import sensor, time, image, pyb
from pyb import Servo
m1 = Servo(1)
sensor.reset() # Initialize the camera sensor.
sensor.set_contrast(3)
sensor.set_gainceiling(16)
sensor.set_framesize(sensor.HQVGA)
sensor.set_pixformat(sensor.GRAYSCALE)
sensor.set_windowing((92,112))
sensor.skip_frames(10) # Let new settings take affect.
sensor.skip_frames(time = 3000) #等待3s
RED_LED_PIN = 1
face_cascade = image.HaarCascade("frontalface", stages=25)
#SUB = "s1"
NUM_SUBJECTS = 3 #图像库中不同人数,一共3人
NUM_SUBJECTS_IMGS = 20 #每人有20张样本图片
pyb.LED(RED_LED_PIN).on()
sensor.skip_frames(time = 3000)
# 拍摄一张照片
img = sensor.snapshot()
pyb.LED(RED_LED_PIN).off()
# Find objects.
# Note: Lower scale factor scales-down the image more and detects smaller objects.
# Higher threshold results in a higher detection rate, with more false positives.
objects = img.find_features(face_cascade, threshold=0.75, scale=1.35)
#image.find_features(cascade, threshold=0.5, scale=1.5),thresholds越大,
#匹配速度越快,错误率也会上升。scale可以缩放被匹配特征的大小。
#在找到的目标上画框,标记出来
for r in objects:
img.draw_rectangle(r)
if (objects==1):
print("get people")
#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/%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。
if pmin > 7500:
print("外来访客")
else:
for i in range(1000):
m1.pulse_width(5000 + i)
time.sleep_ms(5)
for i in range(1000):
m1.pulse_width(1000 - i)
time.sleep_ms(5)
print(pmin)
print(num) # num为当前最匹配的人的编号
print("欢迎回家")
print(num) # num为当前最匹配的人的编号。
time.sleep(3)
else:
print("no people")
time.sleep(3)