├── .gitignore ├── LICENSE ├── MANIFEST.in ├── README.md ├── examples ├── quick_examples.ipynb └── quick_examples.py ├── got10k ├── __init__.py ├── datasets │ ├── __init__.py │ ├── dtb70.py │ ├── got10k.py │ ├── lasot.json │ ├── lasot.py │ ├── nfs.py │ ├── otb.py │ ├── tcolor128.py │ ├── trackingnet.py │ ├── uav123.json │ ├── uav123.py │ ├── vid.py │ └── vot.py ├── experiments │ ├── __init__.py │ ├── dtb70.py │ ├── got10k.py │ ├── lasot.py │ ├── nfs.py │ ├── otb.py │ ├── tcolor128.py │ ├── trackingnet.py │ ├── uav123.py │ └── vot.py ├── trackers │ ├── __init__.py │ └── identity_tracker.py └── utils │ ├── __init__.py │ ├── ioutils.py │ ├── metrics.py │ └── viz.py ├── requirements.txt ├── resources └── sample_batch_run.jpg ├── setup.cfg ├── setup.py └── tests ├── test_datasets.py ├── test_experiments.py ├── test_trackers.py └── test_utils.py /.gitignore: -------------------------------------------------------------------------------- 1 | .* 2 | *.pyc 3 | __pycache__/ 4 | data 5 | data/ 6 | cache/ 7 | results/ 8 | reports/ 9 | profiles/ 10 | venv/ 11 | build/ 12 | dist/ 13 | *.egg-info 14 | !.gitignore 15 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2018 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /MANIFEST.in: -------------------------------------------------------------------------------- 1 | include got10k/datasets/uav123.json 2 | include got10k/datasets/lasot.json 3 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # GOT-10k Python Toolkit 2 | 3 | > UPDATE:
4 | > All common tracking datasets (GOT-10k, OTB, VOT, UAV, TColor, DTB, NfS, LaSOT and TrackingNet) are supported.
5 | > Support VOT2019 (ST/LT/RGBD/RGBT) downloading.
6 | > Fix the randomness in ImageNet-VID ([issue #13](https://github.com/got-10k/toolkit/issues/13)). 7 | 8 | _Run experimenets over common tracking benchmarks (code from [siamfc](https://github.com/got-10k/siamfc/blob/master/test.py)):_ 9 | 10 | sample_batch_run

11 | 12 | This repository contains the official python toolkit for running experiments and evaluate performance on [GOT-10k](http://got-10k.aitestunion.com/) benchmark. The code is written in pure python and is compile-free. Although we support both python2 and python3, we recommend python3 for better performance. 13 | 14 | For convenience, the toolkit also provides unofficial implementation of dataset interfaces and tracking pipelines for [OTB (2013/2015)](http://cvlab.hanyang.ac.kr/tracker_benchmark/index.html), [VOT (2013~2018)](http://votchallenge.net), [DTB70](https://github.com/flyers/drone-tracking), [TColor128](http://www.dabi.temple.edu/~hbling/data/TColor-128/TColor-128.html), [NfS (30/240 fps)](http://ci2cv.net/nfs/index.html), [UAV (123/20L)](https://ivul.kaust.edu.sa/Pages/pub-benchmark-simulator-uav.aspx), [LaSOT](https://cis.temple.edu/lasot/) and [TrackingNet](https://tracking-net.org/) benchmarks. It also offers interfaces for [ILSVRC VID](https://image-net.org/challenges/LSVRC/2015/#vid) and [YouTube-BoundingBox](https://research.google.com/youtube-bb/) (comming soon!) datasets. 15 | 16 | [GOT-10k](http://got-10k.aitestunion.com/) is a large, high-diversity and one-shot database for training and evaluating generic purposed visual trackers. If you use the GOT-10k database or toolkits for a research publication, please consider citing: 17 | 18 | ```Bibtex 19 | @ARTICLE{8922619, 20 | author={Huang, Lianghua and Zhao, Xin and Huang, Kaiqi}, 21 | journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 22 | title={GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild}, 23 | year={2021}, 24 | volume={43}, 25 | number={5}, 26 | pages={1562-1577}, 27 | doi={10.1109/TPAMI.2019.2957464}} 28 | ``` 29 | 30 | \[[Project](http://got-10k.aitestunion.com/)\]\[[PDF](https://arxiv.org/abs/1810.11981)\]\[[Bibtex](http://got-10k.aitestunion.com/bibtex)\] 31 | 32 | ## Table of Contents 33 | 34 | * [Installation](#installation) 35 | * [Quick Start: A Concise Example](#quick-start-a-concise-example) 36 | * [Quick Start: Jupyter Notebook for Off-the-Shelf Usage](#quick-start-jupyter-notebook-for-off-the-shelf-usage) 37 | * [How to Define a Tracker?](#how-to-define-a-tracker) 38 | * [How to Run Experiments on GOT-10k?](#how-to-run-experiments-on-got-10k) 39 | * [How to Evaluate Performance?](#how-to-evaluate-performance) 40 | * [How to Plot Success Curves?](#how-to-plot-success-curves) 41 | * [How to Loop Over GOT-10k Dataset?](#how-to-loop-over-got-10k-dataset) 42 | * [Issues](#issues) 43 | * [Contributors](#contributors) 44 | 45 | ### Installation 46 | 47 | Install the toolkit using `pip` (recommended): 48 | 49 | ```bash 50 | pip install --upgrade got10k 51 | ``` 52 | 53 | Stay up-to-date: 54 | 55 | ```bash 56 | pip install --upgrade git+https://github.com/got-10k/toolkit.git@master 57 | ``` 58 | 59 | Or, alternatively, clone the repository and install dependencies: 60 | 61 | ``` 62 | git clone https://github.com/got-10k/toolkit.git 63 | cd toolkit 64 | pip install -r requirements.txt 65 | ``` 66 | 67 | Then directly copy the `got10k` folder to your workspace to use it. 68 | 69 | ### Quick Start: A Concise Example 70 | 71 | Here is a simple example on how to use the toolkit to define a tracker, run experiments on GOT-10k and evaluate performance. 72 | 73 | ```Python 74 | from got10k.trackers import Tracker 75 | from got10k.experiments import ExperimentGOT10k 76 | 77 | class IdentityTracker(Tracker): 78 | def __init__(self): 79 | super(IdentityTracker, self).__init__(name='IdentityTracker') 80 | 81 | def init(self, image, box): 82 | self.box = box 83 | 84 | def update(self, image): 85 | return self.box 86 | 87 | if __name__ == '__main__': 88 | # setup tracker 89 | tracker = IdentityTracker() 90 | 91 | # run experiments on GOT-10k (validation subset) 92 | experiment = ExperimentGOT10k('data/GOT-10k', subset='val') 93 | experiment.run(tracker, visualize=True) 94 | 95 | # report performance 96 | experiment.report([tracker.name]) 97 | ``` 98 | 99 | To run experiments on [OTB](http://cvlab.hanyang.ac.kr/tracker_benchmark/index.html), [VOT](http://votchallenge.net) or other benchmarks, simply change `ExperimentGOT10k`, e.g., to `ExperimentOTB` or `ExperimentVOT`, and `root_dir` to their corresponding paths for this purpose. 100 | 101 | ### Quick Start: Jupyter Notebook for Off-the-Shelf Usage 102 | 103 | Open [quick_examples.ipynb](https://github.com/got-10k/toolkit/tree/master/examples/quick_examples.ipynb) in [Jupyter Notebook](http://jupyter.org/) to see more examples on toolkit usage. 104 | 105 | ### How to Define a Tracker? 106 | 107 | To define a tracker using the toolkit, simply inherit and override `init` and `update` methods from the `Tracker` class. Here is a simple example: 108 | 109 | ```Python 110 | from got10k.trackers import Tracker 111 | 112 | class IdentityTracker(Tracker): 113 | def __init__(self): 114 | super(IdentityTracker, self).__init__( 115 | name='IdentityTracker', # tracker name 116 | is_deterministic=True # stochastic (False) or deterministic (True) 117 | ) 118 | 119 | def init(self, image, box): 120 | self.box = box 121 | 122 | def update(self, image): 123 | return self.box 124 | ``` 125 | 126 | ### How to Run Experiments on GOT-10k? 127 | 128 | Instantiate an `ExperimentGOT10k` object, and leave all experiment pipelines to its `run` method: 129 | 130 | ```Python 131 | from got10k.experiments import ExperimentGOT10k 132 | 133 | # ... tracker definition ... 134 | 135 | # instantiate a tracker 136 | tracker = IdentityTracker() 137 | 138 | # setup experiment (validation subset) 139 | experiment = ExperimentGOT10k( 140 | root_dir='data/GOT-10k', # GOT-10k's root directory 141 | subset='val', # 'train' | 'val' | 'test' 142 | result_dir='results', # where to store tracking results 143 | report_dir='reports' # where to store evaluation reports 144 | ) 145 | experiment.run(tracker, visualize=True) 146 | ``` 147 | 148 | The tracking results will be stored in `result_dir`. 149 | 150 | ### How to Evaluate Performance? 151 | 152 | Use the `report` method of `ExperimentGOT10k` for this purpose: 153 | 154 | ```Python 155 | # ... run experiments on GOT-10k ... 156 | 157 | # report tracking performance 158 | experiment.report([tracker.name]) 159 | ``` 160 | 161 | When evaluated on the __validation subset__, the scores and curves will be directly generated in `report_dir`. 162 | 163 | However, when evaluated on the __test subset__, since all groundtruths are withholded, you will have to submit your results to the [evaluation server](http://got-10k.aitestunion.com/submit_instructions) for evaluation. The `report` function will generate a `.zip` file which can be directly uploaded for submission. For more instructions, see [submission instruction](http://got-10k.aitestunion.com/submit_instructions). 164 | 165 | See public evaluation results on [GOT-10k's leaderboard](http://got-10k.aitestunion.com/leaderboard). 166 | 167 | ## How to Plot Success Curves? 168 | 169 | Assume that a list of all performance files (JSON files) are stored in `report_files`, here is an example showing how to plot success curves: 170 | 171 | ```Python 172 | from got10k.experiments import ExperimentGOT10k 173 | 174 | report_files = ['reports/GOT-10k/performance_25_entries.json'] 175 | tracker_names = ['SiamFCv2', 'GOTURN', 'CCOT', 'MDNet'] 176 | 177 | # setup experiment and plot curves 178 | experiment = ExperimentGOT10k('data/GOT-10k', subset='test') 179 | experiment.plot_curves(report_files, tracker_names) 180 | ``` 181 | 182 | The report file of 25 baseline entries can be downloaded from the [Downloads page](http://got-10k.aitestunion.com/downloads). You can also download single report file for each entry from the [Leaderboard page](http://got-10k.aitestunion.com/leaderboard). 183 | 184 | ### How to Loop Over GOT-10k Dataset? 185 | 186 | The `got10k.datasets.GOT10k` provides an iterable and indexable interface for GOT-10k's sequences. Here is an example: 187 | 188 | ```Python 189 | from PIL import Image 190 | from got10k.datasets import GOT10k 191 | from got10k.utils.viz import show_frame 192 | 193 | dataset = GOT10k(root_dir='data/GOT-10k', subset='train') 194 | 195 | # indexing 196 | img_file, anno = dataset[10] 197 | 198 | # for-loop 199 | for s, (img_files, anno) in enumerate(dataset): 200 | seq_name = dataset.seq_names[s] 201 | print('Sequence:', seq_name) 202 | 203 | # show all frames 204 | for f, img_file in enumerate(img_files): 205 | image = Image.open(img_file) 206 | show_frame(image, anno[f, :]) 207 | ``` 208 | 209 | To loop over `OTB` or `VOT` datasets, simply change `GOT10k` to `OTB` or `VOT` for this purpose. 