lightweight openpose多线程实现人体姿态估计，python代码实现

以下是使用`OpenCV`和`OpenPose`库实现多线程人体姿态估计的Python代码示例：

import cv2
import threading
import numpy as np
from queue import Queue
from openpose import pyopenpose as op

# OpenPose参数设置
params = dict()
params["model_folder"] = "models/"
params["net_resolution"] = "-1x368"
params["model_pose"] = "BODY_25"

# 初始化OpenPose
opWrapper = op.WrapperPython()
opWrapper.configure(params)
opWrapper.start()

# 用于存储预测结果的队列
result_queue = Queue()

# 处理图像的线程
def process_image(image):
    # 运行OpenPose
    datum = op.Datum()
    datum.cvInputData = image
    opWrapper.emplaceAndPop([datum])

    # 将预测结果存入队列
    result_queue.put(datum)

# 显示结果的线程
def show_result():
    while True:
        if not result_queue.empty():
            datum = result_queue.get()

            # 绘制骨架
            image = datum.cvOutputData
            pose_keypoints = datum.poseKeypoints
            for i in range(pose_keypoints.shape[0]):
                for j in range(pose_keypoints.shape[1]):
                    cv2.circle(image, tuple(pose_keypoints[i][j][:2]), 5, (0, 255, 0), -1)
            cv2.imshow("OpenPose result", image)

            # 按下ESC键退出程序
            if cv2.waitKey(1) == 27:
                break

# 读取视频并进行处理
cap = cv2.VideoCapture("test.mp4")
while True:
    ret, frame = cap.read()
    if not ret:
        break

    # 开启新线程处理图像
    t = threading.Thread(target=process_image, args=(frame,))
    t.start()

# 开启新线程显示结果
t = threading.Thread(target=show_result)
t.start()

# 等待所有线程结束
t.join()
cap.release()
cv2.destroyAllWindows()

在代码中，`process_image`函数用于处理图像，运行OpenPose并将预测结果存入队列中；`show_result`函数用于显示结果，从队列中获取预测结果并绘制骨架；`cap.read()`读取视频帧，每读取一帧就开启一个新线程处理该帧图像。最后，开启新线程显示结果，并等待所有线程结束。