前提：ubuntu+tensorflow-gpu+python3.6
各种环境提前配好
1.下载工程源码
网址：https://github.com/tensorflow/models
下载时会遇到速度过慢或中间因为网络错误停止，可以换移动网络或者用迅雷下载。
2.测试环境
先添加slim路径，每次打开terminal都要加载路径
# From tensorflow/models/research/
export PYTHONPATH=$PYTHONPATH:`pwd`:`pwd`/slim
运行测试1
# From tensorflow/models/research/
python deeplab/model_test.py
测试2
# From tensorflow/models/research/deeplab
sh local_test.sh
3.处理数据标签
处理标签为单通道，运行下面代码即可
import numpy as np
from PIL import Image
from keras.preprocessing.image import load_img, img_to_array
import os classes = ['background', 'aeroplane', 'bicycle', 'bird', 'boat',
'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'dining table',
'dog', 'horse', 'motorbike', 'person', 'potted plant',
'sheep', 'sofa', 'train', 'tv/monitor'] colormap = [[, , ], [, , ], [, , ], [, , ], [, , ],
[, , ], [, , ], [, , ], [, , ], [, , ],
[, , ], [, , ], [, , ], [, , ],
[, , ], [, , ], [, , ], [, , ],
[, , ], [, , ], [, , ]] # 利用下面的代码，将标注的图片转换为单通道的label图像
cm2lbl = np.zeros(**)
for i, cm in enumerate(colormap):
cm2lbl[(cm[]*+cm[])*+cm[]] = i def image2label(im):
# 输入为标记图像的矩阵，输出为单通道映射的label图像
data = im.astype('int32')
idx = (data[:, :, ]*+data[:, :, ])*+data[:, :, ]
return np.array(cm2lbl[idx]) def change_label(label_url, label_name): label_img = load_img(label_url)
label_img = img_to_array(label_img)
label_img = image2label(label_img) # 将图片映射为单通道数据
print(np.max(label_img)) label_single = Image.fromarray(label_img)
label_single = label_single.convert('L') save_path = './datasets/VOC2012/Label'
save_path = os.path.join(save_path, label_name) # 确定保存路径及名称
label_single.save(save_path) val_file_path = './datasets/VOC2012/ImageSets/trainval.txt' # 文件名存放路径
label_file_path = './datasets/VOC2012/SegmentationClass' # 原label存放路径 with open(val_file_path, 'r') as f:
file_names = f.readlines()
count =
for name in file_names:
count +=
name = name.strip('\n') # 去掉换行符
label_name = name + '.png' # label文件名
label_url = os.path.join(label_file_path, label_name)
print('这是第 %s 张' % count)
print(label_url)
change_label(label_url, label_name)