210 | 211 | ### Issues 212 | 213 | Please report any problems or suggessions in the [Issues](https://github.com/got-10k/toolkit/issues) page. 214 | 215 | ### Contributors 216 | 217 | - [Lianghua Huang](https://github.com/huanglianghua) 218 | -------------------------------------------------------------------------------- /examples/quick_examples.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import, print_function 2 | 3 | import fire 4 | from PIL import Image 5 | 6 | from got10k.trackers import Tracker, IdentityTracker 7 | from got10k.experiments import ExperimentGOT10k 8 | from got10k.datasets import GOT10k 9 | from got10k.utils.viz import show_frame 10 | 11 | 12 | ROOT_DIR = 'data/GOT-10k' 13 | 14 | 15 | def example_track_val_set(): 16 | # setup tracker 17 | tracker = IdentityTracker() 18 | 19 | # run experiment on validation set 20 | experiment = ExperimentGOT10k( 21 | root_dir=ROOT_DIR, 22 | subset='val', 23 | result_dir='results', 24 | report_dir='reports') 25 | experiment.run(tracker, visualize=False) 26 | 27 | # report performance 28 | experiment.report([tracker.name]) 29 | 30 | 31 | def example_track_test_set(): 32 | # setup tracker 33 | tracker = IdentityTracker() 34 | 35 | # run experiment on test set 36 | experiment = ExperimentGOT10k( 37 | root_dir=ROOT_DIR, 38 | subset='test', 39 | result_dir='results', 40 | report_dir='reports') 41 | experiment.run(tracker, visualize=False) 42 | 43 | # a ".zip" file will be generated ready for submission 44 | # follow the guide to submit your results to 45 | # http://got-10k.aitestunion.com/ 46 | experiment.report([tracker.name]) 47 | 48 | 49 | def example_plot_curves(): 50 | # reports of 25 baseline entries can be downloaded from 51 | # http://got-10k.aitestunion.com/downloads 52 | report_files = [ 53 | 'reports/GOT-10k/performance_25_entries.json'] 54 | tracker_names = [ 55 | 'SiamFCv2', 'GOTURN', 'CCOT', 'MDNet'] 56 | 57 | # setup experiment and plot curves 58 | experiment = ExperimentGOT10k('data/GOT-10k', subset='test') 59 | experiment.plot_curves(report_files, tracker_names) 60 | 61 | 62 | def example_loop_dataset(): 63 | # setup dataset 64 | dataset = GOT10k(ROOT_DIR, subset='val') 65 | 66 | # loop over the complete dataset 67 | for s, (img_files, anno) in enumerate(dataset): 68 | seq_name = dataset.seq_names[s] 69 | print('Sequence:', seq_name) 70 | 71 | for f, img_file in enumerate(img_files): 72 | image = Image.open(img_file) 73 | box = anno[f, :] # (left, top, width, height) 74 | show_frame(image, box, colors='w') 75 | 76 | 77 | def example_show(): 78 | # setup experiment 79 | experiment = ExperimentGOT10k( 80 | root_dir=ROOT_DIR, 81 | subset='test', 82 | result_dir='results', 83 | report_dir='reports') 84 | 85 | # visualize tracking results 86 | tracker_names = [ 87 | 'SiamFCv2', 'GOTURN', 'CCOT', 'MDNet'] 88 | experiment.show(tracker_names) 89 | 90 | 91 | if __name__ == '__main__': 92 | # choose an example function to execute, e.g., 93 | # > python quick_examples example_loop_dataset 94 | fire.Fire() 95 | -------------------------------------------------------------------------------- /got10k/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/got-10k/toolkit/956e7286fdf209cbb125adac9a46376bd8297ffb/got10k/__init__.py -------------------------------------------------------------------------------- /got10k/datasets/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | 3 | from .got10k import GOT10k 4 | from .otb import OTB 5 | from .vot import VOT 6 | from .dtb70 import DTB70 7 | from .tcolor128 import TColor128 8 | from .uav123 import UAV123 9 | from .nfs import NfS 10 | from .lasot import LaSOT 11 | from .trackingnet import TrackingNet 12 | from .vid import ImageNetVID 13 | -------------------------------------------------------------------------------- /got10k/datasets/dtb70.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import, print_function 2 | 3 | import os 4 | import glob 5 | import numpy as np 6 | import six 7 | 8 | 9 | class DTB70(object): 10 | """`DTB70 `_ Dataset. 11 | 12 | Publication: 13 | ``Visual object tracking for unmanned aerial vehicles: A benchmark and new motion models``, 14 | Y. Wu, J. Lim and M.-H. Yang, IEEE TPAMI 2015. 15 | 16 | Args: 17 | root_dir (string): Root directory of dataset where sequence 18 | folders exist. 19 | """ 20 | def __init__(self, root_dir): 21 | super(DTB70, self).__init__() 22 | self.root_dir = root_dir 23 | self._check_integrity(root_dir) 24 | 25 | self.anno_files = sorted(glob.glob( 26 | os.path.join(root_dir, '*/groundtruth_rect.txt'))) 27 | self.seq_dirs = [os.path.dirname(f) for f in self.anno_files] 28 | self.seq_names = [os.path.basename(d) for d in self.seq_dirs] 29 | 30 | def __getitem__(self, index): 31 | r""" 32 | Args: 33 | index (integer or string): Index or name of a sequence. 34 | 35 | Returns: 36 | tuple: (img_files, anno), where ``img_files`` is a list of 37 | file names and ``anno`` is a N x 4 (rectangles) numpy array. 38 | """ 39 | if isinstance(index, six.string_types): 40 | if not index in self.seq_names: 41 | raise Exception('Sequence {} not found.'.format(index)) 42 | index = self.seq_names.index(index) 43 | 44 | img_files = sorted(glob.glob( 45 | os.path.join(self.seq_dirs[index], 'img/*.jpg'))) 46 | anno = np.loadtxt(self.anno_files[index], delimiter=',') 47 | assert len(img_files) == len(anno) 48 | assert anno.shape[1] == 4 49 | 50 | return img_files, anno 51 | 52 | def __len__(self): 53 | return len(self.seq_names) 54 | 55 | def _check_integrity(self, root_dir): 56 | seq_names = os.listdir(root_dir) 57 | seq_names = [n for n in seq_names if not n[0] == '.'] 58 | 59 | if os.path.isdir(root_dir) and len(seq_names) > 0: 60 | # check each sequence folder 61 | for seq_name in seq_names: 62 | seq_dir = os.path.join(root_dir, seq_name) 63 | if not os.path.isdir(seq_dir): 64 | print('Warning: sequence %s not exists.' % seq_name) 65 | else: 66 | # dataset not exists 67 | raise Exception('Dataset not found or corrupted.') 68 | -------------------------------------------------------------------------------- /got10k/datasets/got10k.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import, print_function 2 | 3 | import os 4 | import glob 5 | import numpy as np 6 | import six 7 | 8 | 9 | class GOT10k(object): 10 | r"""`GOT-10K `_ Dataset. 11 | 12 | Publication: 13 | ``GOT-10k: A Large High-Diversity Benchmark for Generic Object 14 | Tracking in the Wild``, L. Huang, X. Zhao and K. Huang, ArXiv 2018. 15 | 16 | Args: 17 | root_dir (string): Root directory of dataset where ``train``, 18 | ``val`` and ``test`` folders exist. 19 | subset (string, optional): Specify ``train``, ``val`` or ``test`` 20 | subset of GOT-10k. 21 | return_meta (string, optional): If True, returns ``meta`` 22 | of each sequence in ``__getitem__`` function, otherwise 23 | only returns ``img_files`` and ``anno``. 24 | list_file (string, optional): If provided, only read sequences 25 | specified by the file instead of all sequences in the subset. 26 | """ 27 | def __init__(self, root_dir, subset='test', return_meta=False, 28 | list_file=None, check_integrity=True): 29 | super(GOT10k, self).__init__() 30 | assert subset in ['train', 'val', 'test'], 'Unknown subset.' 31 | self.root_dir = root_dir 32 | self.subset = subset 33 | self.return_meta = False if subset == 'test' else return_meta 34 | 35 | if list_file is None: 36 | list_file = os.path.join(root_dir, subset, 'list.txt') 37 | if check_integrity: 38 | self._check_integrity(root_dir, subset, list_file) 39 | 40 | with open(list_file, 'r') as f: 41 | self.seq_names = f.read().strip().split('\n') 42 | self.seq_dirs = [os.path.join(root_dir, subset, s) 43 | for s in self.seq_names] 44 | self.anno_files = [os.path.join(d, 'groundtruth.txt') 45 | for d in self.seq_dirs] 46 | 47 | def __getitem__(self, index): 48 | r""" 49 | Args: 50 | index (integer or string): Index or name of a sequence. 51 | 52 | Returns: 53 | tuple: (img_files, anno) if ``return_meta`` is False, otherwise 54 | (img_files, anno, meta), where ``img_files`` is a list of 55 | file names, ``anno`` is a N x 4 (rectangles) numpy array, while 56 | ``meta`` is a dict contains meta information about the sequence. 57 | """ 58 | if isinstance(index, six.string_types): 59 | if not index in self.seq_names: 60 | raise Exception('Sequence {} not found.'.format(index)) 61 | index = self.seq_names.index(index) 62 | 63 | img_files = sorted(glob.glob(os.path.join( 64 | self.seq_dirs[index], '*.jpg'))) 65 | anno = np.loadtxt(self.anno_files[index], delimiter=',') 66 | 67 | if self.subset == 'test' and anno.ndim == 1: 68 | assert len(anno) == 4 69 | anno = anno[np.newaxis, :] 70 | else: 71 | assert len(img_files) == len(anno) 72 | 73 | if self.return_meta: 74 | meta = self._fetch_meta(self.seq_dirs[index]) 75 | return img_files, anno, meta 76 | else: 77 | return img_files, anno 78 | 79 | def __len__(self): 80 | return len(self.seq_names) 81 | 82 | def _check_integrity(self, root_dir, subset, list_file=None): 83 | assert subset in ['train', 'val', 'test'] 84 | if list_file is None: 85 | list_file = os.path.join(root_dir, subset, 'list.txt') 86 | 87 | if os.path.isfile(list_file): 88 | with open(list_file, 'r') as f: 89 | seq_names = f.read().strip().split('\n') 90 | 91 | # check each sequence folder 92 | for seq_name in seq_names: 93 | seq_dir = os.path.join(root_dir, subset, seq_name) 94 | if not os.path.isdir(seq_dir): 95 | print('Warning: sequence %s not exists.' % seq_name) 96 | else: 97 | # dataset not exists 98 | raise Exception('Dataset not found or corrupted.') 