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这是一个基于 OpenCV 的人体姿态估计系统，能够从摄像头视频流中实时检测人体关键点，并通过简化算法重建 3D 姿态，最后在 3D 空间中进行仿真展示。系统主要包含 2D 姿态检测、3D 姿态重建和 3D 仿真三个核心模块。

模块导入与环境准备

python

运行

import cv2
import numpy as np
import os
import time
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D# 确保目录存在
os.makedirs("results/2d_poses", exist_ok=True)
os.makedirs("results/3d_poses", exist_ok=True)
os.makedirs("results/simulations", exist_ok=True)

• 导入必要的库：计算机视觉 (cv2)、数值计算 (numpy)、文件操作 (os)、时间测量 (time) 和绘图工具 (matplotlib)
• 创建结果保存目录，exist_ok=True 确保目录存在时不会报错

常量定义

python

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JOINT_CONNECTIONS = [(0, 1), (0, 4), (1, 2), (2, 3), (4, 5), (5, 6), (6, 7),  # 头部(0, 11), (0, 12), (11, 12),  # 躯干(11, 13), (13, 15), (15, 17), (17, 19), (19, 21),  # 左臂(12, 14), (14, 16), (16, 18), (18, 20), (20, 22),  # 右臂(11, 23), (12, 24), (23, 24),  # 骨盆(23, 25), (25, 27), (27, 29), (29, 31),  # 左腿(24, 26), (26, 28), (28, 30), (30, 32)  # 右腿
]

• 定义 33 个人体关键点的连接关系，用于后续绘制骨架

2D 姿态估计类

python

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class HumanPoseEstimator:def __init__(self):"""初始化OpenCV人体姿态估计器"""# 使用OpenCV的DNN模块加载预训练的姿态估计模型self.proto_file = "pose_deploy_linevec_faster_4_stages.prototxt"self.weights_file = "pose_iter_160000.caffemodel"self.n_points = 18# 检查模型文件是否存在if not os.path.exists(self.proto_file) or not os.path.exists(self.weights_file):print("警告: 找不到OpenCV姿态估计模型文件")print("请从https://github.com/CMU-Perceptual-Computing-Lab/openpose下载模型文件")self.net = Noneelse:self.net = cv2.dnn.readNetFromCaffe(self.proto_file, self.weights_file)# 定义COCO人体关键点映射到33点格式self.coco_to_mp = {0: 0,    # 鼻子1: 1,    # 脖子2: 12,   # 右肩3: 14,   # 右肘4: 16,   # 右腕5: 11,   # 左肩6: 13,   # 左肘7: 15,   # 左腕8: 24,   # 右髋9: 26,   # 右膝10: 28,  # 右踝11: 23,  # 左髋12: 25,  # 左膝13: 27,  # 左踝14: 5,   # 右眼15: 2,   # 左眼16: 7,   # 右耳17: 4    # 左耳}

• 类初始化：加载 OpenCV 预训练的 Caffe 模型
• 关键点映射表：将 COCO 数据集的 18 个关键点映射到 MediaPipe 的 33 点格式

python

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    def detect_keypoints(self, image):"""从图像中检测人体关键点返回:keypoints_2d: 二维关键点坐标 [33, 3] (x, y, confidence)annotated_image: 标注后的图像"""if self.net is None:print("错误: 姿态估计模型未正确加载")return None, image# 准备输入blob = cv2.dnn.blobFromImage(image, 1.0 / 255, (368, 368), (0, 0, 0), swapRB=False, crop=False)self.net.setInput(blob)# 前向传播output = self.net.forward()# 获取图像尺寸h, w = image.shape[:2]# 初始化33个关键点的数组keypoints_2d = np.zeros((33, 3))# 处理检测结果points = []for i in range(self.n_points):# 查找关键点的置信度图prob_map = output[0, i, :, :]min_val, prob, min_loc, point = cv2.minMaxLoc(prob_map)# 缩放坐标x = (w * point[0]) / output.shape[3]y = (h * point[1]) / output.shape[2]if prob > 0.1:  # 置信度阈值points.append((int(x), int(y)))# 映射到33点格式if i in self.coco_to_mp:mp_idx = self.coco_to_mp[i]keypoints_2d[mp_idx] = [x / w, y / h, prob]else:points.append(None)# 可视化关键点annotated_image = image.copy()for i, p in enumerate(points):if p is not None:cv2.circle(annotated_image, p, 8, (0, 255, 255), thickness=-1, lineType=cv2.FILLED)cv2.putText(annotated_image, f"{i}", p, cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2, lineType=cv2.LINE_AA)# 绘制骨架连接skeleton_pairs = [(1, 2), (1, 5), (2, 3), (3, 4), (5, 6), (6, 7),(1, 8), (8, 9), (9, 10), (1, 11), (11, 12), (12, 13),(1, 0), (0, 14), (14, 16), (0, 15), (15, 17)]for pair in skeleton_pairs:part_a, part_b = pairif points[part_a] and points[part_b]:cv2.line(annotated_image, points[part_a], points[part_b], (0, 255, 0), 2)return keypoints_2d, annotated_image

