def face2database(picture_path,
model_path,
database_path,
batch_size=90,
image_size=160):
# 提取特征到数据库
# picture_path为人脸文件夹的所在路径
# model_path为facenet模型路径
# database_path为人脸数据库路径
with tf.Graph().as_default():
with tf.Session() as sess:
dataset = facenet.get_dataset(picture_path)
paths, labels = facenet.get_image_paths_and_labels(dataset)
print('Number of classes: %d' % len(dataset))
print('Number of images: %d' % len(paths))
# Load the model
print('Loading feature extraction model')
facenet.load_model(model_path)
# Get input and output tensors
images_placeholder = tf.get_default_graph().get_tensor_by_name(
"input:0")
embeddings = tf.get_default_graph().get_tensor_by_name(
"embeddings:0")
phase_train_placeholder = tf.get_default_graph(
).get_tensor_by_name("phase_train:0")
embedding_size = embeddings.get_shape()[1]
# Run forward pass to calculate embeddings
print('Calculating features for images')
nrof_images = len(paths)
nrof_batches_per_epoch = int(
math.ceil(1.0 * nrof_images / batch_size))
emb_array = np.zeros((nrof_images, embedding_size))
for i in range(nrof_batches_per_epoch):
start_index = i * batch_size
end_index = min((i + 1) * batch_size, nrof_images)
paths_batch = paths[start_index:end_index]
images = facenet.load_data(paths_batch, False, False,
image_size)
feed_dict = {
images_placeholder: images,
phase_train_placeholder: False
}
emb_array[start_index:end_index, :] = sess.run(
embeddings, feed_dict=feed_dict)
np.savez(database_path, emb=emb_array, lab=labels)
print("数据库特征提取完毕！")
def __init__(self, sess,
data_path='output', model='20180402-114759',
classifier_name='result1'):
self.sess = sess
self.dataset = facenet.get_dataset(data_path) # 얼굴영역만 저장된 폴더들의 경로를 가져온다.
self.class_names = [cls.name.replace('_', ' ') for cls in self.dataset] # Dataset 폴더 내 각각의 폴더(Class)명
self.paths, self.labels = facenet.get_image_paths_and_labels(self.dataset) # 이미지들의 경로 및 Class 명
print('Number of classes: %d' % len(self.dataset))
print('Number of images: %d' % len(self.paths))
print('Loading feature extraction model')
model_path = 'model/{}/{}.pb'.format(model, model) # 네트워크 모델(얼굴인식)이 저장된 경로
facenet.load_model(model_path)
self.model = SVC(kernel='linear', probability=True) # 분류모델 생성
self.images_placeholder = tf.get_default_graph().get_tensor_by_name("input:0")
self.embeddings = tf.get_default_graph().get_tensor_by_name("embeddings:0")
self.phase_train_placeholder = tf.get_default_graph().get_tensor_by_name("phase_train:0")
self.embedding_size = self.embeddings.get_shape()[1]
classifier_path = 'classifier/classifier_{0}.pkl'.format(classifier_name) # 분류모델이 저장될 이름 및 경로
self.classifier_path = os.path.expanduser(classifier_path)
self.batch_size = 1000
self.image_size = 160
def __init__(self,
sess,
pnet,
rnet,
onet,
out_path='output',
data_path='dataset'):
self.sess = sess
self.pnet, self.rnet, self.onet = pnet, rnet, onet
self.output_path = os.path.expanduser(out_path) # 정렬된 얼굴 이미지가 저장될 경로
if not os.path.exists(self.output_path):
os.makedirs(self.output_path)
self.dataset = facenet.get_dataset(data_path) # 인식 대상이 저장된 이미지 경로
self.minsize = 20 # 사진에서 얼굴 영역의 최소 크기 (20 x 20)
self.threshold = [0.6, 0.7, 0.7] # 얼굴 탐지 시 각 단계별 경계값
self.factor = 0.709 # 얼굴 탐지에 필요한 변수
self.margin = 44 # 얼굴 탐지에 필요한 변수
self.image_size = 182 # 저장될 얼굴 사진의 크기
self.images = [] # 자장된 얼굴 사진들의 경로들이 저장될 배열
self.aligned_images = 0 # 최종적으로 변환된 얼굴 이미지 개수
face_detector = FaceDetector()
face_recognition = FaceRecognition()
face_classfier = FaceClassifier()
def get_image_paths_and_labels(dataset):
image_paths_flat = []
labels_flat = []
for i in range(len(dataset)):
image_paths_flat += dataset[i].image_paths
labels_flat += [dataset[i].name] * len(dataset[i].image_paths)
return image_paths_flat, labels_flat
dataset = get_dataset(datadir)
paths, labels = get_image_paths_and_labels(dataset)
print('Number of classes: %d' % len(dataset))
print('Number of images: %d' % len(paths))
# Run forward pass to calculate embeddings
print('Calculating features for images')
image_size = 160
nrof_images = len(paths)
features = np.zeros((2 * nrof_images, 512))
labels = np.asarray(labels).repeat(2)
for i in range(nrof_images):
img = cv2.imread(paths[i])
if img is None: