这个真的好简单！Opencv & Python 实现的人
2019-01-07 本文已影响0人
b4a0155c6514
image
拍摄代码
功能（检测到人脸后自动拍摄放入照片库中）
image
<pre class="ql-align-justify">
import sensor, time, image, sys, pyb
RED_LED_PIN = 1
BLUE_LED_PIN = 3
# Reset sensor
sensor.reset()
sensor.set_contrast(1)
sensor.set_gainceiling(16)
sensor.set_framesize(sensor.HQVGA)
sensor.set_pixformat(sensor.GRAYSCALE)
sensor.set_framesize(sensor.B128X128)
sensor.set_windowing((128,128))
face_cascade = image.HaarCascade("frontalface", stages=25)
print(face_cascade)
clock = time.clock()
num = 2 #设置被拍摄者序号，第一个人的图片保存到s1文件夹，第二个人的图片保存到s2文件夹，以此类推。每次更换拍摄者时，修改num值。
n = 20 #设置每个人拍摄图片数量。
m = 0
while (True):
clock.tick()
img = sensor.snapshot()
objects = img.find_features(face_cascade, threshold=0.75, scale=1.25)
ddd = clock.fps()
fff = int(ddd)
for r in objects:
img.draw_rectangle(r)
if fff != 0:
print(fff)
m = m + 1
print(m)
pyb.LED(BLUE_LED_PIN).on()
sensor.snapshot().save("face/s%s/%s.pgm" % (num, m) )
print("Done! Reset the camera to see the saved image.")
pyb.LED(BLUE_LED_PIN).off()
if m == 20:
m = 20
else:
print("wrong")
if m is 20:
break
</pre>
识别代码
功能（自动检测到人脸后进行对比，输出匹配的人脸号，或者输出高低电平）
<pre class="ql-align-justify">
import sensor, time, image, sys, pyb, time
# Reset sensor
sensor.reset()
sensor.set_contrast(1)
sensor.set_gainceiling(16)
sensor.set_framesize(sensor.HQVGA)
sensor.set_pixformat(sensor.GRAYSCALE)
sensor.set_framesize(sensor.B128X128)
sensor.set_windowing((128,128))
face_cascade = image.HaarCascade("frontalface", stages=25)
print(face_cascade)
clock = time.clock() #TX RX 管脚控制
#from pyb import UART
#uart = UART(3, 115200, timeout_char=1000) # i使用给定波特率初始化
#uart.init(115200, bits=8, parity=None, stop=1, timeout_char=1000) # 使用给定参数初始化
n = 1
while (True):
clock.tick()
p = pyb.Pin("P0", pyb.Pin.OUT_PP)
p.low()
#p.LOW_POWER
img = sensor.snapshot()
objects = img.find_features(face_cascade, threshold=0.75, scale=1.25)
ddd = clock.fps()
fff = int(ddd)
for r in objects:
img.draw_rectangle(r)
if fff != 0:
#print(fff)
n = n + 1
#print(n)
#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
img = sensor.snapshot()
d0 = img.find_lbp((0, 0, img.width(), img.height()))
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("face/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)
i=0
s=0
if pmin > 100000:
print(0)
else:
print(num)
p.high()
sensor.skip_frames(time = 3000)
p.low()
#uart.writechar(49) #通过uart 引脚输出1
if n == 2:
n=5
else:
print("wrong")
if n is 5:
break
</pre>