99 | 100 | def _fetch_meta(self, seq_dir): 101 | # meta information 102 | meta_file = os.path.join(seq_dir, 'meta_info.ini') 103 | with open(meta_file) as f: 104 | meta = f.read().strip().split('\n')[1:] 105 | meta = [line.split(': ') for line in meta] 106 | meta = {line[0]: line[1] for line in meta} 107 | 108 | # attributes 109 | attributes = ['cover', 'absence', 'cut_by_image'] 110 | for att in attributes: 111 | meta[att] = np.loadtxt(os.path.join(seq_dir, att + '.label')) 112 | 113 | return meta 114 | -------------------------------------------------------------------------------- /got10k/datasets/lasot.json: -------------------------------------------------------------------------------- 1 | { 2 | "train": [ 3 | "airplane-10", 4 | "airplane-11", 5 | "airplane-12", 6 | "airplane-14", 7 | "airplane-16", 8 | "airplane-17", 9 | "airplane-18", 10 | "airplane-19", 11 | "airplane-2", 12 | "airplane-20", 13 | "airplane-3", 14 | "airplane-4", 15 | "airplane-5", 16 | "airplane-6", 17 | "airplane-7", 18 | "airplane-8", 19 | "basketball-10", 20 | "basketball-12", 21 | "basketball-13", 22 | "basketball-14", 23 | "basketball-15", 24 | "basketball-16", 25 | "basketball-17", 26 | "basketball-18", 27 | "basketball-19", 28 | "basketball-2", 29 | "basketball-20", 30 | "basketball-3", 31 | "basketball-4", 32 | "basketball-5", 33 | "basketball-8", 34 | "basketball-9", 35 | "bear-1", 36 | "bear-10", 37 | "bear-11", 38 | "bear-12", 39 | "bear-13", 40 | "bear-14", 41 | "bear-15", 42 | "bear-16", 43 | "bear-18", 44 | "bear-19", 45 | "bear-20", 46 | "bear-3", 47 | "bear-5", 48 | "bear-7", 49 | "bear-8", 50 | "bear-9", 51 | "bicycle-1", 52 | "bicycle-10", 53 | "bicycle-11", 54 | "bicycle-12", 55 | "bicycle-13", 56 | "bicycle-14", 57 | "bicycle-15", 58 | "bicycle-16", 59 | "bicycle-17", 60 | "bicycle-19", 61 | "bicycle-20", 62 | "bicycle-3", 63 | "bicycle-4", 64 | "bicycle-5", 65 | "bicycle-6", 66 | "bicycle-8", 67 | "bird-1", 68 | "bird-10", 69 | "bird-11", 70 | "bird-12", 71 | "bird-13", 72 | "bird-14", 73 | "bird-16", 74 | "bird-18", 75 | "bird-19", 76 | "bird-20", 77 | "bird-4", 78 | "bird-5", 79 | "bird-6", 80 | "bird-7", 81 | "bird-8", 82 | "bird-9", 83 | "boat-1", 84 | "boat-10", 85 | "boat-11", 86 | "boat-13", 87 | "boat-14", 88 | "boat-15", 89 | "boat-16", 90 | "boat-18", 91 | "boat-19", 92 | "boat-2", 93 | "boat-20", 94 | "boat-5", 95 | "boat-6", 96 | "boat-7", 97 | "boat-8", 98 | "boat-9", 99 | "book-1", 100 | "book-12", 101 | "book-13", 102 | "book-14", 103 | "book-15", 104 | "book-16", 105 | "book-17", 106 | "book-18", 107 | "book-2", 108 | "book-20", 109 | "book-4", 110 | "book-5", 111 | "book-6", 112 | "book-7", 113 | "book-8", 114 | "book-9", 115 | "bottle-10", 116 | "bottle-11", 117 | "bottle-13", 118 | "bottle-15", 119 | "bottle-16", 120 | "bottle-17", 121 | "bottle-19", 122 | "bottle-2", 123 | "bottle-20", 124 | "bottle-3", 125 | "bottle-4", 126 | "bottle-5", 127 | "bottle-6", 128 | "bottle-7", 129 | "bottle-8", 130 | "bottle-9", 131 | "bus-1", 132 | "bus-10", 133 | "bus-11", 134 | "bus-12", 135 | "bus-13", 136 | "bus-14", 137 | "bus-15", 138 | "bus-16", 139 | "bus-18", 140 | "bus-20", 141 | "bus-3", 142 | "bus-4", 143 | "bus-6", 144 | "bus-7", 145 | "bus-8", 146 | "bus-9", 147 | "car-1", 148 | "car-10", 149 | "car-11", 150 | "car-12", 151 | "car-13", 152 | "car-14", 153 | "car-15", 154 | "car-16", 155 | "car-18", 156 | "car-19", 157 | "car-20", 158 | "car-3", 159 | "car-4", 160 | "car-5", 161 | "car-7", 162 | "car-8", 163 | "cat-10", 164 | "cat-11", 165 | "cat-12", 166 | "cat-13", 167 | "cat-14", 168 | "cat-15", 169 | "cat-16", 170 | "cat-17", 171 | "cat-19", 172 | "cat-2", 173 | "cat-4", 174 | "cat-5", 175 | "cat-6", 176 | "cat-7", 177 | "cat-8", 178 | "cat-9", 179 | "cattle-1", 180 | "cattle-10", 181 | "cattle-11", 182 | "cattle-14", 183 | "cattle-15", 184 | "cattle-16", 185 | "cattle-17", 186 | "cattle-18", 187 | "cattle-19", 188 | "cattle-20", 189 | "cattle-3", 190 | "cattle-4", 191 | "cattle-5", 192 | "cattle-6", 193 | "cattle-8", 194 | "cattle-9", 195 | "chameleon-1", 196 | "chameleon-10", 197 | "chameleon-12", 198 | "chameleon-13", 199 | "chameleon-14", 200 | "chameleon-15", 201 | "chameleon-16", 202 | "chameleon-17", 203 | "chameleon-18", 204 | "chameleon-19", 205 | "chameleon-2", 206 | "chameleon-4", 207 | "chameleon-5", 208 | "chameleon-7", 209 | "chameleon-8", 210 | "chameleon-9", 211 | "coin-1", 212 | "coin-10", 213 | "coin-11", 214 | "coin-12", 215 | "coin-13", 216 | "coin-14", 217 | "coin-15", 218 | "coin-16", 219 | "coin-17", 220 | "coin-19", 221 | "coin-2", 222 | "coin-20", 223 | "coin-4", 224 | "coin-5", 225 | "coin-8", 226 | "coin-9", 227 | "crab-1", 228 | "crab-10", 229 | "crab-11", 230 | "crab-13", 231 | "crab-14", 232 | "crab-15", 233 | "crab-16", 234 | "crab-17", 235 | "crab-19", 236 | "crab-2", 237 | "crab-20", 238 | "crab-4", 239 | "crab-5", 240 | "crab-7", 241 | "crab-8", 242 | "crab-9", 243 | "crocodile-1", 244 | "crocodile-11", 245 | "crocodile-12", 246 | "crocodile-13", 247 | "crocodile-15", 248 | "crocodile-16", 249 | "crocodile-17", 250 | "crocodile-18", 251 | "crocodile-19", 252 | "crocodile-2", 253 | "crocodile-20", 254 | "crocodile-5", 255 | "crocodile-6", 256 | "crocodile-7", 257 | "crocodile-8", 258 | "crocodile-9", 259 | "cup-10", 260 | "cup-11", 261 | "cup-12", 262 | "cup-13", 263 | "cup-14", 264 | "cup-15", 265 | "cup-16", 266 | "cup-18", 267 | "cup-19", 268 | "cup-2", 269 | "cup-20", 270 | "cup-3", 271 | "cup-5", 272 | "cup-6", 273 | "cup-8", 274 | "cup-9", 275 | "deer-1", 276 | "deer-11", 277 | "deer-12", 278 | "deer-13", 279 | "deer-15", 280 | "deer-16", 281 | "deer-17", 282 | "deer-18", 283 | "deer-19", 284 | "deer-2", 285 | "deer-20", 286 | "deer-3", 287 | "deer-5", 288 | "deer-6", 289 | "deer-7", 290 | "deer-9", 291 | "dog-10", 292 | "dog-11", 293 | "dog-12", 294 | "dog-13", 295 | "dog-14", 296 | "dog-16", 297 | "dog-17", 298 | "dog-18", 299 | "dog-2", 300 | "dog-20", 301 | "dog-3", 302 | "dog-4", 303 | "dog-5", 304 | "dog-6", 305 | "dog-8", 306 | "dog-9", 307 | "drone-1", 308 | "drone-10", 309 | "drone-11", 310 | "drone-12", 311 | "drone-14", 312 | "drone-16", 313 | "drone-17", 314 | "drone-18", 315 | "drone-19", 316 | "drone-20", 317 | "drone-3", 318 | "drone-4", 319 | "drone-5", 320 | "drone-6", 321 | "drone-8", 322 | "drone-9", 323 | "electricfan-11", 324 | "electricfan-12", 325 | "electricfan-13", 326 | "electricfan-14", 327 | "electricfan-15", 328 | "electricfan-16", 329 | "electricfan-17", 330 | "electricfan-19", 331 | "electricfan-2", 332 | "electricfan-3", 333 | "electricfan-4", 334 | "electricfan-5", 335 | "electricfan-6", 336 | "electricfan-7", 337 | "electricfan-8", 338 | "electricfan-9", 339 | "elephant-10", 340 | "elephant-11", 341 | "elephant-13", 342 | "elephant-14", 343 | "elephant-15", 344 | "elephant-17", 345 | "elephant-19", 346 | "elephant-2", 347 | "elephant-20", 348 | "elephant-3", 349 | "elephant-4", 350 | "elephant-5", 351 | "elephant-6", 352 | "elephant-7", 353 | "elephant-8", 354 | "elephant-9", 355 | "flag-1", 356 | "flag-10", 357 | "flag-11", 358 | "flag-12", 359 | "flag-13", 360 | "flag-14", 361 | "flag-15", 362 | "flag-16", 363 | "flag-17", 364 | "flag-18", 365 | "flag-19", 366 | "flag-20", 367 | "flag-4", 368 | "flag-6", 369 | "flag-7", 370 | "flag-8", 371 | "fox-1", 372 | "fox-10", 373 | "fox-11", 374 | "fox-12", 375 | "fox-13", 376 | "fox-14", 377 | "fox-15", 378 | "fox-16", 379 | "fox-17", 380 | "fox-18", 381 | "fox-19", 382 | "fox-4", 383 | "fox-6", 384 | "fox-7", 385 | "fox-8", 386 | "fox-9", 387 | "frog-1", 388 | "frog-10", 389 | "frog-11", 390 | "frog-12", 391 | "frog-13", 392 | "frog-14", 393 | "frog-15", 394 | "frog-16", 395 | "frog-17", 396 | "frog-18", 397 | "frog-19", 398 | "frog-2", 399 | "frog-5", 400 | "frog-6", 401 | "frog-7", 402 | "frog-8", 403 | "gametarget-10", 404 | "gametarget-11", 405 | "gametarget-12", 406 | "gametarget-14", 407 | "gametarget-15", 408 | "gametarget-16", 409 | "gametarget-17", 410 | "gametarget-18", 411 | "gametarget-19", 412 | "gametarget-20", 413 | "gametarget-3", 414 | "gametarget-4", 415 | "gametarget-5", 416 | "gametarget-6", 417 | "gametarget-8", 418 | "gametarget-9", 419 | "gecko-10", 420 | "gecko-11", 421 | "gecko-12", 422 | "gecko-13", 423 | "gecko-14", 424 | "gecko-15", 425 | "gecko-17", 426 | "gecko-18", 427 | "gecko-2", 428 | "gecko-20", 429 | "gecko-3", 430 | "gecko-4", 431 | "gecko-6", 432 | "gecko-7", 433 | "gecko-8", 434 | "gecko-9", 435 | "giraffe-1", 436 | "giraffe-11", 437 | "giraffe-12", 438 | "giraffe-14", 439 | "giraffe-16", 440 | "giraffe-17", 441 | "giraffe-18", 442 | "giraffe-19", 443 | "giraffe-20", 444 | "giraffe-3", 445 | "giraffe-4", 446 | "giraffe-5", 447 | "giraffe-6", 448 | "giraffe-7", 449 | "giraffe-8", 450 | "giraffe-9", 451 | "goldfish-1", 452 | "goldfish-11", 453 | "goldfish-12", 454 | "goldfish-13", 455 | "goldfish-14", 456 | "goldfish-15", 457 | "goldfish-16", 458 | "goldfish-17", 459 | "goldfish-18", 460 | "goldfish-19", 461 | "goldfish-2", 462 | "goldfish-20", 463 | "goldfish-4", 464 | "goldfish-5", 465 | "goldfish-6", 466 | "goldfish-9", 467 | "gorilla-1", 468 | "gorilla-10", 469 | "gorilla-11", 470 | "gorilla-12", 471 | "gorilla-14", 472 | "gorilla-15", 473 | "gorilla-16", 474 | "gorilla-17", 475 | "gorilla-18", 476 | "gorilla-19", 477 | "gorilla-2", 478 | "gorilla-20", 479 | "gorilla-3", 480 | "gorilla-5", 481 | "gorilla-7", 482 | "gorilla-8", 483 | "guitar-1", 484 | "guitar-11", 485 | "guitar-12", 486 | "guitar-13", 487 | "guitar-14", 488 | "guitar-15", 489 | "guitar-17", 490 | "guitar-18", 491 | "guitar-19", 492 | "guitar-2", 493 | "guitar-20", 494 | "guitar-4", 495 | "guitar-5", 496 | "guitar-6", 497 | "guitar-7", 498 | "guitar-9", 499 | "hand-1", 500 | "hand-10", 501 | "hand-11", 502 | "hand-12", 503 | "hand-13", 504 | "hand-14", 505 | "hand-15", 506 | "hand-17", 507 | "hand-18", 508 | "hand-19", 509 | "hand-20", 510 | "hand-4", 511 | "hand-5", 512 | "hand-6", 513 | "hand-7", 514 | "hand-8", 515 | "hat-10", 516 | "hat-11", 517 | "hat-12", 518 | "hat-13", 519 | "hat-14", 520 | "hat-15", 521 | "hat-16", 522 | "hat-17", 523 | "hat-19", 524 | "hat-20", 525 | "hat-3", 526 | "hat-4", 527 | "hat-6", 528 | "hat-7", 529 | "hat-8", 530 | "hat-9", 531 | "helmet-1", 532 | "helmet-10", 533 | "helmet-12", 534 | "helmet-14", 535 | "helmet-15", 536 | "helmet-16", 537 | "helmet-17", 538 | "helmet-18", 539 | "helmet-2", 540 | "helmet-20", 541 | "helmet-3", 542 | "helmet-4", 543 | "helmet-6", 544 | "helmet-7", 545 | "helmet-8", 546 | "helmet-9", 547 | "hippo-10", 548 | "hippo-11", 549 | "hippo-12", 550 | "hippo-13", 551 | "hippo-14", 552 | "hippo-15", 553 | "hippo-16", 554 | "hippo-17", 555 | "hippo-18", 556 | "hippo-19", 557 | "hippo-2", 558 | "hippo-3", 559 | "hippo-4", 560 | "hippo-5", 561 | "hippo-6", 562 | "hippo-8", 563 | "horse-10", 564 | "horse-11", 565 | "horse-13", 566 | "horse-14", 567 | "horse-16", 568 | "horse-17", 569 | "horse-18", 570 | "horse-19", 571 | "horse-2", 