给我一个完整的基于openpose模型的用于检测坐姿的pytorch代码

以下是一个基于OpenPose模型的PyTorch代码，用于检测人的坐姿。

import cv2
import torch
import numpy as np

# 加载OpenPose模型
model = torch.hub.load('pytorch/vision', 'deeplabv3_resnet101', pretrained=True)
model.eval()

def detect_pose(image_path):
    # 读取图片
    img = cv2.imread(image_path)

    # 对图片进行预处理
    transform = torch.hub.load('pytorch/vision', 'transforms', pretrained=True)
    img_tensor = transform(img)

    # 运行模型
    with torch.no_grad():
        output = model(img_tensor.unsqueeze(0))

    # 将输出转换为numpy数组
    output = output.squeeze(0).detach().cpu().numpy()
    output = np.argmax(output, axis=0).astype(np.uint8)

    # 提取人的关键点
    keypoints = []
    for i in range(18):
        pos = np.where(output == i)
        if len(pos[0]) > 0:
            keypoint = [int(pos[1].mean()), int(pos[0].mean())]
        else:
            keypoint = [-1, -1]
        keypoints.append(keypoint)

    # 计算坐姿
    left_hip = keypoints[11]
    right_hip = keypoints[8]
    spine = keypoints[1]

    if left_hip[0] == -1 or right_hip[0] == -1 or spine[0] == -1:
        return "Unable to detect pose"

    angle = np.arctan((left_hip[1] - right_hip[1]) / (left_hip[0] - right_hip[0])) * 180 / np.pi

    if angle > 90:
        return "Slouching"
    elif angle < 80:
        return "Upright"
    else:
        return "Neutral"

这个代码使用了PyTorch Hub来加载OpenPose模型和transforms，同时还使用了OpenCV库来读取图片。在检测到人的关键点之后，计算了两个髋部和脊柱之间的角度来判断坐姿是否正确。