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57
DehazeNet/All_in_One.sh
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57
DehazeNet/All_in_One.sh
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#!/bin/bash
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# 原图像位置
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Dir_src_pics="./img/src"
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Dir_Trans_Refine="./img/Trans_Refine"
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Dir_Trans_Esti="./img/Trans_Esti"
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Dir_result="./img/result"
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Dir_ori_src_pics="/root/Dehaze/SRC_files/src_1280_720"
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mkdir -p $Dir_src_pics $Dir_Trans_Refine $Dir_Trans_Esti $Dir_result
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PS3='All in one choice : '
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applications=("Delete_src_pics" "Delete_generate_pics" "Copy_src_pics" "Run_program" "quit")
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select fav in "${applications[@]}"; do
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case $fav in
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# 删除原始文件选项
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"Delete_src_pics")
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# 删除src文件
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echo "Delete all src files in $Dir_src_pics"
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rm $Dir_src_pics/*
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;;
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# 删除生成文件选项
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"Delete_generate_pics")
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# 删除dark文件
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echo "Delete all src files in $Dir_Trans_Refine"
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rm $Dir_Trans_Refine/*
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# 删除result文件
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echo "Delete all src files in $Dir_result"
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rm $Dir_result/*
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# 删除trans文件
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echo "Delete all src files in $Dir_Trans_Esti"
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rm $Dir_Trans_Esti/*
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;;
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# 复制待处理文件选项
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"Copy_src_pics")
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# 删除src文件
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echo "Copy all src files in $Dir_ori_src_pics"
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ln -s $Dir_ori_src_pics/* $Dir_src_pics
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;;
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# 运行程序
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"Run_program")
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source ~/miniconda/bin/activate Dehaze_DCP
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python DehazeNet.py ./img
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;;
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# 退出选项
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"quit")
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echo "User requested exit"
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exit
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;;
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# 其他选项
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*) echo "invalid option $REPLY";;
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esac
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done
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173
DehazeNet/DehazeFcnTemplate.prototxt
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173
DehazeNet/DehazeFcnTemplate.prototxt
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name: "Dehaze_fullconv"
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input: "data"
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input_dim: 1
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input_dim: 3
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input_dim: {height_15}
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input_dim: {width_15}
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layer {{
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name: "conv1"
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type: "Convolution"
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bottom: "data"
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top: "conv1"
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convolution_param {{
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num_output: 20
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kernel_size: 5
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stride: 1
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pad: 0
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}}
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}}
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layer {{
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name: "relu1"
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type: "ReLU"
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bottom: "conv1"
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top: "conv1"
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}}
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layer {{
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name: "reshape1"
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type: "Reshape"
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bottom: "conv1"
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top: "reshape1"
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reshape_param {{
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shape {{
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dim: 0
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dim: 1
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dim: 20
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dim: -1
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}}
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}}
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}}
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layer {{
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name: "pool1"
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type: "Pooling"
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bottom: "reshape1"
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top: "pool1"
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pooling_param {{
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pool: MAX
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kernel_w: 1
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kernel_h: 5
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stride_w: 1
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stride_h: 5
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}}
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}}
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layer {{
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name: "reshape2"
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type: "Reshape"
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bottom: "pool1"
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top: "reshape2"
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reshape_param {{
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shape {{
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dim: 0
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dim: 4
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dim: {height_11}
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dim: {width_11}
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}}
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}}
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}}
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layer {{
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name: "conv2/1x1"
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type: "Convolution"
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bottom: "reshape2"
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top: "conv2/1x1"
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convolution_param {{
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num_output: 16
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kernel_size: 1
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stride: 1
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pad: 0
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}}
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}}
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layer {{
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name: "conv2/3x3"
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type: "Convolution"
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bottom: "reshape2"
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top: "conv2/3x3"
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convolution_param {{
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num_output: 16
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kernel_size: 3
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stride: 1
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pad: 1
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}}
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}}
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layer {{
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name: "conv2/5x5"
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type: "Convolution"
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bottom: "reshape2"
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top: "conv2/5x5"
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convolution_param {{
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num_output: 16
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kernel_size: 5
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stride: 1
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pad: 2
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}}
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}}
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layer {{
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name: "conv2/7x7"
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type: "Convolution"
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bottom: "reshape2"
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top: "conv2/7x7"
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convolution_param {{
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num_output: 16
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kernel_size: 7
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stride: 1
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pad: 3
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}}
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}}
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layer {{
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name: "conv2/output"
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type: "Concat"
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bottom: "conv2/1x1"
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bottom: "conv2/3x3"
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bottom: "conv2/5x5"
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bottom: "conv2/7x7"
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top: "conv2/output"
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concat_param
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{{
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axis: 1
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}}
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}}
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layer {{
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name: "relu2"
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type: "ReLU"
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bottom: "conv2/output"
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top: "conv2/output"
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}}
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layer {{
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name: "pool2"
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type: "Pooling"
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bottom: "conv2/output"
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top: "pool2"
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pooling_param {{
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pool: MAX
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kernel_size: 8
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stride: 1
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}}
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}}
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layer {{
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name: "ip1-conv"
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type: "Convolution"
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bottom: "pool2"
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top: "ip1-conv"
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convolution_param {{
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num_output: 1
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kernel_size: 5
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}}
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}}
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layer {{
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name: "drelu1"
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type: "ReLU"
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bottom: "ip1-conv"
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top: "ip1-conv"
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}}
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BIN
DehazeNet/DehazeNet.caffemodel
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BIN
DehazeNet/DehazeNet.caffemodel
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Binary file not shown.