• 图像预处理：将输入图像转换为网络可接受的格式 (368x368)
• 模型推理：通过前向传播获取关键点的置信度图
• 后处理：从置信度图中提取关键点坐标，应用阈值过滤低置信度点
• 可视化：在原图上绘制关键点和骨架连接，返回标准化的关键点坐标和可视化后的图像

3D 姿态估计类

python

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class Simple3DPoseEstimator:def __init__(self):"""简单的3D姿态估计器，使用固定比例关系"""# 定义人体各部分的平均比例（单位：米）self.body_proportions = {"head": 0.25,"torso": 0.5,"upper_arm": 0.3,"forearm": 0.25,"hand": 0.1,"upper_leg": 0.5,"lower_leg": 0.5,"foot": 0.2}# 用于可视化self.fig = plt.figure(figsize=(10, 8))self.ax = self.fig.add_subplot(111, projection='3d')

• 初始化：定义人体各部分的标准比例（单位：米）
• 创建 3D 绘图环境用于可视化 3D 姿态

python

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    def estimate_3d_pose(self, keypoints_2d, image_shape, visualize=False):"""简单估计3D姿态参数:keypoints_2d: 二维关键点 [33, 3]image_shape: 图像形状 (h, w)visualize: 是否可视化3D姿态返回:keypoints_3d: 3D关键点 numpy数组 [33, 3]"""if keypoints_2d is None:return Noneh, w = image_shape[:2]# 创建3D关键点数组keypoints_3d = np.zeros((33, 3))# 提取有效关键点valid_mask = keypoints_2d[:, 2] > 0.3if not np.any(valid_mask):return None# 将2D坐标转换为图像坐标系kp_2d_img = keypoints_2d.copy()kp_2d_img[:, 0] *= wkp_2d_img[:, 1] *= h# 计算人体中心center = np.mean(kp_2d_img[valid_mask, :2], axis=0)# 估计人体尺寸# 这里简化为使用肩宽作为参考if valid_mask[11] and valid_mask[12]:  # 左右肩shoulder_width = np.linalg.norm(kp_2d_img[11, :2] - kp_2d_img[12, :2])scale = 0.4 / shoulder_width  # 假设平均肩宽为0.4米else:scale = 0.001  # 默认缩放比例# 基于2D关键点和人体比例估计3D位置# 这里使用简化模型，主要基于深度感知和人体比例for i in range(33):if valid_mask[i]:x, y = kp_2d_img[i, :2]# 计算相对中心的位置rel_x = (x - center[0]) * scalerel_y = (y - center[1]) * scale# 估计深度（z轴）# 这里使用简化方法：离图像中心越远的点假设越远depth_factor = np.sqrt(rel_x**2 + rel_y**2) / max(w, h) * 0.5# 设置3D坐标keypoints_3d[i] = [rel_x, rel_y, depth_factor]# 可视化if visualize:self.visualize_3d_pose(keypoints_3d)return keypoints_3d

