openmv--分辨人脸

import sensor, time, image

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.set_vflip(True)
sensor.skip_frames(10) # Let new settings take affect.
sensor.skip_frames(time = 5000) #等待5s

//SUB = "s1"
NUM_SUBJECTS = 2 #图像库中不同人数，一共6人
NUM_SUBJECTS_IMGS = 4 #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

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"%s+"/%d"%i+".pgm")
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。