好的，谢谢大佬

在代码中 sum+=l.theta()一直报错，改为将l.theta()改为其他类型，还是报错，请问怎样解决啊

import sensor, image, time, math

sensor.reset()
sensor.set_pixformat(sensor.RGB565) # 灰度更快
sensor.set_framesize(sensor.QQVGA)
sensor.skip_frames(time = 2000)
clock = time.clock()

# 所有线段都有 `x1()`, `y1()`, `x2()`, and `y2()` 方法来获得他们的终点
# 一个 `line()` 方法来获得所有上述的四个元组值，可用于 `draw_line()`.

while(True):
    clock.tick()
    img = sensor.snapshot()
    if enable_lens_corr: img.lens_corr(1.8) # for 2.8mm lens...

    for l in img.find_line_segments(merge_distance = 10, max_theta_diff = 10):
        img.draw_line(l.line(), color = (255, 0, 0))
        sum+=l.theta()
    sum-=180

    if sum<110 and sum>1:
        print('三角形')
        num_segment=1

最初的想法就是通过瞳孔识别进行眨眼次数的判断，当闭眼时，则无法识别到瞳孔，则num加一。我是通过调取瞳孔识别函数的返回的一个固定值，作为标志项，进行判断的，但是发现几个问题，一个是打印出的num速度过快，加了定时器还是一样的结果。还有就是将第一个判断语句的等号变成不等号，那代码将不往下执行打印，但显然当识别不到瞳孔的时候，标志不相等应该是恒成立的。代码如下，求大佬解惑

import sensor, time, image
from pyb import Timer

sensor.reset()
sensor.set_contrast(3)
sensor.set_gainceiling(16)
sensor.set_framesize(sensor.VGA)
sensor.set_windowing((220, 190, 200, 100))
sensor.set_pixformat(sensor.GRAYSCALE)

num=0
flag=0

eyes_cascade = image.HaarCascade("eye", stages=24)
clock = time.clock()

while (True):
    clock.tick()
    img = sensor.snapshot()
    eyes = img.find_features(eyes_cascade, threshold=0.5, scale_factor=1.5)

    for e in eyes:
        iris = img.find_eye(e)
        img.draw_rectangle(e)
        img.draw_cross(iris[0], iris[1])

        a=str(img.draw_cross(iris[0], iris[1]).copy())
        flag=int(a[5])

        def ad(timer):
            global num
            num=num+1

        tim = Timer(2, freq=1)
        
        if flag==2:
            tim.callback(ad)
            print(num)
        else:
            print(1)

import sensor, time, image, pyb
from pyb import UART

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

uart = UART(3, 115200)

while(True):
if uart.any():
a=uart.readline().decode().strip()
print(a)
break

#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

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/%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(2)

print(num) # num为当前最匹配的人的编号。
uart.write(str(num)+'\r\n')

我想让人脸识别结束后，将结果发送出去后，再回到程序开始的串口接收部分继续运行程序，请问该怎样弄？

@kidswong999 那请问我是应该用break跳出循环还是其他方法，麻烦你啦

import sensor, time, image, pyb
from pyb import UART
from pyb import Pin, ExtInt

uart = UART(3, 115200)

while(True):
    if uart.any():
        a=uart.readline().decode().strip()
        print(a)

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.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 = 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

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。
    print(pmin)

print(num) # num为当前最匹配的人的编号。
uart.write(str(num)+'\r\n')

我在串口助手上发0给openmv，串行终端读到了0，然后就停在了那一步，我想问一下怎样修改代码，可以让openmv接收到数据后，能继续运行后面的人脸识别程序

import sensor, time, image, pyb
from pyb import UART
from pyb import Pin, ExtInt

pin0 = Pin('P0', Pin.IN, Pin.PULL_UP)

def callback_PIN0(line):

    pin0.value()==0

    pyb.delay(10)

extint = pyb.ExtInt(pin0, pyb.ExtInt.IRQ_FALLING, pyb.Pin.PULL_UP, callback_PIN0)

while(True):

    if pin0.value()==0:

        uart = UART(3, 115200)

        if uart.any():

                a=uart.readline().decode().strip()

        pyb.delay(10)



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.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 = 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

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。
    print(pmin)

print(num) # num为当前最匹配的人的编号。
uart.write(str(num)+'\r\n')


主要想实现stm32发送信息给openmv，用来触发openmv中断下的接收函数，在接受到信息后，能运行人脸识别程序，再将openmv人脸识别结果发给32

@kidswong999 是按照视频的步骤进行的

用给定的人脸分辨的例子，显示说断言失败

你好请问这个人脸识别代码为什么不能输出num的值，串行口显示没有输出值，万分感谢

import sensor, time, image, pyb

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.skip_frames(10) # Let new settings take affect.
sensor.skip_frames(time = 5000) #等待5s
num = 0
def min(pmin, a, s):
    global num
    print(00000000)
    if a < pmin:
        pmin = a
        num = s
        
    return pmin
    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.skip_frames(10) # Let new settings take affect.
    sensor.skip_frames(time = 5000) #等待5s
    
    NUM_SUBJECTS = 2
    NUM_SUBJECTS_IMGS = 20
    img = sensor.snapshot()
    d0 = img.find_lbp((0, 0, img.width(), img.height()))
    img = None
    pmin = 999999
    num = 0
    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()))
            dist += image.match_descriptor(d0, d1)
        print("Average dist for subject %d: %d" % (s, dist / NUM_SUBJECTS_IMGS))
        pmin = min(pmin, dist / NUM_SUBJECTS_IMGS, s)
        print(pmin)
        if dist / NUM_SUBJECTS_IMGS == pmin:
            num = s
    print(num)