### Install RobMOTS Evaluation Code and Run Example Source: https://github.com/yinmayoo185/soccernetgsr/blob/main/sn-trackeval/docs/RobMOTS-Official/Readme.md This script installs the TrackEval repository, sets up a virtual environment, installs dependencies, downloads the training ground truth and an example tracker, and runs the evaluation. Requires Python 3.5+. ```bash # Download the TrackEval repo git clone https://github.com/JonathonLuiten/TrackEval.git # Move to repo folder cd TrackEval # Create a virtual env in the repo for evaluation python3 -m venv ./venv # Activate the virtual env source venv/bin/activate # Update pip to have the latest version of packages pip install --upgrade pip # Install the required packages pip install -r requirements.txt # Download the train gt data wget https://omnomnom.vision.rwth-aachen.de/data/RobMOTS/train_gt.zip # Unzip the train gt data you just downloaded. unzip train_gt.zip # Download the example tracker wget https://omnomnom.vision.rwth-aachen.de/data/RobMOTS/example_tracker.zip # Unzip the example tracker you just downloaded. unzip example_tracker.zip # Run the evaluation on the provided example tracker on the train split (using 4 cores in parallel) python scripts/run_rob_mots.py --ROBMOTS_SPLIT train --TRACKERS_TO_EVAL STP --USE_PARALLEL True --NUM_PARALLEL_CORES 4 ``` -------------------------------- ### Setup Project Installation Source: https://github.com/yinmayoo185/soccernetgsr/blob/main/README.md Installs the project in 'develop' mode, making it editable and available in the current Python environment. ```bash python setup.py develop ``` -------------------------------- ### Install Project Dependencies Source: https://github.com/yinmayoo185/soccernetgsr/blob/main/README.md Installs the main project requirements from the 'requirements.txt' file after installing build dependencies. ```bash pip install numpy cython pip install -r requirements.txt ``` -------------------------------- ### Install CLIP from Source Source: https://github.com/yinmayoo185/soccernetgsr/blob/main/README.md Installs the CLIP library directly from its GitHub repository. This is a specific installation method for the CLIP model. ```bash pip install git+https://github.com/openai/CLIP.git ``` -------------------------------- ### Example Data Line Source: https://github.com/yinmayoo185/soccernetgsr/blob/main/sn-trackeval/docs/MOTChallenge-Official/Readme.md An example line demonstrating the 10-value format for object instances in MOT challenge data files. ```text 1, 3, 794.27, 247.59, 71.245, 174.88, -1, -1, -1, -1 1, 6, 1648.1, 119.61, 66.504, 163.24, -1, -1, -1, -1 1, 8, 875.49, 399.98, 95.303, 233.93, -1, -1, -1, -1 ... ``` -------------------------------- ### Custom Challenge Train/Test Split Example Source: https://github.com/yinmayoo185/soccernetgsr/blob/main/sn-trackeval/docs/MOTChallenge-Official/Readme.md Example text files defining train and test splits for a custom challenge by listing sequence names. ```text name ``` ```text name ``` -------------------------------- ### Custom Challenge Sequence Map Example Source: https://github.com/yinmayoo185/soccernetgsr/blob/main/sn-trackeval/docs/MOTChallenge-Official/Readme.md Example of a text file listing sequence names for a custom challenge, used for training splits. ```text name ``` -------------------------------- ### Install PyTorch with CUDA Support Source: https://github.com/yinmayoo185/soccernetgsr/blob/main/README.md Installs PyTorch, torchvision, and torchaudio compatible with CUDA 12.1. Ensure your CUDA version matches the installation command. ```bash pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121 ``` -------------------------------- ### Example Tracker Result File Source: https://github.com/yinmayoo185/soccernetgsr/blob/main/sn-trackeval/docs/RobMOTS-Official/Readme.md A sample of a tracker result file, demonstrating the format for multiple detections across different timesteps. ```text 0 1 3 0.9917707443237305 1200 1920 