import cv2
import mediapipe as mp
import numpy as np
import time

class PostureDetector:
    def __init__(self, mode=False, upBody=False, smooth=True, 
                 detectionCon=0.5, trackCon=0.5):
        """
        初始化姿勢檢測器
        參數(shù):
            mode: 是否靜態(tài)圖像模式 (False表示視頻流)
            upBody: 是否只檢測上半身
            smooth: 是否平滑處理
            detectionCon: 檢測置信度閾值
            trackCon: 跟蹤置信度閾值
        """
        self.mode = mode
        self.upBody = upBody
        self.smooth = smooth
        self.detectionCon = detectionCon
        self.trackCon = trackCon
        
        self.mpDraw = mp.solutions.drawing_utils
        self.mpPose = mp.solutions.pose
        self.pose = self.mpPose.Pose(
            static_image_mode=self.mode,
            model_complexity=1,
            smooth_landmarks=self.smooth,
            enable_segmentation=False,
            smooth_segmentation=self.smooth,
            min_detection_confidence=self.detectionCon,
            min_tracking_confidence=self.trackCon
        )
        
        # 坐姿評估參數(shù)
        self.good_posture_time = 0
        self.bad_posture_time = 0
        self.posture_status = "Unknown"
        self.last_posture_change = time.time()
        
    def find_pose(self, img, draw=True):
        """
        檢測圖像中的姿勢關(guān)鍵點
        參數(shù):
            img: 輸入圖像 (BGR格式)
            draw: 是否繪制關(guān)鍵點和連接線
        返回:
            帶標(biāo)注的圖像和姿勢關(guān)鍵點
        """
        img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
        self.results = self.pose.process(img_rgb)
        
        if self.results.pose_landmarks and draw:
            self.mpDraw.draw_landmarks(
                img, self.results.pose_landmarks, 
                self.mpPose.POSE_CONNECTIONS)
        
        return img
    
    def get_landmarks(self, img):
        """
        獲取所有姿勢關(guān)鍵點的坐標(biāo)
        參數(shù):
            img: 輸入圖像
        返回:
            關(guān)鍵點坐標(biāo)列表 (x,y,z) 或 None
        """
        self.landmarks = []
        if self.results.pose_landmarks:
            for id, lm in enumerate(self.results.pose_landmarks.landmark):
                h, w, c = img.shape
                cx, cy = int(lm.x * w), int(lm.y * h)
                self.landmarks.append([id, cx, cy, lm.z])
        return self.landmarks
    
    def calculate_angle(self, a, b, c):
        """
        計算三個點之間的角度
        參數(shù):
            a, b, c: 三個點的坐標(biāo) (x,y)
        返回:
            角度 (degrees)
        """
        a = np.array(a)
        b = np.array(b)
        c = np.array(c)
        
        radians = np.arctan2(c[1]-b[1], c[0]-b[0]) - np.arctan2(a[1]-b[1], a[0]-b[0])
        angle = np.abs(radians * 180.0 / np.pi)
        
        if angle > 180.0:
            angle = 360 - angle
            
        return angle
    
    def evaluate_posture(self, img, draw=True):
        """
        評估坐姿是否正確
        參數(shù):
            img: 輸入圖像
            draw: 是否在圖像上繪制評估結(jié)果
        返回:
            圖像和坐姿評估結(jié)果
        """
        current_time = time.time()
        posture_changed = False
        
        if not self.landmarks or len(self.landmarks) < 33:
            return img, "No person detected"
        
        # 獲取需要的關(guān)節(jié)點
        left_shoulder = self.landmarks[11][1:3]  # 11: 左肩
        right_shoulder = self.landmarks[12][1:3]  # 12: 右肩
        left_ear = self.landmarks[7][1:3]  # 7: 左耳
        right_ear = self.landmarks[8][1:3]  # 8: 右耳
        left_hip = self.landmarks[23][1:3]  # 23: 左髖
        right_hip = self.landmarks[24][1:3]  # 24: 右髖
        
