基于MediaPipe的实时人体动作识别

import cv2
import mediapipe as mp
import math
import pandas as pd

# 初始化MediaPipe的人体姿势模型
mp_drawing = mp.solutions.drawing_utils
mp_pose = mp.solutions.pose

# 打开输入视频文件
cap = cv2.VideoCapture('6.mp4')

# 获取输入视频的帧率和分辨率
fps = int(cap.get(cv2.CAP_PROP_FPS))
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))

# 创建输出视频文件
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
out = cv2.VideoWriter('9_1.mp4', fourcc, fps, (width, height))

# 加载动作数据集
warmup_df = pd.read_csv('warmup.csv')
combat_df = pd.read_csv('combat.csv')
attack_df = pd.read_csv('attack.csv')
respect_df = pd.read_csv('respect.csv')

# 处理视频文件中的每一帧
with mp_pose.Pose(min_detection_confidence=0.5, min_tracking_confidence=0.5) as pose:
    while cap.isOpened():
        # 读取一帧
        ret, frame = cap.read()
        if not ret:
            break

        # 将帧转换为RGB格式
        image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)

        # 处理人体姿势检测
        results = pose.process(image)

        # 判断是否检测到人体
        if results.pose_landmarks:
            # 绘制人体骨架
            mp_drawing.draw_landmarks(
                frame, results.pose_landmarks, mp_pose.POSE_CONNECTIONS)
            right_knee = results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_KNEE]
            right_ankle = results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_ANKLE]
            right_wrist = results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_WRIST]

            # 获取左肩、左肘和左手腕关键点的信息
            left_shoulder = results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_SHOULDER]
            left_elbow = results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_ELBOW]
            left_wrist = results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_WRIST]

            # 获取右肩、右肘和右手腕关键点的信息
            right_shoulder = results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_SHOULDER]
            right_elbow = results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_ELBOW]
            right_wrist = results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_WRIST]

            # 获取左臀、左膝和左踝关键点的信息
            left_hip = results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_HIP]
            left_knee = results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_KNEE]
            left_ankle = results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_ANKLE]

            # 获取右臀、右膝和右踝关键点的信息
            right_hip = results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_HIP]
            right_knee = results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_KNEE]
            right_ankle = results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_ANKLE]

            # 计算腿与右手的角度
            angle = math.degrees(math.atan2(right_wrist.y - right_ankle.y, right_wrist.x - right_ankle.x) -
                                 math.atan2(right_knee.y - right_ankle.y, right_knee.x - right_ankle.x))

            # 获取左臀、左膝和左踝的角度
            angle_dl = math.degrees(math.atan2(left_ankle.y - left_knee.y, left_ankle.x - left_knee.x) -
                                    math.atan2(left_hip.y - left_knee.y, left_hip.x - left_knee.x))

            # 获取右臀、右膝和右踝的角度
            angle_dr = math.degrees(math.atan2(right_ankle.y - right_knee.y, right_ankle.x - right_knee.x) -
                                    math.atan2(right_hip.y - right_knee.y, right_hip.x - right_knee.x))

            # 获取右肩、右肘和右手腕的角度
            angle_tr = math.degrees(math.atan2(right_wrist.y - right_elbow.y, right_wrist.x - right_elbow.x) -
                                    math.atan2(right_shoulder.y - right_elbow.y, right_shoulder.x - right_elbow.x))

            # 判断动作类型
            warmup_match = ((warmup_df['angle'] - angle).abs() < 10).any()
            combat_match = ((combat_df['angle'] - angle).abs() < 10).any()
            attack_match = ((attack_df['angle'] - angle_tr).abs() < 10).any()
            respect_match = ((respect_df['angle'] - angle_tr).abs() < 10).any()

            if warmup_match:
                poses = 'warm-up'
            elif combat_match:
                poses = 'combat pose'
            elif attack_match:
                poses = 'attack'
            elif respect_match:
                poses = 'respect'
            else:
                poses = ''

            # 在输出图片上显示角度值
            cv2.putText(frame, 'Angle: {:.2f}'.format(angle), (5, 43),
                        cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)

            cv2.putText(frame, 'Angle_tr: {:.2f}'.format(angle_tr), (5, 60),
                        cv2.FONT_HERSHEY_SIMPLEX, 0.5, (100, 15, 255), 2)

            cv2.putText(frame, 'Angle_dr: {:.2f}'.format(angle_dr), (5, 80),
                        cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 100, 255), 2)

            cv2.putText(frame, poses, (600, 450),
                        cv2.FONT_HERSHEY_SIMPLEX, 0.5, (100, 225, 225), 2)

            cv2.putText(frame, poses, (5, 20),
                        cv2.FONT_HERSHEY_COMPLEX_SMALL, 1.5, (225, 100, 225), 2)

        else:
            # 如果未检测到人体，则跳过本帧处理
            cv2.putText(frame, 'No body detected', (50, 50),
                        cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)

        # 将帧写入输出视频文件
        out.write(frame)

        # 显示当前帧的结果
        cv2.imshow('MediaPipe Pose Detection press q exit', frame)

        # 检测是否按下q键退出
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break

# 释放资源
cap.release()
out.release()
cv2.destroyAllWindows()