def main():
fnet = SiameseFaceNet()
fnet.vgg16_include_top = True
model_dir_path = './models_1'
image_dir_path = "./data/images"
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")]
# 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")]
fnet.fit(database=database, model_dir_path=model_dir_path)
def main():
fnet = SiameseFaceNet()
fnet.vgg16_include_top = True
model_dir_path = './models'
image_dir_path = "./data/images"
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")]
fnet.fit(database=database, model_dir_path=model_dir_path)
def createAug_1():
rootdir = 'data/stu01'
list = os.listdir(rootdir) # 列出文件夹下所有的目录与文件
count = 0
for index in range(0, len(list)):
#print (count)
count = count + 1
path = rootdir + "/" + list[index]
listname = os.listdir(path)
if len(listname) == 0:
print(list[index])
#os.makedirs( 'data/stu01/'+list[index])
for i in range(0, len(listname)):
#print(path + '/' + listname[i])
imgpath = path + '/' + listname[0]
img = cv2.imread(imgpath) # 这是一个PIL图像
#img =cv2.cvtColor( np.array(img), cv2.COLOR_BGR2RGB)
#cv2.imwrite('data/stu01/' +list[index]+'/'+ listname[0], img)
break
if __name__ == '__main__':
#createAug_1()
fnet = SiameseFaceNet()
fnet.vgg16_include_top = True
saveTrainPkl(fnet)
main(fnet)
createAug()