Include evaluation tools for 2024 edition

MTMC_Tracking_2024/eval/3rdParty_Licenses.md

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+# Third-Party Licenses
+
+This project incorporates components from the following open-source software. We have provided links to the licenses for each component below.
+
+| Package / Component Name | Version | License | Link to Component's License |
+|---|---|---|---|
+| pandas | 2.3.1 | GNU General Public License v3.0 | [link](https://github.com/PandasWS/Pandas/blob/master/LICENSE)
+| matplotlib | 3.5.2 | Other (Please describe in Comments) | [link](https://github.com/matplotlib/matplotlib/blob/main/LICENSE/LICENSE) |
+| scipy | 1.15.3 | BSD (any variant) | [link](https://github.com/scipy/scipy/tree/main?tab=BSD-3-Clause-1-ov-file#readme) |
+
+
+
+### MIT License
+```
+MIT License
+
+Copyright (c) [year] [fullname]
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+```
+
+### BSD License 
+```
+BSD License
+
+Copyright (c) [year] [fullname]
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+    * Redistributions of source code must retain the above copyright
+      notice, this list of conditions and the following disclaimer.
+    * Redistributions in binary form must reproduce the above copyright
+      notice, this list of conditions and the following disclaimer in the
+      documentation and/or other materials provided with the distribution.
+    * Neither the name of the copyright holder nor the names of its
+      contributors may be used to endorse or promote products derived from
+      this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+POSSIBILITY OF SUCH DAMAGE.
+```
+
+### Other
+Numpy license 
+```
+Copyright (c) 2005-2023, NumPy Developers.
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are
+met:
+    * Redistributions of source code must retain the above copyright
+       notice, this list of conditions and the following disclaimer.
+    * Redistributions in binary form must reproduce the above
+       copyright notice, this list of conditions and the following
+       disclaimer in the documentation and/or other materials provided
+       with the distribution.
+    * Neither the name of the NumPy Developers nor the names of any
+       contributors may be used to endorse or promote products derived
+       from this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+```
+
+
+### MatplotLib
+
+```
+License agreement for matplotlib versions 1.3.0 and later
+=========================================================
+
+1. This LICENSE AGREEMENT is between the Matplotlib Development Team
+("MDT"), and the Individual or Organization ("Licensee") accessing and
+otherwise using matplotlib software in source or binary form and its
+associated documentation.
+
+2. Subject to the terms and conditions of this License Agreement, MDT
+hereby grants Licensee a nonexclusive, royalty-free, world-wide license
+to reproduce, analyze, test, perform and/or display publicly, prepare
+derivative works, distribute, and otherwise use matplotlib
+alone or in any derivative version, provided, however, that MDT's
+License Agreement and MDT's notice of copyright, i.e., "Copyright (c)
+2012- Matplotlib Development Team; All Rights Reserved" are retained in
+matplotlib  alone or in any derivative version prepared by
+Licensee.
+
+3. In the event Licensee prepares a derivative work that is based on or
+incorporates matplotlib or any part thereof, and wants to
+make the derivative work available to others as provided herein, then
+Licensee hereby agrees to include in any such work a brief summary of
+the changes made to matplotlib .
+
+4. MDT is making matplotlib available to Licensee on an "AS
+IS" basis.  MDT MAKES NO REPRESENTATIONS OR WARRANTIES, EXPRESS OR
+IMPLIED.  BY WAY OF EXAMPLE, BUT NOT LIMITATION, MDT MAKES NO AND
+DISCLAIMS ANY REPRESENTATION OR WARRANTY OF MERCHANTABILITY OR FITNESS
+FOR ANY PARTICULAR PURPOSE OR THAT THE USE OF MATPLOTLIB
+WILL NOT INFRINGE ANY THIRD PARTY RIGHTS.
+
+5. MDT SHALL NOT BE LIABLE TO LICENSEE OR ANY OTHER USERS OF MATPLOTLIB
+ FOR ANY INCIDENTAL, SPECIAL, OR CONSEQUENTIAL DAMAGES OR
+LOSS AS A RESULT OF MODIFYING, DISTRIBUTING, OR OTHERWISE USING
+MATPLOTLIB , OR ANY DERIVATIVE THEREOF, EVEN IF ADVISED OF
+THE POSSIBILITY THEREOF.
+
+6. This License Agreement will automatically terminate upon a material
+breach of its terms and conditions.
+
+7. Nothing in this License Agreement shall be deemed to create any
+relationship of agency, partnership, or joint venture between MDT and
+Licensee.  This License Agreement does not grant permission to use MDT
+trademarks or trade name in a trademark sense to endorse or promote
+products or services of Licensee, or any third party.
+
+8. By copying, installing or otherwise using matplotlib ,
+Licensee agrees to be bound by the terms and conditions of this License
+Agreement.
+
+License agreement for matplotlib versions prior to 1.3.0
+========================================================
+
+1. This LICENSE AGREEMENT is between John D. Hunter ("JDH"), and the
+Individual or Organization ("Licensee") accessing and otherwise using
+matplotlib software in source or binary form and its associated
+documentation.
+
+2. Subject to the terms and conditions of this License Agreement, JDH
+hereby grants Licensee a nonexclusive, royalty-free, world-wide license
+to reproduce, analyze, test, perform and/or display publicly, prepare
+derivative works, distribute, and otherwise use matplotlib
+alone or in any derivative version, provided, however, that JDH's
+License Agreement and JDH's notice of copyright, i.e., "Copyright (c)
+2002-2011 John D. Hunter; All Rights Reserved" are retained in
+matplotlib  alone or in any derivative version prepared by
+Licensee.
+
+3. In the event Licensee prepares a derivative work that is based on or
+incorporates matplotlib  or any part thereof, and wants to
+make the derivative work available to others as provided herein, then
+Licensee hereby agrees to include in any such work a brief summary of
+the changes made to matplotlib.
+
+4. JDH is making matplotlib  available to Licensee on an "AS
+IS" basis.  JDH MAKES NO REPRESENTATIONS OR WARRANTIES, EXPRESS OR
+IMPLIED.  BY WAY OF EXAMPLE, BUT NOT LIMITATION, JDH MAKES NO AND
+DISCLAIMS ANY REPRESENTATION OR WARRANTY OF MERCHANTABILITY OR FITNESS
+FOR ANY PARTICULAR PURPOSE OR THAT THE USE OF MATPLOTLIB
+WILL NOT INFRINGE ANY THIRD PARTY RIGHTS.
+
+5. JDH SHALL NOT BE LIABLE TO LICENSEE OR ANY OTHER USERS OF MATPLOTLIB
+ FOR ANY INCIDENTAL, SPECIAL, OR CONSEQUENTIAL DAMAGES OR
+LOSS AS A RESULT OF MODIFYING, DISTRIBUTING, OR OTHERWISE USING
+MATPLOTLIB , OR ANY DERIVATIVE THEREOF, EVEN IF ADVISED OF
+THE POSSIBILITY THEREOF.
+
+6. This License Agreement will automatically terminate upon a material
+breach of its terms and conditions.
+
+7. Nothing in this License Agreement shall be deemed to create any
+relationship of agency, partnership, or joint venture between JDH and
+Licensee.  This License Agreement does not grant permission to use JDH
+trademarks or trade name in a trademark sense to endorse or promote
+products or services of Licensee, or any third party.
+
+8. By copying, installing or otherwise using matplotlib,
+Licensee agrees to be bound by the terms and conditions of this License
+Agreement.
+```
+
+### GNU
+
+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <http://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
+ of this license document, but changing it is not allowed.
+
+                            Preamble
+
+  The GNU General Public License is a free, copyleft license for
+software and other kinds of works.
+
+  The licenses for most software and other practical works are designed
+to take away your freedom to share and change the works.  By contrast,
+the GNU General Public License is intended to guarantee your freedom to
+share and change all versions of a program--to make sure it remains free
+software for all its users.  We, the Free Software Foundation, use the
+GNU General Public License for most of our software; it applies also to
+any other work released this way by its authors.  You can apply it to
+your programs, too.
+
+  When we speak of free software, we are referring to freedom, not
+price.  Our General Public Licenses are designed to make sure that you
+have the freedom to distribute copies of free software (and charge for
+them if you wish), that you receive source code or can get it if you
+want it, that you can change the software or use pieces of it in new
+free programs, and that you know you can do these things.
+
+  To protect your rights, we need to prevent others from denying you
+these rights or asking you to surrender the rights.  Therefore, you have
+certain responsibilities if you distribute copies of the software, or if
+you modify it: responsibilities to respect the freedom of others.
+
+  For example, if you distribute copies of such a program, whether
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+freedoms that you received.  You must make sure that they, too, receive
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+know their rights.
+
+  Developers that use the GNU GPL protect your rights with two steps:
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+giving you legal permission to copy, distribute and/or modify it.
+
+  For the developers' and authors' protection, the GPL clearly explains
+that there is no warranty for this free software.  For both users' and
+authors' sake, the GPL requires that modified versions be marked as
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+
+  Some devices are designed to deny users access to install or run
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+stand ready to extend this provision to those domains in future versions
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+  Finally, every program is threatened constantly by software patents.
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+
+  The precise terms and conditions for copying, distribution and
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+
+                       TERMS AND CONDITIONS
+
+  0. Definitions.
+
+  "This License" refers to version 3 of the GNU General Public License.
+
+  "Copyright" also means copyright-like laws that apply to other kinds of
+works, such as semiconductor masks.
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+
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+distribution (with or without modification), making available to the
+public, and in some countries other activities as well.
+
+  To "convey" a work means any kind of propagation that enables other
+parties to make or receive copies.  Mere interaction with a user through
+a computer network, with no transfer of a copy, is not conveying.
+
+  An interactive user interface displays "Appropriate Legal Notices"
+to the extent that it includes a convenient and prominently visible
+feature that (1) displays an appropriate copyright notice, and (2)
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+  1. Source Code.
+
+  The "source code" for a work means the preferred form of the work
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+
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+
+  You may convey verbatim copies of the Program's source code as you
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+recipients a copy of this License along with the Program.
+
+  You may charge any price or no price for each copy that you convey,
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+
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+    a) The work must carry prominent notices stating that you modified
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+
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+
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+    License to anyone who comes into possession of a copy.  This
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+
+  You may convey a covered work in object code form under the terms
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+
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+
+  "Additional permissions" are terms that supplement the terms of this
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MTMC_Tracking_2024/eval/README.md

@@ -0,0 +1,41 @@
+# Evaluation Code for - MTMC Tracking 2024 Dataset
+
+Evaluation code for the Multi-Target Multi-Camera (MTMC) 2024 dataset. 
+
+The evaluation utilizes Higher Order Tracking Accuracy (HOTA) score as an evaluation metric for multi-object tracking that addresses the limitations of previous metrics like MOTA and IDF1. It integrates three key aspects of MOT: accurate detection, association, and localization into a unified metric. This comprehensive approach balances the importance of detecting each object (detection), correctly identifying objects across different frames (association), and accurately localizing objects in each frame (localization). Furthermore, HOTA can be decomposed into simpler components, allowing for detailed analysis of different aspects of tracking behavior. 
+
+HOTA scores calculated using 3D distance measurements in a multi-camera setting.
+
+# Environment setup:
+```
+- [Optional] conda create -n mtmc_eval_2024 python=3.10
+- [Optional] conda activate mtmc_eval_2024
+
+- pip3 install pandas
+- pip3 install matplotlib
+- pip3 install scipy
+```
+
+# Usage: 
+
+ - Set the --prediction_file argument to a valid prediction file.
+ - Set the --ground_truth_file argument to a valid test file.
+ - Set the --num_cores argument based on your setup.
+ - Set the --scene_2_camera_id_file argument 
+
+
+Example below:
+```
+python3 main.py --prediction_file ./sample_file/pred.txt --ground_truth_file ./sample_file/ground_truth_test_full.txt --num_cores 16 --scene_2_camera_id_file ./sample_file/scene_name_2_cam_id_full.json
+
+Sample Result:
+Total runtime: 187.0887589454651 seconds.
+HOTA: 49.2825%
+DetA: 49.1998%
+AssA: 49.3655%
+LocA: 77.0546%
+```
+
+## Acknowledgements
+
+This project utilizes a portion of code from [TrackEval](https://github.com/JonathonLuiten/TrackEval), an open-source project by Jonathon Luiten for evaluating multi-camera tracking results. TrackEval is licensed under the MIT License, which you can find in full [here](https://github.com/JonathonLuiten/TrackEval/blob/master/LICENSE).

MTMC_Tracking_2024/eval/main.py

@@ -0,0 +1,228 @@
+#!/usr/bin/python3
+"""
+Evaluation script for the Multi-Camera People Tracking track in the AI City Challenge, Track 1 in 2024.
+
+# Environment setup:
+[Optional] conda create -n aicity24-track1 python=3.10
+[Optional] conda activate aicity24-track1
+
+pip3 install pandas
+pip3 install matplotlib
+pip3 install scipy
+
+# Usage: Set number of cores based on your cpu core count.
+
+python3 aicityeval-track1.py --prediction_file ./sample_file/pred.txt --ground_truth_file ./sample_file/ground_truth_test_full.txt --num_cores 16 --scene_2_camera_id_file ./sample_file/scene_name_2_cam_id_full.json
+
+python3 aicityeval-track1.py --prediction_file ./sample_file/pred.txt --ground_truth_file ./sample_file/ground_truth_test_half.txt --num_cores 16 --scene_2_camera_id_file ./sample_file/scene_name_2_cam_id_half.json
+
+
+"""
+import os
+import sys
+import time
+import tempfile
+import trackeval
+import pandas as pd
+import numpy as np
+
+
+from argparse import ArgumentParser, ArgumentTypeError
+from typing import List
+from utils.io_utils import load_csv_to_dataframe_from_file, write_dataframe_to_csv_to_file, make_seq_maps_file, make_seq_ini_file, make_dir, check_file_size, get_scene_to_camera_id_dict
+
+
+def get_unique_entry_per_scene(dataframe, scene_name, scene_2_camera_id):
+    camera_ids = scene_2_camera_id[scene_name]
+    filtered_df = dataframe[dataframe["CameraId"].isin(camera_ids)]
+    unique_entries_df = filtered_df.drop_duplicates(subset=["FrameId", "Id"])
+    return unique_entries_df
+
+def check_positive(value):
+    int_value = int(value)
+    if int_value <= 0:
+        raise ArgumentTypeError(f"{value} is an invalid num of cores")
+    return int_value
+
+def computes_mot_metrics(prediction_file_path: str, ground_truth_path: str, output_dir: str, num_cores: int, scene_2_cam_id_file: str) -> None:
+
+    
+    check_file_size(prediction_file_path)
+
+    # Create a temp directory if output_dir is not specified
+    is_temp_dir = False
+    if output_dir is None:
+        temp_dir = tempfile.TemporaryDirectory()
+        is_temp_dir = True
+        output_dir = temp_dir.name
+        print(f"Temp files will be created here: {output_dir}")
+
+    # Create a scene 2 camera_id dict
+    scene_2_camera_id = get_scene_to_camera_id_dict(scene_2_cam_id_file)
+    camera_ids = {camera_id for camera_ids in scene_2_camera_id.values() for camera_id in camera_ids}
+
+    # Load ground truth and prediction files in dataframe
+    column_names = ["CameraId", "Id", "FrameId", "X", "Y", "Width", "Height", "Xworld", "Yworld"]
+    mot_pred_dataframe = load_csv_to_dataframe_from_file(prediction_file_path, column_names, camera_ids)
+    ground_truth_dataframe = load_csv_to_dataframe_from_file(ground_truth_path, column_names, camera_ids)
+
+
+    # Create evaluater configs for trackeval lib
+    default_eval_config = trackeval.eval.Evaluator.get_default_eval_config()
+    default_eval_config["PRINT_CONFIG"] = False
+    default_eval_config["USE_PARALLEL"] = True
+    default_eval_config["LOG_ON_ERROR"] = None
+    default_eval_config["NUM_PARALLEL_CORES"] = num_cores
+
+    # Create dataset configs for trackeval lib
+    default_dataset_config = trackeval.datasets.MotChallenge3DLocation.get_default_dataset_config()
+    default_dataset_config["DO_PREPROC"] = False
+    default_dataset_config["SPLIT_TO_EVAL"] = "all"
+    default_dataset_config["GT_FOLDER"] = os.path.join(output_dir, "evaluation", "gt")
+    default_dataset_config["TRACKERS_FOLDER"] = os.path.join(output_dir, "evaluation", "scores")
+    default_dataset_config["PRINT_CONFIG"] = False
+
+    # Make output directory for storing results
+    make_dir(default_dataset_config["GT_FOLDER"])
+    make_dir(default_dataset_config["TRACKERS_FOLDER"])
+
+    # Create sequence maps file for evaluation
+    seq_maps_file = os.path.join(default_dataset_config["GT_FOLDER"], "seqmaps")
+    make_seq_maps_file(seq_maps_file, scene_2_camera_id.keys(), default_dataset_config["BENCHMARK"], default_dataset_config["SPLIT_TO_EVAL"])
+
+    # Set the metrics to obtain
+    default_metrics_config = {"METRICS": ["HOTA"], "THRESHOLD": 0.5}
+    default_metrics_config["PRINT_CONFIG"] = False
+    config = {**default_eval_config, **default_dataset_config, **default_metrics_config}  # Merge default configs
+    eval_config = {k: v for k, v in config.items() if k in default_eval_config.keys()}
+    dataset_config = {k: v for k, v in config.items() if k in default_dataset_config.keys()}
+    metrics_config = {k: v for k, v in config.items() if k in default_metrics_config.keys()}
+
+
+    # Create prediction and ground truth list
+    for scene_name in scene_2_camera_id.keys():
+        
+        # Convert ground truth multi-camera dataframe to single camera in MOT format
+        ground_truth_dataframe_per_scene = get_unique_entry_per_scene(ground_truth_dataframe, scene_name, scene_2_camera_id)
+        ground_truth_dataframe_per_scene = ground_truth_dataframe_per_scene[["FrameId", "Id", "X", "Y", "Width", "Height", "Xworld", "Yworld"]]
+        ground_truth_dataframe_per_scene = ground_truth_dataframe_per_scene.sort_values(by="FrameId")
+
+        # Make ground truth frame-ids 1-based
+        ground_truth_dataframe_per_scene["FrameId"] += 1
+
+        # Set other defaults
+        ground_truth_dataframe_per_scene["Conf"] = 1
+        ground_truth_dataframe_per_scene["Zworld"] = -1
+        ground_truth_dataframe_per_scene = ground_truth_dataframe_per_scene[["FrameId", "Id", "X", "Y", "Width", "Height", "Conf", "Xworld", "Yworld", "Zworld"]]
+
+        # Remove logs for negative frame ids
+        ground_truth_dataframe_per_scene = ground_truth_dataframe_per_scene[ground_truth_dataframe_per_scene["FrameId"] >= 1]
+
+        # Save single camera ground truth in MOT format as CSV
+        mot_version = default_dataset_config["BENCHMARK"] + "-" + default_dataset_config["SPLIT_TO_EVAL"]
+        gt_dir = os.path.join(default_dataset_config["GT_FOLDER"], mot_version)
+        dir_name = os.path.join(gt_dir, str(scene_name))
+        gt_file_dir = os.path.join(gt_dir, str(scene_name), "gt")
+        gt_file_name = os.path.join(gt_file_dir, "gt.txt")
+        make_dir(gt_file_dir)
+        write_dataframe_to_csv_to_file(gt_file_name, ground_truth_dataframe_per_scene)
+
+        # Convert predicted multi-camera dataframe to MOT format
+        mot_pred_dataframe_per_scene = get_unique_entry_per_scene(mot_pred_dataframe, scene_name, scene_2_camera_id)
+        mot_pred_dataframe_per_scene = mot_pred_dataframe_per_scene[["FrameId", "Id", "X", "Y", "Width", "Height", "Xworld", "Yworld"]]
+        mot_pred_dataframe_per_scene = mot_pred_dataframe_per_scene.sort_values(by="FrameId")
+
+        # Make MOT prediction frame-ids 1-based
+        mot_pred_dataframe_per_scene["FrameId"] += 1
+
+        # Remove logs for negative frame ids
+        mot_pred_dataframe_per_scene = mot_pred_dataframe_per_scene[mot_pred_dataframe_per_scene["FrameId"] >= 1]
+
+        # Set other defaults
+        mot_pred_dataframe_per_scene["Conf"] = 1
+        mot_pred_dataframe_per_scene["Zworld"] = -1
+        mot_pred_dataframe_per_scene = mot_pred_dataframe_per_scene[["FrameId", "Id", "X", "Y", "Width", "Height", "Conf", "Xworld", "Yworld", "Zworld"]]
+        
+        # Save single camera prediction in MOT format as CSV
+        mot_file_dir = os.path.join(default_dataset_config["TRACKERS_FOLDER"], mot_version, "data", "data")
+        make_dir(mot_file_dir)
+        tracker_file_name = str(scene_name) + ".txt"
+        mot_file_name = os.path.join(mot_file_dir, tracker_file_name)
+        write_dataframe_to_csv_to_file(mot_file_name, mot_pred_dataframe_per_scene)
+
+        # Make sequence ini file for trackeval library
+        if np.isnan(mot_pred_dataframe_per_scene["FrameId"].max()):
+            last_frame_id = ground_truth_dataframe_per_scene["FrameId"].max()
+        elif np.isnan(ground_truth_dataframe_per_scene["FrameId"].max()):
+            last_frame_id = mot_pred_dataframe_per_scene["FrameId"].max()
+        else:
+            last_frame_id = max(mot_pred_dataframe_per_scene["FrameId"].max(), ground_truth_dataframe_per_scene["FrameId"].max())
+        make_seq_ini_file(dir_name, scene=str(scene_name), seq_length=last_frame_id)          
+
+
+    # Evaluate ground truth & prediction to get all exhaustive metrics
+    evaluator = trackeval.eval.Evaluator(eval_config)
+    dataset_list = [trackeval.datasets.MotChallenge3DLocation(dataset_config)]
+    temp_metrics_list = [trackeval.metrics.HOTA]
+
+    metrics_list = []
+    for metric in temp_metrics_list:
+        if metric.get_name() in metrics_config["METRICS"]:
+            metrics_list.append(metric(metrics_config))
+
+    results = evaluator.evaluate(dataset_list, metrics_list)        
+
+    if is_temp_dir:
+        temp_dir.cleanup()
+    return results
+
+def evaluate(prediction_file: str, ground_truth_file: str, output_dir: str, num_cores: int, scene_2_camera_id_file: str) -> None:
+    
+    # Collect the result
+    sequence_result = computes_mot_metrics(prediction_file, ground_truth_file, output_dir, num_cores, scene_2_camera_id_file)
+    
+    # Compute average
+    final_result = dict()
+    HOTA_scores = []
+    DetA_scores = []
+    AssA_scores = []
+    LocA_scores = []
+    for scene_name, result in sequence_result[0]["MotChallenge3DLocation"]["data"].items():
+        
+        if scene_name == "COMBINED_SEQ":
+            continue
+        result = result["pedestrian"]["HOTA"]
+        HOTA_scores.append(np.mean(result["HOTA"]))
+        DetA_scores.append(np.mean(result["DetA"]))
+        AssA_scores.append(np.mean(result["AssA"]))
+        LocA_scores.append(np.mean(result["LocA"]))
+
+    final_result["FINAL"] = dict()
+    final_result["FINAL"]["HOTA"] = np.mean(np.array(HOTA_scores))
+    final_result["FINAL"]["DetA"] = np.mean(np.array(DetA_scores))
+    final_result["FINAL"]["AssA"] = np.mean(np.array(AssA_scores))
+    final_result["FINAL"]["LocA"] = np.mean(np.array(LocA_scores))
+    return final_result
+
+if __name__ == '__main__':
+    start = time.time() 
+    # Parse arguments
+    parser = ArgumentParser()
+    parser.add_argument('--prediction_file', required=True)
+    parser.add_argument('--ground_truth_file', required=True)
+    parser.add_argument('--output_dir')
+    parser.add_argument('--num_cores', type=check_positive, default=1)
+    parser.add_argument('--scene_2_camera_id_file', required=True)
+    args = parser.parse_args()
+
+    # Run evaluation
+    final_result = evaluate(args.prediction_file, args.ground_truth_file, args.output_dir, args.num_cores, args.scene_2_camera_id_file)
+
+    end = time.time()
+
+    print(f"Total runtime: {end-start} seconds.")
+    print(f"HOTA: {float(final_result['FINAL']['HOTA'] * 100):.4f}%")
+    print(f"DetA: {float(final_result['FINAL']['DetA'] * 100):.4f}%")
+    print(f"AssA: {float(final_result['FINAL']['AssA'] * 100):.4f}%")
+    print(f"LocA: {float(final_result['FINAL']['LocA'] * 100):.4f}%")
+

MTMC_Tracking_2024/eval/sample_file/ground_truth_test_full.txt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:76fc83dae03807622ef62246ba7ebdf43f8109f5a99a2447e681fd8c94955c14
+size 1508235913

