Code for "Reasoning the Occlusion Relationship with Double-correlation and Cascade-refined Network"
The Data Preparation and Evaluation are following Guoxia Wang with his DOOBNet.
You may download the dataset original images from PASCAL VOC 2010 and annotations from here. Then you should copy or move JPEGImages folder in PASCAL VOC 2010 and Data folder and val_doc_2010.txt in PIOD to data/PIOD/. You will have the following directory structure:
PIOD
|_ Data
| |_ <id-1>.mat
| |_ ...
| |_ <id-n>.mat
|_ JPEGImages
| |_ <id-1>.jpg
| |_ ...
| |_ <id-n>.jpg
|_ val_doc_2010.txt
Now, you can use data convert tool to augment and generate HDF5 format data for DFNet.
mkdir data/PIOD/Augmentation
python doobscripts/doobnet_mat2hdf5_edge_ori.py \
--dataset PIOD \
--label-dir data/PIOD/Data \
--img-dir data/PIOD/JPEGImages \
--piod-val-list-file data/PIOD/val_doc_2010.txt \
--output-dir data/PIOD/Augmentation
For BSDS ownership dataset, you may download the dataset original images from BSDS300 and annotations from here. Then you should copy or move BSDS300 folder in BSDS300-images and trainfg and testfg folder in BSDS_theta to data/BSDSownership/. And you will have the following directory structure:
BSDSownership
|_ trainfg
| |_ <id-1>.mat
| |_ ...
| |_ <id-n>.mat
|_ testfg
| |_ <id-1>.mat
| |_ ...
| |_ <id-n>.mat
|_ BSDS300
| |_ images
| |_ train
| |_ <id-1>.jpg
| |_ ...
| |_ <id-n>.jpg
| |_ ...
| |_ ...
Note that BSDS ownership's test set are split from 200 train images (100 for train, 100 for test). More information you can check ids in trainfg and testfg folder and ids in BSDS300/images/train folder, or refer to here
Run the following code for BSDS ownership dataset.
mkdir data/BSDSownership/Augmentation
python doobscripts/doobnet_mat2hdf5_edge_ori.py \
--dataset BSDSownership \
--label-dir data/BSDSownership/trainfg \
--img-dir data/BSDSownership/BSDS300/images/train \
--bsdsownership-testfg data/BSDSownership/testfg \
--output-dir data/BSDSownership/Augmentation
Firstly, you need to download the Res50 weight file from Res50 and save resnet50.caffemodel to the folder $DFNET_ROOT/models/resnet/.
For training DCNet on PIOD training dataset, you can run:
cd $DFNET_ROOT/examples/DCNet
python train.py
bash train.sh
When training completed, you need to modify the save model path model = 'snapshot/dcnet_piod_iter_30000.caffemodel' in eval.py and then run python eval_gcn_dcnet.py to get the results on PIOD testing dataset. For comparation, you can also download our trained model from here. (code: rlux). The testing results are available at here. (code: 8o2c).
For training DCNet on BSDS ownership, you can refer the manner as same as PIOD dataset above. The training model is available at here. (code: akan). The testing results are available at here. (code: ab0e).
Here we provide the PIOD and the BSDS ownership dataset's evaluation and visualization code in doobscripts folder.
Note that you need to config the necessary paths or variables. More information please refers to doobscripts/README.md.
To run the evaluation:
run doobscripts/evaluation/EvaluateOcc.m
Tab.1 Comparisons on the PIOD dataset with the state-of-the-art methods.
| Method | ODS-E | OIS-E | AP-E | ODS-O | OIS-O | AP-O |
|---|---|---|---|---|---|---|
| SRF-OCC | .345 | .369 | .207 | .268 | .286 | .152 |
| DOC-HED | .509 | .532 | .468 | .460 | .479 | .405 |
| DOC-DMLFOV | .669 | .684 | .677 | .601 | .611 | .585 |
| DOOBNet | .736 | .746 | .723 | .702 | .712 | .683 |
| OFNet | .751 | .762 | .773 | .718 | .728 | .729 |
| Net | .774 | .789 | .789 | .741 | .753 | .760 |
Tab.2 Comparisons on the BSDS ownership dataset with the state-of-the-art methods.
| Method | ODS-E | OIS-E | AP-E | ODS-O | OIS-O | AP-O |
|---|---|---|---|---|---|---|
| SRF-OCC | .511 | .544 | .442 | .419 | .448 | .337 |
| DOC-HED | .658 | .685 | .602 | .522 | .545 | .428 |
| DOC-DMLFOV | .579 | .609 | .519 | .463 | .491 | .369 |
| DOOBNet | .647 | .668 | .539 | .555 | .570 | .440 |
| OFNet | .662 | .689 | .585 | .583 | .607 | .501 |
| DCNet | .666 | .692 | .609 | .597 | .607 | .514 |