def images_to_vectors(self, inpath, outjson_path, modelpath):
results = dict()
with tf.Graph().as_default():
with tf.Session() as sess:
src.facenet.load_model(modelpath)
# 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")
image_paths = self.get_image_paths(inpath)
for image_path in image_paths:
# 获取图片中的人脸数
img = misc.imread(os.path.expanduser(image_path), mode='RGB')
images = self.image_array_align_data(img,image_path)
#判断是否检测出人脸 检测不出 就跳出此循环
if images.shape[0] == 1 : continue
feed_dict = {images_placeholder: images, phase_train_placeholder: False}
emb_array = sess.run(embeddings, feed_dict=feed_dict)
filename_base, file_extension = os.path.splitext(image_path)
for j in range(0, len(emb_array)):
results[filename_base + "_" + str(j)] = emb_array[j].tolist()
face_mysql_instant = face_mysql.face_mysql()
face_mysql_instant.insert_facejson(filename_base + "_" + str(j),
",".join(str(li) for li in emb_array[j].tolist()))
# All done, save for later!
json.dump(results, open(outjson_path, "w"))
def face_insert():
# 分别获取post请求中的uid 和ugroup作为图片信息
uid = request.form['uid']
ugroup = request.form['ugroup']
upload_files = request.files['imagefile']
# 从post请求图片保存到本地路径中
file = upload_files
if file and allowed_file(file.filename):
filename = secure_filename(file.filename)
file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename))
image_path = os.path.join(app.config['UPLOAD_FOLDER'], filename)
print(image_path)
# opencv读取图片，开始进行人脸识别
img = misc.imread(os.path.expanduser(image_path), mode='RGB')
# 设置默认插入时 detect_multiple_faces =Flase只检测图中的一张人脸，True则检测人脸中的多张
# 一般入库时只检测一张人脸，查询时检测多张人脸
images = image_array_align_data(img,
image_path,
pnet,
rnet,
onet,
detect_multiple_faces=False)
feed_dict = {
images_placeholder: images,
phase_train_placeholder: False
}
# emb_array保存的是经过facenet转换的128维的向量
# emb_array = sess.run(embeddings, feed_dict=feed_dict)
try:
# emb_array保存的是经过facenet转换的128维的向量
emb_array = sess.run(embeddings, feed_dict=feed_dict)
except:
return render_template('face_insert.html', request_one='error')
filename_base, file_extension = os.path.splitext(image_path)
id_list = []
# 存入数据库
for j in range(0, len(emb_array)):
face_mysql_instant = face_mysql.face_mysql()
last_id = face_mysql_instant.insert_facejson(
filename_base + "_" + str(j),
",".join(str(li) for li in emb_array[j].tolist()), uid, ugroup)
id_list.append(str(last_id))
# 设置返回类型
request_result = {}
request_result['id'] = ",".join(id_list)
if len(id_list) > 0:
request_result['state'] = 'sucess'
return render_template('face_insert.html',
request_one=request_result['state'])
def get_socres(self, A, ugroup):
# 设置每次处理的最大数据库记录数
#如果数据库中记录太多时可分批进行处理
maxlen = 128
fmysql = face_mysql.face_mysql()
results = np.array(fmysql.findall_facejson(ugroup))
#如果没有数据库找到时 直接返回空的list
if results.shape[0] == 0: return [], [], []
pic_scores_all = []
#获取数据库中的入库时的图片名称 pic_names在数据库中存的是数组列索引4这个位置
pic_names = results[:, 4]
#获取入库时图片对象的uid pic_uid在数据库中存的是数组列索引2这个位置
pic_uid = results[:, 2]
pic_floor = results[:, 7]
for i in range(0, len(results), maxlen):
pic_vectors = results[i:i + maxlen, 3]
