利用python生成txt文件后，就可以利用caffe自带工具将图片转换为lmdb格式，其代码如下
#!/usr/bin/env sh # Create the imagenet lmdb inputs # N.B. set the path to the imagenet train + val data dirs set -e EXAMPLE=/home/yh/caffe/examples/H-W DATA=/home/yh/caffe/examples/H-W TOOLS=/home/yh/caffe/build/tools TRAIN_DATA_ROOT=/home/yh/caffe/examples/H-W/train/ VAL_DATA_ROOT=/home/yh/caffe/examples/H-W/val/ # Set RESIZE=true to resize the images to 256x256. Leave as false if images have # already been resized using another tool. RESIZE=true if $RESIZE; then RESIZE_HEIGHT=256 RESIZE_WIDTH=256 else RESIZE_HEIGHT=0 RESIZE_WIDTH=0 fi if [ ! -d "$TRAIN_DATA_ROOT" ]; then echo "Error: TRAIN_DATA_ROOT is not a path to a directory: $TRAIN_DATA_ROOT" echo "Set the TRAIN_DATA_ROOT variable in create_imagenet.sh to the path" \ "where the ImageNet training data is stored." exit 1 fi if [ ! -d "$VAL_DATA_ROOT" ]; then echo "Error: VAL_DATA_ROOT is not a path to a directory: $VAL_DATA_ROOT" echo "Set the VAL_DATA_ROOT variable in create_imagenet.sh to the path" \ "where the ImageNet validation data is stored." exit 1 fi echo "Creating train lmdb..." GLOG_logtostderr=1 $TOOLS/convert_imageset \ --resize_height=$RESIZE_HEIGHT \ --resize_width=$RESIZE_WIDTH \ --shuffle \ $TRAIN_DATA_ROOT \ $DATA/trainlist.txt \ $EXAMPLE/hw_train_lmdb echo "Creating val lmdb..." GLOG_logtostderr=1 $TOOLS/convert_imageset \ --resize_height=$RESIZE_HEIGHT \ --resize_width=$RESIZE_WIDTH \ --shuffle \ $VAL_DATA_ROOT \ $DATA/val.txt \ $EXAMPLE/hw_val_lmdb echo "Done."