133 lines
4.3 KiB
Lua
133 lines
4.3 KiB
Lua
local pairwise_utils = require 'pairwise_transform_utils'
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local gm = {}
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gm.Image = require 'graphicsmagick.Image'
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local iproc = require 'iproc'
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local pairwise_transform = {}
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function pairwise_transform.jpeg_(src, quality, size, offset, n, options)
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local unstable_region_offset = 8
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local y = pairwise_utils.preprocess(src, size, options)
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local x = y
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local factors
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if torch.uniform() < options.jpeg_chroma_subsampling_rate then
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-- YUV 420
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factors = {2.0, 1.0, 1.0}
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else
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-- YUV 444
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factors = {1.0, 1.0, 1.0}
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end
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for i = 1, #quality do
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x = gm.Image(x, "RGB", "DHW")
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local blob, len = x:format("jpeg"):depth(8):samplingFactors(factors):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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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, unstable_region_offset,
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y:size(3) - unstable_region_offset, y:size(2) - unstable_region_offset)
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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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local batch = {}
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local lowres_y = pairwise_utils.low_resolution(y)
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local xs, ys, ls = pairwise_utils.flip_augmentation(x, y, lowres_y)
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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 = pairwise_utils.active_cropping(xs[t], ys[t], ls[t], size, 1,
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options.active_cropping_rate,
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options.active_cropping_tries)
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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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if xc:size(1) > 1 then
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yc = iproc.rgb2y(yc)
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xc = iproc.rgb2y(xc)
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end
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end
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if torch.uniform() < options.nr_rate then
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-- reducing noise
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table.insert(batch, {xc, iproc.crop(yc, offset, offset, size - offset, size - offset)})
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else
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-- ratain useful details
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table.insert(batch, {yc, iproc.crop(yc, offset, offset, size - offset, size - offset)})
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end
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end
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return batch
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end
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function pairwise_transform.jpeg(src, style, level, size, offset, n, options)
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if style == "art" then
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if level == 0 then
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return pairwise_transform.jpeg_(src, {torch.random(85, 95)},
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size, offset, n, options)
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elseif level == 1 then
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return pairwise_transform.jpeg_(src, {torch.random(65, 85)},
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size, offset, n, 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 pairwise_transform.jpeg_(src, {torch.random(27, 70)},
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size, offset, n, 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 pairwise_transform.jpeg_(src, {quality1, quality2},
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size, offset, n, 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 pairwise_transform.jpeg_(src,
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{quality1, quality2, quality3},
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size, offset, n, 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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if level == 0 then
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return pairwise_transform.jpeg_(src, {torch.random(85, 95)},
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size, offset, n,
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options)
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else
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return pairwise_transform.jpeg_(src, {torch.random(37, 70)},
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size, offset, n,
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options)
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end
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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.test_jpeg(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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jpeg_chroma_subsampling_rate = 0.5,
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nr_rate = 1.0,
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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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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, 9 do
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local xy = pairwise_transform.jpeg(src,
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"art",
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torch.random(1, 2),
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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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