/
demo_mhp_keypoints_given.lua
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/
demo_mhp_keypoints_given.lua
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require 'image'
require 'nn'
require 'nngraph'
require 'cunn'
require 'cudnn'
require 'cutorch'
require 'lfs'
require 'stn'
util = paths.dofile('util.lua')
local alphabet = "abcdefghijklmnopqrstuvwxyz0123456789-,;.!?:'\"/\\|_@#$%^&*~`+-=<>()[]{} "
local dict = {}
for i = 1,#alphabet do
dict[alphabet:sub(i,i)] = i
end
ivocab = {}
for k,v in pairs(dict) do
ivocab[v] = k
end
opt = {
dataset = 'mhp',
doc_length = 201,
prefix = 'move',
batchSize = 16, -- number of samples to produce
noisetype = 'normal', -- type of noise distribution (uniform / normal).
imsize = 1, -- used to produce larger images. 1 = 64px. 2 = 80px, 3 = 96px, ...
noisemode = 'random', -- random / line / linefull1d / linefull
gpu = 1, -- gpu mode. 0 = CPU, 1 = GPU
display = 0, -- Display image: 0 = false, 1 = true
nz = 100,
net_gen = '',
net_txt = '',
trainfiles = 'mhp_trainfiles_a5.t7',
loadSize = 140,
fineSize = 128,
txtSize = 1024,
data_root='/mnt/brain3/datasets/txt2img/mhp/t7files_loc_txt_head_ex',
img_dir = '/mnt/brain3/datasets/txt2img/mhp/images',
demo = 'keypoints_given',
num_elt = 17,
nsample = 50,
keypoint_dim = 16,
}
-- demo = 'trans' -- trans|stretch|shrink
for k,v in pairs(opt) do opt[k] = tonumber(os.getenv(k)) or os.getenv(k) or opt[k] end
print(opt)
if opt.display == 0 then opt.display = false end
dofile('data/donkey_folder_mhp_keypoint_and_image.lua')
assert(opt.net_gen ~= '')
assert(opt.net_txt ~= '')
assert(opt.net_kp ~= '')
net_gen = torch.load(opt.net_gen)
net_txt = torch.load(opt.net_txt)
net_gen:evaluate()
net_txt:evaluate()
function decode(txt)
local str = ''
for w_ix = 1,txt:size(1) do
local ch_ix = txt[w_ix]
local ch = ivocab[ch_ix]
if (ch ~= nil) then
str = str .. ch
end
end
return str
end
local html = '<html><body><h1>Generated Images</h1><table border="1" style="width=100%"><tr><td>Caption</td><td>Image</td></tr>'
if opt.trainfiles == '' then
cur_files = dir.getfiles(opt.data_root)
else
cur_files = torch.load(opt.trainfiles)
for k,v in pairs(cur_files) do
cur_files[k] = opt.data_root .. '/' .. cur_files[k]
end
end
local batch_info = {}
while #batch_info < opt.nsample do
local ix_file = torch.randperm(#cur_files)[1]
local info = torch.load(cur_files[ix_file])
if (info.has_kp[{1,1}] == 1) and
(info.points[{3,{},{}}]:sum() > 0) and
(#info.single_person:size() == 2) and
(info.single_person:size(2) == 1) then
batch_info[#batch_info + 1] = info
end
end
for n = 1,opt.nsample do
local info = batch_info[n]
local img_file = opt.img_dir .. '/' .. info.img
-- randomly select a single person
local person_ix = torch.randperm(info.single_person:size(2))[1]
person_ix = info.single_person[{1,person_ix}]
-- normalize image and keypoint locations.
local img, locs, img_dbg = trainHook(img_file,
info.points[{{},{},person_ix}],
info.pos[{{},person_ix}],
info.scale[{{},person_ix}],
info.head[{{},person_ix}])
img:add(1)
img:mul(0.5)
-- prepare text
local txt_ix = info.txt:size(1)
local query = decode(info.char[{{},txt_ix}])
local fea_txt = info.txt[txt_ix]:clone()
fea_txt = torch.repeatTensor(fea_txt, opt.batchSize, 1):cuda()
print(string.format('sample %d of %d: [%s]', n, opt.nsample, query))
-- prepare noise
noise = torch.Tensor(opt.batchSize, opt.nz)
if opt.noisetype == 'uniform' then
noise:uniform(-1, 1)
elseif opt.noisetype == 'normal' then
noise:normal(0, 1)
end
noise = noise:cuda()
-- prepare keypoints
local fea_loc = torch.zeros(1, opt.num_elt, opt.keypoint_dim, opt.keypoint_dim)
for s = 1,opt.num_elt do
local point = locs[s]
if point[3] > 0.1 then
local x = math.min(opt.keypoint_dim,
math.max(1,torch.round(point[1] * opt.keypoint_dim)))
local y = math.min(opt.keypoint_dim,
math.max(1,torch.round(point[2] * opt.keypoint_dim)))
fea_loc[{1,s,y,x}] = 1
end
end
fea_loc = torch.repeatTensor(fea_loc, opt.batchSize, 1, 1, 1)
fea_loc = fea_loc:cuda()
local images = net_gen:forward({ { noise, fea_txt }, fea_loc }):clone()
images:add(1):mul(0.5)
local locs_tmp = locs:clone()
locs_tmp:narrow(2,1,2):mul(opt.fineSize)
images = torch.repeatTensor(images, 2, 1, 1, 1)
for b = 1,opt.batchSize do
images[b] = util.draw_keypoints(images[b], locs_tmp, 0.06, 1)
end
images = images:narrow(1,1,3)
lfs.mkdir('results')
local visdir = 'results/mhp_kp_given'
lfs.mkdir(visdir)
local fname = string.format('%s/mhp_%s_%d', visdir, opt.demo, n)
local fname_png = fname .. '.png'
image.save(fname_png, image.toDisplayTensor(images, 4, 3))
fname = string.format('mhp_kp_given/mhp_%s_%d', opt.demo, n)
local fname_rel = fname .. '.png'
html = html .. string.format('\n<tr><td>%s</td><td><img src=\"%s\"></td></tr>',
query, fname_rel)
end
html = html .. '</html>'
fname_html = string.format('results/%s_%s.html', opt.dataset, opt.demo)
os.execute(string.format('echo "%s" > %s', html, fname_html))