pose_landmarks_proto.landmark.extend([ landmark_pb2.NormalizedLandmark(x=landmark.x, y=landmark.y, z=landmark.z) for landmark in pose_landmarks]) solutions.drawing_utils.draw_landmarks( annotated_image, pose_landmarks_proto, solutions.pose.POSE_CONNECTIONS,solutions.drawing_styles.get_de...
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...
handLandmarker.detectForVideo(video), poseLandmarker.detectForVideo(video) ]); drawResults(handResults, poseResults); } 绘制关键点和连接线 使用drawing_utils中的drawConnectors和drawLandmarks函数来绘制关键点和它们之间的连接线,增强视觉效果和理解。 // 清除画布和绘制检测结果 canvasCtx.clearRect(0, ...
self.pose = mp.solutions.pose.Pose(self.static_image_mode, self.upper_body_only, self.smooth_landmarks,self.min_detection_confidence, self.min_tracking_confidence) def find_pose(self, img, draw=True): ''' 检测姿势方法 :param img: 一帧图像 :param draw: 是否画出人体姿势节点和连接图 :re...
mp_drawing.draw_landmarks(frame, holistic_results.pose_landmarks, mp_holistic.POSE_CONNECTIONS) # 更新避障系统的车辆状态和行人状态 self.obstacle_avoidance_system.update_car_state(car_position, car_rotation) self.obstacle_avoidance_system.update_pedestrians(pedestrian_positions) ...
classPoseDetector(): ''' 人体姿势检测类 ''' def__init__(self, static_image_mode=False, upper_body_only=False, smooth_landmarks=True, min_detection_confidence=0.5, min_tracking_confidence=0.5): ''' 初始化 :param static_image_mode: 是否是静态图片,默认为否 ...
model_complexity:用于指定pose landmark模型的复杂度:0、1或2。随着模型的模型复杂度增加,landmark准确率和延迟增加。默认值为 1。 smooth_landmarks:此参数用于通过过滤不同输入图像中的姿势界标来减少预测中的抖动。默认值为 True。 min_detection_confidence:用于指定人员检测模型中的检测需要被视为成功的最小置信...
if results.pose_landmarks: print(f'Nose coordinates: ('f'{results.pose_landmarks.landmark[mp_holistic.PoseLandmark.NOSE].x * image_width}, 'f'{results.pose_landmarks.landmark[mp_holistic.PoseLandmark.NOSE].y * image_hight})')annotated_image = image.copy()mp_drawing.draw_landmarks...
import cv2import mediapipe as mpimport mathclass PoseDetector():'''人体姿势检测类'''def __init__(self,static_image_mode=False,upper_body_only=False,smooth_landmarks=True,min_detection_confidence=0.5,min_tracking_confidence=0.5):'''初始化:param static_image_mode: 是否是静态图片,默认为否:par...
pose = mp_pose.Pose(static_image_mode=True, model_complexity=2, smooth_landmarks=True, enable_segmentation=True, min_detection_confidence=0.5, min_tracking_confidence=0.5) In [43] img = cv2.imread('1.png') look_img(img) <Figure size 640x480 with 1 Axes> In [44] img_RGB = cv2....