MTMC_Tracking_2024/eval/sample_file/pred.txt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a51d3f9ff529cfcc1ed7c7e5dbe65307f05ce1e634b369cfedb4d23f5c83fcc3
+size 1341211632

MTMC_Tracking_2024/eval/sample_file/scene_name_2_cam_id_full.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f1f1c873d40a50e075d85a364554d902968b2c6717f16ebd5e63d43300f50bac
+size 9555

MTMC_Tracking_2024/eval/trackeval/__init__.py

@@ -0,0 +1,6 @@
+"""MTMC analytics trackeval modules"""
+from .eval import Evaluator
+from . import datasets
+from . import metrics
+from . import plotting
+from . import utils

MTMC_Tracking_2024/eval/trackeval/_timing.py

@@ -0,0 +1,81 @@
+from functools import wraps
+from time import perf_counter
+import inspect
+
+DO_TIMING = False
+DISPLAY_LESS_PROGRESS = False
+timer_dict = {}
+counter = 0
+
+
+def time(f):
+    """
+    Decorator function for timing the execution of a function.
+
+    :param f: The function to be timed.
+    :type f: function
+    :return: A wrapped function that measures the execution time of the original function.
+    :rtype: function
+
+    The wrapped function measures the execution time of the original function `f`. If the `DO_TIMING` flag is set to
+    `True`, the wrapped function records the accumulated time for each function and provides timing analysis when the
+    code is finished. If the flag is set to `False` or certain conditions are met, the wrapped function runs the
+    original function without timing.
+
+    Note that the timing analysis is printed to the console. Modify the implementation to save the timing information
+    in a different format or location if desired.
+    """
+    @wraps(f)
+    def wrap(*args, **kw):
+        if DO_TIMING:
+            # Run function with timing
+            ts = perf_counter()
+            result = f(*args, **kw)
+            te = perf_counter()
+            tt = te-ts
+
+            # Get function name
+            arg_names = inspect.getfullargspec(f)[0]
+            if arg_names[0] == 'self' and DISPLAY_LESS_PROGRESS:
+                return result
+            elif arg_names[0] == 'self':
+                method_name = type(args[0]).__name__ + '.' + f.__name__
+            else:
+                method_name = f.__name__
+
+            # Record accumulative time in each function for analysis
+            if method_name in timer_dict.keys():
+                timer_dict[method_name] += tt
+            else:
+                timer_dict[method_name] = tt
+
+            # If code is finished, display timing summary
+            if method_name == "Evaluator.evaluate":
+                print("")
+                print("Timing analysis:")
+                for key, value in timer_dict.items():
+                    print('%-70s %2.4f sec' % (key, value))
+            else:
+                # Get function argument values for printing special arguments of interest
+                arg_titles = ['tracker', 'seq', 'cls']
+                arg_vals = []
+                for i, a in enumerate(arg_names):
+                    if a in arg_titles:
+                        arg_vals.append(args[i])
+                arg_text = '(' + ', '.join(arg_vals) + ')'
+
+                # Display methods and functions with different indentation.
+                if arg_names[0] == 'self':
+                    print('%-74s %2.4f sec' % (' '*4 + method_name + arg_text, tt))
+                elif arg_names[0] == 'test':
+                    pass
+                else:
+                    global counter
+                    counter += 1
+                    print('%i %-70s %2.4f sec' % (counter, method_name + arg_text, tt))
+
+            return result
+        else:
+            # If config["TIME_PROGRESS"] is false, or config["USE_PARALLEL"] is true, run functions normally without timing.
+            return f(*args, **kw)
+    return wrap

MTMC_Tracking_2024/eval/trackeval/datasets/__init__.py

@@ -0,0 +1,3 @@
+"""MTMC analytics datasets modules"""
+from .mot_challenge_2d_box import MotChallenge2DBox
+from .mot_challenge_3d_location import MotChallenge3DLocation

MTMC_Tracking_2024/eval/trackeval/datasets/_base_dataset.py

@@ -0,0 +1,362 @@
+import csv
+import io
+import zipfile
+import os
+import traceback
+import numpy as np
+from copy import deepcopy
+from abc import ABC, abstractmethod
+from trackeval import _timing
+from trackeval.utils import TrackEvalException
+
+
+class _BaseDataset(ABC):
+    """
+    Module to create a skeleton of dataset formats
+    """
+    @abstractmethod
+    def __init__(self):
+        self.tracker_list = None
+        self.seq_list = None
+        self.class_list = None
+        self.output_fol = None
+        self.output_sub_fol = None
+        self.should_classes_combine = True
+        self.use_super_categories = False
+
+    @staticmethod
+    @abstractmethod
+    def get_default_dataset_config():
+        ...
+
+    @abstractmethod
+    def _load_raw_file(self, tracker, seq, is_gt):
+        ...
+
+    @_timing.time
+    @abstractmethod
+    def get_preprocessed_seq_data(self, raw_data, cls):
+        ...
+
+    @abstractmethod
+    def _calculate_similarities(self, gt_dets_t, tracker_dets_t):
+        ...
+
+    @classmethod
+    def get_class_name(cls):
+        return cls.__name__
+
+    def get_name(self):
+        return self.get_class_name()
+
+    def get_output_fol(self, tracker):
+        return os.path.join(self.output_fol, tracker, self.output_sub_fol)
+
+    def get_display_name(self, tracker):
+        """
+        Can be overwritten if the trackers name (in files) is different to how it should be displayed.
+        By default this method just returns the trackers name as is.
+
+        :param tracker: name of tracker 
+        :return: None
+        """
+        return tracker
+
+    def get_eval_info(self):
+        """Return info about the dataset needed for the Evaluator
+        
+        :return: List[str] tracker_list: list of all trackers
+        :return: List[str] seq_list: list of all sequences
+        :return: List[str] class_list: list of all classes
+        """
+        return self.tracker_list, self.seq_list, self.class_list
+
+    @_timing.time
+    def get_raw_seq_data(self, tracker, seq):
+        """ Loads raw data (tracker and ground-truth) for a single tracker on a single sequence.
+        Raw data includes all of the information needed for both preprocessing and evaluation, for all classes.
+        A later function (get_processed_seq_data) will perform such preprocessing and extract relevant information for
+        the evaluation of each class.
+
+        This returns a dict which contains the fields:
+        [num_timesteps]: integer
+        [gt_ids, tracker_ids, gt_classes, tracker_classes, tracker_confidences]:
+                                                                list (for each timestep) of 1D NDArrays (for each det).
+        [gt_dets, tracker_dets, gt_crowd_ignore_regions]: list (for each timestep) of lists of detections.
+        [similarity_scores]: list (for each timestep) of 2D NDArrays.
+        [gt_extras]: dict (for each extra) of lists (for each timestep) of 1D NDArrays (for each det).
+
+        gt_extras contains dataset specific information used for preprocessing such as occlusion and truncation levels.
+
+        Note that similarities are extracted as part of the dataset and not the metric, because almost all metrics are
+        independent of the exact method of calculating the similarity. However datasets are not (e.g. segmentation
+        masks vs 2D boxes vs 3D boxes).
+        We calculate the similarity before preprocessing because often both preprocessing and evaluation require it and
+        we don't wish to calculate this twice.
+        We calculate similarity between all gt and tracker classes (not just each class individually) to allow for
+        calculation of metrics such as class confusion matrices. Typically the impact of this on performance is low.
+
+        :param: str tracker: name of tracker
+        :param: str sequence: name of sequence
+        :return: raw_data: similarity scores among all gt & tracker classes
+        """
+        # Load raw data.
+        raw_gt_data = self._load_raw_file(tracker, seq, is_gt=True)
+        raw_tracker_data = self._load_raw_file(tracker, seq, is_gt=False)
+        raw_data = {**raw_tracker_data, **raw_gt_data}  # Merges dictionaries
+
+        # Calculate similarities for each timestep.
+        similarity_scores = []
+        for t, (gt_dets_t, tracker_dets_t) in enumerate(zip(raw_data['gt_dets'], raw_data['tracker_dets'])):
+            ious = self._calculate_similarities(gt_dets_t, tracker_dets_t)
+            similarity_scores.append(ious)
+        raw_data['similarity_scores'] = similarity_scores
+        return raw_data
+
+    @staticmethod
+    def _load_simple_text_file(file, time_col=0, id_col=None, remove_negative_ids=False, valid_filter=None,
+                               crowd_ignore_filter=None, convert_filter=None, is_zipped=False, zip_file=None,
+                               force_delimiters=None):
+        """ Function that loads data which is in a commonly used text file format.
+        Assumes each det is given by one row of a text file.
+        There is no limit to the number or meaning of each column,
+        however one column needs to give the timestep of each det (time_col) which is default col 0.
+
+        The file dialect (deliminator, num cols, etc) is determined automatically.
+        This function automatically separates dets by timestep,
+        and is much faster than alternatives such as np.loadtext or pandas.
+
+        If remove_negative_ids is True and id_col is not None, dets with negative values in id_col are excluded.
+        These are not excluded from ignore data.
+
+        valid_filter can be used to only include certain classes.
+        It is a dict with ints as keys, and lists as values,
+        such that a row is included if "row[key].lower() is in value" for all key/value pairs in the dict.
+        If None, all classes are included.
+
+        crowd_ignore_filter can be used to read crowd_ignore regions separately. It has the same format as valid filter.
+
+        convert_filter can be used to convert value read to another format.
+        This is used most commonly to convert classes given as string to a class id.
+        This is a dict such that the key is the column to convert, and the value is another dict giving the mapping.
+
+        Optionally, input files could be a zip of multiple text files for storage efficiency.
+
+        Returns read_data and ignore_data.
+        Each is a dict (with keys as timesteps as strings) of lists (over dets) of lists (over column values).
+        Note that all data is returned as strings, and must be converted to float/int later if needed.
+        Note that timesteps will not be present in the returned dict keys if there are no dets for them
+
+        :param str file: Path to the input text file or the name of the file within the zip file (if is_zipped is True).
+        :param int time_col: Index of the column containing the timestep of each detection, defaults to 0.
+        :param int id_col: Index of the column containing the ID of each detection, defaults to None.
+        :param bool remove_negative_ids: Whether to exclude dets with negative IDs, defaults to False.
+        :param dict valid_filter: Dictionary to include only certain classes, defaults to None.
+        :param dict crowd_ignore_filter: Dictionary to read crowd_ignore regions separately, defaults to None.
+        :param dict convert_filter: Dictionary to convert values read to another format, defaults to None.
+        :param bool is_zipped: Whether the input file is a zip file, defaults to False.
+        :param str zip_file: Path to the zip file (if is_zipped is True), defaults to None.
+        :param list force_delimiters: List of potential delimiters to override the automatic delimiter detection, defaults to None.
+        :raises TrackEvalException: If remove_negative_ids is True but id_col is not given, or if there's an error reading the file.
+        :return: A tuple containing read_data and crowd_ignore_data dictionaries.
+            read_data: dictionary with timesteps as keys (strings) and lists (over detections) of lists (over column values).
+            crowd_ignore_data: dictionary with timesteps as keys (strings) and lists (over detections) of lists (over column values).
+        :rtype: tuple
+        """
+
+        if remove_negative_ids and id_col is None:
+            raise TrackEvalException('remove_negative_ids is True, but id_col is not given.')
+        if crowd_ignore_filter is None:
+            crowd_ignore_filter = {}
+        if convert_filter is None:
+            convert_filter = {}
+        try:
+            if is_zipped:  # Either open file directly or within a zip.
+                if zip_file is None:
+                    raise TrackEvalException('is_zipped set to True, but no zip_file is given.')
+                archive = zipfile.ZipFile(os.path.join(zip_file), 'r')
+                fp = io.TextIOWrapper(archive.open(file, 'r'))
+            else:
+                fp = open(file)
+            read_data = {}
+            crowd_ignore_data = {}
+            fp.seek(0, os.SEEK_END)
+            # check if file is empty
+            if fp.tell():
+                fp.seek(0)
+                dialect = csv.Sniffer().sniff(fp.readline(), delimiters=force_delimiters)  # Auto determine structure.
+                dialect.skipinitialspace = True  # Deal with extra spaces between columns
+                fp.seek(0)
+                reader = csv.reader(fp, dialect)
+                for row in reader:
+                    try:
+                        # Deal with extra trailing spaces at the end of rows
+                        if row[-1] in '':
+                            row = row[:-1]
+                        timestep = str(int(float(row[time_col])))
+                        # Read ignore regions separately.
+                        is_ignored = False
+                        for ignore_key, ignore_value in crowd_ignore_filter.items():
+                            if row[ignore_key].lower() in ignore_value:
+                                # Convert values in one column (e.g. string to id)
+                                for convert_key, convert_value in convert_filter.items():
+                                    row[convert_key] = convert_value[row[convert_key].lower()]
+                                # Save data separated by timestep.
+                                if timestep in crowd_ignore_data.keys():
+                                    crowd_ignore_data[timestep].append(row)
+                                else:
+                                    crowd_ignore_data[timestep] = [row]
+                                is_ignored = True
+                        if is_ignored:  # if det is an ignore region, it cannot be a normal det.
+                            continue
+                        # Exclude some dets if not valid.
+                        if valid_filter is not None:
+                            for key, value in valid_filter.items():
+                                if row[key].lower() not in value:
+                                    continue
+                        if remove_negative_ids:
+                            if int(float(row[id_col])) < 0:
+                                continue
+                        # Convert values in one column (e.g. string to id)
+                        for convert_key, convert_value in convert_filter.items():
+                            row[convert_key] = convert_value[row[convert_key].lower()]
+                        # Save data separated by timestep.
+                        if timestep in read_data.keys():
+                            read_data[timestep].append(row)
+                        else:
+                            read_data[timestep] = [row]
+                    except Exception:
+                        exc_str_init = 'In file %s the following line cannot be read correctly: \n' % os.path.basename(
+                            file)
+                        exc_str = ' '.join([exc_str_init]+row)
+                        raise TrackEvalException(exc_str)
+            fp.close()
+        except Exception:
+            print('Error loading file: %s, printing traceback.' % file)
+            traceback.print_exc()
+            raise TrackEvalException(
+                'File %s cannot be read because it is either not present or invalidly formatted' % os.path.basename(
+                    file))
+        return read_data, crowd_ignore_data
+
+    @staticmethod
+    def _calculate_mask_ious(masks1, masks2, is_encoded=False, do_ioa=False):
+        """ Calculates the IOU (intersection over union) between two arrays of segmentation masks.
+        If is_encoded a run length encoding with pycocotools is assumed as input format, otherwise an input of numpy
+        arrays of the shape (num_masks, height, width) is assumed and the encoding is performed.
+        If do_ioa (intersection over area) , then calculates the intersection over the area of masks1 - this is commonly
+        used to determine if detections are within crowd ignore region.
+        :param masks1:  first set of masks (numpy array of shape (num_masks, height, width) if not encoded,
+                        else pycocotools rle encoded format)
+        :param masks2:  second set of masks (numpy array of shape (num_masks, height, width) if not encoded,
+                        else pycocotools rle encoded format)
+        :param is_encoded: whether the input is in pycocotools rle encoded format
+        :param do_ioa: whether to perform IoA computation
+        :return: the IoU/IoA scores
+        """
+
+        # Only loaded when run to reduce minimum requirements
+        from pycocotools import mask as mask_utils
+
+        # use pycocotools for run length encoding of masks
+        if not is_encoded:
+            masks1 = mask_utils.encode(np.array(np.transpose(masks1, (1, 2, 0)), order='F'))
+            masks2 = mask_utils.encode(np.array(np.transpose(masks2, (1, 2, 0)), order='F'))
+
+        # use pycocotools for iou computation of rle encoded masks
+        ious = mask_utils.iou(masks1, masks2, [do_ioa]*len(masks2))
+        if len(masks1) == 0 or len(masks2) == 0:
+            ious = np.asarray(ious).reshape(len(masks1), len(masks2))
+        assert (ious >= 0 - np.finfo('float').eps).all()
+        assert (ious <= 1 + np.finfo('float').eps).all()
+
+        return ious
+
+    @staticmethod
+    def _calculate_box_ious(bboxes1, bboxes2, box_format='xywh', do_ioa=False):
+        """ Calculates the IOU (intersection over union) between two arrays of boxes.
+        Allows variable box formats ('xywh' and 'x0y0x1y1').
+        If do_ioa (intersection over area) , then calculates the intersection over the area of boxes1 - this is commonly
+        used to determine if detections are within crowd ignore region.
+
+        :param bboxes1: first list of bounding boxes 
+        :param bboxes2: second list of bounding boxes 
+        :return: ious: the IoU/IoA scores
+        """
+        if box_format in 'xywh':
+            # layout: (x0, y0, w, h)
+            bboxes1 = deepcopy(bboxes1)
+            bboxes2 = deepcopy(bboxes2)
+
+            bboxes1[:, 2] = bboxes1[:, 0] + bboxes1[:, 2]
+            bboxes1[:, 3] = bboxes1[:, 1] + bboxes1[:, 3]
+            bboxes2[:, 2] = bboxes2[:, 0] + bboxes2[:, 2]
+            bboxes2[:, 3] = bboxes2[:, 1] + bboxes2[:, 3]
+        elif box_format not in 'x0y0x1y1':
+            raise (TrackEvalException('box_format %s is not implemented' % box_format))
+
+        # layout: (x0, y0, x1, y1)
+        min_ = np.minimum(bboxes1[:, np.newaxis, :], bboxes2[np.newaxis, :, :])
+        max_ = np.maximum(bboxes1[:, np.newaxis, :], bboxes2[np.newaxis, :, :])
+        intersection = np.maximum(min_[..., 2] - max_[..., 0], 0) * np.maximum(min_[..., 3] - max_[..., 1], 0)
+        area1 = (bboxes1[..., 2] - bboxes1[..., 0]) * (bboxes1[..., 3] - bboxes1[..., 1])
+
+        if do_ioa:
+            ioas = np.zeros_like(intersection)
+            valid_mask = area1 > 0 + np.finfo('float').eps
+            ioas[valid_mask, :] = intersection[valid_mask, :] / area1[valid_mask][:, np.newaxis]
+
+            return ioas
+        else:
+            area2 = (bboxes2[..., 2] - bboxes2[..., 0]) * (bboxes2[..., 3] - bboxes2[..., 1])
+            union = area1[:, np.newaxis] + area2[np.newaxis, :] - intersection
+            intersection[area1 <= 0 + np.finfo('float').eps, :] = 0
+            intersection[:, area2 <= 0 + np.finfo('float').eps] = 0
+            intersection[union <= 0 + np.finfo('float').eps] = 0
+            union[union <= 0 + np.finfo('float').eps] = 1
+            ious = intersection / union
+            return ious
+
+    @staticmethod
+    def _calculate_euclidean_similarity(dets1, dets2, zero_distance):
+        """ Calculates the euclidean distance between two sets of detections, and then converts this into a similarity
+        measure with values between 0 and 1 using the following formula: sim = max(0, 1 - dist/zero_distance).
+        The default zero_distance of 2.0, corresponds to the default used in MOT15_3D, such that a 0.5 similarity
+        threshold corresponds to a 1m distance threshold for TPs.
+
+        :param dets1: first list of detections 
+        :param dets2: second list of detections 
+        :return: sim: the similarity score
+        """
+        dist = np.linalg.norm(dets1[:, np.newaxis]-dets2[np.newaxis, :], axis=2)
+        sim = np.maximum(0, 1 - dist/zero_distance)
+        return sim
+
+    @staticmethod
+    def _check_unique_ids(data, after_preproc=False):
+        """Check the requirement that the tracker_ids and gt_ids are unique per timestep"""
+        gt_ids = data['gt_ids']
+        tracker_ids = data['tracker_ids']
+        for t, (gt_ids_t, tracker_ids_t) in enumerate(zip(gt_ids, tracker_ids)):
+            if len(tracker_ids_t) > 0:
+                unique_ids, counts = np.unique(tracker_ids_t, return_counts=True)
+                if np.max(counts) != 1:
+                    duplicate_ids = unique_ids[counts > 1]
+                    exc_str_init = 'Tracker predicts the same ID more than once in a single timestep ' \
+                                   '(seq: %s, frame: %i, ids:' % (data['seq'], t+1)
+                    exc_str = ' '.join([exc_str_init] + [str(d) for d in duplicate_ids]) + ')'
+                    if after_preproc:
+                        exc_str_init += '\n Note that this error occurred after preprocessing (but not before), ' \
+                                        'so ids may not be as in file, and something seems wrong with preproc.'
+                    raise TrackEvalException(exc_str)
+            if len(gt_ids_t) > 0:
+                unique_ids, counts = np.unique(gt_ids_t, return_counts=True)
+                if np.max(counts) != 1:
+                    duplicate_ids = unique_ids[counts > 1]
+                    exc_str_init = 'Ground-truth has the same ID more than once in a single timestep ' \
+                                   '(seq: %s, frame: %i, ids:' % (data['seq'], t+1)
+                    exc_str = ' '.join([exc_str_init] + [str(d) for d in duplicate_ids]) + ')'
+                    if after_preproc:
+                        exc_str_init += '\n Note that this error occurred after preprocessing (but not before), ' \
+                                        'so ids may not be as in file, and something seems wrong with preproc.'
+                    raise TrackEvalException(exc_str)

