基于 MediaPipe 和 CSV 数据集的姿态识别系统
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)) i=0 posess = ''
a=1#拟合精度
读取动作数据集
warm_up = pd.read_csv('warm_up.csv') combat_pose = pd.read_csv('combat_pose.csv') attack = pd.read_csv('attack.csv') respect = 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
if (angle >= -16.5*a and angle <= -8/a) and (angle_dr > 179/a and angle_dr<=186*a or angle_dl > 182/a and angle_dl<=191*a):
poss = 'warm-up'
data = warm_up
elif (angle > -20*a and angle <= 5*a )and ((angle_dr > 130.5/a and angle_dr<=155*a) or (angle_dr > 205/a and angle_dr<=212*a)) and ((angle_tr > 239/a and angle_tr<= 260*a) or (angle_tr > 147/a and angle_tr<= 170*a) ):
poss = 'combat pose'
data = combat_pose
elif (angle > -20*a and angle <= 5*a )and ((angle_tr > -175*a and angle_tr<= -165/a) or (angle_tr > 115/a and angle_tr<= 135*a)):
poss = 'attack'
data = attack
elif (angle > -20*a and angle <= 5*a) and (angle_tr > -43*a and angle_tr <= -31/a or angle_tr > -5*a and angle_tr <= -1/a or angle_tr > 54/a and angle_tr <= 56*a):
poss = 'respect'
data = respect
i=-30
else:
i=i+1
if(i>=60):
poss = ''
i=0
else:
i=i+1
# 在输出图片上显示角度值和动作类型
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, poss, (600, 450),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (100, 225, 225), 2)
cv2.putText(frame, poss, (5, 20),
cv2.FONT_HERSHEY_COMPLEX_SMALL, 1.5, (225, 100, 225), 2)
# 判断当前帧是否符合动作要求
if len(data) > 0:
for index, row in data.iterrows():
if row['angle'] - a <= angle <= row['angle'] + a:
if row['angle_tr'] - a <= angle_tr <= row['angle_tr'] + a:
if row['angle_dr'] - a <= angle_dr <= row['angle_dr'] + a:
cv2.putText(frame, row['action'], (5, 100),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
break
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)
# cv2.imshow('MediaPipe Pose Estimation', 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()
原文地址: https://www.cveoy.top/t/topic/gt3J 著作权归作者所有。请勿转载和采集!