VaTi0b0lT17F8K3M3N1O1N2O0O2M3N2N101O1O1O01O1O0100O100O01O1O100O10O1000O1000000000000000O1000001O0000000000000000O101O00000000000001O0000010O0110O0O100O1O2N1O2N0O2O2M3M2N2O1O2N5J;DgePZ1 0 2 3 0.989478349685669 1200 1920 Ql^c05ZU12O2N001O0O10OTkNIaT17^kNKaT15^kNLbT14^kNMaT13^kNOaT11_kN0`T10_kN1`T11_kN0`T11_kN0`T1a0O00001O1O1O3M;E5K3M2N000000000O100000000000000000001O00001O2N1O1O1O000001O001O0O2O0O2M3M3M3N2O1O1O1N2O002N1O2N10O02N10000O1O101M3N2N2M7H^_g_1 1 2 3 0.964085042476654 1200 1920 o_Uc03\U12O1O1N102N002N001O1O000O2O1O00002N6J1O001O2N1O3L3N2N4L5K2N1O000000000000001O1O2N01O01O010O01N2O0O2O1M4L3N2N101N2O001O1O100O0100000O1O1O1O2N6I4Mdm^`1 ``` -------------------------------- ### Tracker Example Code Source: https://github.com/yinmayoo185/soccernetgsr/blob/main/sn-trackeval/docs/RobMOTS-Official/Readme.md Example Python code demonstrating how to read in supplied detections and utilize them for tracking. This is useful for creating custom trackers or understanding the integration of supplied detections. ```python https://github.com/JonathonLuiten/TrackEval/blob/master/trackeval/baselines/stp.py ``` -------------------------------- ### Install Deep Person ReID and COCO API Source: https://github.com/yinmayoo185/soccernetgsr/blob/main/README.md Installs deep-person-reid and the COCO API from their respective GitHub repositories, using '--no-build-isolation' for specific build requirements. ```bash pip install git+https://github.com/KaiyangZhou/deep-person-reid.git --no-build-isolation pip install 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI' --no-build-isolation ``` -------------------------------- ### Create Conda Virtual Environment Source: https://github.com/yinmayoo185/soccernetgsr/blob/main/README.md Creates a new conda environment named 'SoccernetGSR' with Python 3.12. This is a prerequisite for installing project dependencies. ```bash conda create -n SoccernetGSR python=3.12 conda activate SoccernetGSR ``` -------------------------------- ### Non-Overlap Code Example Source: https://github.com/yinmayoo185/soccernetgsr/blob/main/sn-trackeval/docs/RobMOTS-Official/Readme.md Provides access to the Python code for Non-Maximum Suppression (NMS) and the Non-Overlap algorithm used in processing supplied detections. This is recommended for RobMOTS evaluation to ensure tracking results are non-overlapping. ```python https://github.com/JonathonLuiten/TrackEval/blob/master/trackeval/baselines/non_overlap.py ``` -------------------------------- ### Download Model Weights Source: https://github.com/yinmayoo185/soccernetgsr/blob/main/README.md Executes a Python script to download necessary pre-trained model weights from Google Drive. Ensure you have internet connectivity. ```bash python download_properties.py ``` -------------------------------- ### Run MOT20 Challenge Evaluation Source: https://github.com/yinmayoo185/soccernetgsr/blob/main/sn-trackeval/Readme.md Use this command to evaluate trackers on the MOT20-train benchmark using CLEAR and Identity metrics. Set USE_PARALLEL to False and specify the number of parallel cores. ```bash python scripts/run_mot_challenge.py --BENCHMARK MOT20 --TRACKERS_TO_EVAL LPC_MOT --METRICS CLEAR Identity --USE_PARALLEL False --NUM_PARALLEL_CORES 1 ``` -------------------------------- ### Run SoccerNet Game State Evaluation Source: https://github.com/yinmayoo185/soccernetgsr/blob/main/sn-trackeval/Readme.md Use this command to perform evaluation for SoccerNet Game State Reconstruction. Ensure the dataset and prediction folders are correctly specified. ```bash python3 ./scripts/run_soccernet_gs.py --GT_FOLDER path/to/dataset/SoccerNetGS --TRACKERS_FOLDER path/to/predictions_folder --TRACKER_SUB_FOLDER "" --SPLIT_TO_EVAL "test" ``` -------------------------------- ### Homography Estimation Source: https://github.com/yinmayoo185/soccernetgsr/blob/main/README.md Runs the 'kpts.py' script to generate homography matrices, which are essential for Sports Field Registration. These are