Recognition_Model.py

import cv2
import numpy
import os
haar_file = 'haarcascade_frontalface_default.xml'
face_cascade = cv2.CascadeClassifier(haar_file)
datasets = 'datasets'
(images, labels, names, ide) = ([], [], {}, 0)
for (subdirs, dirs, files) in os.walk(datasets):
    for subdir in dirs:
        names[ide] = subdir
        subjectpath = os.path.join(datasets, subdir)
        for filename in os.listdir(subjectpath):
            path = subjectpath + '/' + filename
            label = ide
            images.append(cv2.imread(path, 0))
            labels.append(int(label))
        ide = ide + 1
(images, labels) = [numpy.array(lis) for lis in [images, labels]]
print(images, labels)
(width, height) = (130, 100)
model = cv2.face.LBPHFaceRecognizer_create()
#model = cv2.face.FisherFaceRecognizer_create()
model.train(images, labels)

cam = cv2.VideoCapture(0)
cnt = 0
while True:
    (_, im) = cam.read()
    gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
    faces = face_cascade.detectMultiScale(gray, 1.3, 5)
    for (x, y, w, h) in faces:
        cv2.rectangle(im, (x, y), (x+w, y+h), (255, 255, 0), 2)
        face = gray[y:y+h, x:x+h]
        face_resize = cv2.resize(face, (width, height))
        prediction = model.predict(face_resize)
        cv2.rectangle(im, (x, y), (x + w, y + h), (0, 255, 0), 3)
        if prediction[1] < 800:
            cv2.putText(im, '%s - %.0f' % (names[prediction[0]], prediction[1]), (x-10, y-10), cv2.FONT_HERSHEY_PLAIN,
                        2, (0, 0, 255))
            print(names[prediction[0]])
            cnt = 0
        else:
            cnt += 1
            cv2.putText(im, 'unknown', (x-10, y-10), cv2.FONT_HERSHEY_PLAIN,
                        2, (0, 0, 255))
            if cnt > 100:
                print("unknown.jpg", im)
                cv2.imwrite("unknown.jpg", im)
                cnt = 0
    cv2.imshow('FaceRecognition', im)
    key = cv2.waitKey(10)
    if key == 27:
        break