@kidswong999 已经好了，不同人脸虽然差异很小但可以分辨出

@kidswong999 https://pan.baidu.com/s/1BW2XXy3weS9N7czQNXiR7g
提取码：1111

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

RED_LED_PIN = 1
BLUE_LED_PIN = 3
NUM_SUBJECTS = 6 #图像库中不同人数，一共6人
NUM_SUBJECTS_IMGS = 10 #每人有3张样本图片

uart = UART(3, 115200)
sensor.reset()
sensor.set_contrast(3)
sensor.set_gainceiling(16)
sensor.set_framesize(sensor.HQVGA) # HQVGA最好,其次QVGA,QQVGA
sensor.set_pixformat(sensor.GRAYSCALE) #注意人脸识别只能用灰度图哦
sensor.skip_frames(10)
sensor.skip_frames(time = 1000) #等待1s

face_cascade = image.HaarCascade("frontalface", stages=25)
clock = time.clock()
lcd.init()

def face1():
    #uart.write("a,ok\r\n")
    print("a,ok")

    j=0
    sensor.reset()
    sensor.set_contrast(3)
    sensor.set_gainceiling(16)
    sensor.set_framesize(sensor.HQVGA) # HQVGA最好,其次QVGA,QQVGA
    sensor.set_pixformat(sensor.GRAYSCALE) #注意人脸识别只能用灰度图哦
    sensor.skip_frames(10) # Let new settings take affect.
    sensor.skip_frames(time = 1000) #等待1s

    while (True):
        pyb.LED(RED_LED_PIN).on()
        img = sensor.snapshot()    # 拍摄一张照片
        lcd.display(sensor.snapshot())
        objects = img.find_features(face_cascade, threshold=0.75, scale=1.35)     #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。
        for r in objects:
            img.draw_rectangle(r)        #在找到的目标上画框，标记出来
        if objects:
            for object in objects:
                fs=object[2]*object[3]
                if fs>=14884:
                    j+=1
                    if j==10:
                        print(fs)
                        print(objects)
                        #uart.write("a,over\r\n")
                        break
            if j==10:
                uart.write("a")
                pyb.LED(RED_LED_PIN).off()    #红灯灭，蓝灯亮
                pyb.LED(BLUE_LED_PIN).on()
                pyb.LED(BLUE_LED_PIN).off()
                break

def dis_face():
    #global NUM_SUBJECTS  #图像库中不同人数，一共6人
    #NUM_SUBJECTS = 4

    #uart.write("c,ok\r\n")
    print("c,ok")

    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 = 3000) #等待1s
    lcd.init()

    #SUB = "s1"
    img = sensor.snapshot() # 拍摄当前人脸。
    #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
    pyb.LED(RED_LED_PIN).on()
    lcd.display(sensor.snapshot())

    for s in range(1, NUM_SUBJECTS+1):
        #lcd.display(sensor.snapshot())
        dist = 0
        for i in range(2, NUM_SUBJECTS_IMGS+1):
            img = image.Image("sample/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))
        if (dist/NUM_SUBJECTS_IMGS)<pmin:
            pmin=(dist/NUM_SUBJECTS_IMGS)
            num=s
        print(pmin)
    if pmin<10000:
        if num==1:
            uart.write("d")
        if num==2:
            uart.write("e")
        if num==3:
            uart.write("f")
        if num==4:
            uart.write("g")
        if num==5:
            uart.write("h")
        if num==6:
            uart.write("i")
    elif pmin>10000:
        uart.write("j")
    print(num) # num为当前最匹配的人的编号。
    #uart.write("%d\r\n" %num)
    #uart.write("c,over\r\n")

    pyb.LED(RED_LED_PIN).off()    #红灯灭，蓝灯亮
    pyb.LED(BLUE_LED_PIN).on()
    pyb.LED(BLUE_LED_PIN).off()

def dis_face1():
    #global NUM_SUBJECTS  #图像库中不同人数，一共6人
    #NUM_SUBJECTS = 4

    #uart.write("c,ok\r\n")
    print("m,ok")

    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 = 1000) #等待1s
    lcd.init()

    #SUB = "s1"
    img = sensor.snapshot() # 拍摄当前人脸。
    #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
    pyb.LED(RED_LED_PIN).on()
    lcd.display(sensor.snapshot())

    for s in range(1, NUM_SUBJECTS+1):
        #lcd.display(sensor.snapshot())
        dist = 0
        for i in range(2, NUM_SUBJECTS_IMGS+1):
            img = image.Image("sample/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))
        if (dist/NUM_SUBJECTS_IMGS)<pmin:
            pmin=(dist/NUM_SUBJECTS_IMGS)
            num=s
        print(pmin)
    #if pmin<6500:
        #if num==1:
            #uart.write("d")
        #if num==2:
            #uart.write("e")
        #if num==3:
            #uart.write("f")
        #if num==4:
            #uart.write("g")
        #if num==5:
            #uart.write("h")
        #if num==6:
            #uart.write("i")
    #elif pmin>6500:
        uart.write("j")
    print(num) # num为当前最匹配的人的编号。
    #uart.write("%d\r\n" %num)
    #uart.write("c,over\r\n")

    pyb.LED(RED_LED_PIN).off()    #红灯灭，蓝灯亮
    pyb.LED(BLUE_LED_PIN).on()
    pyb.LED(BLUE_LED_PIN).off()

def length():
    #uart.write("b,ok\r\n")
    print("b,ok")

