Project O

You can check the code used in the project above in the "apply_to_video.py" file.

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TVCalib: Camera Calibration for Sports Field Registration in Soccer

Contents

• Inference / Demo
• Reproduce Paper Results
  • Datasets
  • Evaluation
• Requirments

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Inference

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.pt

Visualize Results

Reproduce Paper Results

We 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_results

Evaluation

Segment Reprojection Error

See 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 to per_sample_output_json
• --width <int> --height <int>: Source image with and height in pixel, respectively
• --project_from: [Camera, Homography, HDecomp] while Camera requires individual camera parameters, Homography and HDecomp a 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 for project_from=HDecomp.

Projection Error via Intersection over Union (Part):

See python -m scripts.experiments_wacv23.tex.prepare_iou_results.

Datasets

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:

SoccerNet-Calibration-V3:

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-train

Camera 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

WorldCup 2014 (WC14):

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.tar

Requirements

Conda Environment:

conda env create -f environment.yml
conda activate tvcalib

Depending on your hardware, consider to have a look on https://pytorch.org/ for CPU-only installation or other CUDA versions.