import cv2 import mediapipe as mp import math

import numpy as np import pandas as pd from sklearn.neighbors import KNeighborsClassifier

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('5.mp4')

import tkinter as tk from tkinter import filedialog

创建可视化窗口

root = tk.Tk() root.withdraw()

选择输入视频文件

input_path = filedialog.askopenfilename(title='选择输入视频文件', filetypes=[('视频文件', '.mp4;.avi'), ('所有文件', '.')]) if not input_path: print('未选择输入视频文件！') exit()

打开输入视频文件

print('打开输入视频文件') cap = cv2.VideoCapture(input_path)

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('dataset_warm-up.csv') combat_df = pd.read_csv('dataset_SHIZHAN POSE.csv') attack_df = pd.read_csv('dataset_hit.csv') respect_df = pd.read_csv('dataset_respect.csv') gongbu_df = pd.read_csv('dataset_gongbu.csv')

数据清洗

将每个小数据集中偏离平均值超过2倍标准差的样本删除

print('数据清洗') for df in [warmup_df, combat_df, attack_df, respect_df, gongbu_df]: df.drop(df[(np.abs(df[['angle1','angle2','angle3', 'angle4','angle5','angle5_1' ,'angle6', 'angle7','angle8','angle9','angle10','angle11']] - df[['angle1','angle2','angle3', 'angle4','angle5','angle5_1' ,'angle6', 'angle7','angle8','angle9','angle10','angle11']].mean()) > 2 * df[['angle1','angle2','angle3', 'angle4','angle5' ,'angle6', 'angle7','angle8','angle9','angle10','angle11']].std()).any(axis=1)].index, inplace=True)

将数据集合并为一个大的数据集

data = pd.concat([warmup_df, combat_df, attack_df, respect_df,gongbu_df], ignore_index=True)

训练KNN分类器

print('开始训练knn') knn = KNeighborsClassifier(n_neighbors=2, weights='distance', metric='manhattan') knn.fit(data[['angle1','angle2','angle3', 'angle4','angle5','angle5_1' ,'angle6', 'angle7','angle8','angle9','angle10','angle11']], data['label'])

设置距离阈值

distance_threshold = 100

处理视频文件中的每一帧

with mp_pose.Pose(min_detection_confidence=0.3, min_tracking_confidence=0.3) as pose: while cap.isOpened(): # 读取一帧 ret, frame = cap.read() # if ret is None: # continue 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)


        ##################
        # 获取鼻子、右肩和右手腕关键点的信息
        nose = results.pose_landmarks.landmark[mp_pose.PoseLandmark.NOSE]
        right_shoulder = results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_SHOULDER]
        right_wrist = results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_WRIST]

        # 获取鼻子、右肩和右手腕的角度
        angle_nwr = math.degrees(math.atan2(right_wrist.y - nose.y, right_wrist.x - nose.x) -
                                 math.atan2(right_shoulder.y - nose.y, right_shoulder.x - nose.x))


        # 将数据输入KNN分类器进行预测
        distances, indices = knn.kneighbors([[angle, angle1, angle_dl, angle_dr, angle_tr, angle_tr, angle_tl,angle_lka,angle_hls,angle_rka,angle_hrs,angle_nwr]])
        if distances[0][0] > distance_threshold:
            label = ''
        else:
            label = knn.predict([[angle, angle1, angle_dl, angle_dr, angle_tr, angle_tr, angle_tl,angle_lka,angle_hls,angle_rka,angle_hrs,angle_nwr]])
        
        print([angle, angle1, angle_dl, angle_dr, angle_tr, angle_tr, angle_tl,angle_lka,angle_hls,angle_rka,angle_hrs,angle_nwr])
        poses_b = poses


    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()