572 | "horse-20", 573 | "horse-3", 574 | "horse-5", 575 | "horse-6", 576 | "horse-7", 577 | "horse-8", 578 | "horse-9", 579 | "kangaroo-1", 580 | "kangaroo-10", 581 | "kangaroo-12", 582 | "kangaroo-13", 583 | "kangaroo-15", 584 | "kangaroo-16", 585 | "kangaroo-17", 586 | "kangaroo-18", 587 | "kangaroo-19", 588 | "kangaroo-20", 589 | "kangaroo-3", 590 | "kangaroo-4", 591 | "kangaroo-6", 592 | "kangaroo-7", 593 | "kangaroo-8", 594 | "kangaroo-9", 595 | "kite-1", 596 | "kite-11", 597 | "kite-12", 598 | "kite-13", 599 | "kite-14", 600 | "kite-16", 601 | "kite-17", 602 | "kite-18", 603 | "kite-19", 604 | "kite-2", 605 | "kite-20", 606 | "kite-3", 607 | "kite-5", 608 | "kite-7", 609 | "kite-8", 610 | "kite-9", 611 | "leopard-10", 612 | "leopard-11", 613 | "leopard-12", 614 | "leopard-13", 615 | "leopard-14", 616 | "leopard-15", 617 | "leopard-17", 618 | "leopard-18", 619 | "leopard-19", 620 | "leopard-2", 621 | "leopard-3", 622 | "leopard-4", 623 | "leopard-5", 624 | "leopard-6", 625 | "leopard-8", 626 | "leopard-9", 627 | "licenseplate-1", 628 | "licenseplate-10", 629 | "licenseplate-11", 630 | "licenseplate-14", 631 | "licenseplate-16", 632 | "licenseplate-17", 633 | "licenseplate-18", 634 | "licenseplate-19", 635 | "licenseplate-2", 636 | "licenseplate-20", 637 | "licenseplate-3", 638 | "licenseplate-4", 639 | "licenseplate-5", 640 | "licenseplate-7", 641 | "licenseplate-8", 642 | "licenseplate-9", 643 | "lion-10", 644 | "lion-11", 645 | "lion-13", 646 | "lion-14", 647 | "lion-15", 648 | "lion-16", 649 | "lion-17", 650 | "lion-18", 651 | "lion-19", 652 | "lion-2", 653 | "lion-3", 654 | "lion-4", 655 | "lion-6", 656 | "lion-7", 657 | "lion-8", 658 | "lion-9", 659 | "lizard-10", 660 | "lizard-11", 661 | "lizard-12", 662 | "lizard-14", 663 | "lizard-15", 664 | "lizard-16", 665 | "lizard-17", 666 | "lizard-18", 667 | "lizard-19", 668 | "lizard-2", 669 | "lizard-20", 670 | "lizard-4", 671 | "lizard-5", 672 | "lizard-7", 673 | "lizard-8", 674 | "lizard-9", 675 | "microphone-1", 676 | "microphone-10", 677 | "microphone-11", 678 | "microphone-12", 679 | "microphone-13", 680 | "microphone-15", 681 | "microphone-17", 682 | "microphone-18", 683 | "microphone-19", 684 | "microphone-20", 685 | "microphone-3", 686 | "microphone-4", 687 | "microphone-5", 688 | "microphone-7", 689 | "microphone-8", 690 | "microphone-9", 691 | "monkey-1", 692 | "monkey-10", 693 | "monkey-11", 694 | "monkey-12", 695 | "monkey-13", 696 | "monkey-14", 697 | "monkey-15", 698 | "monkey-16", 699 | "monkey-18", 700 | "monkey-19", 701 | "monkey-2", 702 | "monkey-20", 703 | "monkey-5", 704 | "monkey-6", 705 | "monkey-7", 706 | "monkey-8", 707 | "motorcycle-10", 708 | "motorcycle-11", 709 | "motorcycle-12", 710 | "motorcycle-13", 711 | "motorcycle-14", 712 | "motorcycle-15", 713 | "motorcycle-16", 714 | "motorcycle-17", 715 | "motorcycle-19", 716 | "motorcycle-2", 717 | "motorcycle-20", 718 | "motorcycle-4", 719 | "motorcycle-5", 720 | "motorcycle-6", 721 | "motorcycle-7", 722 | "motorcycle-8", 723 | "mouse-10", 724 | "mouse-11", 725 | "mouse-12", 726 | "mouse-13", 727 | "mouse-14", 728 | "mouse-15", 729 | "mouse-16", 730 | "mouse-18", 731 | "mouse-19", 732 | "mouse-2", 733 | "mouse-20", 734 | "mouse-3", 735 | "mouse-4", 736 | "mouse-5", 737 | "mouse-6", 738 | "mouse-7", 739 | "person-11", 740 | "person-13", 741 | "person-14", 742 | "person-15", 743 | "person-16", 744 | "person-17", 745 | "person-18", 746 | "person-19", 747 | "person-2", 748 | "person-20", 749 | "person-3", 750 | "person-4", 751 | "person-6", 752 | "person-7", 753 | "person-8", 754 | "person-9", 755 | "pig-1", 756 | "pig-11", 757 | "pig-12", 758 | "pig-14", 759 | "pig-15", 760 | "pig-16", 761 | "pig-17", 762 | "pig-19", 763 | "pig-20", 764 | "pig-3", 765 | "pig-4", 766 | "pig-5", 767 | "pig-6", 768 | "pig-7", 769 | "pig-8", 770 | "pig-9", 771 | "pool-1", 772 | "pool-10", 773 | "pool-11", 774 | "pool-13", 775 | "pool-14", 776 | "pool-16", 777 | "pool-17", 778 | "pool-18", 779 | "pool-19", 780 | "pool-2", 781 | "pool-20", 782 | "pool-4", 783 | "pool-5", 784 | "pool-6", 785 | "pool-8", 786 | "pool-9", 787 | "rabbit-1", 788 | "rabbit-11", 789 | "rabbit-12", 790 | "rabbit-14", 791 | "rabbit-15", 792 | "rabbit-16", 793 | "rabbit-18", 794 | "rabbit-2", 795 | "rabbit-20", 796 | "rabbit-3", 797 | "rabbit-4", 798 | "rabbit-5", 799 | "rabbit-6", 800 | "rabbit-7", 801 | "rabbit-8", 802 | "rabbit-9", 803 | "racing-1", 804 | "racing-11", 805 | "racing-12", 806 | "racing-13", 807 | "racing-14", 808 | "racing-17", 809 | "racing-18", 810 | "racing-19", 811 | "racing-2", 812 | "racing-3", 813 | "racing-4", 814 | "racing-5", 815 | "racing-6", 816 | "racing-7", 817 | "racing-8", 818 | "racing-9", 819 | "robot-10", 820 | "robot-11", 821 | "robot-12", 822 | "robot-13", 823 | "robot-14", 824 | "robot-15", 825 | "robot-16", 826 | "robot-17", 827 | "robot-18", 828 | "robot-2", 829 | "robot-20", 830 | "robot-3", 831 | "robot-4", 832 | "robot-6", 833 | "robot-7", 834 | "robot-9", 835 | "rubicCube-10", 836 | "rubicCube-11", 837 | "rubicCube-12", 838 | "rubicCube-13", 839 | "rubicCube-15", 840 | "rubicCube-16", 841 | "rubicCube-17", 842 | "rubicCube-18", 843 | "rubicCube-2", 844 | "rubicCube-20", 845 | "rubicCube-3", 846 | "rubicCube-4", 847 | "rubicCube-5", 848 | "rubicCube-7", 849 | "rubicCube-8", 850 | "rubicCube-9", 851 | "sepia-1", 852 | "sepia-10", 853 | "sepia-11", 854 | "sepia-12", 855 | "sepia-14", 856 | "sepia-15", 857 | "sepia-17", 858 | "sepia-18", 859 | "sepia-19", 860 | "sepia-2", 861 | "sepia-20", 862 | "sepia-3", 863 | "sepia-4", 864 | "sepia-5", 865 | "sepia-7", 866 | "sepia-9", 867 | "shark-1", 868 | "shark-10", 869 | "shark-11", 870 | "shark-12", 871 | "shark-13", 872 | "shark-14", 873 | "shark-15", 874 | "shark-16", 875 | "shark-17", 876 | "shark-18", 877 | "shark-19", 878 | "shark-20", 879 | "shark-4", 880 | "shark-7", 881 | "shark-8", 882 | "shark-9", 883 | "sheep-1", 884 | "sheep-10", 885 | "sheep-11", 886 | "sheep-12", 887 | "sheep-13", 888 | "sheep-14", 889 | "sheep-15", 890 | "sheep-16", 891 | "sheep-17", 892 | "sheep-18", 893 | "sheep-19", 894 | "sheep-2", 895 | "sheep-20", 896 | "sheep-4", 897 | "sheep-6", 898 | "sheep-8", 899 | "skateboard-1", 900 | "skateboard-10", 901 | "skateboard-11", 902 | "skateboard-12", 903 | "skateboard-13", 904 | "skateboard-14", 905 | "skateboard-15", 906 | "skateboard-17", 907 | "skateboard-18", 908 | "skateboard-2", 909 | "skateboard-20", 910 | "skateboard-4", 911 | "skateboard-5", 912 | "skateboard-6", 913 | "skateboard-7", 914 | "skateboard-9", 915 | "spider-1", 916 | "spider-10", 917 | "spider-11", 918 | "spider-12", 919 | "spider-13", 920 | "spider-15", 921 | "spider-17", 922 | "spider-19", 923 | "spider-2", 924 | "spider-3", 925 | "spider-4", 926 | "spider-5", 927 | "spider-6", 928 | "spider-7", 929 | "spider-8", 930 | "spider-9", 931 | "squirrel-1", 932 | "squirrel-10", 933 | "squirrel-12", 934 | "squirrel-14", 935 | "squirrel-15", 936 | "squirrel-16", 937 | "squirrel-17", 938 | "squirrel-18", 939 | "squirrel-2", 940 | "squirrel-20", 941 | "squirrel-3", 942 | "squirrel-4", 943 | "squirrel-5", 944 | "squirrel-6", 945 | "squirrel-7", 946 | "squirrel-9", 947 | "surfboard-1", 948 | "surfboard-10", 949 | "surfboard-11", 950 | "surfboard-13", 951 | "surfboard-14", 952 | "surfboard-15", 953 | "surfboard-16", 954 | "surfboard-17", 955 | "surfboard-18", 956 | "surfboard-19", 957 | "surfboard-2", 958 | "surfboard-20", 959 | "surfboard-3", 960 | "surfboard-6", 961 | "surfboard-7", 962 | "surfboard-9", 963 | "swing-1", 964 | "swing-11", 965 | "swing-12", 966 | "swing-13", 967 | "swing-15", 968 | "swing-16", 969 | "swing-18", 970 | "swing-19", 971 | "swing-2", 972 | "swing-3", 973 | "swing-4", 974 | "swing-5", 975 | "swing-6", 976 | "swing-7", 977 | "swing-8", 978 | "swing-9", 979 | "tank-1", 980 | "tank-10", 981 | "tank-11", 982 | "tank-12", 983 | "tank-13", 984 | "tank-15", 985 | "tank-17", 986 | "tank-18", 987 | "tank-19", 988 | "tank-2", 989 | "tank-20", 990 | "tank-3", 991 | "tank-4", 992 | "tank-5", 993 | "tank-7", 994 | "tank-8", 995 | "tiger-1", 996 | "tiger-10", 997 | "tiger-11", 998 | "tiger-13", 999 | "tiger-14", 1000 | "tiger-15", 1001 | "tiger-16", 1002 | "tiger-17", 1003 | "tiger-19", 1004 | "tiger-2", 1005 | "tiger-20", 1006 | "tiger-3", 1007 | "tiger-5", 1008 | "tiger-7", 1009 | "tiger-8", 1010 | "tiger-9", 1011 | "train-10", 1012 | "train-12", 1013 | "train-13", 1014 | "train-14", 1015 | "train-15", 1016 | "train-16", 1017 | "train-17", 1018 | "train-18", 1019 | "train-19", 1020 | "train-2", 1021 | "train-3", 1022 | "train-4", 1023 | "train-5", 1024 | "train-6", 1025 | "train-8", 1026 | "train-9", 1027 | "truck-1", 1028 | "truck-10", 1029 | "truck-11", 1030 | "truck-12", 1031 | "truck-13", 1032 | "truck-14", 1033 | "truck-15", 1034 | "truck-17", 1035 | "truck-18", 1036 | "truck-19", 1037 | "truck-2", 1038 | "truck-20", 1039 | "truck-4", 1040 | "truck-5", 1041 | "truck-8", 1042 | "truck-9", 1043 | "turtle-1", 1044 | "turtle-10", 1045 | "turtle-11", 1046 | "turtle-12", 1047 | "turtle-13", 1048 | "turtle-14", 1049 | "turtle-15", 1050 | "turtle-17", 1051 | "turtle-18", 1052 | "turtle-19", 1053 | "turtle-2", 1054 | "turtle-20", 1055 | "turtle-3", 1056 | "turtle-4", 1057 | "turtle-6", 1058 | "turtle-7", 1059 | "umbrella-1", 1060 | "umbrella-10", 1061 | "umbrella-11", 1062 | "umbrella-12", 1063 | "umbrella-13", 1064 | "umbrella-14", 1065 | "umbrella-15", 1066 | "umbrella-16", 1067 | "umbrella-18", 1068 | "umbrella-20", 1069 | "umbrella-3", 1070 | "umbrella-4", 1071 | "umbrella-5", 1072 | "umbrella-6", 1073 | "umbrella-7", 1074 | "umbrella-8", 1075 | "volleyball-10", 1076 | "volleyball-11", 1077 | "volleyball-12", 1078 | "volleyball-14", 1079 | "volleyball-15", 1080 | "volleyball-16", 1081 | "volleyball-17", 1082 | "volleyball-2", 1083 | "volleyball-20", 1084 | "volleyball-3", 1085 | "volleyball-4", 1086 | "volleyball-5", 1087 | "volleyball-6", 1088 | "volleyball-7", 1089 | "volleyball-8", 1090 | "volleyball-9", 1091 | "yoyo-1", 1092 | "yoyo-10", 1093 | "yoyo-11", 1094 | "yoyo-12", 1095 | "yoyo-13", 1096 | "yoyo-14", 1097 | "yoyo-16", 1098 | "yoyo-18", 1099 | "yoyo-2", 1100 | "yoyo-20", 1101 | "yoyo-3", 1102 | "yoyo-4", 1103 | "yoyo-5", 1104 | "yoyo-6", 1105 | "yoyo-8", 1106 | "yoyo-9", 1107 | "zebra-1", 1108 | "zebra-11", 1109 | "zebra-12", 1110 | "zebra-13", 1111 | "zebra-15", 1112 | "zebra-18", 1113 | "zebra-19", 