inference.ipynb: Given a bunch of images, semantic segmentation, point selection, estimation of camera parameters, and visualization is applied. The pretrained segmentation model can be downloaded here:
mkdir data/segment_localization
wget https://tib.eu/cloud/s/x68XnTcZmsY4Jpg/download/train_59.pt -O data/segment_localization/train_59.ptWe provide scripts (scripts/experiments_wacv23) to reproduce the provided results of the paper for the baseline and TVCalib.
# SN segmentation model & retrained model
scripts/experiments_wacv23/run_segmentation.sh
# choice of self-verification parameter
scripts/experiments_wacv23/run_sncalib-valid-all-tau_tvcalib.sh
# TVCalib & baseline
scripts/experiments_wacv23/run_wc14-test-center_tvcalib_baseline.sh
scripts/experiments_wacv23/run_sncalib-test-center_tvcalib_baseline.sh
scripts/experiments_wacv23/run_lens_distortion_tvcalib.sh
scripts/experiments_wacv23/run_wc14-test-center_manual.sh
# +++ further ablation studies
scripts/experiments_wacv23/run_sncalib-test-all_tvcalib.sh
# table 1
python scripts/experiments_wacv23/tex/generate_table_sncalib-center.py
# table 2, 3
python scripts/experiments_wacv23/tex/generate_table_wc14-center.py
# table appendix: lens distortion
python scripts/experiments_wacv23/tex/generate_table_lens_distortion.py
# figure 2: segment reprojection loss
python scripts/experiments_wacv23/figures/visualize_ndc_losses_multiple_datasets.py
# figure 3: sn-calib-test (main left, center, right)
python scripts/experiments_wacv23/figures/summarize_results_sncalib-test-all.py
# evaluate projection error
python -m scripts.experiments_wacv23.tex.prepare_iou_resultsSee https://github.com/SoccerNet/sn-calibration for details on the evaluation metric.
python -m evaluation.eval_projection
Arguments:
--dir_dataset: Path to ground-truth annotations, i.e., a folder with<image_id>.json--filter_gt_camera_type <str>: Evaluate on a subset according to the available camera types in<dir_dataset>/match_info_cam_gt.json--per_sample_output: File path toper_sample_output_json--width <int> --height <int>: Source image with and height in pixel, respectively--project_from:[Camera, Homography, HDecomp]whileCamerarequires individual camera parameters,HomographyandHDecompa respective homography matrix. Multiple values possible.--evaluate_3d: If set, evaluates the 3D calibration performance (utilizes the 3D pitch model)--evaluate_2d: If set, evaluates the 2D calibration performance from a provided homography (utilizes the 2D pitch model)--distort: Evaluate with provided lens distortion parameters. Default: ignored--taus(optional): Relevant for TVCalib only: Self-verification from loss. Example,--taus inf 0.017--zeta(optional): Relevant forproject_from=HDecomp.
See python -m scripts.experiments_wacv23.tex.prepare_iou_results.
Expected structure for default arguments:
./
├── data
│ └── datasets
│ └── wc14-test/match_info_cam_gt.json
│ └── sncalib-train/match_info_cam_gt.json
│ └── sncalib-valid/match_info_cam_gt.json
│ └── sncalib-test/match_info_cam_gt.json
Download and preparation:
from SoccerNet.Downloader import SoccerNetDownloader
mySoccerNetDownloader = SoccerNetDownloader(LocalDirectory="</nfs/data/soccernet>")
mySoccerNetDownloader.downloadDataTask(task="calibration", split=["train","valid","test"])Already downloaded? May consider to create a soft link for each subset:
ln -s /nfs/data/soccernet/calibration/valid data/datasets/sncalib-valid
ln -s /nfs/data/soccernet/calibration/test data/datasets/sncalib-test
ln -s /nfs/data/soccernet/calibration/train data/datasets/sncalib-trainCamera type annotations
# move annotation file to respective dataset directory
wget https://tib.eu/cloud/s/483Bqf78dDMcx2H/download/test_match_info_cam_gt.json -O sncalib-test/match_info_cam_gt.json
wget https://tib.eu/cloud/s/WdSqM3WbyKQ36pm/download/val_match_info_cam_gt.json -O sncalib-valid/match_info_cam_gt.json
mkdir -p data/datasets/wc14-test && cd data/datasets/wc14-test/
# Images and provided homography matrices from test split
wget https://nhoma.github.io/data/soccer_data.tar.gz
tar -zxvf soccer_data.tar.gz raw/test --strip-components 2
# Our additional segment annotations
wget https://tib.eu/cloud/s/Jz4x2KsjinEEkwQ/download/wc14-test-additional_annotations_wacv23_theiner.tar -O wc14-test-additional_annotations_wacv23_theiner.tar
tar xvf wc14-test-additional_annotations_wacv23_theiner.tarconda env create -f environment.yml
conda activate tvcalibDepending on your hardware, consider to have a look on https://pytorch.org/ for CPU-only installation or other CUDA versions.