256
DehazeNet/DehazeNet.prototxt
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256
DehazeNet/DehazeNet.prototxt
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@@ -0,0 +1,256 @@
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name: "Dehaze"
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input: "data"
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input_dim: 1
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input_dim: 3
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input_dim: 16
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input_dim: 16
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layer {
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name: "conv1"
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type: "Convolution"
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bottom: "data"
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top: "conv1"
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param {
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lr_mult: 1
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}
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param {
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lr_mult: 0.1
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}
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convolution_param {
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num_output: 20
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kernel_size: 5
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stride: 1
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pad: 0
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weight_filler {
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type: "gaussian"
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std: 0.001
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}
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bias_filler {
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type: "constant"
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value: 0
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}
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}
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}
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layer {
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name: "relu1"
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type: "ReLU"
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bottom: "conv1"
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top: "conv1"
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}
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layer {
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name: "reshape1"
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type: "Reshape"
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bottom: "conv1"
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top: "reshape1"
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reshape_param {
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shape {
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dim: 0
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dim: 1
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dim: 20
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dim: -1
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}
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}
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}
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layer {
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name: "pool1"
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type: "Pooling"
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bottom: "reshape1"
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top: "pool1"
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pooling_param {
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pool: MAX
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kernel_w: 1
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kernel_h: 5
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stride_w: 1
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stride_h: 5
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}
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}
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layer {
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name: "reshape2"
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type: "Reshape"
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bottom: "pool1"
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top: "reshape2"
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reshape_param {
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shape {
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dim: 0
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dim: 4
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dim: 12
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dim: 12
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}
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}
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}
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layer {
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name: "conv2/1x1"
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type: "Convolution"
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bottom: "reshape2"
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top: "conv2/1x1"
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param {
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lr_mult: 0.1
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}
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param {
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lr_mult: 0.1
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}
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convolution_param {
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num_output: 16
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kernel_size: 1
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stride: 1
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pad: 0
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weight_filler {
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type: "gaussian"
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std: 0.001
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}
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bias_filler {
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type: "constant"
