错误是：
Arena size is too small forall buffers.Needed 225840 but only 110912 was available.
这怎么解决

但是运行起来看图像太卡了

from pyb import UART,LED            ###串口要用
import json
import ustruct                      ###串口要用

LED_R = pyb.LED(1) # Red LED = 1, Green LED = 2, Blue LED = 3, IR LEDs = 4.
LED_G = pyb.LED(2)
LED_B = pyb.LED(3)

LED_R.off()
LED_G.off()
LED_B.off()

uart = UART(1, 115200)                       # init with given baudrate
uart.init(115200, bits=8,parity=None, stop=1) # init with given parameters

def mask_face():
    net = None
    labels = None
    try:
        # load the model, alloc the model file on the heap if we have at least 64K free after loading
        net = tf.load("trained.tflite", load_to_fb=uos.stat('trained.tflite')[6] > (gc.mem_free() - (64*1024)))
    except Exception as e:
        print(e)
        raise Exception('Failed to load "trained.tflite", did you copy the .tflite and labels.txt file onto the mass-storage device? (' + str(e) + ')')

    try:
        labels = [line.rstrip('\n') for line in open("labels.txt")]
    except Exception as e:
        raise Exception('Failed to load "labels.txt", did you copy the .tflite and labels.txt file onto the mass-storage device? (' + str(e) + ')')
    clock = time.clock()

    clock.tick()
    img = sensor.snapshot()
    # default settings just do one detection... change them to search the image...
    for obj in net.classify(img, min_scale=1.0, scale_mul=0.8, x_overlap=0.5, y_overlap=0.5):
        print("**********\nPredictions at [x=%d,y=%d,w=%d,h=%d]" % obj.rect())
        img.draw_rectangle(obj.rect())
        # This combines the labels and confidence values into a list of tuples
        predictions_list = list(zip(labels, obj.output()))
        for i in range(len(predictions_list)):
            print("%s = %f" % (predictions_list[i][0], predictions_list[i][1]))
    uart.write("mask:%.2f" % predictions_list[1][1])
    print(clock.fps(), "fps")
    LED_G.on()

def min(pmin, a, s):
    global num
    if a<pmin:
        pmin=a
        num=s
    return pmin

def people_shibie():
    #SUB = "s1"
    NUM_SUBJECTS = 2 #图像库中不同人数，一共2人
    NUM_SUBJECTS_IMGS = 20 #每人有20张样本图片
    # 拍摄当前人脸。
    img = sensor.snapshot()
    #img = image.Image("singtown/%s/1.pgm"%(SUB))
    d0 = img.find_lbp((0, 0, img.width(), img.height()))
    #d0为当前人脸的lbp特征
    img = None
    pmin = 999999
    num=0
    for s in range(1, NUM_SUBJECTS+1):
        dist = 0
        for i in range(2, NUM_SUBJECTS_IMGS+1):
            img = image.Image("singtown/s%d/%d.pgm"%(s, i))
            d1 = img.find_lbp((0, 0, img.width(), img.height()))
            #d1为第s文件夹中的第i张图片的lbp特征
            dist += image.match_descriptor(d0, d1)#计算d0 d1即样本图像与被检测人脸的特征差异度。
        print("Average dist for subject %d: %d"%(s, dist/NUM_SUBJECTS_IMGS))
    pmin = min(pmin, dist/NUM_SUBJECTS_IMGS, s)#特征差异度越小，被检测人脸与此样本更相似更匹配。
    print(pmin)
    print(num) # num为当前最匹配的人的编号。
    uart.write("face:%d" % num)

def main():
    mode = 0
    omode = 1
    while(True):

        if mode == 0:
            if omode == 1:
                omode = 0
                sensor.reset() # Initialize the camera sensor.
                sensor.set_pixformat(sensor.GRAYSCALE) # or sensor.GRAYSCALE
                sensor.set_framesize(sensor.B128X128) # or sensor.QQVGA (or others)
                sensor.set_windowing((92,112))
                sensor.skip_frames(10) # Let new settings take affect.
                sensor.skip_frames(time = 5000) #等待5s
            people_shibie()
        else:
            if omode == 0:
                omode = 1
                sensor.reset()                         # Reset and initialize the sensor.
                sensor.set_pixformat(sensor.RGB565)    # Set pixel format to RGB565 (or GRAYSCALE)
                sensor.set_framesize(sensor.QVGA)      # Set frame size to QVGA (320x240)
                sensor.set_windowing((240, 240))       # Set 240x240 window.
                sensor.skip_frames(time=2000)          # Let the camera adjust.
            mask_face()
        if uart.read() != None:
            print("Change")
            mode = not mode

main()