#數(shù)據(jù)采集
cam = cv2.VideoCapture(0)#補(bǔ)獲圖片
cam.set(3, 640) # set video width
cam.set(4, 480) # set video height
face_detector = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')#導(dǎo)入分類器
# For each person, enter one numeric face id
face_id = input('\n 輸入用戶id')
print("\n 數(shù)據(jù)采集中，請(qǐng)正視攝像頭輕微扭轉(zhuǎn)")
# Initialize individual sampling face count
count = 0
while(True):
 ret, img = cam.read()#ret為是否成功讀取，是一個(gè)布爾值
 gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)#轉(zhuǎn)化為灰度圖
 faces = face_detector.detectMultiScale(gray, 1.3, 5,minSize=(100,100))
 for (x,y,w,h) in faces:#此處faces是一個(gè)array數(shù)組或空的元組，原因我后面會(huì)分析
  cv2.rectangle(img, (x,y), (x+w,y+h), (255,0,0), 2)
  count += 1
 # Save the captured image into the datasets folder
  cv2.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 video
 if k == 27:
  break
 elif count >= 10: # Take 10 face sample and stop video
  break
# Do a bit of cleanup
print("\n [INFO] Exiting Program and cleanup stuff")
cam.release()
cv2.destroyAllWindows()

import cv2
#訓(xùn)練器
import numpy as np
from PIL import Image
import os
# Path for face image database
path = 'dataset'
recognizer = cv2.face.LBPHFaceRecognizer_create()#識(shí)別器的導(dǎo)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)]#在這里os模塊可以幫助我們很好的建立路徑，建議可以先查看一下相關(guān)函數(shù)的使用方法。
 faceSamples=[]
 ids = []
 for imagePath in imagePaths:
  PIL_img = Image.open(imagePath).convert('L') #轉(zhuǎn)化為灰度圖
  img_numpy = np.array(PIL_img,'uint8')#轉(zhuǎn)化為數(shù)組
  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 訓(xùn)練數(shù)據(jù)中，請(qǐng)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} 張臉訓(xùn)練完畢. 程序關(guān)閉".format(len(np.unique(ids))))

# -*- coding: UTF-8 -*-
#識(shí)別器
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文件是支持漢語(yǔ)的字體，稍后我會(huì)說(shuō)明。
font1= ImageFont.truetype(path_to_ttf, size=20)
recognizer = cv2.face.LBPHFaceRecognizer_create()
recognizer.read('trainer/trainer.yml')#讀取識(shí)別器
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?yàn)槲覀儾粫?huì)在人臉識(shí)別時(shí)只顯示你的代號(hào)而是要顯示你的具體信息。
cam = cv2.VideoCapture(0)
cam.set(3, 640) # set video widht
cam.set(4, 480) # set video height
#最小識(shí)別的臉的大小，在識(shí)別過(guò)程中，我們不會(huì)捕獲到距離你很遠(yuǎn)的街上路人的信息，這會(huì)導(dǎo)致很多問(wèn)題，因此我們只需要識(shí)別想?yún)⑴c識(shí)別的人，而設(shè)置最小人臉識(shí)別大小可以規(guī)避這一點(diǎn)
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)),
 )#相關(guān)參數(shù)設(shè)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 match
  if (confidence < 100):
   id = names[id]
   confidence = " {0}%".format(round(100 - confidence))#confidence是置信度指數(shù)，等于100-概率，相信大家的概率統(tǒng)計(jì)一定比我優(yōu)秀
  else:
   id = "未識(shí)別"
   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 video
 if k == 27:
  break
# Do a bit of cleanup
print("\n [INFO] Exiting Program and cleanup stuff")
cam.release()
cv2.destroyAllWindows()