目录

• 一。人脸检测和数据收集
• 二.训练识别器
• 三.人脸识别和显示

#数据采集
cam = cv2.VideoCapture(0)#补获图片
cam.set(3, 640) # set video width
cam.set(4, 480) # set video height
face_detector = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')#导入分类器
# For each person, enter one numeric face id
face_id = input('\n 输入用户id')
print("\n 数据采集中，请正视摄像头轻微扭转")
# Initialize individual sampling face count
count = 0
while(True):ret, img = cam.read()#ret为是否成功读取，是一个布尔值gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)#转化为灰度图faces = face_detector.detectMultiScale(gray, 1.3, 5,minSize=(100,100))for (x,y,w,h) in faces:#此处faces是一个array数组或空的元组，原因我后面会分析cv2.rectangle(img, (x,y), (x+w,y+h), (255,0,0), 2)count += 1# Save the captured image into the datasets foldercv2.imwrite("dataset/User." + str(face_id) + '.' + str(count) + ".jpg", gray[y:y+h,x:x+w])cv2.imshow('image', img)k = cv2.waitKey(100) & 0xff # Press 'ESC' for exiting videoif k == 27:breakelif count >= 10: # Take 10 face sample and stop videobreak
# Do a bit of cleanup
print("\n [INFO] Exiting Program and cleanup stuff")
cam.release()
cv2.destroyAllWindows()

import cv2
#训练器
import numpy as np
from PIL import Image
import os
# Path for face image database
path = 'dataset'
recognizer = cv2.face.LBPHFaceRecognizer_create()#识别器的导入
detector = cv2.CascadeClassifier("haarcascade_frontalface_default.xml")
# function to get the images and label data
def getImagesAndLabels(path):imagePaths = [os.path.join(path,f) for f in os.listdir(path)]#在这里os模块可以帮助我们很好的建立路径，建议可以先查看一下相关函数的使用方法。faceSamples=[]ids = []for imagePath in imagePaths:PIL_img = Image.open(imagePath).convert('L') #转化为灰度图img_numpy = np.array(PIL_img,'uint8')#转化为数组id = int(os.path.split(imagePath)[-1].split(".")[1])faces = detector.detectMultiScale(img_numpy)for (x,y,w,h) in faces:faceSamples.append(img_numpy[y:y+h,x:x+w])ids.append(id)return faceSamples,ids
print ("\n 训练数据中，请稍后")
faces,ids = getImagesAndLabels(path)
recognizer.train(faces, np.array(ids))
# Save the model into trainer/trainer.yml
recognizer.write('trainer/trainer.yml') # recognizer.save() worked on Mac, but not on Pi
# Print the numer of faces trained and end program
print("\n {0} 张脸训练完毕. 程序关闭".format(len(np.unique(ids))))

# -*- coding: UTF-8 -*-
#识别器
import cv2
import numpy as np
import os
from PIL import Image, ImageFont, ImageDraw
path_to_ttf = 'C:\Windows\Fonts\Microsoft YaHei UI\msyh.ttc'#ttc文件是支持汉语的字体，稍后我会说明。
font1= ImageFont.truetype(path_to_ttf, size=20)
recognizer = cv2.face.LBPHFaceRecognizer_create()
recognizer.read('trainer/trainer.yml')#读取识别器
cascadePath = "haarcascade_frontalface_default.xml"
faceCascade = cv2.CascadeClassifier(cascadePath);
font = cv2.FONT_HERSHEY_SIMPLEX
#iniciate id counter
id = 0
# names related to ids: example ==> Marcelo: id=1, etc
names = ['None', '段林晨', 'Paula', 'Ilza', 'Z', 'W']#因为我们不会在人脸识别时只显示你的代号而是要显示你的具体信息。
cam = cv2.VideoCapture(0)
cam.set(3, 640) # set video widht
cam.set(4, 480) # set video height
#最小识别的脸的大小，在识别过程中，我们不会捕获到距离你很远的街上路人的信息，这会导致很多问题，因此我们只需要识别想参与识别的人，而设置最小人脸识别大小可以规避这一点
minW = 0.1*cam.get(3)
minH = 0.1*cam.get(4)
while True:ret, img =cam.read()gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)faces = faceCascade.detectMultiScale(gray,scaleFactor = 1.2,minNeighbors = 5,minSize = (int(minW), int(minH)),)#相关参数设置可以自行搜索for(x,y,w,h) in faces:cv2.rectangle(img, (x,y), (x+w,y+h), (0,255,0), 2)id, confidence = recognizer.predict(gray[y:y+h,x:x+w])# Check if confidence is less them 100 ==> "0" is perfect matchif (confidence < 100):id = names[id]confidence = " {0}%".format(round(100 - confidence))#confidence是置信度指数，等于100-概率，相信大家的概率统计一定比我优秀else:id = "未识别"confidence = " {0}%".format(round(100 - confidence))img=Image.fromarray(img)draw = ImageDraw.Draw(img)draw.text(xy=(x+5,y-5), text=str(id), font=font1,fill=(255,255,255))img=np.array(img)cv2.putText(img, str(confidence), (x+5,y+h-5), font, 1, (255,255,0), 1)cv2.imshow('camera',img)k = cv2.waitKey(10) & 0xff # Press 'ESC' for exiting videoif k == 27:break
# Do a bit of cleanup
print("\n [INFO] Exiting Program and cleanup stuff")
cam.release()
cv2.destroyAllWindows()