forked from lengstrom/fast-style-transfer
-
Notifications
You must be signed in to change notification settings - Fork 0
/
evaluate.py
212 lines (181 loc) · 8.31 KB
/
evaluate.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
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
from __future__ import print_function
import sys
sys.path.insert(0, 'src')
import transform, numpy as np, vgg, pdb, os
import scipy.misc
import tensorflow as tf
from utils import save_img, get_img, exists, list_files
from argparse import ArgumentParser
from collections import defaultdict
import time
import json
import subprocess
import numpy
from moviepy.video.io.VideoFileClip import VideoFileClip
import moviepy.video.io.ffmpeg_writer as ffmpeg_writer
BATCH_SIZE = 4
DEVICE = '/gpu:0'
def ffwd_video(path_in, path_out, checkpoint_dir, device_t='/gpu:0', batch_size=4):
video_clip = VideoFileClip(path_in, audio=False)
video_writer = ffmpeg_writer.FFMPEG_VideoWriter(path_out, video_clip.size, video_clip.fps, codec="libx264",
preset="medium", bitrate="2000k",
audiofile=path_in, threads=None,
ffmpeg_params=None)
g = tf.Graph()
soft_config = tf.ConfigProto(allow_soft_placement=True)
soft_config.gpu_options.allow_growth = True
with g.as_default(), g.device(device_t), \
tf.Session(config=soft_config) as sess:
batch_shape = (batch_size, video_clip.size[1], video_clip.size[0], 3)
img_placeholder = tf.placeholder(tf.float32, shape=batch_shape,
name='img_placeholder')
preds = transform.net(img_placeholder)
saver = tf.train.Saver()
if os.path.isdir(checkpoint_dir):
ckpt = tf.train.get_checkpoint_state(checkpoint_dir)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
else:
raise Exception("No checkpoint found...")
else:
saver.restore(sess, checkpoint_dir)
X = np.zeros(batch_shape, dtype=np.float32)
def style_and_write(count):
for i in range(count, batch_size):
X[i] = X[count - 1] # Use last frame to fill X
_preds = sess.run(preds, feed_dict={img_placeholder: X})
for i in range(0, count):
video_writer.write_frame(np.clip(_preds[i], 0, 255).astype(np.uint8))
frame_count = 0 # The frame count that written to X
for frame in video_clip.iter_frames():
X[frame_count] = frame
frame_count = 1
if frame_count == batch_size:
style_and_write(frame_count)
frame_count = 0
if frame_count != 0:
style_and_write(frame_count)
video_writer.close()
# get img_shape
def ffwd(data_in, paths_out, checkpoint_dir, device_t='/gpu:0', batch_size=4):
assert len(paths_out) > 0
is_paths = type(data_in[0]) == str
if is_paths:
assert len(data_in) == len(paths_out)
img_shape = get_img(data_in[0]).shape
else:
assert data_in.size[0] == len(paths_out)
img_shape = X[0].shape
g = tf.Graph()
batch_size = min(len(paths_out), batch_size)
curr_num = 0
soft_config = tf.ConfigProto(allow_soft_placement=True)
soft_config.gpu_options.allow_growth = True
with g.as_default(), g.device(device_t), \
tf.Session(config=soft_config) as sess:
batch_shape = (batch_size,) img_shape
img_placeholder = tf.placeholder(tf.float32, shape=batch_shape,
name='img_placeholder')
preds = transform.net(img_placeholder)
saver = tf.train.Saver()
if os.path.isdir(checkpoint_dir):
ckpt = tf.train.get_checkpoint_state(checkpoint_dir)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
else:
raise Exception("No checkpoint found...")
else:
saver.restore(sess, checkpoint_dir)
num_iters = int(len(paths_out)/batch_size)
for i in range(num_iters):
pos = i * batch_size
curr_batch_out = paths_out[pos:pos batch_size]
if is_paths:
curr_batch_in = data_in[pos:pos batch_size]
X = np.zeros(batch_shape, dtype=np.float32)
for j, path_in in enumerate(curr_batch_in):
img = get_img(path_in)
assert img.shape == img_shape, \
'Images have different dimensions. ' \
'Resize images or use --allow-different-dimensions.'
