应用场景：ai换脸，根据左右手势选择图片，上下则表示选中。

版本号：python3.7（一开始是3.6，但是mediapipe最低就是3.7

因为网上检测的都不太准，所以我在判断的时候加入了如果70次里55次检测的是左才返回左，测试完之后效果还可以，蛮准的。判断方法想的头都要秃了。

def gesture3(cap, gesture_buffer_size=70, gesture_threshold=55):
    direction = None
    mp_drawing = mp.solutions.drawing_utils
    mp_hands = mp.solutions.hands

    # 初始化手部检测模型
    hands = mp_hands.Hands(static_image_mode=False, max_num_hands=2, min_detection_confidence=0.5)

    gesture_buffer = deque(maxlen=gesture_buffer_size)  # 用于存储最近的几次手势识别结果

    while cap.isOpened():
        success, image = cap.read()
        if not success:
            print("Ignoring empty camera frame.")
            continue

        # 将图像转换为RGB格式
        image = cv2.cvtColor(cv2.flip(image, 1), cv2.COLOR_BGR2RGB)

        # 执行手部检测
        results = hands.process(image)

        if results.multi_hand_landmarks:
            for hand_landmarks in results.multi_hand_landmarks:
                # 在图像上绘制手部标记
                mp_drawing.draw_landmarks(
                    image, hand_landmarks, mp_hands.HAND_CONNECTIONS,
                    mp_drawing.DrawingSpec(color=(121, 22, 76), thickness=2, circle_radius=4),
                    mp_drawing.DrawingSpec(color=(250, 44, 250), thickness=2, circle_radius=2)
                )

                # 获取手部关键点坐标
                landmarks = hand_landmarks.landmark

                # 获取手掌位置（关键点索引为0）
                palm_landmark = hand_landmarks.landmark[0]
                palm_x = palm_landmark.x
                palm_y = palm_landmark.y

                # 获取除大拇指之外的其他手指的指尖位置
                index_tip = hand_landmarks.landmark[mp.solutions.hands.HandLandmark.INDEX_FINGER_TIP]
                middle_tip = hand_landmarks.landmark[mp.solutions.hands.HandLandmark.MIDDLE_FINGER_TIP]
                ring_tip = hand_landmarks.landmark[mp.solutions.hands.HandLandmark.RING_FINGER_TIP]
                pinky_tip = hand_landmarks.landmark[mp.solutions.hands.HandLandmark.PINKY_TIP]

                # 计算其他手指指尖到手掌位置的距离
                index_distance = math.sqrt((index_tip.x - palm_x) ** 2 + (index_tip.y - palm_y) ** 2)
                middle_distance = math.sqrt((middle_tip.x - palm_x) ** 2 + (middle_tip.y - palm_y) ** 2)
                ring_distance = math.sqrt((ring_tip.x - palm_x) ** 2 + (ring_tip.y - palm_y) ** 2)
                pinky_distance = math.sqrt((pinky_tip.x - palm_x) ** 2 + (pinky_tip.y - palm_y) ** 2)

                # 假设第二个关键点代表食指指尖，第三个关键点代表拇指指尖
                index_finger_tip = landmarks[8]
                # 获取拇指 MCP （第一关节）和 TIP（指尖）的坐标
                thumb_mcp = hand_landmarks.landmark[mp_hands.HandLandmark.THUMB_MCP]
                thumb_tip = hand_landmarks.landmark[mp_hands.HandLandmark.THUMB_TIP]

                # 获取手腕的坐标
                wrist = landmarks[0]

                # 计算食指和拇指指尖之间的距离
                distance = math.sqrt((thumb_tip.x - index_finger_tip.x)**2 + (thumb_tip.y - index_finger_tip.y)**2 + (thumb_tip.z - index_finger_tip.z)**2)
                # 计算手指和手掌在X轴和Y轴上的距离
                x_distance = index_tip.x - palm_x
                y_distance = index_tip.y - palm_y
                if abs(x_distance) > abs(y_distance):
                    # 当手腕在食指的左边，并且食指和拇指指尖之间的距离较大时，判断为向左的手势。 判断手心是否超前，以及食指和拇指指尖的距离来确定手势
                    if index_tip.x < palm_x and middle_tip.x < palm_x and ring_tip.x < palm_x and pinky_tip.x < palm_x and wrist.x > index_finger_tip.x and distance > 0.2 and index_finger_tip.x < thumb_tip.x - 0.05:  # 假设手心超前且食指和拇指指尖的距离大于0.1
                        print('Left')
                        gesture_buffer.append("Left")
                    elif index_tip.x > palm_x and middle_tip.x > palm_x and ring_tip.x > palm_x and pinky_tip.x > palm_x and wrist.x < index_finger_tip.x and distance > 0.1 and index_finger_tip.x > thumb_tip.x + 0.05:  # 假设手心超前且食指和拇指指尖的距离大于0.1
                        print('Right')
                        gesture_buffer.append("Right")
                else:
                    # 当手腕在食指的上方，并且食指和拇指指尖之间的距离较大时，判断为向上的手势
                    if index_tip.y < palm_y and middle_tip.y < palm_y and ring_tip.y < palm_y and pinky_tip.y < palm_y and wrist.y > index_finger_tip.y and distance > 0.2:  # 假设手心超前且食指和拇指指尖的距离大于0.1
                        print('Up')
                        gesture_buffer.append("Up")
                    # 这里我不用下了，如果你们需要直接把注释去掉就可以。当手腕在食指的下方，并且食指和拇指指尖之间的距离较大时，判断为向下的手势
                    # elif index_tip.y > palm_y and middle_tip.y > palm_y and ring_tip.y > palm_y and pinky_tip.y > palm_y and wrist.y < index_finger_tip.y and distance > 0.1:  # 假设手心超前且食指和拇指指尖的距离大于0.1
                    #     print('Down')
                    #     gesture_buffer.append("Down")
               


        # 统计手势识别结果
        gesture_counts = {gesture: gesture_buffer.count(gesture) for gesture in set(gesture_buffer)}

        # 判断最终手势
        if len(gesture_buffer) == gesture_buffer_size and max(gesture_counts.values()) >= gesture_threshold:
            direction = max(gesture_counts, key=gesture_counts.get)

        cv2.imshow('Hand Gestures', cv2.cvtColor(image, cv2.COLOR_RGB2BGR))
        if direction or cv2.waitKey(5) & 0xFF == 27:  # 按ESC键退出
            break

    return direction

实现效果如下：