不好意思 麻烦各位，问题已经解决了

#使用LBP描述符的人脸识别。

import sensor, time, image, pyb

sensor.reset() # 初始化相机传感器
sensor.set_pixformat(sensor.GRAYSCALE) # 设置摄像头像素模式
sensor.set_framesize(sensor.B128X128) # 设置摄像机模块的帧的阿晓
sensor.set_windowing((92,112)) # 设置采集图像的相框大小
sensor.skip_frames(10) # 让新设置生效
sensor.skip_frames(time = 5000) # 等待5s

blueled = pyb.LED(3) # Red LED = 1, Green LED = 2, Blue LED = 3, IR LEDs = 4.
redled = pyb.LED(1) # Red LED = 1, Green LED = 2, Blue LED = 3, IR LEDs = 4.

#SUB = "s1"
NUM_SUBJECTS = 1 #图像库中不同人数，一共6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

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

global dist
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。

if dist>7000:
    redled.on()
    time.sleep(3000)
    redled.off()
else :
    blueled.on()
    time.sleep(3000)
    blueled.off()       
print("Average dist for subject %d: %d"%(s, dist/NUM_SUBJECTS_IMGS))