w...
import numpyasnp from src import model from src import util from src.body import Body from src.hand import Hand# 加载模型body_estimation=Body('model/body_pose_model.pth')hand_estimation=Hand('model/hand_pose_model.pth')# 加载待检测的图片 ==> 这里可以自行修改test_image='images/ski.jpg'or...
body_estimation = Body('model/body_pose_model.pth') hand_estimation = Hand('model/hand_pose_model.pth') print(f"Torch device: {torch.cuda.get_device_name()}") cap = cv2.VideoCapture("D:/3code/6pytorch/opencv_demo/12_open_pose/11.mp4") ...
http://posefs1.perception.cs.cmu.edu/OpenPose/models/pose/body_25/pose_iter_584000.caffemodel face,下载好后放置models/face中或替换原有模型: http://posefs1.perception.cs.cmu.edu/OpenPose/models/face/pose_iter_116000.caffemodel hand,下载好后放置models/hand中或替换原有模型: ...
--model_pose MPI: 模型的使用,影响数字关键点、速度和精度 --logging_level 3: 日志消息阈值,范围[0,255]:0将输出任何消息,255将输出无消息。当前消息的范围为[1-4],低优先级消息为1,重要消息为4。 不进行可视化展示、直接输出 JSON # Only body ...
from src.body import Body from src.hand import Hand body_estimation = Body('model/body_pose_model.pth') hand_estimation = Hand('model/hand_pose_model.pth') print(f"Torch device: {torch.cuda.get_device_name()}") cap = cv2.VideoCapture("D:/3code/6pytorch/opencv_demo/12_open_pose/11...
hand模型:http://posefs1.perception.cs.cmu.edu/OpenPose/models/hand/pose_iter_102000.caffemodel pose/body_25模型:http://posefs1.perception.cs.cmu.edu/OpenPose/models/pose/body_25/pose_iter_584000.caffemodel pose/coco模型:http://posefs1.perception.cs.cmu.edu/OpenPose/models/pose/coco/pose_...
例如openpose,测试时发现,只有在body_pose_model.pth、facenet.pth、hand_pose_model.pth这三个模型都成功下载好之后,才能成功生成骨骼图,我们可以在预处理器中逐个选中身体、脸部、手部相关的几个选项,跑一次,让系统自动帮忙下载; depth等其他功能如果也报错盲猜是可以同样处理; ...