使用欧几里德距离进行人脸识别

Question

我有一个项目正在用Python进行脸部识别。我想在我的代码中添加欧几里得距离，以便了解实时视频和我的数据集（图像）之间的距离。

我很困惑，因为它是实时的。例如，许多项目只是解释图像“X”和图像“Y”之间的欧几里德距离。任何人都可以帮助我理解如何为实时视频做到这一点？

这是代码：

import sys 
import os 
impo rt numpy as np 
from face_recognition_system.videocamera import VideoCamera 
from face_recognition_system.detectors import FaceDetector 
import face_recognition_system.operations as op 
import cv2 
from cv2 import __version__ 

def get_images(frame, faces_coord, shape): 

if shape == "rectangle": 
    faces_img = op.cut_face_rectangle(frame, faces_coord) 
    frame = op.draw_face_rectangle(frame, faces_coord) 
elif shape == "ellipse": 
    faces_img = op.cut_face_ellipse(frame, faces_coord) 
    frame = op.draw_face_ellipse(frame, faces_coord) 
faces_img = op.normalize_intensity(faces_img) 
faces_img = op.resize(faces_img) 
return (frame, faces_img) 

def add_person(people_folder, shape): 
    person_name = raw_input('What is the name of the new person: ').lower() 
folder = people_folder + person_name 
if not os.path.exists(folder): 
    raw_input("I will now take 20 pictures. Press ENTER when ready.") 
    os.mkdir(folder) 
    video = VideoCamera() 
    detector = FaceDetector('face_recognition_system/frontal_face.xml') 
    counter = 1 
    timer = 0 
    cv2.namedWindow('Video Feed', cv2.WINDOW_AUTOSIZE) 
    cv2.namedWindow('Saved Face', cv2.WINDOW_NORMAL) 
    while counter < 21: 
     frame = video.get_frame() 
     face_coord = detector.detect(frame) 
     if len(face_coord): 
      frame, face_img = get_images(frame, face_coord, shape) 
      # save a face every second, we start from an offset '5' because 
      # the first frame of the camera gets very high intensity 
      # readings. 
      if timer % 100 == 5: 
       cv2.imwrite(folder + '/' + str(counter) + '.jpg', 
          face_img[0]) 
       print 'Images Saved:' + str(counter) 
       counter += 1 
       cv2.imshow('Saved Face', face_img[0]) 

     cv2.imshow('Video Feed', frame) 
     cv2.waitKey(50) 
     timer += 5 
else: 
    print "This name already exists." 
    sys.exit() 

def recognize_people(people_folder, shape): 
try: 
    people = [person for person in os.listdir(people_folder)] 
except: 
    print "Have you added at least one person to the system?" 
    sys.exit() 
print "This are the people in the Recognition System:" 
for person in people: 
    print "-" + person 

print 30 * '-' 
print " POSSIBLE RECOGNIZERS TO USE" 
print 30 * '-' 
print "1. EigenFaces" 
print "2. FisherFaces" 
print "3. LBPHFaces" 
print 30 * '-' 

choice = check_choice() 

detector = FaceDetector('face_recognition_system/frontal_face.xml') 
if choice == 1: 
    recognizer = cv2.face.createEigenFaceRecognizer() 
    threshold = 4000 
elif choice == 2: 
    recognizer = cv2.face.createFisherFaceRecognizer() 
    threshold = 300 
elif choice == 3: 
    recognizer = cv2.face.createLBPHFaceRecognizer() 
    threshold = 105 
images = [] 
labels = [] 
labels_people = {} 
for i, person in enumerate(people): 
    labels_people[i] = person 
    for image in os.listdir(people_folder + person): 
     images.append(cv2.imread(people_folder + person + '/' + image, 0)) 
     labels.append(i) 
try: 
    recognizer.train(images, np.array(labels)) 
except: 
    print "\nOpenCV Error: Do you have at least two people in the database?\n" 
    sys.exit() 

video = VideoCamera() 
while True: 
    frame = video.get_frame() 
    faces_coord = detector.detect(frame, False) 
    if len(faces_coord): 
     frame, faces_img = get_images(frame, faces_coord, shape) 
     for i, face_img in enumerate(faces_img): 
      if __version__ == "3.1.0": 
       collector = cv2.face.MinDistancePredictCollector() 
       recognizer.predict(face_img, collector) 
       conf = collector.getDist() 
       pred = collector.getLabel() 
      else: 
       pred, conf = recognizer.predict(face_img) 
      print "Prediction: " + str(pred) 
      print 'Confidence: ' + str(round(conf)) 
      print 'Threshold: ' + str(threshold) 
      if conf < threshold: 
       cv2.putText(frame, labels_people[pred].capitalize(), 
          (faces_coord[i][0], faces_coord[i][1] - 2), 
          cv2.FONT_HERSHEY_PLAIN, 1.7, (206, 0, 209), 2, 
          cv2.LINE_AA) 
      else: 
       cv2.putText(frame, "Unknown", 
          (faces_coord[i][0], faces_coord[i][1]), 
          cv2.FONT_HERSHEY_PLAIN, 1.7, (206, 0, 209), 2, 
          cv2.LINE_AA) 

    cv2.putText(frame, "ESC to exit", (5, frame.shape[0] - 5), 
       cv2.FONT_HERSHEY_PLAIN, 1.2, (206, 0, 209), 2, cv2.LINE_AA) 
    cv2.imshow('Video', frame) 
    if cv2.waitKey(100) & 0xFF == 27: 
     sys.exit() 

def check_choice(): 
""" Check if choice is good 
""" 
is_valid = 0 
while not is_valid: 
    try: 
     choice = int(raw_input('Enter your choice [1-3] : ')) 
     if choice in [1, 2, 3]: 
      is_valid = 1 
     else: 
      print "'%d' is not an option.\n" % choice 
    except ValueError, error: 
     print "%s is not an option.\n" % str(error).split(": ")[1] 
return choice 

if __name__ == '__main__': 
print 30 * '-' 
print " POSSIBLE ACTIONS" 
print 30 * '-' 
print "1. Add person to the recognizer system" 
print "2. Start recognizer" 
print "3. Exit" 
print 30 * '-' 

CHOICE = check_choice() 

PEOPLE_FOLDER = "face_recognition_system/people/" 
SHAPE = "ellipse" 

if CHOICE == 1: 
    if not os.path.exists(PEOPLE_FOLDER): 
     os.makedirs(PEOPLE_FOLDER) 
    add_person(PEOPLE_FOLDER, SHAPE) 
elif CHOICE == 2: 
    recognize_people(PEOPLE_FOLDER, SHAPE) 
elif CHOICE == 3: 
sys.exit() 

Answer 1

如果要比较的脸在数据集，并出现在视频中的面之间的欧氏距离，你必须首先从视频中提取单个帧，检测人脸在各个帧中，然后将人脸图像与数据集中的图像进行比较。

它可以使用Opencv轻松完成。