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require 'torch'
require 'nn'
require 'image'
local flowFile = require 'flowFileLoader'
local cmd = torch.CmdLine()
cmd:option('-flow_file', 'examples/example.flo', 'Target optical flow file')
cmd:option('-source_image', 'examples/example.png', 'Source image to warp')
cmd:option('-output_image', 'example_output.png', 'Destination warped image')
cmd:option('-scale', '0.5', 'Scale for optical flow, from 0-1')
local function main(params)
local flow = flowFile.load(params.flow_file, params.scale)
local imageWarped = warpImage(image.load(params.source_image, 3), flow)
image.save(params.output_image, imageWarped)
end
-- warp a given image according to the given optical flow.
-- Disocclusions at the borders will be filled with the VGG mean pixel.
function warpImage(img, flow)
local mean_pixel = torch.DoubleTensor({123.68/256.0, 116.779/256.0, 103.939/256.0})
result = image.warp(img, flow, 'bilinear', true, 'pad', -1)
for x=1, result:size(2) do
for y=1, result:size(3) do
if result[1][x][y] == -1 and result[2][x][y] == -1 and result[3][x][y] == -1 then
result[1][x][y] = mean_pixel[1]
result[2][x][y] = mean_pixel[2]
result[3][x][y] = mean_pixel[3]
end
end
end
return result
end
local tmpParams = cmd:parse(arg)
local params = nil
local file = nil
if tmpParams.args == '' or file == nil then
params = cmd:parse(arg)
else
local args = {}
io.input(file)
local argPos = 1
while true do
local line = io.read()
if line == nil then break end
if line:sub(0, 1) == '-' then
local splits = str_split(line, " ", 2)
args[argPos] = splits[1]
args[argPos + 1] = splits[2]
argPos = argPos + 2
end
end
for i=1, #arg do
args[argPos] = arg[i]
argPos = argPos + 1
end
params = cmd:parse(args)
io.close(file)
end
main(params)
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