249 lines
8.6 KiB
Lua
249 lines
8.6 KiB
Lua
require 'image'
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local gm = require 'graphicsmagick'
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local iproc = require 'iproc'
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local data_augmentation = require 'data_augmentation'
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local pairwise_transform = {}
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local function random_half(src, p)
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if torch.uniform() < p then
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local filter = ({"Box","Box","Blackman","Sinc","Lanczos", "Catrom"})[torch.random(1, 6)]
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return iproc.scale(src, src:size(3) * 0.5, src:size(2) * 0.5, filter)
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else
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return src
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end
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end
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local function crop_if_large(src, max_size)
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local tries = 4
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if src:size(2) > max_size and src:size(3) > max_size then
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local rect
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for i = 1, tries do
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local yi = torch.random(0, src:size(2) - max_size)
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local xi = torch.random(0, src:size(3) - max_size)
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rect = iproc.crop(src, xi, yi, xi + max_size, yi + max_size)
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-- ignore simple background
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if rect:float():std() >= 0 then
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break
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end
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end
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return rect
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else
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return src
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end
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end
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local function preprocess(src, crop_size, options)
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local dest = src
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dest = random_half(dest, options.random_half_rate)
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dest = crop_if_large(dest, math.max(crop_size * 2, options.max_size))
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dest = data_augmentation.flip(dest)
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dest = data_augmentation.color_noise(dest, options.random_color_noise_rate)
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dest = data_augmentation.overlay(dest, options.random_overlay_rate)
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dest = data_augmentation.unsharp_mask(dest, options.random_unsharp_mask_rate)
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dest = data_augmentation.shift_1px(dest)
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return dest
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end
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local function active_cropping(x, y, size, p, tries)
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assert("x:size == y:size", x:size(2) == y:size(2) and x:size(3) == y:size(3))
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local r = torch.uniform()
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local t = "float"
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if x:type() == "torch.ByteTensor" then
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t = "byte"
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end
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if p < r then
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local xi = torch.random(0, y:size(3) - (size + 1))
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local yi = torch.random(0, y:size(2) - (size + 1))
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local xc = iproc.crop(x, xi, yi, xi + size, yi + size)
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local yc = iproc.crop(y, xi, yi, xi + size, yi + size)
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return xc, yc
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else
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local lowres = gm.Image(x, "RGB", "DHW"):
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size(x:size(3) * 0.5, x:size(2) * 0.5, "Box"):
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size(x:size(3), x:size(2), "Box"):
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toTensor(t, "RGB", "DHW")
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local best_se = 0.0
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local best_xc, best_yc
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local m = torch.FloatTensor(x:size(1), size, size)
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for i = 1, tries do
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local xi = torch.random(0, y:size(3) - (size + 1))
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local yi = torch.random(0, y:size(2) - (size + 1))
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local xc = iproc.crop(x, xi, yi, xi + size, yi + size)
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local lc = iproc.crop(lowres, xi, yi, xi + size, yi + size)
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local xcf = iproc.byte2float(xc)
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local lcf = iproc.byte2float(lc)
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local se = m:copy(xcf):add(-1.0, lcf):pow(2):sum()
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if se >= best_se then
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best_xc = xcf
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best_yc = iproc.byte2float(iproc.crop(y, xi, yi, xi + size, yi + size))
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best_se = se
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end
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end
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return best_xc, best_yc
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end
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end
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function pairwise_transform.scale(src, scale, size, offset, n, options)
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local filters = options.downsampling_filters
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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 y = 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 = iproc.scale(iproc.scale(y, y:size(3) * down_scale,
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y:size(2) * down_scale, downsampling_filter),
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y:size(3), y:size(2))
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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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for i = 1, n do
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local xc, yc = active_cropping(x, y,
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size,
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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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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.jpeg_(src, quality, size, offset, n, options)
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local unstable_region_offset = 8
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local y = preprocess(src, size, options)
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local x = y
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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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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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for i = 1, n do
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local xc, yc = active_cropping(x, y, size,
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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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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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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 == 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 > 0.6 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 > 0.3 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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-- level adjusting by -nr_rate
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return pairwise_transform.jpeg_(src, {torch.random(30, 70)},
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size, offset, n,
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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.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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function pairwise_transform.test_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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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.scale(src, 2.0, 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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