基于OpenMV的人脸识别，支持人脸注册、人脸检测、人脸识别

本文主要是介绍基于OpenMV的人脸识别，支持人脸注册、人脸检测、人脸识别，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

https://github.com/1061700625/OpenMV_Face_Recognition

'''
>> author: SXF
>> email: songxf1024@163.com
>> description:用LBP特征进行人脸识别，可进行人脸注册、人脸检测与人脸识别Pin7高电平一次，触发人脸注册；默认低电平UART1（Pin1）输出调试信息UART3（Pin4）输出识别结果，当识别成功后，返回“Find It”（可自定义修改），可连接IoT平台注：需配备SD卡，最大3支持2G，将main.py等文件放至SD卡根目录后上电
'''import sensor, time, image
import os, time
import pyb
from pyb import Pinred   = pyb.LED(1)
green = pyb.LED(2)
blue  = pyb.LED(3)
infrared = pyb.LED(4)
usart1 = pyb.UART(1, 115200)
usart3 = pyb.UART(3, 115200)
REGISTER_MODE = 0sensor.reset()
sensor.set_contrast(1)
sensor.set_gainceiling(16)
sensor.set_framesize(sensor.HQVGA)
sensor.set_pixformat(sensor.GRAYSCALE)
sensor.skip_frames(10)Path_Backup = {'path':'', 'id':0}
rootpath = "/orl_faces"
DIST_THRESHOLD = 15000  # 差异度阈值def debug(strings):print(strings)usart1.write(str(strings)+"\r\n")def find(face_cascade, img):objects = img.find_features(face_cascade, threshold=0.75, scale_factor=1.25)  # 人脸检测if objects:green.on()time.sleep(500)green.off()width_old = 0height_old = 0index = 0for r in objects:  # 寻找最大的faceif r[2] > width_old and r[3] > height_old:width_old = r[2]height_old = r[3]index += 1index -= 1#print("index:", index)img.draw_rectangle(objects[index])d0 = img.find_lbp((0, 0, img.width(), img.height()))res = match(d0)if res != 0:debug(res)return 1def match(d0):  # 人脸识别dir_lists = os.listdir(rootpath)  # 路径下文件夹dir_num = len(dir_lists)          # 文件夹数量debug("*" * 60)debug("Total %d Folders -> %s"%(dir_num, str(dir_lists)))for i in range(0, dir_num):item_lists = os.listdir(rootpath+'/'+dir_lists[i])  # 路径下文件item_num = len(item_lists)                          # 文件数量debug("The %d Folder[%s], Total %d Files -> %s" %(i+1, dir_lists[i], item_num, str(item_lists)))Path_Backup['path'] = rootpath+'/'+dir_lists[i]  # 马上记录当前路径Path_Backup['id'] = item_num                     # 马上记录当前文件数量for j in range(0, item_num):  # 文件依次对比debug(">> Current File: " + item_lists[j])try:img = image.Image("/orl_faces/%s/%s" % (dir_lists[i], item_lists[j]), copy_to_fb=True)except Exception as e:debug(e)breakd1 = img.find_lbp((0, 0, img.width(), img.height()))  # 提取特征值dist = image.match_descriptor(d0, d1)                 # 计算差异度debug(">> Difference Degree: " + str(dist))if dist < DIST_THRESHOLD:debug(">> ** Find It! **")green.on()time.sleep(1000)green.off()return item_lists[j]debug(">> ** No Match! **")return 0def register(face_cascade, img):global REGISTER_MODEif find(face_cascade, img) == 1:debug(">> Existing without registration!")REGISTER_MODE = 0return 0dir_lists = os.listdir(rootpath)  # 路径下文件夹dir_num = len(dir_lists)          # 文件夹数量new_dir = ("%s/%d") % (rootpath, int(dir_num)+1)os.mkdir(new_dir)                 # 创建文件夹cnt = 5  # 拍摄5次图片while cnt:img = sensor.snapshot()objects = img.find_features(face_cascade, threshold=0.75, scale_factor=1.25)  # 人脸检测if objects:width_old = 0height_old = 0index = 0for r in objects:  # 寻找最大的faceif r[2] > width_old and r[3] > height_old:width_old = r[2]height_old = r[3]index += 1index -= 1#print("index:", index)item_lists = os.listdir(new_dir)  # 新路径下文件item_num = len(item_lists)        # 文件数量img.save("%s/%d.pgm" % (new_dir, item_num))  # 写入文件debug(">> [%d]Regist OK!" % cnt)img.draw_rectangle(objects[index])green.on()time.sleep(50)green.off()cnt -= 1if cnt==0:green.on()time.sleep(1000)green.off()REGISTER_MODE = 0def main():global REGISTER_MODEtry:os.mkdir(rootpath)except:passpin7 = Pin('P7', Pin.IN, Pin.PULL_DOWN)  # 1为注册模式，即拍照存入face_cascade = image.HaarCascade("frontalface", stages=25)#try:#face_cascade = image.HaarCascade("/haarcascade_frontalcatface.cascade", stages=25)  # "frontalface"#except:#face_cascade = image.HaarCascade("frontalface", stages=25)print(face_cascade)clock = time.clock()img = Nonewhile (True):clock.tick()img = sensor.snapshot()if pin7.value() == 1:REGISTER_MODE = 1if REGISTER_MODE == 1:debug("REGISTER_MODE\r\n")register(face_cascade, img)else:res = find(face_cascade, img)if res==1:usart3.write("Find It\r\n")# 程序开始
#debug(os.listdir())
main()

过摄像头可进行人脸检测，但由于SD卡内无文件，无法匹配人脸