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flow-cam.lua
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flow-cam.lua
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torch.setdefaulttensortype('torch.FloatTensor')
require 'opticalflow'
require 'image'
require 'matching'
require 'prettydisplay'
require 'framegrabber'
require 'filtering'
require 'planning'
local hwin = 10
local wwin = 16
local filters0 = torch.LongTensor{
{0, 4, 8, 8, 12, 0, 16, 8},
{0, 8, 8, 12, 12, 4, 16, 12},
{8, 0, 12, 12, 4, 4, 16, 8},
{0, 0, 4, 8, 8, 0, 16, 4},
{8, 4, 12, 16, 4, 8, 16, 12},
{4, 0, 8, 12, 0, 4, 12, 8},
{0, 12, 8, 16, 12, 8, 16, 16},
{4, 4, 8, 16, 0, 8, 12, 12},
{0, 4, 8, 12, 0, 0, 4, 16},
{0, 4, 4, 12, 8, 4, 16, 8},
{4, 0, 12, 16, 0, 4, 16, 12},
{4, 0, 8, 4, 0, 0, 4, 4},
{4, 4, 12, 12, 4, 0, 8, 16},
{4, 4, 8, 8, 0, 4, 4, 8},
{0, 8, 4, 16, 8, 8, 16, 12},
{8, 0, 12, 4, 4, 0, 8, 4},
{4, 8, 8, 12, 0, 8, 4, 12},
{4, 12, 8, 16, 0, 12, 4, 16},
{8, 4, 12, 8, 4, 4, 8, 8},
{8, 4, 16, 12, 8, 0, 12, 16},
{8, 8, 12, 12, 4, 8, 8, 12},
{8, 12, 12, 16, 4, 12, 8, 16},
{12, 0, 16, 8, 0, 4, 8, 8},
{0, 4, 12, 8, 0, 0, 4, 12},
{12, 0, 16, 4, 8, 0, 12, 4},
{12, 4, 16, 12, 0, 8, 8, 12},
{4, 0, 12, 4, 0, 0, 8, 4},
{0, 8, 12, 12, 0, 4, 4, 16},
{12, 4, 16, 8, 8, 4, 12, 8},
{12, 8, 16, 12, 8, 8, 12, 12},
{12, 12, 16, 16, 8, 12, 12, 16},
{4, 4, 12, 8, 0, 4, 8, 8},
}
--[[
local filters0= torch.LongTensor{{ 0, 0, 8, 8, 8, 8,16,16},
{ 8, 0,16, 8, 0, 8, 8,16},
{ 0, 0, 8,16, 8, 0,16,16},
{ 0, 0,16, 8, 0, 8,16,16},
{ 4, 4, 8, 8, 8, 8,12,12},
{ 8, 4,12, 8, 4, 8, 8,12},
{ 4, 0, 8,16, 8, 0,12,16},
{ 0, 4,16, 8, 0, 8,16,12},
{ 0, 0, 4,16, 4, 0, 8,16},
{ 8, 0,12,16, 12, 0,16,16},
{ 0, 0,16, 4, 0, 4,16, 8},
{ 0, 8,16,12, 0,12,16,16},
}
--]]
local filters = nn.Sequential()
--filters:add(nn.SpatialContrastiveNormalization(n_chans,image.gaussian1D(k_norm)))
filters:add(nn.BlockFilter(filters0))
local filters2 = filters:clone()
--TODO depends on the size of the filters
local matcher = nn.BinaryMatching(hwin, wwin, 7, 8, 7, 8)
local matcher2 = nn.BinaryMatching(hwin, wwin, 7, 8, 7, 8)
function loadImgFile(i)
return image.rgb2y(image.scale(image.load(string.format("data/%09d.jpg", i)),
320, 180, 'bilinear'))
end
local fg
function loadImgCam(i)
fg = fg or new_framegrabber(240, 320, 0)
return image.rgb2y(fg:grab())
end
loadImg = loadImgCam
local i_img = 1
local im1 = loadImg(i_img)
local im12 = image.scale(im1, im1:size(3)/2, im1:size(2)/2)
local im1filtered = filters:forward(im1):clone()
local im1filtered2 = filters2:forward(im12):clone()
local win, win2, win3, win4, win5
local timer = torch.Timer()
local flow = torch.ByteTensor()
local flowreal = torch.Tensor()
local flowrealdisp = torch.Tensor()
local meantfilter = 0
local nfilter = 0
local totaltime = 0
while true do
--sys.execute('sleep 1')
i_img = i_img + 1
local im2 = loadImg(i_img)
local im22 = image.scale(im2, im1:size(3)/2, im1:size(2)/2)
timer:reset()
local im2filtered = filters:forward(im2)
local im2filtered2 = filters2:forward(im22)
meantfilter = nfilter/(nfilter+1)*meantfilter + timer:time()['real']/(nfilter+1)
nfilter = nfilter + 1
print("toc filters : ", timer:time()['real'])
print("mean filters : ", meantfilter)
local flow1, score1 = matcher:forward{im1filtered, im2filtered}
local flow2, score2 = matcher2:forward{im1filtered2, im2filtered2}
print("toc match : ", timer:time()['real'])
--local tmed = torch.Timer()
MergeFlow(flow1, score1, flow2, score2, flow,
math.floor((hwin-1)/2), math.floor((wwin-1)/2), 1,1)
--print("merge ", tmed:time()['real'])
MedianFilter(flow, 3)
print("toc median : ", timer:time()['real'])
totaltime = totaltime + timer:time()['real']
print(" ===== FPS : ",1/(timer:time()['real']), " =====")
flowreal:resize(flow:size())
flowreal:copy(flow)
flowreal[1]:add(-math.floor((hwin-1)/2)*2)
flowreal[2]:add(-math.floor((wwin-1)/2)*2)
flowrealdisp:resizeAs(flowreal)
flowrealdisp:copy(flowreal)
local norm = (flowreal[1]:cmul(flowreal[1])+flowreal[2]:cmul(flowreal[2])):sqrt()
local x1,y1,x2,y2 = planningOld(norm)
--norm[{{y1+1,y2},{x1+1,x2}}]:add(10)
local score2b = image.scale(score2:real(), score1:size(2),
score1:size(1), 'simple')
local select = score1:real():gt(score2b)
local maxflow = math.max(math.ceil((hwin-1)/2)*2,math.ceil((wwin-1)/2)*2)
im2c = torch.Tensor(3, im2:size(2), im2:size(3))
for i = 1,3 do
im2c[i]:copy(im2[1])
end
im2c[{2,{y1+1,y2},{x1+1,x2}}]:zero()
local todisp=prettydisplay({{im2c,norm,flow1},
{flowrealdisp,flow2},
{score1:real(), score2b:real(),select}},
{{{},{},{}},
{{-maxflow,maxflow},{}},
{{0,10},{0,10},{}}})
win=image.display{image=todisp, win=win}
--image.save(string.format('output_video/%07d.jpg', i_img), todisp)
--[[win = image.display{image=norm, win=win, min=0, max=20}
win2 = image.display{image=im2, win=win2}
win3 = image.display{image=flow1, win=win3}
win4 = image.display{image=flow2, win=win4}
win5 = image.display{image=flow, win=win5}--]]
im1filtered = im2filtered:clone()
im1filtered2 = im2filtered2:clone()
print("---")
end