        # 計算肩膀中點
        shoulder_mid = (
            (left_shoulder[0] + right_shoulder[0]) // 2,
            (left_shoulder[1] + right_shoulder[1]) // 2
        )
        
        # 計算耳朵中點
        ear_mid = (
            (left_ear[0] + right_ear[0]) // 2,
            (left_ear[1] + right_ear[1]) // 2
        )
        
        # 計算髖部中點
        hip_mid = (
            (left_hip[0] + right_hip[0]) // 2,
            (left_hip[1] + right_hip[1]) // 2
        )
        
        # 計算脊柱角度 (肩膀-髖部-垂直線)
        spine_angle = self.calculate_angle(
            (shoulder_mid[0], shoulder_mid[1] - 100),  # 肩膀上方一點
            shoulder_mid,
            hip_mid
        )
        
        # 計算頸部角度 (耳朵-肩膀-水平線)
        neck_angle = self.calculate_angle(
            (ear_mid[0] - 100, ear_mid[1]),
            ear_mid,
            shoulder_mid
        )
        
        # 坐姿評估標(biāo)準(zhǔn)
        good_spine = 160 < spine_angle < 200  # 脊柱應(yīng)該接近垂直
        good_neck = 70 < neck_angle < 110  # 頸部應(yīng)該接近垂直
        
        # 判斷坐姿
        new_status = "Good" if good_spine and good_neck else "Bad"
        
        # 更新姿勢狀態(tài)時間
        if new_status != self.posture_status:
            posture_changed = True
            self.last_posture_change = current_time
            self.posture_status = new_status
        else:
            if new_status == "Good":
                self.good_posture_time += 1
            else:
                self.bad_posture_time += 1
        
        # 在圖像上繪制結(jié)果
        if draw:
            # 繪制關(guān)鍵點和連接線
            cv2.circle(img, ear_mid, 8, (255, 0, 0), cv2.FILLED)
            cv2.circle(img, shoulder_mid, 8, (255, 0, 0), cv2.FILLED)
            cv2.circle(img, hip_mid, 8, (255, 0, 0), cv2.FILLED)
            cv2.line(img, ear_mid, shoulder_mid, (255, 0, 0), 3)
            cv2.line(img, shoulder_mid, hip_mid, (255, 0, 0), 3)
            
            # 顯示角度信息
            cv2.putText(img, f"Spine Angle: {int(spine_angle)}", 
                        (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
            cv2.putText(img, f"Neck Angle: {int(neck_angle)}", 
                        (10, 60), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
            
            # 顯示坐姿狀態(tài)
            color = (0, 255, 0) if new_status == "Good" else (0, 0, 255)
            cv2.putText(img, f"Posture: {new_status}", 
                        (10, 90), cv2.FONT_HERSHEY_SIMPLEX, 0.7, color, 2)
            
            # 顯示時間統(tǒng)計
            cv2.putText(img, f"Good Time: {self.good_posture_time//10}s", 
                        (10, 120), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1)
            cv2.putText(img, f"Bad Time: {self.bad_posture_time//10}s", 
                        (10, 140), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 1)
            
            # 如果姿勢不良，添加警告
            if new_status == "Bad":
                cv2.putText(img, "WARNING: Bad Posture!", 
                            (img.shape[1]//2 - 150, 30), 
                            cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
        
        return img, new_status

def main():
    cap = cv2.VideoCapture(0)  # 使用攝像頭
    detector = PostureDetector()
    
    while True:
        success, img = cap.read()
        if not success:
            break
            
        img = detector.find_pose(img)
        landmarks = detector.get_landmarks(img)
        
        if landmarks:
            img, posture = detector.evaluate_posture(img)
        
        cv2.imshow("Posture Detection", img)
        
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break
    
    cap.release()
    cv2.destroyAllWindows()

if __name__ == "__main__":
    main()

pip install opencv-python mediapipe numpy

python posture_detection.py