Я работаю над системой управления посещаемостью распознавания лиц.Я построил конвейер с нуля, но в итоге сценарий распознает неправильное лицо среди группы из 10 классов.Я реализовал следующий конвейер, используя Tensorflow и Python.
- Захватывайте изображения, изменяйте их размер, выравнивайте их, используя предиктор формы dlib, и сохраняйте их в именованных папках для последующего сравнения при выполнении распознавания.
Извлечение изображений в файл data.pickle для последующей десериализации.
Использование OpenCV для реализации алгоритма MTCNN для обнаружения лиц в кадре, снятом веб-камерой
- передача этих фреймов в сеть facenet для создания вложений 128-D и, соответственно, сравнение с вложениями, присутствующими в базе данных pickle.
Ниже приведен основной файл, который выполняет шаги 3 и 4:
from keras import backend as K
import time
from multiprocessing.dummy import Pool
K.set_image_data_format('channels_first')
import cv2
import os
import glob
import numpy as np
from numpy import genfromtxt
import tensorflow as tf
from keras.models import load_model
from fr_utils import *
from inception_blocks_v2 import *
from mtcnn.mtcnn import MTCNN
import dlib
from imutils import face_utils
import imutils
import pickle
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import train_test_split
FRmodel = load_model('face-rec_Google.h5')
# detector = dlib.get_frontal_face_detector()
detector = MTCNN()
# FRmodel = faceRecoModel(input_shape=(3, 96, 96))
#
# # detector = dlib.get_frontal_face_detector()
# # predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")
# def triplet_loss(y_true, y_pred, alpha = 0.3):
# """
# Implementation of the triplet loss as defined by formula (3)
#
# Arguments:
# y_pred -- python list containing three objects:
# anchor -- the encodings for the anchor images, of shape (None, 128)
# positive -- the encodings for the positive images, of shape (None, 128)
# negative -- the encodings for the negative images, of shape (None, 128)
#
# Returns:
# loss -- real number, value of the loss
# """
#
# anchor, positive, negative = y_pred[0], y_pred[1], y_pred[2]
#
# pos_dist = tf.reduce_sum(tf.square(tf.subtract(anchor, positive)), axis=-1)
# neg_dist = tf.reduce_sum(tf.square(tf.subtract(anchor, negative)), axis=-1)
# basic_loss = tf.add(tf.subtract(pos_dist, neg_dist), alpha)
# loss = tf.reduce_sum(tf.maximum(basic_loss, 0.0))
#
# return loss
#
# FRmodel.compile(optimizer = 'adam', loss = triplet_loss, metrics = ['accuracy'])
# load_weights_from_FaceNet(FRmodel)
def ret_model():
return FRmodel
def prepare_database():
pickle_in = open("data.pickle","rb")
database = pickle.load(pickle_in)
return database
def unpickle_something(pickle_file):
pickle_in = open(pickle_file,"rb")
unpickled_file = pickle.load(pickle_in)
return unpickled_file
def webcam_face_recognizer(database):
cv2.namedWindow("preview")
vc = cv2.VideoCapture(0)
while vc.isOpened():
ret, frame = vc.read()
img_rgb = cv2.cvtColor(frame,cv2.COLOR_BGR2RGB)
img = frame
# We do not want to detect a new identity while the program is in the process of identifying another person
img = process_frame(img,img)
cv2.imshow("Preview", img)
cv2.waitKey(1)
vc.release()
def process_frame(img, frame):
"""
Determine whether the current frame contains the faces of people from our database
"""
# rects = detector(img)
rects = detector.detect_faces(img)
# Loop through all the faces detected and determine whether or not they are in the database
identities = []
for (i,rect) in enumerate(rects):
(x,y,w,h) = rect['box'][0],rect['box'][1],rect['box'][2],rect['box'][3]
img = cv2.rectangle(frame,(x, y),(x+w, y+h),(255,0,0),2)
identity = find_identity(frame, x-50, y-50, x+w+50, y+h+50)
cv2.putText(img, identity,(10,500), cv2.FONT_HERSHEY_SIMPLEX , 4,(255,255,255),2,cv2.LINE_AA)
if identity is not None:
identities.append(identity)
if identities != []:
cv2.imwrite('example.png',img)
return img
def find_identity(frame, x,y,w,h):
"""
Determine whether the face contained within the bounding box exists in our database
x1,y1_____________
| |
| |
|_________________x2,y2
"""
height, width, channels = frame.shape
# The padding is necessary since the OpenCV face detector creates the bounding box around the face and not the head
part_image = frame[y:y+h, x:x+w]
return who_is_it(part_image, database, FRmodel)
def who_is_it(image, database, model):
encoding = img_to_encoding(image, model)
min_dist = 100
# Loop over the database dictionary's names and encodings.
for (name, db_enc) in database.items():
# Compute L2 distance between the target "encoding" and the current "emb" from the database.
dist = np.linalg.norm(db_enc.flatten() - encoding.flatten())
print('distance for %s is %s' %(name, dist))
# If this distance is less than the min_dist, then set min_dist to dist, and identity to name
if dist < min_dist:
min_dist = dist
identity = name
if min_dist >0.1:
print('Unknown person')
else:
print(identity)
return identity
if __name__ == "__main__":
database = prepare_database()
webcam_face_recognizer(database)
Что я здесь не так делаю? Здесь FRmodel - обученная модель facenet