使用歐氏距離的人臉識別

Question

我有一個使用Python進行人臉識別的項目。 我想在我的代碼中加入歐幾里得距離，以了解實時視頻和數據集（圖像）之間的距離。

我很困惑，因為它是實時的。 例如，許多項目只是解釋圖像“ X”和圖像“ Y”之間的歐幾里得距離。 有人可以幫助我了解如何針對實時視頻執行此操作嗎？

這是代碼：

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

如果要比較數據集中的面部和視頻中出現的面部之間的歐式距離，則必須首先從視頻中提取單個幀，檢測各個幀中的面部，然后將面部圖像與數據集中的圖像進行比較。

使用Opencv可以輕松完成。