mediapipe手势识别 内存越来越大

网上流传的几乎都是这种代码

def see():
    # cap = cv2.VideoCapture(path)
    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_tracking_confidence=0.5
                          )
    mpDraw = mp.solutions.drawing_utils

    img = np.zeros((500, 500, 3), np.uint8)
    # 浅灰色背景
    img.fill(200)

    i = 0
    while True:

        ret, frame = cap.read()
        if not ret:
            print("读取帧失败！")
            break
        # frame = cv2.imread('img.png')
        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
        # 因为摄像头是镜像的，所以将摄像头水平翻转
        # 不是镜像的可以不翻转
        # frame = cv2.flip(frame, M_17912)
        results = hands.process(frame)
        frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
        # if results.multi_handedness:
        #      for hand_label in results.multi_handedness:
        #     print(type(hand_label))

        if results.multi_hand_landmarks:
            for hand_landmarks in results.multi_hand_landmarks:
                # print('hand_landmarks:',hand_landmarks)
                # print("n")
                # m = hand_landmarks.landmark
                # for id, lm in enumerate(m):
                #     print("x = {}, y = {}， z = {}".format(lm.x, lm.y, lm.z))
                # 关键点可视化
                mpDraw.draw_landmarks(
                    frame, hand_landmarks, mpHands.HAND_CONNECTIONS,
                    mpDraw.DrawingSpec(color=(255, 255, 255), thickness=0, circle_radius=0),
                    mpDraw.DrawingSpec(thickness=1, circle_radius=1))
        cv2.imshow('MediaPipe Hands', frame)
        i = i + 1
        if cv2.waitKey(1) & 0xFF == 27:
            break
        print(cap.get(7), i)

    cap.release()

但是，批量运行，内存会越来越大，根本原因还是内存没有释放。实际上，hands这个对象和openCV里cap类似的，都是需要释放的。因此，在程序运行最后要加上hands.close()
改正代码如下：

def see():
    # cap = cv2.VideoCapture(path)
    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_tracking_confidence=0.5
                          )
    mpDraw = mp.solutions.drawing_utils

    img = np.zeros((500, 500, 3), np.uint8)
    # 浅灰色背景
    img.fill(200)

    i = 0
    while True:

        ret, frame = cap.read()
        if not ret:
            print("读取帧失败！")
            break
        # frame = cv2.imread('img.png')
        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
        # 因为摄像头是镜像的，所以将摄像头水平翻转
        # 不是镜像的可以不翻转
        # frame = cv2.flip(frame, M_17912)
        results = hands.process(frame)
        frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
        # if results.multi_handedness:
        #      for hand_label in results.multi_handedness:
        #     print(type(hand_label))

        if results.multi_hand_landmarks:
            for hand_landmarks in results.multi_hand_landmarks:
                # print('hand_landmarks:',hand_landmarks)
                # print("n")
                # m = hand_landmarks.landmark
                # for id, lm in enumerate(m):
                #     print("x = {}, y = {}， z = {}".format(lm.x, lm.y, lm.z))
                # 关键点可视化
                mpDraw.draw_landmarks(
                    frame, hand_landmarks, mpHands.HAND_CONNECTIONS,
                    mpDraw.DrawingSpec(color=(255, 255, 255), thickness=0, circle_radius=0),
                    mpDraw.DrawingSpec(thickness=1, circle_radius=1))
        cv2.imshow('MediaPipe Hands', frame)
        i = i + 1
        if cv2.waitKey(1) & 0xFF == 27:
            break
        print(cap.get(7), i)
	hands.close()
    cap.release()