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fuy34
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Description

[CVPR‘20] SpixelFCN: Superpixel Segmentation with Fully Convolutional Network

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SpixelFCN: Superpixel Segmentation with Fully Convolutional Network

This is is a PyTorch implementation of the superpixel segmentation network introduced in our CVPR-20 paper:

Superpixel Segmentation with Fully Convolutional Network

Fengting Yang, Qian Sun, Hailin Jin, and Zihan Zhou

Please contact Fengting Yang ([email protected]) if you have any question.

Prerequisites

The training code was mainly developed and tested with python 2.7, PyTorch 0.4.1, CUDA 9, and Ubuntu 16.04.

During test, we make use of the component connection method in SSN to enforce the connectivity in superpixels. The code has been included in

/third_paty/cython
. To compile it:
cd third_party/cython/
python setup.py install --user
cd ../..

Demo

The demo script

run_demo.py
provides the superpixels with grid size
16 x 16
using our pre-trained model (in
/pretrained_ckpt
). Please feel free to provide your own images by copying them into
/demo/inputs
, and run
python run_demo.py --data_dir=./demo/inputs --data_suffix=jpg --output=./demo 
The results will be generate in a new folder under
/demo
called
spixel_viz
.

Data preparation

To generate training and test dataset, please first download the data from the original BSDS500 dataset, and extract it to

. Then, run 
cd data_preprocessing
python pre_process_bsd500.py --dataset= --dump_root=
python pre_process_bsd500_ori_sz.py --dataset= --dump_root=
cd ..
The code will generate three folders under the
, named as 
/train
,
/val
, and
/test
, and three
.txt
files record the absolute path of the images, named as
train.txt
,
val.txt
, and
test.txt
.

Training

Once the data is prepared, we should be able to train the model by running the following command

python main.py --data= --savepath=

if we wish to continue a train process or fine-tune from a pre-trained model, we can run

python main.py --data= --savepath= --pretrained= 
The code will start from the recorded status, which includes the optimizer status and epoch number.

The training log can be viewed from the

tensorboard
session by running
tensorboard --logdir= --port=8888

If everything is set up properly, reasonable segmentation should be observed after 10 epochs.

Testing

We provide test code to generate: 1) superpixel visualization and 2) the

.csv
files for evaluation.

To test on BSDS500, run

python run_infer_bsds.py --data_dir= --output= --pretrained=

To test on NYUv2, please first extract our pre-processed dataset from

/nyu_test_set/nyu_preprocess_tst.tar.gz
to
 , or follow the intruction on the superpixel benchmark
 to generate the test dataset, and then run
python run_infer_nyu.py --data_dir= --output= --pretrained=

To test on other datasets, please first collect all the images into one folder

, and then convert them into the same 
format (e.g. 
.png
or
.jpg
) if necessary, and run
python run_demo.py --data_dir= --data_suffix= --output= --pretrained=
Superpixels with grid size
16 x 16
will be generated by default. To generate the superpixel with a different grid size, we simply need to resize the images into the approporate resolution before passing them through the code. Please refer to
run_infer_nyu.py
for the details.

Evaluation

We use the code from superpixel benchmark for superpixel evaluation. A detailed instruction is available in the repository, please

(1) download the code and build it accordingly;

(2) edit the variables

$SUPERPIXELS
,
IMG_PATH
and
GT_PATH
in
/eval_spixel/my_eval.sh
,

(3) run

cp /eval_spixel/my_eval.sh /examples/bash/
cd  /examples/
bash my_eval.sh
several files should be generated in the
map_csv
folders in the corresponding test outputs;

(4) run

cd eval_spixel
python copy_resCSV.py --src= --dst=
(5) open
/eval_spixel/plot_benchmark_curve.m
, set the
our1l_res_path
as
 and modify the 
num_list
according to the test setting. The default setting is for our BSDS500 test set.;

(6) run the

plot_benchmark_curve.m
, the
ASA Score
,
CO Score
, and
BR-BP curve
of our method should be shown on the screen. If you wish to compare our method with the others, you can first run the method and organize the data as we state above, and uncomment the code in the
plot_benchmark_curve.m
to generate a similar figure shown in our papers.

Acknowledgement

Our code is developed based on the training framework provided by FlowNetPytorch.

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