require 'torch'
require 'image'
local setting = {parent_root =
'/home/pxu/image'}
function list_children_root(path)
local i,t,popen =
0,{},
io.popen
for file_name
in popen(
'ls -a ' .. path):lines()
do
i = i +
1
if i>
2 then
t[i-
2] =
file_name
--if i>0 then
--t[i] = file_name
end
end
return t
end
function list_img(path)
--print(path)
local i,t,popen =
0,{},
io.popen
for file_name
in popen(
'ls -a ' .. path ..
' |grep jpg'):lines()
do
i = i +
1
t[i] =
file_name
end
return t
end
print(
'obtain children root path ...')
train_paths,train_labels =
{},{}
test_paths,test_labels =
{}, {}
children_paths =
list_children_root(setting.parent_root)
print(children_paths)
num_train,num_test =
1,
1
print(
'spit data begin')
for i=
1,table.getn(children_paths)
do
children_root = setting.parent_root ..
'/'..children_paths[i]
print(children_root)
img_names =
list_img(children_root)
ranIdx =
torch.randperm(table.getn(img_names))
for j=
1,table.getn(img_names)
do
if j<=
math.floor(
0.6*table.getn(img_names))
then
local idx =
ranIdx[{j}]
train_paths[num_train] = children_root ..
'/'..img_names[idx]
train_labels[num_train]=
i
num_train = num_train+
1
else
local idx =
ranIdx[{j}]
test_paths[num_test]=children_root ..
'/' ..img_names[idx]
test_labels[num_test]=
i
num_test = num_test+
1
end
end
end
print(
'begin copy')
local nTrain,nTest =
table.getn(train_paths),table.getn(test_paths)
for i=1,nTrain do
local aimpath = '/home/yqcui/image/train/'..train_labels[i]..'/'..i..'.jpg'
local todo='cp '..train_paths[i]..' ' ..aimpath
print(todo)
os.execute(todo)
end
for i=
1,nTest
do
local aimpath =
'/home/yqcui/image/train/'..test_labels[i]..
'/'..i..
'.jpg'
local todo=
'cp '..test_paths[i]..
' ' .. aimpath
print(todo)
os.execute(todo)
end
将数据分为数据集和训练集，比例为6:4
转载于:https://www.cnblogs.com/cyq041804/p/5737374.html