MTMC_Tracking_2024/eval/trackeval/datasets/mot_challenge_2d_box.py

@@ -0,0 +1,471 @@
+import os
+import csv
+import configparser
+import numpy as np
+from scipy.optimize import linear_sum_assignment
+from trackeval import utils
+from trackeval import _timing
+from trackeval.utils import TrackEvalException
+from trackeval.datasets._base_dataset import _BaseDataset
+
+
+class MotChallenge2DBox(_BaseDataset):
+    """
+    Dataset class for MOT Challenge 2D bounding box tracking
+    
+    :param dict config: configuration for the app
+    ::
+
+        default_dataset = trackeeval.datasets.MotChallenge2DBox(config)
+    """
+    @staticmethod
+    def get_default_dataset_config():
+        """Default class config values"""
+        code_path = utils.get_code_path()
+        default_config = {
+            'GT_FOLDER': os.path.join(code_path, 'data/gt/mot_challenge/'),  # Location of GT data
+            'TRACKERS_FOLDER': os.path.join(code_path, 'data/trackers/mot_challenge/'),  # Trackers location
+            'OUTPUT_FOLDER': None,  # Where to save eval results (if None, same as TRACKERS_FOLDER)
+            'TRACKERS_TO_EVAL': None,  # Filenames of trackers to eval (if None, all in folder)
+            'CLASSES_TO_EVAL': ['pedestrian'],  # Valid: ['pedestrian']
+            'BENCHMARK': 'MOT17',  # Valid: 'MOT17', 'MOT16', 'MOT20', 'MOT15'
+            'SPLIT_TO_EVAL': 'train',  # Valid: 'train', 'test', 'all'
+            'INPUT_AS_ZIP': False,  # Whether tracker input files are zipped
+            'PRINT_CONFIG': True,  # Whether to print current config
+            'DO_PREPROC': True,  # Whether to perform preprocessing (never done for MOT15)
+            'TRACKER_SUB_FOLDER': 'data',  # Tracker files are in TRACKER_FOLDER/tracker_name/TRACKER_SUB_FOLDER
+            'OUTPUT_SUB_FOLDER': '',  # Output files are saved in OUTPUT_FOLDER/tracker_name/OUTPUT_SUB_FOLDER
+            'TRACKER_DISPLAY_NAMES': None,  # Names of trackers to display, if None: TRACKERS_TO_EVAL
+            'SEQMAP_FOLDER': None,  # Where seqmaps are found (if None, GT_FOLDER/seqmaps)
+            'SEQMAP_FILE': None,  # Directly specify seqmap file (if none use seqmap_folder/benchmark-split_to_eval)
+            'SEQ_INFO': None,  # If not None, directly specify sequences to eval and their number of timesteps
+            'GT_LOC_FORMAT': '{gt_folder}/{seq}/gt/gt.txt',  # '{gt_folder}/{seq}/gt/gt.txt'
+            'SKIP_SPLIT_FOL': False,  # If False, data is in GT_FOLDER/BENCHMARK-SPLIT_TO_EVAL/ and in
+                                      # TRACKERS_FOLDER/BENCHMARK-SPLIT_TO_EVAL/tracker/
+                                      # If True, then the middle 'benchmark-split' folder is skipped for both.
+        }
+        return default_config
+
+    def __init__(self, config=None):
+        """Initialise dataset, checking that all required files are present"""
+        super().__init__()
+        # Fill non-given config values with defaults
+        self.config = utils.init_config(config, self.get_default_dataset_config(), self.get_name())
+
+        self.benchmark = self.config['BENCHMARK']
+        gt_set = self.config['BENCHMARK'] + '-' + self.config['SPLIT_TO_EVAL']
+        self.gt_set = gt_set
+        if not self.config['SKIP_SPLIT_FOL']:
+            split_fol = gt_set
+        else:
+            split_fol = ''
+        self.gt_fol = os.path.join(self.config['GT_FOLDER'], split_fol)
+        self.tracker_fol = os.path.join(self.config['TRACKERS_FOLDER'], split_fol)
+        self.should_classes_combine = False
+        self.use_super_categories = False
+        self.data_is_zipped = self.config['INPUT_AS_ZIP']
+        self.do_preproc = self.config['DO_PREPROC']
+
+        self.output_fol = self.config['OUTPUT_FOLDER']
+        if self.output_fol is None:
+            self.output_fol = self.tracker_fol
+
+        self.tracker_sub_fol = self.config['TRACKER_SUB_FOLDER']
+        self.output_sub_fol = self.config['OUTPUT_SUB_FOLDER']
+
+        # Get classes to eval
+        self.valid_classes = ['pedestrian']
+        self.class_list = [cls.lower() if cls.lower() in self.valid_classes else None
+                           for cls in self.config['CLASSES_TO_EVAL']]
+        if not all(self.class_list):
+            raise TrackEvalException('Attempted to evaluate an invalid class. Only pedestrian class is valid.')
+        self.class_name_to_class_id = {'pedestrian': 1, 'person_on_vehicle': 2, 'car': 3, 'bicycle': 4, 'motorbike': 5,
+                                       'non_mot_vehicle': 6, 'static_person': 7, 'distractor': 8, 'occluder': 9,
+                                       'occluder_on_ground': 10, 'occluder_full': 11, 'reflection': 12, 'crowd': 13}
+        self.valid_class_numbers = list(self.class_name_to_class_id.values())
+
+        # Get sequences to eval and check gt files exist
+        self.seq_list, self.seq_lengths = self._get_seq_info()
+        if len(self.seq_list) < 1:
+            raise TrackEvalException('No sequences are selected to be evaluated.')
+
+        # Check gt files exist
+        for seq in self.seq_list:
+            if not self.data_is_zipped:
+                curr_file = self.config["GT_LOC_FORMAT"].format(gt_folder=self.gt_fol, seq=seq)
+                if not os.path.isfile(curr_file):
+                    print('GT file not found ' + curr_file)
+                    raise TrackEvalException('GT file not found for sequence: ' + seq)
+        if self.data_is_zipped:
+            curr_file = os.path.join(self.gt_fol, 'data.zip')
+            if not os.path.isfile(curr_file):
+                print('GT file not found ' + curr_file)
+                raise TrackEvalException('GT file not found: ' + os.path.basename(curr_file))
+
+        # Get trackers to eval
+        if self.config['TRACKERS_TO_EVAL'] is None:
+            self.tracker_list = os.listdir(self.tracker_fol)
+        else:
+            self.tracker_list = self.config['TRACKERS_TO_EVAL']
+
+        if self.config['TRACKER_DISPLAY_NAMES'] is None:
+            self.tracker_to_disp = dict(zip(self.tracker_list, self.tracker_list))
+        elif (self.config['TRACKERS_TO_EVAL'] is not None) and (
+                len(self.config['TRACKER_DISPLAY_NAMES']) == len(self.tracker_list)):
+            self.tracker_to_disp = dict(zip(self.tracker_list, self.config['TRACKER_DISPLAY_NAMES']))
+        else:
+            raise TrackEvalException('List of tracker files and tracker display names do not match.')
+
+        for tracker in self.tracker_list:
+            if self.data_is_zipped:
+                curr_file = os.path.join(self.tracker_fol, tracker, self.tracker_sub_fol + '.zip')
+                if not os.path.isfile(curr_file):
+                    print('Tracker file not found: ' + curr_file)
+                    raise TrackEvalException('Tracker file not found: ' + tracker + '/' + os.path.basename(curr_file))
+            else:
+                for seq in self.seq_list:
+                    curr_file = os.path.join(self.tracker_fol, tracker, self.tracker_sub_fol, seq + '.txt')
+                    if not os.path.isfile(curr_file):
+                        print('Tracker file not found: ' + curr_file)
+                        raise TrackEvalException(
+                            'Tracker file not found: ' + tracker + '/' + self.tracker_sub_fol + '/' + os.path.basename(
+                                curr_file))
+
+    def get_display_name(self, tracker):
+        """
+        Gets the display name of the tracker
+
+        :param str tracker: Class of tracker
+        :return: str
+        ::
+
+            dataset.get_display_name(tracker)
+        """
+
+        return self.tracker_to_disp[tracker]
+
+    def _get_seq_info(self):
+        seq_list = []
+        seq_lengths = {}
+        if self.config["SEQ_INFO"]:
+            seq_list = list(self.config["SEQ_INFO"].keys())
+            seq_lengths = self.config["SEQ_INFO"]
+
+            # If sequence length is 'None' tries to read sequence length from .ini files.
+            for seq, seq_length in seq_lengths.items():
+                if seq_length is None:
+                    ini_file = os.path.join(self.gt_fol, seq, 'seqinfo.ini')
+                    if not os.path.isfile(ini_file):
+                        raise TrackEvalException('ini file does not exist: ' + seq + '/' + os.path.basename(ini_file))
+                    ini_data = configparser.ConfigParser()
+                    ini_data.read(ini_file)
+                    seq_lengths[seq] = int(float(ini_data['Sequence']['seqLength']))
+
+        else:
+            if self.config["SEQMAP_FILE"]:
+                seqmap_file = self.config["SEQMAP_FILE"]
+            else:
+                if self.config["SEQMAP_FOLDER"] is None:
+                    seqmap_file = os.path.join(self.config['GT_FOLDER'], 'seqmaps', self.gt_set + '.txt')
+                else:
+                    seqmap_file = os.path.join(self.config["SEQMAP_FOLDER"], self.gt_set + '.txt')
+            if not os.path.isfile(seqmap_file):
+                print('no seqmap found: ' + seqmap_file)
+                raise TrackEvalException('no seqmap found: ' + os.path.basename(seqmap_file))
+            with open(seqmap_file) as fp:
+                reader = csv.reader(fp)
+                for i, row in enumerate(reader):
+                    if i == 0 or row[0] == '':
+                        continue
+                    seq = row[0]
+                    seq_list.append(seq)
+                    ini_file = os.path.join(self.gt_fol, seq, 'seqinfo.ini')
+                    if not os.path.isfile(ini_file):
+                        raise TrackEvalException('ini file does not exist: ' + seq + '/' + os.path.basename(ini_file))
+                    ini_data = configparser.ConfigParser()
+                    ini_data.read(ini_file)
+                    seq_lengths[seq] = int(float(ini_data['Sequence']['seqLength']))
+        return seq_list, seq_lengths
+
+    def _load_raw_file(self, tracker, seq, is_gt):
+        """Load a file (gt or tracker) in the MOT Challenge 2D box format
+
+        If is_gt, this returns a dict which contains the fields:
+        [gt_ids, gt_classes] : list (for each timestep) of 1D NDArrays (for each det).
+        [gt_dets, gt_crowd_ignore_regions]: list (for each timestep) of lists of detections.
+        [gt_extras] : list (for each timestep) of dicts (for each extra) of 1D NDArrays (for each det).
+
+        if not is_gt, this returns a dict which contains the fields:
+        [tracker_ids, tracker_classes, tracker_confidences] : list (for each timestep) of 1D NDArrays (for each det).
+        [tracker_dets]: list (for each timestep) of lists of detections.
+
+        :param str tracker: Name of the tracker.
+        :param str seq: Sequence identifier.
+        :param bool is_gt: Indicates whether the file is ground truth or from a tracker.
+        :raises TrackEvalException: If there's an error loading the file or if the data is corrupted.
+        :return: dictionary containing the loaded data.
+        :rtype: dict
+        """
+        # File location
+        if self.data_is_zipped:
+            if is_gt:
+                zip_file = os.path.join(self.gt_fol, 'data.zip')
+            else:
+                zip_file = os.path.join(self.tracker_fol, tracker, self.tracker_sub_fol + '.zip')
+            file = seq + '.txt'
+        else:
+            zip_file = None
+            if is_gt:
+                file = self.config["GT_LOC_FORMAT"].format(gt_folder=self.gt_fol, seq=seq)
+            else:
+                file = os.path.join(self.tracker_fol, tracker, self.tracker_sub_fol, seq + '.txt')
+
+        # Load raw data from text file
+        read_data, ignore_data = self._load_simple_text_file(file, is_zipped=self.data_is_zipped, zip_file=zip_file)
+
+        # Convert data to required format
+        num_timesteps = self.seq_lengths[seq]
+        data_keys = ['ids', 'classes', 'dets']
+        if is_gt:
+            data_keys += ['gt_crowd_ignore_regions', 'gt_extras']
+        else:
+            data_keys += ['tracker_confidences']
+        raw_data = {key: [None] * num_timesteps for key in data_keys}
+
+        # Check for any extra time keys
+        current_time_keys = [str( t+ 1) for t in range(num_timesteps)]
+        extra_time_keys = [x for x in read_data.keys() if x not in current_time_keys]
+        if len(extra_time_keys) > 0:
+            if is_gt:
+                text = 'Ground-truth'
+            else:
+                text = 'Tracking'
+            raise TrackEvalException(
+                text + ' data contains the following invalid timesteps in seq %s: ' % seq + ', '.join(
+                    [str(x) + ', ' for x in extra_time_keys]))
+
+        for t in range(num_timesteps):
+            time_key = str(t+1)
+            if time_key in read_data.keys():
+                try:
+                    time_data = np.asarray(read_data[time_key], dtype=float)
+                except ValueError:
+                    if is_gt:
+                        raise TrackEvalException(
+                            'Cannot convert gt data for sequence %s to float. Is data corrupted?' % seq)
+                    else:
+                        raise TrackEvalException(
+                            'Cannot convert tracking data from tracker %s, sequence %s to float. Is data corrupted?' % (
+                                tracker, seq))
+                try:
+                    raw_data['dets'][t] = np.atleast_2d(time_data[:, 2:6])
+                    raw_data['ids'][t] = np.atleast_1d(time_data[:, 1]).astype(int)
+                except IndexError:
+                    if is_gt:
+                        err = 'Cannot load gt data from sequence %s, because there is not enough ' \
+                              'columns in the data.' % seq
+                        raise TrackEvalException(err)
+                    else:
+                        err = 'Cannot load tracker data from tracker %s, sequence %s, because there is not enough ' \
+                              'columns in the data.' % (tracker, seq)
+                        raise TrackEvalException(err)
+                if time_data.shape[1] >= 8:
+                    raw_data['classes'][t] = np.atleast_1d(time_data[:, 7]).astype(int)
+                else:
+                    if not is_gt:
+                        raw_data['classes'][t] = np.ones_like(raw_data['ids'][t])
+                    else:
+                        raise TrackEvalException(
+                            'GT data is not in a valid format, there is not enough rows in seq %s, timestep %i.' % (
+                                seq, t))
+                if is_gt:
+                    gt_extras_dict = {'zero_marked': np.atleast_1d(time_data[:, 6].astype(int))}
+                    raw_data['gt_extras'][t] = gt_extras_dict
+                else:
+                    raw_data['tracker_confidences'][t] = np.atleast_1d(time_data[:, 6])
+            else:
+                raw_data['dets'][t] = np.empty((0, 4))
+                raw_data['ids'][t] = np.empty(0).astype(int)
+                raw_data['classes'][t] = np.empty(0).astype(int)
+                if is_gt:
+                    gt_extras_dict = {'zero_marked': np.empty(0)}
+                    raw_data['gt_extras'][t] = gt_extras_dict
+                else:
+                    raw_data['tracker_confidences'][t] = np.empty(0)
+            if is_gt:
+                raw_data['gt_crowd_ignore_regions'][t] = np.empty((0, 4))
+
+        if is_gt:
+            key_map = {'ids': 'gt_ids',
+                       'classes': 'gt_classes',
+                       'dets': 'gt_dets'}
+        else:
+            key_map = {'ids': 'tracker_ids',
+                       'classes': 'tracker_classes',
+                       'dets': 'tracker_dets'}
+        for k, v in key_map.items():
+            raw_data[v] = raw_data.pop(k)
+        raw_data['num_timesteps'] = num_timesteps
+        raw_data['seq'] = seq
+        return raw_data
+
+    @_timing.time
+    def get_preprocessed_seq_data(self, raw_data, cls):
+        """ Preprocess data for a single sequence for a single class ready for evaluation.
+        Inputs:
+             - raw_data is a dict containing the data for the sequence already read in by get_raw_seq_data().
+             - cls is the class to be evaluated.
+        Outputs:
+             - data is a dict containing all of the information that metrics need to perform evaluation.
+                It contains the following fields:
+                    [num_timesteps, num_gt_ids, num_tracker_ids, num_gt_dets, num_tracker_dets] : integers.
+                    [gt_ids, tracker_ids, tracker_confidences]: list (for each timestep) of 1D NDArrays (for each det).
+                    [gt_dets, tracker_dets]: list (for each timestep) of lists of detections.
+                    [similarity_scores]: list (for each timestep) of 2D NDArrays.
+        Notes:
+            General preprocessing (preproc) occurs in 4 steps. Some datasets may not use all of these steps.
+                1) Extract only detections relevant for the class to be evaluated (including distractor detections).
+                2) Match gt dets and tracker dets. Remove tracker dets that are matched to a gt det that is of a
+                    distractor class, or otherwise marked as to be removed.
+                3) Remove unmatched tracker dets if they fall within a crowd ignore region or don't meet a certain
+                    other criteria (e.g. are too small).
+                4) Remove gt dets that were only useful for preprocessing and not for actual evaluation.
+            After the above preprocessing steps, this function also calculates the number of gt and tracker detections
+                and unique track ids. It also relabels gt and tracker ids to be contiguous and checks that ids are
+                unique within each timestep.
+
+        MOT Challenge:
+            In MOT Challenge, the 4 preproc steps are as follow:
+                1) There is only one class (pedestrian) to be evaluated, but all other classes are used for preproc.
+                2) Predictions are matched against all gt boxes (regardless of class), those matching with distractor
+                    objects are removed.
+                3) There is no crowd ignore regions.
+                4) All gt dets except pedestrian are removed, also removes pedestrian gt dets marked with zero_marked.
+        
+        :param raw_data: A dict containing the data for the sequence already read in by `get_raw_seq_data()`.
+        :param cls: The class to be evaluated.
+
+        :return: A dict containing all of the information that metrics need to perform evaluation.
+                It contains the following fields:
+                - [num_timesteps, num_gt_ids, num_tracker_ids, num_gt_dets, num_tracker_dets]: Integers.
+                - [gt_ids, tracker_ids, tracker_confidences]: List (for each timestep) of 1D NDArrays (for each detection).
+                - [gt_dets, tracker_dets]: List (for each timestep) of lists of detections.
+                - [similarity_scores]: List (for each timestep) of 2D NDArrays.
+
+        """
+        # Check that input data has unique ids
+        self._check_unique_ids(raw_data)
+
+        distractor_class_names = ['person_on_vehicle', 'static_person', 'distractor', 'reflection']
+        if self.benchmark == 'MOT20':
+            distractor_class_names.append('non_mot_vehicle')
+        distractor_classes = [self.class_name_to_class_id[x] for x in distractor_class_names]
+        cls_id = self.class_name_to_class_id[cls]
+
+        data_keys = ['gt_ids', 'tracker_ids', 'gt_dets', 'tracker_dets', 'tracker_confidences', 'similarity_scores']
+        data = {key: [None] * raw_data['num_timesteps'] for key in data_keys}
+        unique_gt_ids = []
+        unique_tracker_ids = []
+        num_gt_dets = 0
+        num_tracker_dets = 0
+        for t in range(raw_data['num_timesteps']):
+
+            # Get all data
+            gt_ids = raw_data['gt_ids'][t]
+            gt_dets = raw_data['gt_dets'][t]
+            gt_classes = raw_data['gt_classes'][t]
+            gt_zero_marked = raw_data['gt_extras'][t]['zero_marked']
+
+            tracker_ids = raw_data['tracker_ids'][t]
+            tracker_dets = raw_data['tracker_dets'][t]
+            tracker_classes = raw_data['tracker_classes'][t]
+            tracker_confidences = raw_data['tracker_confidences'][t]
+            similarity_scores = raw_data['similarity_scores'][t]
+
+            # Evaluation is ONLY valid for pedestrian class
+            if len(tracker_classes) > 0 and np.max(tracker_classes) > 1:
+                raise TrackEvalException(
+                    'Evaluation is only valid for pedestrian class. Non pedestrian class (%i) found in sequence %s at '
+                    'timestep %i.' % (np.max(tracker_classes), raw_data['seq'], t))
+
+            # Match tracker and gt dets (with hungarian algorithm) and remove tracker dets which match with gt dets
+            # which are labeled as belonging to a distractor class.
+            to_remove_tracker = np.array([], int)
+            if self.do_preproc and self.benchmark != 'MOT15' and gt_ids.shape[0] > 0 and tracker_ids.shape[0] > 0:
+
+                # Check all classes are valid:
+                invalid_classes = np.setdiff1d(np.unique(gt_classes), self.valid_class_numbers)
+                if len(invalid_classes) > 0:
+                    print(' '.join([str(x) for x in invalid_classes]))
+                    raise(TrackEvalException('Attempting to evaluate using invalid gt classes. '
+                                             'This warning only triggers if preprocessing is performed, '
+                                             'e.g. not for MOT15 or where prepropressing is explicitly disabled. '
+                                             'Please either check your gt data, or disable preprocessing. '
+                                             'The following invalid classes were found in timestep ' + str(t) + ': ' +
+                                             ' '.join([str(x) for x in invalid_classes])))
+
+                matching_scores = similarity_scores.copy()
+                matching_scores[matching_scores < 0.5 - np.finfo('float').eps] = 0
+                match_rows, match_cols = linear_sum_assignment(-matching_scores)
+                actually_matched_mask = matching_scores[match_rows, match_cols] > 0 + np.finfo('float').eps
+                match_rows = match_rows[actually_matched_mask]
+                match_cols = match_cols[actually_matched_mask]
+
+                is_distractor_class = np.isin(gt_classes[match_rows], distractor_classes)
+                to_remove_tracker = match_cols[is_distractor_class]
+
+            # Apply preprocessing to remove all unwanted tracker dets.
+            data['tracker_ids'][t] = np.delete(tracker_ids, to_remove_tracker, axis=0)
+            data['tracker_dets'][t] = np.delete(tracker_dets, to_remove_tracker, axis=0)
+            data['tracker_confidences'][t] = np.delete(tracker_confidences, to_remove_tracker, axis=0)
+            similarity_scores = np.delete(similarity_scores, to_remove_tracker, axis=1)
+
+            # Remove gt detections marked as to remove (zero marked), and also remove gt detections not in pedestrian
+            # class (not applicable for MOT15)
+            if self.do_preproc and self.benchmark != 'MOT15':
+                gt_to_keep_mask = (np.not_equal(gt_zero_marked, 0)) & \
+                                  (np.equal(gt_classes, cls_id))
+            else:
+                # There are no classes for MOT15
+                gt_to_keep_mask = np.not_equal(gt_zero_marked, 0)
+            data['gt_ids'][t] = gt_ids[gt_to_keep_mask]
+            data['gt_dets'][t] = gt_dets[gt_to_keep_mask, :]
+            data['similarity_scores'][t] = similarity_scores[gt_to_keep_mask]
+
+            unique_gt_ids += list(np.unique(data['gt_ids'][t]))
+            unique_tracker_ids += list(np.unique(data['tracker_ids'][t]))
+            num_tracker_dets += len(data['tracker_ids'][t])
+            num_gt_dets += len(data['gt_ids'][t])
+
+        # Re-label IDs such that there are no empty IDs
+        if len(unique_gt_ids) > 0:
+            unique_gt_ids = np.unique(unique_gt_ids)
+            gt_id_map = np.nan * np.ones((np.max(unique_gt_ids) + 1))
+            gt_id_map[unique_gt_ids] = np.arange(len(unique_gt_ids))
+            for t in range(raw_data['num_timesteps']):
+                if len(data['gt_ids'][t]) > 0:
+                    data['gt_ids'][t] = gt_id_map[data['gt_ids'][t]].astype(int)
+        if len(unique_tracker_ids) > 0:
+            unique_tracker_ids = np.unique(unique_tracker_ids)
+            tracker_id_map = np.nan * np.ones((np.max(unique_tracker_ids) + 1))
+            tracker_id_map[unique_tracker_ids] = np.arange(len(unique_tracker_ids))
+            for t in range(raw_data['num_timesteps']):
+                if len(data['tracker_ids'][t]) > 0:
+                    data['tracker_ids'][t] = tracker_id_map[data['tracker_ids'][t]].astype(int)
+
+        # Record overview statistics.
+        data['num_tracker_dets'] = num_tracker_dets
+        data['num_gt_dets'] = num_gt_dets
+        data['num_tracker_ids'] = len(unique_tracker_ids)
+        data['num_gt_ids'] = len(unique_gt_ids)
+        data['num_timesteps'] = raw_data['num_timesteps']
+        data['seq'] = raw_data['seq']
+
+        # Ensure again that ids are unique per timestep after preproc.
+        self._check_unique_ids(data, after_preproc=True)
+
+        return data
+
+    def _calculate_similarities(self, gt_dets_t, tracker_dets_t):
+        similarity_scores = self._calculate_box_ious(gt_dets_t, tracker_dets_t, box_format='xywh')
+        return similarity_scores

