VOC2012 分割数据 转 lmdb 格式 python 代码

参考 caffe 将三通道或四通道图片转换为lmdb格式，将标签（单通道灰度图）转换为lmdb格式 http://blog.csdn.net/c_qianbo/article/details/53375476

代码语言：javascript复制

import numpy as np
import sys
from PIL import Image
import lmdb
import random
import os

sys.path.append('/home/guest/caffe/python/')

import caffe

if __name__ == '__main__' :
    train_list_file = '/home/guest/caffe/examples
    /VOC2012ext/VOCdevkit/VOC2012/ImageSets/Segmentation/val.txt'
    train_images_root = '/home/guest/caffe/examples
    /VOC2012ext/VOCdevkit/VOC2012/JPEGImages/'

    f = open(train_list_file, 'r')
    trainlist = f.readlines()
    f.close()

    random.shuffle(trainlist)

    # creating images lmdb
    in_db = lmdb.open('/home/guest/caffe/VOC2012ext_val_img_lmdb',
     map_size=int(1e12))
    with in_db.begin(write=True) as in_txn :
        for in_idx, in_ in enumerate(trainlist) :
            fid = in_.strip() '.jpg'
            fn = os.path.join(train_images_root, fid)
            im = np.array(Image.open(fn))
            Dtype = im.dtype


            im = im[:,:,::-1]
            im = Image.fromarray(im)  
            im = np.array(im, Dtype)  
            im = im.transpose((2, 0, 1)) 
            im_dat = caffe.io.array_to_datum(im)
            in_txn.put('{:0>10d}'.format(in_idx), im_dat.SerializeToString())
    in_db.close()


 # creating label lmdb
    in_db = lmdb.open('/home/guest/caffe/VOC2012ext_val_label_lmdb',
     map_size=int(1e12))
    train_images_root = '/home/guest/caffe/examples
    /VOC2012ext/VOCdevkit/VOC2012/SegmentationClass/'
    with in_db.begin(write=True) as in_txn :
        for in_idx, in_ in enumerate(trainlist) :
            fid = in_.strip() '.png'
            fn = os.path.join(train_images_root, fid)
        Dtype = 'uint8'  
            L = np.array(Image.open(fn), Dtype)  
            Limg = Image.fromarray(L)  
            L = np.array(Limg,Dtype)  
            L = L.reshape(L.shape[0],L.shape[1],1)  
            L = L.transpose((2,0,1))  
            L_dat = caffe.io.array_to_datum(L)  
            in_txn.put('{:0>10d}'.format(in_idx),L_dat.SerializeToString())  
    in_db.close()

caffe

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