From 2402696d92578e16306914100830405df9597638 Mon Sep 17 00:00:00 2001 From: wangqianwu
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index 0000000..07cf51c --- /dev/null +++ b/trackeval/.idea/trackeval.iml @@ -0,0 +1,12 @@ + + + + + + + + + + \ No newline at end of file diff --git a/trackeval/Readme.md b/trackeval/Readme.md new file mode 100644 index 0000000..ff8e198 --- /dev/null +++ b/trackeval/Readme.md @@ -0,0 +1,363 @@ +# 修改mot官网指标测试代码 + +我是以mot17数据测试作为基准修改的,其余不相干脚本,我将删除,或相关较麻烦的脚本,我已修改。 + + +##指标数据测试结构 +###mot17 gt.txt数据格式: +frame, id, bb_left, bb_top, bb_width, bb_height, active, label_id, visibility ratio + +第0个代表第几帧; +第1个值为目标运动轨迹的ID号; +第2个到第5个数代表物体框的左上角坐标及长宽; +第6个值为目标轨迹是否进入考虑范围内的标志,0表示忽略,1表示active; +第7个值为该轨迹对应的目标种类(种类见下面的表格中的label-ID对应情况); +第8个值为box的visibility ratio,表示目标运动时被其他目标box包含/覆盖或者目标之间box边缘裁剪情况。 + +特别说明: +第6个activate若为0表示该行目标不考虑计算。 +第7个目标种类,目标名称和数字一定要对齐,依照数字指定为目标选择,如下: +"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} +具体解释如图: +![](trackeval/data/images/mot-format.png) + +###预测数据结构 + +frame_id, id, bbox_left, bbox_top, bbox_w, bbox_h, -1, -1, -1, i +第0个的frame_id表示第几帧,没有0帧,只有第1帧开始,与gt.txt的帧对应; +第1个id表示跟踪id,由跟踪算法决定,实际为常说的track_id; +第2个到第5个数代表物体框的左上角坐标及长宽; +第9个i表示第frame_idx+1帧第几个目标id,也可以固定为-1,该参数不需要。 + +特别说明: +指标预测模型是第一帧到最后一帧by顺序一张一张图预测; +输出结果为预测txt保存结果,名称需和seq相同,如MOT17-02-FRCNN.txt + + +![](trackeval/data/images/mot-predect-format.png) + +##文件结构 +###gt文件夹: +主路径下是被测文件夹列表,每个列表文件(如:MOT17-02-FRCNN)下有一个gt.txt文件和seqinfo.ini文件, +该路径可由config['GT_LOC_FORMAT'] = '{gt_folder}/{seq}/gt/gt.txt'参数控制。 +其中seqinfo.ini文件用于记录name=MOT17-02-FRCNN文件下相关信息,主要使用seqLength信息。 +seqinfo.ini如下: + +''' + + [Sequence] + name=MOT17-02-FRCNN + imDir=img1 + frameRate=30 + seqLength=600 + imWidth=1920 + imHeight=1080 + imExt=.jpg + +''' +也可变为如下: +''' + + [Sequence] + seqLength=600 + +''' + + +###predect文件夹: +主路径下只有对应txt文件,命名分别为gt文件对应的文件列表名称,MOT17-02-FRCNN.txt +具体方法如下图: +![](trackeval/data/images/file-format.png) + + + +##运行参数说明 +在mot_challenge_2d_box.py文件夹下有类MotChallenge2DBox(_BaseDataset),用于处理相关数据, +而在该类中有一个默认default_config字典,我已做了修改,具体用法如下解释。 +''' + + default_config = { + 'GT_FOLDER': os.path.join(code_path, 'data/'), # 真实标签路径Location of GT data + 'TRACKERS_FOLDER': os.path.join(code_path, 'data/predect_mot/'), # 预测标签路径 Trackers location + 'OUTPUT_FOLDER': None, # 指标保存路径,若为None将保存在TRACKERS_FOLDER文件下 + # 'TRACKERS_TO_EVAL': None, # Filenames of predect_mot to eval (if None, all in folder) + 'CLASSES_TO_EVAL': ['pedestrian'], # 规定哪些类别预测指标 Valid: ['pedestrian'] + 'BENCHMARK': 'MOT17', # 用于显示名字,随便起名,我是以mot17为基准修改,因此默认为MOT17 + + '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 predect_mot 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, # 需要预测的列表,若为None则将GT_FOLDER文件均当做seq预测指标 + + 'GT_LOC_FORMAT': '{gt_folder}/{seq}/gt/gt.txt', # 能找到真实标签txt路径,gt_folder为GT_FOLDER,seq为seq_list遍历值 + '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. + "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}, + 'use_super_categories':False, # 决定是否合并类别为key类预测 + 'super_categories' : {"FF": ['pedestrian', 'car']} # 表示将字典value类统一到key上,给出指标结果 + } + + +''' +###参数重点说明 +①合并类指标测试方法 +use_super_categories:参数为True将会将我们在super_categories指定合并类一起测试 +'super_categories' :指定合并类,如将'pedestrian', 'car'合并为’FF‘,如: {"FF": ['pedestrian', 'car']} + +②测试类指定方法 +CLASSES_TO_EVAL:列表,指定gt.txt需要测试的类别,特别说明,它和gt.txt的第6个activate共同决定有效gt目标 +③指定gt路径方法 +GT_LOC_FORMAT:如{gt_folder}/{seq}/gt/gt.txt,确定gt路径 +④指定测试方法 +SEQ_INFO:指定gt_folder测试方法 +⑤确定gt类别方法 +class_name_to_class_id:指定名称和类别对应字典,gt.txt中的label id为对应数字,此为②提供依据 + + +##自定义参数 + +通过config字典,修改MotChallenge2DBox(_BaseDataset)中的默认参数,如下: + +''' + + config={} + config['TRACKERS_FOLDER'] = ROOT+'/data/predect_mot' # 预测路径 + config['GT_FOLDER'] = ROOT+'/data/mot17_gt' # 给出gt路径 + config['OUTPUT_FOLDER'] = ROOT+'/data/out_dir' + # 确定文件内gt.txt的路径,gt_folder=config['GT_FOLDER'],seq为os.listdir(gt_folder)列表 + config['GT_LOC_FORMAT'] = '{gt_folder}/{seq}/gt/gt.txt' + config['CLASSES_TO_EVAL'] = ['pedestrian'] # 确定预测指标的类别 + dataset = MotChallenge2DBox(config) # dataset_list是存放数据信息列表 + + +''' + + + +##运行命令 + +按照以上参数修改,直接运行run_mot_challenge.py文件 + +#tools文件工具 +mot_vision.py文件用于将gt.txt信息可视化图像上,效果如下图: + +![](trackeval/tools/img.png) + + +##测试结果 + +![](data/images/result.png) + + +# TrackEval +*Code for evaluating object tracking.* + +This codebase provides code for a number of different tracking evaluation metrics (including the [HOTA metrics](https://link.springer.com/article/10.1007/s11263-020-01375-2)), as well as supporting running all of these metrics on a number of different tracking benchmarks. Plus plotting of results and other things one may want to do for tracking evaluation. + +## **NEW**: RobMOTS Challenge 2021 + +Call for submission to our [RobMOTS Challenge](https://eval.vision.rwth-aachen.de/rvsu-workshop21/?page_id=110) (Robust Multi-Object Tracking and Segmentation) held in conjunction with our [RVSU CVPR'21 Workshop](https://eval.vision.rwth-aachen.de/rvsu-workshop21/). Robust tracking evaluation against 8 tracking benchmarks. Challenge submission deadline June 15th. Also check out our workshop [call for papers](https://eval.vision.rwth-aachen.de/rvsu-workshop21/?page_id=74). + +## Official Evaluation Code + +The following benchmarks use TrackEval as their official evaluation code, check out the links to see TrackEval in action: + + - **[RobMOTS](https://eval.vision.rwth-aachen.de/rvsu-workshop21/?page_id=110)** ([Official Readme](docs/RobMOTS-Official/Readme.md)) + - **[KITTI Tracking](http://www.cvlibs.net/datasets/kitti/eval_tracking.php)** + - **[KITTI MOTS](http://www.cvlibs.net/datasets/kitti/eval_mots.php)** + - **[MOTChallenge](https://motchallenge.net/)** ([Official Readme](docs/MOTChallenge-Official/Readme.md)) + - **[Open World Tracking](https://openworldtracking.github.io)** ([Official Readme](docs/OpenWorldTracking-Official)) + - **[PersonPath22](https://amazon-research.github.io/tracking-dataset/personpath22.html)** + + +If you run a tracking benchmark and want to use TrackEval as your official evaluation code, please contact Jonathon (contact details below). + +## Currently implemented metrics + +The following metrics are currently implemented: + +Metric Family | Sub metrics | Paper | Code | Notes | +|----- | ----------- |----- | ----------- | ----- | +| | | | | | +|**HOTA metrics**|HOTA, DetA, AssA, LocA, DetPr, DetRe, AssPr, AssRe|[paper](https://link.springer.com/article/10.1007/s11263-020-01375-2)|[code](trackeval/metrics/hota.py)|**Recommended tracking metric**| +|**CLEARMOT metrics**|MOTA, MOTP, MT, ML, Frag, etc.|[paper](https://link.springer.com/article/10.1155/2008/246309)|[code](trackeval/metrics/clear.py)| | +|**Identity metrics**|IDF1, IDP, IDR|[paper](https://arxiv.org/abs/1609.01775)|[code](trackeval/metrics/identity.py)| | +|**VACE metrics**|ATA, SFDA|[paper](https://link.springer.com/chapter/10.1007/11612704_16)|[code](trackeval/metrics/vace.py)| | +|**Track mAP metrics**|Track mAP|[paper](https://arxiv.org/abs/1905.04804)|[code](trackeval/metrics/track_map.py)|Requires confidence scores| +|**J & F metrics**|J&F, J, F|[paper](https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Perazzi_A_Benchmark_Dataset_CVPR_2016_paper.pdf)|[code](trackeval/metrics/j_and_f.py)|Only for Seg Masks| +|**ID Euclidean**|ID Euclidean|[paper](https://arxiv.org/pdf/2103.13516.pdf)|[code](trackeval/metrics/ideucl.py)| | + + +## Currently implemented benchmarks + +The following benchmarks are currently implemented: + +Benchmark | Sub-benchmarks | Type | Website | Code | Data Format | +|----- | ----------- |----- | ----------- | ----- | ----- | +| | | | | | | +|**RobMOTS**|Combination of 8 benchmarks|Seg Masks|[website](https://eval.vision.rwth-aachen.de/rvsu-workshop21/?page_id=110)|[code](trackeval/datasets/rob_mots.py)|[format](docs/RobMOTS-Official/Readme.md)| +|**Open World Tracking**|TAO-OW|OpenWorld / Seg Masks|[website](https://openworldtracking.github.io)|[code](trackeval/datasets/tao_ow.py)|[format](docs/OpenWorldTracking-Official/Readme.md)| +|**MOTChallenge**|MOT15/16/17/20|2D BBox|[website](https://motchallenge.net/)|[code](trackeval/datasets/mot_challenge_2d_box.py)|[format](docs/MOTChallenge-format.txt)| +|**KITTI Tracking**| |2D BBox|[website](http://www.cvlibs.net/datasets/kitti/eval_tracking.php)|[code](trackeval/datasets/kitti_2d_box.py)|[format](docs/KITTI-format.txt)| +|**BDD-100k**| |2D BBox|[website](https://bdd-data.berkeley.edu/)|[code](trackeval/datasets/bdd100k.py)|[format](docs/BDD100k-format.txt)| +|**TAO**| |2D BBox|[website](https://taodataset.org/)|[code](trackeval/datasets/tao.py)|[format](docs/TAO-format.txt)| +|**MOTS**|KITTI-MOTS, MOTS-Challenge|Seg Mask|[website](https://www.vision.rwth-aachen.de/page/mots)|[code](trackeval/datasets/mots_challenge.py) and [code](trackeval/datasets/kitti_mots.py)|[format](docs/MOTS-format.txt)| +|**DAVIS**|Unsupervised|Seg Mask|[website](https://davischallenge.org/)|[code](trackeval/datasets/davis.py)|[format](docs/DAVIS-format.txt)| +|**YouTube-VIS**| |Seg Mask|[website](https://youtube-vos.org/dataset/vis/)|[code](trackeval/datasets/youtube_vis.py)|[format](docs/YouTube-VIS-format.txt)| +|**Head Tracking Challenge**| |2D BBox|[website](https://arxiv.org/pdf/2103.13516.pdf)|[code](trackeval/datasets/head_tracking_challenge.py)|[format](docs/MOTChallenge-format.txt)| +|**PersonPath22**| |2D BBox|[website](https://github.com/amazon-research/tracking-dataset)|[code](trackeval/datasets/person_path_22.py)|[format](docs/MOTChallenge-format.txt)| +|**BURST**| {Common, Long-tail, Open-world} Class-guided, {Point, Box, Mask} Exemplar-guided |Seg Mask|[website](https://github.com/Ali2500/BURST-benchmark)|[format](https://github.com/Ali2500/BURST-benchmark/blob/main/ANNOTATION_FORMAT.md)| + +## HOTA metrics + +This code is also the official reference implementation for the HOTA metrics: + +*[HOTA: A Higher Order Metric for Evaluating Multi-Object Tracking](https://link.springer.com/article/10.1007/s11263-020-01375-2). IJCV 2020. Jonathon Luiten, Aljosa Osep, Patrick Dendorfer, Philip Torr, Andreas Geiger, Laura Leal-Taixe and Bastian Leibe.