require 'torch'
require 'image'
require 'sys'
require 'cunn'
require 'cutorch'
require 'cudnn'

imgPath = 'testdata_inpaint_BSDS500'
savePath = 'result_inpaint'

model = torch.load('CEILNet_inpaint.net')
model = model:cuda()
model:training()
model_edge = nn.computeEdge(100)

files = {}
for file in paths.files(imgPath) do
  if string.find(file,'-input.png') then
    table.insert(files, paths.concat(imgPath,file))
  end
end

for _,inputFile in ipairs(files) do
  local inputImg = image.load(inputFile)
  local height = inputImg:size(2)
  local width = inputImg:size(3)
  local input = torch.CudaTensor(1, 3, height, width)
  input[1] = inputImg:cuda()
  input = input * 255

  local inputs = torch.CudaTensor(1, 4, height, width)
  inputs[{{},{1,3},{},{}}] = input
  inputs[{{},{4},{},{}}] = model_edge:forward(input)
  inputs = inputs - 115
  local inputs = {inputs,input}
  local inputC = input:clone()

  local predictions = model:forward(inputs)
  local pred_b = predictions[2]

  for m = 1,3 do
    local numerator = torch.dot(pred_b[1][m], inputC[1][m])
    local denominator = torch.dot(pred_b[1][m], pred_b[1][m])
    local alpha = numerator/denominator
    pred_b[1][m] = pred_b[1][m] * alpha
  end

  local savColor = string.gsub(inputFile,imgPath,savePath)

  pred_b = pred_b/255
  local sav = string.gsub(savColor,'input.png','predict.png')
  image.save(sav,pred_b[1])

  ::done::
end