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convert_data.lua
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convert_data.lua
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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 'image'
local cjson = require 'cjson'
local csvigo = require 'csvigo'
local compression = require 'compression'
local settings = require 'settings'
local image_loader = require 'image_loader'
local iproc = require 'iproc'
local alpha_util = require 'alpha_util'
local function crop_if_large(src, max_size)
if max_size < 0 then
return src
end
local tries = 4
if src:size(2) >= max_size and src:size(3) >= max_size then
local rect
for i = 1, tries do
local yi = torch.random(0, src:size(2) - max_size)
local xi = torch.random(0, src:size(3) - max_size)
rect = iproc.crop(src, xi, yi, xi + max_size, yi + max_size)
-- ignore simple background
if rect:float():std() >= 0 then
break
end
end
return rect
else
return src
end
end
local function crop_if_large_pair(x, y, max_size)
if max_size < 0 then
return x, y
end
local scale_y = y:size(2) / x:size(2)
local mod = 4
assert(x:size(3) == (y:size(3) / scale_y))
local tries = 4
if y:size(2) > max_size and y:size(3) > max_size then
assert(max_size % 4 == 0)
local rect_x, rect_y
for i = 1, tries do
local yi = torch.random(0, y:size(2) - max_size)
local xi = torch.random(0, y:size(3) - max_size)
if mod then
yi = yi - (yi % mod)
xi = xi - (xi % mod)
end
rect_y = iproc.crop(y, xi, yi, xi + max_size, yi + max_size)
rect_x = iproc.crop(y, xi / scale_y, yi / scale_y, xi / scale_y + max_size / scale_y, yi / scale_y + max_size / scale_y)
-- ignore simple background
if rect_y:float():std() >= 0 then
break
end
end
return rect_x, rect_y
else
return x, y
end
end
local function padding_x(x, pad, x_zero)
if pad > 0 then
if x_zero then
x = iproc.zero_padding(x, pad, pad, pad, pad)
else
x = iproc.padding(x, pad, pad, pad, pad)
end
end
return x
end
local function padding_xy(x, y, pad, x_zero, y_zero)
local scale = y:size(2) / x:size(2)
if pad > 0 then
if x_zero then
x = iproc.zero_padding(x, pad, pad, pad, pad)
else
x = iproc.padding(x, pad, pad, pad, pad)
end
if y_zero then
y = iproc.zero_padding(y, pad * scale, pad * scale, pad * scale, pad * scale)
else
y = iproc.padding(y, pad * scale, pad * scale, pad * scale, pad * scale)
end
end
return x, y
end
local function load_images(list)
local MARGIN = 32
local csv = csvigo.load({path = list, verbose = false, mode = "raw"})
local x = {}
local skip_notice = false
for i = 1, #csv do
local filters = nil
local filename = csv[i][1]
local csv_meta = csv[i][2]
if csv_meta and csv_meta:len() > 0 then
csv_meta = cjson.decode(csv_meta)
end
if csv_meta and csv_meta.filters then
filters = csv_meta.filters
end
local basename_y = path.basename(filename)
local im, meta = image_loader.load_byte(filename)
local skip = false
local alpha_color = torch.random(0, 1)
if im then
if meta and meta.alpha then
if settings.use_transparent_png then
im = alpha_util.fill(im, meta.alpha, alpha_color)
else
skip = true
end
end
if skip then
if not skip_notice then
io.stderr:write("skip transparent png (settings.use_transparent_png=0)\n")
skip_notice = true
end
else
if csv_meta and csv_meta.x then
-- method == user
local yy = im
local xx, meta2 = image_loader.load_byte(csv_meta.x)
if settings.invert_x then
xx = (-(xx:long()) + 255):byte()
end
if xx then
if meta2 and meta2.alpha then
xx = alpha_util.fill(xx, meta2.alpha, alpha_color)
end
xx, yy = crop_if_large_pair(xx, yy, settings.max_training_image_size)
xx, yy = padding_xy(xx, yy, settings.padding, settings.padding_x_zero, settings.padding_y_zero)
if settings.grayscale then
xx = iproc.rgb2y(xx)
yy = iproc.rgb2y(yy)
end
table.insert(x, {{y = compression.compress(yy), x = compression.compress(xx)},
{data = {filters = filters, has_x = true, basename = basename_y}}})
else
io.stderr:write(string.format("\n%s: skip: load error.\n", csv_meta.x))
end
else
im = crop_if_large(im, settings.max_training_image_size)
im = iproc.crop_mod4(im)
im = padding_x(im, settings.padding, settings.padding_x_zero)
local scale = 1.0
if settings.random_half_rate > 0.0 then
scale = 2.0
end
if im:size(2) > (settings.crop_size * scale + MARGIN) and im:size(3) > (settings.crop_size * scale + MARGIN) then
if settings.grayscale then
im = iproc.rgb2y(im)
end
table.insert(x, {compression.compress(im), {data = {filters = filters, basename = basename_y}}})
else
io.stderr:write(string.format("\n%s: skip: image is too small (%d > size).\n", filename, settings.crop_size * scale + MARGIN))
end
end
end
else
io.stderr:write(string.format("\n%s: skip: load error.\n", filename))
end
xlua.progress(i, #csv)
if i % 10 == 0 then
collectgarbage()
end
end
return x
end
torch.manualSeed(settings.seed)
print(settings)
local x = load_images(settings.image_list)
torch.save(settings.images, x)