forked from jiny2001/dcscn-super-resolution
-
Notifications
You must be signed in to change notification settings - Fork 0
/
convert_y.py
executable file
·45 lines (29 loc) · 1.21 KB
/
convert_y.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
"""
Paper: "Fast and Accurate Image Super Resolution by Deep CNN with Skip Connection and Network in Network"
Author: Jin Yamanaka
Github: https://github.com/jiny2001/dcscn-image-super-resolution
Convert RGB(A)-(PNG or Jpeg) Image to Y-BMP images
Put your images under data/[your dataset name]/ and specify [your dataset name] for --dataset.
"""
import os
import tensorflow as tf
from helper import args, utilty as util
FLAGS = args.get()
def main(not_parsed_args):
if len(not_parsed_args) > 1:
print("Unknown args:%s" % not_parsed_args)
exit()
print("Building Y channel data...")
training_filenames = util.get_files_in_directory(FLAGS.data_dir + "/" + FLAGS.dataset + "/")
target_dir = FLAGS.data_dir + "/" + FLAGS.dataset + "_y/"
util.make_dir(target_dir)
for file_path in training_filenames:
org_image = util.load_image(file_path)
if org_image.shape[2] == 3:
org_image = util.convert_rgb_to_y(org_image)
filename = os.path.basename(file_path)
filename, extension = os.path.splitext(filename)
new_filename = target_dir + filename
util.save_image(new_filename + ".bmp", org_image)
if __name__ == '__main__':
tf.app.run()