local data_verbose = false

function getdata(datafile, inputsize, std)

local data = torch.load(datafile, 'ascii')

local dataset ={}

local std = std or 0.2

local nsamples = data:size(1)

local nrows = data:size(2)

local ncols = data:size(3)

function dataset:size()

return nsamples

end

function dataset:selectPatch(nr,nc)

local imageok = false

if simdata_verbose then

print('selectPatch')

end

while not imageok do

--image index

local i = math.ceil(torch.uniform(1e-12,nsamples))

local im = data:select(1,i)

-- select some patch for original that contains original + pos

local ri = math.ceil(torch.uniform(1e-12,nrows-nr))

local ci = math.ceil(torch.uniform(1e-12,ncols-nc))

local patch = im:narrow(1,ri,nr)

patch = patch:narrow(2,ci,nc)

local patchstd = patch:std()

if data_verbose then

print('Image ' .. i .. ' ri= ' .. ri .. ' ci= ' .. ci .. ' std= ' .. patchstd)

end

if patchstd > std then

if data_verbose then

print(patch:min(),patch:max())

end

return patch,i,im

end

end

end

local dsample = torch.Tensor(inputsize*inputsize)

function dataset:conv()

dsample = torch.Tensor(1,inputsize,inputsize)

end

setmetatable(dataset, {__index = function(self, index)

local sample,i,im = self:selectPatch(inputsize, inputsize)

dsample:copy(sample)

return {dsample,dsample,im}

end})

return dataset

end

function getdatacam(inputsize, std)

require 'camera'

local frow = 60

local fcol = 80

local gs = 5

local cam = image.Camera{width=fcol,height=frow}

local dataset ={}

local counter = 1

local std = std or 0.2

local nsamples = 10000

local gfh = image.gaussian{width=gs,height=1,normalize=true}

local gfv = image.gaussian{width=1,height=gs,normalize=true}

local gf = image.gaussian{width=gs,height=gs,normalize=true}

function dataset:size()

return nsamples

end

local imsq = torch.Tensor()

local lmnh = torch.Tensor()

local lmn = torch.Tensor()

local lmnsqh = torch.Tensor()

local lmnsq = torch.Tensor()

local lvar = torch.Tensor()

local function lcn(im)

local mn = im:mean()

local std = im:std()

if data_verbose then

print('im',mn,std,im:min(),im:max())

end

im:add(-mn)

im:div(std)

if data_verbose then

print('im',im:min(),im:max(),im:mean(), im:std())

end

imsq:resizeAs(im):copy(im):cmul(im)

if data_verbose then

print('imsq',imsq:min(),imsq:max())

end

torch.conv2(lmnh,im,gfh)

torch.conv2(lmn,lmnh,gfv)

if data_verbose then

print('lmn',lmn:min(),lmn:max())

end

--local lmn = torch.conv2(im,gf)

torch.conv2(lmnsqh,imsq,gfh)

torch.conv2(lmnsq,lmnsqh,gfv)

if data_verbose then

print('lmnsq',lmnsq:min(),lmnsq:max())

end

lvar:resizeAs(lmn):copy(lmn):cmul(lmn)

lvar:mul(-1)

lvar:add(lmnsq)

if data_verbose then

print('2',lvar:min(),lvar:max())

end

lvar:apply(function (x) if x<0 then return 0 else return x end end)

if data_verbose then

print('2',lvar:min(),lvar:max())

end

local lstd = lvar

lstd:sqrt()

lstd:apply(function (x) if x<1 then return 1 else return x end end)

if data_verbose then

print('lstd',lstd:min(),lstd:max())

end

local shift = (gs+1)/2

local nim = im:narrow(1,shift,im:size(1)-(gs-1)):narrow(2,shift,im:size(2)-(gs-1))

nim:add(-1,lmn)

nim:cdiv(lstd)

if data_verbose then

print('nim',nim:min(),nim:max())

end

return nim

end

function dataset:selectPatch(nr,nc)

local imageok = false

if simdata_verbose then

print('selectPatch')

end

counter = counter + 1

local imgray = image.rgb2y(cam:forward())

local nim = lcn(imgray[1]:clone())

while not imageok do

-- select some patch for original that contains original + pos

local ri = math.ceil(torch.uniform(1e-12,nim:size(1)-nr))

local ci = math.ceil(torch.uniform(1e-12,nim:size(2)-nc))

local patch = nim:narrow(1,ri,nr)

patch = patch:narrow(2,ci,nc)

local patchstd = patch:std()

if data_verbose then

print('Image ' .. 0 .. ' ri= ' .. ri .. ' ci= ' .. ci .. ' std= ' .. patchstd)

end

if patchstd > std then

if data_verbose then

print(patch:min(),patch:max())

end

return patch,i,nim

end

end

end

local dsample = torch.Tensor(inputsize*inputsize)

setmetatable(dataset, {__index = function(self, index)

local sample,i,im = self:selectPatch(inputsize, inputsize)

dsample:copy(sample)

return {dsample,dsample,im}

end})

return dataset

end

-- dataset, dataset=createDataset(....)

-- nsamples, how many samples to display from dataset

-- nrow, number of samples per row for displaying samples

-- zoom, zoom at which to draw dataset

function displayData(dataset, nsamples, nrow, zoom)

require 'image'

local nsamples = nsamples or 100

local zoom = zoom or 1

local nrow = nrow or 10

cntr = 1

local ex = {}

for i=1,nsamples do

local exx = dataset[1]

ex[cntr] = exx[1]:clone():unfold(1,math.sqrt(exx[1]:size(1)),math.sqrt(exx[1]:size(1)))

cntr = cntr + 1

end

if itorch then

itorch.image(ex)

else

print('For visualization, run the script in itorch')

end

end