    K=4148#the value should be measured
    length=0

    sensor.reset()
    sensor.set_contrast(3)
    sensor.set_gainceiling(16)
    sensor.set_framesize(sensor.HQVGA) # HQVGA最好,其次QVGA,QQVGA
    sensor.set_pixformat(sensor.GRAYSCALE) #注意人脸识别只能用灰度图哦
    sensor.skip_frames(10) # Let new settings take affect.
    sensor.skip_frames(time = 1000) #等待1s

    while (True):
        pyb.LED(RED_LED_PIN).on()
        img = sensor.snapshot()    # 拍摄一张照片
        lcd.display(sensor.snapshot())
        objects = img.find_features(face_cascade, threshold=0.75, scale=1.35)     #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。

        for r in objects:
            img.draw_rectangle(r)        #在找到的目标上画框，标记出来
        if objects:
            for object in objects:
                print(object[2],object[3])
                cx = int(object[0]+object[2]/2)
                cy = int(object[1]+object[3]/2)

                Lm = (object[2]+object[3])/2
                length = K/Lm
                img.draw_cross(cx, cy) # cx, cy

                print(length)
                #uart.write("%d\r\n" %length)
                #uart.write("b,over\r\n")
                break
        if length!=0:
            uart.write("l")
            pyb.LED(RED_LED_PIN).off()    #红灯灭，蓝灯亮
            pyb.LED(BLUE_LED_PIN).on()
            pyb.LED(BLUE_LED_PIN).off()
            break

def snapshot1():
    #uart.write("k,ok\r\n")
    print("k,ok")


    print("%d\r\n" %NUM_SUBJECTS)

    sensor.reset() # Initialize the camera sensor.
    sensor.set_pixformat(sensor.GRAYSCALE) # or sensor.GRAYSCALE
    sensor.set_framesize(sensor.HQVGA) # B128X128or sensor.QQVGA (or others)
    sensor.set_windowing((92,112))
    sensor.skip_frames(10) # Let new settings take affect.
    sensor.skip_frames(time = 1000)
    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 = 10 #设置每个人拍摄图片数量。

    while(n):
        pyb.LED(RED_LED_PIN).on()     #红灯亮
        lcd.display(sensor.snapshot())
        sensor.skip_frames(time = 3000) #等待3s，准备一下表情。

        pyb.LED(RED_LED_PIN).off()    #红灯灭，蓝灯亮
        pyb.LED(BLUE_LED_PIN).on()
        print(n)
        sensor.snapshot().save("data/s%s/%s.pgm" % (num, n) ) #保存截取到的图片到SD卡
        n -= 1
        pyb.LED(BLUE_LED_PIN).off()

        if n==1:
            print("Done! Reset the camera to see the saved image.")

        #uart.write("k,over\r\n")
        uart.write("k\r\n")


while(True):

    #clock.tick()


    #img = sensor.snapshot()        #1
    #lcd.display(sensor.snapshot()) #2  这两行无论哪一行运行，串口都无法接收到数据

    a='0'

    if uart.any():
        a=uart.readline().decode()
        print(a)
   #pyb.delay(100)

    if a=='a':
        #uart.write("a,ready\r\n")
        face1()              #人脸识别（日常模式）
        print("face1")

    if a=='b':
        #uart.write("b,ready\r\n")
        length()              #人脸识别（测额头距）
        print("length")

    if a=='c':
        #uart.write("c,ready\r\n")
        dis_face()           #分辨人脸
        print("dis_face")

    if a=='k':
        #uart.write("k,ready\r\n")
        snapshot1()           #拍照保存（人脸样本）
        print("snapshot1")

    if a=='m':
        #uart.write("c,ready\r\n")
        dis_face1()           #分辨人脸
        print("dis_face1")

    #else:
        #print("not a ")
        #print("a:",a)

@kidswong999 谢谢你，问题解决了

@kidswong999 这两行，你可以把代码贴到IDE中去试一下，只要这两行不注释掉，就无法发送数据

@kidswong999 三次截图的不同之处在于，我把代码中的两行代码注释与否，所以才会得出不同结果，我想问的是代码中标记的两行代码为什么会影响串口发送数据？因为我看不到库是怎么写的，只有使用方法，所以现在不知道是哪里冲突还是怎样

@kidswong999 这是第三张，接收到数据

@kidswong999 这是第二张，没接到数据

@kidswong999 这是第一张，没接到数据

@kidswong999 你可以截下图我看下嘛？