def align_face(self,opic,faceKeyPoint):
img = cv2.imread(opic)
faceKeyPoint = faceKeyPoint[0]
#根据两个鼻子和眼睛进行3点对齐
eye1 = faceKeyPoint[0]
eye2 = faceKeyPoint[1]
noise = faceKeyPoint[2]
source_point = np.array(
[eye1, eye2, noise], dtype=np.float32
)
eye1_noraml= [int(x) for x in face_comm.get_conf('alignment','left_eye').split(',')]
eye2_noraml=[int(x) for x in face_comm.get_conf('alignment','right_eye').split(',')]
noise_normal=[int(x) for x in face_comm.get_conf('alignment','noise').split(',')]
#设置的人脸标准模型
dst_point = np.array(
[eye1_noraml,
eye2_noraml,
noise_normal],
dtype=np.float32)
tranform = cv2.getAffineTransform(source_point, dst_point)
imagesize=tuple([int(x) for x in face_comm.get_conf('alignment','imgsize').split(',')])
img_new = cv2.warpAffine(img, tranform, imagesize)
new_image= os.path.abspath(face_comm.get_conf('alignment','aligment_face_dir'))
new_image= new_image+'/'+'%d_%d.png'%(time.time(),random.randint(0,100))
if cv2.imwrite(new_image, img_new):
return new_image
return None
def __init__(self):
self.f = int(face_comm.get_conf('annoy','face_vector'))
self.annoy_index_path = os.path.abspath(face_comm.get_conf('annoy','index_path'))
self.lmdb_file =os.path.abspath(face_comm.get_conf('lmdb','lmdb_path'))
self.num_trees =int(face_comm.get_conf('annoy','num_trees'))
self.annoy = AnnoyIndex(self.f)
if os.path.isfile(self.annoy_index_path):
self.annoy.load(self.annoy_index_path)
def add_embed_to_lmdb(self,id,vector):
self.db_file=os.path.abspath(face_comm.get_conf('lmdb','lmdb_path'))
id = str(id)
evn = lmdb.open(self.db_file);
wfp = evn.begin(write=True)
wfp.put(key=id, value=face_comm.embed_to_str(vector))
wfp.commit()
evn.close()
def add_embed_to_lmdb(self, id, vector):
self.db_file = os.path.abspath(face_comm.get_conf('lmdb', 'lmdb_path'))
id = str(id)
evn = lmdb.open(self.db_file)
wfp = evn.begin(write=True)
print(vector.dtype)
wfp.put(key=id.encode(), value=vector.tostring())
wfp.commit()
evn.close()
def load_index_from_lmdb(self, id_list, name_list):
# 遍历
self.db_file = os.path.abspath(face_comm.get_conf('lmdb', 'lmdb_path'))
if os.path.isdir(self.db_file):
evn = lmdb.open(self.db_file)
wfp = evn.begin()
for key, value in wfp.cursor():
str_list = key.split(',')
id_list.append(str_list[0])
name_list.append(str_list[1])
import cv2
import numpy as np
import tensorflow as tf
from scipy import misc
import face_net.src.facenet as facenet
import face_net.src.align.detect_face
import face_comm
import time
np.set_printoptions(suppress=True)
gpu_memory_fraction = 0.3
facenet_model_checkpoint = os.path.abspath(
face_comm.get_conf('facedetect', 'model'))
class Encoder:
def __init__(self):
self.dectection = Detection()
self.sess = tf.Session()
start = time.time()
with self.sess.as_default():
facenet.load_model(facenet_model_checkpoint)
print('Model loading finised,cost: %ds' % ((time.time() - start)))
def generate_embedding(self, image):
# Get input and output tensors
images_placeholder = self.sess.graph.get_tensor_by_name("input:0")
embeddings = self.sess.graph.get_tensor_by_name("embeddings:0")
# coding: utf-8
import mxnet as mx
from mtcnn.mtcnn_detector import MtcnnDetector
import cv2
import os
import time
import numpy as np
import face_comm
model = os.path.abspath(face_comm.get_conf('mtcnn', 'model'))
class Detect:
def __init__(self):
self.detector = MtcnnDetector(model_folder=model,
ctx=mx.cpu(0),
num_worker=4,
accurate_landmark=False)
def detect_face(self, image):
img = cv2.imread(image)
results = self.detector.detect_face(img)
boxes = []
key_points = []
if results is not None:
#box框
boxes = results[0]
#人脸5个关键点
points = results[1]
for i in results[0]:
def query_vector(self, face_vector):
n = int(face_comm.get_conf('annoy', 'num_nn_nearst'))
return self.annoy.get_nns_by_vector(face_vector,
n,
include_distances=True)
def show_lmdb(self):
self.db_file = os.path.abspath(face_comm.get_conf('lmdb', 'lmdb_path'))
evn = lmdb.open(self.db_file)
wfp = evn.begin()
import cv2
import numpy as np
import tensorflow as tf
from scipy import misc
import face_net.src.facenet as facenet
import face_net.src.align.detect_face
import face_comm
import time
np.set_printoptions(suppress=True)
gpu_memory_fraction = 0.3
facenet_model_checkpoint = os.path.abspath(face_comm.get_conf('facedetect','model'))
class Encoder:
def __init__(self):
self.dectection= Detection()
self.sess = tf.Session()
start=time.time()
with self.sess.as_default():
facenet.load_model(facenet_model_checkpoint)
print 'Model loading finised,cost: %ds'%((time.time()-start))
def generate_embedding(self, image):
# 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")
