370 lines
13 KiB
Lua
370 lines
13 KiB
Lua
require 'pl'
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local __FILE__ = (function() return string.gsub(debug.getinfo(2, 'S').source, "^@", "") end)()
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local ROOT = path.dirname(__FILE__)
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package.path = path.join(ROOT, "lib", "?.lua;") .. package.path
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_G.TURBO_SSL = true
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require 'w2nn'
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local uuid = require 'uuid'
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local ffi = require 'ffi'
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local md5 = require 'md5'
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local iproc = require 'iproc'
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local reconstruct = require 'reconstruct'
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local image_loader = require 'image_loader'
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local alpha_util = require 'alpha_util'
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local gm = require 'graphicsmagick'
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-- Note: turbo and xlua has different implementation of string:split().
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-- Therefore, string:split() has conflict issue.
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-- In this script, use turbo's string:split().
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local turbo = require 'turbo'
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local cmd = torch.CmdLine()
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cmd:text()
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cmd:text("waifu2x-api")
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cmd:text("Options:")
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cmd:option("-port", 8812, 'listen port')
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cmd:option("-gpu", 1, 'Device ID')
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cmd:option("-upsampling_filter", "Box", 'Upsampling filter (for dev)')
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cmd:option("-crop_size", 128, 'patch size per process')
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cmd:option("-batch_size", 1, 'batch size')
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cmd:option("-thread", -1, 'number of CPU threads')
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local opt = cmd:parse(arg)
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cutorch.setDevice(opt.gpu)
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torch.setdefaulttensortype('torch.FloatTensor')
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if opt.thread > 0 then
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torch.setnumthreads(opt.thread)
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end
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if cudnn then
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cudnn.fastest = true
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cudnn.benchmark = false
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end
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local ART_MODEL_DIR = path.join(ROOT, "models", "upconv_7", "art")
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local PHOTO_MODEL_DIR = path.join(ROOT, "models", "photo")
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local art_scale2_model = torch.load(path.join(ART_MODEL_DIR, "scale2.0x_model.t7"), "ascii")
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local art_noise1_model = torch.load(path.join(ART_MODEL_DIR, "noise1_model.t7"), "ascii")
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local art_noise2_model = torch.load(path.join(ART_MODEL_DIR, "noise2_model.t7"), "ascii")
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local art_noise3_model = torch.load(path.join(ART_MODEL_DIR, "noise3_model.t7"), "ascii")
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local photo_scale2_model = torch.load(path.join(PHOTO_MODEL_DIR, "scale2.0x_model.t7"), "ascii")
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local photo_noise1_model = torch.load(path.join(PHOTO_MODEL_DIR, "noise1_model.t7"), "ascii")
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local photo_noise2_model = torch.load(path.join(PHOTO_MODEL_DIR, "noise2_model.t7"), "ascii")
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local photo_noise3_model = torch.load(path.join(PHOTO_MODEL_DIR, "noise3_model.t7"), "ascii")
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local CLEANUP_MODEL = false -- if you are using the low memory GPU, you could use this flag.
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local CACHE_DIR = path.join(ROOT, "cache")
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local MAX_NOISE_IMAGE = 2560 * 2560
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local MAX_SCALE_IMAGE = 1280 * 1280
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local CURL_OPTIONS = {
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request_timeout = 60,
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connect_timeout = 60,
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allow_redirects = true,
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max_redirects = 2
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}
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local CURL_MAX_SIZE = 3 * 1024 * 1024
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local function valid_size(x, scale)
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if scale == 0 then
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return x:size(2) * x:size(3) <= MAX_NOISE_IMAGE
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else
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return x:size(2) * x:size(3) <= MAX_SCALE_IMAGE
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end
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end
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local function cache_url(url)
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local hash = md5.sumhexa(url)
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local cache_file = path.join(CACHE_DIR, "url_" .. hash)
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if path.exists(cache_file) then
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return image_loader.load_float(cache_file)
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else
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local res = coroutine.yield(
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turbo.async.HTTPClient({verify_ca=false},
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nil,
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CURL_MAX_SIZE):fetch(url, CURL_OPTIONS)
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)
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if res.code == 200 then
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local content_type = res.headers:get("Content-Type", true)
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if type(content_type) == "table" then
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content_type = content_type[1]
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end
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if content_type and content_type:find("image") then
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local fp = io.open(cache_file, "wb")
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local blob = res.body
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fp:write(blob)
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fp:close()
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return image_loader.decode_float(blob)
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end
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end
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end
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return nil, nil
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end
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local function get_image(req)
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local file_info = req:get_arguments("file")
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local url = req:get_argument("url", "")
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local file = nil
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local filename = nil
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if file_info and #file_info == 1 then
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file = file_info[1][1]
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local disp = file_info[1]["content-disposition"]
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if disp and disp["filename"] then
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filename = path.basename(disp["filename"])
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end
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end
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if file and file:len() > 0 then
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local x, meta = image_loader.decode_float(file)
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return x, meta, filename
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elseif url and url:len() > 0 then
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local x, meta = cache_url(url)
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return x, meta, filename
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end
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return nil, nil, nil
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end
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local function cleanup_model(model)
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if CLEANUP_MODEL then
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model:clearState() -- release GPU memory
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end