* + +HOTA is a novel set of MOT evaluation metrics which enable better understanding of tracking behavior than previous metrics. + +For more information check out the following links: + - [Short blog post on HOTA](https://jonathonluiten.medium.com/how-to-evaluate-tracking-with-the-hota-metrics-754036d183e1) - **HIGHLY RECOMMENDED READING** + - [IJCV version of paper](https://link.springer.com/article/10.1007/s11263-020-01375-2) (Open Access) + - [ArXiv version of paper](https://arxiv.org/abs/2009.07736) + - [Code](trackeval/metrics/hota.py) + +## Properties of this codebase + +The code is written 100% in python with only numpy and scipy as minimum requirements. + +The code is designed to be easily understandable and easily extendable. + +The code is also extremely fast, running at more than 10x the speed of the both [MOTChallengeEvalKit](https://github.com/dendorferpatrick/MOTChallengeEvalKit), and [py-motmetrics](https://github.com/cheind/py-motmetrics) (see detailed speed comparison below). + +The implementation of CLEARMOT and ID metrics aligns perfectly with the [MOTChallengeEvalKit](https://github.com/dendorferpatrick/MOTChallengeEvalKit). + +By default the code prints results to the screen, saves results out as both a summary txt file and a detailed results csv file, and outputs plots of the results. All outputs are by default saved to the 'tracker' folder for each tracker. + +## Running the code + +The code can be run in one of two ways: + + - From the terminal via one of the scripts [here](scripts/). See each script for instructions and arguments, hopefully this is self-explanatory. + - Directly by importing this package into your code, see the same scripts above for how. + +## Quickly evaluate on supported benchmarks + +To enable you to use TrackEval for evaluation as quickly and easily as possible, we provide ground-truth data, meta-data and example trackers for all currently supported benchmarks. +You can download this here: [data.zip](https://omnomnom.vision.rwth-aachen.de/data/TrackEval/data.zip) (~150mb). + +The data for RobMOTS is separate and can be found here: [rob_mots_train_data.zip](https://omnomnom.vision.rwth-aachen.de/data/RobMOTS/train_data.zip) (~750mb). + +The data for PersonPath22 is separate and can be found here: [person_path_22_data.zip](https://tracking-dataset-eccv-2022.s3.us-east-2.amazonaws.com/person_path_22_data.zip) (~3mb). + +The easiest way to begin is to extract this zip into the repository root folder such that the file paths look like: TrackEval/data/gt/... + +This then corresponds to the default paths in the code. You can now run each of the scripts [here](scripts/) without providing any arguments and they will by default evaluate all trackers present in the supplied file structure. To evaluate your own tracking results, simply copy your files as a new tracker folder into the file structure at the same level as the example trackers (MPNTrack, CIWT, track_rcnn, qdtrack, ags, Tracktor++, STEm_Seg), ensuring the same file structure for your trackers as in the example. + +Of course, if your ground-truth and tracker files are located somewhere else you can simply use the script arguments to point the code toward your data. + +To ensure your tracker outputs data in the correct format, check out our format guides for each of the supported benchmarks [here](docs), or check out the example trackers provided. + +## Evaluate on your own custom benchmark + +To evaluate on your own data, you have two options: + - Write custom dataset code (more effort, rarely worth it). + - Convert your current dataset and trackers to the same format of an already implemented benchmark. + +To convert formats, check out the format specifications defined [here](docs). + +By default, we would recommend the MOTChallenge format, although any implemented format should work. Note that for many cases you will want to use the argument ```--DO_PREPROC False``` unless you want to run preprocessing to remove distractor objects. + +## Requirements + Code tested on Python 3.7. + + - Minimum requirements: numpy, scipy + - For plotting: matplotlib + - For segmentation datasets (KITTI MOTS, MOTS-Challenge, DAVIS, YouTube-VIS): pycocotools + - For DAVIS dataset: Pillow + - For J & F metric: opencv_python, scikit_image + - For simples test-cases for metrics: pytest + +use ```pip3 -r install requirements.txt``` to install all possible requirements. + +use ```pip3 -r install minimum_requirments.txt``` to only install the minimum if you don't need the extra functionality as listed above. + +## Timing analysis + +Evaluating CLEAR + ID metrics on Lift_T tracker on MOT17-train (seconds) on a i7-9700K CPU with 8 physical cores (median of 3 runs): +Num Cores|TrackEval|MOTChallenge|Speedup vs MOTChallenge|py-motmetrics|Speedup vs py-motmetrics +:---|:---|:---|:---|:---|:--- +1|9.64|66.23|6.87x|99.65|10.34x +4|3.01|29.42|9.77x| |33.11x* +8|1.62|29.51|18.22x| |61.51x* + +*using a different number of cores as py-motmetrics doesn't allow multiprocessing. + +``` +python scripts/run_mot_challenge.py --BENCHMARK MOT17 --TRACKERS_TO_EVAL Lif_T --METRICS CLEAR Identity --USE_PARALLEL False --NUM_PARALLEL_CORES 1 +``` + +Evaluating CLEAR + ID metrics on LPC_MOT tracker on MOT20-train (seconds) on a i7-9700K CPU with 8 physical cores (median of 3 runs): +Num Cores|TrackEval|MOTChallenge|Speedup vs MOTChallenge|py-motmetrics|Speedup vs py-motmetrics +:---|:---|:---|:---|:---|:--- +1|18.63|105.3|5.65x|175.17|9.40x + +``` +python scripts/run_mot_challenge.py --BENCHMARK MOT20 --TRACKERS_TO_EVAL LPC_MOT --METRICS CLEAR Identity --USE_PARALLEL False --NUM_PARALLEL_CORES 1 +``` + +## License + +TrackEval is released under the [MIT License](LICENSE). + +## Contact + +If you encounter any problems with the code, please contact [Jonathon Luiten](https://www.vision.rwth-aachen.de/person/216/) ([luiten@vision.rwth-aachen.de](mailto:luiten@vision.rwth-aachen.de)). +If anything is unclear, or hard to use, please leave a comment either via email or as an issue and I would love to help. + +## Dedication + +This codebase was built for you, in order to make your life easier! For anyone doing research on tracking or using trackers, please don't hesitate to reach out with any comments or suggestions on how things could be improved. + +## Contributing + +We welcome contributions of new metrics and new supported benchmarks. Also any other new features or code improvements. Send a PR, an email, or open an issue detailing what you'd like to add/change to begin a conversation. + +## Citing TrackEval + +If you use this code in your research, please use the following BibTeX entry: + +```BibTeX +@misc{luiten2020trackeval, + author = {Jonathon Luiten, Arne Hoffhues}, + title = {TrackEval}, + howpublished = {\url{https://github.com/JonathonLuiten/TrackEval}}, + year = {2020} +} +``` + +Furthermore, if you use the HOTA metrics, please cite the following paper: + +``` +@article{luiten2020IJCV, + title={HOTA: A Higher Order Metric for Evaluating Multi-Object Tracking}, + author={Luiten, Jonathon and Osep, Aljosa and Dendorfer, Patrick and Torr, Philip and Geiger, Andreas and Leal-Taix{\'e}, Laura and Leibe, Bastian}, + journal={International Journal of Computer Vision}, + pages={1--31}, + year={2020}, + publisher={Springer} +} +``` + +If you use any other metrics please also cite the relevant papers, and don't forget to cite each of the benchmarks you evaluate on. 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HcmV?d00001 diff --git a/trackeval/_timing.py b/trackeval/_timing.py new file mode 100644 index 0000000..4614ba3 --- /dev/null +++ b/trackeval/_timing.py @@ -0,0 +1,65 @@ +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): + @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 diff --git a/trackeval/datasets/__init__.py b/trackeval/datasets/__init__.py new file mode 100644 index 0000000..d48a4b3 --- /dev/null +++ b/trackeval/datasets/__init__.py @@ -0,0 +1,18 @@ +# from .kitti_2d_box import Kitti2DBox +# from .kitti_mots import KittiMOTS +# from .mots_challenge import MOTSChallenge +# from .bdd100k import BDD100K +# from .davis import DAVIS +# from .tao import TAO +# from .tao_ow import TAO_OW +# try: +# from .burst import BURST +# from .burst_ow import BURST_OW +# except ImportError as err: +# print(f"Error importing BURST due to missing underlying dependency: {err}") +# from .youtube_vis import YouTubeVIS +# from .head_tracking_challenge import HeadTrackingChallenge +# from .rob_mots import RobMOTS +# from .person_path_22 import PersonPath22 + +# from .mot_challenge_2d_box import MotChallenge2DBox diff --git a/trackeval/datasets/__pycache__/__init__.cpython-311.pyc b/trackeval/datasets/__pycache__/__init__.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..54f9ea43d91a24d52714b07722712d856e3d79a2 GIT 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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=2.0): + """ 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. + """ + dist = np.linalg.norm(dets1[:, np.newaxis]-dets2[np.newaxis, :], axis=2) + sim = np.maximum(0, 1 - dist/zero_distance) + return sim + + @staticmethod # 核对每帧图有唯一的id + 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) diff --git a/trackeval/datasets/mot_challenge_2d_box.py b/trackeval/datasets/mot_challenge_2d_box.py new file mode 100644 index 0000000..13c5b8f --- /dev/null +++ b/trackeval/datasets/mot_challenge_2d_box.py @@ -0,0 +1,385 @@ +import os +import csv +import configparser +import numpy as np +from scipy.optimize import linear_sum_assignment +from ._base_dataset import _BaseDataset +from .. import utils +from .. import _timing +from ..utils import TrackEvalException + + + + +class MotChallenge2DBox(_BaseDataset): + """Dataset class for MOT Challenge 2D bounding box tracking""" + + @staticmethod + def get_default_dataset_config(): + """Default class config values""" + code_path = utils.get_code_path() #/home/oil/Wangqianwu/trackeval + default_config = { + 'GT_FOLDER': os.path.join(code_path, 'data/'), # 真实标签路径Location of GT data + 'TRACKERS_FOLDER': os.path.join(code_path, 'data/predect_mot/'), # 预测标签路径 Trackers location + 'OUTPUT_FOLDER': None, # 指标保存路径,若为None将保存在TRACKERS_FOLDER文件下 + # 'TRACKERS_TO_EVAL': None, # Filenames of predect_mot to eval (if None, all in folder) + 'CLASSES_TO_EVAL': ['pedestrian'], # 规定哪些类别预测指标 Valid: ['pedestrian'] + #'BENCHMARK': 'MOT17', # 用于显示名字,随便起名,我是以mot17为基准修改,因此默认为MOT17 + 'BENCHMARK': 'MOT_RGBT', # 用于显示名字,随便起名,我是以mot17为基准修改,因此默认为MOT17 + '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 predect_mot 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, # 需要预测的列表,若为None则将GT_FOLDER文件均当做seq预测指标 + + 'GT_LOC_FORMAT': '{gt_folder}/{seq}/gt/gt.txt', # 能找到真实标签txt路径,gt_folder为GT_FOLDER,seq为seq_list遍历值 + '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. + "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}, + 'use_super_categories':True, # 决定是否合并类别为key类预测 + 'super_categories' : {"FF": ['pedestrian', 'person_on_vehicle']} # 表示将字典value类统一到key上,给出指标结果 + } + 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'] + self.gt_fol = self.config['GT_FOLDER'] + self.tracker_fol = self.config['TRACKERS_FOLDER'] + self.seq_info = self.config['SEQ_INFO'] + + + self.should_classes_combine = False + self.use_super_categories = self.config['use_super_categories'] # 启用需要与下面共同使用 + self.super_categories = self.config['super_categories'] # 表示将字典value类统一到key上,给出指标结果 + + + self.do_preproc = True + + self.output_fol = self._get_out_folder() + + self.class_list = self.config['CLASSES_TO_EVAL'] # 需要评估的类别 + 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 = self.config['class_name_to_class_id'] + self.valid_class_numbers = list(self.class_name_to_class_id.values()) # 获得对应类别整数值 + + + self.seq_list=self._get_seq_list(self.seq_info) + self.seq_lengths = self._get_seq_length() + + if len(self.seq_list) < 1: + raise TrackEvalException('No sequences are selected to be evaluated.') + + + + def _get_seq_list(self,seq_info): + ''' + 获得指标测试的列表 + ''' + seq_list=seq_info + if seq_info is None: + seq_list=os.listdir(self.gt_fol) + seq_list=[seq for seq in seq_list if not os.path.isfile(os.path.join(self.gt_fol,seq))] + + return seq_list + + + def _get_seq_length(self): + ''' + 需要获取预测文件名与对应的图像数字 + ''' + + seq_lengths = {} + # If sequence length is 'None' tries to read sequence length from .ini files. + for seq in self.seq_list: + 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(ini_data['Sequence']['seqLength']) + + return seq_lengths + + def _load_raw_file(self, 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. + """ + # File location + + 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, seq + '.txt') + + # Load raw data from text file file='../data/train/MOT17\\/MOT17-02-FRCNN/gt/gt.txt' false none + read_data, ignore_data = self._load_simple_text_file(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} # 每个 + # raw_data=['track_id','class_id','box','','activate'] + # 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=np.float) # time_key帧的所有数据 + 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 , sequence %s to float. Is data corrupted?' % ( + seq)) + try: + raw_data['dets'][t] = np.atleast_2d(time_data[:, 2:6]) # 获得box + raw_data['ids'][t] = np.atleast_1d(time_data[:, 1]).astype(int) # trackid + 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 = ' sequence %s, because there is not enough ' \ + 'columns in the data.' % (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 + + def _get_out_folder(self): + if self.config['OUTPUT_FOLDER'] is None: + out_folder=self.tracker_fol + else: + out_folder=self.config['OUTPUT_FOLDER'] + if not os.path.exists(out_folder): + os.makedirs(out_folder) + return out_folder + + + + @_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. + """ + # 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([], np.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(np.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(np.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 + + diff --git a/trackeval/datasets/rob_mots_classmap.py b/trackeval/datasets/rob_mots_classmap.py new file mode 100644 index 0000000..1b3644d --- /dev/null +++ b/trackeval/datasets/rob_mots_classmap.py @@ -0,0 +1,81 @@ +cls_id_to_name = { + 1: 'person', + 2: 'bicycle', + 3: 'car', + 4: 'motorcycle', + 5: 'airplane', + 6: 'bus', + 7: 'train', + 8: 'truck', + 9: 'boat', + 10: 'traffic light', + 11: 'fire hydrant', + 12: 'stop sign', + 13: 'parking meter', + 14: 'bench', + 15: 'bird', + 16: 'cat', + 17: 'dog', + 18: 'horse', + 19: 'sheep', + 20: 'cow', + 21: 'elephant', + 22: 'bear', + 23: 'zebra', + 24: 'giraffe', + 25: 'backpack', + 26: 'umbrella', + 27: 'handbag', + 28: 'tie', + 29: 'suitcase', + 30: 'frisbee', + 31: 'skis', + 32: 'snowboard', + 33: 'sports ball', + 34: 'kite', + 35: 'baseball bat', + 36: 'baseball glove', + 37: 'skateboard', + 38: 'surfboard', + 39: 'tennis racket', + 40: 'bottle', + 41: 'wine glass', + 42: 'cup', + 43: 'fork', + 44: 'knife', + 45: 'spoon', + 46: 'bowl', + 47: 'banana', + 48: 'apple', + 49: 'sandwich', + 50: 'orange', + 51: 'broccoli', + 52: 'carrot', + 53: 'hot dog', + 54: 'pizza', + 55: 'donut', + 56: 'cake', + 57: 'chair', + 58: 'couch', + 59: 'potted plant', + 60: 'bed', + 61: 'dining table', + 62: 'toilet', + 63: 'tv', + 64: 'laptop', + 65: 'mouse', + 66: 'remote', + 67: 'keyboard', + 68: 'cell phone', + 69: 'microwave', + 70: 'oven', + 71: 'toaster', + 72: 'sink', + 73: 'refrigerator', + 74: 'book', + 75: 'clock', + 76: 'vase', + 77: 'scissors', + 78: 'teddy bear', + 79: 'hair drier', + 80: 'toothbrush'} \ No newline at end of file diff --git a/trackeval/eval.py b/trackeval/eval.py new file mode 100644 index 0000000..e69e798 --- /dev/null +++ b/trackeval/eval.py @@ -0,0 +1,199 @@ +import time +import traceback +import os +from . import utils +from .utils import TrackEvalException +from . import _timing +from .metrics import Count + +import tqdm + + + +class Evaluator: + """Evaluator class for evaluating different metrics for different datasets""" + + @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): + """Initialise the evaluator with a config file""" + 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, metrics_list, show_progressbar=False): + """Evaluate a set of metrics on a set of datasets""" + config = self.config + metrics_list = metrics_list + [Count()] # Count metrics are always run + metric_names = utils.validate_metrics_list(metrics_list) + dataset_name = dataset.get_name() # 获得处理数据类的名称 + output_res = {} + output_msg = {} + + + # Get dataset info about what to evaluate + output_res[dataset_name] = {} + output_msg[dataset_name] = {} + + seq_list=dataset.seq_list # seq_list表示测试列表名字 + class_list=dataset.class_list # class_list表示测试的类别名称 + + print('\n tracker(s) on %i sequence(s) for %i class(es) on %s dataset using the following ' + 'metrics: %s\n' % ( len(seq_list), len(class_list), dataset_name, + ', '.join(metric_names))) + + # Evaluate each tracker + tracker ='tracker-result' + # 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] + print('\nEvaluating %s\n' % tracker) + time_start = time.time() + + res = {} + + seq_list_sorted = sorted(seq_list) # 遍历列表,分别计算 + for curr_seq in tqdm.tqdm(seq_list_sorted): + res[curr_seq] = eval_sequence(curr_seq, dataset, 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'} + res['COMBINED_SEQ'][c_cls][metric_name] = metric.combine_sequences(curr_res) + # 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']: + print('\nAll sequences for %s finished in %.2f seconds' % (tracker, time.time() - time_start)) + + output_fol = dataset.get_output_fol() + tracker_display_name = dataset.benchmark#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']: + 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.' + print('Tracker %s was unable to be evaluated.' % tracker) + print(err) + traceback.print_exc() + if config['LOG_ON_ERROR'] is not None: + with open(config['LOG_ON_ERROR'], 'a') as f: + print(dataset_name, file=f) + print(tracker, file=f) + print(traceback.format_exc(), file=f) + print('\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, class_list, metrics_list, metric_names): + """Function for evaluating a single sequence""" + # + raw_data = dataset.get_raw_seq_data( 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 diff --git a/trackeval/metrics/__init__.py b/trackeval/metrics/__init__.py new file mode 100644 index 0000000..1f84774 --- /dev/null +++ b/trackeval/metrics/__init__.py @@ -0,0 +1,8 @@ +from .hota import HOTA +from .clear import CLEAR +from .identity import Identity +from .count import Count +from .j_and_f import JAndF +from .track_map import TrackMAP +from .vace import VACE +from .ideucl import IDEucl \ No newline at end of file diff --git a/trackeval/metrics/__pycache__/__init__.cpython-311.pyc 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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""" + 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""" + 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. + """ + 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. + """ + 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 diff --git a/trackeval/metrics/count.py b/trackeval/metrics/count.py new file mode 100644 index 0000000..49049b1 --- /dev/null +++ b/trackeval/metrics/count.py @@ -0,0 +1,44 @@ + +from ._base_metric import _BaseMetric +from .. import _timing + + +class Count(_BaseMetric): + """Class which simply counts the number of tracker and gt detections and ids.""" + 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""" + # 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""" + 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""" + 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""" + res = {} + for field in self.integer_fields: + res[field] = self._combine_sum(all_res, field) + return res diff --git a/trackeval/metrics/hota.py b/trackeval/metrics/hota.py new file mode 100644 index 0000000..f551b76 --- /dev/null +++ b/trackeval/metrics/hota.py @@ -0,0 +1,203 @@ + +import os +import numpy as np +from scipy.optimize import linear_sum_assignment +from ._base_metric import _BaseMetric +from .. import _timing + + +class HOTA(_BaseMetric): + """Class which implements the HOTA metrics. + See: https://link.springer.com/article/10.1007/s11263-020-01375-2 + """ + + 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""" + + # Initialise results + res = {} + for field in self.float_array_fields + self.integer_array_fields: + res[field] = np.zeros((len(self.array_labels)), dtype=np.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=np.float) + res['LocA'] = np.ones((len(self.array_labels)), dtype=np.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=np.float) + res['LocA'] = np.ones((len(self.array_labels)), dtype=np.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(1e-10, 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""" + 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. + """ + 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""" + 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. + """ + 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""" + + # 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() diff --git a/trackeval/metrics/identity.py b/trackeval/metrics/identity.py new file mode 100644 index 0000000..c8c6c80 --- /dev/null +++ b/trackeval/metrics/identity.py @@ -0,0 +1,135 @@ +import numpy as np +from scipy.optimize import linear_sum_assignment +from ._base_metric import _BaseMetric +from .. import _timing +from .. import utils + + +class Identity(_BaseMetric): + """Class which implements the ID metrics""" + + @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""" + # 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(np.int) + res['IDFP'] = fp_mat[match_rows, match_cols].sum().astype(np.int) + res['IDTP'] = (gt_id_count.sum() - res['IDFN']).astype(np.