/DeepGuidedFilter

Official Implementation for Deep Guided Filter, CVPR 2018

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Fast End-to-End Trainable Guided Filter

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Official implementation of Fast End-to-End Trainable Guided Filter.
Faster, Better and Lighter for image processing and dense prediction.

Overview

DeepGuidedFilter is the author's implementation of the deep learning building block for joint upsampling described in:

Fast End-to-End Trainable Guided Filter
Huikai Wu, Shuai Zheng, Junge Zhang, Kaiqi Huang
CVPR 2018

Given a reference image pair in high-resolution and low-resolution, our algorithm generates high-resolution target from the low-resolution input. Through joint training with CNNs, our algorithm achieves the state-of-the-art performance while runs 10-100 times faster.

Contact: Hui-Kai Wu (huikaiwu@icloud.com)

Try it on an image!

Prepare Environment

  1. Download source code from GitHub. 
    git clone https://github.com/wuhuikai/DeepGuidedFilter

cd DeepGuidedFilter && git checkout release
  2. Install dependencies (PyTorch version). 
    conda install opencv
conda install pytorch=0.2.0 cuda80 -c soumith

pip install -r requirements.txt
  3. (Optional) Install dependencies for MonoDepth (Tensorflow version). 
    cd ComputerVision/MonoDepth

pip install -r requirements.txt

Ready to GO !

Image Processing

cd ImageProcessing/DeepGuidedFilteringNetwork

python predict.py  --task auto_ps \
                   --img_path ../../images/auto_ps.jpg \
                   --save_folder . \
                   --model deep_guided_filter_advanced \
                   --low_size 64 \
                   --gpu 0

See Here or python predict.py -h for more details.

Semantic Segmentation with Deeplab-Resnet

  1. Enter the directory. 
    cd ComputerVision/Deeplab-Resnet
  2. Download the pretrained model [Google Drive|BaiduYunPan].
  3. Run it now ! 
    python predict_dgf.py --img_path ../../images/segmentation.jpg --snapshots [MODEL_PATH]

Note:

  1. Result is in ../../images.
  2. Run python predict_dgf.py -h for more details.

Saliency Detection with DSS

  1. Enter the directory. 
    cd ComputerVision/Saliency_DSS
  2. Download the pretrained model [Google Drive|BaiduYunPan].
  3. Try it now ! 
    python predict.py --im_path ../../images/saliency.jpg \
                  --netG [MODEL_PATH] \
                  --thres 161 \
                  --dgf --nn_dgf \
                  --post_sigmoid --cuda

Note:

  1. Result is in ../../images.
  2. See Here or python predict.py -h for more details.

Monocular Depth Estimation (TensorFlow version)

  1. Enter the directory. 
    cd ComputerVision/MonoDepth
  2. Download and Unzip Pretrained Model [Google Drive|BaiduYunPan]
  3. Run on an Image ! 
    python monodepth_simple.py --image_path ../../images/depth.jpg --checkpoint_path [MODEL_PATH] --guided_filter

Note:

  1. Result is in ../../images.
  2. See Here or python monodepth_simple.py -h for more details.

Guided Filtering Layer

Install Released Version

  • PyTorch Version 
    pip install guided-filter-pytorch
  • Tensorflow Version 
    pip install guided-filter-tf

Usage

  • PyTorch Version 
    from guided_filter_pytorch.guided_filter import FastGuidedFilter

hr_y = FastGuidedFilter(r, eps)(lr_x, lr_y, hr_x)
    from guided_filter_pytorch.guided_filter import GuidedFilter

hr_y = GuidedFilter(r, eps)(hr_x, init_hr_y)
  • Tensorflow Version 
    from guided_filter_tf.guided_filter import fast_guided_filter

hr_y = fast_guided_filter(lr_x, lr_y, hr_x, r, eps, nhwc)
    from guided_filter_tf.guided_filter import guided_filter

hr_y = guided_filter(hr_x, init_hr_y, r, eps, nhwc)

Training from scratch

Prepare Training Environment

git checkout master

conda install opencv
conda install pytorch=0.2.0 cuda80 -c soumith
    
pip install -r requirements.txt

# (Optional) For MonoDepth (TF Version).
pip install -r ComputerVision/MonoDepth/requirements.txt

Start to Train

Citation

@inproceedings{wu2017fast,
  title     = {Fast End-to-End Trainable Guided Filter},
  author    = {Wu, Huikai and Zheng, Shuai and Zhang, Junge and Huang, Kaiqi},
  booktitle = {CVPR},
  year = {2018}
}