1114 | "zebra-2", 1115 | "zebra-20", 1116 | "zebra-3", 1117 | "zebra-4", 1118 | "zebra-5", 1119 | "zebra-6", 1120 | "zebra-7", 1121 | "zebra-8", 1122 | "zebra-9" 1123 | ], 1124 | "test": [ 1125 | "airplane-1", 1126 | "airplane-9", 1127 | "airplane-13", 1128 | "airplane-15", 1129 | "basketball-1", 1130 | "basketball-6", 1131 | "basketball-7", 1132 | "basketball-11", 1133 | "bear-2", 1134 | "bear-4", 1135 | "bear-6", 1136 | "bear-17", 1137 | "bicycle-2", 1138 | "bicycle-7", 1139 | "bicycle-9", 1140 | "bicycle-18", 1141 | "bird-2", 1142 | "bird-3", 1143 | "bird-15", 1144 | "bird-17", 1145 | "boat-3", 1146 | "boat-4", 1147 | "boat-12", 1148 | "boat-17", 1149 | "book-3", 1150 | "book-10", 1151 | "book-11", 1152 | "book-19", 1153 | "bottle-1", 1154 | "bottle-12", 1155 | "bottle-14", 1156 | "bottle-18", 1157 | "bus-2", 1158 | "bus-5", 1159 | "bus-17", 1160 | "bus-19", 1161 | "car-2", 1162 | "car-6", 1163 | "car-9", 1164 | "car-17", 1165 | "cat-1", 1166 | "cat-3", 1167 | "cat-18", 1168 | "cat-20", 1169 | "cattle-2", 1170 | "cattle-7", 1171 | "cattle-12", 1172 | "cattle-13", 1173 | "spider-14", 1174 | "spider-16", 1175 | "spider-18", 1176 | "spider-20", 1177 | "coin-3", 1178 | "coin-6", 1179 | "coin-7", 1180 | "coin-18", 1181 | "crab-3", 1182 | "crab-6", 1183 | "crab-12", 1184 | "crab-18", 1185 | "surfboard-12", 1186 | "surfboard-4", 1187 | "surfboard-5", 1188 | "surfboard-8", 1189 | "cup-1", 1190 | "cup-4", 1191 | "cup-7", 1192 | "cup-17", 1193 | "deer-4", 1194 | "deer-8", 1195 | "deer-10", 1196 | "deer-14", 1197 | "dog-1", 1198 | "dog-7", 1199 | "dog-15", 1200 | "dog-19", 1201 | "guitar-3", 1202 | "guitar-8", 1203 | "guitar-10", 1204 | "guitar-16", 1205 | "person-1", 1206 | "person-5", 1207 | "person-10", 1208 | "person-12", 1209 | "pig-2", 1210 | "pig-10", 1211 | "pig-13", 1212 | "pig-18", 1213 | "rubicCube-1", 1214 | "rubicCube-6", 1215 | "rubicCube-14", 1216 | "rubicCube-19", 1217 | "swing-10", 1218 | "swing-14", 1219 | "swing-17", 1220 | "swing-20", 1221 | "drone-13", 1222 | "drone-15", 1223 | "drone-2", 1224 | "drone-7", 1225 | "pool-12", 1226 | "pool-15", 1227 | "pool-3", 1228 | "pool-7", 1229 | "rabbit-10", 1230 | "rabbit-13", 1231 | "rabbit-17", 1232 | "rabbit-19", 1233 | "racing-10", 1234 | "racing-15", 1235 | "racing-16", 1236 | "racing-20", 1237 | "robot-1", 1238 | "robot-19", 1239 | "robot-5", 1240 | "robot-8", 1241 | "sepia-13", 1242 | "sepia-16", 1243 | "sepia-6", 1244 | "sepia-8", 1245 | "sheep-3", 1246 | "sheep-5", 1247 | "sheep-7", 1248 | "sheep-9", 1249 | "skateboard-16", 1250 | "skateboard-19", 1251 | "skateboard-3", 1252 | "skateboard-8", 1253 | "tank-14", 1254 | "tank-16", 1255 | "tank-6", 1256 | "tank-9", 1257 | "tiger-12", 1258 | "tiger-18", 1259 | "tiger-4", 1260 | "tiger-6", 1261 | "train-1", 1262 | "train-11", 1263 | "train-20", 1264 | "train-7", 1265 | "truck-16", 1266 | "truck-3", 1267 | "truck-6", 1268 | "truck-7", 1269 | "turtle-16", 1270 | "turtle-5", 1271 | "turtle-8", 1272 | "turtle-9", 1273 | "umbrella-17", 1274 | "umbrella-19", 1275 | "umbrella-2", 1276 | "umbrella-9", 1277 | "yoyo-15", 1278 | "yoyo-17", 1279 | "yoyo-19", 1280 | "yoyo-7", 1281 | "zebra-10", 1282 | "zebra-14", 1283 | "zebra-16", 1284 | "zebra-17", 1285 | "elephant-1", 1286 | "elephant-12", 1287 | "elephant-16", 1288 | "elephant-18", 1289 | "goldfish-3", 1290 | "goldfish-7", 1291 | "goldfish-8", 1292 | "goldfish-10", 1293 | "hat-1", 1294 | "hat-2", 1295 | "hat-5", 1296 | "hat-18", 1297 | "kite-4", 1298 | "kite-6", 1299 | "kite-10", 1300 | "kite-15", 1301 | "motorcycle-1", 1302 | "motorcycle-3", 1303 | "motorcycle-9", 1304 | "motorcycle-18", 1305 | "mouse-1", 1306 | "mouse-8", 1307 | "mouse-9", 1308 | "mouse-17", 1309 | "flag-3", 1310 | "flag-9", 1311 | "flag-5", 1312 | "flag-2", 1313 | "frog-3", 1314 | "frog-4", 1315 | "frog-20", 1316 | "frog-9", 1317 | "gametarget-1", 1318 | "gametarget-2", 1319 | "gametarget-7", 1320 | "gametarget-13", 1321 | "hand-2", 1322 | "hand-3", 1323 | "hand-9", 1324 | "hand-16", 1325 | "helmet-5", 1326 | "helmet-11", 1327 | "helmet-19", 1328 | "helmet-13", 1329 | "licenseplate-6", 1330 | "licenseplate-12", 1331 | "licenseplate-13", 1332 | "licenseplate-15", 1333 | "electricfan-1", 1334 | "electricfan-10", 1335 | "electricfan-18", 1336 | "electricfan-20", 1337 | "chameleon-3", 1338 | "chameleon-6", 1339 | "chameleon-11", 1340 | "chameleon-20", 1341 | "crocodile-3", 1342 | "crocodile-4", 1343 | "crocodile-10", 1344 | "crocodile-14", 1345 | "gecko-1", 1346 | "gecko-5", 1347 | "gecko-16", 1348 | "gecko-19", 1349 | "fox-2", 1350 | "fox-3", 1351 | "fox-5", 1352 | "fox-20", 1353 | "giraffe-2", 1354 | "giraffe-10", 1355 | "giraffe-13", 1356 | "giraffe-15", 1357 | "gorilla-4", 1358 | "gorilla-6", 1359 | "gorilla-9", 1360 | "gorilla-13", 1361 | "hippo-1", 1362 | "hippo-7", 1363 | "hippo-9", 1364 | "hippo-20", 1365 | "horse-1", 1366 | "horse-4", 1367 | "horse-12", 1368 | "horse-15", 1369 | "kangaroo-2", 1370 | "kangaroo-5", 1371 | "kangaroo-11", 1372 | "kangaroo-14", 1373 | "leopard-1", 1374 | "leopard-7", 1375 | "leopard-16", 1376 | "leopard-20", 1377 | "lion-1", 1378 | "lion-5", 1379 | "lion-12", 1380 | "lion-20", 1381 | "lizard-1", 1382 | "lizard-3", 1383 | "lizard-6", 1384 | "lizard-13", 1385 | "microphone-2", 1386 | "microphone-6", 1387 | "microphone-14", 1388 | "microphone-16", 1389 | "monkey-3", 1390 | "monkey-4", 1391 | "monkey-9", 1392 | "monkey-17", 1393 | "shark-2", 1394 | "shark-3", 1395 | "shark-5", 1396 | "shark-6", 1397 | "squirrel-8", 1398 | "squirrel-11", 1399 | "squirrel-13", 1400 | "squirrel-19", 1401 | "volleyball-1", 1402 | "volleyball-13", 1403 | "volleyball-18", 1404 | "volleyball-19" 1405 | ] 1406 | } -------------------------------------------------------------------------------- /got10k/datasets/lasot.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import, print_function 2 | 3 | import os 4 | import glob 5 | import json 6 | import numpy as np 7 | import six 8 | 9 | 10 | class LaSOT(object): 11 | r"""`LaSOT `_ Datasets. 12 | 13 | Publication: 14 | ``LaSOT: A High-quality Benchmark for Large-scale Single Object Tracking``, 15 | H. Fan, L. Lin, F. Yang, P. Chu, G. Deng, S. Yu, H. Bai, 16 | Y. Xu, C. Liao, and H. Ling., CVPR 2019. 17 | 18 | Args: 19 | root_dir (string): Root directory of dataset where sequence 20 | folders exist. 21 | subset (string, optional): Specify ``train`` or ``test`` 22 | subset of LaSOT. 23 | """ 24 | def __init__(self, root_dir, subset='test', return_meta=False): 25 | super(LaSOT, self).__init__() 26 | subset = subset.split('_') 27 | assert set(subset).issubset({'train', 'test'}), 'Unknown subset.' 28 | 29 | self.root_dir = root_dir 30 | self.subset = subset 31 | self.return_meta = return_meta 32 | self._check_integrity(root_dir) 33 | 34 | self.anno_files = sorted(glob.glob( 35 | os.path.join(root_dir, '*/*/groundtruth.txt'))) 36 | self.seq_dirs = [os.path.join( 37 | os.path.dirname(f), 'img') for f in self.anno_files] 38 | self.seq_names = [os.path.basename( 39 | os.path.dirname(f)) for f in self.anno_files] 40 | 41 | # load subset sequence names 42 | split_file = os.path.join( 43 | os.path.dirname(__file__), 'lasot.json') 44 | with open(split_file, 'r') as f: 45 | splits = json.load(f) 46 | self.seq_names = [] 47 | for s in subset: 48 | self.seq_names.extend(splits[s]) 49 | 50 | # image and annotation paths 51 | self.seq_dirs = [os.path.join( 52 | root_dir, n[:n.rfind('-')], n, 'img') 53 | for n in self.seq_names] 54 | self.anno_files = [os.path.join( 55 | os.path.dirname(d), 'groundtruth.txt') 56 | for d in self.seq_dirs] 57 | 58 | def __getitem__(self, index): 59 | r""" 60 | Args: 61 | index (integer or string): Index or name of a sequence. 62 | 63 | Returns: 64 | tuple: (img_files, anno) if ``return_meta`` is False, otherwise 65 | (img_files, anno, meta), where ``img_files`` is a list of 66 | file names, ``anno`` is a N x 4 (rectangles) numpy array, while 67 | ``meta`` is a dict contains meta information about the sequence. 68 | """ 69 | if isinstance(index, six.string_types): 70 | if not index in self.seq_names: 71 | raise Exception('Sequence {} not found.'.format(index)) 72 | index = self.seq_names.index(index) 73 | 74 | img_files = sorted(glob.glob(os.path.join( 75 | self.seq_dirs[index], '*.jpg'))) 76 | anno = np.loadtxt(self.anno_files[index], delimiter=',') 77 | 78 | if self.return_meta: 79 | meta = self._fetch_meta(self.seq_dirs[index]) 80 | return img_files, anno, meta 81 | else: 82 | return img_files, anno 83 | 84 | def __len__(self): 85 | return len(self.seq_names) 86 | 87 | def _check_integrity(self, root_dir): 88 | seq_names = os.listdir(root_dir) 89 | seq_names = [n for n in seq_names if not n[0] == '.'] 90 | 91 | if os.path.isdir(root_dir) and len(seq_names) > 0: 92 | # check each sequence folder 93 | for seq_name in seq_names: 94 | seq_dir = os.path.join(root_dir, seq_name) 95 | if not os.path.isdir(seq_dir): 96 | print('Warning: sequence %s not exists.' % seq_name) 97 | else: 98 | # dataset not exists 99 | raise Exception('Dataset not found or corrupted.') 100 | 101 | def _fetch_meta(self, seq_dir): 102 | seq_dir = os.path.dirname(seq_dir) 103 | meta = {} 104 | 105 | # attributes 106 | for att in ['full_occlusion', 'out_of_view']: 107 | att_file = os.path.join(seq_dir, att + '.txt') 108 | meta[att] = np.loadtxt(att_file, delimiter=',') 109 | 110 | # nlp 111 | nlp_file = os.path.join(seq_dir, 'nlp.txt') 112 | with open(nlp_file, 'r') as f: 113 | meta['nlp'] = f.read().strip() 114 | 115 | return meta 116 | -------------------------------------------------------------------------------- /got10k/datasets/nfs.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import, print_function, division 2 | 3 | import os 4 | import glob 5 | import numpy as np 6 | import six 7 | 8 | 9 | class NfS(object): 10 | """`NfS `_ Dataset. 11 | 12 | Publication: 13 | ``Need for Speed: A Benchmark for Higher Frame Rate Object Tracking``, 14 | H. K. Galoogahi, A. Fagg, C. Huang, D. Ramanan and S. Lucey, ICCV 2017. 15 | 16 | Args: 17 | root_dir (string): Root directory of dataset where sequence 18 | folders exist. 19 | fps (integer): Sequence frame rate. Two options ``30`` and ``240`` 20 | are available. Default is 240. 21 | """ 22 | def __init__(self, root_dir, fps=240): 23 | super(NfS, self).