MTMC_Tracking_2024/eval/trackeval/datasets/mot_challenge_3d_location.py

@@ -0,0 +1,475 @@
+import os
+import csv
+import configparser
+import numpy as np
+from scipy.optimize import linear_sum_assignment
+from trackeval import utils
+from trackeval import _timing
+from trackeval.utils import TrackEvalException
+from trackeval.datasets._base_dataset import _BaseDataset
+
+
+class MotChallenge3DLocation(_BaseDataset):
+    """
+    Dataset class for MOT Challenge 3D tracking
+    
+    :param dict config: configuration for the app
+    ::
+
+        default_dataset = trackeeval.datasets.MotChallenge2DBox(config)
+    """
+    @staticmethod
+    def get_default_dataset_config():
+        """Default class config values"""
+        code_path = utils.get_code_path()
+        default_config = {
+            'GT_FOLDER': os.path.join(code_path, 'data/gt/mot_challenge/'),  # Location of GT data
+            'TRACKERS_FOLDER': os.path.join(code_path, 'data/trackers/mot_challenge/'),  # Trackers location
+            'OUTPUT_FOLDER': None,  # Where to save eval results (if None, same as TRACKERS_FOLDER)
+            'TRACKERS_TO_EVAL': None,  # Filenames of trackers to eval (if None, all in folder)
+            'CLASSES_TO_EVAL': ['pedestrian'],  # Valid: ['pedestrian']
+            'BENCHMARK': 'MOT17',  # Valid: 'MOT17', 'MOT16', 'MOT20', 'MOT15'
+            'SPLIT_TO_EVAL': 'train',  # Valid: 'train', 'test', 'all'
+            'INPUT_AS_ZIP': False,  # Whether tracker input files are zipped
+            'PRINT_CONFIG': True,  # Whether to print current config
+            'DO_PREPROC': True,  # Whether to perform preprocessing (never done for MOT15)
+            'TRACKER_SUB_FOLDER': 'data',  # Tracker files are in TRACKER_FOLDER/tracker_name/TRACKER_SUB_FOLDER
+            'OUTPUT_SUB_FOLDER': '',  # Output files are saved in OUTPUT_FOLDER/tracker_name/OUTPUT_SUB_FOLDER
+            'TRACKER_DISPLAY_NAMES': None,  # Names of trackers to display, if None: TRACKERS_TO_EVAL
+            'SEQMAP_FOLDER': None,  # Where seqmaps are found (if None, GT_FOLDER/seqmaps)
+            'SEQMAP_FILE': None,  # Directly specify seqmap file (if none use seqmap_folder/benchmark-split_to_eval)
+            'SEQ_INFO': None,  # If not None, directly specify sequences to eval and their number of timesteps
+            'GT_LOC_FORMAT': '{gt_folder}/{seq}/gt/gt.txt',  # '{gt_folder}/{seq}/gt/gt.txt'
+            'SKIP_SPLIT_FOL': False,  # If False, data is in GT_FOLDER/BENCHMARK-SPLIT_TO_EVAL/ and in
+                                      # TRACKERS_FOLDER/BENCHMARK-SPLIT_TO_EVAL/tracker/
+                                      # If True, then the middle 'benchmark-split' folder is skipped for both.
+        }
+        return default_config
+
+    def __init__(self, config=None, zd=2.0):
+        """Initialise dataset, checking that all required files are present"""
+        super().__init__()
+        # Fill non-given config values with defaults
+        self.config = utils.init_config(config, self.get_default_dataset_config(), self.get_name())
+        self.zero_distance = zd
+        self.benchmark = self.config['BENCHMARK']
+        gt_set = self.config['BENCHMARK'] + '-' + self.config['SPLIT_TO_EVAL']
+        self.gt_set = gt_set
+        if not self.config['SKIP_SPLIT_FOL']:
+            split_fol = gt_set
+        else:
+            split_fol = ''
+        self.gt_fol = os.path.join(self.config['GT_FOLDER'], split_fol)
+        self.tracker_fol = os.path.join(self.config['TRACKERS_FOLDER'], split_fol)
+        self.should_classes_combine = False
+        self.use_super_categories = False
+        self.data_is_zipped = self.config['INPUT_AS_ZIP']
+        self.do_preproc = self.config['DO_PREPROC']
+
+        self.output_fol = self.config['OUTPUT_FOLDER']
+        if self.output_fol is None:
+            self.output_fol = self.tracker_fol
+
+        self.tracker_sub_fol = self.config['TRACKER_SUB_FOLDER']
+        self.output_sub_fol = self.config['OUTPUT_SUB_FOLDER']
+
+        # Get classes to eval
+        self.valid_classes = ['pedestrian']
+        self.class_list = [cls.lower() if cls.lower() in self.valid_classes else None
+                           for cls in self.config['CLASSES_TO_EVAL']]
+        if not all(self.class_list):
+            raise TrackEvalException('Attempted to evaluate an invalid class. Only pedestrian class is valid.')
+        self.class_name_to_class_id = {'pedestrian': 1, 'person_on_vehicle': 2, 'car': 3, 'bicycle': 4, 'motorbike': 5,
+                                       'non_mot_vehicle': 6, 'static_person': 7, 'distractor': 8, 'occluder': 9,
+                                       'occluder_on_ground': 10, 'occluder_full': 11, 'reflection': 12, 'crowd': 13}
+        self.valid_class_numbers = list(self.class_name_to_class_id.values())
+
+        # Get sequences to eval and check gt files exist
+        self.seq_list, self.seq_lengths = self._get_seq_info()
+        if len(self.seq_list) < 1:
+            raise TrackEvalException('No sequences are selected to be evaluated.')
+
+        # Check gt files exist
+        for seq in self.seq_list:
+            if not self.data_is_zipped:
+                curr_file = self.config["GT_LOC_FORMAT"].format(gt_folder=self.gt_fol, seq=seq)
+                if not os.path.isfile(curr_file):
+                    print('GT file not found ' + curr_file)
+                    raise TrackEvalException('GT file not found for sequence: ' + seq)
+        if self.data_is_zipped:
+            curr_file = os.path.join(self.gt_fol, 'data.zip')
+            if not os.path.isfile(curr_file):
+                print('GT file not found ' + curr_file)
+                raise TrackEvalException('GT file not found: ' + os.path.basename(curr_file))
+
+        # Get trackers to eval
+        if self.config['TRACKERS_TO_EVAL'] is None:
+            self.tracker_list = os.listdir(self.tracker_fol)
+        else:
+            self.tracker_list = self.config['TRACKERS_TO_EVAL']
+
+        if self.config['TRACKER_DISPLAY_NAMES'] is None:
+            self.tracker_to_disp = dict(zip(self.tracker_list, self.tracker_list))
+        elif (self.config['TRACKERS_TO_EVAL'] is not None) and (
+                len(self.config['TRACKER_DISPLAY_NAMES']) == len(self.tracker_list)):
+            self.tracker_to_disp = dict(zip(self.tracker_list, self.config['TRACKER_DISPLAY_NAMES']))
+        else:
+            raise TrackEvalException('List of tracker files and tracker display names do not match.')
+
+        for tracker in self.tracker_list:
+            if self.data_is_zipped:
+                curr_file = os.path.join(self.tracker_fol, tracker, self.tracker_sub_fol + '.zip')
+                if not os.path.isfile(curr_file):
+                    print('Tracker file not found: ' + curr_file)
+                    raise TrackEvalException('Tracker file not found: ' + tracker + '/' + os.path.basename(curr_file))
+            else:
+                for seq in self.seq_list:
+                    curr_file = os.path.join(self.tracker_fol, tracker, self.tracker_sub_fol, seq + '.txt')
+                    if not os.path.isfile(curr_file):
+                        print('Tracker file not found: ' + curr_file)
+                        raise TrackEvalException(
+                            'Tracker file not found: ' + tracker + '/' + self.tracker_sub_fol + '/' + os.path.basename(
+                                curr_file))
+
+    def get_display_name(self, tracker):
+        """
+        Gets the display name of the tracker
+
+        :param str tracker: Class of tracker
+        :return: str
+        ::
+
+            dataset.get_display_name(tracker)
+        """
+
+        return self.tracker_to_disp[tracker]
+
+    def _get_seq_info(self):
+        seq_list = []
+        seq_lengths = {}
+        if self.config["SEQ_INFO"]:
+            seq_list = list(self.config["SEQ_INFO"].keys())
+            seq_lengths = self.config["SEQ_INFO"]
+
+            # If sequence length is 'None' tries to read sequence length from .ini files.
+            for seq, seq_length in seq_lengths.items():
+                if seq_length is None:
+                    ini_file = os.path.join(self.gt_fol, seq, 'seqinfo.ini')
+                    if not os.path.isfile(ini_file):
+                        raise TrackEvalException('ini file does not exist: ' + seq + '/' + os.path.basename(ini_file))
+                    ini_data = configparser.ConfigParser()
+                    ini_data.read(ini_file)
+                    seq_lengths[seq] = int(float(ini_data['Sequence']['seqLength']))
+
+        else:
+            if self.config["SEQMAP_FILE"]:
+                seqmap_file = self.config["SEQMAP_FILE"]
+            else:
+                if self.config["SEQMAP_FOLDER"] is None:
+                    seqmap_file = os.path.join(self.config['GT_FOLDER'], 'seqmaps', self.gt_set + '.txt')
+                else:
+                    seqmap_file = os.path.join(self.config["SEQMAP_FOLDER"], self.gt_set + '.txt')
+            if not os.path.isfile(seqmap_file):
+                print('no seqmap found: ' + seqmap_file)
+                raise TrackEvalException('no seqmap found: ' + os.path.basename(seqmap_file))
+            with open(seqmap_file) as fp:
+                reader = csv.reader(fp)
+                for i, row in enumerate(reader):
+                    if i == 0 or row[0] == '':
+                        continue
+                    seq = row[0]
+                    seq_list.append(seq)
+                    ini_file = os.path.join(self.gt_fol, seq, 'seqinfo.ini')
+                    if not os.path.isfile(ini_file):
+                        raise TrackEvalException('ini file does not exist: ' + seq + '/' + os.path.basename(ini_file))
+                    ini_data = configparser.ConfigParser()
+                    ini_data.read(ini_file)
+                    seq_lengths[seq] = int(float(ini_data['Sequence']['seqLength']))
+        return seq_list, seq_lengths
+
+    def _load_raw_file(self, tracker, seq, is_gt):
+        """Load a file (gt or tracker) in the MOT Challenge 3D location format
+
+        If is_gt, this returns a dict which contains the fields:
+        [gt_ids, gt_classes] : list (for each timestep) of 1D NDArrays (for each det).
+        [gt_dets, gt_crowd_ignore_regions]: list (for each timestep) of lists of detections.
+        [gt_extras] : list (for each timestep) of dicts (for each extra) of 1D NDArrays (for each det).
+
+        if not is_gt, this returns a dict which contains the fields:
+        [tracker_ids, tracker_classes, tracker_confidences] : list (for each timestep) of 1D NDArrays (for each det).
+        [tracker_dets]: list (for each timestep) of lists of detections.
+
+        :param str tracker: Name of the tracker.
+        :param str seq: Sequence identifier.
+        :param bool is_gt: Indicates whether the file is ground truth or from a tracker.
+        :raises TrackEvalException: If there's an error loading the file or if the data is corrupted.
+        :return: dictionary containing the loaded data.
+        :rtype: dict
+        """
+        # File location
+        if self.data_is_zipped:
+            if is_gt:
+                zip_file = os.path.join(self.gt_fol, 'data.zip')
+            else:
+                zip_file = os.path.join(self.tracker_fol, tracker, self.tracker_sub_fol + '.zip')
+            file = seq + '.txt'
+        else:
+            zip_file = None
+            if is_gt:
+                file = self.config["GT_LOC_FORMAT"].format(gt_folder=self.gt_fol, seq=seq)
+            else:
+                file = os.path.join(self.tracker_fol, tracker, self.tracker_sub_fol, seq + '.txt')
+
+        # Load raw data from text file
+        read_data, ignore_data = self._load_simple_text_file(file, is_zipped=self.data_is_zipped, zip_file=zip_file)
+
+        # Convert data to required format
+        num_timesteps = self.seq_lengths[seq]
+        data_keys = ['ids', 'classes', 'dets']
+        if is_gt:
+            data_keys += ['gt_crowd_ignore_regions', 'gt_extras']
+        else:
+            data_keys += ['tracker_confidences']
+        raw_data = {key: [None] * num_timesteps for key in data_keys}
+
+        # Check for any extra time keys
+        current_time_keys = [str( t+ 1) for t in range(num_timesteps)]
+        extra_time_keys = [x for x in read_data.keys() if x not in current_time_keys]
+        if len(extra_time_keys) > 0:
+            if is_gt:
+                text = 'Ground-truth'
+            else:
+                text = 'Tracking'
+            raise TrackEvalException(
+                text + ' data contains the following invalid timesteps in seq %s: ' % seq + ', '.join(
+                    [str(x) + ', ' for x in extra_time_keys]))
+
+        for t in range(num_timesteps):
+            time_key = str(t+1)
+            if time_key in read_data.keys():
+                try:
+                    time_data = np.asarray(read_data[time_key], dtype=float)
+                except ValueError:
+                    if is_gt:
+                        raise TrackEvalException(
+                            'Cannot convert gt data for sequence %s to float. Is data corrupted?' % seq)
+                    else:
+                        raise TrackEvalException(
+                            'Cannot convert tracking data from tracker %s, sequence %s to float. Is data corrupted?' % (
+                                tracker, seq))
+                try:
+                    if is_gt:
+                        raw_data['dets'][t] = np.atleast_2d(time_data[:, 7:9])
+                    else:
+                        raw_data['dets'][t] = np.atleast_2d(time_data[:, 7:9])
+                    raw_data['ids'][t] = np.atleast_1d(time_data[:, 1]).astype(int)
+                except IndexError:
+                    if is_gt:
+                        err = 'Cannot load gt data from sequence %s, because there is not enough ' \
+                              'columns in the data.' % seq
+                        raise TrackEvalException(err)
+                    else:
+                        err = 'Cannot load tracker data from tracker %s, sequence %s, because there is not enough ' \
+                              'columns in the data.' % (tracker, seq)
+                        raise TrackEvalException(err)
+                if time_data.shape[1] >= 8:
+                    raw_data['classes'][t] = np.ones_like(raw_data['ids'][t])
+                    # raw_data['classes'][t] = np.atleast_1d(time_data[:, 7]).astype(int)
+                else:
+                    if not is_gt:
+                        raw_data['classes'][t] = np.ones_like(raw_data['ids'][t])
+                    else:
+                        raise TrackEvalException(
+                            'GT data is not in a valid format, there is not enough rows in seq %s, timestep %i.' % (
+                                seq, t))
+                if is_gt:
+                    gt_extras_dict = {'zero_marked': np.atleast_1d(time_data[:, 6].astype(int))}
+                    raw_data['gt_extras'][t] = gt_extras_dict
+                else:
+                    raw_data['tracker_confidences'][t] = np.atleast_1d(time_data[:, 6])
+            else:
+                raw_data['dets'][t] = np.empty((0, 2))
+                raw_data['ids'][t] = np.empty(0).astype(int)
+                raw_data['classes'][t] = np.empty(0).astype(int)
+                if is_gt:
+                    gt_extras_dict = {'zero_marked': np.empty(0)}
+                    raw_data['gt_extras'][t] = gt_extras_dict
+                else:
+                    raw_data['tracker_confidences'][t] = np.empty(0)
+            if is_gt:
+                raw_data['gt_crowd_ignore_regions'][t] = np.empty((0, 2))
+
+        if is_gt:
+            key_map = {'ids': 'gt_ids',
+                       'classes': 'gt_classes',
+                       'dets': 'gt_dets'}
+        else:
+            key_map = {'ids': 'tracker_ids',
+                       'classes': 'tracker_classes',
+                       'dets': 'tracker_dets'}
+        for k, v in key_map.items():
+            raw_data[v] = raw_data.pop(k)
+        raw_data['num_timesteps'] = num_timesteps
+        raw_data['seq'] = seq
+        return raw_data
+
+    @_timing.time
+    def get_preprocessed_seq_data(self, raw_data, cls):
+        """ Preprocess data for a single sequence for a single class ready for evaluation.
+        Inputs:
+             - raw_data is a dict containing the data for the sequence already read in by get_raw_seq_data().
+             - cls is the class to be evaluated.
+        Outputs:
+             - data is a dict containing all of the information that metrics need to perform evaluation.
+                It contains the following fields:
+                    [num_timesteps, num_gt_ids, num_tracker_ids, num_gt_dets, num_tracker_dets] : integers.
+                    [gt_ids, tracker_ids, tracker_confidences]: list (for each timestep) of 1D NDArrays (for each det).
+                    [gt_dets, tracker_dets]: list (for each timestep) of lists of detections.
+                    [similarity_scores]: list (for each timestep) of 2D NDArrays.
+        Notes:
+            General preprocessing (preproc) occurs in 4 steps. Some datasets may not use all of these steps.
+                1) Extract only detections relevant for the class to be evaluated (including distractor detections).
+                2) Match gt dets and tracker dets. Remove tracker dets that are matched to a gt det that is of a
+                    distractor class, or otherwise marked as to be removed.
+                3) Remove unmatched tracker dets if they fall within a crowd ignore region or don't meet a certain
+                    other criteria (e.g. are too small).
+                4) Remove gt dets that were only useful for preprocessing and not for actual evaluation.
+            After the above preprocessing steps, this function also calculates the number of gt and tracker detections
+                and unique track ids. It also relabels gt and tracker ids to be contiguous and checks that ids are
+                unique within each timestep.
+
+        MOT Challenge:
+            In MOT Challenge, the 4 preproc steps are as follow:
+                1) There is only one class (pedestrian) to be evaluated, but all other classes are used for preproc.
+                2) Predictions are matched against all gt boxes (regardless of class), those matching with distractor
+                    objects are removed.
+                3) There is no crowd ignore regions.
+                4) All gt dets except pedestrian are removed, also removes pedestrian gt dets marked with zero_marked.
+        
+        :param raw_data: A dict containing the data for the sequence already read in by `get_raw_seq_data()`.
+        :param cls: The class to be evaluated.
+
+        :return: A dict containing all of the information that metrics need to perform evaluation.
+                It contains the following fields:
+                - [num_timesteps, num_gt_ids, num_tracker_ids, num_gt_dets, num_tracker_dets]: Integers.
+                - [gt_ids, tracker_ids, tracker_confidences]: List (for each timestep) of 1D NDArrays (for each detection).
+                - [gt_dets, tracker_dets]: List (for each timestep) of lists of detections.
+                - [similarity_scores]: List (for each timestep) of 2D NDArrays.
+
+        """
+        # Check that input data has unique ids
+        self._check_unique_ids(raw_data)
+
+        distractor_class_names = ['person_on_vehicle', 'static_person', 'distractor', 'reflection']
+        if self.benchmark == 'MOT20':
+            distractor_class_names.append('non_mot_vehicle')
+        distractor_classes = [self.class_name_to_class_id[x] for x in distractor_class_names]
+        cls_id = self.class_name_to_class_id[cls]
+
+        data_keys = ['gt_ids', 'tracker_ids', 'gt_dets', 'tracker_dets', 'tracker_confidences', 'similarity_scores']
+        data = {key: [None] * raw_data['num_timesteps'] for key in data_keys}
+        unique_gt_ids = []
+        unique_tracker_ids = []
+        num_gt_dets = 0
+        num_tracker_dets = 0
+        for t in range(raw_data['num_timesteps']):
+
+            # Get all data
+            gt_ids = raw_data['gt_ids'][t]
+            gt_dets = raw_data['gt_dets'][t]
+            gt_classes = raw_data['gt_classes'][t]
+            gt_zero_marked = raw_data['gt_extras'][t]['zero_marked']
+
+            tracker_ids = raw_data['tracker_ids'][t]
+            tracker_dets = raw_data['tracker_dets'][t]
+            tracker_classes = raw_data['tracker_classes'][t]
+            tracker_confidences = raw_data['tracker_confidences'][t]
+            similarity_scores = raw_data['similarity_scores'][t]
+
+            # Evaluation is ONLY valid for pedestrian class
+            if len(tracker_classes) > 0 and np.max(tracker_classes) > 1:
+                raise TrackEvalException(
+                    'Evaluation is only valid for pedestrian class. Non pedestrian class (%i) found in sequence %s at '
+                    'timestep %i.' % (np.max(tracker_classes), raw_data['seq'], t))
+
+            # Match tracker and gt dets (with hungarian algorithm) and remove tracker dets which match with gt dets
+            # which are labeled as belonging to a distractor class.
+            to_remove_tracker = np.array([], int)
+            if self.do_preproc and self.benchmark != 'MOT15' and gt_ids.shape[0] > 0 and tracker_ids.shape[0] > 0:
+
+                # Check all classes are valid:
+                invalid_classes = np.setdiff1d(np.unique(gt_classes), self.valid_class_numbers)
+                if len(invalid_classes) > 0:
+                    print(' '.join([str(x) for x in invalid_classes]))
+                    raise(TrackEvalException('Attempting to evaluate using invalid gt classes. '
+                                             'This warning only triggers if preprocessing is performed, '
+                                             'e.g. not for MOT15 or where prepropressing is explicitly disabled. '
+                                             'Please either check your gt data, or disable preprocessing. '
+                                             'The following invalid classes were found in timestep ' + str(t) + ': ' +
+                                             ' '.join([str(x) for x in invalid_classes])))
+
+                matching_scores = similarity_scores.copy()
+                matching_scores[matching_scores < 0.5 - np.finfo('float').eps] = 0
+                match_rows, match_cols = linear_sum_assignment(-matching_scores)
+                actually_matched_mask = matching_scores[match_rows, match_cols] > 0 + np.finfo('float').eps
+                match_rows = match_rows[actually_matched_mask]
+                match_cols = match_cols[actually_matched_mask]
+
+                is_distractor_class = np.isin(gt_classes[match_rows], distractor_classes)
+                to_remove_tracker = match_cols[is_distractor_class]
+
+            # Apply preprocessing to remove all unwanted tracker dets.
+            data['tracker_ids'][t] = np.delete(tracker_ids, to_remove_tracker, axis=0)
+            data['tracker_dets'][t] = np.delete(tracker_dets, to_remove_tracker, axis=0)
+            data['tracker_confidences'][t] = np.delete(tracker_confidences, to_remove_tracker, axis=0)
+            similarity_scores = np.delete(similarity_scores, to_remove_tracker, axis=1)
+
+            # Remove gt detections marked as to remove (zero marked), and also remove gt detections not in pedestrian
+            # class (not applicable for MOT15)
+            if self.do_preproc and self.benchmark != 'MOT15':
+                gt_to_keep_mask = (np.not_equal(gt_zero_marked, 0)) & \
+                                  (np.equal(gt_classes, cls_id))
+            else:
+                # There are no classes for MOT15
+                gt_to_keep_mask = np.not_equal(gt_zero_marked, 0)
+            data['gt_ids'][t] = gt_ids[gt_to_keep_mask]
+            data['gt_dets'][t] = gt_dets[gt_to_keep_mask, :]
+            data['similarity_scores'][t] = similarity_scores[gt_to_keep_mask]
+
+            unique_gt_ids += list(np.unique(data['gt_ids'][t]))
+            unique_tracker_ids += list(np.unique(data['tracker_ids'][t]))
+            num_tracker_dets += len(data['tracker_ids'][t])
+            num_gt_dets += len(data['gt_ids'][t])
+
+        # Re-label IDs such that there are no empty IDs
+        if len(unique_gt_ids) > 0:
+            unique_gt_ids = np.unique(unique_gt_ids)
+            gt_id_map = np.nan * np.ones((np.max(unique_gt_ids) + 1))
+            gt_id_map[unique_gt_ids] = np.arange(len(unique_gt_ids))
+            for t in range(raw_data['num_timesteps']):
+                if len(data['gt_ids'][t]) > 0:
+                    data['gt_ids'][t] = gt_id_map[data['gt_ids'][t]].astype(int)
+        if len(unique_tracker_ids) > 0:
+            unique_tracker_ids = np.unique(unique_tracker_ids)
+            tracker_id_map = np.nan * np.ones((np.max(unique_tracker_ids) + 1))
+            tracker_id_map[unique_tracker_ids] = np.arange(len(unique_tracker_ids))
+            for t in range(raw_data['num_timesteps']):
+                if len(data['tracker_ids'][t]) > 0:
+                    data['tracker_ids'][t] = tracker_id_map[data['tracker_ids'][t]].astype(int)
+
+        # Record overview statistics.
+        data['num_tracker_dets'] = num_tracker_dets
+        data['num_gt_dets'] = num_gt_dets
+        data['num_tracker_ids'] = len(unique_tracker_ids)
+        data['num_gt_ids'] = len(unique_gt_ids)
+        data['num_timesteps'] = raw_data['num_timesteps']
+        data['seq'] = raw_data['seq']
+
+        # Ensure again that ids are unique per timestep after preproc.
+        self._check_unique_ids(data, after_preproc=True)
+
+        return data
+
+    def _calculate_similarities(self, gt_dets_t, tracker_dets_t):
+        similarity_scores = self._calculate_euclidean_similarity(gt_dets_t, tracker_dets_t, zero_distance=self.zero_distance)
+        return similarity_scores