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. + """ + 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""" + 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""" + 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. + """ + 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 diff --git a/trackeval/metrics/ideucl.py b/trackeval/metrics/ideucl.py new file mode 100644 index 0000000..db9b57b --- /dev/null +++ b/trackeval/metrics/ideucl.py @@ -0,0 +1,135 @@ +import numpy as np +from scipy.optimize import linear_sum_assignment +from ._base_metric import _BaseMetric +from .. import _timing +from collections import defaultdict +from .. import utils + + +class IDEucl(_BaseMetric): + """Class which implements the ID metrics""" + + @staticmethod + def get_default_config(): + """Default class config values""" + default_config = { + 'THRESHOLD': 0.4, # Similarity score threshold required for a IDTP match. 0.4 for IDEucl. + 'PRINT_CONFIG': True, # Whether to print the config information on init. Default: False. + } + return default_config + + def __init__(self, config=None): + super().__init__() + self.fields = ['IDEucl'] + self.float_fields = self.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 IDEucl metrics for all frames""" + # Initialise results + res = {'IDEucl' : 0} + + # Return result quickly if tracker or gt sequence is empty + if data['num_tracker_dets'] == 0 or data['num_gt_dets'] == 0.: + return res + + data['centroid'] = [] + for t, gt_det in enumerate(data['gt_dets']): + # import pdb;pdb.set_trace() + data['centroid'].append(self._compute_centroid(gt_det)) + + oid_hid_cent = defaultdict(list) + oid_cent = defaultdict(list) + for t, (gt_ids_t, tracker_ids_t) in enumerate(zip(data['gt_ids'], data['tracker_ids'])): + matches_mask = np.greater_equal(data['similarity_scores'][t], self.threshold) + + # I hope the orders of ids and boxes are maintained in `data` + for ind, gid in enumerate(gt_ids_t): + oid_cent[gid].append(data['centroid'][t][ind]) + + match_idx_gt, match_idx_tracker = np.nonzero(matches_mask) + for m_gid, m_tid in zip(match_idx_gt, match_idx_tracker): + oid_hid_cent[gt_ids_t[m_gid], tracker_ids_t[m_tid]].append(data['centroid'][t][m_gid]) + + oid_hid_dist = {k : np.sum(np.linalg.norm(np.diff(np.array(v), axis=0), axis=1)) for k, v in oid_hid_cent.items()} + oid_dist = {int(k) : np.sum(np.linalg.norm(np.diff(np.array(v), axis=0), axis=1)) for k, v in oid_cent.items()} + + unique_oid = np.unique([i[0] for i in oid_hid_dist.keys()]).tolist() + unique_hid = np.unique([i[1] for i in oid_hid_dist.keys()]).tolist() + o_len = len(unique_oid) + h_len = len(unique_hid) + dist_matrix = np.zeros((o_len, h_len)) + for ((oid, hid), dist) in oid_hid_dist.items(): + oid_ind = unique_oid.index(oid) + hid_ind = unique_hid.index(hid) + dist_matrix[oid_ind, hid_ind] = dist + + # opt_hyp_dist contains GT ID : max dist covered by track + opt_hyp_dist = dict.fromkeys(oid_dist.keys(), 0.) + cost_matrix = np.max(dist_matrix) - dist_matrix + rows, cols = linear_sum_assignment(cost_matrix) + for (row, col) in zip(rows, cols): + value = dist_matrix[row, col] + opt_hyp_dist[int(unique_oid[row])] = value + + assert len(opt_hyp_dist.keys()) == len(oid_dist.keys()) + hyp_length = np.sum(list(opt_hyp_dist.values())) + gt_length = np.sum(list(oid_dist.values())) + id_eucl =np.mean([np.divide(a, b, out=np.zeros_like(a), where=b!=0) for a, b in zip(opt_hyp_dist.values(), oid_dist.values())]) + res['IDEucl'] = np.divide(hyp_length, gt_length, out=np.zeros_like(hyp_length), where=gt_length!=0) + 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. + """ + res = {} + + for field in self.float_fields: + if ignore_empty_classes: + res[field] = np.mean([v[field] for v in all_res.values() + if v['IDEucl'] > 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""" + res = {} + for field in self.float_fields: + res[field] = self._combine_sum(all_res, field) + res = self._compute_final_fields(res, len(all_res)) + return res + + def combine_sequences(self, all_res): + """Combines metrics across all sequences""" + res = {} + for field in self.float_fields: + res[field] = self._combine_sum(all_res, field) + res = self._compute_final_fields(res, len(all_res)) + return res + + + @staticmethod + def _compute_centroid(box): + box = np.array(box) + if len(box.shape) == 1: + centroid = (box[0:2] + box[2:4])/2 + else: + centroid = (box[:, 0:2] + box[:, 2:4])/2 + return np.flip(centroid, axis=1) + + + @staticmethod + def _compute_final_fields(res, res_len): + """ + Exists only to match signature with the original Identiy class. + + """ + return {k:v/res_len for k,v in res.items()} diff --git a/trackeval/metrics/j_and_f.py b/trackeval/metrics/j_and_f.py new file mode 100644 index 0000000..1b18f04 --- /dev/null +++ b/trackeval/metrics/j_and_f.py @@ -0,0 +1,310 @@ + +import numpy as np +import math +from scipy.optimize import linear_sum_assignment +from ..utils import TrackEvalException +from ._base_metric import _BaseMetric +from .. import _timing + + +class JAndF(_BaseMetric): + """Class which implements the J&F metrics""" + def __init__(self, config=None): + super().__init__() + self.integer_fields = ['num_gt_tracks'] + self.float_fields = ['J-Mean', 'J-Recall', 'J-Decay', 'F-Mean', 'F-Recall', 'F-Decay', 'J&F'] + self.fields = self.float_fields + self.integer_fields + self.summary_fields = self.float_fields + self.optim_type = 'J' # possible values J, J&F + + @_timing.time + def eval_sequence(self, data): + """Returns J&F metrics for one sequence""" + + # Only loaded when run to reduce minimum requirements + from pycocotools import mask as mask_utils + + num_timesteps = data['num_timesteps'] + num_tracker_ids = data['num_tracker_ids'] + num_gt_ids = data['num_gt_ids'] + gt_dets = data['gt_dets'] + tracker_dets = data['tracker_dets'] + gt_ids = data['gt_ids'] + tracker_ids = data['tracker_ids'] + + # get shape of frames + frame_shape = None + if num_gt_ids > 0: + for t in range(num_timesteps): + if len(gt_ids[t]) > 0: + frame_shape = gt_dets[t][0]['size'] + break + elif num_tracker_ids > 0: + for t in range(num_timesteps): + if len(tracker_ids[t]) > 0: + frame_shape = tracker_dets[t][0]['size'] + break + + if frame_shape: + # append all zero masks for timesteps in which tracks do not have a detection + zero_padding = np.zeros((frame_shape), order= 'F').astype(np.uint8) + padding_mask = mask_utils.encode(zero_padding) + for t in range(num_timesteps): + gt_id_det_mapping = {gt_ids[t][i]: gt_dets[t][i] for i in range(len(gt_ids[t]))} + gt_dets[t] = [gt_id_det_mapping[index] if index in gt_ids[t] else padding_mask for index + in range(num_gt_ids)] + tracker_id_det_mapping = {tracker_ids[t][i]: tracker_dets[t][i] for i in range(len(tracker_ids[t]))} + tracker_dets[t] = [tracker_id_det_mapping[index] if index in tracker_ids[t] else padding_mask for index + in range(num_tracker_ids)] + # also perform zero padding if number of tracker IDs < number of ground truth IDs + if num_tracker_ids < num_gt_ids: + diff = num_gt_ids - num_tracker_ids + for t in range(num_timesteps): + tracker_dets[t] = tracker_dets[t] + [padding_mask for _ in range(diff)] + num_tracker_ids += diff + + j = self._compute_j(gt_dets, tracker_dets, num_gt_ids, num_tracker_ids, num_timesteps) + + # boundary threshold for F computation + bound_th = 0.008 + + # perform matching + if self.optim_type == 'J&F': + f = np.zeros_like(j) + for k in range(num_tracker_ids): + for i in range(num_gt_ids): + f[k, i, :] = self._compute_f(gt_dets, tracker_dets, k, i, bound_th) + optim_metrics = (np.mean(j, axis=2) + np.mean(f, axis=2)) / 2 + row_ind, col_ind = linear_sum_assignment(- optim_metrics) + j_m = j[row_ind, col_ind, :] + f_m = f[row_ind, col_ind, :] + elif self.optim_type == 'J': + optim_metrics = np.mean(j, axis=2) + row_ind, col_ind = linear_sum_assignment(- optim_metrics) + j_m = j[row_ind, col_ind, :] + f_m = np.zeros_like(j_m) + for i, (tr_ind, gt_ind) in enumerate(zip(row_ind, col_ind)): + f_m[i] = self._compute_f(gt_dets, tracker_dets, tr_ind, gt_ind, bound_th) + else: + raise TrackEvalException('Unsupported optimization type %s for J&F metric.' % self.optim_type) + + # append zeros for false negatives + if j_m.shape[0] < data['num_gt_ids']: + diff = data['num_gt_ids'] - j_m.shape[0] + j_m = np.concatenate((j_m, np.zeros((diff, j_m.shape[1]))), axis=0) + f_m = np.concatenate((f_m, np.zeros((diff, f_m.shape[1]))), axis=0) + + # compute the metrics for each ground truth track + res = { + 'J-Mean': [np.nanmean(j_m[i, :]) for i in range(j_m.shape[0])], + 'J-Recall': [np.nanmean(j_m[i, :] > 0.5 + np.finfo('float').eps) for i in range(j_m.shape[0])], + 'F-Mean': [np.nanmean(f_m[i, :]) for i in range(f_m.shape[0])], + 'F-Recall': [np.nanmean(f_m[i, :] > 0.5 + np.finfo('float').eps) for i in range(f_m.shape[0])], + 'J-Decay': [], + 'F-Decay': [] + } + n_bins = 4 + ids = np.round(np.linspace(1, data['num_timesteps'], n_bins + 1) + 1e-10) - 1 + ids = ids.astype(np.uint8) + + for k in range(j_m.shape[0]): + d_bins_j = [j_m[k][ids[i]:ids[i + 1] + 1] for i in range(0, n_bins)] + res['J-Decay'].append(np.nanmean(d_bins_j[0]) - np.nanmean(d_bins_j[3])) + for k in range(f_m.shape[0]): + d_bins_f = [f_m[k][ids[i]:ids[i + 1] + 1] for i in range(0, n_bins)] + res['F-Decay'].append(np.nanmean(d_bins_f[0]) - np.nanmean(d_bins_f[3])) + + # count number of tracks for weighting of the result + res['num_gt_tracks'] = len(res['J-Mean']) + for field in ['J-Mean', 'J-Recall', 'J-Decay', 'F-Mean', 'F-Recall', 'F-Decay']: + res[field] = np.mean(res[field]) + res['J&F'] = (res['J-Mean'] + res['F-Mean']) / 2 + return res + + def combine_sequences(self, all_res): + """Combines metrics across all sequences""" + res = {'num_gt_tracks': self._combine_sum(all_res, 'num_gt_tracks')} + for field in self.summary_fields: + res[field] = self._combine_weighted_av(all_res, field, res, weight_field='num_gt_tracks') + 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 + 'ignore empty classes' is not yet implemented here. + """ + res = {'num_gt_tracks': self._combine_sum(all_res, 'num_gt_tracks')} + for field in self.float_fields: + res[field] = np.mean([v[field] for v in all_res.values()]) + return res + + def combine_classes_det_averaged(self, all_res): + """Combines metrics across all classes by averaging over the detection values""" + res = {'num_gt_tracks': self._combine_sum(all_res, 'num_gt_tracks')} + for field in self.float_fields: + res[field] = np.mean([v[field] for v in all_res.values()]) + return res + + @staticmethod + def _seg2bmap(seg, width=None, height=None): + """ + From a segmentation, compute a binary boundary map with 1 pixel wide + boundaries. The boundary pixels are offset by 1/2 pixel