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Fast End-to-End Trainable Guided Filter

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

Overview

DeepGuidedFilter is the author's implementation of:

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

With our method, FCNs can run 10-100 times faster w/o performance drop.

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

Get Started

Prepare Environment [Python>=3.6]

Download source code from GitHub.

git clone https://github.com/wuhuikai/DeepGuidedFilter

cd DeepGuidedFilter && git checkout release

Install dependencies.

conda install opencv=3.4
conda install pytorch=1.1 torchvision=0.2 cudatoolkit=9.0 -c pytorch

pip install -r requirements.txt

(Optional) Install dependencies for MonoDepth.

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

Enter the directory.
```
cd ComputerVision/Deeplab-Resnet
```
Download the pretrained model [Google Drive|BaiduYunPan].

Run it now !

python predict_dgf.py --img_path ../../images/segmentation.jpg --snapshots [MODEL_PATH]

Note:

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

Saliency Detection with DSS

Enter the directory.
```
cd ComputerVision/Saliency_DSS
```
Download the pretrained model [Google Drive|BaiduYunPan].

Try it now !

python predict.py --im_path ../../images/saliency.jpg \
                  --netG [MODEL_PATH] \
                  --thres 161 \
                  --dgf --nn_dgf \
                  --post_sigmoid --cuda

Note:

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

Monocular Depth Estimation

Enter the directory.
```
cd ComputerVision/MonoDepth
```
Download and Unzip Pretrained Model [Google Drive|BaiduYunPan]

Run on an Image

python monodepth_simple.py --image_path ../../images/depth.jpg --checkpoint_path [MODEL_PATH] --guided_filter

Note:

Result is in ../../images.
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)

from guided_filter_pytorch.guided_filter import ConvGuidedFilter

hr_y = ConvGuidedFilter(r, norm)(lr_x, lr_y, hr_x)

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=3.4
conda install pytorch=1.1 torchvision=0.2 cudatoolkit=9.0 -c pytorch

pip uninstall Pillow
pip install -r requirements.txt

# (Optional) For MonoDepth
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}
}