VOC2012

第一步：将VOC数据集的类别标签放入Label文件夹中，使用以下代码将VOC2012数据集的图片按照类别分组保存新的文件夹

import os
import shutil

# 文件目录结构：
# D:/VOC2012/JPEGImages
# D:/VOC2012/Label
# D:/VOC2012/SegmentationClass
# D:/VOC2012/VOC2012-CoSaliency

Set_Data_Dir = 'D:/VOC2012/Label/'

for group_file in os.listdir(Set_Data_Dir):

Group_File_Dir = Set_Data_Dir + group_file
with open(Group_File_Dir) as f:
lines = f.read().splitlines()

for line in lines:

if line[-2:] != '-1':

origin_gt = 'D:/VOC2012/SegmentationClass/' + line[:-3] + '.png'

if os.path.exists(origin_gt):

origin_image = 'D:/VOC2012/JPEGImages/' + line[:-3] + '.jpg'

new_fold = 'D:/VOC2012/VOC2012-CoSaliency-Origin/' + str(group_file[:-13])
if not os.path.exists(new_fold):
os.mkdir(new_fold)

new_image = 'D:/VOC2012/VOC2012-CoSaliency-Origin/' + str(group_file[:-13]) + '/' + line[:-3] + '.jpg'
new_gt = 'D:/VOC2012/VOC2012-CoSaliency-Origin/' + str(group_file[:-13]) + '/' + line[:-3] + '.png'

shutil.copyfile(origin_image, new_image)
shutil.copyfile(origin_gt, new_gt)

第二步转化语义分割真值图为显著性检测真值图

import os
import shutil
import cv2
from PIL import Image
import numpy as np

Set_Data_Dir = 'D:/VOC2012/VOC2012-CoSaliency-Origin/'
New_Data_Dir = 'D:/VOC2012/VOC2012-CoSaliency/'

i = 0

for group_file in os.listdir(Set_Data_Dir):

i = i + 1

for imgPath in [imgName for imgName in os.listdir(Set_Data_Dir + group_file + "/") if 'jpg' in imgName]:

origin_image = Set_Data_Dir + group_file + "/" + imgPath
new_fold = New_Data_Dir + group_file

if not os.path.exists(new_fold):
os.mkdir(new_fold)
newImg = New_Data_Dir + group_file + "/" + imgPath

shutil.copyfile(origin_image, newImg)

for gtPath in [gtName for gtName in os.listdir(Set_Data_Dir + group_file + "/") if 'png' in gtName]:

origin_gt_path = Set_Data_Dir + group_file + "/" + gtPath

origin_gt = Image.open(origin_gt_path)
origin_gt = np.array(origin_gt)

origin_gt[origin_gt != i] = 0
origin_gt[origin_gt == i] = 255

newGt_path = New_Data_Dir + group_file + "/" + gtPath

cv2.imwrite(newGt_path, origin_gt)

第三步每张图片在组中与最相似的一张组成新组

import cv2
import os
import numpy as np
import shutil
import random

def CopyPic(img_path, j, Set_dir_len, group_dir_len, img_dir_len):

rgbOld = img_path
rgbFolder = "D:/VOC2012/processed_datasets/" + img_path[Set_dir_len:group_dir_len-1] + '_' + str(j)
print(rgbFolder)
if not os.path.exists(rgbFolder):
os.mkdir(rgbFolder)
rgbNew = "D:/VOC2012/processed_datasets/" + img_path[Set_dir_len:group_dir_len-1] + '_' + str(j) + "/" + img_path[group_dir_len:-4] + ".jpg"
print(rgbNew)
gtOld = img_path[:-4] + ".png"
print(gtOld)
gtNew = "D:/VOC2012/processed_datasets/" + img_path[Set_dir_len:group_dir_len-1] + '_' + str(j) + "/" + img_path[group_dir_len:-4] + ".png"
print(gtNew)

print()

shutil.copyfile(rgbOld, rgbNew)
shutil.copyfile(gtOld, gtNew)
return

Set_Data_Dir = "D:/VOC2012/VOC2012-CoSaliency-Delete/"
Set_dir_len = len(Set_Data_Dir)

#[p for p in os.listdir(Group_Data_Dir) if 'jpg' in p]

for group_name in os.listdir(Set_Data_Dir):
i = 1
Group_Data_Dir = Set_Data_Dir + group_name + "/"
group_dir_len = len(Group_Data_Dir)
for img_name in [p for p in os.listdir(Group_Data_Dir) if 'jpg' in p]:

img_path = Group_Data_Dir + img_name
img_dir_len = len(img_path)

img = cv2.imread(img_path, cv2.IMREAD_GRAYSCALE)
hist = cv2.calcHist(img, [0], None, [256], [0, 256])

list_loss = []
list_path = []
for o_img_name in [p for p in os.listdir(Group_Data_Dir) if 'jpg' in p]:
o_img_path = Group_Data_Dir + o_img_name

if img_path != o_img_path:

o_img = cv2.imread(o_img_path, cv2.IMREAD_GRAYSCALE)
o_hist = cv2.calcHist(o_img, [0], None, [256], [0, 256])
o_loss = cv2.compareHist(hist, o_hist, cv2.HISTCMP_BHATTACHARYYA)

list_loss.append(o_loss)
list_path.append(o_img_path)

loss_sort_index = np.argsort(list_loss)

CopyPic(img_path, i, Set_dir_len, group_dir_len, img_dir_len)
CopyPic(list_path[loss_sort_index[0]], i, Set_dir_len, group_dir_len, img_dir_len)

i = i + 1

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