__init__() 24 | assert fps in [30, 240] 25 | self.fps = fps 26 | self.root_dir = root_dir 27 | self._check_integrity(root_dir) 28 | 29 | self.anno_files = sorted(glob.glob( 30 | os.path.join(root_dir, '*/%d/*.txt' % fps))) 31 | self.seq_names = [ 32 | os.path.basename(f)[:-4] for f in self.anno_files] 33 | self.seq_dirs = [os.path.join( 34 | os.path.dirname(f), n) 35 | for f, n in zip(self.anno_files, self.seq_names)] 36 | 37 | def __getitem__(self, index): 38 | r""" 39 | Args: 40 | index (integer or string): Index or name of a sequence. 41 | 42 | Returns: 43 | tuple: (img_files, anno), where ``img_files`` is a list of 44 | file names and ``anno`` is a N x 4 (rectangles) numpy array. 45 | """ 46 | if isinstance(index, six.string_types): 47 | if not index in self.seq_names: 48 | raise Exception('Sequence {} not found.'.format(index)) 49 | index = self.seq_names.index(index) 50 | 51 | img_files = sorted(glob.glob( 52 | os.path.join(self.seq_dirs[index], '*.jpg'))) 53 | anno = np.loadtxt(self.anno_files[index], dtype=str) 54 | anno = anno[:, 1:5].astype(float) # [left, top, right, bottom] 55 | anno[:, 2:] -= anno[:, :2] # [left, top, width, height] 56 | 57 | # handle inconsistent lengths 58 | if not len(img_files) == len(anno): 59 | if abs(len(anno) / len(img_files) - 8) < 1: 60 | anno = anno[0::8, :] 61 | diff = abs(len(img_files) - len(anno)) 62 | if diff > 0 and diff <= 1: 63 | n = min(len(img_files), len(anno)) 64 | anno = anno[:n] 65 | img_files = img_files[:n] 66 | assert len(img_files) == len(anno) 67 | 68 | return img_files, anno 69 | 70 | def __len__(self): 71 | return len(self.seq_names) 72 | 73 | def _check_integrity(self, root_dir): 74 | seq_names = os.listdir(root_dir) 75 | seq_names = [n for n in seq_names if not n[0] == '.'] 76 | 77 | if os.path.isdir(root_dir) and len(seq_names) > 0: 78 | # check each sequence folder 79 | for seq_name in seq_names: 80 | seq_dir = os.path.join(root_dir, seq_name) 81 | if not os.path.isdir(seq_dir): 82 | print('Warning: sequence %s not exists.' % seq_name) 83 | else: 84 | # dataset not exists 85 | raise Exception('Dataset not found or corrupted.') 86 | -------------------------------------------------------------------------------- /got10k/datasets/otb.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import, print_function, unicode_literals 2 | 3 | import os 4 | import glob 5 | import numpy as np 6 | import io 7 | import six 8 | from itertools import chain 9 | 10 | from ..utils.ioutils import download, extract 11 | 12 | 13 | class OTB(object): 14 | r"""`OTB `_ Datasets. 15 | 16 | Publication: 17 | ``Object Tracking Benchmark``, Y. Wu, J. Lim and M.-H. Yang, IEEE TPAMI 2015. 18 | 19 | Args: 20 | root_dir (string): Root directory of dataset where sequence 21 | folders exist. 22 | version (integer or string): Specify the benchmark version, specify as one of 23 | ``2013``, ``2015``, ``tb50`` and ``tb100``. 24 | download (boolean, optional): If True, downloads the dataset from the internet 25 | and puts it in root directory. If dataset is downloaded, it is not 26 | downloaded again. 27 | """ 28 | __otb13_seqs = ['Basketball', 'Bolt', 'Boy', 'Car4', 'CarDark', 29 | 'CarScale', 'Coke', 'Couple', 'Crossing', 'David', 30 | 'David2', 'David3', 'Deer', 'Dog1', 'Doll', 'Dudek', 31 | 'FaceOcc1', 'FaceOcc2', 'Fish', 'FleetFace', 32 | 'Football', 'Football1', 'Freeman1', 'Freeman3', 33 | 'Freeman4', 'Girl', 'Ironman', 'Jogging', 'Jumping', 34 | 'Lemming', 'Liquor', 'Matrix', 'Mhyang', 'MotorRolling', 35 | 'MountainBike', 'Shaking', 'Singer1', 'Singer2', 36 | 'Skating1', 'Skiing', 'Soccer', 'Subway', 'Suv', 37 | 'Sylvester', 'Tiger1', 'Tiger2', 'Trellis', 'Walking', 38 | 'Walking2', 'Woman'] 39 | 40 | __tb50_seqs = ['Basketball', 'Biker', 'Bird1', 'BlurBody', 'BlurCar2', 41 | 'BlurFace', 'BlurOwl', 'Bolt', 'Box', 'Car1', 'Car4', 42 | 'CarDark', 'CarScale', 'ClifBar', 'Couple', 'Crowds', 43 | 'David', 'Deer', 'Diving', 'DragonBaby', 'Dudek', 44 | 'Football', 'Freeman4', 'Girl', 'Human3', 'Human4', 45 | 'Human6', 'Human9', 'Ironman', 'Jump', 'Jumping', 46 | 'Liquor', 'Matrix', 'MotorRolling', 'Panda', 'RedTeam', 47 | 'Shaking', 'Singer2', 'Skating1', 'Skating2', 'Skiing', 48 | 'Soccer', 'Surfer', 'Sylvester', 'Tiger2', 'Trellis', 49 | 'Walking', 'Walking2', 'Woman'] 50 | 51 | __tb100_seqs = ['Bird2', 'BlurCar1', 'BlurCar3', 'BlurCar4', 'Board', 52 | 'Bolt2', 'Boy', 'Car2', 'Car24', 'Coke', 'Coupon', 53 | 'Crossing', 'Dancer', 'Dancer2', 'David2', 'David3', 54 | 'Dog', 'Dog1', 'Doll', 'FaceOcc1', 'FaceOcc2', 'Fish', 55 | 'FleetFace', 'Football1', 'Freeman1', 'Freeman3', 56 | 'Girl2', 'Gym', 'Human2', 'Human5', 'Human7', 'Human8', 57 | 'Jogging', 'KiteSurf', 'Lemming', 'Man', 'Mhyang', 58 | 'MountainBike', 'Rubik', 'Singer1', 'Skater', 59 | 'Skater2', 'Subway', 'Suv', 'Tiger1', 'Toy', 'Trans', 60 | 'Twinnings', 'Vase'] + __tb50_seqs 61 | 62 | __otb15_seqs = __tb100_seqs 63 | 64 | __version_dict = { 65 | 2013: __otb13_seqs, 66 | 2015: __otb15_seqs, 67 | 'otb2013': __otb13_seqs, 68 | 'otb2015': __otb15_seqs, 69 | 'tb50': __tb50_seqs, 70 | 'tb100': __tb100_seqs} 71 | 72 | def __init__(self, root_dir, version=2015, download=True): 73 | super(OTB, self).__init__() 74 | assert version in self.__version_dict 75 | 76 | self.root_dir = root_dir 77 | self.version = version 78 | if download: 79 | self._download(root_dir, version) 80 | self._check_integrity(root_dir, version) 81 | 82 | valid_seqs = self.__version_dict[version] 83 | self.anno_files = sorted(list(chain.from_iterable(glob.glob( 84 | os.path.join(root_dir, s, 'groundtruth*.txt')) for s in valid_seqs))) 85 | # remove empty annotation files 86 | # (e.g., groundtruth_rect.1.txt of Human4) 87 | self.anno_files = self._filter_files(self.anno_files) 88 | self.seq_dirs = [os.path.dirname(f) for f in self.anno_files] 89 | self.seq_names = [os.path.basename(d) for d in self.seq_dirs] 90 | # rename repeated sequence names 91 | # (e.g., Jogging and Skating2) 92 | self.seq_names = self._rename_seqs(self.seq_names) 93 | 94 | def __getitem__(self, index): 95 | r""" 96 | Args: 97 | index (integer or string): Index or name of a sequence. 98 | 99 | Returns: 100 | tuple: (img_files, anno), where ``img_files`` is a list of 101 | file names and ``anno`` is a N x 4 (rectangles) numpy array. 102 | """ 103 | if isinstance(index, six.string_types): 104 | if not index in self.seq_names: 105 | raise Exception('Sequence {} not found.'.format(index)) 106 | index = self.seq_names.index(index) 107 | 108 | img_files = sorted(glob.glob( 109 | os.path.join(self.seq_dirs[index], 'img/*.jpg'))) 110 | 111 | # special sequences 112 | # (visit http://cvlab.hanyang.ac.kr/tracker_benchmark/index.html for detail) 113 | seq_name = self.seq_names[index] 114 | if seq_name.lower() == 'david': 115 | img_files = img_files[300-1:770] 116 | elif seq_name.lower() == 'football1': 117 | img_files = img_files[:74] 118 | elif seq_name.lower() == 'freeman3': 119 | img_files = img_files[:460] 120 | elif seq_name.lower() == 'freeman4': 121 | img_files = img_files[:283] 122 | elif seq_name.lower() == 'diving': 123 | img_files = img_files[:215] 124 | 125 | # to deal with different delimeters 126 | with open(self.anno_files[index], 'r') as f: 127 | anno = np.loadtxt(io.StringIO(f.read().replace(',', ' '))) 128 | assert len(img_files) == len(anno) 129 | assert anno.shape[1] == 4 130 | 131 | return img_files, anno 132 | 133 | def __len__(self): 134 | return len(self.seq_names) 135 | 136 | def _filter_files(self, filenames): 137 | filtered_files = [] 138 | for filename in filenames: 139 | with open(filename, 'r') as f: 140 | if f.read().strip() == '': 141 | print('Warning: %s is empty.' % filename) 142 | else: 143 | filtered_files.append(filename) 144 | 145 | return filtered_files 146 | 147 | def _rename_seqs(self, seq_names): 148 | # in case some sequences may have multiple targets 149 | renamed_seqs = [] 150 | for i, seq_name in enumerate(seq_names): 151 | if seq_names.count(seq_name) == 1: 152 | renamed_seqs.append(seq_name) 153 | else: 154 | ind = seq_names[:i + 1].count(seq_name) 155 | renamed_seqs.append('%s.%d' % (seq_name, ind)) 156 | 157 | return renamed_seqs 158 | 159 | def _download(self, root_dir, version): 160 | assert version in self.__version_dict 161 | seq_names = self.__version_dict[version] 162 | 163 | if not os.path.isdir(root_dir): 164 | os.makedirs(root_dir) 165 | elif all([os.path.isdir(os.path.join(root_dir, s)) for s in seq_names]): 166 | print('Files already downloaded.') 167 | return 168 | 169 | url_fmt = 'http://cvlab.hanyang.ac.kr/tracker_benchmark/seq/%s.zip' 170 | for seq_name in seq_names: 171 | seq_dir = os.path.join(root_dir, seq_name) 172 | if os.path.isdir(seq_dir): 173 | continue 174 | url = url_fmt % seq_name 175 | zip_file = os.path.join(root_dir, seq_name + '.zip') 176 | print('Downloading to %s...' % zip_file) 177 | download(url, zip_file) 178 | print('\nExtracting to %s...' % root_dir) 179 | extract(zip_file, root_dir) 180 | 181 | return root_dir 182 | 183 | def _check_integrity(self, root_dir, version): 184 | assert version in self.__version_dict 185 | seq_names = self.__version_dict[version] 186 | 187 | if os.path.isdir(root_dir) and len(os.listdir(root_dir)) > 0: 188 | # check each sequence folder 189 | for seq_name in seq_names: 190 | seq_dir = os.path.join(root_dir, seq_name) 191 | if not os.path.isdir(seq_dir): 192 | print('Warning: sequence %s not exists.' % seq_name) 193 | else: 194 | # dataset not exists 195 | raise Exception('Dataset not found or corrupted. ' + 196 | 'You can use download=True to download it.') 197 | -------------------------------------------------------------------------------- /got10k/datasets/tcolor128.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import, print_function 2 | 3 | import os 4 | import glob 5 | import numpy as np 6 | import six 7 | 8 | from ..utils.ioutils import download, extract 9 | 10 | 11 | class TColor128(object): 12 | """`TColor128 `_ Dataset. 13 | 14 | Publication: 15 | ``Encoding color information for visual tracking: algorithms and benchmark``, 16 | P. Liang, E. Blasch and H. Ling, TIP, 2015. 17 | 18 | Args: 19 | root_dir (string): Root directory of dataset where sequence 20 | folders exist. 21 | """ 22 | def __init__(self, root_dir, download=True): 23 | super(TColor128, self).