• 3D 姿态估计：基于 2D 关键点和人体比例关系计算 3D 坐标
• 坐标缩放：使用肩宽作为参考来估计人体尺寸比例
• 深度估计：使用离图像中心的距离来粗略估计深度信息（z 轴）

python

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    def visualize_3d_pose(self, keypoints_3d, frame_id=None):"""可视化3D姿态"""self.ax.clear()# 设置坐标轴范围max_range = np.max(np.abs(keypoints_3d))self.ax.set_xlim(-max_range, max_range)self.ax.set_ylim(-max_range, max_range)self.ax.set_zlim(-max_range, max_range)# 设置坐标轴标签self.ax.set_xlabel('X')self.ax.set_ylabel('Y')self.ax.set_zlabel('Z')# 绘制关键点self.ax.scatter(keypoints_3d[:, 0], keypoints_3d[:, 1], keypoints_3d[:, 2], c='r', s=50)# 绘制连接关系for connection in JOINT_CONNECTIONS:start_idx, end_idx = connectionif start_idx < len(keypoints_3d) and end_idx < len(keypoints_3d):self.ax.plot([keypoints_3d[start_idx, 0], keypoints_3d[end_idx, 0]],[keypoints_3d[start_idx, 1], keypoints_3d[end_idx, 1]],[keypoints_3d[start_idx, 2], keypoints_3d[end_idx, 2]],c='b', linewidth=2)# 设置视角self.ax.view_init(elev=-90, azim=90)  # 俯视视角# 保存图像if frame_id is not None:plt.savefig(f"results/3d_poses/3d_pose_frame_{frame_id}.png", dpi=300, bbox_inches='tight')else:plt.pause(0.01)

• 3D 姿态可视化：在 3D 空间中绘制关键点和骨架连接
• 视角设置：默认使用俯视视角 (-90 度仰角，90 度方位角)
• 图像保存：根据需要保存 3D 姿态图像

3D 仿真器类

python

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class SimpleSimulator:def __init__(self, use_gui=True):"""简单的3D仿真器，使用matplotlib进行可视化"""self.use_gui = use_gui# 用于可视化self.fig = plt.figure(figsize=(10, 8))self.ax = self.fig.add_subplot(111, projection='3d')# 设置固定的相机位置self.ax.set_xlim(-1.5, 1.5)self.ax.set_ylim(-1.5, 1.5)self.ax.set_zlim(0, 2)self.ax.set_xlabel('X')self.ax.set_ylabel('Y')self.ax.set_zlabel('Z')# 绘制地面x = np.linspace(-1.5, 1.5, 100)y = np.linspace(-1.5, 1.5, 100)X, Y = np.meshgrid(x, y)Z = np.zeros_like(X)self.ax.plot_surface(X, Y, Z, alpha=0.3, color='g')print("使用简单的3D可视化模拟器")

• 初始化：创建 3D 绘图环境和固定大小的场景
• 绘制地面平面：使用绿色半透明平面表示地面

python

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    def update_pose(self, keypoints_3d):"""根据3D姿态更新仿真模型参数:keypoints_3d: 3D关键点 [33, 3]"""if keypoints_3d is None:returnself.ax.clear()# 设置坐标轴范围self.ax.set_xlim(-1.5, 1.5)self.ax.set_ylim(-1.5, 1.5)self.ax.set_zlim(0, 2)# 设置坐标轴标签self.ax.set_xlabel('X')self.ax.set_ylabel('Y')self.ax.set_zlabel('Z')# 绘制地面x = np.linspace(-1.5, 1.5, 100)y = np.linspace(-1.5, 1.5, 100)X, Y = np.meshgrid(x, y)Z = np.zeros_like(X)self.ax.plot_surface(X, Y, Z, alpha=0.3, color='g')# 绘制关键点self.ax.scatter(keypoints_3d[:, 0], keypoints_3d[:, 1], keypoints_3d[:, 2], c='r', s=50)# 绘制连接关系for connection in JOINT_CONNECTIONS:start_idx, end_idx = connectionif start_idx < len(keypoints_3d) and end_idx < len(keypoints_3d):self.ax.plot([keypoints_3d[start_idx, 0], keypoints_3d[end_idx, 0]],[keypoints_3d[start_idx, 1], keypoints_3d[end_idx, 1]],[keypoints_3d[start_idx, 2], keypoints_3d[end_idx, 2]],c='b', linewidth=2)# 设置视角self.ax.view_init(elev=30, azim=45)  # 侧视视角if self.use_gui:plt.pause(0.01)def render_scene(self, frame_id):"""渲染当前场景并保存参数:frame_id: 帧ID"""plt.savefig(f"results/simulations/simulation_frame_{frame_id}.png", dpi=300, bbox_inches='tight')