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value: 0
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}
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}
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}
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layer {
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name: "conv2/3x3"
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type: "Convolution"
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bottom: "reshape2"
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top: "conv2/3x3"
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param {
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lr_mult: 0.1
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}
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param {
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lr_mult: 0.1
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}
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convolution_param {
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num_output: 16
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kernel_size: 3
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stride: 1
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pad: 1
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weight_filler {
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type: "gaussian"
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std: 0.001
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}
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bias_filler {
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type: "constant"
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value: 0
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}
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}
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}
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layer {
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name: "conv2/5x5"
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type: "Convolution"
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bottom: "reshape2"
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top: "conv2/5x5"
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param {
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lr_mult: 0.1
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}
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param {
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lr_mult: 0.1
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}
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convolution_param {
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num_output: 16
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kernel_size: 5
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stride: 1
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pad: 2
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weight_filler {
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type: "gaussian"
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std: 0.001
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}
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bias_filler {
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type: "constant"
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value: 0
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}
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}
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}
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layer {
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name: "conv2/7x7"
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type: "Convolution"
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bottom: "reshape2"
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top: "conv2/7x7"
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param {
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lr_mult: 0.1
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}
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param {
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lr_mult: 0.1
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}
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convolution_param {
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num_output: 16
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kernel_size: 7
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stride: 1
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pad: 3
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weight_filler {
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type: "gaussian"
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std: 0.001
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}
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bias_filler {
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type: "constant"
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value: 0
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}
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}
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}
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layer {
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name: "conv2/output"
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type: "Concat"
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bottom: "conv2/1x1"
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bottom: "conv2/3x3"
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bottom: "conv2/5x5"
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bottom: "conv2/7x7"
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top: "conv2/output"
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concat_param
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{
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axis: 1
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}
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}
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layer {
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name: "relu2"
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type: "ReLU"
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bottom: "conv2/output"
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top: "conv2/output"
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}
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layer {
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name: "pool2"
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type: "Pooling"