towards the + origin from the actual segment boundary. + Arguments: + seg : Segments labeled from 1..k. + width : Width of desired bmap <= seg.shape[1] + height : Height of desired bmap <= seg.shape[0] + Returns: + bmap (ndarray): Binary boundary map. + David Martin + January 2003 + """ + + seg = seg.astype(np.bool) + seg[seg > 0] = 1 + + assert np.atleast_3d(seg).shape[2] == 1 + + width = seg.shape[1] if width is None else width + height = seg.shape[0] if height is None else height + + h, w = seg.shape[:2] + + ar1 = float(width) / float(height) + ar2 = float(w) / float(h) + + assert not ( + width > w | height > h | abs(ar1 - ar2) > 0.01 + ), "Can" "t convert %dx%d seg to %dx%d bmap." % (w, h, width, height) + + e = np.zeros_like(seg) + s = np.zeros_like(seg) + se = np.zeros_like(seg) + + e[:, :-1] = seg[:, 1:] + s[:-1, :] = seg[1:, :] + se[:-1, :-1] = seg[1:, 1:] + + b = seg ^ e | seg ^ s | seg ^ se + b[-1, :] = seg[-1, :] ^ e[-1, :] + b[:, -1] = seg[:, -1] ^ s[:, -1] + b[-1, -1] = 0 + + if w == width and h == height: + bmap = b + else: + bmap = np.zeros((height, width)) + for x in range(w): + for y in range(h): + if b[y, x]: + j = 1 + math.floor((y - 1) + height / h) + i = 1 + math.floor((x - 1) + width / h) + bmap[j, i] = 1 + + return bmap + + @staticmethod + def _compute_f(gt_data, tracker_data, tracker_data_id, gt_id, bound_th): + """ + Perform F computation for a given gt and a given tracker ID. Adapted from + https://github.com/davisvideochallenge/davis2017-evaluation + :param gt_data: the encoded gt masks + :param tracker_data: the encoded tracker masks + :param tracker_data_id: the tracker ID + :param gt_id: the ground truth ID + :param bound_th: boundary threshold parameter + :return: the F value for the given tracker and gt ID + """ + + # Only loaded when run to reduce minimum requirements + from pycocotools import mask as mask_utils + from skimage.morphology import disk + import cv2 + + f = np.zeros(len(gt_data)) + + for t, (gt_masks, tracker_masks) in enumerate(zip(gt_data, tracker_data)): + curr_tracker_mask = mask_utils.decode(tracker_masks[tracker_data_id]) + curr_gt_mask = mask_utils.decode(gt_masks[gt_id]) + + bound_pix = bound_th if bound_th >= 1 - np.finfo('float').eps else \ + np.ceil(bound_th * np.linalg.norm(curr_tracker_mask.shape)) + + # Get the pixel boundaries of both masks + fg_boundary = JAndF._seg2bmap(curr_tracker_mask) + gt_boundary = JAndF._seg2bmap(curr_gt_mask) + + # fg_dil = binary_dilation(fg_boundary, disk(bound_pix)) + fg_dil = cv2.dilate(fg_boundary.astype(np.uint8), disk(bound_pix).astype(np.uint8)) + # gt_dil = binary_dilation(gt_boundary, disk(bound_pix)) + gt_dil = cv2.dilate(gt_boundary.astype(np.uint8), disk(bound_pix).astype(np.uint8)) + + # Get the intersection + gt_match = gt_boundary * fg_dil + fg_match = fg_boundary * gt_dil + + # Area of the intersection + n_fg = np.sum(fg_boundary) + n_gt = np.sum(gt_boundary) + + # % Compute precision and recall + if n_fg == 0 and n_gt > 0: + precision = 1 + recall = 0 + elif n_fg > 0 and n_gt == 0: + precision = 0 + recall = 1 + elif n_fg == 0 and n_gt == 0: + precision = 1 + recall = 1 + else: + precision = np.sum(fg_match) / float(n_fg) + recall = np.sum(gt_match) / float(n_gt) + + # Compute F measure + if precision + recall == 0: + f_val = 0 + else: + f_val = 2 * precision * recall / (precision + recall) + + f[t] = f_val + + return f + + @staticmethod + def _compute_j(gt_data, tracker_data, num_gt_ids, num_tracker_ids, num_timesteps): + """ + Computation of J value for all ground truth IDs and all tracker IDs in the given sequence. Adapted from + https://github.com/davisvideochallenge/davis2017-evaluation + :param gt_data: the ground truth masks + :param tracker_data: the tracker masks + :param num_gt_ids: the number of ground truth IDs + :param num_tracker_ids: the number of tracker IDs + :param num_timesteps: the number of timesteps + :return: the J values + """ + + # Only loaded when run to reduce minimum requirements + from pycocotools import mask as mask_utils + + j = np.zeros((num_tracker_ids, num_gt_ids, num_timesteps)) + + for t, (time_gt, time_data) in enumerate(zip(gt_data, tracker_data)): + # run length encoded masks with pycocotools + area_gt = mask_utils.area(time_gt) + time_data = list(time_data) + area_tr = mask_utils.area(time_data) + + area_tr = np.repeat(area_tr[:, np.newaxis], len(area_gt), axis=1) + area_gt = np.repeat(area_gt[np.newaxis, :], len(area_tr), axis=0) + + # mask iou computation with pycocotools + ious = np.atleast_2d(mask_utils.iou(time_data, time_gt, [0]*len(time_gt))) + # set iou to 1 if both masks are close to 0 (no ground truth and no predicted mask in timestep) + ious[np.isclose(area_tr, 0) & np.isclose(area_gt, 0)] = 1 + assert (ious >= 0 - np.finfo('float').eps).all() + assert (ious <= 1 + np.finfo('float').eps).all() + + j[..., t] = ious + + return j diff --git a/trackeval/metrics/track_map.py b/trackeval/metrics/track_map.py new file mode 100644 index 0000000..039f890 --- /dev/null +++ b/trackeval/metrics/track_map.py @@ -0,0 +1,462 @@ +import numpy as np +from ._base_metric import _BaseMetric +from .. import _timing +from functools import partial +from .. import utils +from ..utils import TrackEvalException + + +class TrackMAP(_BaseMetric): + """Class which implements the TrackMAP metrics""" + + @staticmethod + def get_default_metric_config(): + """Default class config values""" + default_config = { + 'USE_AREA_RANGES': True, # whether to evaluate for certain area ranges + 'AREA_RANGES': [[0 ** 2, 32 ** 2], # additional area range sets for which TrackMAP is evaluated + [32 ** 2, 96 ** 2], # (all area range always included), default values for TAO + [96 ** 2, 1e5 ** 2]], # evaluation + 'AREA_RANGE_LABELS': ["area_s", "area_m", "area_l"], # the labels for the area ranges + 'USE_TIME_RANGES': True, # whether to evaluate for certain time ranges (length of tracks) + 'TIME_RANGES': [[0, 3], [3, 10], [10, 1e5]], # additional time range sets for which TrackMAP is evaluated + # (all time range always included) , default values for TAO evaluation + 'TIME_RANGE_LABELS': ["time_s", "time_m", "time_l"], # the labels for the time ranges + 'IOU_THRESHOLDS': np.arange(0.5, 0.96, 0.05), # the IoU thresholds + 'RECALL_THRESHOLDS': np.linspace(0.0, 1.00, int(np.round((1.00 - 0.0) / 0.01) + 1), endpoint=True), + # recall thresholds at which precision is evaluated + 'MAX_DETECTIONS': 0, # limit the maximum number of considered tracks per sequence (0 for unlimited) + 'PRINT_CONFIG': True + } + return default_config + + def __init__(self, config=None): + super().__init__() + self.config = utils.init_config(config, self.get_default_metric_config(), self.get_name()) + + self.num_ig_masks = 1 + self.lbls = ['all'] + self.use_area_rngs = self.config['USE_AREA_RANGES'] + if self.use_area_rngs: + self.area_rngs = self.config['AREA_RANGES'] + self.area_rng_lbls = self.config['AREA_RANGE_LABELS'] + self.num_ig_masks += len(self.area_rng_lbls) + self.lbls += self.area_rng_lbls + + self.use_time_rngs = self.config['USE_TIME_RANGES'] + if self.use_time_rngs: + self.time_rngs = self.config['TIME_RANGES'] + self.time_rng_lbls = self.config['TIME_RANGE_LABELS'] + self.num_ig_masks += len(self.time_rng_lbls) + self.lbls += self.time_rng_lbls + + self.array_labels = self.config['IOU_THRESHOLDS'] + self.rec_thrs = self.config['RECALL_THRESHOLDS'] + + self.maxDet = self.config['MAX_DETECTIONS'] + self.float_array_fields = ['AP_' + lbl for lbl in self.lbls] + ['AR_' + lbl for lbl in self.lbls] + self.fields = self.float_array_fields + self.summary_fields = self.float_array_fields + + @_timing.time + def eval_sequence(self, data): + """Calculates GT and Tracker matches for one sequence for TrackMAP metrics. Adapted from + https://github.com/TAO-Dataset/""" + + # Initialise results to zero for each sequence as the fields are only defined over the set of all sequences + res = {} + for field in self.fields: + res[field] = [0 for _ in self.array_labels] + + gt_ids, dt_ids = data['gt_track_ids'], data['dt_track_ids'] + + if len(gt_ids) == 0 and len(dt_ids) == 0: + for idx in range(self.num_ig_masks): + res[idx] = None + return res + + # get track data + gt_tr_areas = data.get('gt_track_areas', None) if self.use_area_rngs else None + gt_tr_lengths = data.get('gt_track_lengths', None) if self.use_time_rngs else None + gt_tr_iscrowd = data.get('gt_track_iscrowd', None) + dt_tr_areas = data.get('dt_track_areas', None) if self.use_area_rngs else None + dt_tr_lengths = data.get('dt_track_lengths', None) if self.use_time_rngs else None + is_nel = data.get('not_exhaustively_labeled', False) + + # compute ignore masks for different track sets to eval + gt_ig_masks = self._compute_track_ig_masks(len(gt_ids), track_lengths=gt_tr_lengths, track_areas=gt_tr_areas, + iscrowd=gt_tr_iscrowd) + dt_ig_masks = self._compute_track_ig_masks(len(dt_ids), track_lengths=dt_tr_lengths, track_areas=dt_tr_areas, + is_not_exhaustively_labeled=is_nel, is_gt=False) + + boxformat = data.get('boxformat', 'xywh') + ious = self._compute_track_ious(data['dt_tracks'], data['gt_tracks'], iou_function=data['iou_type'], + boxformat=boxformat) + + for mask_idx in range(self.num_ig_masks): + gt_ig_mask = gt_ig_masks[mask_idx] + + # Sort gt ignore last + gt_idx = np.argsort([g for g in gt_ig_mask], kind="mergesort") + gt_ids = [gt_ids[i] for i in gt_idx] + + ious_sorted = ious[:, gt_idx] if len(ious) > 0 else ious + + num_thrs = len(self.array_labels) + num_gt = len(gt_ids) + num_dt = len(dt_ids) + + # Array to store the "id" of the matched dt/gt + gt_m = np.zeros((num_thrs, num_gt)) - 1 + dt_m = np.zeros((num_thrs, num_dt)) - 1 + + gt_ig = np.array([gt_ig_mask[idx] for idx in gt_idx]) + dt_ig = np.zeros((num_thrs, num_dt)) + + for iou_thr_idx, iou_thr in enumerate(self.array_labels): + if len(ious_sorted) == 0: + break + + for dt_idx, _dt in enumerate(dt_ids): + iou = min([iou_thr, 1 - 1e-10]) + # information about best match so far (m=-1 -> unmatched) + # store the gt_idx which matched for _dt + m = -1 + for gt_idx, _ in enumerate(gt_ids): + # if this gt already matched continue + if gt_m[iou_thr_idx, gt_idx] > 0: + continue + # if _dt matched to reg gt, and on ignore gt, stop + if m > -1 and gt_ig[m] == 0 and gt_ig[gt_idx] == 1: + break + # continue to next gt unless better match made + if ious_sorted[dt_idx, gt_idx] < iou - np.finfo('float').eps: + continue + # if match successful and best so far, store appropriately + iou = ious_sorted[dt_idx, gt_idx] + m = gt_idx + + # No match found for _dt, go to next _dt + if m == -1: + continue + + # if gt to ignore for some reason update dt_ig. + # Should not be used in evaluation. + dt_ig[iou_thr_idx, dt_idx] = gt_ig[m] + # _dt