MTMC_Tracking_2024/eval/trackeval/datasets/test_mot.py

@@ -0,0 +1,475 @@
+import os
+import csv
+import configparser
+import numpy as np
+from scipy.optimize import linear_sum_assignment
+from trackeval import utils
+from trackeval import _timing
+from trackeval.utils import TrackEvalException
+from trackeval.datasets._base_dataset import _BaseDataset
+
+
+class MotChallenge2DLocation(_BaseDataset):
+    """
+    Dataset class for MOT Challenge 2D bounding box tracking
+    
+    :param dict config: configuration for the app
+    ::
+
+        default_dataset = trackeeval.datasets.MotChallenge2DBox(config)
+    """
+    @staticmethod
+    def get_default_dataset_config():
+        """Default class config values"""
+        code_path = utils.get_code_path()
+        default_config = {
+            'GT_FOLDER': os.path.join(code_path, 'data/gt/mot_challenge/'),  # Location of GT data
+            'TRACKERS_FOLDER': os.path.join(code_path, 'data/trackers/mot_challenge/'),  # Trackers location
+            'OUTPUT_FOLDER': None,  # Where to save eval results (if None, same as TRACKERS_FOLDER)
+            'TRACKERS_TO_EVAL': None,  # Filenames of trackers to eval (if None, all in folder)
+            'CLASSES_TO_EVAL': ['pedestrian'],  # Valid: ['pedestrian']
+            'BENCHMARK': 'MOT17',  # Valid: 'MOT17', 'MOT16', 'MOT20', 'MOT15'
+            'SPLIT_TO_EVAL': 'train',  # Valid: 'train', 'test', 'all'
+            'INPUT_AS_ZIP': False,  # Whether tracker input files are zipped
+            'PRINT_CONFIG': True,  # Whether to print current config
+            'DO_PREPROC': True,  # Whether to perform preprocessing (never done for MOT15)
+            'TRACKER_SUB_FOLDER': 'data',  # Tracker files are in TRACKER_FOLDER/tracker_name/TRACKER_SUB_FOLDER
+            'OUTPUT_SUB_FOLDER': '',  # Output files are saved in OUTPUT_FOLDER/tracker_name/OUTPUT_SUB_FOLDER
+            'TRACKER_DISPLAY_NAMES': None,  # Names of trackers to display, if None: TRACKERS_TO_EVAL
+            'SEQMAP_FOLDER': None,  # Where seqmaps are found (if None, GT_FOLDER/seqmaps)
+            'SEQMAP_FILE': None,  # Directly specify seqmap file (if none use seqmap_folder/benchmark-split_to_eval)
+            'SEQ_INFO': None,  # If not None, directly specify sequences to eval and their number of timesteps
+            'GT_LOC_FORMAT': '{gt_folder}/{seq}/gt/gt.txt',  # '{gt_folder}/{seq}/gt/gt.txt'
+            'SKIP_SPLIT_FOL': False,  # If False, data is in GT_FOLDER/BENCHMARK-SPLIT_TO_EVAL/ and in
+                                      # TRACKERS_FOLDER/BENCHMARK-SPLIT_TO_EVAL/tracker/
+                                      # If True, then the middle 'benchmark-split' folder is skipped for both.
+        }
+        return default_config
+
+    def __init__(self, config=None):
+        """Initialise dataset, checking that all required files are present"""
+        super().__init__()
+        # Fill non-given config values with defaults
+        self.config = utils.init_config(config, self.get_default_dataset_config(), self.get_name())
+
+        self.benchmark = self.config['BENCHMARK']
+        gt_set = self.config['BENCHMARK'] + '-' + self.config['SPLIT_TO_EVAL']
+        self.gt_set = gt_set
+        if not self.config['SKIP_SPLIT_FOL']:
+            split_fol = gt_set
+        else:
+            split_fol = ''
+        self.gt_fol = os.path.join(self.config['GT_FOLDER'], split_fol)
+        self.tracker_fol = os.path.join(self.config['TRACKERS_FOLDER'], split_fol)
+        self.should_classes_combine = False
+        self.use_super_categories = False
+        self.data_is_zipped = self.config['INPUT_AS_ZIP']
+        self.do_preproc = self.config['DO_PREPROC']
+
+        self.output_fol = self.config['OUTPUT_FOLDER']
+        if self.output_fol is None:
+            self.output_fol = self.tracker_fol
+
+        self.tracker_sub_fol = self.config['TRACKER_SUB_FOLDER']
+        self.output_sub_fol = self.config['OUTPUT_SUB_FOLDER']
+
+        # Get classes to eval
+        self.valid_classes = ['pedestrian']
+        self.class_list = [cls.lower() if cls.lower() in self.valid_classes else None
+                           for cls in self.config['CLASSES_TO_EVAL']]
+        if not all(self.class_list):
+            raise TrackEvalException('Attempted to evaluate an invalid class. Only pedestrian class is valid.')
+        self.class_name_to_class_id = {'pedestrian': 1, 'person_on_vehicle': 2, 'car': 3, 'bicycle': 4, 'motorbike': 5,
+                                       'non_mot_vehicle': 6, 'static_person': 7, 'distractor': 8, 'occluder': 9,
+                                       'occluder_on_ground': 10, 'occluder_full': 11, 'reflection': 12, 'crowd': 13}
+        self.valid_class_numbers = list(self.class_name_to_class_id.values())
+
+        # Get sequences to eval and check gt files exist
+        self.seq_list, self.seq_lengths = self._get_seq_info()
+        if len(self.seq_list) < 1:
+            raise TrackEvalException('No sequences are selected to be evaluated.')
+
+        # Check gt files exist
+        for seq in self.seq_list:
+            if not self.data_is_zipped:
+                curr_file = self.config["GT_LOC_FORMAT"].format(gt_folder=self.gt_fol, seq=seq)
+                if not os.path.isfile(curr_file):
+                    print('GT file not found ' + curr_file)
+                    raise TrackEvalException('GT file not found for sequence: ' + seq)
+        if self.data_is_zipped:
+            curr_file = os.path.join(self.gt_fol, 'data.zip')
+            if not os.path.isfile(curr_file):
+                print('GT file not found ' + curr_file)
+                raise TrackEvalException('GT file not found: ' + os.path.basename(curr_file))
+
+        # Get trackers to eval
+        if self.config['TRACKERS_TO_EVAL'] is None:
+            self.tracker_list = os.listdir(self.tracker_fol)
+        else:
+            self.tracker_list = self.config['TRACKERS_TO_EVAL']
+
+        if self.config['TRACKER_DISPLAY_NAMES'] is None:
+            self.tracker_to_disp = dict(zip(self.tracker_list, self.tracker_list))
+        elif (self.config['TRACKERS_TO_EVAL'] is not None) and (
+                len(self.config['TRACKER_DISPLAY_NAMES']) == len(self.tracker_list)):
+            self.tracker_to_disp = dict(zip(self.tracker_list, self.config['TRACKER_DISPLAY_NAMES']))
+        else:
+            raise TrackEvalException('List of tracker files and tracker display names do not match.')
+
+        for tracker in self.tracker_list:
+            if self.data_is_zipped:
+                curr_file = os.path.join(self.tracker_fol, tracker, self.tracker_sub_fol + '.zip')
+                if not os.path.isfile(curr_file):
+                    print('Tracker file not found: ' + curr_file)
+                    raise TrackEvalException('Tracker file not found: ' + tracker + '/' + os.path.basename(curr_file))
+            else:
+                for seq in self.seq_list:
+                    curr_file = os.path.join(self.tracker_fol, tracker, self.tracker_sub_fol, seq + '.txt')
+                    if not os.path.isfile(curr_file):
+                        print('Tracker file not found: ' + curr_file)
+                        raise TrackEvalException(
+                            'Tracker file not found: ' + tracker + '/' + self.tracker_sub_fol + '/' + os.path.basename(
+                                curr_file))
+
+    def get_display_name(self, tracker):
+        """
+        Gets the display name of the tracker
+
+        :param str tracker: Class of tracker
+        :return: str
+        ::
+
+            dataset.get_display_name(tracker)
+        """
+
+        return self.tracker_to_disp[tracker]
+
+    def _get_seq_info(self):
+        seq_list = []
+        seq_lengths = {}
+        if self.config["SEQ_INFO"]:
+            seq_list = list(self.config["SEQ_INFO"].keys())
+            seq_lengths = self.config["SEQ_INFO"]
+
+            # If sequence length is 'None' tries to read sequence length from .ini files.
+            for seq, seq_length in seq_lengths.items():
+                if seq_length is None:
+                    ini_file = os.path.join(self.gt_fol, seq, 'seqinfo.ini')
+                    if not os.path.isfile(ini_file):
+                        raise TrackEvalException('ini file does not exist: ' + seq + '/' + os.path.basename(ini_file))
+                    ini_data = configparser.ConfigParser()
+                    ini_data.read(ini_file)
+                    seq_lengths[seq] = int(float(ini_data['Sequence']['seqLength']))
+
+        else:
+            if self.config["SEQMAP_FILE"]:
+                seqmap_file = self.config["SEQMAP_FILE"]
+            else:
+                if self.config["SEQMAP_FOLDER"] is None:
+                    seqmap_file = os.path.join(self.config['GT_FOLDER'], 'seqmaps', self.gt_set + '.txt')
+                else:
+                    seqmap_file = os.path.join(self.config["SEQMAP_FOLDER"], self.gt_set + '.txt')
+            if not os.path.isfile(seqmap_file):
+                print('no seqmap found: ' + seqmap_file)
+                raise TrackEvalException('no seqmap found: ' + os.path.basename(seqmap_file))
+            with open(seqmap_file) as fp:
+                reader = csv.reader(fp)
+                for i, row in enumerate(reader):
+                    if i == 0 or row[0] == '':
+                        continue
+                    seq = row[0]
+                    seq_list.append(seq)
+                    ini_file = os.path.join(self.gt_fol, seq, 'seqinfo.ini')
+                    if not os.path.isfile(ini_file):
+                        raise TrackEvalException('ini file does not exist: ' + seq + '/' + os.path.basename(ini_file))
+                    ini_data = configparser.ConfigParser()
+                    ini_data.read(ini_file)
+                    seq_lengths[seq] = int(float(ini_data['Sequence']['seqLength']))
+        return seq_list, seq_lengths
+
+    def _load_raw_file(self, tracker, seq, is_gt):
+        """Load a file (gt or tracker) in the MOT Challenge 2D box format
+
+        If is_gt, this returns a dict which contains the fields:
+        [gt_ids, gt_classes] : list (for each timestep) of 1D NDArrays (for each det).
+        [gt_dets, gt_crowd_ignore_regions]: list (for each timestep) of lists of detections.
+        [gt_extras] : list (for each timestep) of dicts (for each extra) of 1D NDArrays (for each det).
+
+        if not is_gt, this returns a dict which contains the fields:
+        [tracker_ids, tracker_classes, tracker_confidences] : list (for each timestep) of 1D NDArrays (for each det).
+        [tracker_dets]: list (for each timestep) of lists of detections.
+
+        :param str tracker: Name of the tracker.
+        :param str seq: Sequence identifier.
+        :param bool is_gt: Indicates whether the file is ground truth or from a tracker.
+        :raises TrackEvalException: If there's an error loading the file or if the data is corrupted.
+        :return: dictionary containing the loaded data.
+        :rtype: dict
+        """
+        # File location
+        if self.data_is_zipped:
+            if is_gt:
+                zip_file = os.path.join(self.gt_fol, 'data.zip')
+            else:
+                zip_file = os.path.join(self.tracker_fol, tracker, self.tracker_sub_fol + '.zip')
+            file = seq + '.txt'
+        else:
+            zip_file = None
+            if is_gt:
+                file = self.config["GT_LOC_FORMAT"].format(gt_folder=self.gt_fol, seq=seq)
+            else:
+                file = os.path.join(self.tracker_fol, tracker, self.tracker_sub_fol, seq + '.txt')
+
+        # Load raw data from text file
+        read_data, ignore_data = self._load_simple_text_file(file, is_zipped=self.data_is_zipped, zip_file=zip_file)
+
+        # Convert data to required format
+        num_timesteps = self.seq_lengths[seq]
+        data_keys = ['ids', 'classes', 'dets']
+        if is_gt:
+            data_keys += ['gt_crowd_ignore_regions', 'gt_extras']
+        else:
+            data_keys += ['tracker_confidences']
+        raw_data = {key: [None] * num_timesteps for key in data_keys}
+
+        # Check for any extra time keys
+        current_time_keys = [str( t+ 1) for t in range(num_timesteps)]
+        extra_time_keys = [x for x in read_data.keys() if x not in current_time_keys]
+        if len(extra_time_keys) > 0:
+            if is_gt:
+                text = 'Ground-truth'
+            else:
+                text = 'Tracking'
+            raise TrackEvalException(
+                text + ' data contains the following invalid timesteps in seq %s: ' % seq + ', '.join(
+                    [str(x) + ', ' for x in extra_time_keys]))
+
+        for t in range(num_timesteps):
+            time_key = str(t+1)
+            if time_key in read_data.keys():
+                try:
+                    time_data = np.asarray(read_data[time_key], dtype=float)
+                except ValueError:
+                    if is_gt:
+                        raise TrackEvalException(
+                            'Cannot convert gt data for sequence %s to float. Is data corrupted?' % seq)
+                    else:
+                        raise TrackEvalException(
+                            'Cannot convert tracking data from tracker %s, sequence %s to float. Is data corrupted?' % (
+                                tracker, seq))
+                try:
+                    if is_gt:
+                        raw_data['dets'][t] = np.atleast_2d(time_data[:, 7:9])
+                    else:
+                        raw_data['dets'][t] = np.atleast_2d(time_data[:, 7:9])
+                    raw_data['ids'][t] = np.atleast_1d(time_data[:, 1]).astype(int)
+                except IndexError:
+                    if is_gt:
+                        err = 'Cannot load gt data from sequence %s, because there is not enough ' \
+                              'columns in the data.' % seq
+                        raise TrackEvalException(err)
+                    else:
+                        err = 'Cannot load tracker data from tracker %s, sequence %s, because there is not enough ' \
+                              'columns in the data.' % (tracker, seq)
+                        raise TrackEvalException(err)
+                if time_data.shape[1] >= 8:
+                    raw_data['classes'][t] = np.ones_like(raw_data['ids'][t])
+                    # raw_data['classes'][t] = np.atleast_1d(time_data[:, 7]).astype(int)
+                else:
+                    if not is_gt:
+                        raw_data['classes'][t] = np.ones_like(raw_data['ids'][t])
+                    else:
+                        raise TrackEvalException(
+                            'GT data is not in a valid format, there is not enough rows in seq %s, timestep %i.' % (
+                                seq, t))
+                if is_gt:
+                    gt_extras_dict = {'zero_marked': np.atleast_1d(time_data[:, 6].astype(int))}
+                    raw_data['gt_extras'][t] = gt_extras_dict
+                else:
+                    raw_data['tracker_confidences'][t] = np.atleast_1d(time_data[:, 6])
+            else:
+                raw_data['dets'][t] = np.empty((0, 4))
+                raw_data['ids'][t] = np.empty(0).astype(int)
+                raw_data['classes'][t] = np.empty(0).astype(int)
+                if is_gt:
+                    gt_extras_dict = {'zero_marked': np.empty(0)}
+                    raw_data['gt_extras'][t] = gt_extras_dict
+                else:
+                    raw_data['tracker_confidences'][t] = np.empty(0)
+            if is_gt:
+                raw_data['gt_crowd_ignore_regions'][t] = np.empty((0, 4))
+
+        if is_gt:
+            key_map = {'ids': 'gt_ids',
+                       'classes': 'gt_classes',
+                       'dets': 'gt_dets'}
+        else:
+            key_map = {'ids': 'tracker_ids',
+                       'classes': 'tracker_classes',
+                       'dets': 'tracker_dets'}
+        for k, v in key_map.items():
+            raw_data[v] = raw_data.pop(k)
+        raw_data['num_timesteps'] = num_timesteps
+        raw_data['seq'] = seq
+        return raw_data
+
+    @_timing.time
+    def get_preprocessed_seq_data(self, raw_data, cls):
+        """ Preprocess data for a single sequence for a single class ready for evaluation.
+        Inputs:
+             - raw_data is a dict containing the data for the sequence already read in by get_raw_seq_data().
+             - cls is the class to be evaluated.
+        Outputs:
+             - data is a dict containing all of the information that metrics need to perform evaluation.
+                It contains the following fields:
+                    [num_timesteps, num_gt_ids, num_tracker_ids, num_gt_dets, num_tracker_dets] : integers.
+                    [gt_ids, tracker_ids, tracker_confidences]: list (for each timestep) of 1D NDArrays (for each det).
+                    [gt_dets, tracker_dets]: list (for each timestep) of lists of detections.
+                    [similarity_scores]: list (for each timestep) of 2D NDArrays.
+        Notes:
+            General preprocessing (preproc) occurs in 4 steps. Some datasets may not use all of these steps.
+                1) Extract only detections relevant for the class to be evaluated (including distractor detections).
+                2) Match gt dets and tracker dets. Remove tracker dets that are matched to a gt det that is of a
+                    distractor class, or otherwise marked as to be removed.
+                3) Remove unmatched tracker dets if they fall within a crowd ignore region or don't meet a certain
+                    other criteria (e.g. are too small).
+                4) Remove gt dets that were only useful for preprocessing and not for actual evaluation.
+            After the above preprocessing steps, this function also calculates the number of gt and tracker detections
+                and unique track ids. It also relabels gt and tracker ids to be contiguous and checks that ids are
+                unique within each timestep.
+
+        MOT Challenge:
+            In MOT Challenge, the 4 preproc steps are as follow:
+                1) There is only one class (pedestrian) to be evaluated, but all other classes are used for preproc.
+                2) Predictions are matched against all gt boxes (regardless of class), those matching with distractor
+                    objects are removed.
+                3) There is no crowd ignore regions.
+                4) All gt dets except pedestrian are removed, also removes pedestrian gt dets marked with zero_marked.
+        
+        :param raw_data: A dict containing the data for the sequence already read in by `get_raw_seq_data()`.
+        :param cls: The class to be evaluated.
+
+        :return: A dict containing all of the information that metrics need to perform evaluation.
+                It contains the following fields:
+                - [num_timesteps, num_gt_ids, num_tracker_ids, num_gt_dets, num_tracker_dets]: Integers.
+                - [gt_ids, tracker_ids, tracker_confidences]: List (for each timestep) of 1D NDArrays (for each detection).
+                - [gt_dets, tracker_dets]: List (for each timestep) of lists of detections.
+                - [similarity_scores]: List (for each timestep) of 2D NDArrays.
+
+        """
+        # Check that input data has unique ids
+        self._check_unique_ids(raw_data)
+
+        distractor_class_names = ['person_on_vehicle', 'static_person', 'distractor', 'reflection']
+        if self.benchmark == 'MOT20':
+            distractor_class_names.append('non_mot_vehicle')
+        distractor_classes = [self.class_name_to_class_id[x] for x in distractor_class_names]
+        cls_id = self.class_name_to_class_id[cls]
+
+        data_keys = ['gt_ids', 'tracker_ids', 'gt_dets', 'tracker_dets', 'tracker_confidences', 'similarity_scores']
+        data = {key: [None] * raw_data['num_timesteps'] for key in data_keys}
+        unique_gt_ids = []
+        unique_tracker_ids = []
+        num_gt_dets = 0
+        num_tracker_dets = 0
+        for t in range(raw_data['num_timesteps']):
+
+            # Get all data
+            gt_ids = raw_data['gt_ids'][t]
+            gt_dets = raw_data['gt_dets'][t]
+            gt_classes = raw_data['gt_classes'][t]
+            gt_zero_marked = raw_data['gt_extras'][t]['zero_marked']
+
+            tracker_ids = raw_data['tracker_ids'][t]
+            tracker_dets = raw_data['tracker_dets'][t]
+            tracker_classes = raw_data['tracker_classes'][t]
+            tracker_confidences = raw_data['tracker_confidences'][t]
+            similarity_scores = raw_data['similarity_scores'][t]
+
+            # Evaluation is ONLY valid for pedestrian class
+            if len(tracker_classes) > 0 and np.max(tracker_classes) > 1:
+                raise TrackEvalException(
+                    'Evaluation is only valid for pedestrian class. Non pedestrian class (%i) found in sequence %s at '
+                    'timestep %i.' % (np.max(tracker_classes), raw_data['seq'], t))
+
+            # Match tracker and gt dets (with hungarian algorithm) and remove tracker dets which match with gt dets
+            # which are labeled as belonging to a distractor class.
+            to_remove_tracker = np.array([], int)
+            if self.do_preproc and self.benchmark != 'MOT15' and gt_ids.shape[0] > 0 and tracker_ids.shape[0] > 0:
+
+                # Check all classes are valid:
+                invalid_classes = np.setdiff1d(np.unique(gt_classes), self.valid_class_numbers)
+                if len(invalid_classes) > 0:
+                    print(' '.join([str(x) for x in invalid_classes]))
+                    raise(TrackEvalException('Attempting to evaluate using invalid gt classes. '
+                                             'This warning only triggers if preprocessing is performed, '
+                                             'e.g. not for MOT15 or where prepropressing is explicitly disabled. '
+                                             'Please either check your gt data, or disable preprocessing. '
+                                             'The following invalid classes were found in timestep ' + str(t) + ': ' +
+                                             ' '.join([str(x) for x in invalid_classes])))
+
+                matching_scores = similarity_scores.copy()
+                matching_scores[matching_scores < 0.5 - np.finfo('float').eps] = 0
+                match_rows, match_cols = linear_sum_assignment(-matching_scores)
+                actually_matched_mask = matching_scores[match_rows, match_cols] > 0 + np.finfo('float').eps
+                match_rows = match_rows[actually_matched_mask]
+                match_cols = match_cols[actually_matched_mask]
+
+                is_distractor_class = np.isin(gt_classes[match_rows], distractor_classes)
+                to_remove_tracker = match_cols[is_distractor_class]
+
+            # Apply preprocessing to remove all unwanted tracker dets.
+            data['tracker_ids'][t] = np.delete(tracker_ids, to_remove_tracker, axis=0)
+            data['tracker_dets'][t] = np.delete(tracker_dets, to_remove_tracker, axis=0)
+            data['tracker_confidences'][t] = np.delete(tracker_confidences, to_remove_tracker, axis=0)
+            similarity_scores = np.delete(similarity_scores, to_remove_tracker, axis=1)
+
+            # Remove gt detections marked as to remove (zero marked), and also remove gt detections not in pedestrian
+            # class (not applicable for MOT15)
+            if self.do_preproc and self.benchmark != 'MOT15':
+                gt_to_keep_mask = (np.not_equal(gt_zero_marked, 0)) & \
+                                  (np.equal(gt_classes, cls_id))
+            else:
+                # There are no classes for MOT15
+                gt_to_keep_mask = np.not_equal(gt_zero_marked, 0)
+            data['gt_ids'][t] = gt_ids[gt_to_keep_mask]
+            data['gt_dets'][t] = gt_dets[gt_to_keep_mask, :]
+            data['similarity_scores'][t] = similarity_scores[gt_to_keep_mask]
+
+            unique_gt_ids += list(np.unique(data['gt_ids'][t]))
+            unique_tracker_ids += list(np.unique(data['tracker_ids'][t]))
+            num_tracker_dets += len(data['tracker_ids'][t])
+            num_gt_dets += len(data['gt_ids'][t])
+
+        # Re-label IDs such that there are no empty IDs
+        if len(unique_gt_ids) > 0:
+            unique_gt_ids = np.unique(unique_gt_ids)
+            gt_id_map = np.nan * np.ones((np.max(unique_gt_ids) + 1))
+            gt_id_map[unique_gt_ids] = np.arange(len(unique_gt_ids))
+            for t in range(raw_data['num_timesteps']):
+                if len(data['gt_ids'][t]) > 0:
+                    data['gt_ids'][t] = gt_id_map[data['gt_ids'][t]].astype(int)
+        if len(unique_tracker_ids) > 0:
+            unique_tracker_ids = np.unique(unique_tracker_ids)
+            tracker_id_map = np.nan * np.ones((np.max(unique_tracker_ids) + 1))
+            tracker_id_map[unique_tracker_ids] = np.arange(len(unique_tracker_ids))
+            for t in range(raw_data['num_timesteps']):
+                if len(data['tracker_ids'][t]) > 0:
+                    data['tracker_ids'][t] = tracker_id_map[data['tracker_ids'][t]].astype(int)
+
+        # Record overview statistics.
+        data['num_tracker_dets'] = num_tracker_dets
+        data['num_gt_dets'] = num_gt_dets
+        data['num_tracker_ids'] = len(unique_tracker_ids)
+        data['num_gt_ids'] = len(unique_gt_ids)
+        data['num_timesteps'] = raw_data['num_timesteps']
+        data['seq'] = raw_data['seq']
+
+        # Ensure again that ids are unique per timestep after preproc.
+        self._check_unique_ids(data, after_preproc=True)
+
+        return data
+
+    def _calculate_similarities(self, gt_dets_t, tracker_dets_t):
+        similarity_scores = self._calculate_euclidean_similarity(gt_dets_t, tracker_dets_t)        
+        return similarity_scores

