#导入相关包
import matplotlib.pyplot as plt
import numpy as np
import glob
import scipy.misc
from PIL import Image
import re
#这是我的标签数据对应像素值
'''
label2color_dict = {
0:[0,0,0],
1:[128,0,0],
2:[0,128,150],
3:[128,128,0],
4:[0,0,128],
5:[128,0,128],
6:[0,128,128],
7:[128,128,128],
8:[64,0,0],
9:[192,0,0],
10:[64,128,0],
11:[192,128,0],
12:[64,0,128],
13:[192,0,128],
14:[64,128,128],
15:[192,128,128],
16:[0,64,0],
17:[128,64,0],
18:[0,192,0],
19:[128,128,128]
}
'''
VOC_COLORMAP = [[0, 0, 0], [128, 0, 0], [0, 128, 0], [128, 128, 0],
[0, 0, 128], [128, 0, 128], [0, 128, 128], [128, 128, 128],
[64, 0, 0], [192, 0, 0], [64, 128, 0], [192, 128, 0],
[64, 0, 128], [192, 0, 128], [64, 128, 128], [192, 128, 128],
[0, 64, 0], [128, 64, 0], [0, 192, 0], [128, 192, 0],
[0, 64, 128]]
zero = [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20]
label2color_dict = dict(zip(zero, VOC_COLORMAP))
# VOC_COLORMAP相当于一个调色板
'''
VOC_CLASSES = [
'background', 'aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus',
'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike',
'person', 'potted plant', 'sheep', 'sofa', 'train', 'tv/monitor']
'''
def get_keys(value):
#获取图片各像素value对应的key值
p=0
i=0
for k,v in label2color_dict.items():
if v == value:
p = k
break
i += 1
if i > 15:
p = 0
return p
#glob.glob方法获取全部文件名
img_path = glob.glob("/home/cjk/Downloads/VOCdevkit/VOC2012/SegmentationClass/*.png")
img_path = sorted(img_path, key=lambda img_path:int(re.search(r'(\d+)',img_path).group()))
for k in range(0, 10000):
img = img_path[k]
img = plt.imread(img)*255.0
img = img[:,:,:3]
img_label = np.zeros((img.shape[0],img.shape[1]),np.uint8)
img_new_label = np.zeros((img.shape[0],img.shape[1]),np.uint8)
for i in range(img.shape[0]):
for j in range(img.shape[1]):
value = list(img[i,j,:])
img_label[i,j] = get_keys(value)
#import pdb
#pdb.set_trace()
img_new_label[i,j] = img_label[i,j]
label0 = Image.fromarray(np.uint8(img_new_label))
p = str(k)
scipy.misc.toimage(img_label).save("/home/cjk/Downloads/VOCdevkit/VOC2012/label_my/"+p+".jpg")