match found, update gt_m, and dt_m with "id" + dt_m[iou_thr_idx, dt_idx] = gt_ids[m] + gt_m[iou_thr_idx, m] = _dt + + dt_ig_mask = dt_ig_masks[mask_idx] + + dt_ig_mask = np.array(dt_ig_mask).reshape((1, num_dt)) # 1 X num_dt + dt_ig_mask = np.repeat(dt_ig_mask, num_thrs, 0) # num_thrs X num_dt + + # Based on dt_ig_mask ignore any unmatched detection by updating dt_ig + dt_ig = np.logical_or(dt_ig, np.logical_and(dt_m == -1, dt_ig_mask)) + # store results for given video and category + res[mask_idx] = { + "dt_ids": dt_ids, + "gt_ids": gt_ids, + "dt_matches": dt_m, + "gt_matches": gt_m, + "dt_scores": data['dt_track_scores'], + "gt_ignore": gt_ig, + "dt_ignore": dt_ig, + } + + return res + + def combine_sequences(self, all_res): + """Combines metrics across all sequences. Computes precision and recall values based on track matches. + Adapted from https://github.com/TAO-Dataset/ + """ + num_thrs = len(self.array_labels) + num_recalls = len(self.rec_thrs) + + # -1 for absent categories + precision = -np.ones( + (num_thrs, num_recalls, self.num_ig_masks) + ) + recall = -np.ones((num_thrs, self.num_ig_masks)) + + for ig_idx in range(self.num_ig_masks): + ig_idx_results = [res[ig_idx] for res in all_res.values() if res[ig_idx] is not None] + + # Remove elements which are None + if len(ig_idx_results) == 0: + continue + + # Append all scores: shape (N,) + # limit considered tracks for each sequence if maxDet > 0 + if self.maxDet == 0: + dt_scores = np.concatenate([res["dt_scores"] for res in ig_idx_results], axis=0) + + dt_idx = np.argsort(-dt_scores, kind="mergesort") + + dt_m = np.concatenate([e["dt_matches"] for e in ig_idx_results], + axis=1)[:, dt_idx] + dt_ig = np.concatenate([e["dt_ignore"] for e in ig_idx_results], + axis=1)[:, dt_idx] + elif self.maxDet > 0: + dt_scores = np.concatenate([res["dt_scores"][0:self.maxDet] for res in ig_idx_results], axis=0) + + dt_idx = np.argsort(-dt_scores, kind="mergesort") + + dt_m = np.concatenate([e["dt_matches"][:, 0:self.maxDet] for e in ig_idx_results], + axis=1)[:, dt_idx] + dt_ig = np.concatenate([e["dt_ignore"][:, 0:self.maxDet] for e in ig_idx_results], + axis=1)[:, dt_idx] + else: + raise Exception("Number of maximum detections must be >= 0, but is set to %i" % self.maxDet) + + gt_ig = np.concatenate([res["gt_ignore"] for res in ig_idx_results]) + # num gt anns to consider + num_gt = np.count_nonzero(gt_ig == 0) + + if num_gt == 0: + continue + + tps = np.logical_and(dt_m != -1, np.logical_not(dt_ig)) + fps = np.logical_and(dt_m == -1, np.logical_not(dt_ig)) + + tp_sum = np.cumsum(tps, axis=1).astype(dtype=np.float) + fp_sum = np.cumsum(fps, axis=1).astype(dtype=np.float) + + for iou_thr_idx, (tp, fp) in enumerate(zip(tp_sum, fp_sum)): + tp = np.array(tp) + fp = np.array(fp) + num_tp = len(tp) + rc = tp / num_gt + if num_tp: + recall[iou_thr_idx, ig_idx] = rc[-1] + else: + recall[iou_thr_idx, ig_idx] = 0 + + # np.spacing(1) ~= eps + pr = tp / (fp + tp + np.spacing(1)) + pr = pr.tolist() + + # Ensure precision values are monotonically decreasing + for i in range(num_tp - 1, 0, -1): + if pr[i] > pr[i - 1]: + pr[i - 1] = pr[i] + + # find indices at the predefined recall values + rec_thrs_insert_idx = np.searchsorted(rc, self.rec_thrs, side="left") + + pr_at_recall = [0.0] * num_recalls + + try: + for _idx, pr_idx in enumerate(rec_thrs_insert_idx): + pr_at_recall[_idx] = pr[pr_idx] + except IndexError: + pass + + precision[iou_thr_idx, :, ig_idx] = (np.array(pr_at_recall)) + + res = {'precision': precision, 'recall': recall} + + # compute the precision and recall averages for the respective alpha thresholds and ignore masks + for lbl in self.lbls: + res['AP_' + lbl] = np.zeros((len(self.array_labels)), dtype=np.float) + res['AR_' + lbl] = np.zeros((len(self.array_labels)), dtype=np.float) + + for a_id, alpha in enumerate(self.array_labels): + for lbl_idx, lbl in enumerate(self.lbls): + p = precision[a_id, :, lbl_idx] + if len(p[p > -1]) == 0: + mean_p = -1 + else: + mean_p = np.mean(p[p > -1]) + res['AP_' + lbl][a_id] = mean_p + res['AR_' + lbl][a_id] = recall[a_id, lbl_idx] + + return res + + def combine_classes_class_averaged(self, all_res, ignore_empty_classes=True): + """Combines metrics across all classes by averaging over the class values + Note mAP is not well defined for 'empty classes' so 'ignore empty classes' is always true here. + """ + res = {} + for field in self.fields: + res[field] = np.zeros((len(self.array_labels)), dtype=np.float) + field_stacked = np.array([res[field] for res in all_res.values()]) + + for a_id, alpha in enumerate(self.array_labels): + values = field_stacked[:, a_id] + if len(values[values > -1]) == 0: + mean = -1 + else: + mean = np.mean(values[values > -1]) + res[field][a_id] = mean + return res + + def combine_classes_det_averaged(self, all_res): + """Combines metrics across all classes by averaging over the detection values""" + + res = {} + for field in self.fields: + res[field] = np.zeros((len(self.array_labels)), dtype=np.float) + field_stacked = np.array([res[field] for res in all_res.values()]) + + for a_id, alpha in enumerate(self.array_labels): + values = field_stacked[:, a_id] + if len(values[values > -1]) == 0: + mean = -1 + else: + mean = np.mean(values[values > -1]) + res[field][a_id] = mean + return res + + def _compute_track_ig_masks(self, num_ids, track_lengths=None, track_areas=None, iscrowd=None, + is_not_exhaustively_labeled=False, is_gt=True): + """ + Computes ignore masks for different track sets to evaluate + :param num_ids: the number of track IDs + :param track_lengths: the lengths of the tracks (number of timesteps) + :param track_areas: the average area of a track + :param iscrowd: whether a track is marked as crowd + :param is_not_exhaustively_labeled: whether the track category is not exhaustively labeled + :param is_gt: whether it is gt + :return: the track ignore masks + """ + # for TAO tracks for classes which are not exhaustively labeled are not evaluated + if not is_gt and is_not_exhaustively_labeled: + track_ig_masks = [[1 for _ in range(num_ids)] for i in range(self.num_ig_masks)] + else: + # consider all tracks + track_ig_masks = [[0 for _ in range(num_ids)]] + + # consider tracks with certain area + if self.use_area_rngs: + for rng in self.area_rngs: + track_ig_masks.append([0 if rng[0] - np.finfo('float').eps <= area <= rng[1] + np.finfo('float').eps + else 1 for area in track_areas]) + + # consider tracks with certain duration + if self.use_time_rngs: + for rng in self.time_rngs: + track_ig_masks.append([0 if rng[0] - np.finfo('float').eps <= length + <= rng[1] + np.finfo('float').eps else 1 for length in track_lengths]) + + # for YouTubeVIS evaluation tracks with crowd tag are not evaluated + if is_gt and iscrowd: + track_ig_masks = [np.logical_or(mask, iscrowd) for mask in track_ig_masks] + + return track_ig_masks + + @staticmethod + def _compute_bb_track_iou(dt_track, gt_track, boxformat='xywh'): + """ + Calculates the track IoU for one detected track and one ground truth track for bounding boxes + :param dt_track: the detected track (format: dictionary with frame index as keys and + numpy arrays as values) + :param gt_track: the ground truth track (format: dictionary with frame index as keys and + numpy array as values) + :param boxformat: the format of the boxes + :return: the track IoU + """ + intersect = 0 + union = 0 + image_ids = set(gt_track.keys()) | set(dt_track.keys()) + for image in image_ids: + g = gt_track.get(image, None) + d = dt_track.get(image, None) + if boxformat == 'xywh': + if d is not None and g is not None: + dx, dy, dw, dh = d + gx, gy, gw, gh = g + w = max(min(dx + dw, gx + gw) - max(dx, gx), 0) + h = max(min(dy + dh, gy + gh) - max(dy, gy), 0) + i = w * h + u = dw * dh + gw * gh - i + intersect += i + union += u + elif d is None and g is not None: + union += g[2] * g[3] + elif d is not None and g is None: + union += d[2] * d[3] + elif boxformat == 'x0y0x1y1': + if d is not None and g is not None: + dx0, dy0, dx1, dy1 = d + gx0, gy0, gx1, gy1 = g + w = max(min(dx1, gx1) - max(dx0, gx0), 0) + h = max(min(dy1, gy1) - max(dy0, gy0), 0) + i = w * h + u = (dx1 - dx0) * (dy1 - dy0) + (gx1 - gx0) * (gy1 - gy0) - i + intersect += i + union += u + elif d is None and g is not None: + union += (g[2] - g[0]) * (g[3] - g[1]) + elif d is not None and g is None: + union += (d[2] - d[0]) * (d[3] - d[1]) + else: + raise TrackEvalException('BoxFormat not implemented') + if intersect > union: + raise TrackEvalException("Intersection value > union value. Are the box values corrupted?") + return intersect / union if union > 0 else 0 + + @staticmethod + def _compute_mask_track_iou(dt_track, gt_track): + """ + Calculates the track IoU for one detected track and one ground truth track for segmentation masks + :param dt_track: the detected track (format: dictionary with frame index as keys and + pycocotools rle encoded masks as values) + :param gt_track: the ground truth track (format: dictionary with frame index as keys and + pycocotools rle encoded masks as values) + :return: the track IoU + """ + # only loaded when needed to reduce minimum requirements + from pycocotools import mask as mask_utils + + intersect = .0 + union = .0 + image_ids = set(gt_track.keys()) | set(dt_track.keys()) + for image in image_ids: + g = gt_track.get(image, None) + d = dt_track.get(image, None) + if d and g: + intersect += mask_utils.area(mask_utils.merge([d, g], True)) + union += mask_utils.area(mask_utils.merge([d, g], False)) + elif not d and g: + union += mask_utils.area(g) + elif d and not g: + union += mask_utils.area(d) + if union < 0.0 - np.finfo('float').eps: + raise TrackEvalException("Union value < 0. Are the segmentaions corrupted?") + if intersect > union: + raise TrackEvalException("Intersection value > union value. Are the segmentations corrupted?") + iou = intersect / union if union > 0.0 + np.finfo('float').eps else 0.0 + return iou + + @staticmethod + def _compute_track_ious(dt, gt, iou_function='bbox', boxformat='xywh'): + """ + Calculate track IoUs for a set of ground truth tracks and a set of detected tracks + """ + + if len(gt) == 0 and len(dt) == 0: + return [] + + if iou_function == 'bbox': + track_iou_function = partial(TrackMAP._compute_bb_track_iou, boxformat=boxformat) + elif iou_function == 'mask': + track_iou_function = partial(TrackMAP._compute_mask_track_iou) + else: + raise Exception('IoU function not implemented') + + ious = np.zeros([len(dt), len(gt)]) + for i, j in np.ndindex(ious.shape): + ious[i, j] = track_iou_function(dt[i], gt[j]) + return ious + + @staticmethod + def _row_print(*argv): + """Prints results in an evenly spaced rows, with more space in first row""" + if len(argv) == 1: + argv = argv[0] + to_print = '%-40s' % argv[0] + for v in argv[1:]: + to_print += '%-12s' % str(v) + print(to_print) diff --git a/trackeval/metrics/vace.py b/trackeval/metrics/vace.py new file mode 100644 index 0000000..81858d4 --- /dev/null +++ b/trackeval/metrics/vace.py @@ -0,0 +1,131 @@ +import numpy as np +from scipy.optimize import linear_sum_assignment +from ._base_metric import _BaseMetric +from .. import _timing + + +class VACE(_BaseMetric): + """Class which implements the VACE metrics. + + The metrics are described in: + Manohar et al. (2006) "Performance Evaluation of Object Detection and Tracking in Video" + https://link.springer.com/chapter/10.1007/11612704_16 + + This implementation uses the "relaxed" variant of the metrics, + where an overlap threshold is applied in each frame. + """ + + def __init__(self, config=None): + super().