MTMC_Tracking_2024/eval/trackeval/eval.py

@@ -0,0 +1,233 @@
+import time
+import logging
+import traceback
+from multiprocessing.pool import Pool
+from functools import partial
+import os
+from . import utils
+from .utils import TrackEvalException
+from . import _timing
+from .metrics import Count
+
+
+class Evaluator:
+    """
+    Evaluator class for evaluating different metrics for different datasets
+    
+    :param dict config: configuration for the app
+    ::
+
+        evaluator = Evaluator(config)
+    """
+
+    @staticmethod
+    def get_default_eval_config():
+        """Returns the default config values for evaluation"""
+        code_path = utils.get_code_path()
+        default_config = {
+            'USE_PARALLEL': False,
+            'NUM_PARALLEL_CORES': 8,
+            'BREAK_ON_ERROR': True,  # Raises exception and exits with error
+            'RETURN_ON_ERROR': False,  # if not BREAK_ON_ERROR, then returns from function on error
+            'LOG_ON_ERROR': os.path.join(code_path, 'error_log.txt'),  # if not None, save any errors into a log file.
+
+            'PRINT_RESULTS': True,
+            'PRINT_ONLY_COMBINED': False,
+            'PRINT_CONFIG': True,
+            'TIME_PROGRESS': True,
+            'DISPLAY_LESS_PROGRESS': True,
+
+            'OUTPUT_SUMMARY': True,
+            'OUTPUT_EMPTY_CLASSES': True,  # If False, summary files are not output for classes with no detections
+            'OUTPUT_DETAILED': True,
+            'PLOT_CURVES': True,
+        }
+        return default_config
+
+    def __init__(self, config=None):
+        self.config = utils.init_config(config, self.get_default_eval_config(), 'Eval')
+        # Only run timing analysis if not run in parallel.
+        if self.config['TIME_PROGRESS'] and not self.config['USE_PARALLEL']:
+            _timing.DO_TIMING = True
+            if self.config['DISPLAY_LESS_PROGRESS']:
+                _timing.DISPLAY_LESS_PROGRESS = True
+
+    @_timing.time
+    def evaluate(self, dataset_list, metrics_list):
+        """
+        Evaluate a list of datasets with a list of metrics
+        
+        :param List[str] dataset_list: list of all datasets
+        :param List[str] metrics_list: list of all metrics
+
+        :return: str output_res: results of the evaluation
+        :return: str output_msg: status of the evaluation
+
+        ::
+
+            trackeval.eval.evaluate(dataset_list, metrics_list)
+        """
+        config = self.config
+        metrics_list = metrics_list + [Count()]  # Count metrics are always run
+        metric_names = utils.validate_metrics_list(metrics_list)
+        dataset_names = [dataset.get_name() for dataset in dataset_list]
+        output_res = {}
+        output_msg = {}
+
+        for dataset, dataset_name in zip(dataset_list, dataset_names):
+            # Get dataset info about what to evaluate
+            output_res[dataset_name] = {}
+            output_msg[dataset_name] = {}
+            tracker_list, seq_list, class_list = dataset.get_eval_info()
+            logging.info('Evaluating %i tracker(s) on %i sequence(s) for %i class(es) on %s dataset using the following '
+                  'metrics: %s\n' % (len(tracker_list), len(seq_list), len(class_list), dataset_name,
+                                     ', '.join(metric_names)))
+
+            # Evaluate each tracker
+            for tracker in tracker_list:
+                # if not config['BREAK_ON_ERROR'] then go to next tracker without breaking
+                try:
+                    # Evaluate each sequence in parallel or in series.
+                    # returns a nested dict (res), indexed like: res[seq][class][metric_name][sub_metric field]
+                    # e.g. res[seq_0001][pedestrian][hota][DetA]
+                    logging.info('Evaluating %s\n' % tracker)
+                    time_start = time.time()
+                    if config['USE_PARALLEL']:
+                        with Pool(config['NUM_PARALLEL_CORES']) as pool:
+                            _eval_sequence = partial(eval_sequence, dataset=dataset, tracker=tracker,
+                                                        class_list=class_list, metrics_list=metrics_list,
+                                                        metric_names=metric_names)
+                            results = pool.map(_eval_sequence, seq_list)
+                            res = dict(zip(seq_list, results))
+                    else:
+                        res = {}
+                        for curr_seq in sorted(seq_list):
+                            res[curr_seq] = eval_sequence(curr_seq, dataset, tracker, class_list, metrics_list,
+                                                            metric_names)
+
+                    # Combine results over all sequences and then over all classes
+
+                    # collecting combined cls keys (cls averaged, det averaged, super classes)
+                    combined_cls_keys = []
+                    res['COMBINED_SEQ'] = {}
+                    # combine sequences for each class
+                    for c_cls in class_list:
+                        res['COMBINED_SEQ'][c_cls] = {}
+                        for metric, metric_name in zip(metrics_list, metric_names):
+                            curr_res = {seq_key: seq_value[c_cls][metric_name] for seq_key, seq_value in res.items() if
+                                        seq_key != 'COMBINED_SEQ'}
+                            # print(curr_res)
+                            res['COMBINED_SEQ'][c_cls][metric_name] = metric.combine_sequences(curr_res)
+                            # print(res['COMBINED_SEQ'][c_cls][metric_name])
+                    # combine classes
+                    if dataset.should_classes_combine:
+                        combined_cls_keys += ['cls_comb_cls_av', 'cls_comb_det_av', 'all']
+                        res['COMBINED_SEQ']['cls_comb_cls_av'] = {}
+                        res['COMBINED_SEQ']['cls_comb_det_av'] = {}
+                        for metric, metric_name in zip(metrics_list, metric_names):
+                            cls_res = {cls_key: cls_value[metric_name] for cls_key, cls_value in
+                                       res['COMBINED_SEQ'].items() if cls_key not in combined_cls_keys}
+                            res['COMBINED_SEQ']['cls_comb_cls_av'][metric_name] = \
+                                metric.combine_classes_class_averaged(cls_res)
+                            res['COMBINED_SEQ']['cls_comb_det_av'][metric_name] = \
+                                metric.combine_classes_det_averaged(cls_res)
+                    # combine classes to super classes
+                    if dataset.use_super_categories:
+                        for cat, sub_cats in dataset.super_categories.items():
+                            combined_cls_keys.append(cat)
+                            res['COMBINED_SEQ'][cat] = {}
+                            for metric, metric_name in zip(metrics_list, metric_names):
+                                cat_res = {cls_key: cls_value[metric_name] for cls_key, cls_value in
+                                           res['COMBINED_SEQ'].items() if cls_key in sub_cats}
+                                res['COMBINED_SEQ'][cat][metric_name] = metric.combine_classes_det_averaged(cat_res)
+
+                    # Print and output results in various formats
+                    if config['TIME_PROGRESS']:
+                        logging.info('All sequences for %s finished in %.2f seconds' % (tracker, time.time() - time_start))
+                    output_fol = dataset.get_output_fol(tracker)
+                    tracker_display_name = dataset.get_display_name(tracker)
+                    for c_cls in res['COMBINED_SEQ'].keys():  # class_list + combined classes if calculated
+                        summaries = []
+                        details = []
+                        num_dets = res['COMBINED_SEQ'][c_cls]['Count']['Dets']
+                        if config['OUTPUT_EMPTY_CLASSES'] or num_dets > 0:
+                            for metric, metric_name in zip(metrics_list, metric_names):
+                                # for combined classes there is no per sequence evaluation
+                                if c_cls in combined_cls_keys:
+                                    table_res = {'COMBINED_SEQ': res['COMBINED_SEQ'][c_cls][metric_name]}
+                                else:
+                                    table_res = {seq_key: seq_value[c_cls][metric_name] for seq_key, seq_value
+                                                 in res.items()}
+
+                                if config['PRINT_RESULTS'] and config['PRINT_ONLY_COMBINED']:
+                                    dont_print = dataset.should_classes_combine and c_cls not in combined_cls_keys
+                                    if not dont_print:
+                                        metric.print_table({'COMBINED_SEQ': table_res['COMBINED_SEQ']},
+                                                           tracker_display_name, c_cls)
+                                elif config['PRINT_RESULTS']:
+                                    # print(table_res['FINAL'])
+                                    metric.print_table(table_res, tracker_display_name, c_cls)
+                                if config['OUTPUT_SUMMARY']:
+                                    summaries.append(metric.summary_results(table_res))
+                                if config['OUTPUT_DETAILED']:
+                                    details.append(metric.detailed_results(table_res))
+                                if config['PLOT_CURVES']:
+                                    metric.plot_single_tracker_results(table_res, tracker_display_name, c_cls,
+                                                                       output_fol)
+                            if config['OUTPUT_SUMMARY']:
+                                utils.write_summary_results(summaries, c_cls, output_fol)
+                            if config['OUTPUT_DETAILED']:
+                                utils.write_detailed_results(details, c_cls, output_fol)
+
+                    # Output for returning from function
+                    output_res[dataset_name][tracker] = res
+                    output_msg[dataset_name][tracker] = 'Success'
+
+                except Exception as err:
+                    output_res[dataset_name][tracker] = None
+                    if type(err) == TrackEvalException:
+                        output_msg[dataset_name][tracker] = str(err)
+                    else:
+                        output_msg[dataset_name][tracker] = 'Unknown error occurred.'
+                    logging.info('Tracker %s was unable to be evaluated.' % tracker)
+                    logging.error(err)
+                    traceback.print_exc()
+                    if config['LOG_ON_ERROR'] is not None:
+                        with open(config['LOG_ON_ERROR'], 'a') as f:
+                            logging.info(dataset_name, file=f)
+                            logging.info(tracker, file=f)
+                            logging.info(traceback.format_exc(), file=f)
+                            logging.info('\n\n\n', file=f)
+                    if config['BREAK_ON_ERROR']:
+                        raise err
+                    elif config['RETURN_ON_ERROR']:
+                        return output_res, output_msg
+
+        return output_res, output_msg
+
+
+@_timing.time
+def eval_sequence(seq, dataset, tracker, class_list, metrics_list, metric_names):
+    """
+    Function for evaluating a single sequence
+
+    :param str seq: name of the sequence
+    :param str dataset: name of the dataset
+    :param str tracker: name of the tracker
+    :param List[str] class_list: list of all classes to be evaluated
+    :param List[str] metrics_list: list of all metrics
+    :param List[str] metric_names: list of all metrics names 
+
+    :return: Dict[str] seq_res: results of the eval sequence
+    ::
+
+        trackeval.eval.eval_sequence(seq, dataset, tracker, class_list, metrics_list, metric_names)
+    """
+    raw_data = dataset.get_raw_seq_data(tracker, seq)
+    seq_res = {}
+    for cls in class_list:
+        seq_res[cls] = {}
+        data = dataset.get_preprocessed_seq_data(raw_data, cls)
+        for metric, met_name in zip(metrics_list, metric_names):
+            seq_res[cls][met_name] = metric.eval_sequence(data)
+    return seq_res

MTMC_Tracking_2024/eval/trackeval/metrics/__init__.py

@@ -0,0 +1,5 @@
+"""MTMC analytics hota-metrics submodules"""
+from .hota import HOTA
+from .clear import CLEAR
+from .identity import Identity
+from .count import Count

MTMC_Tracking_2024/eval/trackeval/metrics/_base_metric.py

@@ -0,0 +1,199 @@
+import numpy as np
+from abc import ABC, abstractmethod
+from trackeval import _timing
+from trackeval.utils import TrackEvalException
+
+
+class _BaseMetric(ABC):
+    @abstractmethod
+    def __init__(self):
+        self.plottable = False
+        self.integer_fields = []
+        self.float_fields = []
+        self.array_labels = []
+        self.integer_array_fields = []
+        self.float_array_fields = []
+        self.fields = []
+        self.summary_fields = []
+        self.registered = False
+
+    #####################################################################
+    # Abstract functions for subclasses to implement
+
+    @_timing.time
+    @abstractmethod
+    def eval_sequence(self, data):
+        ...
+
+    @abstractmethod
+    def combine_sequences(self, all_res):
+        ...
+
+    @abstractmethod
+    def combine_classes_class_averaged(self, all_res, ignore_empty_classes=False):
+        ...
+
+    @ abstractmethod
+    def combine_classes_det_averaged(self, all_res):
+        ...
+
+    def plot_single_tracker_results(self, all_res, tracker, output_folder, cls):
+        """
+        Plot results of metrics, only valid for metrics with self.plottable
+        
+        :param Dict all_res: dictionary containing all results
+        :param str tracker: The tracker to plot results for
+        :param str output_folder: The output folder for saving the plots
+        :param str cls: The class to plot results for
+
+        :raises NotImplementedError: If the metric does not have self.plottable
+
+        """
+        if self.plottable:
+            raise NotImplementedError('plot_results is not implemented for metric %s' % self.get_name())
+        else:
+            pass
+
+    #####################################################################
+    # Helper functions which are useful for all metrics:
+
+    @classmethod
+    def get_name(cls):
+        return cls.__name__
+
+    @staticmethod
+    def _combine_sum(all_res, field):
+        """
+        Combine sequence results via sum
+        
+        :param Dict all_res: dictionary containing sequence results
+        :param str field: The field to be combined
+        :return: The sum of the combined results
+        :rtype: float
+        """
+        return sum([all_res[k][field] for k in all_res.keys()])
+
+    @staticmethod
+    def _combine_weighted_av(all_res, field, comb_res, weight_field):
+        """
+        Combine sequence results via weighted average
+
+        :param Dict all_res: dictionary containing sequence results
+        :param str field: The field to be combined
+        :param Dict comb_res: dictionary containing combined results
+        :param str weight_field: The field representing the weight
+        :return: The weighted average of the combined results
+        :rtype: float
+        """
+        return sum([all_res[k][field] * all_res[k][weight_field] for k in all_res.keys()]) / np.maximum(1.0, comb_res[
+            weight_field])
+
+    def print_table(self, table_res, tracker, cls):
+        """
+        Prints table of results for all sequences
+        
+        :param Dict table_res: dictionary containing the results for each sequence.
+        :param str tracker: The name of the tracker.
+        :param str cls: The name of the class.
+        :return None
+        """
+        print('')
+        metric_name = self.get_name()
+        self._row_print([metric_name + ': ' + tracker + '-' + cls] + self.summary_fields)
+        for seq, results in sorted(table_res.items()):
+            if seq == 'COMBINED_SEQ':
+                continue
+            # if seq == 'FINAL':
+            summary_res = self._summary_row(results)
+            self._row_print([seq] + summary_res)
+        summary_res = self._summary_row(table_res['COMBINED_SEQ'])
+        # self._row_print(['COMBINED'] + summary_res)
+
+    def _summary_row(self, results_):
+        """
+        Generate a summary row of values based on the provided results.
+        :param Dict results_: dictionary containing the metric results.
+
+        :return: A list of formatted values for the summary row.
+        :rtype: list
+        :raises NotImplementedError: If the summary function is not implemented for a field type.
+        """
+        vals = []
+        for h in self.summary_fields:
+            if h in self.float_array_fields:
+                vals.append("{0:1.5g}".format(100 * np.mean(results_[h])))
+            elif h in self.float_fields:
+                vals.append("{0:1.5g}".format(100 * float(results_[h])))
+            elif h in self.integer_fields:
+                vals.append("{0:d}".format(int(results_[h])))
+            else:
+                raise NotImplementedError("Summary function not implemented for this field type.")
+        return vals
+
+    @staticmethod
+    def _row_print(*argv):
+        """
+        Prints results in an evenly spaced rows, with more space in first row
+        
+        :param argv: The values to be printed in each column of the row.
+        :type argv: tuple or list
+        """
+        if len(argv) == 1:
+            argv = argv[0]
+        to_print = '%-35s' % argv[0]
+        for v in argv[1:]:
+            to_print += '%-10s' % str(v)
+        print(to_print)
+
+    def summary_results(self, table_res):
+        """
+        Returns a simple summary of final results for a tracker
+        
+        :param Dict table_res: The table of results containing per-sequence and combined sequence results.
+        :return: dictionary representing the summary of final results.
+        :rtype: Dict
+        """
+        return dict(zip(self.summary_fields, self._summary_row(table_res['COMBINED_SEQ'])))
+
+    def detailed_results(self, table_res):
+        """
+        Returns detailed final results for a tracker
+        
+        :param Dict table_res: The table of results containing per-sequence and combined sequence results.
+        :return: Detailed results for each sequence as a dictionary of dictionaries.
+        :rtype: Dict
+        :raises TrackEvalException: If the field names and data have different sizes.
+        """
+        # Get detailed field information
+        detailed_fields = self.float_fields + self.integer_fields
+        for h in self.float_array_fields + self.integer_array_fields:
+            for alpha in [int(100*x) for x in self.array_labels]:
+                detailed_fields.append(h + '___' + str(alpha))
+            detailed_fields.append(h + '___AUC')
+
+        # Get detailed results
+        detailed_results = {}
+        for seq, res in table_res.items():
+            detailed_row = self._detailed_row(res)
+            if len(detailed_row) != len(detailed_fields):
+                raise TrackEvalException(
+                    'Field names and data have different sizes (%i and %i)' % (len(detailed_row), len(detailed_fields)))
+            detailed_results[seq] = dict(zip(detailed_fields, detailed_row))
+        return detailed_results
+
+    def _detailed_row(self, res):
+        """
+        Calculates a detailed row of results for a given set of metrics.
+
+        :param Dict res: The results containing the metrics.
+        :return: Detailed row of results.
+        :rtype: list
+        """
+        detailed_row = []
+        for h in self.float_fields + self.integer_fields:
+            detailed_row.append(res[h])
+        for h in self.float_array_fields + self.integer_array_fields:
+            for i, alpha in enumerate([int(100 * x) for x in self.array_labels]):
+                detailed_row.append(res[h][i])
+            detailed_row.append(np.mean(res[h]))
+        return detailed_row

MTMC_Tracking_2024/eval/trackeval/metrics/clear.py

@@ -0,0 +1,223 @@
+import numpy as np
+from scipy.optimize import linear_sum_assignment
+from ._base_metric import _BaseMetric
+from trackeval import _timing
+from trackeval import utils
+
+
+class CLEAR(_BaseMetric):
+    """
+    Class which implements the CLEAR metrics
+    
+    :param Dict config: configuration for the app
+    ::
+
+        identity = trackeval.metrics.CLEAR(config)
+    """
+
+    @staticmethod
+    def get_default_config():
+        """Default class config values"""
+        default_config = {
+            'THRESHOLD': 0.5,  # Similarity score threshold required for a TP match. Default 0.5.
+            'PRINT_CONFIG': True,  # Whether to print the config information on init. Default: False.
+        }
+        return default_config
+
+    def __init__(self, config=None):
+        super().__init__()
+        main_integer_fields = ['CLR_TP', 'CLR_FN', 'CLR_FP', 'IDSW', 'MT', 'PT', 'ML', 'Frag']
+        extra_integer_fields = ['CLR_Frames']
+        self.integer_fields = main_integer_fields + extra_integer_fields
+        main_float_fields = ['MOTA', 'MOTP', 'MODA', 'CLR_Re', 'CLR_Pr', 'MTR', 'PTR', 'MLR', 'sMOTA']
+        extra_float_fields = ['CLR_F1', 'FP_per_frame', 'MOTAL', 'MOTP_sum']
+        self.float_fields = main_float_fields + extra_float_fields
+        self.fields = self.float_fields + self.integer_fields
+        self.summed_fields = self.integer_fields + ['MOTP_sum']
+        self.summary_fields = main_float_fields + main_integer_fields
+
+        # Configuration options:
+        self.config = utils.init_config(config, self.get_default_config(), self.get_name())
+        self.threshold = float(self.config['THRESHOLD'])
+
+
+    @_timing.time
+    def eval_sequence(self, data):
+        """
+        Calculates CLEAR metrics for one sequence
+        
+        :param Dict[str, float] data: dictionary containing the data for the sequence
+        
+        :return: dictionary containing the calculated count metrics
+        :rtype: Dict[str, float]
+        """
+        # Initialise results
+        res = {}
+        for field in self.fields:
+            res[field] = 0
+
+        # Return result quickly if tracker or gt sequence is empty
+        if data['num_tracker_dets'] == 0:
+            res['CLR_FN'] = data['num_gt_dets']
+            res['ML'] = data['num_gt_ids']
+            res['MLR'] = 1.0
+            return res
+        if data['num_gt_dets'] == 0:
+            res['CLR_FP'] = data['num_tracker_dets']
+            res['MLR'] = 1.0
+            return res
+
+        # Variables counting global association
+        num_gt_ids = data['num_gt_ids']
+        gt_id_count = np.zeros(num_gt_ids)  # For MT/ML/PT
+        gt_matched_count = np.zeros(num_gt_ids)  # For MT/ML/PT
+        gt_frag_count = np.zeros(num_gt_ids)  # For Frag
+
+        # Note that IDSWs are counted based on the last time each gt_id was present (any number of frames previously),
+        # but are only used in matching to continue current tracks based on the gt_id in the single previous timestep.
+        prev_tracker_id = np.nan * np.zeros(num_gt_ids)  # For scoring IDSW
+        prev_timestep_tracker_id = np.nan * np.zeros(num_gt_ids)  # For matching IDSW
+
+        # Calculate scores for each timestep
+        for t, (gt_ids_t, tracker_ids_t) in enumerate(zip(data['gt_ids'], data['tracker_ids'])):
+            # Deal with the case that there are no gt_det/tracker_det in a timestep.
+            if len(gt_ids_t) == 0:
+                res['CLR_FP'] += len(tracker_ids_t)
+                continue
+            if len(tracker_ids_t) == 0:
+                res['CLR_FN'] += len(gt_ids_t)
+                gt_id_count[gt_ids_t] += 1
+                continue
+
+            # Calc score matrix to first minimise IDSWs from previous frame, and then maximise MOTP secondarily
+            similarity = data['similarity_scores'][t]
+            score_mat = (tracker_ids_t[np.newaxis, :] == prev_timestep_tracker_id[gt_ids_t[:, np.newaxis]])
+            score_mat = 1000 * score_mat + similarity
+            score_mat[similarity < self.threshold - np.finfo('float').eps] = 0
+
+            # Hungarian algorithm to find best matches
+            match_rows, match_cols = linear_sum_assignment(-score_mat)
+            actually_matched_mask = score_mat[match_rows, match_cols] > 0 + np.finfo('float').eps
+            match_rows = match_rows[actually_matched_mask]
+            match_cols = match_cols[actually_matched_mask]
+
+            matched_gt_ids = gt_ids_t[match_rows]
+            matched_tracker_ids = tracker_ids_t[match_cols]
+
+            # Calc IDSW for MOTA
+            prev_matched_tracker_ids = prev_tracker_id[matched_gt_ids]
+            is_idsw = (np.logical_not(np.isnan(prev_matched_tracker_ids))) & (
+                np.not_equal(matched_tracker_ids, prev_matched_tracker_ids))
+            res['IDSW'] += np.sum(is_idsw)
+
+            # Update counters for MT/ML/PT/Frag and record for IDSW/Frag for next timestep
+            gt_id_count[gt_ids_t] += 1
+            gt_matched_count[matched_gt_ids] += 1
+            not_previously_tracked = np.isnan(prev_timestep_tracker_id)
+            prev_tracker_id[matched_gt_ids] = matched_tracker_ids
+            prev_timestep_tracker_id[:] = np.nan
+            prev_timestep_tracker_id[matched_gt_ids] = matched_tracker_ids
+            currently_tracked = np.logical_not(np.isnan(prev_timestep_tracker_id))
+            gt_frag_count += np.logical_and(not_previously_tracked, currently_tracked)
+
+            # Calculate and accumulate basic statistics
+            num_matches = len(matched_gt_ids)
+            res['CLR_TP'] += num_matches
+            res['CLR_FN'] += len(gt_ids_t) - num_matches
+            res['CLR_FP'] += len(tracker_ids_t) - num_matches
+            if num_matches > 0:
+                res['MOTP_sum'] += sum(similarity[match_rows, match_cols])
+
+        # Calculate MT/ML/PT/Frag/MOTP
+        tracked_ratio = gt_matched_count[gt_id_count > 0] / gt_id_count[gt_id_count > 0]
+        res['MT'] = np.sum(np.greater(tracked_ratio, 0.8))
+        res['PT'] = np.sum(np.greater_equal(tracked_ratio, 0.2)) - res['MT']
+        res['ML'] = num_gt_ids - res['MT'] - res['PT']
+        res['Frag'] = np.sum(np.subtract(gt_frag_count[gt_frag_count > 0], 1))
+        res['MOTP'] = res['MOTP_sum'] / np.maximum(1.0, res['CLR_TP'])
+
+        res['CLR_Frames'] = data['num_timesteps']
+
+        # Calculate final CLEAR scores
+        res = self._compute_final_fields(res)
+        return res
+
+    def combine_sequences(self, all_res):
+        """
+        Combines metrics across all sequences
+        
+        :param Dict[str, float] all_res: dictionary containing the metrics for each sequence        
+        :return: dictionary containing the combined metrics across sequences
+        :rtype: Dict[str, float]
+        """
+        res = {}
+        for field in self.summed_fields:
+            res[field] = self._combine_sum(all_res, field)
+        res = self._compute_final_fields(res)
+        return res
+
+    def combine_classes_det_averaged(self, all_res):
+        """
+        Combines metrics across all classes by averaging over the detection values
+        
+        :param Dict[str, float] all_res: dictionary containing the metrics for each class        
+        :return: dictionary containing the combined metrics averaged over detections
+        :rtype: Dict[str, float]
+        """
+        res = {}
+        for field in self.summed_fields:
+            res[field] = self._combine_sum(all_res, field)
+        res = self._compute_final_fields(res)
+        return res
+
+    def combine_classes_class_averaged(self, all_res, ignore_empty_classes=False):
+        """
+        Combines metrics across all classes by averaging over the class values.
+        If 'ignore_empty_classes' is True, then it only sums over classes with at least one gt or predicted detection.
+        
+        :param Dict[str, float] all_res: dictionary containing the ID metrics for each class
+        :param bool ignore_empty_classes: Flag to ignore empty classes, defaults to False
+        :return: dictionary containing the combined metrics averaged over classes
+        :rtype: Dict[str, float]
+        """
+        res = {}
+        for field in self.integer_fields:
+            if ignore_empty_classes:
+                res[field] = self._combine_sum(
+                    {k: v for k, v in all_res.items() if v['CLR_TP'] + v['CLR_FN'] + v['CLR_FP'] > 0}, field)
+            else:
+                res[field] = self._combine_sum({k: v for k, v in all_res.items()}, field)
+        for field in self.float_fields:
+            if ignore_empty_classes:
+                res[field] = np.mean(
+                    [v[field] for v in all_res.values() if v['CLR_TP'] + v['CLR_FN'] + v['CLR_FP'] > 0], axis=0)
+            else:
+                res[field] = np.mean([v[field] for v in all_res.values()], axis=0)
+        return res
+
+    @staticmethod
+    def _compute_final_fields(res):
+        """
+        Calculate sub-metric ('field') values which only depend on other sub-metric values.
+        This function is used both for both per-sequence calculation, and in combining values across sequences.
+        
+        :param Dict[str, float] res: dictionary containing the sub-metric values        
+        :return: dictionary containing the updated sub-metric values
+        :rtype: Dict[str, float]
+        """
+        num_gt_ids = res['MT'] + res['ML'] + res['PT']
+        res['MTR'] = res['MT'] / np.maximum(1.0, num_gt_ids)
+        res['MLR'] = res['ML'] / np.maximum(1.0, num_gt_ids)
+        res['PTR'] = res['PT'] / np.maximum(1.0, num_gt_ids)
+        res['CLR_Re'] = res['CLR_TP'] / np.maximum(1.0, res['CLR_TP'] + res['CLR_FN'])
+        res['CLR_Pr'] = res['CLR_TP'] / np.maximum(1.0, res['CLR_TP'] + res['CLR_FP'])
+        res['MODA'] = (res['CLR_TP'] - res['CLR_FP']) / np.maximum(1.0, res['CLR_TP'] + res['CLR_FN'])
+        res['MOTA'] = (res['CLR_TP'] - res['CLR_FP'] - res['IDSW']) / np.maximum(1.0, res['CLR_TP'] + res['CLR_FN'])
+        res['MOTP'] = res['MOTP_sum'] / np.maximum(1.0, res['CLR_TP'])
+        res['sMOTA'] = (res['MOTP_sum'] - res['CLR_FP'] - res['IDSW']) / np.maximum(1.0, res['CLR_TP'] + res['CLR_FN'])
+
+        res['CLR_F1'] = res['CLR_TP'] / np.maximum(1.0, res['CLR_TP'] + 0.5*res['CLR_FN'] + 0.5*res['CLR_FP'])
+        res['FP_per_frame'] = res['CLR_FP'] / np.maximum(1.0, res['CLR_Frames'])
+        safe_log_idsw = np.log10(res['IDSW']) if res['IDSW'] > 0 else res['IDSW']
+        res['MOTAL'] = (res['CLR_TP'] - res['CLR_FP'] - safe_log_idsw) / np.maximum(1.0, res['CLR_TP'] + res['CLR_FN'])
+        return res

