forked from nagadomi/waifu2x
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathwaifu2x.lua
203 lines (196 loc) · 7.15 KB
/
waifu2x.lua
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
require 'pl'
local __FILE__ = (function() return string.gsub(debug.getinfo(2, 'S').source, "^@", "") end)()
package.path = path.join(path.dirname(__FILE__), "lib", "?.lua;") .. package.path
require 'sys'
require 'w2nn'
local iproc = require 'iproc'
local reconstruct = require 'reconstruct'
local image_loader = require 'image_loader'
torch.setdefaulttensortype('torch.FloatTensor')
local function convert_image(opt)
local x, alpha = image_loader.load_float(opt.i)
local new_x = nil
local t = sys.clock()
local scale_f, image_f
if opt.tta == 1 then
scale_f = reconstruct.scale_tta
image_f = reconstruct.image_tta
else
scale_f = reconstruct.scale
image_f = reconstruct.image
end
if opt.o == "(auto)" then
local name = path.basename(opt.i)
local e = path.extension(name)
local base = name:sub(0, name:len() - e:len())
opt.o = path.join(path.dirname(opt.i), string.format("%s_%s.png", base, opt.m))
end
if opt.m == "noise" then
local model_path = path.join(opt.model_dir, ("noise%d_model.t7"):format(opt.noise_level))
local model = torch.load(model_path, "ascii")
if not model then
error("Load Error: " .. model_path)
end
new_x = image_f(model, x, opt.crop_size)
elseif opt.m == "scale" then
local model_path = path.join(opt.model_dir, ("scale%.1fx_model.t7"):format(opt.scale))
local model = torch.load(model_path, "ascii")
if not model then
error("Load Error: " .. model_path)
end
new_x = scale_f(model, opt.scale, x, opt.crop_size)
elseif opt.m == "noise_scale" then
local noise_model_path = path.join(opt.model_dir, ("noise%d_model.t7"):format(opt.noise_level))
local noise_model = torch.load(noise_model_path, "ascii")
local scale_model_path = path.join(opt.model_dir, ("scale%.1fx_model.t7"):format(opt.scale))
local scale_model = torch.load(scale_model_path, "ascii")
if not noise_model then
error("Load Error: " .. noise_model_path)
end
if not scale_model then
error("Load Error: " .. scale_model_path)
end
x = image_f(noise_model, x, opt.crop_size)
new_x = scale_f(scale_model, opt.scale, x, opt.crop_size)
else
error("undefined method:" .. opt.method)
end
if opt.white_noise == 1 then
new_x = iproc.white_noise(new_x, opt.white_noise_std, {1.0, 0.8, 1.0})
end
image_loader.save_png(opt.o, new_x, alpha, opt.depth)
print(opt.o .. ": " .. (sys.clock() - t) .. " sec")
end
local function convert_frames(opt)
local model_path, noise1_model, noise2_model, scale_model
local scale_f, image_f
if opt.tta == 1 then
scale_f = reconstruct.scale_tta
image_f = reconstruct.image_tta
else
scale_f = reconstruct.scale
image_f = reconstruct.image
end
if opt.m == "scale" then
model_path = path.join(opt.model_dir, ("scale%.1fx_model.t7"):format(opt.scale))
scale_model = torch.load(model_path, "ascii")
if not scale_model then
error("Load Error: " .. model_path)
end
elseif opt.m == "noise" and opt.noise_level == 1 then
model_path = path.join(opt.model_dir, "noise1_model.t7")
noise1_model = torch.load(model_path, "ascii")
if not noise1_model then
error("Load Error: " .. model_path)
end
elseif opt.m == "noise" and opt.noise_level == 2 then
model_path = path.join(opt.model_dir, "noise2_model.t7")
noise2_model = torch.load(model_path, "ascii")
if not noise2_model then
error("Load Error: " .. model_path)
