import sensor, time, image, pyb  
def face_fb():
    while(True):
        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 = 3 #图像库中不同人数，一共6人
        NUM_SUBJECTS_IMGS = 10 #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("face/F%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。
face_fb()

import sensor, image, time

from pid import PID
from pyb import Servo

pan_servo=Servo(1)
tilt_servo=Servo(2)

#pan_servo.calibration(500,2500,500)
#tilt_servo.calibration(500,2500,500)

#red_threshold = (13, 49, 18, 61, 6, 47)

pan_pid = PID(p=0.17, i=0, imax=90) #脱机运行或者禁用图像传输，使用这个PID
tilt_pid = PID(p=0.085, i=0, imax=90) #脱机运行或者禁用图像传输，使用这个PID
#pan_pid = PID(p=0.1, i=0, imax=90)#在线调试使用这个PID
#tilt_pid = PID(p=0.1, i=0, imax=90)#在线调试使用这个PID

sensor.reset() # Initialize the camera sensor.
sensor.set_contrast(3)
sensor.set_gainceiling(16)
sensor.set_pixformat(sensor.GRAYSCALE) # use RGB565.
sensor.set_framesize(sensor.B160X160) # use QQVGA for speed.
sensor.set_vflip(True)
sensor.skip_frames(10) # Let new settings take affect.
sensor.set_auto_whitebal(False) # turn this off.

降低环境因素的影响

sensor.set_auto_gain(True) # 开启自动增益
sensor.set_auto_exposure(True) # 开启自动曝光

clock = time.clock() # Tracks FPS.

face_cascade = image.HaarCascade("frontalface", stages=25)
def find_max(blobs):
max_size=0
for blob in blobs:
if blob[2]*blob[3] > max_size:
max_blob=blob
max_size = blob[2]*blob[3]
return max_blob

while(True):
clock.tick() # Track elapsed milliseconds between snapshots().
img = sensor.snapshot() # Take a picture and return the image.

blobs = img.find_features(face_cascade, threshold=0.75, scale=1.35)
if blobs:
    max_blob = find_max(blobs)
    pan_error = max_blob[0]+max_blob[2]/2-img.width()/2
    tilt_error = max_blob[1]+max_blob[3]/2-img.height()/2

    print("pan_error: ", pan_error)

    img.draw_rectangle(max_blob) # rect
    img.draw_cross(int(max_blob[0]+max_blob[2]/2),int(max_blob[1]+max_blob[3]/2)) # cx, cy

    pan_output=pan_pid.get_pid(pan_error,1)/2
    tilt_output=tilt_pid.get_pid(tilt_error,1)
    print("pan_output",pan_output)
    pan_servo.angle(pan_servo.angle()+pan_output)
    tilt_servo.angle(tilt_servo.angle()-tilt_output)

粘贴代码