python import mediapipe as mp # 初始化MediaPipe手部模型 mp_hands = mp.solutions.hands hands = mp_hands.Hands(static_image_mode=False, max_num_hands=2, min_detection_confidence=0.5) mp_drawing = mp.solutions.drawing_utils 3. 使用MediaPipe手部识别模型处理图像 接下来,你需要使用加载的模型来处理...
# print(results.multi_hand_landmarks) if results.multi_hand_landmarks: for handLms in results.multi_hand_landmarks: for id, lm in enumerate(handLms.landmark): ## 存储0关键点的三个坐标 if id == 0: lst = [lm.x,lm.y,lm.z] h, w, c = img.shape cx, cy = int(lm.x *w)...
1为打开你设备列表的第二个摄像头,以此类推; mpHands = mp.solutions.hands #使用Mediapipe库的手部姿势估计模型 hands = mpHands.Hands(static_image_mode=False, max_num_hands=4, model_complexity=1, min_detection_confidence=0.5, min_tracking_confidence=0.5) #...
hands = mpHands.Hands(min_detection_confidence=0.5, min_tracking_confidence=0.5)#最小检测置信度,最小追踪置信度 mpDraw = mp.solutions.drawing_utils #获取mediapipe解决方案的绘画工具包 #第二步:参数设定 handLmsStyle = mpDraw.DrawingSpec(color=(0, 0, 255), thickness=3)#绘制手部关键点的颜色与...
with mp_hands.Hands( static_image_mode=True, max_num_hands=1, min_detection_confidence=0.6) as hands: image = cv2.imread('hand.jpg') image = cv2.flip(image, 1) results = hands.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB)) 用mediapipe 识别手指信息 if results.multi_hand_landmarks:...
[mp_hands.HandLandmark.RING_FINGER_TIP].y pinky_tip=landmarks.landmark[mp_hands.HandLandmark.PINKY_TIP].yifdistance<0.05and middle_finger_tip<ring_finger_tip and ring_finger_tip<pinky_tip:returnTruereturnFalse defdetect_hand_actions(image):withmp_hands.Hands(min_detection_...
注意:这里的python版本尽量在3.8以上,不然会报各种错误!! 首先,让我们检查网络摄像头的工作情况。 代码语言:javascript 复制 importcv2importmediapipeasmpimporttime cap=cv2.VideoCapture(0)mpHands=mp.solutions.hands hands=mpHands.Hands(static_image_mode=False,max_num_hands=2,min_detection_confidence=0.5,min...
min_detection_confidence=0.5, min_tracking_confidence=0.5)ashands: whilecap.isOpened: success, image = cap.read ifnotsuccess: print("Ignoring empty camera frame.") # If loading a video, use 'break' instead of 'continue'. continue # To improve performance, optionally mark the image as not ...
(双)手并进行关键点的标注及连线导入OpenCV和MediaPipe库模块;定义变量camera,并赋值为0,如果默认的摄像头编号不是0则更改为1,打开摄像头;建立变量hand_detector,用于创建检测人手关键点的检测对象,判断并获取视频画面中是否有符合“手”特征的信息,该参数保持为空时默认状态是“model_complexity=0,min_detection_...
首先新建文件,我们将它命名为"HandTrackingModule" 回车确认后,我们先将原来Min中的代码全部复制到新的py文件中,以便进行下一步改造。 接着,我们在底部加上代码 if __name__ == "__main__": main() #当运行“if __name__=='__main__':”语句时,如果当前模块时被直接执行,__name__的值就是__main...