人体动作识别：使用随机森林分类器识别运动姿势
import cv2 import mediapipe as mp import math import pandas as pd from sklearn.ensemble import RandomForestClassifier
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('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')
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], ignore_index=True)
训练随机森林分类器
rf = RandomForestClassifier(n_estimators=100, max_depth=5, random_state=42) rf.fit(data[['angle1','angle2','angle3', 'angle4','angle5' ,'angle6', 'angle7','angle8','angle9','angle10','angle11']], data['label'])
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)
# 获取右膝、右踝和右手腕关键点的信息
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]
# 获取左肩、左肘和左手腕关键点的信息 new
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]
# 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))
# 获取左肩、左肘和左手腕 new
angle_tl = math.degrees(math.atan2(left_wrist.y - left_elbow.y, left_wrist.x - left_elbow.x) -
math.atan2(left_shoulder.y - left_elbow.y, left_shoulder.x - left_elbow.x))
##############
#获取左髋、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]
# 获取左髋、左膝和左踝的角度
angle_lka = 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))
# 获取腰部、左髋和左肩的角度
waist = results.pose_landmarks.landmark[12]
angle_hls = math.degrees(math.atan2(left_shoulder.y - waist.y, left_shoulder.x - waist.x) -
math.atan2(left_hip.y - waist.y, left_hip.x - waist.x))
# 获取右髋、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]
# 获取右髋、右膝和右踝的角度
angle_rka = 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))
# 获取腰部、右髋和右肩的角度
angle_hrs = math.degrees(math.atan2(right_shoulder.y - waist.y, right_shoulder.x - waist.x) -
math.atan2(right_hip.y - waist.y, right_hip.x - waist.x))
##################
# 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
label = rf.predict([[angle, angle1, angle_dl, angle_dr, angle_tr, angle_tl,angle_lka,angle_hls,angle_rka,angle_hrs,angle_nwr]])
# 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, label[0], (600, 450),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (100, 225, 225), 2)
cv2.putText(frame, label[0], (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()
原文地址: https://www.cveoy.top/t/topic/gGae 著作权归作者所有。请勿转载和采集!