Python 姿势估计：提取关键点并添加到列表中

本文将介绍如何使用 Python 进行姿势估计，并提取关键点坐标添加到列表中。

提取关键点坐标并添加到列表

首先，我们需要获取姿势估计的结果。假设 results 包含姿势估计的结果，并且 mp_pose.PoseLandmark 是一个枚举类型，包含所有关键点的名称。

# 将姿势估计结果添加到列表中
pose_results.append([folder_name, 
                         results.pose_landmarks.landmark[mp_pose.PoseLandmark.NOSE].x,
                         results.pose_landmarks.landmark[mp_pose.PoseLandmark.NOSE].y,
                         results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_SHOULDER].x,
                         results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_SHOULDER].y,
                         results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_SHOULDER].x,
                         results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_SHOULDER].y,
                         results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_ELBOW].x,
                         results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_ELBOW].y,
                         results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_ELBOW].x,
                         results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_ELBOW].y,
                         results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_WRIST].x,
                         results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_WRIST].y,
                         results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_WRIST].x,
                         results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_WRIST].y,
                         results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_HIP].x,
                         results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_HIP].y,
                         results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_HIP].x,
                         results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_HIP].y,
                         results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_KNEE].x,
                         results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_KNEE].y,
                         results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_KNEE].x,
                         results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_KNEE].y,
                         results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_ANKLE].x,
                         results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_ANKLE].y,
                         results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_ANKLE].x,
                         results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_ANKLE].y])

# 获取人体姿势信息
pose_data = []
for landmark in results.pose_landmarks.landmark:
    pose_data.append(landmark.x)
    pose_data.append(landmark.y)
    pose_data.append(landmark.z if landmark.z is not None else 0)
pose_data.extend([results.pose_landmarks.landmark[mp_pose.PoseLandmark.NOSE].x,
                  results.pose_landmarks.landmark[mp_pose.PoseLandmark.NOSE].y,
                  results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_SHOULDER].x,
                  results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_SHOULDER].y,
                  results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_SHOULDER].x,
                  results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_SHOULDER].y,
                  results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_ELBOW].x,
                  results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_ELBOW].y,
                  results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_ELBOW].x,
                  results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_ELBOW].y,
                  results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_WRIST].x,
                  results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_WRIST].y,
                  results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_WRIST].x,
                  results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_WRIST].y,
                  results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_HIP].x,
                  results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_HIP].y,
                  results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_HIP].x,
                  results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_HIP].y,
                  results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_KNEE].x,
                  results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_KNEE].y,
                  results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_KNEE].x,
                  results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_KNEE].y,
                  results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_ANKLE].x,
                  results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_ANKLE].y,
                  results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_ANKLE].x,
                  results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_ANKLE].y])

代码解释：

1. pose_results 是一个列表，用于存储每个姿势的提取结果。
2. folder_name 是当前图像所在的文件夹名称，可以根据需要修改。
3. results.pose_landmarks.landmark 是一个列表，包含每个关键点的坐标信息，每个关键点包含 x, y, z 三个坐标值。
4. mp_pose.PoseLandmark.NOSE 等是枚举类型，代表每个关键点的名称。
5. 代码使用 append 方法将关键点坐标添加到 pose_results 列表中。
6. pose_data 列表用于存储所有关键点的坐标值。
7. 代码使用循环遍历 results.pose_landmarks.landmark 列表，并将每个关键点的 x, y, z 坐标值添加到 pose_data 列表中。

总结

本文介绍了如何使用 Python 进行姿势估计，并提取关键点坐标添加到列表中。希望能对大家有所帮助。

注意事项

• 代码需要依赖 mediapipe 库，请确保已安装该库。
• 代码中的关键点名称和坐标值需要根据实际情况进行修改。

相关资源

• MediaPipe 文档
• MediaPipe Python 示例