def main():
fnet = SiameseFaceNet()
model_dir_path = './models'
image_dir_path = "./data/images"
fnet.load_model(model_dir_path)
database = dict()
database["danielle"] = [fnet.img_to_encoding(image_dir_path + "/danielle.png")]
database["younes"] = [fnet.img_to_encoding(image_dir_path + "/younes.jpg")]
database["tian"] = [fnet.img_to_encoding(image_dir_path + "/tian.jpg")]
database["andrew"] = [fnet.img_to_encoding(image_dir_path + "/andrew.jpg")]
database["kian"] = [fnet.img_to_encoding(image_dir_path + "/kian.jpg")]
database["dan"] = [fnet.img_to_encoding(image_dir_path + "/dan.jpg")]
database["sebastiano"] = [fnet.img_to_encoding(image_dir_path + "/sebastiano.jpg")]
database["bertrand"] = [fnet.img_to_encoding(image_dir_path + "/bertrand.jpg")]
database["kevin"] = [fnet.img_to_encoding(image_dir_path + "/kevin.jpg")]
database["felix"] = [fnet.img_to_encoding(image_dir_path + "/felix.jpg")]
database["benoit"] = [fnet.img_to_encoding(image_dir_path + "/benoit.jpg")]
database["arnaud"] = [fnet.img_to_encoding(image_dir_path + "/arnaud.jpg")]
database["deepak"] = [fnet.img_to_encoding(image_dir_path + "/deepak.jpg")]
database["momo"] = [fnet.img_to_encoding(image_dir_path + "/momo.jpg")]
fnet.verify(image_dir_path + "/camera_0.jpg", "younes", database)
fnet.verify(image_dir_path + "/camera_2.jpg", "kian", database)
# fnet.verify(image_dir_path + "/test_deepak_1.jpg", "deepak", database)
# fnet.verify(image_dir_path + "/test_momo_1.jpg", "momo", database)
# fnet.verify(image_dir_path + "/test_momo.jpg", "deepak", database)
fnet.who_is_it(image_dir_path + "/camera_0.jpg", database)
fnet.who_is_it(image_dir_path + "/younes.jpg", database)
def predict(self, database):
fnet = SiameseFaceNet()
model_dir_path = './models_1'
image_dir_path = "./data/images"
fnet.load_model(model_dir_path)
print("-------------------------")
# fnet.verify(image_dir_path + "/001.png", "aipengfei", database)
# fnet.verify(image_dir_path + "/002.png", "aipengfei", database)
# fnet.who_is_it(image_dir_path + "/001.png", database)
#fnet.who_is_it(image_dir_path + "/002.png", database)
fnet.who_is_it("data/5test.jpg", database)
def main():
fnet = SiameseFaceNet()
model_dir_path = './model'
image_dir_path = "./data/test/align"
fnet.load_model(model_dir_path)
database1 = dict() #dictionary
#fnet.who_is_it(image_dir_path + "/younes.jpg", database)
database1["chenaifang"] = [
fnet.img_to_encoding(image_dir_path + "/bailu1.jpg")
]
fnet.verify(image_dir_path + "/wangtiange1.jpg", "chenaifang", database1)
def predict(self):
fnet = SiameseFaceNet()
model_dir_path = './models_1'
image_dir_path = "./data/images"
fnet.load_model(model_dir_path)
database = dict()
database["aipengfei"] = [fnet.img_to_encoding(image_dir_path + "/aipengfei.png")]
database["anyaru"] = [fnet.img_to_encoding(image_dir_path + "/anyaru.png")]
database["baozhiqian"] = [fnet.img_to_encoding(image_dir_path + "/baozhiqian.png")]
print("-------------------------")
fnet.verify(image_dir_path + "/001.png", "aipengfei", database)
fnet.verify(image_dir_path + "/002.png", "aipengfei", database)
fnet.who_is_it(image_dir_path + "/001.png", database)
fnet.who_is_it(image_dir_path + "/002.png", database)
fnet.who_is_it(image_dir_path + "/003.png", database)
def fileName(self):
fnet = SiameseFaceNet()
model_dir_path = './models_1'
image_dir_path = "./data/images"
fnet.load_model(model_dir_path)
database = dict()
rootdir = 'data/stu'
list = os.listdir(rootdir) # 列出文件夹下所有的目录与文件
for i in range(0, len(list)):
path = rootdir + "/" + list[i]
listname = os.listdir(path)
for i in range(0, len(listname)):
print(path + '/' + listname[i])
database[listname[i].split('.')[0]] = [
fnet.img_to_encoding(path + '/' + listname[i])
]
#print (path+'/'+listname[i])
return database
def fileName(self):
fnet = SiameseFaceNet()
model_dir_path = './models_2'
fnet.load_model(model_dir_path)
database = dict()
rootdir = 'data/stu100'
list = os.listdir(rootdir) # 列出文件夹下所有的目录与文件
for index in range(0, len(list)):
path = rootdir+"/"+list[index]
listname = os.listdir(path)
for i in range(0, len(listname)):
print(path+'/'+listname[i])
database[list[index]] = [fnet.img_to_encoding(path+'/'+listname[i])]
#print (path+'/'+listname[i])
output = open('myfile.pkl', 'wb')
pickle.dump(database, output)
output.close()
return database
def predict(self,database):
fnet = SiameseFaceNet()
model_dir_path = './models_2'
fnet.load_model(model_dir_path)
print("-------------------------")
# fnet.verify(image_dir_path + "/001.png", "aipengfei", database)
# fnet.verify(image_dir_path + "/002.png", "aipengfei", database)
# fnet.who_is_it(image_dir_path + "/001.png", database)
#fnet.who_is_it(image_dir_path + "/002.png", database)
fnet.who_is_it("data/test/chencongcong.png", database)
fnet.who_is_it("data/test/chendandan.png", database)
# fnet.who_is_it("data/test/chendaocheng.png", database)
# fnet.who_is_it("data/test/chenfuyuan.png", database)
# fnet.who_is_it("data/test/chenge.png", database)
# fnet.who_is_it("data/test/chenguang.png", database)
# fnet.who_is_it("data/test/chenguangwei.png", database)
# fnet.who_is_it("data/test/chenguoyan.png", database)
# fnet.who_is_it("data/test/chenhaiyan.png", database)
# fnet.who_is_it("data/test/chenhao.png", database)