基於opencv和pillow實現人臉識別系統(附demo)

#數據采集
cam = cv2.VideoCapture(0)#補獲圖片
cam.set(3, 640) # set video width
cam.set(4, 480) # set video height
face_detector = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')#導入分類器
# For each person, enter one numeric face id
face_id = input('\n 輸入用戶id')
print("\n 數據采集中，請正視攝像頭輕微扭轉")
# Initialize individual sampling face count
count = 0
while(True):
 ret, img = cam.read()#ret為是否成功讀取，是一個佈爾值
 gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)#轉化為灰度圖
 faces = face_detector.detectMultiScale(gray, 1.3, 5,minSize=(100,100))
 for (x,y,w,h) in faces:#此處faces是一個array數組或空的元組，原因我後面會分析
  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()#識別器的導入
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)]#在這裡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函數的使用方法。
 faceSamples=[]
 ids = []
 for imagePath in imagePaths:
  PIL_img = Image.open(imagePath).convert('L') #轉化為灰度圖
  img_numpy = np.array(PIL_img,'uint8')#轉化為數組
  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 訓練數據中，請稍後")
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} 張臉訓練完畢. 程序關閉".format(len(np.unique(ids))))

# -*- coding: UTF-8 -*-
#識別器
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文件是支持漢語的字體，稍後我會說明。
font1= ImageFont.truetype(path_to_ttf, size=20)
recognizer = cv2.face.LBPHFaceRecognizer_create()
recognizer.read('trainer/trainer.yml')#讀取識別器
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']#因為我們不會在人臉識別時隻顯示你的代號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
#最小識別的臉的大小，在識別過程中，我們不會捕獲到距離你很遠的街上路人的信息，這會導致很多問題，因此我們隻需要識別想參與識別的人，而設置最小人臉識別大小可以規避這一點
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)),
 )#相關參數設置可以自行搜索
 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，等於100-概率，相信大傢的概率統計一定比我優秀
  else:
   id = "未識別"
   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()