saved as .npy files. ```bash python kpts.py ``` -------------------------------- ### Reading MOTS PNG Annotations with PIL Source: https://github.com/yinmayoo185/soccernetgsr/blob/main/sn-trackeval/docs/MOTS-format.txt Demonstrates how to read MOTS annotation PNG files using the Pillow library and extract object IDs, class IDs, and instance IDs. ```python import PIL.Image as Image import numpy as np img = np.array(Image.open("000005.png")) obj_ids = np.unique(img) # to correctly interpret the id of a single object obj_id = obj_ids[0] class_id = obj_id // 1000 obj_instance_id = obj_id % 1000 ``` -------------------------------- ### Run MOT Challenge Evaluation Source: https://github.com/yinmayoo185/soccernetgsr/blob/main/sn-trackeval/Readme.md Use this command to evaluate trackers on the MOT17-train benchmark using CLEAR and Identity metrics. Set USE_PARALLEL to False and specify the number of parallel cores. ```bash python scripts/run_mot_challenge.py --BENCHMARK MOT17 --TRACKERS_TO_EVAL Lif_T --METRICS CLEAR Identity --USE_PARALLEL False --NUM_PARALLEL_CORES 1 ``` -------------------------------- ### Run MOT Challenge Evaluation Source: https://github.com/yinmayoo185/soccernetgsr/blob/main/sn-trackeval/docs/MOTChallenge-Official/Readme.md Execute the evaluation script for a specified benchmark, split, trackers, and metrics. Supports parallel processing. ```bash python scripts/run_mot_challenge.py --BENCHMARK MOT17 --SPLIT_TO_EVAL train --TRACKERS_TO_EVAL MPNTrack --METRICS HOTA CLEAR Identity VACE --USE_PARALLEL False --NUM_PARALLEL_CORES 1 ``` -------------------------------- ### Run Inference for Tracking and Recognition Source: https://github.com/yinmayoo185/soccernetgsr/blob/main/README.md Executes the main inference script for player tracking, ReID, and jersey number/role recognition. The 'JERSEY_MODE' (CLIP or LLaMA) should be set in 'configs/config.yaml'. ```bash python inference_soccernetGSR.py ``` -------------------------------- ### Run RobMOTS Evaluation Source: https://github.com/yinmayoo185/soccernetgsr/blob/main/sn-trackeval/docs/RobMOTS-Official/Readme.md Execute the evaluation script for your custom tracker on the RobMOTS dataset. Ensure your tracker's results are placed in the correct directory. ```bash python scripts/run_rob_mots.py --ROBMOTS_SPLIT train --TRACKERS_TO_EVAL YOUR_TRACKER --USE_PARALLEL True --NUM_PARALLEL_CORES 4 ``` -------------------------------- ### Visualize Prediction Results Source: https://github.com/yinmayoo185/soccernetgsr/blob/main/README.md Generates visualizations of the predicted game states based on the output JSON files. Specify the directory containing the JSON results. ```bash python visualize_prediction_results.py --json_dir results/predicted_SNGamestate_results/test ``` -------------------------------- ### Base Metric Class Structure Source: https://github.com/yinmayoo185/soccernetgsr/blob/main/sn-trackeval/docs/How_To/Add_a_new_metric.md Illustrates the expected structure for a custom metric class, inheriting from _BaseMetric and defining key functions. ```python from trackeval.metrics._base_metric import _BaseMetric class MyCustomMetric(_BaseMetric): def __init__(self, **kwargs): super().__init__(**kwargs) self.fields = [ 'my_custom_field_1', 'my_custom_field_2' ] self.param_keys = ['my_param'] def eval_sequence(self, data, **kwargs): # Calculate metric for a single sequence # data is a dictionary containing sequence data # returns a dictionary of results results = {} # ... calculation logic ... return results def combine_sequences(self, sequences_results, **kwargs): # Combine results over sequences # sequences_results is a list of results dictionaries from eval_sequence # returns a dictionary of combined results combined = {} # ... combination logic ... return combined def combine_classes_class_averaged(self, class_results, **kwargs): # Combine results over classes (class-averaged) # class_results is a dictionary mapping class names to results dictionaries # returns a dictionary of combined results combined = {} # ... combination logic ... return combined def combine_classes_det_averaged(self, class_results, **kwargs): # Combine results over classes (detection-averaged) # class_results is a dictionary mapping class names to results dictionaries # returns a dictionary of combined results combined = {} # ... combination logic ... return combined def _compute_final_fields(self, results, **kwargs): # Optional: Compute final fields from intermediate results # results is a dictionary of computed fields # returns a dictionary of final fields final_fields = {} # ... final calculation logic ... return final_fields ``` -------------------------------- ### Evaluate Tracking Results Source: https://github.com/yinmayoo185/soccernetgsr/blob/main/README.md Uses the 'sn-trackeval' toolkit to evaluate the prediction results against ground truth data. Ensure the GT_FOLDER and TRACKERS_FOLDER paths are correctly set. ```bash python ./sn-trackeval/scripts/run_soccernet_gs.py \ --GT_FOLDER /path/to/SoccerNetGS \ --TRACKERS_FOLDER /path/to/Pred_SN_GS2025/ \ --TRACKER_SUB_FOLDER "" \ --SPLIT_TO_EVAL "test" ``` -------------------------------- ### YouTube-VIS Label File Structure Source: https://github.com/yinmayoo185/soccernetgsr/blob/main/sn-trackeval/docs/YouTube-VIS-format.txt Defines the JSON structure for label files, including info, videos, annotations, and categories. ```json { "info" : info, "videos" : [video], "annotations" : [annotation], "categories" : [category], } ``` -------------------------------- ### Reading MOTS PNG Annotations with TensorFlow Source: https://github.com/yinmayoo185/soccernetgsr/blob/main/sn-trackeval/docs/MOTS-format.txt Shows how to read MOTS annotation PNG files within a TensorFlow input pipeline, decoding them as 16-bit, single-channel images. ```python import tensorflow as tf ann_data = tf.read_file(ann_filename) ann = tf.image.decode_image(ann_data, dtype=tf.uint16, channels=1) ``` -------------------------------- ### Load DAVIS Annotation with PIL Source: https://github.com/yinmayoo185/soccernetgsr/blob/main/sn-trackeval/docs/DAVIS-format.txt Loads a DAVIS annotation PNG file into a NumPy array using the Pillow library. Ensure the image is treated as a single channel. ```python import PIL.Image as Image import numpy as np img = np.array(Image.open("000005.png")) ``` -------------------------------- ### Load DAVIS Annotation with TensorFlow Source: https://github.com/yinmayoo185/soccernetgsr/blob/main/sn-trackeval/docs/DAVIS-format.txt Loads and decodes a DAVIS annotation PNG file using TensorFlow, ensuring it's read as a single-channel uint8 image. ```python import tensorflow as tf ann_data = tf.read_file(ann_filename) ann = tf.image.decode_image(ann_data, dtype=tf.uint8, channels=1) ``` -------------------------------- ### YouTube-VIS Submission File Structure Source: https://github.com/yinmayoo185/soccernetgsr/blob/main/sn-trackeval/docs/YouTube-VIS-format.txt Defines the JSON structure for submission files, containing a list of predictions. ```json prediction{ "video_id" : int, "category_id" : int, "segmentations" : [RLE or [polygon] or None], "score" : float, } ``` -------------------------------- ### YouTube-VIS Video Object Structure Source: https://github.com/yinmayoo185/soccernetgsr/blob/main/sn-trackeval/docs/YouTube-VIS-format.txt Defines the structure of a video object within the label file. ```json video{ "id" : int, "width" : int, "height" : int, "length" : int, "file_names" : [file_name], } ``` -------------------------------- ### YouTube-VIS Annotation Object Structure Source: https://github.com/yinmayoo185/soccernetgsr/blob/main/sn-trackeval/docs/YouTube-VIS-format.txt Defines the structure of an annotation object within the label file. ```json annotation{ "id" : int, "video_id" : int, "category_id" : int, "segmentations" : [RLE or [polygon] or None], "areas" : [float or None], "bboxes" : [[x,y,width,height] or None], "iscrowd" : 0 or 