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end
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local function convert(x, meta, options)
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local cache_file = path.join(CACHE_DIR, options.prefix .. ".png")
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local alpha_cache_file = path.join(CACHE_DIR, options.alpha_prefix .. ".png")
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local alpha = meta.alpha
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local alpha_orig = alpha
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if path.exists(alpha_cache_file) then
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alpha = image_loader.load_float(alpha_cache_file)
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if alpha:dim() == 2 then
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alpha = alpha:reshape(1, alpha:size(1), alpha:size(2))
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end
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if alpha:size(1) == 3 then
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alpha = image.rgb2y(alpha)
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end
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end
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if path.exists(cache_file) then
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x = image_loader.load_float(cache_file)
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meta = tablex.copy(meta)
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meta.alpha = alpha
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return x, meta
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else
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if options.style == "art" then
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if options.border then
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x = alpha_util.make_border(x, alpha_orig, reconstruct.offset_size(art_scale2_model))
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end
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if options.method == "scale" then
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x = reconstruct.scale(art_scale2_model, 2.0, x,
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opt.crop_size, opt.batch_size, opt.upsampling_filter)
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if alpha then
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if not (alpha:size(2) == x:size(2) and alpha:size(3) == x:size(3)) then
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alpha = reconstruct.scale(art_scale2_model, 2.0, alpha,
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opt.crop_size, opt.batch_size, opt.upsampling_filter)
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image_loader.save_png(alpha_cache_file, alpha)
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end
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end
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cleanup_model(art_scale2_model)
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elseif options.method == "noise1" then
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x = reconstruct.image(art_noise1_model, x, opt.crop_size, opt.batch_size)
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cleanup_model(art_noise1_model)
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elseif options.method == "noise2" then
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x = reconstruct.image(art_noise2_model, x, opt.crop_size, opt.batch_size)
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cleanup_model(art_noise2_model)
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elseif options.method == "noise3" then
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x = reconstruct.image(art_noise3_model, x, opt.crop_size, opt.batch_size)
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cleanup_model(art_noise3_model)
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end
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else -- photo
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if options.border then
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x = alpha_util.make_border(x, alpha, reconstruct.offset_size(photo_scale2_model))
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end
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if options.method == "scale" then
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x = reconstruct.scale(photo_scale2_model, 2.0, x,
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opt.crop_size, opt.batch_size, opt.upsampling_filter)
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if alpha then
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if not (alpha:size(2) == x:size(2) and alpha:size(3) == x:size(3)) then
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alpha = reconstruct.scale(photo_scale2_model, 2.0, alpha,
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opt.crop_size, opt.batch_size, opt.upsampling_filter)
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image_loader.save_png(alpha_cache_file, alpha)
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end
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end
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cleanup_model(photo_scale2_model)
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elseif options.method == "noise1" then
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x = reconstruct.image(photo_noise1_model, x, opt.crop_size, opt.batch_size)
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cleanup_model(photo_noise1_model)
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elseif options.method == "noise2" then
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x = reconstruct.image(photo_noise2_model, x, opt.crop_size, opt.batch_size)
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cleanup_model(photo_noise2_model)
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elseif options.method == "noise3" then
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x = reconstruct.image(photo_noise3_model, x, opt.crop_size, opt.batch_size)
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cleanup_model(photo_noise3_model)
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end
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end
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image_loader.save_png(cache_file, x)
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meta = tablex.copy(meta)
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meta.alpha = alpha
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return x, meta
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end
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end
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local function client_disconnected(handler)
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return not(handler.request and
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handler.request.connection and
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handler.request.connection.stream and
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(not handler.request.connection.stream:closed()))
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end
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local function make_output_filename(filename, mode)
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local e = path.extension(filename)
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local base = filename:sub(0, filename:len() - e:len())
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if mode then
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return base .. "_waifu2x_" .. mode .. ".png"
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else
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return base .. ".png"
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end
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end
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local APIHandler = class("APIHandler", turbo.web.RequestHandler)
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function APIHandler:post()
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if client_disconnected(self) then
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self:set_status(400)
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self:write("client disconnected")
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return
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end
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local x, meta, filename = get_image(self)
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local scale = tonumber(self:get_argument("scale", "0"))
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local noise = tonumber(self:get_argument("noise", "0"))
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local style = self:get_argument("style", "art")
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local download = (self:get_argument("download", "")):len()
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if style ~= "art" then
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style = "photo" -- style must be art or photo
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end
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if x and valid_size(x, scale) then
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local prefix = nil
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if (noise ~= 0 or scale ~= 0) then
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local hash = md5.sumhexa(meta.blob)
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local alpha_prefix = style .. "_" .. hash .. "_alpha"
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local border = false
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if scale ~= 0 and meta.alpha then
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border = true
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end
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if noise == 1 then
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prefix = style .. "_noise1_"
