# 语义分割数据集准备
Dataset 数据集下载
PASCAL VOC 2012 datasetaugmented PASCAL VOC dataset
# augmented PASCAL VOC
cd $DATASETS
wget http://www
.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/semantic_contours/benchmark
.tgz # 1.3 GB
tar -zxvf benchmark
.tgz
mv benchmark_RELEASE VOC_aug
# original PASCAL VOC 2012
wget http://host
.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-
2012.tar
# 2 GB
tar -xvf VOCtrainval_11-May-
2012.tar
mv VOCdevkit/VOC2012 VOC2012_orig && rm -r VOCdevkit
Data conversions 数据转换
augmented PASCAL VOC 数据集的 ground truth labels 是以 Matlab data files的格式存在的，需要进行转换：
Step1 定义 mat2png 脚本； Step2 转换 mat 成 png.
from __future__
import print_function
import os
import sys
import glob
import scipy.io
from PIL
import Image
as PILImage
def mat2png_hariharan(mat_file, key='GTcls'):
mat = scipy.io.loadmat(mat_file, mat_dtype=
True, squeeze_me=
True, struct_as_record=
False)
return mat[key].Segmentation
def main():
input_path, output_path = process_arguments(sys.argv)
if os.path.isdir(input_path)
and os.path.isdir(output_path):
mat_files = glob.glob(os.path.join(input_path,
'*.mat'))
convert_mat2png(mat_files, output_path)
else:
help(
'Input or output path does not exist!\n')
def process_arguments(argv):
num_args = len(argv)
input_path =
None
output_path =
None
if num_args ==
3:
input_path = argv[
1]
output_path = argv[
2]
else:
help()
return input_path, output_path
def convert_mat2png(mat_files, output_path):
if not mat_files:
help(
'Input directory does not contain any Matlab files!\n')
for mat
in mat_files:
numpy_img = mat2png_hariharan(mat)
pil_img = PILImage.fromarray(numpy_img)
pil_img.save(os.path.join(output_path, modify_image_name(mat,
'png')))
def modify_image_name(path, ext):
return os.path.basename(path).split(
'.')[
0] +
'.' + ext
def help(msg=''):
print(msg +
'Usage: python mat2png.py INPUT_PATH OUTPUT_PATH\n'
'INPUT_PATH denotes path containing Matlab files for conversion.\n'
'OUTPUT_PATH denotes path where converted Png files ar going to be saved.'
, file=sys.stderr)
exit()
if __name__ ==
'__main__':
main()
cd $DATASETS/VOC_aug/dataset
mkdir cls_png
cd $DATASETSDIR
./mat2png.py
$DATASETS/VOC_aug/dataset/cls
$DATASETS/VOC_aug/dataset/cls_png
Caffe的softmax loss函数只能接受一维的 ground truth labels. 但 original PASCAL VOC 2012中的 ground truth labels 是以RGB图像的形式保存的，因此需要降维：
Step1 定义转换python脚本：convert_labels.py；Step2 转换 ground truth labels 为 1D.
from __future__
import print_function
import os
import sys
import numpy
as np
from skimage.io
import imread, imsave
def pascal_palette():
palette = {(
0,
0,
0) :
0 ,
(
128,
0,
0) :
1 ,
(
0,
128,
0) :
2 ,
(
128,
128,
0) :
3 ,
(
0,
0,
128) :
4 ,
(
128,
0,
128) :
5 ,
(
0,
128,
128) :
6 ,
(
128,
128,
128) :
7 ,
(
64,
0,
0) :
8 ,
(
192,
0,
0) :
9 ,
(
64,
128,
0) :
10,
(
192,
128,
0) :
11,
(
64,
0,
128) :
12,
(
192,
0,
128) :
13,
(
64,
128,
128) :
14,
(
192,
128,
128) :
15,
(
0,
64,
0) :
16,
(
128,
64,
0) :
17,
(
0,
192,
0) :
18,
(
128,
192,
0) :
19,
(
0,
64,
128) :
20 }
return palette
def convert_from_color_segmentation(arr_3d):
arr_2d = np.zeros((arr_3d.shape[
0], arr_3d.shape[
1]), dtype=np.uint8)
palette = pascal_palette()
for c, i
in palette.items():
m = np.all(arr_3d == np.array(c).reshape(
1,
1,
3), axis=
2)
arr_2d[m] = i
return arr_2d
def main():
ext =
'.png'
path, txt_file, path_converted = process_arguments(sys.argv)
if not os.path.isdir(path_converted):
os.makedirs(path_converted)
with open(txt_file,
'rb')
as f:
for img_name
in f:
img_base_name = img_name.strip()
img_name = os.path.join(path, img_base_name) + ext
img = imread(img_name)
if (len(img.shape) >
2):
img = convert_from_color_segmentation(img)
imsave(os.path.join(path_converted, img_base_name) + ext, img)
else:
print(img_name +
" is not composed of three dimensions, therefore "
"shouldn't be processed by this script.\n"
"Exiting." , file=sys.stderr)
exit()
def process_arguments(argv):
if len(argv) !=
4:
help()
path = argv[
1]
list_file = argv[
2]
new_path = argv[
3]
return path, list_file, new_path
def help():
print(
'Usage: python convert_labels.py PATH LIST_FILE NEW_PATH\n'
'PATH points to directory with segmentation image labels.\n'
'LIST_FILE denotes text file containing names of images in PATH.\n'
'Names do not include extension of images.\n'
'NEW_PATH points to directory where converted labels will be stored.'
, file=sys.stderr)
exit()
if __name__ ==
'__main__':
main()
cd $DATASETS/VOC2012_orig
mkdir SegmentationClass_1D
cd $DATASETSDIR
./convert_labels.py
$DATASETS/VOC2012_orig/SegmentationClass/ \
$DATASETS/VOC2012_orig/ImageSets/Segmentation/trainval.txt \
$DATASETS/VOC2012_orig/SegmentationClass_1D/
