我是跟随这篇博客进行转换的
博客
图像语义分割网络实战（一）增强数据集PASCAL VOC2012 Aug的制作
https://blog.csdn.net/qq_39444453/article/details/107231357?utm_medium=distribute.pc_relevant_download.none-task-blog-2~default~BlogCommendFromBaidu~default-4.test_version_3&depth_1-utm_source=distribute.pc_relevant_download.none-task-blog-2~default~BlogCommendFromBaidu~default-4.test_version_
博客2
SBD数据集从这里下载：https://www.jianshu.com/p/da4641f50000
简书： pascal VOC 2012 分割数据集及其增强版SBD介绍
SBD官网：http://home.bharathh.info/pubs/codes/SBD/download.html
博客3
可视化：灰度标签转化为彩色标签可视化
https://www.freesion.com/article/77741310718/
博客4
下载tools
tools我是从这里下载的（VOC增强数据集数据转换代码）：https://download.csdn.net/download/qq_31347869/11850943?utm_medium=distribute.pc_relevant.none-task-download-2%7Edefault%7EBlogCommendFromMachineLearnPai2%7Edefault-1.control&depth_1-utm_source=distribute.pc_relevant.none-task-download-2%7Edefault%7EBlogCommendFromMachineLearnPai2%7Edefault-1.control
一、根据博客2下载SBD数据集
二、博客1介绍SBD数据集情况如下
SBD
增强版的VOC跟原版没什么区别，只是增加了数据。介绍下载的文件夹中每个文件的作用。
- img: 增强版的原图，共11355张图片
- cls: 用于语义分割的label，共11355个.mat文件，每个.mat文件对应一张原图
- inst: 用于实例分割的label，也是11355个.mat文件
- tools: 3个用于数据转换的脚本，在后面数据转换时用到（这个文件我没有）
- train.txt: 训练集索引
- val.txt: 验证集索引
三、下载tools
四、开始转换mat2png
由于pascal voc 2012增强版的数据集的label是.mat格式的文件，需要将其转换为.png格式的图片。转化后的图片是8-bit的灰度图。
-
1）在benchmark_RELEASE目录下创建一个名为cls_png的目录用于存放转化后的png图片 -
2）在benchmark_RELEASE目录下执行mat2png.py脚本 -
python tools/mat2png.py cls cls_png（cls是读入文件夹、cls_png是输出文件夹）
mat2png.py文件如下
from __future__ import print_function
import os
import sys
import glob
from PIL import Image as PILImage
import os
import cv2
from utils import mat2png_hariharan
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')))
cv2.imwrite(os.path.join(output_path, modify_image_name(mat, 'png')), numpy_img)
# Extract name of image from given path, replace its extension with specified one
# and return new name only, not path.
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()
值得注意的是这里转化语义cls标签直接按照上述进行操作就行了，如果转化实力标签inst需要做两件事：
1 记得将mat2png调用的utils.mat2png_hariharan这里的key改成GTinst2 这里会出现PIL的报错，可能是我自己的PIL库的版本问题，
不能将数组型array转化成PIL类型(方案一)
因此我们采用方案二，由于cv2可以直接保存array格式直接cv2.imwrite(path, img)即可
五、根据博客3灰度标签转化为彩色标签
仅需要改变成如下读入、输出路径即可，这是两个文件夹对应创建即可
path = './cls_png/*.png'
path_converted = './cls_png_color'
from __future__ import print_function
import os
import sys
import numpy as np
from skimage.io import imread, imsave
import glob
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(seg):
color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8)
palette = pascal_palette()
for c, i in palette.items():
color_seg[ seg == i] = c
color_seg = color_seg[..., ::-1]
return color_seg
def main():
##
ext = '.png'
##
# path, txt_file, path_converted = process_arguments(sys.argv)
path = './voc12_aug/benchmark_RELEASE/dataset/seg/*.png' # 前面转换后的语义图片
path_converted = './voc12_aug/benchmark_RELEASE/dataset/seg_visualize' # 着色后的图片的保存位置
# Create dir for converted labels
if not os.path.isdir(path_converted):
os.makedirs(path_converted)
f = glob.glob(path)
for img_name in f:
img_base_name = os.path.basename(img_name)
img = imread(img_name)
if (len(img.shape) == 2):
img = convert_from_color_segmentation(img)
imsave(os.path.join(path_converted, img_base_name), 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()
最后展示下我自己目录：
绿色是我自己创建的文件夹
褐色是从博客4下载
紫色从博客3下载
---benchmark_RELEASE
------dataset
---------cls
---------cls_png
---------cls_png_color
---------img
---------inst
---------tools
------------convert_labels.py
------------grey2color.py
------------mat2png.py
------------utils.py