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))

读取不同动作的骨骼角度信息

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))
        # 获取左肩、左肘和左手腕
        angle1 = 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
        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
        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
        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))

        # 判断当前帧的动作类型
        current_pose = ""
        if (angle >= -16.5 and angle <= -8) and (angle_dr > 179 and angle_dr <= 186 or angle_dl > 182 and angle_dl <= 191):
            current_pose = "warm-up"
            current_df = warmup_df
        elif (angle > -20 and angle <= 5) and ((angle_dr > 130.5 and angle_dr <= 155) or (angle_dr > 205 and angle_dr <= 212)) and ((angle_tr > 239 and angle_tr <= 260) or (angle_tr > 147 and angle_tr <= 170)):
            current_pose = "combat pose"
            current_df = combat_df
        elif (angle > -20 and angle <= 5) and ((angle_tr > -175 and angle_tr <= -165) or (angle_tr > 115 and angle_tr <= 135)):
            current_pose = "attack"
            current_df = attack_df
        elif (angle > -20 and angle <= 5) and (angle_tr > -43 and angle_tr <= -31 or angle_tr > -5 and angle_tr <= -1 or angle_tr > 54 and angle_tr <= 56):
            current_pose = "respect"
            current_df = respect_df

        # 判断当前帧的姿势是否匹配当前动作类型
        if current_pose != "":
            for idx, row in current_df.iterrows():
                if abs(angle - row['angle']) <= 5 and abs(angle_tr - row['angle_tr']) <= 5 and abs(angle_dr - row['angle_dr']) <= 5:
                    cv2.putText(frame, current_pose + ": " + row['name'], (600, 450),
                                cv2.FONT_HERSHEY_SIMPLEX, 0.5, (100, 225, 225), 2)
                    break

        # 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)

    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(