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bottom: "conv2/output"
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top: "pool2"
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pooling_param {
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pool: MAX
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kernel_size: 8
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stride: 1
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}
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}
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layer {
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name: "ip1"
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type: "InnerProduct"
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bottom: "pool2"
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top: "ip1"
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param {
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lr_mult: 1
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}
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param {
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lr_mult: 2
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}
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inner_product_param {
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num_output: 1
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weight_filler {
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type: "xavier"
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}
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bias_filler {
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type: "constant"
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}
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}
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}
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layer {
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name: "drelu1"
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type: "ReLU"
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bottom: "ip1"
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top: "ip1"
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}
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208
DehazeNet/DehazeNet.py
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208
DehazeNet/DehazeNet.py
Normal file
@@ -0,0 +1,208 @@
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import sys,os
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import caffe
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import numpy as np
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import cv2
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import math
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def EditFcnProto(templateFile, height, width):
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with open(templateFile, 'r') as ft:
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template = ft.read()
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outFile = 'DehazeNetFcn.prototxt'
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with open(outFile, 'w') as fd:
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fd.write(template.format(height_15=height+15, width_15=width+15,
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height_11=height+11, width_11=width+11))
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def TransmissionEstimate(im_path, height, width):
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caffe.set_mode_cpu()
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# Define a safe tile size to prevent INT_MAX overflow (approx 512x512 is safe)
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SAFE_TILE_SIZE = 512
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# Use tiling if the image is larger than the safe size
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if height > SAFE_TILE_SIZE or width > SAFE_TILE_SIZE:
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print(f"Image size ({width}x{height}) is large. Using tiling to avoid memory overflow...")
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# Determine effective tile size (cannot be larger than image)
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tile_h = min(height, SAFE_TILE_SIZE)
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tile_w = min(width, SAFE_TILE_SIZE)
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# Generate prototxt for the TILE size, not the full image size
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EditFcnProto('DehazeFcnTemplate.prototxt', tile_h, tile_w)
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# Load networks
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net = caffe.Net('DehazeNet.prototxt', 'DehazeNet.caffemodel', caffe.TEST)
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net_full_conv = caffe.Net('DehazeNetFcn.prototxt', 'DehazeNet.caffemodel', caffe.TEST)
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net_full_conv.params['ip1-conv'][0].data.flat = net.params['ip1'][0].data.flat
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net_full_conv.params['ip1-conv'][1].data[...] = net.params['ip1'][1].data
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|
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# Load and pad image
|
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im = caffe.io.load_image(im_path)
|
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npad = ((7,8), (7,8), (0,0))
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im_padded = np.pad(im, npad, 'symmetric')
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transmission = np.zeros((height, width))
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# Setup transformer for the tile size
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transformers = caffe.io.Transformer({'data': net_full_conv.blobs['data'].data.shape})
|
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transformers.set_transpose('data', (2,0,1))
|
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transformers.set_channel_swap('data', (2,1,0))
|
||||
|
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# Process in tiles
|
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for h in range(0, height, tile_h):
|
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for w in range(0, width, tile_w):
|
||||
# Calculate start/end to handle edges/overlap
|
||||
# If we are at the end, shift back to ensure we feed a full tile
|
||||
h_start = min(h, height - tile_h)
|
||||
w_start = min(w, width - tile_w)
|
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|