# 效率待优化，现在是每行处理
pic_vectors = [[float(j) for j in i.split(',')]
for i in pic_vectors]
pic_socores = self.EuclideanDistances(
A, np.array(pic_vectors)) #比较两个128向量的欧氏距离
pic_socores_list = np.array(pic_socores).tolist()
pic_scores_all.extend(pic_socores_list)
pic_scores_all = np.array(pic_scores_all).transpose()
# 获取距离最近的值
# np.argsort() 返回排序后的索引
pic_min_scores = np.amin(pic_scores_all, axis=1)
pic_min_names = []
pic_min_uid = []
pic_min_floor = []
for i in range(0, len(pic_min_scores)):
# 获取最小值的index
index = np.where(pic_scores_all[i] == pic_min_scores[i])
# print(int(index[0]))
# 有多个符合条件的只取第一个
pic_min_names.append(pic_names[index[0][0]])
pic_min_uid.append(pic_uid[index[0][0]])
pic_min_floor.append(pic_floor[index[0][0]])
# print(pic_min_names)
return pic_min_scores.tolist(
), pic_min_names, pic_min_uid, pic_min_floor
def images_to_vectors(self, inpath, outjson_path, modelpath):
results = dict()
with tf.Graph().as_default():
with tf.Session() as sess:
src.facenet.load_model(modelpath)
# 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")
image_paths = self.get_image_paths(inpath)
for image_path in image_paths:
# 获取图片中的人脸数
img = misc.imread(os.path.expanduser(image_path),
mode='RGB')
images = self.image_array_align_data(img, image_path)
#判断是否检测出人脸 检测不出 就跳出此循环
if images.shape[0] == 1: continue
feed_dict = {
images_placeholder: images,
phase_train_placeholder: False
}
emb_array = sess.run(embeddings, feed_dict=feed_dict)
filename_base, file_extension = os.path.splitext(
image_path)
for j in range(0, len(emb_array)):
results[filename_base + "_" +
str(j)] = emb_array[j].tolist()
face_mysql_instant = face_mysql.face_mysql()
face_mysql_instant.insert_facejson(
filename_base + "_" + str(j),
",".join(str(li) for li in emb_array[j].tolist()),
555, 333)
# All done, save for later!
json.dump(results, open(outjson_path, "w"))
def get_socres(self, A, ugroup):
# 设置每次处理的最大数据库记录数
#如果数据库中记录太多时可分批进行处理
maxlen = 128
fmysql = face_mysql.face_mysql()
results = np.array(fmysql.findall_facejson(ugroup))
#如果没有数据库找到时 直接返回空的list
if results.shape[0] == 0: return [],[],[]
pic_scores_all = []
#获取数据库中的入库时的图片名称 pic_names在数据库中存的是数组列索引4这个位置
pic_names = results[:, 4]
#获取入库时图片对象的uid pic_uid在数据库中存的是数组列索引2这个位置
pic_uid = results[:, 2]
for i in range(0, len(results), maxlen):
pic_vectors = results[i:i + maxlen, 3]
# 效率待优化，现在是每行处理
pic_vectors = [[float(j) for j in i.split(',')] for i in pic_vectors]
pic_socores = self.EuclideanDistances(A, np.array(pic_vectors))
pic_socores_list = np.array(pic_socores).tolist()
pic_scores_all.extend(pic_socores_list)
pic_scores_all = np.array(pic_scores_all).transpose()
# 获取距离最近的值
# np.argsort() 返回排序后的索引
pic_min_scores = np.amin(pic_scores_all, axis=1)
pic_min_names = []
pic_min_uid = []
for i in range(0, len(pic_min_scores)):
# 获取最小值的index
index = np.where(pic_scores_all[i] == pic_min_scores[i])
# print(int(index[0]))
# 有多个符合条件的只取第一个
pic_min_names.append(pic_names[index[0][0]])
pic_min_uid.append(pic_uid[index[0][0]])
# print(pic_min_names)
return pic_min_scores.tolist(), pic_min_names, pic_min_uid