Python 基于欧氏距离的人脸识别
我有一个用Python做人脸识别的项目。我想在我的代码中加入欧几里德距离，以了解实时视频和数据集（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。有人能帮我理解如何在实时视频中做到这一点吗 代码如下：Python 基于欧氏距离的人脸识别,python,opencv,raspberry-pi,raspbian,Python,Opencv,Raspberry Pi,Raspbian,我有一个用Python做人脸识别的项目。我想在我的代码中加入欧几里德距离，以了解实时视频和数据集（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。有人能帮我理解如何在实时视频中做到这一点吗 代码如下： import sys import os impo rt numpy as np from face_recognition_system.videocamera import VideoCamera from face_recognit
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()