Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/bowenc0221/boundary-iou-api
Boundary IoU API (Beta version)
https://github.com/bowenc0221/boundary-iou-api
Last synced: 14 days ago
JSON representation
Boundary IoU API (Beta version)
- Host: GitHub
- URL: https://github.com/bowenc0221/boundary-iou-api
- Owner: bowenc0221
- License: other
- Created: 2021-03-29T19:47:22.000Z (over 3 years ago)
- Default Branch: master
- Last Pushed: 2021-04-05T00:08:06.000Z (over 3 years ago)
- Last Synced: 2023-11-07T16:55:49.120Z (about 1 year ago)
- Language: Python
- Homepage:
- Size: 48.8 KB
- Stars: 195
- Watchers: 8
- Forks: 22
- Open Issues: 7
-
Metadata Files:
- Readme: README.md
- License: license.txt
Awesome Lists containing this project
README
# Boundary IoU API (Beta version)
Bowen Cheng, Ross Girshick, Piotr Dollár, Alexander C. Berg, Alexander Kirillov
[[`arXiv`](https://arxiv.org/abs/2103.16562)] [[`Project`](https://bowenc0221.github.io/boundary-iou/)] [[`BibTeX`](#CitingBoundaryIoU)]
This API is an experimental version of Boundary IoU for 5 datasets:
- [COCO instance segmentation](https://cocodataset.org/#detection-eval)
- [LVIS instance segmentation](https://www.lvisdataset.org/)
- [Cityscapes instance segmentation](https://www.cityscapes-dataset.com/benchmarks/)
- [COCO panoptic segmentation](https://cocodataset.org/#panoptic-eval)
- [Cityscapes panoptic segmentation](https://www.cityscapes-dataset.com/benchmarks/)
To install Boundary IoU API, run:
```bash
pip install git+https://github.com/bowenc0221/boundary-iou-api.git
```
or
```bash
git clone [email protected]:bowenc0221/boundary-iou-api.git
cd boundary_iou_api
pip install -e .
```
OpenCV is required to use Boundary IoU API.
## Summary of usage
We provide two ways to use this api, you can either replace imports with our api or do offline evaluation.
### Replacing imports
Our Boundary IoU API supports both evaluation with Mask IoU and Boundary IoU with the same interface as original ones.
Thus, you only need to change the import, without worried about breaking your existing code.
1. COCO instance segmentation
replace
```python
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval
```
with
```python
from boundary_iou.coco_instance_api.coco import COCO
from boundary_iou.coco_instance_api.cocoeval import COCOeval
```
and set
```python
COCOeval(..., iouType="boundary")
```
2. LVIS instance segmentation
replace
```python
from lvis import LVISEval
```
with
```python
from boundary_iou.lvis_instance_api.eval import LVISEval
```
and set
```python
LVISEval(..., iou_type="boundary")
```
3. Cityscapes instance segmentation
replace
```python
import cityscapesscripts.evaluation.evalInstanceLevelSemanticLabeling as cityscapes_eval
```
with
```python
import boundary_iou.cityscapes_instance_api.evalInstanceLevelSemanticLabeling as cityscapes_eval
```
and set
```python
cityscapes_eval.args.iou_type = "boundary"
```
4. COCO panoptic segmentation
replace
```python
from panopticapi.evaluation import pq_compute
```
with
```python
from boundary_iou.coco_panoptic_api.evaluation import pq_compute
```
and set
```python
pq_compute(..., iou_type="boundary")
```
5. Cityscapes panoptic segmentation
replace
```python
from cityscapesscripts.evaluation.evalPanopticSemanticLabeling as evaluatePanoptic
```
with
```python
from boundary_iou.cityscapes_panoptic_api.evalPanopticSemanticLabeling import evaluatePanoptic
```
and set
```python
evaluatePanoptic(..., iou_type="boundary")
```
### Offline evaluation
We also provide evaluation code that can evaluates your prediction files for each dataset.
1. COCO instance segmentation
```bash
python ./tools/coco_instance_evaluation.py \
--gt-json-file COCO_GT_JSON \
--dt-json-file COCO_DT_JSON \
--iou-type boundary
```
2. LVIS instance segmentation
```bash
python ./tools/lvis_instance_evaluation.py \
--gt-json-file LVIS_GT_JSON \
--dt-json-file LVIS_DT_JSON \
--iou-type boundary
```
3. Cityscapes instance segmentation
```bash
python ./tools/cityscapes_instance_evaluation.py \
--gt_dir GT_DIR \
--result_dir RESULT_DIR \
--iou-type boundary
```
4. COCO panoptic segmentation
```bash
python ./tools/coco_panoptic_evaluation.py \
--gt_json_file PANOPTIC_GT_JSON \
--gt_folder PANOPTIC_GT_DIR \
--pred_json_file PANOPTIC_PRED_JSON \
--pred_folder PANOPTIC_PRED_DIR \
--iou-type boundary
```
5. Cityscapes panoptic segmentation
```bash
python ./tools/cityscapes_panoptic_evaluation.py \
--gt_json_file PANOPTIC_GT_JSON \
--gt_folder PANOPTIC_GT_DIR \
--pred_json_file PANOPTIC_PRED_JSON \
--pred_folder PANOPTIC_PRED_DIR \
--iou-type boundary
```
## Citing Boundary IoU
If you find Boundary IoU helpful in your research or wish to refer to the referenced results, please use the following BibTeX entry.
```BibTeX
@inproceedings{cheng2021boundary,
title={Boundary {IoU}: Improving Object-Centric Image Segmentation Evaluation},
author={Bowen Cheng and Ross Girshick and Piotr Doll{\'a}r and Alexander C. Berg and Alexander Kirillov},
booktitle={CVPR},
year={2021}
}
```
## Contact
If you have any questions regarding this API, please contact us at bcheng9 AT illinois.edu