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 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 ([email protected])
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
- Image Processing
- Semantic Segmentation with Deeplab-Resnet
- Saliency Detection with DSS
- Monocular Depth Estimation
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}
}