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x = convert(x, meta, {method = "noise1", style = style,
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prefix = prefix .. hash,
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alpha_prefix = alpha_prefix, border = border})
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border = false
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elseif noise == 2 then
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prefix = style .. "_noise2_"
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x = convert(x, meta, {method = "noise2", style = style,
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prefix = prefix .. hash,
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alpha_prefix = alpha_prefix, border = border})
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border = false
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elseif noise == 3 then
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prefix = style .. "_noise3_"
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x = convert(x, meta, {method = "noise3", style = style,
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prefix = prefix .. hash,
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alpha_prefix = alpha_prefix, border = border})
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border = false
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end
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if scale == 1 or scale == 2 then
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if noise == 1 then
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prefix = style .. "_noise1_scale_"
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elseif noise == 2 then
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prefix = style .. "_noise2_scale_"
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elseif noise == 3 then
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prefix = style .. "_noise3_scale_"
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else
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prefix = style .. "_scale_"
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end
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x, meta = convert(x, meta, {method = "scale", style = style, prefix = prefix .. hash, alpha_prefix = alpha_prefix, border = border})
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if scale == 1 then
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x = iproc.scale(x, x:size(3) * (1.6 / 2.0), x:size(2) * (1.6 / 2.0), "Sinc")
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end
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end
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end
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local name = nil
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if filename then
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if prefix then
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name = make_output_filename(filename, prefix:sub(0, prefix:len()-1))
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else
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name = make_output_filename(filename, nil)
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end
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else
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name = uuid() .. ".png"
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end
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local blob = image_loader.encode_png(alpha_util.composite(x, meta.alpha),
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tablex.update({depth = 8, inplace = true}, meta))
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self:set_header("Content-Length", string.format("%d", #blob))
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if download > 0 then
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self:set_header("Content-Type", "application/octet-stream")
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self:set_header("Content-Disposition", string.format('attachment; filename="%s"', name))
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else
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self:set_header("Content-Type", "image/png")
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self:set_header("Content-Disposition", string.format('inline; filename="%s"', name))
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end
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self:write(blob)
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else
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if not x then
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self:set_status(400)
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self:write("ERROR: An error occurred. (unsupported image format/connection timeout/file is too large)")
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else
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self:set_status(400)
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self:write("ERROR: image size exceeds maximum allowable size.")
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end
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end
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collectgarbage()
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end
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local FormHandler = class("FormHandler", turbo.web.RequestHandler)
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local index_ja = file.read(path.join(ROOT, "assets", "index.ja.html"))
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local index_ru = file.read(path.join(ROOT, "assets", "index.ru.html"))
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local index_pt = file.read(path.join(ROOT, "assets", "index.pt.html"))
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local index_es = file.read(path.join(ROOT, "assets", "index.es.html"))
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local index_fr = file.read(path.join(ROOT, "assets", "index.fr.html"))
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local index_de = file.read(path.join(ROOT, "assets", "index.de.html"))
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local index_tr = file.read(path.join(ROOT, "assets", "index.tr.html"))
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local index_zh_cn = file.read(path.join(ROOT, "assets", "index.zh-CN.html"))
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local index_en = file.read(path.join(ROOT, "assets", "index.html"))
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function FormHandler:get()
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local lang = self.request.headers:get("Accept-Language")
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if lang then
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local langs = utils.split(lang, ",")
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for i = 1, #langs do
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langs[i] = utils.split(langs[i], ";")[1]
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end
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if langs[1] == "ja" then
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self:write(index_ja)
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elseif langs[1] == "ru" then
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self:write(index_ru)
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elseif langs[1] == "pt" or langs[1] == "pt-BR" then
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self:write(index_pt)
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elseif langs[1] == "es" or langs[1] == "es-ES" then
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self:write(index_es)
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elseif langs[1] == "fr" then
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self:write(index_fr)
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elseif langs[1] == "de" then
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self:write(index_de)
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elseif langs[1] == "tr" then
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self:write(index_tr)
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elseif langs[1] == "zh-CN" or langs[1] == "zh" then
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self:write(index_zh_cn)
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else
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self:write(index_en)
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end
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else
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self:write(index_en)
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end
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end
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turbo.log.categories = {
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["success"] = true,
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["notice"] = false,
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["warning"] = true,
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["error"] = true,
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["debug"] = false,
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["development"] = false
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}
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local app = turbo.web.Application:new(
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{
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{"^/$", FormHandler},
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{"^/api$", APIHandler},
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{"^/([%a%d%.%-_]+)$", turbo.web.StaticFileHandler, path.join(ROOT, "assets/")},
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}
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)
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app:listen(opt.port, "0.0.0.0", {max_body_size = CURL_MAX_SIZE})
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turbo.ioloop.instance():start()
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