这是我使用 python、opencv 和 haar 级联分类器创建的面部识别应用程序,该应用程序在对受过训练的人进行分类方面效果很好,但是该应用程序存在将未知人检测为先前受过训练的已知人的问题,如何解决此类问题?
this is the dataset creation code
import cv2
import os
import time
cam = cv2.VideoCapture(0)
cam.set(3, 640) # set video width
cam.set(4, 480) # set video height
face_detector = cv2.CascadeClassifier('Cascades/haarcascade_frontalface_default.xml')
# For each person, enter one numeric face id
face_id = input('\n enter user id end press <return> ==> ')
print("\n [INFO] Initializing face capture. Look the camera and wait ...")
# Initialize individual sampling face count
count = 0
while(True):
ret, img = cam.read()
img = cv2.flip(img, 1)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = face_detector.detectMultiScale(gray, 1.3, 5)
width_d, height_d = 150, 150
for (x,y,w,h) in faces:
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", cv2.resize(gray[y:y+h,x:x+w] , (width_d, height_d)))
cv2.imshow('image', img)
k = cv2.waitKey(100) & 0xff # Press 'ESC' for exiting video
if k == 27:
break
elif count >= 400: # Take 30 face sample and stop video
break
# Do a bit of cleanup
print("\n [INFO] Exiting Program and cleanup stuff")
cam.release()
cv2.destroyAllWindows()
这是训练阶段
import cv2
import numpy as np
from PIL import Image
import os
# Path for face image database
path = 'dataset'
recognizer = cv2.face.LBPHFaceRecognizer_create()
detector = cv2.CascadeClassifier("Cascades/haarcascade_frontalface_default.xml");
# function to get the images and label data
def getImagesAndLabels(path):
width_d, height_d = 150, 150 # Declare your own width and height
imagePaths = [os.path.join(path,f) for f in os.listdir(path)]
faceSamples=[]
ids = []
for imagePath in imagePaths:
PIL_img = Image.open(imagePath).convert('L') # convert it to grayscale
img_numpy = np.array(PIL_img,'uint8')
id = int(os.path.split(imagePath)[-1].split(".")[1])
faces = detector.detectMultiScale(img_numpy)
for (x,y,w,h) in faces:
faceSamples.append(cv2.resize(img_numpy[y:y+h,x:x+w], (width_d, height_d)))
ids.append(id)
return faceSamples,ids
print ("\n [INFO] Training faces. It will take a few seconds. Wait ...")
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 [INFO] {0} faces trained. Exiting Program".format(len(np.unique(ids))))
这是识别阶段
import cv2
import numpy as np
import os
recognizer = cv2.face.LBPHFaceRecognizer_create()
recognizer.read('trainer/trainer.yml')
cascadePath = "Cascades/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 = ['Mamdouh Alaa' , 'Dr.Ahmed Seddawy' , 'Dr.Ismail Abdulghaffar']
# Initialize and start realtime video capture
cam = cv2.VideoCapture(0)
cam.set(3, 1366) # set video widht
cam.set(4, 768) # set video height[enter image description here][1]
# Define min window size to be recognized as a face
minW = 0.1*cam.get(3)
minH = 0.1*cam.get(4)
while True:
ret, img =cam.read()
img = cv2.flip(img, 1)
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
width_d, height_d = 150, 150
faces = faceCascade.detectMultiScale(
gray,
scaleFactor = 1.2,
minNeighbors = 5,
minSize = (int(minW), int(minH)),
)
for(x,y,w,h) in faces:
cv2.rectangle(img, (x,y), (x+w,y+h), (255,0,0), 2)
id, confidence = recognizer.predict(cv2.resize(gray[y:y+h,x:x+w], (width_d, height_d)))
# Check if confidence is less them 100 ==> "0" is perfect match
if (confidence < 100) :
id = names[id]
confidence = " {0}%".format(round(100 - confidence))
else:
id = "unknown person"
confidence = " {0}%".format(round(100 - confidence))
cv2.putText(img, str(id), (x+5,y-5), font, 1, (255,255,255), 2)
cv2.putText(img, str(confidence), (x+5,y+h-5), font, 1, (255,255,0), 1)
cv2.imshow('IFR',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()