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# Extract patch from PADDED image
|
||||
# Network expects input size of (Tile + 15), so we slice accordingly
|
||||
patch = im_padded[h_start : h_start + tile_h + 15, w_start : w_start + tile_w + 15, :]
|
||||
|
||||
# Forward pass
|
||||
out = net_full_conv.forward_all(data=np.array([transformers.preprocess('data', patch-0.2)]))
|
||||
|
||||
# Reshape output
|
||||
block_trans = np.reshape(out['ip1-conv'], (tile_h, tile_w))
|
||||
|
||||
# Assign to result buffer
|
||||
transmission[h_start : h_start + tile_h, w_start : w_start + tile_w] = block_trans
|
||||
|
||||
return transmission
|
||||
|
||||
else:
|
||||
# Original logic for small images
|
||||
EditFcnProto('DehazeFcnTemplate.prototxt', height, width)
|
||||
net = caffe.Net('DehazeNet.prototxt', 'DehazeNet.caffemodel', caffe.TEST)
|
||||
net_full_conv = caffe.Net('DehazeNetFcn.prototxt', 'DehazeNet.caffemodel', caffe.TEST)
|
||||
net_full_conv.params['ip1-conv'][0].data.flat = net.params['ip1'][0].data.flat
|
||||
net_full_conv.params['ip1-conv'][1].data[...] = net.params['ip1'][1].data
|
||||
im = caffe.io.load_image(im_path)
|
||||
npad = ((7,8), (7,8), (0,0))
|
||||
im = np.pad(im, npad, 'symmetric')
|
||||
transformers = caffe.io.Transformer({'data': net_full_conv.blobs['data'].data.shape})
|
||||
transformers.set_transpose('data', (2,0,1))
|
||||
transformers.set_channel_swap('data', (2,1,0))
|
||||
out = net_full_conv.forward_all(data=np.array([transformers.preprocess('data', im-0.2)]))
|
||||
transmission = np.reshape(out['ip1-conv'], (height,width))
|
||||
return transmission
|
||||
|
||||
def DarkChannel(im,sz):
|
||||
b,g,r = cv2.split(im)
|
||||
dc = cv2.min(cv2.min(r,g),b)
|
||||
kernel = cv2.getStructuringElement(cv2.MORPH_RECT,(sz,sz))
|
||||
dark = cv2.erode(dc,kernel)
|
||||
return dark
|
||||
|
||||
def AtmLight(im,dark):
|
||||
[h,w] = im.shape[:2]
|
||||
imsz = h*w
|
||||
numpx = int(max(math.floor(imsz/1000),1))
|
||||
darkvec = dark.reshape(imsz,1)
|
||||
imvec = im.reshape(imsz,3)
|
||||
indices = darkvec.argsort()
|
||||
indices = indices[imsz-numpx::]
|
||||
atmsum = np.zeros([1,3])
|
||||
for ind in range(1,numpx):
|
||||
atmsum = atmsum + imvec[indices[ind]]
|
||||
A = atmsum / numpx
|
||||
return A
|
||||
|
||||
def Guidedfilter(im,p,r,eps):
|
||||
mean_I = cv2.boxFilter(im,cv2.CV_64F,(r,r))
|
||||
mean_p = cv2.boxFilter(p, cv2.CV_64F,(r,r))
|
||||
mean_Ip = cv2.boxFilter(im*p,cv2.CV_64F,(r,r))
|
||||
cov_Ip = mean_Ip - mean_I*mean_p
|
||||
mean_II = cv2.boxFilter(im*im,cv2.CV_64F,(r,r))
|
||||
var_I = mean_II - mean_I*mean_I
|
||||
a = cov_Ip/(var_I + eps)
|
||||
b = mean_p - a*mean_I
|
||||
mean_a = cv2.boxFilter(a,cv2.CV_64F,(r,r))
|
||||
mean_b = cv2.boxFilter(b,cv2.CV_64F,(r,r))
|
||||
q = mean_a*im + mean_b
|
||||
return q
|
||||
|
||||
def TransmissionRefine(im,et):
|
||||
gray = cv2.cvtColor(im,cv2.COLOR_BGR2GRAY)
|
||||
gray = np.float64(gray)/255
|
||||
r = 60
|
||||
eps = 0.0001
|
||||
t = Guidedfilter(gray,et,r,eps)
|
||||
return t
|
||||
|
||||
def Recover(im,t,A,tx = 0.1):
|
||||
res = np.empty(im.shape,im.dtype)
|
||||
t = cv2.max(t,tx)
|
||||
for ind in range(0,3):
|
||||
res[:,:,ind] = (im[:,:,ind]-A[0,ind])/t + A[0,ind]
|
||||
return res
|
||||
|
||||
def getFileList(dir,Filelist, ext=None):
|
||||
"""
|
||||
获取文件夹及其子文件夹中文件列表
|
||||
输入 dir：文件夹根目录
|
||||
输入 ext: 扩展名
|
||||
返回： 文件路径列表
|
||||
"""
|
||||
newDir = dir
|
||||
if os.path.isfile(dir):
|
||||
if ext is None:
|
||||
Filelist.append(dir)
|
||||
else:
|
||||
if ext in dir[-3:]:
|
||||
Filelist.append(dir)
|
||||
|
||||
elif os.path.isdir(dir):
|
||||
for s in os.listdir(dir):
|
||||
newDir=os.path.join(dir,s)
|
||||
getFileList(newDir, Filelist, ext)
|
||||
|
||||
return Filelist
|
||||
|
||||
if __name__ == '__main__':
|
||||
if not len(sys.argv) == 2:
|
||||
print ('Usage: python DeHazeNet.py haze_img_path')
|
||||
exit()
|
||||
else:
|
||||
im_path = sys.argv[1]
|
||||
|
||||
# 检索文件
|
||||
src_img_folder = os.path.join(im_path, 'src')
|
||||
imglist = getFileList(src_img_folder, [], '')
|
||||
print('本次执行检索到 '+str(len(imglist))+' 张图像\n')
|
||||
for img_path in imglist:
|
||||
imgname= os.path.splitext(os.path.basename(img_path))[0]
|
||||
src = cv2.imread(img_path)
|
||||
|
||||
height = src.shape[0]
|
||||
width = src.shape[1]
|
||||
|
||||
# Note: EditFcnProto is also called inside TransmissionEstimate if tiling is used
|
||||
# We call it here for initialization but it may be overwritten.
|
||||
templateFile = 'DehazeFcnTemplate.prototxt'
|
||||
EditFcnProto(templateFile, height, width)
|
||||
print("-"*5, ' 完成EditFcnProto ',"-"*5)
|
||||
|
||||
I = src/255.0
|
||||
dark = DarkChannel(I,15)
|
||||
A = AtmLight(I,dark)
|
||||
|
||||
te = TransmissionEstimate(img_path, height, width)
|
||||
t = TransmissionRefine(src,te)
|
||||
J = Recover(I,t,A,0.1)
|
||||
|
||||
print("Finsh All the operation")
|
||||
Trans_Esti_imgdir = os.path.join(im_path, 'Trans_Esti/')
|
||||
if not os.path.exists(Trans_Esti_imgdir): os.makedirs(Trans_Esti_imgdir)
|
||||
print(Trans_Esti_imgdir + imgname + "_Trans_Esti.png")
|
||||
cv2.imwrite(Trans_Esti_imgdir + imgname + "_Trans_Esti.png",te*255);
|
||||
|
||||
Trans_Refine_imgdir = os.path.join(im_path, 'Trans_Refine/')
|
||||
if not os.path.exists(Trans_Refine_imgdir): os.makedirs(Trans_Refine_imgdir)
|
||||
print(Trans_Refine_imgdir + imgname + "_Trans_Refine.png")
|
||||
cv2.imwrite(Trans_Refine_imgdir + imgname + "_Trans_Refine.png",t*255);
|
||||
|
||||
result_imgdir = os.path.join(im_path, 'result/')
|
||||
if not os.path.exists(result_imgdir): os.makedirs(result_imgdir)
|
||||
print(result_imgdir + imgname + "_result.png")
|
||||
cv2.imwrite(result_imgdir + imgname + "_result.png",J*255);
|
||||
31
DehazeNet/readme.md
Normal file
31
DehazeNet/readme.md
Normal file
@@ -0,0 +1,31 @@
|
||||
## Reimplement
|
||||
## *DehazeNet: An End-to-End System for Single Image Haze Removal*
|
||||
Bolun Cai, Xiangmin Xu, Kui Jia, Chunmei Qing, Dacheng Tao
|
||||
|
||||
## Requirement
|
||||
> * caffe
|
||||
> * opencv2
|
||||
|
||||
## Usage:
|
||||
simply type
|
||||
```shell
|
||||
python DehazeNet.py image_path
|
||||
```
|
||||
|
||||
## Demo:
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
## Site:
|
||||
@article{cai2016dehazenet,
|
||||
title={Dehazenet: An end-to-end system for single image haze removal},
|
||||
author={Cai, Bolun and Xu, Xiangmin and Jia, Kui and Qing, Chunmei and Tao, Dacheng},
|
||||
journal={IEEE Transactions on Image Processing},
|
||||
volume={25},
|
||||
number={11},
|
||||
pages={5187--5198},
|
||||
year={2016},
|
||||
publisher={IEEE}
|
||||
}
|
||||
Reference in New Issue
Block a user