# Face recognition with LBP descriptors.
# See Timo Ahonen's "Face Recognition with Local Binary Patterns".
#
# Before running the example:
# 1) Download the AT&T faces database http://www.cl.cam.ac.uk/Research/DTG/attarchive/pub/data/att_faces.zip
# 2) Exract and copy the orl_faces directory to the SD card root.
import sensor, time, image, pyb, lcd
from pyb import UART
uart = UART(3, 115200)
sensor.reset() # Initialize the camera sensor.
sensor.set_pixformat(sensor.GRAYSCALE) # or sensor.GRAYSCALE
sensor.set_framesize(sensor.QQVGA2) # or sensor.QQVGA (or others)
sensor.skip_frames(10) # Let new settings take affect.
lcd.init()
sensor.skip_frames(time = 3000) #等待5s
#SUB = "s1"
NUM_SUBJECTS = 2 #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
while True:
# 拍摄当前人脸。
img = sensor.snapshot()
#img.draw_string(0,0,"hjhjk")
pyb.LED(2).off()
lcd.display(img) # 拍照并显示图像。
#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
for s in range(1, NUM_SUBJECTS+1):
dist = 0
for i in range(2, NUM_SUBJECTS_IMGS+1):
img = image.Image("facedb/face%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。
if(dist/NUM_SUBJECTS_IMGS < 10000) :
pyb.LED(2).on()
if num == 1:
imgstr = sensor.snapshot()
imgstr.draw_string(0,0,"Ms.Pu",0)
lcd.display(imgstr) # 拍照并显示图像。
uart.write("1\r\n")
elif num ==2:
imgstr = sensor.snapshot()
imgstr.draw_string(0,0,"Ms.Yuan",0)
lcd.display(imgstr) # 拍照并显示图像。
uart.write("2\r\n")
time.sleep(5)
