python监测人体姿势摔倒算法

通过mediapipe来获取人体关节点再分别通过重心下降检测(CGDD),身体倾斜检测(BTD)外形轮廓变形检测(CSDD)进行当前状态分析最后进行总体评估来分析是否摔倒!from collections import deque class CGDD:# def __init__(self, window_size7, threshold0.06): self.window_size window_size self.threshold threshold self.cog_history deque(maxlenwindow_size) def update(self, left_hip, right_hip): cog_y (left_hip[1] right_hip[1]) / 2 self.cog_history.append(cog_y) if len(self.cog_history) self.window_size: return False delta self.cog_history[-1] - self.cog_history[0] return delta self.thresholdimport math class BTD: def __init__(self, angle_threshold45): self.angle_threshold angle_threshold def calculate_angle(self, shoulder_mid, hip_mid): dx shoulder_mid[0] - hip_mid[0] dy hip_mid[1] - shoulder_mid[1] angle abs(math.degrees(math.atan2(dx, dy))) return angle def detect(self, shoulder_mid, hip_mid): angle self.calculate_angle(shoulder_mid, hip_mid) return angle self.angle_threshold, angleclass SCDD: def __init__(self, ratio_threshold0.9): self.ratio_threshold ratio_threshold def detect(self, landmarks): xs [lm[0] for lm in landmarks] ys [lm[1] for lm in landmarks] width max(xs) - min(xs) height max(ys) - min(ys) if height 0: return False, 0 ratio width / height return ratio self.ratio_threshold, ratioimport cv2 from pose_detector import PoseDetector from cgdd import CGDD from btd import BTD from scdd import SCDD from posture_state import PostureState import mediapipe as mp pose_detector PoseDetector() cgdd CGDD() btd BTD() scdd SCDD() posture PostureState() # cap cv2.VideoCapture(0) cap cv2.VideoCapture(这里传入是测试的视频当然你也可以改成摄像头播放流地址) while cap.isOpened(): ret, frame cap.read() if not ret: break result pose_detector.process(frame) if result.pose_landmarks: lm result.pose_landmarks.landmark l_hip (lm[mp.solutions.pose.PoseLandmark.LEFT_HIP].x, lm[mp.solutions.pose.PoseLandmark.LEFT_HIP].y) r_hip (lm[mp.solutions.pose.PoseLandmark.RIGHT_HIP].x, lm[mp.solutions.pose.PoseLandmark.RIGHT_HIP].y) l_sh (lm[mp.solutions.pose.PoseLandmark.LEFT_SHOULDER].x, lm[mp.solutions.pose.PoseLandmark.LEFT_SHOULDER].y) r_sh (lm[mp.solutions.pose.PoseLandmark.RIGHT_SHOULDER].x, lm[mp.solutions.pose.PoseLandmark.RIGHT_SHOULDER].y) shoulder_mid ((l_sh[0] r_sh[0]) / 2, (l_sh[1] r_sh[1]) / 2) hip_mid ((l_hip[0] r_hip[0]) / 2, (l_hip[1] r_hip[1]) / 2) cgdd_flag cgdd.update(l_hip, r_hip) btd_flag, angle btd.detect(shoulder_mid, hip_mid) scdd_flag, ratio scdd.detect([(lm[i].x, lm[i].y) for i in range(33)]) state posture.update((l_hip[1] r_hip[1]) / 2, angle) # print(cgdd_flag) # print(btd_flag) # print(scdd_flag) # print(state) if cgdd_flag True: cv2.putText(frame, CGDD, (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 3) if btd_flag True: cv2.putText(frame, BTD, (60, 60), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 3) if scdd_flag True: cv2.putText(frame, SCDD, (100, 100), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 3) if state FALLEN : cv2.putText(frame, STATE FALLEN, (120, 120), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 0), 3) pose_detector.draw(frame, result.pose_landmarks) cv2.imshow(Fall Detection, frame) if cv2.waitKey(1) 0xFF 27: break cap.release() cv2.destroyAllWindows()原作者开源地址https://github.com/hao-7835/Fall-detection-algorithm