X[j] = img
else:
X = data_in[pos:pos batch_size]
_preds = sess.run(preds, feed_dict={img_placeholder:X})
for j, path_out in enumerate(curr_batch_out):
save_img(path_out, _preds[j])
remaining_in = data_in[num_iters*batch_size:]
remaining_out = paths_out[num_iters*batch_size:]
if len(remaining_in) > 0:
ffwd(remaining_in, remaining_out, checkpoint_dir,
device_t=device_t, batch_size=1)
def ffwd_to_img(in_path, out_path, checkpoint_dir, device='/cpu:0'):
paths_in, paths_out = [in_path], [out_path]
ffwd(paths_in, paths_out, checkpoint_dir, batch_size=1, device_t=device)
def ffwd_different_dimensions(in_path, out_path, checkpoint_dir,
device_t=DEVICE, batch_size=4):
in_path_of_shape = defaultdict(list)
out_path_of_shape = defaultdict(list)
for i in range(len(in_path)):
in_image = in_path[i]
out_image = out_path[i]
shape = "%dx%dx%d" % get_img(in_image).shape
in_path_of_shape[shape].append(in_image)
out_path_of_shape[shape].append(out_image)
for shape in in_path_of_shape:
print('Processing images of shape %s' % shape)
ffwd(in_path_of_shape[shape], out_path_of_shape[shape],
checkpoint_dir, device_t, batch_size)
def build_parser():
parser = ArgumentParser()
parser.add_argument('--checkpoint', type=str,
dest='checkpoint_dir',
help='dir or .ckpt file to load checkpoint from',
metavar='CHECKPOINT', required=True)
parser.add_argument('--in-path', type=str,
dest='in_path',help='dir or file to transform',
metavar='IN_PATH', required=True)
help_out = 'destination (dir or file) of transformed file or files'
parser.add_argument('--out-path', type=str,
dest='out_path', help=help_out, metavar='OUT_PATH',
required=True)
parser.add_argument('--device', type=str,
dest='device',help='device to perform compute on',
metavar='DEVICE', default=DEVICE)
parser.add_argument('--batch-size', type=int,
dest='batch_size',help='batch size for feedforwarding',
metavar='BATCH_SIZE', default=BATCH_SIZE)
parser.add_argument('--allow-different-dimensions', action='store_true',
dest='allow_different_dimensions',
help='allow different image dimensions')
return parser
def check_opts(opts):
exists(opts.checkpoint_dir, 'Checkpoint not found!')
exists(opts.in_path, 'In path not found!')
if os.path.isdir(opts.out_path):
exists(opts.out_path, 'out dir not found!')
assert opts.batch_size > 0
def main():
parser = build_parser()
opts = parser.parse_args()
check_opts(opts)
if not os.path.isdir(opts.in_path):
if os.path.exists(opts.out_path) and os.path.isdir(opts.out_path):
out_path = \
os.path.join(opts.out_path,os.path.basename(opts.in_path))
else:
out_path = opts.out_path
ffwd_to_img(opts.in_path, out_path, opts.checkpoint_dir,
device=opts.device)
else:
files = list_files(opts.in_path)
full_in = [os.path.join(opts.in_path,x) for x in files]
full_out = [os.path.join(opts.out_path,x) for x in files]
if opts.allow_different_dimensions:
ffwd_different_dimensions(full_in, full_out, opts.checkpoint_dir,
device_t=opts.device, batch_size=opts.batch_size)
else :
ffwd(full_in, full_out, opts.checkpoint_dir, device_t=opts.device,
batch_size=opts.batch_size)
if __name__ == '__main__':
main()