MTMC_Tracking_2024/eval/trackeval/metrics/count.py

@@ -0,0 +1,76 @@
+from ._base_metric import _BaseMetric
+from trackeval import _timing
+
+
+class Count(_BaseMetric):
+    """
+    Class which simply counts the number of tracker and gt detections and ids.
+    
+    :param Dict config: configuration for the app
+    ::
+
+        identity = trackeval.metrics.Count(config)
+    """
+    def __init__(self, config=None):
+        super().__init__()
+        self.integer_fields = ['Dets', 'GT_Dets', 'IDs', 'GT_IDs']
+        self.fields = self.integer_fields
+        self.summary_fields = self.fields
+
+    @_timing.time
+    def eval_sequence(self, data):
+        """
+        Returns counts for one sequence
+        
+        :param Dict data: dictionary containing the data for the sequence
+        
+        :return: dictionary containing the calculated count metrics
+        :rtype: Dict[str, Dict[str]]
+        """
+        # Get results
+        res = {'Dets': data['num_tracker_dets'],
+               'GT_Dets': data['num_gt_dets'],
+               'IDs': data['num_tracker_ids'],
+               'GT_IDs': data['num_gt_ids'],
+               'Frames': data['num_timesteps']}
+        return res
+
+    def combine_sequences(self, all_res):
+        """
+        Combines metrics across all sequences
+        
+        :param Dict[str, float] all_res: dictionary containing the metrics for each sequence        
+        :return: dictionary containing the combined metrics across sequences
+        :rtype: Dict[str, float]
+        """
+        res = {}
+        for field in self.integer_fields:
+            res[field] = self._combine_sum(all_res, field)
+        return res
+
+    def combine_classes_class_averaged(self, all_res, ignore_empty_classes=None):
+        """
+        Combines metrics across all classes by averaging over the class values
+        
+        :param Dict[str, float] all_res: dictionary containing the ID metrics for each class
+        :param bool ignore_empty_classes: Flag to ignore empty classes, defaults to False
+        :return: dictionary containing the combined metrics averaged over classes
+        :rtype: Dict[str, float]
+        """
+        res = {}
+        for field in self.integer_fields:
+            res[field] = self._combine_sum(all_res, field)
+        return res
+
+    def combine_classes_det_averaged(self, all_res):
+        """
+        Combines metrics across all classes by averaging over the detection values
+        
+        :param Dict[str, float] all_res: dictionary containing the metrics for each class        
+        :return: dictionary containing the combined metrics averaged over detections
+        :rtype: Dict[str, float]
+        """
+        res = {}
+        for field in self.integer_fields:
+            res[field] = self._combine_sum(all_res, field)
+        return res

MTMC_Tracking_2024/eval/trackeval/metrics/hota.py

@@ -0,0 +1,245 @@
+import os
+import numpy as np
+from trackeval import _timing
+from scipy.optimize import linear_sum_assignment
+from trackeval.metrics._base_metric import _BaseMetric
+
+
+class HOTA(_BaseMetric):
+    """
+    Class which implements the HOTA metrics.
+    See: https://link.springer.com/article/10.1007/s11263-020-01375-2
+    
+    :param Dict config: configuration for the app
+    ::
+
+        identity = trackeval.metrics.HOTA(config)
+    """
+
+    def __init__(self, config=None):
+        super().__init__()
+        self.plottable = True
+        self.array_labels = np.arange(0.05, 0.99, 0.05)
+        self.integer_array_fields = ['HOTA_TP', 'HOTA_FN', 'HOTA_FP']
+        self.float_array_fields = ['HOTA', 'DetA', 'AssA', 'DetRe', 'DetPr', 'AssRe', 'AssPr', 'LocA', 'OWTA']
+        self.float_fields = ['HOTA(0)', 'LocA(0)', 'HOTALocA(0)']
+        self.fields = self.float_array_fields + self.integer_array_fields + self.float_fields
+        self.summary_fields = self.float_array_fields + self.float_fields
+
+    @_timing.time
+    def eval_sequence(self, data):
+        """
+        Calculates the HOTA metrics for one sequence
+        
+        :param Dict data: dictionary containing the data for the sequence
+        
+        :return: dictionary containing the calculated hota metrics
+        :rtype: Dict
+        """
+        
+        # Initialise results
+        res = {}
+        for field in self.float_array_fields + self.integer_array_fields:
+            res[field] = np.zeros((len(self.array_labels)), dtype=float)
+        for field in self.float_fields:
+            res[field] = 0
+
+        # Return result quickly if tracker or gt sequence is empty
+        if data['num_tracker_dets'] == 0:
+            res['HOTA_FN'] = data['num_gt_dets'] * np.ones((len(self.array_labels)), dtype=float)
+            res['LocA'] = np.ones((len(self.array_labels)), dtype=float)
+            res['LocA(0)'] = 1.0
+            return res
+        if data['num_gt_dets'] == 0:
+            res['HOTA_FP'] = data['num_tracker_dets'] * np.ones((len(self.array_labels)), dtype=float)
+            res['LocA'] = np.ones((len(self.array_labels)), dtype=float)
+            res['LocA(0)'] = 1.0
+            return res
+
+        # Variables counting global association
+        potential_matches_count = np.zeros((data['num_gt_ids'], data['num_tracker_ids']))
+        gt_id_count = np.zeros((data['num_gt_ids'], 1))
+        tracker_id_count = np.zeros((1, data['num_tracker_ids']))
+
+        # First loop through each timestep and accumulate global track information.
+        for t, (gt_ids_t, tracker_ids_t) in enumerate(zip(data['gt_ids'], data['tracker_ids'])):
+            # Count the potential matches between ids in each timestep
+            # These are normalised, weighted by the match similarity.
+            similarity = data['similarity_scores'][t]
+            sim_iou_denom = similarity.sum(0)[np.newaxis, :] + similarity.sum(1)[:, np.newaxis] - similarity
+            sim_iou = np.zeros_like(similarity)
+            sim_iou_mask = sim_iou_denom > 0 + np.finfo('float').eps
+            sim_iou[sim_iou_mask] = similarity[sim_iou_mask] / sim_iou_denom[sim_iou_mask]
+            potential_matches_count[gt_ids_t[:, np.newaxis], tracker_ids_t[np.newaxis, :]] += sim_iou
+
+            # Calculate the total number of dets for each gt_id and tracker_id.
+            gt_id_count[gt_ids_t] += 1
+            tracker_id_count[0, tracker_ids_t] += 1
+
+        # Calculate overall jaccard alignment score (before unique matching) between IDs
+        global_alignment_score = potential_matches_count / (gt_id_count + tracker_id_count - potential_matches_count)
+        matches_counts = [np.zeros_like(potential_matches_count) for _ in self.array_labels]
+
+        # Calculate scores for each timestep
+        for t, (gt_ids_t, tracker_ids_t) in enumerate(zip(data['gt_ids'], data['tracker_ids'])):
+            # Deal with the case that there are no gt_det/tracker_det in a timestep.
+            if len(gt_ids_t) == 0:
+                for a, alpha in enumerate(self.array_labels):
+                    res['HOTA_FP'][a] += len(tracker_ids_t)
+                continue
+            if len(tracker_ids_t) == 0:
+                for a, alpha in enumerate(self.array_labels):
+                    res['HOTA_FN'][a] += len(gt_ids_t)
+                continue
+
+            # Get matching scores between pairs of dets for optimizing HOTA
+            similarity = data['similarity_scores'][t]
+            score_mat = global_alignment_score[gt_ids_t[:, np.newaxis], tracker_ids_t[np.newaxis, :]] * similarity
+
+            # Hungarian algorithm to find best matches
+            match_rows, match_cols = linear_sum_assignment(-score_mat)
+
+            # Calculate and accumulate basic statistics
+            for a, alpha in enumerate(self.array_labels):
+                actually_matched_mask = similarity[match_rows, match_cols] >= alpha - np.finfo('float').eps
+                alpha_match_rows = match_rows[actually_matched_mask]
+                alpha_match_cols = match_cols[actually_matched_mask]
+                num_matches = len(alpha_match_rows)
+                res['HOTA_TP'][a] += num_matches
+                res['HOTA_FN'][a] += len(gt_ids_t) - num_matches
+                res['HOTA_FP'][a] += len(tracker_ids_t) - num_matches
+                if num_matches > 0:
+                    res['LocA'][a] += sum(similarity[alpha_match_rows, alpha_match_cols])
+                    matches_counts[a][gt_ids_t[alpha_match_rows], tracker_ids_t[alpha_match_cols]] += 1
+
+        # Calculate association scores (AssA, AssRe, AssPr) for the alpha value.
+        # First calculate scores per gt_id/tracker_id combo and then average over the number of detections.
+        for a, alpha in enumerate(self.array_labels):
+            matches_count = matches_counts[a]
+            ass_a = matches_count / np.maximum(1, gt_id_count + tracker_id_count - matches_count)
+            res['AssA'][a] = np.sum(matches_count * ass_a) / np.maximum(1, res['HOTA_TP'][a])
+            ass_re = matches_count / np.maximum(1, gt_id_count)
+            res['AssRe'][a] = np.sum(matches_count * ass_re) / np.maximum(1, res['HOTA_TP'][a])
+            ass_pr = matches_count / np.maximum(1, tracker_id_count)
+            res['AssPr'][a] = np.sum(matches_count * ass_pr) / np.maximum(1, res['HOTA_TP'][a])
+
+        # Calculate final scores
+        res['LocA'] = np.maximum(0, res['LocA']) / np.maximum(1e-10, res['HOTA_TP'])
+        res = self._compute_final_fields(res)
+        return res
+
+    def combine_sequences(self, all_res):
+        """
+        Combines metrics across all sequences
+        
+        :param Dict[str, float] all_res: dictionary containing the metrics for each sequence        
+        :return: dictionary containing the combined metrics across sequences
+        :rtype: Dict[str, float]
+        """
+        res = {}
+        for field in self.integer_array_fields:
+            res[field] = self._combine_sum(all_res, field)
+        for field in ['AssRe', 'AssPr', 'AssA']:
+            res[field] = self._combine_weighted_av(all_res, field, res, weight_field='HOTA_TP')
+        loca_weighted_sum = sum([all_res[k]['LocA'] * all_res[k]['HOTA_TP'] for k in all_res.keys()])
+        res['LocA'] = np.maximum(1e-10, loca_weighted_sum) / np.maximum(1e-10, res['HOTA_TP'])
+        res = self._compute_final_fields(res)
+        return res
+
+    def combine_classes_class_averaged(self, all_res, ignore_empty_classes=False):
+        """
+        Combines metrics across all classes by averaging over the class values.
+        If 'ignore_empty_classes' is True, then it only sums over classes with at least one gt or predicted detection.
+        
+        :param Dict[str, float] all_res: dictionary containing the ID metrics for each class
+        :param bool ignore_empty_classes: Flag to ignore empty classes, defaults to False
+        :return: dictionary containing the combined metrics averaged over classes
+        :rtype: Dict[str, float]
+        """
+        res = {}
+        for field in self.integer_array_fields:
+            if ignore_empty_classes:
+                res[field] = self._combine_sum(
+                    {k: v for k, v in all_res.items()
+                     if (v['HOTA_TP'] + v['HOTA_FN'] + v['HOTA_FP'] > 0 + np.finfo('float').eps).any()}, field)
+            else:
+                res[field] = self._combine_sum({k: v for k, v in all_res.items()}, field)
+
+        for field in self.float_fields + self.float_array_fields:
+            if ignore_empty_classes:
+                res[field] = np.mean([v[field] for v in all_res.values() if
+                                      (v['HOTA_TP'] + v['HOTA_FN'] + v['HOTA_FP'] > 0 + np.finfo('float').eps).any()],
+                                     axis=0)
+            else:
+                res[field] = np.mean([v[field] for v in all_res.values()], axis=0)
+        return res
+
+    def combine_classes_det_averaged(self, all_res):
+        """
+        Combines metrics across all classes by averaging over the detection values
+        
+        :param Dict[str, float] all_res: dictionary containing the metrics for each class        
+        :return: dictionary containing the combined metrics averaged over detections
+        :rtype: Dict[str, float]
+        """
+        res = {}
+        for field in self.integer_array_fields:
+            res[field] = self._combine_sum(all_res, field)
+        for field in ['AssRe', 'AssPr', 'AssA']:
+            res[field] = self._combine_weighted_av(all_res, field, res, weight_field='HOTA_TP')
+        loca_weighted_sum = sum([all_res[k]['LocA'] * all_res[k]['HOTA_TP'] for k in all_res.keys()])
+        res['LocA'] = np.maximum(1e-10, loca_weighted_sum) / np.maximum(1e-10, res['HOTA_TP'])
+        res = self._compute_final_fields(res)
+        return res
+
+    @staticmethod
+    def _compute_final_fields(res):
+        """
+        Calculate sub-metric ('field') values which only depend on other sub-metric values.
+        This function is used both for both per-sequence calculation, and in combining values across sequences.
+        
+        :param Dict[str, float] res: dictionary containing the sub-metric values        
+        :return: dictionary containing the updated sub-metric values
+        :rtype: Dict[str, float]
+        """
+        res['DetRe'] = res['HOTA_TP'] / np.maximum(1, res['HOTA_TP'] + res['HOTA_FN'])
+        res['DetPr'] = res['HOTA_TP'] / np.maximum(1, res['HOTA_TP'] + res['HOTA_FP'])
+        res['DetA'] = res['HOTA_TP'] / np.maximum(1, res['HOTA_TP'] + res['HOTA_FN'] + res['HOTA_FP'])
+        res['HOTA'] = np.sqrt(res['DetA'] * res['AssA'])
+        res['OWTA'] = np.sqrt(res['DetRe'] * res['AssA'])
+
+        res['HOTA(0)'] = res['HOTA'][0]
+        res['LocA(0)'] = res['LocA'][0]
+        res['HOTALocA(0)'] = res['HOTA(0)']*res['LocA(0)']
+        return res
+
+    def plot_single_tracker_results(self, table_res, tracker, cls, output_folder):
+        """
+        Create plot of results
+        
+        :param Dict table_res: dictionary containing the evaluation results
+        :param str tracker: The name of the tracker
+        :param str cls: The class name
+        :param str output_folder: The output folder path for saving the plot
+        """
+
+        # Only loaded when run to reduce minimum requirements
+        from matplotlib import pyplot as plt
+
+        res = table_res['COMBINED_SEQ']
+        styles_to_plot = ['r', 'b', 'g', 'b--', 'b:', 'g--', 'g:', 'm']
+        for name, style in zip(self.float_array_fields, styles_to_plot):
+            plt.plot(self.array_labels, res[name], style)
+        plt.xlabel('alpha')
+        plt.ylabel('score')
+        plt.title(tracker + ' - ' + cls)
+        plt.axis([0, 1, 0, 1])
+        legend = []
+        for name in self.float_array_fields:
+            legend += [name + ' (' + str(np.round(np.mean(res[name]), 2)) + ')']
+        plt.legend(legend, loc='lower left')
+        out_file = os.path.join(output_folder, cls + '_plot.pdf')
+        os.makedirs(os.path.dirname(out_file), exist_ok=True)
+        plt.savefig(out_file)
+        plt.savefig(out_file.replace('.pdf', '.png'))
+        plt.clf()

MTMC_Tracking_2024/eval/trackeval/metrics/identity.py

@@ -0,0 +1,172 @@
+import numpy as np
+from scipy.optimize import linear_sum_assignment
+from trackeval import _timing
+from trackeval import utils
+from trackeval.metrics._base_metric import _BaseMetric
+
+
+class Identity(_BaseMetric):
+    """
+    Class which implements the Identity metrics
+    
+    :param Dict config: configuration for the app
+    ::
+
+        identity = trackeval.metrics.Identity(config)
+    """
+
+    @staticmethod
+    def get_default_config():
+        """Default class config values"""
+        default_config = {
+            'THRESHOLD': 0.5,  # Similarity score threshold required for a IDTP match. Default 0.5.
+            'PRINT_CONFIG': True,  # Whether to print the config information on init. Default: False.
+        }
+        return default_config
+
+    def __init__(self, config=None):
+        super().__init__()
+        self.integer_fields = ['IDTP', 'IDFN', 'IDFP']
+        self.float_fields = ['IDF1', 'IDR', 'IDP']
+        self.fields = self.float_fields + self.integer_fields
+        self.summary_fields = self.fields
+
+        # Configuration options:
+        self.config = utils.init_config(config, self.get_default_config(), self.get_name())
+        self.threshold = float(self.config['THRESHOLD'])
+
+    @_timing.time
+    def eval_sequence(self, data):
+        """
+        Calculates ID metrics for one sequence
+        
+        :param Dict[str, float] data: dictionary containing the data for the sequence
+        
+        :return: dictionary containing the calculated ID metrics
+        :rtype: Dict[str, float]
+        """
+        # Initialise results
+        res = {}
+        for field in self.fields:
+            res[field] = 0
+
+        # Return result quickly if tracker or gt sequence is empty
+        if data['num_tracker_dets'] == 0:
+            res['IDFN'] = data['num_gt_dets']
+            return res
+        if data['num_gt_dets'] == 0:
+            res['IDFP'] = data['num_tracker_dets']
+            return res
+
+        # Variables counting global association
+        potential_matches_count = np.zeros((data['num_gt_ids'], data['num_tracker_ids']))
+        gt_id_count = np.zeros(data['num_gt_ids'])
+        tracker_id_count = np.zeros(data['num_tracker_ids'])
+
+        # First loop through each timestep and accumulate global track information.
+        for t, (gt_ids_t, tracker_ids_t) in enumerate(zip(data['gt_ids'], data['tracker_ids'])):
+            # Count the potential matches between ids in each timestep
+            matches_mask = np.greater_equal(data['similarity_scores'][t], self.threshold)
+            match_idx_gt, match_idx_tracker = np.nonzero(matches_mask)
+            potential_matches_count[gt_ids_t[match_idx_gt], tracker_ids_t[match_idx_tracker]] += 1
+
+            # Calculate the total number of dets for each gt_id and tracker_id.
+            gt_id_count[gt_ids_t] += 1
+            tracker_id_count[tracker_ids_t] += 1
+
+        # Calculate optimal assignment cost matrix for ID metrics
+        num_gt_ids = data['num_gt_ids']
+        num_tracker_ids = data['num_tracker_ids']
+        fp_mat = np.zeros((num_gt_ids + num_tracker_ids, num_gt_ids + num_tracker_ids))
+        fn_mat = np.zeros((num_gt_ids + num_tracker_ids, num_gt_ids + num_tracker_ids))
+        fp_mat[num_gt_ids:, :num_tracker_ids] = 1e10
+        fn_mat[:num_gt_ids, num_tracker_ids:] = 1e10
+        for gt_id in range(num_gt_ids):
+            fn_mat[gt_id, :num_tracker_ids] = gt_id_count[gt_id]
+            fn_mat[gt_id, num_tracker_ids + gt_id] = gt_id_count[gt_id]
+        for tracker_id in range(num_tracker_ids):
+            fp_mat[:num_gt_ids, tracker_id] = tracker_id_count[tracker_id]
+            fp_mat[tracker_id + num_gt_ids, tracker_id] = tracker_id_count[tracker_id]
+        fn_mat[:num_gt_ids, :num_tracker_ids] -= potential_matches_count
+        fp_mat[:num_gt_ids, :num_tracker_ids] -= potential_matches_count
+
+        # Hungarian algorithm
+        match_rows, match_cols = linear_sum_assignment(fn_mat + fp_mat)
+
+        # Accumulate basic statistics
+        res['IDFN'] = fn_mat[match_rows, match_cols].sum().astype(int)
+        res['IDFP'] = fp_mat[match_rows, match_cols].sum().astype(int)
+        res['IDTP'] = (gt_id_count.sum() - res['IDFN']).astype(int)
+
+        # Calculate final ID scores
+        res = self._compute_final_fields(res)
+        return res
+
+    def combine_classes_class_averaged(self, all_res, ignore_empty_classes=False):
+        """
+        Combines metrics across all classes by averaging over the class values.
+        If 'ignore_empty_classes' is True, then it only sums over classes with at least one gt or predicted detection.
+        
+        :param Dict[str, float] all_res: dictionary containing the ID metrics for each class
+        :param bool ignore_empty_classes: flag to ignore empty classes, defaults to False
+        :return: dictionary containing the combined metrics averaged over classes
+        :rtype: Dict[str, float]
+        """
+        res = {}
+        for field in self.integer_fields:
+            if ignore_empty_classes:
+                res[field] = self._combine_sum({k: v for k, v in all_res.items()
+                                                if v['IDTP'] + v['IDFN'] + v['IDFP'] > 0 + np.finfo('float').eps},
+                                               field)
+            else:
+                res[field] = self._combine_sum({k: v for k, v in all_res.items()}, field)
+        for field in self.float_fields:
+            if ignore_empty_classes:
+                res[field] = np.mean([v[field] for v in all_res.values()
+                                      if v['IDTP'] + v['IDFN'] + v['IDFP'] > 0 + np.finfo('float').eps], axis=0)
+            else:
+                res[field] = np.mean([v[field] for v in all_res.values()], axis=0)
+        return res
+
+    def combine_classes_det_averaged(self, all_res):
+        """
+        Combines metrics across all classes by averaging over the detection values
+        
+        :param Dict[str, float] all_res: dictionary containing the metrics for each class        
+        :return: dictionary containing the combined metrics averaged over detections
+        :rtype: Dict[str, float]
+        """
+        res = {}
+        for field in self.integer_fields:
+            res[field] = self._combine_sum(all_res, field)
+        res = self._compute_final_fields(res)
+        return res
+
+    def combine_sequences(self, all_res):
+        """
+        Combines metrics across all sequences
+        
+        :param Dict[str, float] all_res: dictionary containing the metrics for each sequence        
+        :return: dictionary containing the combined metrics across sequences
+        :rtype: Dict[str, float][str, float]
+        """
+        res = {}
+        for field in self.integer_fields:
+            res[field] = self._combine_sum(all_res, field)
+        res = self._compute_final_fields(res)
+        return res
+
+    @staticmethod
+    def _compute_final_fields(res):
+        """
+        Calculate sub-metric ('field') values which only depend on other sub-metric values.
+        This function is used both for both per-sequence calculation, and in combining values across sequences.
+        
+        :param Dict[str, float] res: dictionary containing the sub-metric values        
+        :return: dictionary containing the updated sub-metric values
+        :rtype: Dict[str, float]
+        """
+        res['IDR'] = res['IDTP'] / np.maximum(1.0, res['IDTP'] + res['IDFN'])
+        res['IDP'] = res['IDTP'] / np.maximum(1.0, res['IDTP'] + res['IDFP'])
+        res['IDF1'] = res['IDTP'] / np.maximum(1.0, res['IDTP'] + 0.5 * res['IDFP'] + 0.5 * res['IDFN'])
+        return res

