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...
Mediapipe入门——搭建姿态检测模型并实时输出人体关节点3d坐标(2024.1.4更新)_mediapipe模型-CSDN博客 Hand landmarks detection guide | MediaPipe | Google for Developers
Realtimeflowlimiter:实时限流器;handdetection:手部探测;detectiontorectangle:检测到矩形; image cropping:图像裁剪;handlandmark:手部标志;landmarktorectangle:标志成矩形;annotationrender:注释渲染 手部标志模型的输出(REJECT_HAND_FLAG)控制何时触发手部检测模型。这种行为是通过MediaPipe强大的同步构建块实现的,从而实现...
# Draw the hand annotations on the image. image.flags.writeable =True image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR) ifresults.multi_hand_landmarks: forhand_landmarksinresults.multi_hand_landmarks: mp_drawing.draw_landmarks( image, hand_landmarks, mp_hands.HAND_CONNECTIONS) cv2.imwrite...
cx,cy=int(lm.x*w),int(lm.y*h)#ifid==0:cv2.circle(img,(cx,cy),3,(255,0,255),cv2.FILLED)mpDraw.draw_landmarks(img,handLms,mpHands.HAND_CONNECTIONS)cTime=time.time()fps=1/(cTime-pTime)pTime=cTime cv2.putText(img,str(int(fps)),(10,70),cv2.FONT_HERSHEY_PLAIN,3,(255,0...
print(f'{hand_landmarks.landmark[mp_hands.HandLandmark(i).value]}') 输出: 代码分解: 第一步,我们使用Mediapipe 库中的process函数将手部地标检测结果存储在变量results中,同时我们将图像从 BGR 格式转换为 RGB 格式。 在进入下一步时,我们将首先检查一些验证,是否检测到点,即变量results应该存放了一些结果...
只有当置信度低于某个阈值时,手部探测器模型才会重新检测整个帧。 Realtimeflowlimiter:实时限流器;handdetection:手部探测;detectiontorectangle:检测到矩形; image cropping:图像裁剪;handlandmark:手部标志;landmarktorectangle:标志成矩形;annotationrender:注释渲染 手部标志模型的输出(REJECT_HAND_FLAG)控制何时触发手部...
{mp_hands.HandLandmark(i).name}:') print(f'x: {hand_landmarks.landmark[mp_hands.HandLandmark(i).value].x * image_width}') print(f'y: {hand_landmarks.landmark[mp_hands.HandLandmark(i).value].y * image_height}') print(f'z: {hand_landmarks.landmark[mp_hands.HandLandmark(...
num_hands=2) detector = vision.HandLandmarker.create_from_options(options) # 加载图片 image = mp.Image.create_from_file("image.jpg") # 人手坐标点检测 detection_result = detector.detect(image) # 可视化人手检测 annotated_image = draw_landmarks_on_image(image.numpy_view(), detection_result)...
thumb_tip=landmarks.landmark[mp_hands.HandLandmark.THUMB_TIP]index_finger_tip=landmarks.landmark[mp_hands.HandLandmark.INDEX_FINGER_TIP]# 这里只是一个简单示例,您可以添加更多的逻辑来识别特定的手部动作ifthumb_tip.x>index_finger_tip.x:print("右手")else:print("左手")returnimage ...