__init__() + self.integer_fields = ['VACE_IDs', 'VACE_GT_IDs', 'num_non_empty_timesteps'] + self.float_fields = ['STDA', 'ATA', 'FDA', 'SFDA'] + self.fields = self.integer_fields + self.float_fields + self.summary_fields = ['SFDA', 'ATA'] + + # Fields that are accumulated over multiple videos. + self._additive_fields = self.integer_fields + ['STDA', 'FDA'] + + self.threshold = 0.5 + + @_timing.time + def eval_sequence(self, data): + """Calculates VACE metrics for one sequence. + + Depends on the fields: + data['num_gt_ids'] + data['num_tracker_ids'] + data['gt_ids'] + data['tracker_ids'] + data['similarity_scores'] + """ + res = {} + + # Obtain Average Tracking Accuracy (ATA) using track correspondence. + # Obtain counts necessary to compute temporal IOU. + # Assume that integer counts can be represented exactly as floats. + 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']) + both_present_count = np.zeros((data['num_gt_ids'], data['num_tracker_ids'])) + for t, (gt_ids_t, tracker_ids_t) in enumerate(zip(data['gt_ids'], data['tracker_ids'])): + # Count the number of frames in which two tracks satisfy the overlap criterion. + 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 + # Count the number of frames in which the tracks are present. + gt_id_count[gt_ids_t] += 1 + tracker_id_count[tracker_ids_t] += 1 + both_present_count[gt_ids_t[:, np.newaxis], tracker_ids_t[np.newaxis, :]] += 1 + # Number of frames in which either track is present (union of the two sets of frames). + union_count = (gt_id_count[:, np.newaxis] + + tracker_id_count[np.newaxis, :] + - both_present_count) + # The denominator should always be non-zero if all tracks are non-empty. + with np.errstate(divide='raise', invalid='raise'): + temporal_iou = potential_matches_count / union_count + # Find assignment that maximizes temporal IOU. + match_rows, match_cols = linear_sum_assignment(-temporal_iou) + res['STDA'] = temporal_iou[match_rows, match_cols].sum() + res['VACE_IDs'] = data['num_tracker_ids'] + res['VACE_GT_IDs'] = data['num_gt_ids'] + + # Obtain Frame Detection Accuracy (FDA) using per-frame correspondence. + non_empty_count = 0 + fda = 0 + for t, (gt_ids_t, tracker_ids_t) in enumerate(zip(data['gt_ids'], data['tracker_ids'])): + n_g = len(gt_ids_t) + n_d = len(tracker_ids_t) + if not (n_g or n_d): + continue + # n_g > 0 or n_d > 0 + non_empty_count += 1 + if not (n_g and n_d): + continue + # n_g > 0 and n_d > 0 + spatial_overlap = data['similarity_scores'][t] + match_rows, match_cols = linear_sum_assignment(-spatial_overlap) + overlap_ratio = spatial_overlap[match_rows, match_cols].sum() + fda += overlap_ratio / (0.5 * (n_g + n_d)) + res['FDA'] = fda + res['num_non_empty_timesteps'] = non_empty_count + + res.update(self._compute_final_fields(res)) + return res + + def combine_classes_class_averaged(self, all_res, ignore_empty_classes=True): + """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. + """ + res = {} + for field in self.fields: + if ignore_empty_classes: + res[field] = np.mean([v[field] for v in all_res.values() + if v['VACE_GT_IDs'] > 0 or v['VACE_IDs'] > 0], 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""" + res = {} + for field in self._additive_fields: + res[field] = _BaseMetric._combine_sum(all_res, field) + res = self._compute_final_fields(res) + return res + + def combine_sequences(self, all_res): + """Combines metrics across all sequences""" + res = {} + for header in self._additive_fields: + res[header] = _BaseMetric._combine_sum(all_res, header) + res.update(self._compute_final_fields(res)) + return res + + @staticmethod + def _compute_final_fields(additive): + final = {} + with np.errstate(invalid='ignore'): # Permit nan results. + final['ATA'] = (additive['STDA'] / + (0.5 * (additive['VACE_IDs'] + additive['VACE_GT_IDs']))) + final['SFDA'] = additive['FDA'] / additive['num_non_empty_timesteps'] + return final diff --git a/trackeval/minimum_requirements.txt b/trackeval/minimum_requirements.txt new file mode 100644 index 0000000..b63c322 --- /dev/null +++ b/trackeval/minimum_requirements.txt @@ -0,0 +1,2 @@ +scipy==1.4.1 +numpy==1.18.1 diff --git a/trackeval/plotting.py b/trackeval/plotting.py new file mode 100644 index 0000000..f3dece1 --- /dev/null +++ b/trackeval/plotting.py @@ -0,0 +1,230 @@ + +import os +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 predect_mot.""" + # Define what to plot + 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 + for args in plots_list: + create_comparison_plot(data, out_loc, *args) + + +def get_default_plots_list(): + # y_label, x_label, sort_label, bg_label, bg_function + 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 predect_mot.""" + 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 predect_mot 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. + + Inputs: + data: dict of dicts such that data[tracker_name][metric_field_name] = float + y_label: the metric_field_name to be plotted on the y-axis + x_label: the metric_field_name to be plotted on the x-axis + sort_label: the metric_field_name by which predect_mot are ordered and ranked + bg_label: the metric_field_name by which (optional) background contours are plotted + bg_function: the (optional) function bg_function(x,y) which converts the x_label / y_label values into bg_label. + settings: dict of plot settings with keys: + 'gap_val': gap between axis ticks and bg curves. + 'num_to_plot': maximum number of predect_mot to plot + """ + + # 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] + print('\nPlotting %s vs %s, for the following (ordered) predect_mot:' % (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): + 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): + 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): + 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. """ + + # 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 """ + + # 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') diff --git a/trackeval/requirements.txt b/trackeval/requirements.txt new file mode 100644 index 0000000..40d2a57 --- /dev/null +++ b/trackeval/requirements.txt @@ -0,0 +1,10 @@ +numpy==1.18.1 +scipy==1.4.1 +pycocotools==2.0.2 +matplotlib==3.2.1 +opencv_python==4.4.0.46 +scikit_image==0.16.2 +pytest==6.0.1 +Pillow==8.1.2 +tqdm==4.64.0 +tabulate diff --git a/trackeval/run_mot_challenge.py b/trackeval/run_mot_challenge.py new file mode 100644 index 0000000..33e34bf --- /dev/null +++ b/trackeval/run_mot_challenge.py @@ -0,0 +1,43 @@ + + +import sys +import os + + +sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) + +ROOT=os.path.dirname(__file__) +# Run code +from trackeval.eval import Evaluator +from trackeval.datasets.mot_challenge_2d_box import MotChallenge2DBox +from trackeval.metrics import * + +if __name__ == '__main__': + + config={} + #"/data1/Datasets/Tracking/MOT/VTMOT/images/results/PIDMOT_RGBT/model_1_/" + #"/data1/Datasets/Tracking/MOT/VTMOT/images/results/FairMOT_V3/model_1/" + config['TRACKERS_FOLDER'] = "/home/zhuyabin/MOT/TraDeS/exp/tracking/VTMOT_RGBT_PFM_RGBT/results_motmot_rgbt_test_11" + config['GT_FOLDER'] = "/data1/Datasets/Tracking/MOT/VTMOT/images/test/" # 给出gt路径 + config['OUTPUT_FOLDER'] = '/home/zhuyabin/MOT/MOTRv2-Ours/T_R' + + # 确定文件内gt.txt的路径,gt_folder=config['GT_FOLDER'],seq为os.listdir(gt_folder)列表 + config['GT_LOC_FORMAT'] = '{gt_folder}/{seq}/gt/gt1.txt' + config['CLASSES_TO_EVAL'] = ['pedestrian'] # 确定预测指标的类别 + + + + + + dataset = MotChallenge2DBox(config) # dataset_list是存放数据信息列表 + + evaluator = Evaluator() + + metrics_config = {'METRICS': ['HOTA', 'CLEAR', 'Identity'], 'THRESHOLD': 0.5} + metrics_list = [] + for metric in [HOTA, CLEAR, Identity, VACE]: + if metric.get_name() in metrics_config['METRICS']: + metrics_list.append(metric(metrics_config)) + if len(metrics_list) == 0: + raise Exception('No metrics selected for evaluation') + evaluator.evaluate(dataset, metrics_list) diff --git a/trackeval/tools/__init__.py b/trackeval/tools/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/trackeval/tools/img.png b/trackeval/tools/img.png new file mode 100644 index 0000000000000000000000000000000000000000..ca709572a4632da3efeff7f247e735bcff8c3d7f GIT binary patch literal 2107003 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+ gt: + gt.txt + MOT17-02-FRCNN: + 000001.jpg + 000002.jpg + 000003.jpg + seqinfo.ini + MOT17-02-DPM: + 与上面格式类似 + test: + MOT17-01-DPM: + det: + det.txt + img1: + 000001.jpg + 000002.jpg + 000003.jpg + seqinfo.ini + ...... + +''' + + +from utils import read_txt,chinese2img,show_img,build_dir +import os +import numpy as np +import cv2 +from tqdm import tqdm + +def draw_mot(img, info_lst,label_size=20,label_color=(0, 255, 20)): + + for i, f in enumerate(info_lst): + + track_id=int(f[1]) + x1, y1, w,h = f[2:6] + x2,y2=x1+w,y1+h + cv2.rectangle(img, (int(x1), int(y1)), (int(x2), int(y2)), (255, 0, 0), 2) # 画大矩形 + if len(f)==9:#表示gt + act = str(int(f[6])) + cls_id = str(int(f[7])) + vision_ratio = str(np.round(float(f[8]), 3)) + label = 'track_id:{}\ncls_id:{}\nact:{}\nratio:{}'.format(str(track_id), cls_id, act, vision_ratio) + else: # 表示det 因为det有时候没有后三个-1,为10或7 + conf = str(np.round(float(f[6]), 3)) + label = 'track_id:{}\nconf:{}'.format(str(track_id), conf) + + img = chinese2img(img, label, coord=(int(x1 + 2), int(y1 + 6)),label_size=label_size,label_color=label_color) + + return img + + +def mot_vision_demo(): + + ''' + mot17数据集的det.txt与gt.txt信息画图显示 + root:需给到这样路径下-->D:\tracker\data\MOT17\train\MOT17-02-DPM + + + ''' + root='/data1/Datasets/Tracking/MOT/MOT_RGBT_all/images/test/' + video_name = 'LasHeR-004' + + results_dir_path = '' + path_img=os.path.join(root,video_name,'visible') + + out_root = '/home/zhuyabin/MOT/FairMOT/output' + + + out_dir=build_dir(os.path.join(out_root,video_name)) + # out_dir_det=build_dir(os.path.join(root, 