• 更新姿态：根据新的 3D 关键点数据更新场景
• 固定视角：使用侧视视角 (30 度仰角，45 度方位角)
• 场景渲染：将当前场景保存为图像

主函数

python

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def main(camera_id=0, use_gui=True):"""完整流程：从摄像头读取到3D仿真参数:camera_id: 摄像头ID，0表示默认摄像头use_gui: 是否使用GUI模式"""# 1. 初始化模块pose_estimator = HumanPoseEstimator()pose_3d_estimator = Simple3DPoseEstimator()simulator = SimpleSimulator(use_gui=use_gui)# 2. 打开摄像头cap = cv2.VideoCapture(camera_id)# 检查摄像头是否成功打开if not cap.isOpened():print(f"无法打开摄像头 {camera_id}")return# 获取摄像头信息fps = cap.get(cv2.CAP_PROP_FPS)width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))print(f"摄像头参数: {width}x{height}, 帧率: {fps}")# 创建窗口cv2.namedWindow("2D Pose Estimation", cv2.WINDOW_NORMAL)cv2.resizeWindow("2D Pose Estimation", 800, 600)frame_id = 0# 3. 处理摄像头帧while True:ret, frame = cap.read()if not ret:print("无法获取帧，退出...")break# 翻转帧，使其成为镜像效果frame = cv2.flip(frame, 1)print(f"处理第{frame_id}帧...")# 3.1 2D姿态识别start_time = time.time()keypoints_2d, vis_frame = pose_estimator.detect_keypoints(frame)# 显示2D姿态结果cv2.imshow("2D Pose Estimation", vis_frame)# 保存2D姿态结果cv2.imwrite(f"results/2d_poses/2d_pose_frame_{frame_id}.png", vis_frame)# 3.2 3D姿态重建keypoints_3d = pose_3d_estimator.estimate_3d_pose(keypoints_2d, frame.shape, visualize=False)# 可视化3D姿态if keypoints_3d is not None:pose_3d_estimator.visualize_3d_pose(keypoints_3d, frame_id)# 3.3 更新3D仿真simulator.update_pose(keypoints_3d)# 3.4 渲染场景simulator.render_scene(frame_id)# 计算处理时间process_time = time.time() - start_timeprint(f"处理时间: {process_time:.3f}秒")frame_id += 1# 按ESC键退出key = cv2.waitKey(1)if key == 27:  # ESC键break# 4. 释放资源cap.release()cv2.destroyAllWindows()print(f"处理完成，共{frame_id}帧，结果保存在results目录")

• 初始化所有模块：2D 姿态估计器、3D 姿态估计器和 3D 仿真器
• 打开摄像头并获取视频流参数
• 主循环处理每一帧：
  1. 读取摄像头帧并翻转
  2. 进行 2D 姿态检测
  3. 基于 2D 结果进行 3D 姿态重建
  4. 更新 3D 仿真场景
  5. 保存所有处理结果
  6. 计算处理时间
• 资源释放：关闭摄像头和窗口

程序入口

python

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if __name__ == "__main__":# 运行主程序main(camera_id=0,  # 摄像头ID，0表示默认摄像头use_gui=True  # 是否使用GUI模式)

• 程序入口点，调用 main 函数启动整个系统
• 可以通过修改参数来调整系统行为

总结

这段代码实现了一个完整的人体姿态估计和 3D 仿真系统，主要特点包括：

1. 使用 OpenCV 预训练模型进行 2D 姿态检测
2. 基于人体比例关系的简化 3D 姿态重建方法
3. 使用 matplotlib 进行 3D 姿态可视化和仿真
4. 实时处理摄像头视频流
5. 保存所有处理结果到指定目录

