python - OpenCv人脸识别app检测问题

Question

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以前受过训练的已知人的问题，如何修复这样的问题？

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()

标签： python