188 lines
6.2 KiB
Lua
188 lines
6.2 KiB
Lua
local pairwise_utils = require 'pairwise_transform_utils'
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local iproc = require 'iproc'
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local gm = require 'graphicsmagick'
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local pairwise_transform = {}
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local function add_jpeg_noise_(x, quality, options)
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for i = 1, #quality do
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x = gm.Image(x, "RGB", "DHW")
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x:format("jpeg"):depth(8)
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if torch.uniform() < options.jpeg_chroma_subsampling_rate then
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-- YUV 420
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x:samplingFactors({2.0, 1.0, 1.0})
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else
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-- YUV 444
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x:samplingFactors({1.0, 1.0, 1.0})
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end
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local blob, len = x:toBlob(quality[i])
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x:fromBlob(blob, len)
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x = x:toTensor("byte", "RGB", "DHW")
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end
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return x
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end
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local function add_jpeg_noise(src, style, level, options)
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if style == "art" then
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if level == 1 then
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return add_jpeg_noise_(src, {torch.random(65, 85)}, options)
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elseif level == 2 or level == 3 then
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-- level 2/3 adjusting by -nr_rate. for level3, -nr_rate=1
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local r = torch.uniform()
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if r > 0.4 then
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return add_jpeg_noise_(src, {torch.random(27, 70)}, options)
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elseif r > 0.1 then
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local quality1 = torch.random(37, 70)
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local quality2 = quality1 - torch.random(5, 10)
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return add_jpeg_noise_(src, {quality1, quality2}, options)
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else
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local quality1 = torch.random(52, 70)
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local quality2 = quality1 - torch.random(5, 15)
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local quality3 = quality1 - torch.random(15, 25)
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return add_jpeg_noise_(src, {quality1, quality2, quality3}, options)
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end
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else
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error("unknown noise level: " .. level)
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end
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elseif style == "photo" then
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-- level adjusting by -nr_rate
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return add_jpeg_noise_(src, {torch.random(30, 70)}, options)
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else
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error("unknown style: " .. style)
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end
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end
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function pairwise_transform.jpeg_scale(src, scale, style, noise_level, size, offset, n, options)
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local filters = options.downsampling_filters
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if options.data.filters then
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filters = options.data.filters
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end
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local unstable_region_offset = 8
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local downsampling_filter = filters[torch.random(1, #filters)]
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local blur = torch.uniform(options.resize_blur_min, options.resize_blur_max)
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local y = pairwise_utils.preprocess(src, size, options)
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assert(y:size(2) % 4 == 0 and y:size(3) % 4 == 0)
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local down_scale = 1.0 / scale
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local x
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if options.gamma_correction then
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local small = iproc.scale_with_gamma22(y, y:size(3) * down_scale,
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y:size(2) * down_scale, downsampling_filter, blur)
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if options.x_upsampling then
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x = iproc.scale(small, y:size(3), y:size(2), "Box")
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else
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x = small
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end
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else
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local small = iproc.scale(y, y:size(3) * down_scale,
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y:size(2) * down_scale, downsampling_filter, blur)
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if options.x_upsampling then
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x = iproc.scale(small, y:size(3), y:size(2), "Box")
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else
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x = small
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end
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end
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local scale_inner = scale
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if options.x_upsampling then
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scale_inner = 1
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end
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x = iproc.crop(x, unstable_region_offset, unstable_region_offset,
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x:size(3) - unstable_region_offset, x:size(2) - unstable_region_offset)
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y = iproc.crop(y, unstable_region_offset * scale_inner, unstable_region_offset * scale_inner,
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y:size(3) - unstable_region_offset * scale_inner, y:size(2) - unstable_region_offset * scale_inner)
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if options.x_upsampling then
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assert(x:size(2) % 4 == 0 and x:size(3) % 4 == 0)
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assert(x:size(1) == y:size(1) and x:size(2) == y:size(2) and x:size(3) == y:size(3))
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else
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assert(x:size(1) == y:size(1) and x:size(2) * scale == y:size(2) and x:size(3) * scale == y:size(3))
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end
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local batch = {}
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local lowres_y = gm.Image(y, "RGB", "DHW"):
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size(y:size(3) * 0.5, y:size(2) * 0.5, "Box"):
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size(y:size(3), y:size(2), "Box"):
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toTensor(t, "RGB", "DHW")
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local xs = {}
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local ns = {}
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local ys = {}
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local lowreses = {}
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for j = 1, 2 do
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-- TTA
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local xi, yi, ri
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if j == 1 then
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xi = x
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yi = y
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ri = lowres_y
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else
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xi = x:transpose(2, 3):contiguous()
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yi = y:transpose(2, 3):contiguous()
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ri = lowres_y:transpose(2, 3):contiguous()
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end
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local xv = image.vflip(xi)
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local yv = image.vflip(yi)
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local rv = image.vflip(ri)
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table.insert(xs, xi)
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table.insert(ys, yi)
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table.insert(lowreses, ri)
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table.insert(xs, xv)
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table.insert(ys, yv)
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table.insert(lowreses, rv)
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table.insert(xs, image.hflip(xi))
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table.insert(ys, image.hflip(yi))
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table.insert(lowreses, image.hflip(ri))
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table.insert(xs, image.hflip(xv))
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table.insert(ys, image.hflip(yv))
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table.insert(lowreses, image.hflip(rv))
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end
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for i = 1, n do
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local t = (i % #xs) + 1
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local xc, yc
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if torch.uniform() < options.nr_rate then
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-- scale + noise reduction
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if not ns[t] then
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ns[t] = add_jpeg_noise(xs[t], style, noise_level, options)
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end
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xc, yc = pairwise_utils.active_cropping(ns[t], ys[t], lowreses[t],
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size,
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scale_inner,
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options.active_cropping_rate,
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options.active_cropping_tries)
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else
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-- scale
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xc, yc = pairwise_utils.active_cropping(xs[t], ys[t], lowreses[t],
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size,
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scale_inner,
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options.active_cropping_rate,
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options.active_cropping_tries)
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end
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xc = iproc.byte2float(xc)
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yc = iproc.byte2float(yc)
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if options.rgb then
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else
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yc = image.rgb2yuv(yc)[1]:reshape(1, yc:size(2), yc:size(3))
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xc = image.rgb2yuv(xc)[1]:reshape(1, xc:size(2), xc:size(3))
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end
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table.insert(batch, {xc, iproc.crop(yc, offset, offset, size - offset, size - offset)})
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end
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return batch
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end
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function pairwise_transform.test_jpeg_scale(src)
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torch.setdefaulttensortype("torch.FloatTensor")
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local options = {random_color_noise_rate = 0.5,
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random_half_rate = 0.5,
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random_overlay_rate = 0.5,
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random_unsharp_mask_rate = 0.5,
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active_cropping_rate = 0.5,
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active_cropping_tries = 10,
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max_size = 256,
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x_upsampling = false,
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downsampling_filters = "Box",
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rgb = true
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}
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local image = require 'image'
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local src = image.lena()
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for i = 1, 10 do
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local xy = pairwise_transform.jpeg_scale(src, 2.0, "art", 1, 128, 7, 1, options)
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image.display({image = xy[1][1], legend = "y:" .. (i * 10), min = 0, max = 1})
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image.display({image = xy[1][2], legend = "x:" .. (i * 10), min = 0, max = 1})
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end
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end
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return pairwise_transform
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