本文的手跟踪pipeline包括两个模型: (1)一个手掌检测器,通过预测一个有方向性的手的bounding box在输入的整张图片上进行手定位。 (2)一个hand landmark模型,在手掌检测器根据bounding box裁剪之后的手图片上运行,并返回高保真的2.5D landmarks. 在实时的跟踪方案中,我们从上一帧的landmark预测中推导出一个边界...
hand_landmarks, mp_hands.HAND_CONNECTIONS) cv2.imwrite( 'D:/result.png', cv2.flip(image, 1)) # Flip the image horizontally for a selfie-view display. cv2.imshow( 'MediaPipe Hands', cv2.flip(image, 1)) ifcv2.waitKey( 5) & 0xFF== 27: break cap.release 手势识别 基于最...
image_rgb=cv2.cvtColor(image,cv2.COLOR_BGR2RGB)results=hands.process(image_rgb)# 处理图像ifresults.multi_hand_landmarks:# 检查是否检测到手部forlandmarksinresults.multi_hand_landmarks:# 遍历每一只手 mp_drawing.draw_landmarks(image,landmarks,mp_hands.HAND_CONNECTIONS)# 绘制关键点连线ifis_ok...
results=hands.process(imgRGB)# 调用process方法处理图像,检测手ifresults.multi_hand_landmarks:# results.multi_hand_landmarks是一个列表,列表中的每个元素代表一个手。每个手有20个位置标记。forhand_landmarkinresults.multi_hand_landmarks:mp_draw.draw_landmarks(img,hand_landmark,mp_hands.HAND_CONNECT...
手势landmark的关键点编号与解释如下: 修改后的代码如下: 代码语言:javascript 代码运行次数:0 运行 AI代码解释 importcv2importmediapipeasmp mp_drawing=mp.solutions.drawing_utils mp_hands=mp.solutions.hands # For webcam input:cap=cv2.VideoCapture(0)withmp_hands.Hands(min_detection_confidence=0.5,min_tra...
(frame_rgb)ifresults.multi_hand_landmarks:forhand_landmarksinresults.multi_hand_landmarks:# 绘制手部标记mp.solutions.drawing_utils.draw_landmarks(frame,hand_landmarks,mp_hands.HAND_CONNECTIONS)# TODO: 解析手势,识别数字(0-5)cv2.imshow("Hand Gesture Recognition",frame)ifcv2.waitKey(10)&0x...
mpDraw.draw_landmarks(img, handLms, mpHands.HAND_CONNECTIONS) command = 'UnKnown' if distanceDict == {}: MaxId = 0 else: MaxId = [key for key,value in distanceDict.items() if value == max(distanceDict.values())][0] ## 判断哪个结点距离根节点最远,并由此给出相应的命令 ...
hands = mp_hands.Hands(static_image_mode=True, max_num_hands=2, min_detection_confidence=0.3)# Last step is to set up the drawing function of hands landmarks on the imagemp_drawing = mp.solutions.drawing_utils 代码分解: 首先,使用mp.solutions.hands初始化变量 mp_hands。 然后使用相同的...
COLOR_BGR2RGB) # 处理RGB图像并返回手的标志点和检测到的每个手对象 results = hands.process(imgRGB) # results.multi_hand_landmarks返回None或手的标志点坐标 if results.multi_hand_landmarks: for handLms in results.multi_hand_landmarks: # landmark有21个（具体查阅上面的参考网...
(base_options=base_options, 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....