该系统可以用于姿势分析、运动跟踪、人机交互等多种应用场景，并且提供了良好的扩展性，可以根据需求进一步优化 3D 姿态估计算法或添加更多功能。

完整代码

import cv2
import numpy as np
import os
import time
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D# 确保目录存在
os.makedirs("results/2d_poses", exist_ok=True)
os.makedirs("results/3d_poses", exist_ok=True)
os.makedirs("results/simulations", exist_ok=True)# 定义常量
JOINT_CONNECTIONS = [(0, 1), (0, 4), (1, 2), (2, 3), (4, 5), (5, 6), (6, 7),  # 头部(0, 11), (0, 12), (11, 12),  # 躯干(11, 13), (13, 15), (15, 17), (17, 19), (19, 21),  # 左臂(12, 14), (14, 16), (16, 18), (18, 20), (20, 22),  # 右臂(11, 23), (12, 24), (23, 24),  # 骨盆(23, 25), (25, 27), (27, 29), (29, 31),  # 左腿(24, 26), (26, 28), (28, 30), (30, 32)  # 右腿
]class HumanPoseEstimator:def __init__(self):"""初始化OpenCV人体姿态估计器"""# 使用OpenCV的DNN模块加载预训练的姿态估计模型self.proto_file = "pose_deploy_linevec_faster_4_stages.prototxt"self.weights_file = "pose_iter_160000.caffemodel"self.n_points = 18# 检查模型文件是否存在if not os.path.exists(self.proto_file) or not os.path.exists(self.weights_file):print("警告: 找不到OpenCV姿态估计模型文件")print("请从https://github.com/CMU-Perceptual-Computing-Lab/openpose下载模型文件")self.net = Noneelse:self.net = cv2.dnn.readNetFromCaffe(self.proto_file, self.weights_file)# 定义COCO人体关键点映射到33点格式self.coco_to_mp = {0: 0,    # 鼻子1: 1,    # 脖子2: 12,   # 右肩3: 14,   # 右肘4: 16,   # 右腕5: 11,   # 左肩6: 13,   # 左肘7: 15,   # 左腕8: 24,   # 右髋9: 26,   # 右膝10: 28,  # 右踝11: 23,  # 左髋12: 25,  # 左膝13: 27,  # 左踝14: 5,   # 右眼15: 2,   # 左眼16: 7,   # 右耳17: 4    # 左耳}def detect_keypoints(self, image):"""从图像中检测人体关键点返回:keypoints_2d: 二维关键点坐标 [33, 3] (x, y, confidence)annotated_image: 标注后的图像"""if self.net is None:print("错误: 姿态估计模型未正确加载")return None, image# 准备输入blob = cv2.dnn.blobFromImage(image, 1.0 / 255, (368, 368), (0, 0, 0), swapRB=False, crop=False)self.net.setInput(blob)# 前向传播output = self.net.forward()# 获取图像尺寸h, w = image.shape[:2]# 初始化33个关键点的数组keypoints_2d = np.zeros((33, 3))# 处理检测结果points = []for i in range(self.n_points):# 查找关键点的置信度图prob_map = output[0, i, :, :]min_val, prob, min_loc, point = cv2.minMaxLoc(prob_map)# 缩放坐标x = (w * point[0]) / output.shape[3]y = (h * point[1]) / output.shape[2]if prob > 0.1:  # 置信度阈值points.append((int(x), int(y)))# 映射到33点格式if i in self.coco_to_mp:mp_idx = self.coco_to_mp[i]keypoints_2d[mp_idx] = [x / w, y / h, prob]else:points.append(None)# 可视化关键点annotated_image = image.copy()for i, p in enumerate(points):if p is not None:cv2.circle(annotated_image, p, 8, (0, 255, 255), thickness=-1, lineType=cv2.FILLED)cv2.putText(annotated_image, f"{i}", p, cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2, lineType=cv2.LINE_AA)# 绘制骨架连接skeleton_pairs = [(1, 2), (1, 5), (2, 3), (3, 4), (5, 6), (6, 7),(1, 8), (8, 9), (9, 10), (1, 11), (11, 12), (12, 13),(1, 0), (0, 14), (14, 16), (0, 15), (15, 17)]for pair in skeleton_pairs:part_a, part_b = pairif points[part_a] and points[part_b]:cv2.line(annotated_image, points[part_a], points[part_b], (0, 