-
Notifications
You must be signed in to change notification settings - Fork 0
/
test_3dunet_sid.py
130 lines (105 loc) · 4.67 KB
/
test_3dunet_sid.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
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
# uniform content loss + adaptive threshold + per_class_input + recursive G
# improvement upon cqf37
from __future__ import division
import os, scipy.io
import rawpy
import tensorflow as tf
tf.set_random_seed(819)
import tensorflow.contrib.slim as slim
import numpy as np
# import rawpy
import glob
from model import UNet3D
input_dir = '../../datasets/SID/Sony/short/'
gt_dir = '../../datasets/SID/Sony/long/'
checkpoint_dir = './checkpoint/Sony_3d/'
result_dir = './result_Sony_3d/'
# get test IDs
test_fns = glob.glob(gt_dir + '/0*.ARW')
test_ids = [int(os.path.basename(test_fn)[0:5]) for test_fn in test_fns]
ps = 512
DEBUG = 1
if DEBUG == 1:
save_freq = 2
test_ids = test_ids[0:5]
def pack_raw(raw):
# pack Bayer image to 4 channels
im = raw.raw_image_visible.astype(np.float32)
im = np.maximum(im - 512, 0) / (16383 - 512) # subtract the black level
im = np.expand_dims(im, axis=2)
img_shape = im.shape
H = img_shape[0]
W = img_shape[1]
out = np.concatenate((im[0:H:2, 0:W:2, :],
im[0:H:2, 1:W:2, :],
im[1:H:2, 1:W:2, :],
im[1:H:2, 0:W:2, :]), axis=2)
return out
sess = tf.Session()
in_image = tf.placeholder(tf.float32, [None, None, None, 4])
gt_image = tf.placeholder(tf.float32, [None, None, None, 3])
net = UNet3D()
out_image = net.construct_model(in_image)
saver = tf.train.Saver()
sess.run(tf.global_variables_initializer())
ckpt = tf.train.get_checkpoint_state(checkpoint_dir)
if ckpt:
print('loaded ' + ckpt.model_checkpoint_path)
saver.restore(sess, ckpt.model_checkpoint_path)
if not os.path.isdir(result_dir + 'final/'):
os.makedirs(result_dir + 'final/')
psnr = {}
ssim = {}
for test_id in test_ids:
# test the first image in each sequence
in_files = glob.glob(input_dir + '%05d_00*.ARW' % test_id)
for k in range(len(in_files)):
in_path = in_files[k]
in_fn = os.path.basename(in_path)
print(in_fn)
gt_files = glob.glob(gt_dir + '%05d_00*.ARW' % test_id)
gt_path = gt_files[0]
gt_fn = os.path.basename(gt_path)
in_exposure = float(in_fn[9:-5])
gt_exposure = float(gt_fn[9:-5])
ratio = min(gt_exposure / in_exposure, 300)
raw = rawpy.imread(in_path)
input_full = np.expand_dims(pack_raw(raw), axis=0) * ratio
im = raw.postprocess(use_camera_wb=True, half_size=False, no_auto_bright=True, output_bps=16)
# scale_full = np.expand_dims(np.float32(im/65535.0),axis = 0)*ratio
scale_full = np.expand_dims(np.float32(im / 65535.0), axis=0)
gt_raw = rawpy.imread(gt_path)
im = gt_raw.postprocess(use_camera_wb=True, half_size=False, no_auto_bright=True, output_bps=16)
gt_full = np.expand_dims(np.float32(im / 65535.0), axis=0)
input_full = np.minimum(input_full, 1.0)
H = input_full.shape[1]
W = input_full.shape[2]
xx = np.random.randint(0, W - ps)
yy = np.random.randint(0, H - ps)
input_full = input_full[:, yy:yy + ps, xx:xx + ps, :]
gt_full = gt_full[:, yy * 2:yy * 2 + ps * 2, xx * 2:xx * 2 + ps * 2, :]
scale_full = scale_full[:, yy * 2:yy * 2 + ps * 2, xx * 2:xx * 2 + ps * 2, :]
output = sess.run(out_image, feed_dict={in_image: input_full, net.is_training: False})
output = np.minimum(np.maximum(output, 0), 1)
output = output[0, :, :, :]
gt_full = gt_full[0, :, :, :]
scale_full = scale_full[0, :, :, :]
scale_full = scale_full * np.mean(gt_full) / np.mean(
scale_full) # scale the low-light image to the same mean of the groundtruth
output_im = tf.image.convert_image_dtype(output, tf.float32)
gt_im = tf.image.convert_image_dtype(gt_full, tf.float32)
psnr[str(test_id) + '_' + str(k)] = sess.run(
tf.image.psnr(output_im, gt_im, max_val=1.0))
ssim[str(test_id) + '_' + str(k)] = sess.run(
tf.image.ssim(output_im, gt_im, max_val=1.0))
print("psnr: ", psnr[str(test_id) + '_' + str(k)])
print("ssim: ", ssim[str(test_id) + '_' + str(k)])
scipy.misc.toimage(output * 255, high=255, low=0, cmin=0, cmax=255).save(
result_dir + 'final/%5d_00_%d_out.png' % (test_id, ratio))
scipy.misc.toimage(scale_full * 255, high=255, low=0, cmin=0, cmax=255).save(
result_dir + 'final/%5d_00_%d_scale.png' % (test_id, ratio))
scipy.misc.toimage(gt_full * 255, high=255, low=0, cmin=0, cmax=255).save(
result_dir + 'final/%5d_00_%d_gt.png' % (test_id, ratio))
if len(psnr) > 0 and len(ssim) > 0:
print("average psnr:", sum(psnr.values()) / len(psnr))
print("average ssim:", sum(ssim.values()) / len(ssim))