#!/usr/bin/env python
# -*- coding: UTF-8 -*-
import os, cv2
from pickled import *
data_path = './data/train'
file_list = './data/train/images.lst'
save_path = './bin'
testing_data_path = './data/testing'
testing_file_list = './data/testing/images.lst'
if __name__ == '__main__':
#Build training data
build_filelist(data_path, file_list)
data, label, lst = read_data(file_list, data_path, shape=[32, 32])
pickled(save_path, data, label, lst, bin_num=2)
build_meta(data_path, save_path)
#Build test data
build_filelist(testing_data_path, testing_file_list)
data, label, lst = read_data(testing_file_list,
testing_data_path,
shape=[32, 32])
pickled(save_path, data, label, lst, bin_num=1, mode="test")
# -*- coding: utf-8 -*-
import os, cv2
from pickled import *
from load_data import *
data_path = './data'
file_list = './data/images.lst'
save_path = './bin'
if __name__ == '__main__':
data, label, lst = read_data(file_list, data_path, shape=32)
pickled(save_path, data, label, lst, bin_num=1)
os.path.dirname(current_path) + os.path.sep + ".")
source_image_path = os.path.join(
os.path.abspath(os.path.dirname(current_path) + os.path.sep + "../.."),
"Source_image")
mosaic_image_path = os.path.join(
os.path.abspath(os.path.dirname(current_path) + os.path.sep + "../.."),
"Mosaic_image")
dataset_train_path = os.path.join(
os.path.abspath(os.path.dirname(current_path) + os.path.sep + "../.."),
"Data_train")
dataset_valiate_path = os.path.join(
os.path.abspath(os.path.dirname(current_path) + os.path.sep + "../.."),
"Data_valiate")
#Path of data and datasets
image_train_path = source_image_path
image_train_record_file = source_image_path + os.path.sep + "data.json"
dataset_train_save_path = dataset_train_path
dataset_valiate_save_path = dataset_valiate_path
# pickled train_dataset
data, label, file_name_list = read_data(image_train_record_file,
image_train_path,
shape=32)
pickled(dataset_train_save_path,
data,
label,
file_name_list,
bin_num=1,
mode="train")
parser = ArgumentParser()
parser.add_argument('--folder_path', default="train", help='choose a image folder')
parser.add_argument('--mode', default="train", help='train or test')
parser.add_argument('--read_image', default="ori_data", help='choose read_data or face_encoding')
args = parser.parse_args()
data_path = args.folder_path
file_list = 'image_list/image_{}_list.txt'.format(data_path)
save_path = './bin'
mode = args.mode
if __name__ == '__main__':
if os.path.isfile(file_list):
os.remove(file_list)
imagelist(data_path, file_list)
if args.read_image == 'ori_data':
data, label, lst = read_data(file_list, data_path, shape=80)
elif args.read_image == 'face_encoding':
data, label, lst = face_encoding_read(file_list, data_path)
pickled(save_path, data, label, lst, mode, args.read_image, data_path, bin_num=1)
train_image = np.reshape(np.stack(train_image, axis=0), [num_cifar_train, 32*32*3])
train_label = np.reshape(np.array(np.stack(train_label, axis=0)), [num_cifar_train])
fd = os.path.join(target_path, 'test_batch')
dict = unpickle(fd)
test_image = np.reshape(dict['data'], [num_cifar_test, 32*32*3])
test_label = np.reshape(dict['labels'], [num_cifar_test])
prepare_h5py(train_image, train_label, test_image, test_label, data_dir, [32, 32, 3])
if __name__ == '__main__':
#代码未解耦合&判存，每次仅active其中一步 其余注释掉
#1
# resize_pic(image_dir,resized_dir) #train
# resize_pic(image_dir_test,resized_dir_test)#test
#2. for Lung experiment I re-write the code in Lab's computer and get the .lst files
####Make_pic_list(resized_dir, lst_file_outdir) # train
# #3.
data, label, lst = read_data(lst_file_outdir, resized_dir, shape=dim)
pickled(save_path, data, label, lst_file_outdir, bin_num)
# # # 4.
args = parser.parse_args()
if not os.path.exists(save_path): os.mkdir(save_path)
data_process_h5py(save_path)
#本文件用于封装数据 最后获得 data.hy 和 id.txt 作为SSGAN网络的输入文件