end
elseif opt.m == "noise_scale" then
model_path = path.join(opt.model_dir, ("scale%.1fx_model.t7"):format(opt.scale))
scale_model = torch.load(model_path, "ascii")
if not scale_model then
error("Load Error: " .. model_path)
end
if opt.noise_level == 1 then
model_path = path.join(opt.model_dir, "noise1_model.t7")
noise1_model = torch.load(model_path, "ascii")
if not noise1_model then
error("Load Error: " .. model_path)
end
elseif opt.noise_level == 2 then
model_path = path.join(opt.model_dir, "noise2_model.t7")
noise2_model = torch.load(model_path, "ascii")
if not noise2_model then
error("Load Error: " .. model_path)
end
end
end
local fp = io.open(opt.l)
if not fp then
error("Open Error: " .. opt.l)
end
local count = 0
local lines = {}
for line in fp:lines() do
table.insert(lines, line)
end
fp:close()
for i = 1, #lines do
if opt.resume == 0 or path.exists(string.format(opt.o, i)) == false then
local x, alpha = image_loader.load_float(lines[i])
local new_x = nil
if opt.m == "noise" and opt.noise_level == 1 then
new_x = image_f(noise1_model, x, opt.crop_size)
elseif opt.m == "noise" and opt.noise_level == 2 then
new_x = image_func(noise2_model, x, opt.crop_size)
elseif opt.m == "scale" then
new_x = scale_f(scale_model, opt.scale, x, opt.crop_size)
elseif opt.m == "noise_scale" and opt.noise_level == 1 then
x = image_f(noise1_model, x, opt.crop_size)
new_x = scale_f(scale_model, opt.scale, x, opt.crop_size)
elseif opt.m == "noise_scale" and opt.noise_level == 2 then
x = image_f(noise2_model, x, opt.crop_size)
new_x = scale_f(scale_model, opt.scale, x, opt.crop_size)
else
error("undefined method:" .. opt.method)
end
if opt.white_noise == 1 then
new_x = iproc.white_noise(new_x, opt.white_noise_std, {1.0, 0.8, 1.0})
end
local output = nil
if opt.o == "(auto)" then
local name = path.basename(lines[i])
local e = path.extension(name)
local base = name:sub(0, name:len() - e:len())
output = path.join(path.dirname(opt.i), string.format("%s(%s).png", base, opt.m))
else
output = string.format(opt.o, i)
end
image_loader.save_png(output, new_x, alpha, opt.depth)
xlua.progress(i, #lines)
if i % 10 == 0 then
collectgarbage()
end
else
xlua.progress(i, #lines)
end
end
end
local function waifu2x()
local cmd = torch.CmdLine()
cmd:text()
cmd:text("waifu2x")
cmd:text("Options:")
cmd:option("-i", "images/miku_small.png", 'path to input image')
cmd:option("-l", "", 'path to image-list.txt')
cmd:option("-scale", 2, 'scale factor')
cmd:option("-o", "(auto)", 'path to output file')
cmd:option("-depth", 8, 'bit-depth of the output image (8|16)')
cmd:option("-model_dir", "./models/anime_style_art_rgb", 'path to model directory')
cmd:option("-m", "noise_scale", 'method (noise|scale|noise_scale)')
cmd:option("-noise_level", 1, '(1|2)')
cmd:option("-crop_size", 128, 'patch size per process')
cmd:option("-resume", 0, "skip existing files (0|1)")
cmd:option("-thread", -1, "number of CPU threads")
cmd:option("-tta", 0, '8x slower and slightly high quality (0|1)')
cmd:option("-white_noise", 0, 'adding white noise to output image (0|1)')
cmd:option("-white_noise_std", 0.0055, 'standard division of white noise')
local opt = cmd:parse(arg)
if opt.thread > 0 then
torch.setnumthreads(opt.thread)
end
if cudnn then
cudnn.fastest = true
cudnn.benchmark = false
end
if string.len(opt.l) == 0 then
convert_image(opt)
else
convert_frames(opt)
end
end
waifu2x()