1, } ``` -------------------------------- ### RobMOTS Tracker Result File Format Source: https://github.com/yinmayoo185/soccernetgsr/blob/main/sn-trackeval/docs/RobMOTS-Official/Readme.md Format for each row in a tracker result file, representing a single detection. Includes timestep, track ID, class, confidence, image dimensions, and compressed RLE mask. ```text (int), (int), (int), (float), (int), (int), (string), ``` -------------------------------- ### Refine Tracklets using IDATR Source: https://github.com/yinmayoo185/soccernetgsr/blob/main/README.md Applies ID-Aware Tracklets Refinement (IDATR) to the initial tracklet results obtained from the inference step. ```bash ./refine_tracklets.sh ``` -------------------------------- ### YouTube-VIS Category Object Structure Source: https://github.com/yinmayoo185/soccernetgsr/blob/main/sn-trackeval/docs/YouTube-VIS-format.txt Defines the structure of a category object within the label file. ```json category{ "id" : int, "name" : str, "supercategory" : str, } ``` -------------------------------- ### MOT Challenge Data Format Source: https://github.com/yinmayoo185/soccernetgsr/blob/main/sn-trackeval/docs/MOTChallenge-Official/Readme.md Defines the CSV text-file format for tracker and ground truth data, including frame, ID, bounding box coordinates, and confidence. ```text , , , , , , , , , ``` -------------------------------- ### HOTA Metrics Citation Source: https://github.com/yinmayoo185/soccernetgsr/blob/main/sn-trackeval/docs/RobMOTS-Official/Readme.md Citation for the HOTA metrics paper. Use this when referencing the HOTA metric in your work. ```bibtex @article{luiten2020IJCV, title={HOTA: A Higher Order Metric for Evaluating Multi-Object Tracking}, author={Luiten, Jonathon and Osep, Aljosa and Dendorfer, Patrick and Torr, Philip and Geiger, Andreas and Leal-Taix{\'e}, Laura and Leibe, Bastian}, journal={International Journal of Computer Vision}, pages={1--31}, year={2020}, publisher={Springer} } ``` -------------------------------- ### TrackEval Citation Source: https://github.com/yinmayoo185/soccernetgsr/blob/main/sn-trackeval/docs/MOTChallenge-Official/Readme.md BibTeX entry for citing the TrackEval toolkit in academic work. ```bibtex @misc{luiten2020trackeval, author = {Jonathon Luiten, Arne Hoffhues}, title = {TrackEval}, howpublished = {\url{https://github.com/JonathonLuiten/TrackEval}}, year = {2020} } ``` -------------------------------- ### Convert Inference Results to JSON Source: https://github.com/yinmayoo185/soccernetgsr/blob/main/README.md Converts the processed inference results into the required JSON format for subsequent evaluation. ```bash python write_json_file_team.py ``` -------------------------------- ### Accessing TrackEval Results Source: https://github.com/yinmayoo185/soccernetgsr/blob/main/sn-trackeval/docs/RobMOTS-Official/Readme.md Information on the location and format of tracking evaluation results, including overall summaries and detailed per-sequence data. The 'c', 'd', and 'f' prefixes for metrics denote class averaged, detection averaged, and final geometric mean, respectively. ```text TrackEval/data/trackers/rob_mots/train/STP/ ``` ```text ./final_results.csv ``` ```text ./results/* ``` -------------------------------- ### MOTChallenge Journal Citation Source: https://github.com/yinmayoo185/soccernetgsr/blob/main/sn-trackeval/docs/MOTChallenge-Official/Readme.md BibTeX entry for citing the MOTChallenge journal article. ```bibtex @article{dendorfer2020motchallenge, title={MOTChallenge: A Benchmark for Single-camera Multiple Target Tracking}, author={Dendorfer, Patrick and Osep, Aljosa and Milan, Anton and Schindler, Konrad and Cremers, Daniel and Reid, Ian and Roth, Stefan and Leal-Taix{\'e}, Laura}, journal={International Journal of Computer Vision}, pages={1--37}, year={2020}, } ``` === COMPLETE CONTENT === This response contains all available snippets from this library. No additional content exists. Do not make further requests.