Gesichtserkennung mit euklidischen Abstand

Question

Ich habe ein Projekt, Gesichtserkennung mit Python zu tun. Ich möchte euklidische Distanz in meinen Code eingeben, um den Abstand zwischen Echtzeitvideo und meinem Datensatz (Bild) zu kennen.

Ich bin verwirrt, weil es in Echtzeit ist. Viele Projekte erklären nur den euklidischen Abstand zwischen Bild "X" und Bild "Y". Kann mir jemand helfen zu verstehen, wie man das für Echtzeit-Video macht?

Dies ist der Code:

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

Wenn Sie den euklidischen Abstand zwischen der Fläche in dem Datensatz und die Gesichter auf dem Video vergleichen wollen, können Sie bekommen von Video-Einzelbilder zuerst zu extrahieren, erkennt Gesichter in den einzelnen Frames und vergleichen Sie dann das Gesichtsbild mit den Bildern im Datensatz.

Es kann leicht mit Opencv getan werden.