import mediapipe as mp
import cv2
import os
import pandas as pd


# 定义保存姿势估计结果的函数
def save_pose_results(pose_results, action_name, folder_name):
    filename = f'{folder_name}_{action_name}.csv'
    df = pd.DataFrame(pose_results, columns=['label', 'nose_x', 'nose_y', 'left_shoulder_x', 'left_shoulder_y',
                                              'right_shoulder_x', 'right_shoulder_y', 'left_elbow_x', 'left_elbow_y',
                                              'right_elbow_x', 'right_elbow_y', 'left_wrist_x', 'left_wrist_y',
                                              'right_wrist_x', 'right_wrist_y', 'left_hip_x', 'left_hip_y',
                                              'right_hip_x', 'right_hip_y', 'left_knee_x', 'left_knee_y',
                                              'right_knee_x', 'right_knee_y', 'left_ankle_x', 'left_ankle_y',
                                              'right_ankle_x', 'right_ankle_y'])
    df.to_csv(filename, index=False, float_format='%.2f', header=True, encoding='utf-8-sig',
          columns=['label']+list(df.columns[1:]), dtype={'label': str})
    print(f'{filename} saved successfully')

# 初始化 mediapipe
mp_drawing = mp.solutions.drawing_utils
mp_pose = mp.solutions.pose

# 遍历不同动作的文件夹
for folder_name in os.listdir('actions'):
    # 创建一个空的姿势估计结果列表
    pose_results = []
    # 遍历文件夹中的所有图片
    for filename in os.listdir(f'actions/{folder_name}'):
        # 读取图片
        image = cv2.imread(f'actions/{folder_name}/{filename}')
        # 将图片转换为 RGB 格式
        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
        # 处理图片，进行姿势估计
        with mp_pose.Pose(min_detection_confidence=0.5, min_tracking_confidence=0.5) as pose:
            results = pose.process(image)
            if results.pose_landmarks is None:
                continue
            # 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])
            # 在图像上绘制姿势估计结果
            annotated_image = image.copy()
            mp_drawing.draw_landmarks(annotated_image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS)
        # 保存绘制好姿势估计结果的图像
        cv2.imwrite(f'annotated_images/{filename}', cv2.cvtColor(annotated_image, cv2.COLOR_RGB2BGR))
    # 将姿势估计结果列表保存为 csv 文件
    save_pose_results(pose_results, folder_name, 'dataset')

# 报错 ValueError: could not convert string to float: 'warm-up' 解决内容：
# 这个错误是因为在保存姿势估计结果的函数中，将动作名称作为第一列保存为字符串类型，而在保存为 csv 文件时，pandas 默认将字符串类型保存为 object 类型，而不是 float 类型。
# 解决方法是在保存 csv 文件时，将第一列的数据类型设置为字符串类型，可以使用以下代码：

```python
# df.to_csv(filename, index=False, float_format='%.2f', header=True, encoding='utf-8-sig',
#           columns=['label']+list(df.columns[1:]), dtype={'label': str})

其中，dtype={'label': str} 表示将第一列的数据类型设置为字符串类型。