'out_dir_draw', 'draw_det_imgs')) + + # path_det_txt=os.path.join(root,'det\det.txt') + path_result_txt = os.path.join(results_dir_path, video_name+'.txt') + + + # det_info=read_txt(path_det_txt) + result_info=read_txt(path_result_txt) + # det_val_lst=np.array([[float(v) for v in det.split(',')] for det in det_info ]) + gt_val_lst = np.array([[float(v) for v in gt.split(',')] for gt in result_info]) + + for img_name in tqdm(os.listdir(path_img)): + name_float=float(img_name[:-4]) + # det_lst=det_val_lst[det_val_lst[:,0]==name_float] + gt_lst=gt_val_lst[gt_val_lst[:,0]==name_float] + img=cv2.imread(os.path.join(path_img,img_name)) + img_gt=draw_mot(img.copy(),gt_lst) + # img_det=draw_mot(img.copy(),det_lst) + cv2.imwrite(os.path.join(out_dir_gt,img_name),img_gt) + # cv2.imwrite(os.path.join(out_dir_det, img_name), img_det) + + + # show_img(img_gt) + # show_img(img_det) + + + + + +if __name__ == '__main__': + mot_vision_demo() + + + + + + + + + + diff --git a/trackeval/tools/utils.py b/trackeval/tools/utils.py new file mode 100644 index 0000000..6389b75 --- /dev/null +++ b/trackeval/tools/utils.py @@ -0,0 +1,389 @@ +import os +import numpy as np +import pandas as pd +import matplotlib.pyplot as plt +import json +import cv2 + + +img_format=['.jpg','.png','.bmp','.PNG','.JPG'] + +def build_dir(out_dir): + if not os.path.exists(out_dir): + os.makedirs(out_dir,exist_ok=True) + return out_dir + + +def build_files(root): + ''' + :得到该路径下的所有文件 + ''' + files = [os.path.join(root, file) for file in os.listdir(root)] + files_true = [] + for file in files: + if not os.path.isfile(file): + files_true.append(file) + return files_true + + +def get_strfile(file_str,pos=-1): + ''' + 得到file_str / or \\ 的最后一个名称 + ''' + endstr_f_filestr = file_str.split('\\')[pos] if '\\' in file_str else file_str.split('/')[pos] + return endstr_f_filestr + +def get_cat_txt(code_txt_path): + code_name = [] + with open(code_txt_path) as f: + while True: + line = f.readline() + if not line: + break + if '\n' in line: + code_name.append(line[:-1]) + else: + code_name.append(line) + code_name=sorted(code_name) + return code_name + +def write_txt(text_lst,out_txt=None): + ''' + 每行内容为列表,将其写入text中 + ''' + out_dir =out_txt if out_txt is not None else 'classes.txt' + file_write_obj = open(out_dir, 'w') # 以写的方式打开文件,如果文件不存在,就会自动创建 + for text in text_lst: + file_write_obj.writelines(text) + file_write_obj.write('\n') + file_write_obj.close() + + return out_dir +def write_img(img,img_name,out_dir): + assert img_name[-4:] in img_format,'suffix must be {}'.format(img_format) + + out_dir=build_dir(out_dir) + cv2.imwrite(os.path.join(out_dir,img_name),img) + + + + + +def root2lst(read_path): + ''' + 将字符串路径拆成列表的字符串保存 + :param read_path: + :return: + ''' + root_slice = [] + for read_path in read_path.split('\\'): + root_slice.append(read_path) + root_lst = [] + for slice1 in root_slice: + for slice in slice1.split('/'): + root_lst.append(slice) + return root_lst + + +def lst2root(root_lst, exept_pos=None): + if exept_pos is None: + root_lst = root_lst + elif exept_pos == -1: + assert len(root_lst) > 1, 'root_lst count must >=2' + root_lst = root_lst[:-1] + else: + root_lst = root_lst[:exept_pos - 1] + root_lst[exept_pos:] + result = root_lst[0] + for root_str in root_lst[1:]: + result = result + '/' + root_str + return str(result) + +def read_txt(file_path): + with open(file_path, 'r') as f: + content = f.read().splitlines() + return content +def delete_empty_files(read_path): + ''' + :删除任意路径的空文件 + ''' + count_root = len(root2lst(read_path)) + files_path = build_files(read_path) + dict_info = {'del_file': [], 'not_del_file': []} + for filepath in files_path: + root_file_lst = root2lst(filepath) + N = count_root - len(root_file_lst) + if N < 0: + print('it is problem : ', filepath) + for i in range(N): + sub_files_path = lst2root(root_file_lst, exept_pos=-1) + try: + os.rmdir(sub_files_path) # 删除空文件夹 + dict_info['del_file'].append(sub_files_path) + except: + dict_info['not_del_file'].append(sub_files_path) + continue + print('[del empty info]', dict_info) + + +def get_pre_dir(root): + root_new = os.path.abspath(os.path.join(root, "..")) + return root_new + + +def get_file_root(root): + ''' + :return: 寻找root下面有文件夹的路径,输出所有文件夹绝对路径的列表 + ''' + files_lst = [root] + files_root = [] + + if build_files(root) == []: + files_root = files_lst + else: + while True: + if files_lst != []: + for path in files_lst: + files = build_files(path) + for file in files: + files_root.append(os.path.join(path, file)) + if build_files(files_root[0]) != []: + files_lst = files_root + files_root = [] + else: + files_lst = [] + else: + break + return files_root + + +def get_files_root(root): + ''' + :return: 寻找root下面有文件夹的路径,输出所有文件夹绝对路径的列表 + ''' + files_lst = [root] + result_lst = files_lst + if build_files(root) == []: + result_lst = files_lst + else: + is_while = True + files_all_path = [file for file in files_lst] + while is_while: + for file_root in files_lst: + F1 = build_files(file_root) + for F1 in F1: + files_all_path.append(F1) + is_while = False + # 排除主文件夹 + record = np.ones((len(files_all_path))) + for i, F3 in enumerate(files_all_path): + F3 = files_all_path[i] + for j, F4 in enumerate(files_all_path): + if F3 + '\\' in F4 or F3 + '/' in F4: + record[i] = 0 + break + # 将需要循环聚集 + files_lst = [] + for i, F3 in enumerate(files_all_path): + if record[i] == 1: + files_lst.append(F3) + # 判断是否有子文件夹 + for F4 in files_lst: + file_judge = build_files(F4) + if file_judge != []: + is_while = True + break + + result_lst = files_lst + + return result_lst + + +# excel相关处理 + +def read_excel(root, sheet_name=1): + ''' + :param root: excel 路径 + :param sheet_name: 指定excel的sheet + :return: 通过字典形式返回对应名称的列表 + ''' + df = pd.read_excel(root, sheet_name=sheet_name) + dict_excel={} + for key in df.keys(): + dict_excel[key]=df[key].values + return dict_excel + +def read_csv(root): + ''' + :param root: csv 路径 + :param sheet_name: 指定csv的sheet + :return: 通过字典形式返回对应名称的列表 + ''' + df = pd.read_csv(root) + dict_csv={} + for key in df.keys(): + dict_csv[key]=df[key].values + return dict_csv + + + +def chinese2img(img, str, coord=(0, 0),label_size=20,label_color=(255, 0, 0)): + # 将具有中文的字符打印到图上 + from PIL import Image, ImageDraw, ImageFont + cv2img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # cv2和PIL中颜色的hex码的储存顺序不同 + pilimg = Image.fromarray(cv2img) + + # PIL图片上打印汉字 + draw = ImageDraw.Draw(pilimg) # 图片上打印 + font = ImageFont.truetype("font/simsun.ttc", label_size, encoding="utf-8") + # font = ImageFont.truetype("./simhei.ttf", 20, encoding="utf-8") # 参数1:字体文件路径,参数2:字体大小 + draw.text(tuple(coord), str, label_color, font=font) # 参数1:打印坐标,参数2:文本,参数3:字体颜色,参数4:字体 + # PIL图片转cv2 图片 + cv2charimg = cv2.cvtColor(np.array(pilimg), cv2.COLOR_RGB2BGR) + return cv2charimg + + +def show_img(img): + plt.imshow(img) + plt.show() + + + + + +def draw_img(img, boxes, labels, scores=None,label_size=20,label_color=(255, 0, 0)): + for i, box in enumerate(boxes): + x1, y1, x2, y2 = box + cv2.rectangle(img, (int(x1), int(y1)), (int(x2), int(y2)), (255, 0, 0), 2) # 画大矩形 + if scores is not None: + label = str(labels[i]) + str(scores[i]) + else: + label = str(labels[i]) + + img = chinese2img(img, label, coord=(int(x1 + 5), int(y1 + 4)),label_size=label_size,label_color=label_color) + return img + + +def draw_bar_graph(num_dict, out_path): + ''' + :param num_dict: key为字符串,value为数字 + :param out_path: + ''' + + # 创建一个点数为 8 x 6 的窗口, 并设置分辨率为 80像素/每英寸 + plt.figure(figsize=(28, 20), dpi=100) + # 再创建一个规格为 1 x 1 的子图 + # plt.subplot(1, 1, 1) + # 柱子总数 + N = len(num_dict) + # 包含每个柱子对应值的序列 + values=[] + code_name=[] + for code in num_dict.keys(): + code_name.append(code) + values.append(num_dict[code]) + code_name=tuple(code_name) + values=tuple(values) + + # values = (56796, 42996, 24872, 13849, 8609, 5331, 1971, 554, 169, 26) + # 包含每个柱子下标的序列 + index = np.arange(N) + # 柱子的宽度 + width = 0.8 + # 绘制柱状图, 每根柱子的颜色为紫罗兰色 + p2 = plt.bar(index, values, width, label="num", color="#87CEFA") + # 设置横轴标签 + plt.xlabel('code') + # 设置纵轴标签 + plt.ylabel('number of code') + # 添加标题 + plt.title('Cluster Distribution of codes') + # 添加纵横轴的刻度 + plt.xticks(index, code_name) + + for a, b in zip(index, values): ##控制标签位置 + plt.text(a , b + 0.1, '%.1f' % b, ha='center', va='bottom', fontsize=14) + + + # plt.xticks(index, ( + # 'mentioned1cluster', 'mentioned2cluster', 'mentioned3cluster', 'mentioned4cluster', 'mentioned5cluster', + # 'mentioned6cluster', 'mentioned7cluster', 'mentioned8cluster', 'mentioned9cluster', 'mentioned10cluster')) + # plt.yticks(np.arange(0, 1400, 200)) + # 添加图例 + plt.legend(loc="upper right") + plt.savefig(os.path.join(out_path,"out_dir.png")) + plt.show() + + + + + + +def exclude_file(files_root,exclude_file_name): + # 在filses_root 文件中排除名字未exclude_file_name的文件 + files_root_new=[] + for file in files_root: + + if exclude_file_name not in file: + files_root_new.append(file) + return files_root_new + +def read_json(json_root): + with open(json_root,'r') as f: + json_info=json.load(f) + + return json_info + + + + + + +def write_csv(df,out_dir=None): + if out_dir is not None: + out_csv=build_dir(os.path.join(out_dir,'out_dir.csv')) + else: + out_csv='./out_dir.csv' + df.to_csv(out_csv) + + +def show_img(img): + img=np.array(img,dtype=np.uint8) + img_shape=img.shape + if len(img_shape)==2: + img=np.expand_dims(img,axis=-1).repeat(3,axis=-1) + plt.imshow(img) + plt.show() + + + + + + +def find_build_root(root,file_path,out_dir,is_build_dir=True): + # file_path路径前面字符串被out_dir替换 + out_path=file_path.replace(root,out_dir) + if is_build_dir: + out_path=build_dir(out_path) + return out_path + + + + + +if __name__ == '__main__': + root = r'D:\DATA\whtm\data_52902' + file_path=r'D:\DATA\whtm\data_52902\data1\52902_DRM1.xlsx' + out_dir=r'D:\DATA' + out=find_build_root(root, file_path, out_dir, is_build_dir=False) + print(out) + + + + + + + + + + + diff --git a/trackeval/utils.py b/trackeval/utils.py new file mode 100644 index 0000000..8c7c916 --- /dev/null +++ b/trackeval/utils.py @@ -0,0 +1,146 @@ + +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""" + 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 config: the config to update + :return: the updated config + """ + 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 code is""" + 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. + """ + 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""" + + 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""" + 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.""" + 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.""" + ...

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