255, 0), 2)return keypoints_2d, annotated_imageclass Simple3DPoseEstimator:def __init__(self):"""简单的3D姿态估计器，使用固定比例关系"""# 定义人体各部分的平均比例（单位：米）self.body_proportions = {"head": 0.25,"torso": 0.5,"upper_arm": 0.3,"forearm": 0.25,"hand": 0.1,"upper_leg": 0.5,"lower_leg": 0.5,"foot": 0.2}# 用于可视化self.fig = plt.figure(figsize=(10, 8))self.ax = self.fig.add_subplot(111, projection='3d')def estimate_3d_pose(self, keypoints_2d, image_shape, visualize=False):"""简单估计3D姿态参数:keypoints_2d: 二维关键点 [33, 3]image_shape: 图像形状 (h, w)visualize: 是否可视化3D姿态返回:keypoints_3d: 3D关键点 numpy数组 [33, 3]"""if keypoints_2d is None:return Noneh, w = image_shape[:2]# 创建3D关键点数组keypoints_3d = np.zeros((33, 3))# 提取有效关键点valid_mask = keypoints_2d[:, 2] > 0.3if not np.any(valid_mask):return None# 将2D坐标转换为图像坐标系kp_2d_img = keypoints_2d.copy()kp_2d_img[:, 0] *= wkp_2d_img[:, 1] *= h# 计算人体中心center = np.mean(kp_2d_img[valid_mask, :2], axis=0)# 估计人体尺寸# 这里简化为使用肩宽作为参考if valid_mask[11] and valid_mask[12]:  # 左右肩shoulder_width = np.linalg.norm(kp_2d_img[11, :2] - kp_2d_img[12, :2])scale = 0.4 / shoulder_width  # 假设平均肩宽为0.4米else:scale = 0.001  # 默认缩放比例# 基于2D关键点和人体比例估计3D位置# 这里使用简化模型，主要基于深度感知和人体比例for i in range(33):if valid_mask[i]:x, y = kp_2d_img[i, :2]# 计算相对中心的位置rel_x = (x - center[0]) * scalerel_y = (y - center[1]) * scale# 估计深度（z轴）# 这里使用简化方法：离图像中心越远的点假设越远depth_factor = np.sqrt(rel_x**2 + rel_y**2) / max(w, h) * 0.5# 设置3D坐标keypoints_3d[i] = [rel_x, rel_y, depth_factor]# 可视化if visualize:self.visualize_3d_pose(keypoints_3d)return keypoints_3ddef visualize_3d_pose(self, keypoints_3d, frame_id=None):"""可视化3D姿态"""self.ax.clear()# 设置坐标轴范围max_range = np.max(np.abs(keypoints_3d))self.ax.set_xlim(-max_range, max_range)self.ax.set_ylim(-max_range, max_range)self.ax.set_zlim(-max_range, max_range)# 设置坐标轴标签self.ax.set_xlabel('X')self.ax.set_ylabel('Y')self.ax.set_zlabel('Z')# 绘制关键点self.ax.scatter(keypoints_3d[:, 0], keypoints_3d[:, 1], keypoints_3d[:, 2], c='r', s=50)# 绘制连接关系for connection in JOINT_CONNECTIONS:start_idx, end_idx = connectionif start_idx < len(keypoints_3d) and end_idx < len(keypoints_3d):self.ax.plot([keypoints_3d[start_idx, 0], keypoints_3d[end_idx, 0]],[keypoints_3d[start_idx, 1], keypoints_3d[end_idx, 1]],[keypoints_3d[start_idx, 2], keypoints_3d[end_idx, 2]],c='b', linewidth=2)# 设置视角self.ax.view_init(elev=-90, azim=90)  # 俯视视角# 保存图像if frame_id is not None:plt.savefig(f"results/3d_poses/3d_pose_frame_{frame_id}.png", dpi=300, bbox_inches='tight')else:plt.pause(0.01)class SimpleSimulator:def __init__(self, use_gui=True):"""简单的3D仿真器，使用matplotlib进行可视化"""self.use_gui = use_gui# 用于可视化self.fig = plt.figure(figsize=(10, 8))self.ax = self.fig.add_subplot(111, projection='3d')# 设置固定的相机位置self.ax.set_xlim(-1.5, 1.5)self.ax.set_ylim(-1.5, 1.5)self.ax.set_zlim(0, 2)self.ax.set_xlabel('X')self.ax.set_ylabel('Y')self.ax.set_zlabel('Z')# 绘制地面x = np.linspace(-1.5, 1.5, 100)y = np.linspace(-1.5, 1.5, 100)X, Y = np.meshgrid(x, y)Z = np.zeros_like(X)self.ax.plot_surface(X, Y, Z, alpha=0.3, color='g')print("使用简单的3D可视化模拟器")def update_pose(self, keypoints_3d):"""根据3D姿态更新仿真模型参数:keypoints_3d: 3D关键点 [33, 3]"""if keypoints_3d is None:returnself.ax.clear()# 设置坐标轴范围self.ax.set_xlim(-1.5, 1.5)self.ax.set_ylim(-1.5, 1.5)self.ax.set_zlim(0, 2)# 设置坐标轴标签self.ax.set_xlabel('X')self.ax.set_ylabel('Y')self.ax.set_zlabel('Z')# 绘制地面x = np.linspace(-1.5, 1.5, 100)y = np.linspace(-1.5, 1.5, 100)X, Y = np.meshgrid(x, y)Z = np.zeros_like(X)self.ax.plot_surface(X, Y, Z, alpha=0.3, color='g')# 绘制关键点self.ax.scatter(keypoints_3d[:, 0], keypoints_3d[:, 1], keypoints_3d[:, 2], c='r', s=50)# 绘制连接关系for connection in JOINT_CONNECTIONS:start_idx, end_idx = connectionif start_idx < len(keypoints_3d) and end_idx < len(keypoints_3d):self.ax.plot([keypoints_3d[start_idx, 0], keypoints_3d[end_idx, 0]],[keypoints_3d[start_idx, 1], keypoints_3d[end_idx, 1]],[keypoints_3d[start_idx, 2], keypoints_3d[end_idx, 2]],c='b', linewidth=2)# 设置视角self.ax.view_init(elev=30, azim=45)  # 侧视视角if self.use_gui:plt.pause(0.01)def render_scene(self, frame_id):"""渲染当前场景并保存参数:frame_id: 帧ID"""plt.savefig(f"results/simulations/simulation_frame_{frame_id}.png", dpi=300, bbox_inches='tight')def main(camera_id=0, use_gui=True):"""完整流程：从摄像头读取到3D仿真参数:camera_id: 摄像头ID，0表示默认摄像头use_gui: 是否使用GUI模式"""# 1. 初始化模块pose_estimator = HumanPoseEstimator()pose_3d_estimator = Simple3DPoseEstimator()simulator = SimpleSimulator(use_gui=use_gui)# 2. 打开摄像头cap = cv2.VideoCapture(camera_id)# 检查摄像头是否成功打开if not cap.isOpened():print(f"无法打开摄像头 {camera_id}")return# 获取摄像头信息fps = cap.get(cv2.CAP_PROP_FPS)width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))print(f"摄像头参数: {width}x{height}, 帧率: {fps}")# 创建窗口cv2.namedWindow("2D Pose Estimation", cv2.WINDOW_NORMAL)cv2.resizeWindow("2D Pose Estimation", 800, 600)frame_id = 0# 3. 处理摄像头帧while True:ret, frame = cap.read()if not ret:print("无法获取帧，退出...")break# 翻转帧，使其成为镜像效果frame = cv2.flip(frame, 1)print(f"处理第{frame_id}帧...")# 3.1 2D姿态识别start_time = time.time()keypoints_2d, vis_frame = pose_estimator.detect_keypoints(frame)# 显示2D姿态结果cv2.imshow("2D Pose Estimation", vis_frame)# 保存2D姿态结果cv2.imwrite(f"results/2d_poses/2d_pose_frame_{frame_id}.png", vis_frame)# 3.2 3D姿态重建keypoints_3d = pose_3d_estimator.estimate_3d_pose(keypoints_2d, frame.shape, visualize=False)# 可视化3D姿态if keypoints_3d is not None:pose_3d_estimator.visualize_3d_pose(keypoints_3d, frame_id)# 3.3 更新3D仿真simulator.update_pose(keypoints_3d)# 3.4 渲染场景simulator.render_scene(frame_id)# 计算处理时间process_time = time.time() - start_timeprint(f"处理时间: {process_time:.3f}秒")frame_id += 1# 按ESC键退出key = cv2.waitKey(1)if key == 27:  # ESC键break# 4. 释放资源cap.release()cv2.destroyAllWindows()print(f"处理完成，共{frame_id}帧，结果保存在results目录")if __name__ == "__main__":# 运行主程序main(camera_id=0,  # 摄像头ID，0表示默认摄像头use_gui=True  # 是否使用GUI模式)