MTMC_Tracking_2024/eval/trackeval/plotting.py

@@ -0,0 +1,322 @@
+import os
+import logging
+import numpy as np
+from .utils import TrackEvalException
+
+
+def plot_compare_trackers(tracker_folder, tracker_list, cls, output_folder, plots_list=None):
+    """
+    Create plots which compare metrics across different trackers
+
+    :param str tracker_folder: root tracker folder
+    :param str tracker_list: names of all trackers
+    :param List[cls] cls: names of classes
+    :param str output_folder: root folder to save the plots in
+    :param List[str] plots_list: list of all plots to generate
+    :return: None
+    ::
+
+        plotting.plot_compare_trackers(tracker_folder, tracker_list, cls, output_folder, plots_list)
+    """
+    if plots_list is None:
+        plots_list = get_default_plots_list()
+
+    # Load data
+    data = load_multiple_tracker_summaries(tracker_folder, tracker_list, cls)
+    out_loc = os.path.join(output_folder, cls)
+
+    # Plot
+    print("\n")
+    for args in plots_list:
+        create_comparison_plot(data, out_loc, *args)
+
+
+def get_default_plots_list():
+    """
+    Create a intermediate config to define the type of plots.
+    The plot uses the following order to generate the charts:
+    y_label, x_label, sort_label, bg_label, bg_function
+
+    :param None
+    :return: List[List[str]] plots_list: detailed description of the plots 
+    ::
+
+        plotting.get_default_plots_list(tracker_folder, tracker_list, cls, output_folder, plots_list)
+    """
+    plots_list = [
+        ['AssA', 'DetA', 'HOTA', 'HOTA', 'geometric_mean'],
+        ['AssPr', 'AssRe', 'HOTA', 'AssA', 'jaccard'],
+        ['DetPr', 'DetRe', 'HOTA', 'DetA', 'jaccard'],
+        ['HOTA(0)', 'LocA(0)', 'HOTA', 'HOTALocA(0)', 'multiplication'],
+        ['HOTA', 'LocA', 'HOTA', None, None],
+
+        ['HOTA', 'MOTA', 'HOTA', None, None],
+        ['HOTA', 'IDF1', 'HOTA', None, None],
+        ['IDF1', 'MOTA', 'HOTA', None, None],
+    ]
+    return plots_list
+
+
+def load_multiple_tracker_summaries(tracker_folder, tracker_list, cls):
+    """
+    Loads summary data for multiple trackers 
+
+    :param str tracker_folder: directory of the tracker folder
+    :param str tracker_list: names of the trackers
+    :param str cls: names of all classes
+
+    :return: Dict[str] data: summaried data of the trackers 
+    ::
+
+        plotting.load_multiple_tracker_summaries(tracker_folder, tracker_list, cls, output_folder, plots_list)
+    """
+    data = {}
+    for tracker in tracker_list:
+        with open(os.path.join(tracker_folder, tracker, cls + '_summary.txt')) as f:
+            keys = next(f).split(' ')
+            done = False
+            while not done:
+                values = next(f).split(' ')
+                if len(values) == len(keys):
+                    done = True
+            data[tracker] = dict(zip(keys, map(float, values)))
+    return data
+
+
+def create_comparison_plot(data, out_loc, y_label, x_label, sort_label, bg_label=None, bg_function=None, settings=None):
+    """ 
+    Creates a scatter plot comparing multiple trackers between two metric fields, with one on the x-axis and the
+    other on the y axis. Adds pareto optical lines and (optionally) a background contour.
+
+    :param data: dict of dicts such that data[tracker_name][metric_field_name] = float
+    :param str y_label: the metric_field_name to be plotted on the y-axis
+    :param strx_label: the metric_field_name to be plotted on the x-axis
+    :param str sort_label: the metric_field_name by which trackers are ordered and ranked
+    :param str bg_label: the metric_field_name by which (optional) background contours are plotted
+    :param str bg_function: the (optional) function bg_function(x,y) which converts the x_label / y_label values into bg_label.
+    :param Dict[str] settings: dict of plot settings with keys:
+        'gap_val': gap between axis ticks and bg curves.
+        'num_to_plot': maximum number of trackers to plot
+
+    :return: None
+    ::
+
+        plotting.create_comparison_plot(x_values, y_values)
+    """
+
+    # Only loaded when run to reduce minimum requirements
+    from matplotlib import pyplot as plt
+
+    # Get plot settings
+    if settings is None:
+        gap_val = 2
+        num_to_plot = 20
+    else:
+        gap_val = settings['gap_val']
+        num_to_plot = settings['num_to_plot']
+
+    if (bg_label is None) != (bg_function is None):
+        raise TrackEvalException('bg_function and bg_label must either be both given or neither given.')
+
+    # Extract data
+    tracker_names = np.array(list(data.keys()))
+    sort_index = np.array([data[t][sort_label] for t in tracker_names]).argsort()[::-1]
+    x_values = np.array([data[t][x_label] for t in tracker_names])[sort_index][:num_to_plot]
+    y_values = np.array([data[t][y_label] for t in tracker_names])[sort_index][:num_to_plot]
+
+    # Print info on what is being plotted
+    tracker_names = tracker_names[sort_index][:num_to_plot]
+    logging.info('Plotting %s vs %s...' % (y_label, x_label))
+    #for i, name in enumerate(tracker_names):
+        #print('%i: %s' % (i+1, name))
+
+    # Find best fitting boundaries for data
+    boundaries = _get_boundaries(x_values, y_values, round_val=gap_val/2)
+
+    fig = plt.figure()
+
+    # Plot background contour
+    if bg_function is not None:
+        _plot_bg_contour(bg_function, boundaries, gap_val)
+
+    # Plot pareto optimal lines
+    _plot_pareto_optimal_lines(x_values, y_values)
+
+    # Plot data points with number labels
+    labels = np.arange(len(y_values)) + 1
+    plt.plot(x_values, y_values, 'b.', markersize=15)
+    for xx, yy, l in zip(x_values, y_values, labels):
+        plt.text(xx, yy, str(l), color="red", fontsize=15)
+
+    # Add extra explanatory text to plots
+    plt.text(0, -0.11, 'label order:\nHOTA', horizontalalignment='left', verticalalignment='center',
+             transform=fig.axes[0].transAxes, color="red", fontsize=12)
+    if bg_label is not None:
+        plt.text(1, -0.11, 'curve values:\n' + bg_label, horizontalalignment='right', verticalalignment='center',
+                 transform=fig.axes[0].transAxes, color="grey", fontsize=12)
+
+    plt.xlabel(x_label, fontsize=15)
+    plt.ylabel(y_label, fontsize=15)
+    title = y_label + ' vs ' + x_label
+    if bg_label is not None:
+        title += ' (' + bg_label + ')'
+    plt.title(title, fontsize=17)
+    plt.xticks(np.arange(0, 100, gap_val))
+    plt.yticks(np.arange(0, 100, gap_val))
+    min_x, max_x, min_y, max_y = boundaries
+    plt.xlim(min_x, max_x)
+    plt.ylim(min_y, max_y)
+    plt.gca().set_aspect('equal', adjustable='box')
+    plt.tight_layout()
+
+    os.makedirs(out_loc, exist_ok=True)
+    filename = os.path.join(out_loc, title.replace(' ', '_'))
+    plt.savefig(filename + '.pdf', bbox_inches='tight', pad_inches=0.05)
+    plt.savefig(filename + '.png', bbox_inches='tight', pad_inches=0.05)
+
+
+def _get_boundaries(x_values, y_values, round_val):
+    """
+    Computes boundaries of a plot
+
+    :param List[Float] x_values: x values
+    :param List[Float] y_values: y values
+    :param Float round_val: interval
+
+    :return: Float, Float, Float, Float: boundaries of the plot
+    ::
+
+        plotting._get_boundaries(x_values, y_values)
+    """
+    x1 = np.min(np.floor((x_values - 0.5) / round_val) * round_val)
+    x2 = np.max(np.ceil((x_values + 0.5) / round_val) * round_val)
+    y1 = np.min(np.floor((y_values - 0.5) / round_val) * round_val)
+    y2 = np.max(np.ceil((y_values + 0.5) / round_val) * round_val)
+    x_range = x2 - x1
+    y_range = y2 - y1
+    max_range = max(x_range, y_range)
+    x_center = (x1 + x2) / 2
+    y_center = (y1 + y2) / 2
+    min_x = max(x_center - max_range / 2, 0)
+    max_x = min(x_center + max_range / 2, 100)
+    min_y = max(y_center - max_range / 2, 0)
+    max_y = min(y_center + max_range / 2, 100)
+    return min_x, max_x, min_y, max_y
+
+
+def geometric_mean(x, y):
+    """
+    Computes geometric mean
+
+    :param Float x: x values
+    :param Float y: y values
+
+    :return: Float: geometric mean value 
+    ::
+
+        plotting.geometric_mean(x_values, y_values)
+    """
+    return np.sqrt(x * y)
+
+
+def jaccard(x, y):
+    x = x / 100
+    y = y / 100
+    return 100 * (x * y) / (x + y - x * y)
+
+
+def multiplication(x, y):
+    """
+    Computes multiplication for plots
+
+    :param Float x: x values
+    :param Float y: y values
+
+    :return: Float: multiplied value 
+    ::
+
+        plotting.multiplication(x_values, y_values)
+    """
+    return x * y / 100
+
+
+bg_function_dict = {
+    "geometric_mean": geometric_mean,
+    "jaccard": jaccard,
+    "multiplication": multiplication,
+    }
+
+
+def _plot_bg_contour(bg_function, plot_boundaries, gap_val):
+    """
+    Plot background contour
+
+    :param Dict[str:func()] bg_function: sort order function
+    :param List[float] plot_boundaries: limit values for the plot
+    :param int gap_val: interval value
+
+    :return: None 
+    ::
+
+        plotting._plot_bg_contour(x_values, y_values)
+    """
+    # Only loaded when run to reduce minimum requirements
+    from matplotlib import pyplot as plt
+
+    # Plot background contour
+    min_x, max_x, min_y, max_y = plot_boundaries
+    x = np.arange(min_x, max_x, 0.1)
+    y = np.arange(min_y, max_y, 0.1)
+    x_grid, y_grid = np.meshgrid(x, y)
+    if bg_function in bg_function_dict.keys():
+        z_grid = bg_function_dict[bg_function](x_grid, y_grid)
+    else:
+        raise TrackEvalException("background plotting function '%s' is not defined." % bg_function)
+    levels = np.arange(0, 100, gap_val)
+    con = plt.contour(x_grid, y_grid, z_grid, levels, colors='grey')
+
+    def bg_format(val):
+        s = '{:1f}'.format(val)
+        return '{:.0f}'.format(val) if s[-1] == '0' else s
+
+    con.levels = [bg_format(val) for val in con.levels]
+    plt.clabel(con, con.levels, inline=True, fmt='%r', fontsize=8)
+
+
+def _plot_pareto_optimal_lines(x_values, y_values):
+    """
+    Plot pareto optimal lines
+
+    :param List[float] x_values: values to plot on x axis
+    :param List[float] y_values: values to plot on y axis
+
+    :return: None 
+    ::
+
+        plotting._plot_pareto_optimal_lines(x_values, y_values)
+    """
+
+    # Only loaded when run to reduce minimum requirements
+    from matplotlib import pyplot as plt
+
+    # Plot pareto optimal lines
+    cxs = x_values
+    cys = y_values
+    best_y = np.argmax(cys)
+    x_pareto = [0, cxs[best_y]]
+    y_pareto = [cys[best_y], cys[best_y]]
+    t = 2
+    remaining = cxs > x_pareto[t - 1]
+    cys = cys[remaining]
+    cxs = cxs[remaining]
+    while len(cxs) > 0 and len(cys) > 0:
+        best_y = np.argmax(cys)
+        x_pareto += [x_pareto[t - 1], cxs[best_y]]
+        y_pareto += [cys[best_y], cys[best_y]]
+        t += 2
+        remaining = cxs > x_pareto[t - 1]
+        cys = cys[remaining]
+        cxs = cxs[remaining]
+    x_pareto.append(x_pareto[t - 1])
+    y_pareto.append(0)
+    plt.plot(np.array(x_pareto), np.array(y_pareto), '--r')

MTMC_Tracking_2024/eval/trackeval/utils.py

@@ -0,0 +1,204 @@
+import os
+import csv
+import argparse
+from collections import OrderedDict
+
+
+def init_config(config, default_config, name=None):
+    """
+    Initialise non-given config values with defaults
+
+    :param str config: config
+    :param str default_config: default config
+    :param str name: name of dataset/metric
+    :return: None
+    ::
+
+        trackeval.utils.init_config(config, default_config, name)
+    """
+    if config is None:
+        config = default_config
+    else:
+        for k in default_config.keys():
+            if k not in config.keys():
+                config[k] = default_config[k]
+    if name and config['PRINT_CONFIG']:
+        print('\n%s Config:' % name)
+        for c in config.keys():
+            print('%-20s : %-30s' % (c, config[c]))
+    return config
+
+
+def update_config(config):
+    """
+    Parse the arguments of a script and updates the config values for a given value if specified in the arguments.
+
+    :param str config: the config to update
+    :return: the updated config
+    ::
+
+        trackeval.utils.update_config(config, default_config, name)
+    """
+    parser = argparse.ArgumentParser()
+    for setting in config.keys():
+        if type(config[setting]) == list or type(config[setting]) == type(None):
+            parser.add_argument("--" + setting, nargs='+')
+        else:
+            parser.add_argument("--" + setting)
+    args = parser.parse_args().__dict__
+    for setting in args.keys():
+        if args[setting] is not None:
+            if type(config[setting]) == type(True):
+                if args[setting] == 'True':
+                    x = True
+                elif args[setting] == 'False':
+                    x = False
+                else:
+                    raise Exception('Command line parameter ' + setting + 'must be True or False')
+            elif type(config[setting]) == type(1):
+                x = int(args[setting])
+            elif type(args[setting]) == type(None):
+                x = None
+            else:
+                x = args[setting]
+            config[setting] = x
+    return config
+
+
+def get_code_path():
+    """
+    Get base path where the trackeval library is located
+
+    :param None
+    :return: str: base path of trackeval library
+    ::
+
+        trackeval.utils.get_code_path(config, default_config, name)
+    """
+    return os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
+
+
+def validate_metrics_list(metrics_list):
+    """
+    Get names of metric class and ensures they are unique, further checks that the fields within each metric class
+    do not have overlapping names.
+
+    :param List[str] metrics_list: list of all metrics to test
+    :return: List[str] metric_names: valid list of all metrics to test
+    ::
+
+        trackeval.utils.get_code_path(config, default_config, name)
+    """
+    metric_names = [metric.get_name() for metric in metrics_list]
+    # check metric names are unique
+    if len(metric_names) != len(set(metric_names)):
+        raise TrackEvalException('Code being run with multiple metrics of the same name')
+    fields = []
+    for m in metrics_list:
+        fields += m.fields
+    # check metric fields are unique
+    if len(fields) != len(set(fields)):
+        raise TrackEvalException('Code being run with multiple metrics with fields of the same name')
+    return metric_names
+
+
+def write_summary_results(summaries, cls, output_folder):
+    """
+    Write summary results to file
+
+    :param List[str] summaries: list of all summaries
+    :param List[str] cls: list of classes 
+    :param List[str] output_folder: directory to store the summary results
+
+    :return: None
+    ::
+
+        trackeval.utils.write_summary_results(config, default_config, name)
+    """
+    fields = sum([list(s.keys()) for s in summaries], [])
+    values = sum([list(s.values()) for s in summaries], [])
+
+    # In order to remain consistent upon new fields being adding, for each of the following fields if they are present
+    # they will be output in the summary first in the order below. Any further fields will be output in the order each
+    # metric family is called, and within each family either in the order they were added to the dict (python >= 3.6) or
+    # randomly (python < 3.6).
+    default_order = ['HOTA', 'DetA', 'AssA', 'DetRe', 'DetPr', 'AssRe', 'AssPr', 'LocA', 'OWTA', 'HOTA(0)', 'LocA(0)',
+                     'HOTALocA(0)', 'MOTA', 'MOTP', 'MODA', 'CLR_Re', 'CLR_Pr', 'MTR', 'PTR', 'MLR', 'CLR_TP', 'CLR_FN',
+                     'CLR_FP', 'IDSW', 'MT', 'PT', 'ML', 'Frag', 'sMOTA', 'IDF1', 'IDR', 'IDP', 'IDTP', 'IDFN', 'IDFP',
+                     'Dets', 'GT_Dets', 'IDs', 'GT_IDs']
+    default_ordered_dict = OrderedDict(zip(default_order, [None for _ in default_order]))
+    for f, v in zip(fields, values):
+        default_ordered_dict[f] = v
+    for df in default_order:
+        if default_ordered_dict[df] is None:
+            del default_ordered_dict[df]
+    fields = list(default_ordered_dict.keys())
+    values = list(default_ordered_dict.values())
+
+    out_file = os.path.join(output_folder, cls + '_summary.txt')
+    os.makedirs(os.path.dirname(out_file), exist_ok=True)
+    with open(out_file, 'w', newline='') as f:
+        writer = csv.writer(f, delimiter=' ')
+        writer.writerow(fields)
+        writer.writerow(values)
+
+
+def write_detailed_results(details, cls, output_folder):
+    """
+    Write detailed results to file
+    
+    :param Dict[str, Object] details: dictionary of all trackers 
+    :param List[str] cls: list of classes 
+    :param List[str] output_folder: directory to store the detailed results
+
+    :return: None
+    ::
+
+        trackeval.utils.write_detailed_results(config, default_config, name)
+    """
+    sequences = details[0].keys()
+    fields = ['seq'] + sum([list(s['COMBINED_SEQ'].keys()) for s in details], [])
+    out_file = os.path.join(output_folder, cls + '_detailed.csv')
+    os.makedirs(os.path.dirname(out_file), exist_ok=True)
+    with open(out_file, 'w', newline='') as f:
+        writer = csv.writer(f)
+        writer.writerow(fields)
+        for seq in sorted(sequences):
+            if seq == 'COMBINED_SEQ':
+                continue
+            writer.writerow([seq] + sum([list(s[seq].values()) for s in details], []))
+        writer.writerow(['COMBINED'] + sum([list(s['COMBINED_SEQ'].values()) for s in details], []))
+
+
+def load_detail(file):
+    """
+    Loads detailed data for a tracker.
+    
+    :param Dict[str] file: file to load the detailed results from 
+
+    :return: Dict[str] :data 
+    ::
+
+        trackeval.utils.load_detail(config, default_config, name)
+    """
+    data = {}
+    with open(file) as f:
+        for i, row_text in enumerate(f):
+            row = row_text.replace('\r', '').replace('\n', '').split(',')
+            if i == 0:
+                keys = row[1:]
+                continue
+            current_values = row[1:]
+            seq = row[0]
+            if seq == 'COMBINED':
+                seq = 'COMBINED_SEQ'
+            if (len(current_values) == len(keys)) and seq != '':
+                data[seq] = {}
+                for key, value in zip(keys, current_values):
+                    data[seq][key] = float(value)
+    return data
+
+
+class TrackEvalException(Exception):
+    """Custom exception for catching expected errors."""
+    ...

MTMC_Tracking_2024/eval/utils/__init__.py

@@ -0,0 +1 @@
+"""Utils modules"""

MTMC_Tracking_2024/eval/utils/io_utils.py

@@ -0,0 +1,217 @@
+import os
+import re
+import argparse
+import shutil
+import json
+import csv
+import numpy as np
+import pandas as pd
+from typing import List, Set, Any
+
+
+class ValidateFile(argparse.Action):
+    """
+    Module to validate files
+    """
+    def __call__(self, parser, namespace, values, option_string = None):
+        
+        if not os.path.exists(values):
+            parser.error(f"Please enter a valid file path. Got: {values}")
+        elif not os.access(values, os.R_OK):
+            parser.error(f"File {values} doesn't have read access")
+        setattr(namespace, self.dest, values)
+
+
+def validate_file_path(input_string: str) -> str:
+    """
+    Validates whether the input string matches a file path pattern
+
+    :param str input_string: input string
+    :return: validated file path
+    :rtype: str
+    ::
+
+        file_path = validate_file_path(input_string)
+    """
+    file_path_pattern = r"^[a-zA-Z0-9_\-\/.#]+$"
+
+    if re.match(file_path_pattern, input_string):
+        return input_string
+    else:
+        raise ValueError(f"Invalid file path: {input_string}")
+
+
+def load_csv_to_dataframe_from_file(file_path: str, column_names: List[str], camera_ids: Set, interval: int = 1) -> pd.DataFrame:
+    """
+    Loads dataframe from a CSV file
+
+    :param str file_path: file path
+    :param List[str] column_names: column names
+    :return: dataframe in the file
+    :rtype: pd.DataFrame
+    ::
+
+        dataFrame = load_csv_to_dataframe_from_file(file_path, column_names)
+    """
+    data: List[List[str]] = list()
+    valid_file_path = validate_file_path(file_path)
+    
+    df = pd.read_csv(valid_file_path, sep=" ", header=None, names=column_names, dtype={"CameraId": int, "Id": int, "FrameId": int})
+    
+    # Ensure non-negative values for CameraId, Id, FrameId
+    if (df[['CameraId', 'Id', 'FrameId']] < 0).any().any():
+        raise ValueError("Invalid negative values found for CameraId, Id, or FrameId.")
+
+
+    # Filter by camera_id
+    df = df[df['CameraId'].isin(camera_ids)]
+    
+    # Filter rows where FrameId % interval == 0
+    df = df[df['FrameId'] % interval == 0]
+    
+    # Round the last two columns (assuming these are 'Xworld' and 'Yworld')
+    df['Xworld'] = df['Xworld'].round(3)
+    df['Yworld'] = df['Yworld'].round(3)
+    
+    if len(df) == 0:
+        raise ValueError("DataFrame is empty after filtering process.")
+
+    return df
+
+
+def write_dataframe_to_csv_to_file(file_path: str, data: pd.DataFrame, delimiter: str = " ") -> None:
+    """
+    Writes dataframe to a CSV file
+
+    :param str file_path: file path
+    :param pd.DataFrame data: dataframe to be written
+    :param str delimiter: delimiter of the CSV file
+    :return: None
+    ::
+
+        write_dataframe_to_csv_to_file(file_path, data, delimiter)
+    """
+    data.to_csv(file_path, sep=delimiter, index=False, header=False)
+
+
+def make_dir(dir_path: str) -> None:
+    """
+    Makes a directory without removing other files
+
+    :param str dir_path: directory path
+    :return: None
+    ::
+
+        make_dir(dir_path)
+    """
+    valid_dir_path = validate_file_path(dir_path)
+    if not os.path.isdir(valid_dir_path):
+        os.makedirs(validate_file_path(dir_path))
+
+
+def make_seq_maps_file(file_dir: str, scenes: List[str], benchmark: str, split_to_eval: str) -> None:
+    """
+    Makes a sequence-maps file used by TrackEval library
+
+    :param str file_dir: file path
+    :param Set(str) sensor_ids: names of sensors
+    :param str benchmark: name of the benchmark
+    :param str split_to_eval: name of the split of data
+    :return: None
+    ::
+
+        make_seq_maps_file(file_dir, sensor_ids)
+    """
+    make_clean_dir(file_dir)
+    file_name = benchmark + "-" +split_to_eval + ".txt"
+    seq_maps_file = file_dir +  "/" + file_name
+    f = open(seq_maps_file, "w")
+    f.write("name\n")
+    for name in scenes:
+        sensor_name = str(name) + "\n"
+        f.write(sensor_name)
+    # f.write("FINAL")
+    f.close()
+
+
+def make_seq_ini_file(gt_dir: str, scene: str, seq_length: int) -> None:
+    """
+    Makes a sequence-ini file used by TrackEval library
+
+    :param str gt_dir: file path
+    :param str scene: Name of a single scene 
+    :param int seq_length: Number of frames
+
+    :return: None
+    ::
+
+        make_seq_ini_file(gt_dir, scene, seq_length)
+    """
+    ini_file_name = gt_dir + "/seqinfo.ini"
+    f = open(ini_file_name, "w")
+    f.write("[Sequence]\n")
+    name= "name=" +str(scene)+ "\n"
+    f.write(name)
+    f.write("imDir=img1\n")
+    f.write("frameRate=30\n")
+    seq = "seqLength=" + str(seq_length) + "\n"
+    f.write(seq)
+    f.write("imWidth=1920\n")
+    f.write("imHeight=1080\n")
+    f.write("imExt=.jpg\n")
+    f.close()
+
+
+def get_scene_to_camera_id_dict(file_path):
+    """
+    Loads a mapping of scene names to camera IDs from a JSON file.
+
+    :param str file_path: Path to the JSON file containing scenes data.
+    :return: A dictionary where keys are scene names and values are lists of camera IDs.
+    ::
+
+        scene_to_camera_id_dict = get_scene_to_camera_id_dict(file_path)
+    """
+    scene_2_cam_id = dict()
+    valid_file_path = validate_file_path(file_path)
+    with open(valid_file_path, "r") as file:
+        scenes_data = json.load(file)
+        for scene_data in scenes_data:
+            scene_name = scene_data["scene_name"]
+            camera_ids = scene_data["camera_ids"]
+            if scene_name not in scene_2_cam_id:
+                scene_2_cam_id[scene_name] = []
+            scene_2_cam_id[scene_name].extend(camera_ids)
+    return scene_2_cam_id
+    
+
+def check_file_size(file_path):
+    """
+    Checks the size of a file and raises an exception if it exceeds 2 GB.
+
+    :param str file_path: Path to the file to be checked.
+    :return: None
+    :raises ValueError: If the file size is greater than 2 GB.
+    ::
+
+        check_file_size(file_path)
+    """
+    file_size_bytes = os.path.getsize(file_path)
+    file_size_gb = file_size_bytes / (2**30)
+    if file_size_gb > 2:
+        raise ValueError(f"The size of the file is {file_size_gb:.2f} GB, which is greater than the 2 GB")
+
+
+def make_clean_dir(dir_path: str) -> None:
+    """
+    Makes a clean directory
+    :param str dir_path: directory path
+    :return: None
+    ::
+        make_clean_dir(dir_path)
+    """
+    valid_dir_path = validate_file_path(dir_path)
+    if os.path.exists(valid_dir_path):
+        shutil.rmtree(dir_path, ignore_errors=True)
+    if not os.path.isdir(valid_dir_path):
+        os.makedirs(validate_file_path(dir_path))