landmarks[mp_pose.PoseLandmark.LEFT_KNEE.value].y] rshoulder=[landmarks[mp_pose.PoseLandmark.RIGHT_SHOULDER.value].x, landmarks[mp_pose.PoseLandmark.RIGHT_SHOULDER.value].y] relbow=[landmarks[mp_pose.PoseLandmark.RIGHT_ELBOW.value].x, landmarks[mp_pose.PoseLandmark.RIGHT_ELBOW.value...
mediapipe坐标 h, w, c = img.shape try: for index, landmarks in enumerate(results.pose_landmarks.landmark): cx = int(results.pose_landmarks.landmark[index].x * w) cy = int(results.pose_landmarks.landmark[index].y * h) cz = results.pose_landmarks.landmark[index].z v = resu...
def detect_finger_positions(landmarks, frame):"""检测并标记手指指向和数量:param landmarks: 手部关键点:param frame: 视频帧:return: 伸出的手指数和各手指的指向角度"""h, w, _ = frame.shape# 食指指尖index_finger_tip = (int(landmarks[mp_hands.HandLandmark.INDEX_FINGER_TIP].x * w),int...
模型检测完成后的结果保存在results里面,我们需要访问此结果,并把检测到的人脸,人手,以及姿态评估的数据点画在原始检测的图片上,以便查看 if results.pose_landmarks: print( f'Nose coordinates: (' f'{results.pose_landmarks.landmark[mp_holistic.PoseLandmark.NOSE].x * image_width}, ' f'{results.pos...
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) lmList.append([id,cx,cy]) if draw:cv2.circle(img,(cx,cy),5,(255,0,0),cv2.FILLED) ...
这部分代码导入MediaPipe的绘图工具和Pose解决方案,并创建一个Pose对象,用于处理姿态检测。 4.开始视频流处理 cap = cv2.VideoCapture('01.mp4') 1. 创建一个VideoCapture对象,从指定的视频文件中读取帧。 5. 初始化俯卧撑计数器变量 counter = 0 stage = None ...
results = pose.process(imgRGB) # mediapipe姿态检测只能检测一个人 if results.pose_landmarks: for lm in results.pose_landmarks.landmark: h, w, c = image.shape cx, cy = int(lm.x * w), int(lm.y * h) # 画关键点 annotated_image = image.copy() ...
pose = mp_pose.Pose(static_image_mode=True, min_detection_confidence=0.5, min_tracking_confidence=0.5) res = pose.process(img) img_copy = img.copy() if res.pose_landmarks is not None: mp_drawing = mp.solutions.drawing_utils # mp_drawing.draw_landmarks( ...
if (results.pose_landmarks): for id,lm in enumerate(results.pose_landmarks.landmark): #print (id,"\n",lm) h,w,c = img.shape cx,cy = int(lm.x*w),int(lm.y*h) print (id,cx,cy) if id == 0 : cv2.circle(img,(cx,cy),12,(0,255,255),cv2.FILLED) ...
process(image)# 重新转为BGRimage.flags.writeable = Trueimage = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)# 获取姿态关键点try:landmarks = results.pose_landmarks.landmark# 获取左肩、左肘和左腕的坐标shoulder = [landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value].x,landmarks[mp_pose.PoseLand...