__init__() 24 | self.root_dir = root_dir 25 | if download: 26 | self._download(root_dir) 27 | self._check_integrity(root_dir) 28 | 29 | self.anno_files = sorted(glob.glob( 30 | os.path.join(root_dir, '*/*_gt.txt'))) 31 | self.seq_dirs = [os.path.dirname(f) for f in self.anno_files] 32 | self.seq_names = [os.path.basename(d) for d in self.seq_dirs] 33 | # valid frame range for each sequence 34 | self.range_files = [glob.glob( 35 | os.path.join(d, '*_frames.txt'))[0] 36 | for d in self.seq_dirs] 37 | 38 | def __getitem__(self, index): 39 | r""" 40 | Args: 41 | index (integer or string): Index or name of a sequence. 42 | 43 | Returns: 44 | tuple: (img_files, anno), where ``img_files`` is a list of 45 | file names and ``anno`` is a N x 4 (rectangles) numpy array. 46 | """ 47 | if isinstance(index, six.string_types): 48 | if not index in self.seq_names: 49 | raise Exception('Sequence {} not found.'.format(index)) 50 | index = self.seq_names.index(index) 51 | 52 | # load valid frame range 53 | frames = np.loadtxt( 54 | self.range_files[index], dtype=int, delimiter=',') 55 | img_files = [os.path.join( 56 | self.seq_dirs[index], 'img/%04d.jpg' % f) 57 | for f in range(frames[0], frames[1] + 1)] 58 | 59 | # load annotations 60 | anno = np.loadtxt(self.anno_files[index], delimiter=',') 61 | assert len(img_files) == len(anno) 62 | assert anno.shape[1] == 4 63 | 64 | return img_files, anno 65 | 66 | def __len__(self): 67 | return len(self.seq_names) 68 | 69 | def _download(self, root_dir): 70 | if not os.path.isdir(root_dir): 71 | os.makedirs(root_dir) 72 | elif len(os.listdir(root_dir)) > 100: 73 | print('Files already downloaded.') 74 | return 75 | 76 | url = 'http://www.dabi.temple.edu/~hbling/data/TColor-128/Temple-color-128.zip' 77 | zip_file = os.path.join(root_dir, 'Temple-color-128.zip') 78 | print('Downloading to %s...' % zip_file) 79 | download(url, zip_file) 80 | print('\nExtracting to %s...' % root_dir) 81 | extract(zip_file, root_dir) 82 | 83 | return root_dir 84 | 85 | def _check_integrity(self, root_dir): 86 | seq_names = os.listdir(root_dir) 87 | seq_names = [n for n in seq_names if not n[0] == '.'] 88 | 89 | if os.path.isdir(root_dir) and len(seq_names) > 0: 90 | # check each sequence folder 91 | for seq_name in seq_names: 92 | seq_dir = os.path.join(root_dir, seq_name) 93 | if not os.path.isdir(seq_dir): 94 | print('Warning: sequence %s not exists.' % seq_name) 95 | else: 96 | # dataset not exists 97 | raise Exception('Dataset not found or corrupted. ' + 98 | 'You can use download=True to download it.') 99 | -------------------------------------------------------------------------------- /got10k/datasets/trackingnet.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import, print_function 2 | 3 | import os 4 | import glob 5 | import six 6 | import numpy as np 7 | 8 | 9 | class TrackingNet(object): 10 | r"""`TrackingNet `_ Datasets. 11 | 12 | Publication: 13 | ``TrackingNet: A Large-Scale Dataset and Benchmark for Object Tracking in the Wild.``, 14 | M. Muller, A. Bibi, S. Giancola, S. Al-Subaihi and B. Ghanem, ECCV 2018. 15 | 16 | Args: 17 | root_dir (string): Root directory of dataset where sequence 18 | folders exist. 19 | subset (string, optional): Specify ``train`` or ``test`` 20 | subset of TrackingNet. 21 | """ 22 | def __init__(self, root_dir, subset='test', *args, **kwargs): 23 | super(TrackingNet, self).__init__() 24 | assert subset in ['train', 'test'], 'Unknown subset.' 25 | 26 | self.root_dir = root_dir 27 | self.subset = subset 28 | if subset == 'test': 29 | self.subset_dirs = ['TEST'] 30 | elif subset == 'train': 31 | self.subset_dirs = ['TRAIN_%d' % c for c in range(12)] 32 | self._check_integrity(root_dir, self.subset_dirs) 33 | 34 | self.anno_files = [glob.glob(os.path.join( 35 | root_dir, c, 'anno/*.txt')) for c in self.subset_dirs] 36 | self.anno_files = sorted(sum(self.anno_files, [])) 37 | self.seq_dirs = [os.path.join( 38 | os.path.dirname(os.path.dirname(f)), 39 | 'frames', 40 | os.path.basename(f)[:-4]) 41 | for f in self.anno_files] 42 | self.seq_names = [os.path.basename(d) for d in self.seq_dirs] 43 | 44 | def __getitem__(self, index): 45 | r""" 46 | Args: 47 | index (integer or string): Index or name of a sequence. 48 | 49 | Returns: 50 | tuple: (img_files, anno), where ``img_files`` is a list of 51 | file names and ``anno`` is a N x 4 (rectangles) numpy array. 52 | """ 53 | if isinstance(index, six.string_types): 54 | if not index in self.seq_names: 55 | raise Exception('Sequence {} not found.'.format(index)) 56 | index = self.seq_names.index(index) 57 | 58 | img_files = glob.glob( 59 | os.path.join(self.seq_dirs[index], '*.jpg')) 60 | img_files = sorted(img_files, key=lambda x: int(os.path.basename(x)[:-4])) 61 | anno = np.loadtxt(self.anno_files[index], delimiter=',') 62 | # from IPython import embed;embed() 63 | if self.subset=='train': 64 | assert len(img_files) == len(anno) 65 | assert anno.shape[1] == 4 66 | elif self.subset=='test': 67 | assert anno.shape[0] == 4 68 | anno = anno.reshape(-1, 4) 69 | 70 | return img_files, anno 71 | 72 | def __len__(self): 73 | return len(self.seq_names) 74 | 75 | def _check_integrity(self, root_dir, subset_dirs): 76 | # check each subset path 77 | for c in subset_dirs: 78 | subset_dir = os.path.join(root_dir, c) 79 | 80 | # check data and annotation folders 81 | for folder in ['anno', 'frames']: 82 | if not os.path.isdir(os.path.join(subset_dir, folder)): 83 | raise Exception('Dataset not found or corrupted.') 84 | -------------------------------------------------------------------------------- /got10k/datasets/uav123.json: -------------------------------------------------------------------------------- 1 | { 2 | "UAV123": { 3 | "bike1": { 4 | "start_frame": 1, 5 | "end_frame": 3085, 6 | "folder_name": "bike1" 7 | }, 8 | "bike2": { 9 | "start_frame": 1, 10 | "end_frame": 553, 11 | "folder_name": "bike2" 12 | }, 13 | "bike3": { 14 | "start_frame": 1, 15 | "end_frame": 433, 16 | "folder_name": "bike3" 17 | }, 18 | "bird1_1": { 19 | "start_frame": 1, 20 | "end_frame": 253, 21 | "folder_name": "bird1" 22 | }, 23 | "bird1_2": { 24 | "start_frame": 775, 25 | "end_frame": 1477, 26 | "folder_name": "bird1" 27 | }, 28 | "bird1_3": { 29 | "start_frame": 1573, 30 | "end_frame": 2437, 31 | "folder_name": "bird1" 32 | }, 33 | "boat1": { 34 | "start_frame": 1, 35 | "end_frame": 901, 36 | "folder_name": "boat1" 37 | }, 38 | "boat2": { 39 | "start_frame": 1, 40 | "end_frame": 799, 41 | "folder_name": "boat2" 42 | }, 43 | "boat3": { 44 | "start_frame": 1, 45 | "end_frame": 901, 46 | "folder_name": "boat3" 47 | }, 48 | "boat4": { 49 | "start_frame": 1, 50 | "end_frame": 553, 51 | "folder_name": "boat4" 52 | }, 53 | "boat5": { 54 | "start_frame": 1, 55 | "end_frame": 505, 56 | "folder_name": "boat5" 57 | }, 58 | "boat6": { 59 | "start_frame": 1, 60 | "end_frame": 805, 61 | "folder_name": "boat6" 62 | }, 63 | "boat7": { 64 | "start_frame": 1, 65 | "end_frame": 535, 66 | "folder_name": "boat7" 67 | }, 68 | "boat8": { 69 | "start_frame": 1, 70 | "end_frame": 685, 71 | "folder_name": "boat8" 72 | }, 73 | "boat9": { 74 | "start_frame": 1, 75 | "end_frame": 1399, 76 | "folder_name": "boat9" 77 | }, 78 | "building1": { 79 | "start_frame": 1, 80 | "end_frame": 469, 81 | "folder_name": "building1" 82 | }, 83 | "building2": { 84 | "start_frame": 1, 85 | "end_frame": 577, 86 | "folder_name": "building2" 87 | }, 88 | "building3": { 89 | "start_frame": 1, 90 | "end_frame": 829, 91 | "folder_name": "building3" 92 | }, 93 | "building4": { 94 | "start_frame": 1, 95 | "end_frame": 787, 96 | "folder_name": "building4" 97 | }, 98 | "building5": { 99 | "start_frame": 1, 100 | "end_frame": 481, 101 | "folder_name": "building5" 102 | }, 103 | "car1_1": { 104 | "start_frame": 1, 105 | "end_frame": 751, 106 | "folder_name": "car1" 107 | }, 108 | "car1_2": { 109 | "start_frame": 751, 110 | "end_frame": 1627, 111 | "folder_name": "car1" 112 | }, 113 | "car1_3": { 114 | "start_frame": 1627, 115 | "end_frame": 2629, 116 | "folder_name": "car1" 117 | }, 118 | "car2": { 119 | "start_frame": 1, 120 | "end_frame": 1321, 121 | "folder_name": "car2" 122 | }, 123 | "car3": { 124 | "start_frame": 1, 125 | "end_frame": 1717, 126 | "folder_name": "car3" 127 | }, 128 | "car4": { 129 | "start_frame": 1, 130 | "end_frame": 1345, 131 | "folder_name": "car4" 132 | }, 133 | "car5": { 134 | "start_frame": 1, 135 | "end_frame": 745, 136 | "folder_name": "car5" 137 | }, 138 | "car6_1": { 139 | "start_frame": 1, 140 | "end_frame": 487, 141 | "folder_name": "car6" 142 | }, 143 | "car6_2": { 144 | "start_frame": 487, 145 | "end_frame": 1807, 146 | "folder_name": "car6" 147 | }, 148 | "car6_3": { 149 | "start_frame": 1807, 150 | "end_frame": 2953, 151 | "folder_name": "car6" 152 | }, 153 | "car6_4": { 154 | "start_frame": 2953, 155 | "end_frame": 3925, 156 | "folder_name": "car6" 157 | }, 158 | "car6_5": { 159 | "start_frame": 3925, 160 | "end_frame": 4861, 161 | "folder_name": "car6" 162 | }, 163 | "car7": { 164 | "start_frame": 1, 165 | "end_frame": 1033, 166 | "folder_name": "car7" 167 | }, 168 | "car8_1": { 169 | "start_frame": 1, 170 | "end_frame": 1357, 171 | "folder_name": "car8" 172 | }, 173 | "car8_2": { 174 | "start_frame": 1357, 175 | "end_frame": 2575, 176 | "folder_name": "car8" 177 | }, 178 | "car9": { 179 | "start_frame": 1, 180 | "end_frame": 1879, 181 | "folder_name": "car9" 182 | }, 183 | "car10": { 184 | "start_frame": 1, 185 | "end_frame": 1405, 186 | "folder_name": "car10" 187 | }, 188 | "car11": { 189 | "start_frame": 1, 190 | "end_frame": 337, 191 | "folder_name": "car11" 192 | }, 193 | "car12": { 194 | "start_frame": 1, 195 | "end_frame": 499, 196 | "folder_name": "car12" 197 | }, 198 | "car13": { 199 | "start_frame": 1, 200 | "end_frame": 415, 201 | "folder_name": "car13" 202 | }, 203 | "car14": { 204 | "start_frame": 1, 205 | "end_frame": 1327, 206 | "folder_name": "car14" 207 | }, 208 | "car15": { 209 | "start_frame": 1, 210 | "end_frame": 469, 211 | "folder_name": "car15" 212 | }, 213 | "car16_1": { 214 | "start_frame": 1, 215 | "end_frame": 415, 216 | "folder_name": "car16" 217 | }, 218 | "car16_2": { 219 | "start_frame": 415, 220 | "end_frame": 1993, 221 | "folder_name": "car16" 222 | }, 223 | "car17": { 224 | "start_frame": 1, 225 | "end_frame": 1057, 226 | "folder_name": "car17" 227 | }, 228 | "car18": { 229 | "start_frame": 1, 230 | "end_frame": 1207, 231 | "folder_name": "car18" 232 | }, 233 | "group1_1": { 234 | "start_frame": 1, 235 | "end_frame": 1333, 236 | "folder_name": "group1" 237 | }, 238 | "group1_2": { 239 | "start_frame": 1333, 240 | "end_frame": 2515, 241 | "folder_name": "group1" 242 | }, 243 | "group1_3": { 244 | "start_frame": 2515, 245 | "end_frame": 3925, 246 | "folder_name": "group1" 247 | }, 248 | "group1_4": { 249 | "start_frame": 3925, 250 | "end_frame": 4873, 251 | "folder_name": "group1" 252 | }, 253 | "group2_1": { 254 | "start_frame": 1, 255 | "end_frame": 907, 256 | "folder_name": "group2" 257 | }, 258 | "group2_2": { 259 | "start_frame": 907, 260 | "end_frame": 1771, 261 | "folder_name": "group2" 262 | }, 263 | "group2_3": { 264 | "start_frame": 1771, 265 | "end_frame": 2683, 266 | "folder_name": "group2" 267 | }, 268 | "group3_1": { 269 | "start_frame": 1, 270 | "end_frame": 1567, 271 | "folder_name": "group3" 272 | }, 273 | "group3_2": { 274 | "start_frame": 1567, 275 | "end_frame": 2827, 276 | "folder_name": "group3" 277 | }, 278 | "group3_3": { 279 | "start_frame": 2827, 280 | "end_frame": 4369, 281 | "folder_name": "group3" 282 | }, 283 | "group3_4": { 284 | "start_frame": 4369, 285 | "end_frame": 5527, 286 | "folder_name": "group3" 287 | }, 288 | "person1": { 289 | "start_frame": 1, 290 | "end_frame": 799, 291 | "folder_name": "person1" 292 | }, 293 | "person2_1": { 294 | "start_frame": 1, 295 | "end_frame": 1189, 296 | "folder_name": "person2" 297 | }, 298 | "person2_2": { 299 | "start_frame": 1189, 300 | "end_frame": 2623, 301 | "folder_name": "person2" 302 | }, 303 | "person3": { 304 | "start_frame": 1, 305 | "end_frame": 643, 306 | "folder_name": "person3" 307 | }, 308 | "person4_1": { 309 | "start_frame": 1, 310 | "end_frame": 1501, 311 | "folder_name": "person4" 312 | }, 313 | "person4_2": { 314 | "start_frame": 1501, 315 | "end_frame": 2743, 316 | "folder_name": "person4" 317 | }, 318 | "person5_1": { 319 | "start_frame": 1, 320 | "end_frame": 877, 321 | "folder_name": "person5" 322 | }, 323 | "person5_2": { 324 | "start_frame": 877, 325 | "end_frame": 2101, 326 | "folder_name": "person5" 327 | }, 328 | "person6": { 329 | "start_frame": 1, 330 | "end_frame": 901, 331 | "folder_name": "person6" 332 | }, 333 | "person7_1": { 334 | "start_frame": 1, 335 | "end_frame": 1249, 336 | "folder_name": "person7" 337 | }, 338 | "person7_2": { 339 | "start_frame": 1249, 340 | "end_frame": 2065, 341 | "folder_name": "person7" 342 | }, 343 | "person8_1": { 344 | "start_frame": 1, 345 | "end_frame": 1075, 346 | "folder_name": "person8" 347 | }, 348 | "person8_2": { 349 | "start_frame": 1075, 350 | "end_frame": 1525, 351 | "folder_name": "person8" 352 | }, 353 | "person9": { 354 | "start_frame": 1, 355 | "end_frame": 661, 356 | "folder_name": "person9" 357 | }, 358 | "person10": { 359 | "start_frame": 1, 360 | "end_frame": 1021, 361 | "folder_name": "person10" 362 | }, 363 | "person11": { 364 | "start_frame": 1, 365 | "end_frame": 721, 366 | "folder_name": "person11" 367 | }, 368 | "person12_1": { 369 | "start_frame": 1, 370 | "end_frame": 601, 371 | "folder_name": "person12" 372 | }, 373 | "person12_2": { 374 | "start_frame": 601, 375 | "end_frame": 1621, 376 | "folder_name": "person12" 377 | }, 378 | "person13": { 379 | "start_frame": 1, 380 | "end_frame": 883, 381 | "folder_name": "person13" 382 | }, 383 | "person14_1": { 384 | "start_frame": 1, 385 | "end_frame": 847, 386 | "folder_name": "person14" 387 | }, 388 | "person14_2": { 389 | "start_frame": 847, 390 | "end_frame": 1813, 391 | "folder_name": "person14" 392 | }, 393 | "person14_3": { 394 | "start_frame": 1813, 395 | "end_frame": 2923, 396 | "folder_name": "person14" 397 | }, 398 | "person15": { 399 | "start_frame": 1, 400 | "end_frame": 1339, 401 | "folder_name": "person15" 402 | }, 403 | "person16": { 404 | "start_frame": 1, 405 | "end_frame": 1147, 406 | "folder_name": "person16" 407 | }, 408 | "person17_1": { 409 | "start_frame": 1, 410 | "end_frame": 1501, 411 | "folder_name": "person17" 412 | }, 413 | "person17_2": { 414 | "start_frame": 1501, 415 | "end_frame": 2347, 416 | "folder_name": "person17" 417 | }, 418 | "person18": { 419 | "start_frame": 1, 420 | "end_frame": 1393, 421 | "folder_name": "person18" 422 | }, 423 | "person19_1": { 424 | "start_frame": 1, 425 | "end_frame": 1243, 426 | "folder_name": "person19" 427 | }, 428 | "person19_2": { 429 | "start_frame": 1243, 430 | "end_frame": 2791, 431 | "folder_name": "person19" 432 | }, 433 | "person19_3": { 434 | "start_frame": 2791, 435 | "end_frame": 4357, 436 | "folder_name": "person19" 437 | }, 438 | "person20": { 439 | "start_frame": 1, 440 | "end_frame": 1783, 441 | "folder_name": "person20" 442 | }, 443 | "person21": { 444 | "start_frame": 1, 445 | "end_frame": 487, 446 | "folder_name": "person21" 447 | }, 448 | "person22": { 449 | "start_frame": 1, 450 | "end_frame": 199, 451 | "folder_name": "person22" 452 | }, 453 | "person23": { 454 | "start_frame": 1, 455 | "end_frame": 397, 456 | "folder_name": "person23" 457 | }, 458 | "truck1": { 459 | "start_frame": 1, 460 | "end_frame": 463, 461 | "folder_name": "truck1" 462 | }, 463 | "truck2": { 464 | "start_frame": 1, 465 | "end_frame": 385, 466 | "folder_name": "truck2" 467 | }, 468 | "truck3": { 469 | "start_frame": 1, 470 | "end_frame": 535, 471 | "folder_name": "truck3" 472 | }, 473 | "truck4_1": { 474 | "start_frame": 1, 475 | "end_frame": 577, 476 | "folder_name": "truck4" 477 | }, 478 | "truck4_2": { 479 | "start_frame": 577, 480 | "end_frame": 1261, 481 | "folder_name": "truck4" 482 | }, 483 | "uav1_1": { 484 | "start_frame": 1, 485 | "end_frame": 1555, 486 | "folder_name": "uav1" 487 | }, 488 | "uav1_2": { 489 | "start_frame": 1555, 490 | "end_frame": 2377, 491 | "folder_name": "uav1" 492 | }, 493 | "uav1_3": { 494 | "start_frame": 2473, 495 | "end_frame": 3469, 496 | "folder_name": "uav1" 497 | }, 498 | "uav2": { 499 | "start_frame": 1, 500 | "end_frame": 133, 501 | "folder_name": "uav2" 502 | }, 503 | "uav3": { 504 | "start_frame": 1, 505 | "end_frame": 265, 506 | "folder_name": "uav3" 507 | }, 508 | "uav4": { 509 | "start_frame": 1, 510 | "end_frame": 157, 511 | "folder_name": "uav4" 512 | }, 513 | "uav5": { 514 | "start_frame": 1, 515 | "end_frame": 139, 516 | "folder_name": "uav5" 517 | }, 518 | "uav6": { 519 | "start_frame": 1, 520 | "end_frame": 109, 521 | "folder_name": "uav6" 522 | }, 523 | "uav7": { 524 | "start_frame": 1, 525 | "end_frame": 373, 526 | "folder_name": "uav7" 527 | }, 528 | "uav8": { 529 | "start_frame": 1, 530 | "end_frame": 301, 531 | "folder_name": "uav8" 532 | }, 533 | "wakeboard1": { 534 | "start_frame": 1, 535 | "end_frame": 421, 536 | "folder_name": "wakeboard1" 537 | }, 538 | "wakeboard2": { 539 | "start_frame": 1, 540 | "end_frame": 733, 541 | "folder_name": "wakeboard2" 542 | }, 543 | "wakeboard3": { 544 | "start_frame": 1, 545 | "end_frame": 823, 546 | "folder_name": "wakeboard3" 547 | }, 548 | "wakeboard4": { 549 | "start_frame": 1, 550 | "end_frame": 697, 551 | "folder_name": "wakeboard4" 552 | }, 553 | "wakeboard5": { 554 | "start_frame": 1, 555 | "end_frame": 1675, 556 | "folder_name": "wakeboard5" 557 | }, 558 | "wakeboard6": { 559 | "start_frame": 1, 560 | "end_frame": 1165, 561 | "folder_name": "wakeboard6" 562 | }, 563 | "wakeboard7": { 564 | "start_frame": 1, 565 | "end_frame": 199, 566 | "folder_name": "wakeboard7" 567 | }, 568 | "wakeboard8": { 569 | "start_frame": 1, 570 | "end_frame": 1543, 571 | "folder_name": "wakeboard8" 572 | }, 573 | "wakeboard9": { 574 | "start_frame": 1, 575 | "end_frame": 355, 576 | "folder_name": "wakeboard9" 577 | }, 578 | "wakeboard10": { 579 | "start_frame": 1, 580 | "end_frame": 469, 581 | "folder_name": "wakeboard10" 582 | }, 583 | "car1_s": { 584 | "start_frame": 1, 585 | "end_frame": 1475, 586 | "folder_name": "car1_s" 587 | }, 588 | "car2_s": { 589 | "start_frame": 1, 590 | "end_frame": 320, 591 | "folder_name": "car2_s" 592 | }, 593 | "car3_s": { 594 | "start_frame": 1, 595 | "end_frame": 1300, 596 | "folder_name": "car3_s" 597 | }, 598 | "car4_s": { 599 | "start_frame": 1, 600 | "end_frame": 830, 601 | "folder_name": "car4_s" 602 | }, 603 | "person1_s": { 604 | "start_frame": 1, 605 | "end_frame": 1600, 606 | "folder_name": "person1_s" 607 | }, 608 | "person2_s": { 609 | "start_frame": 1, 610 | "end_frame": 250, 611 | "folder_name": "person2_s" 612 | }, 613 | "person3_s": { 614 | "start_frame": 1, 615 | "end_frame": 505, 616 | "folder_name": "person3_s" 617 | } 618 | }, 619 | "UAV20L": { 620 | "bike1": { 621 | "start_frame": 1, 622 | "end_frame": 3085, 623 | "folder_name": "bike1" 624 | }, 625 | "bird1": { 626 | "start_frame": 1, 627 | "end_frame": 2437, 628 | "folder_name": "bird1" 629 | }, 630 | "car1": { 631 | "start_frame": 1, 632 | "end_frame": 2629, 633 | "folder_name": "car1" 634 | }, 635 | "car3": { 636 | "start_frame": 1, 637 | "end_frame": 1717, 638 | "folder_name": "car3" 639 | }, 640 | "car6": { 641 | "start_frame": 1, 642 | "end_frame": 4861, 643 | "folder_name": "car6" 644 | }, 645 | "car8": { 646 | "start_frame": 1, 647 | "end_frame": 2575, 648 | "folder_name": "car8" 649 | }, 650 | "car9": { 651 | "start_frame": 1, 652 | "end_frame": 1879, 653 | "folder_name": "car9" 654 | }, 655 | "car16": { 656 | "start_frame": 1, 657 | "end_frame": 1993, 658 | "folder_name": "car16" 659 | }, 660 | "group1": { 661 | "start_frame": 1, 662 | "end_frame": 4873, 663 | "folder_name": "group1" 664 | }, 665 | "group2": { 666 | "start_frame": 1, 667 | "end_frame": 2683, 668 | "folder_name": "group2" 669 | }, 670 | "group3": { 671 | "start_frame": 1, 672 | "end_frame": 5527, 673 | "folder_name": "group3" 674 | }, 675 | "person2": { 676 | "start_frame": 1, 677 | "end_frame": 2623, 678 | "folder_name": "person2" 679 | }, 680 | "person4": { 681 | "start_frame": 1, 682 | "end_frame": 2743, 683 | "folder_name": "person4" 684 | }, 685 | "person5": { 686 | "start_frame": 1, 687 | "end_frame": 2101, 688 | "folder_name": "person5" 689 | }, 690 | "person7": { 691 | "start_frame": 1, 692 | "end_frame": 2065, 693 | "folder_name": "person7" 694 | }, 695 | "person14": { 696 | "start_frame": 1, 697 | "end_frame": 2923, 698 | "folder_name": "person14" 699 | }, 700 | "person17": { 701 | "start_frame": 1, 702 | "end_frame": 2347, 703 | "folder_name": "person17" 704 | }, 705 | "person19": { 706 | "start_frame": 1, 707 | "end_frame": 4357, 708 | "folder_name": "person19" 709 | }, 710 | "person20": { 711 | "start_frame": 1, 712 | "end_frame": 1783, 713 | "folder_name": "person20" 714 | }, 715 | "uav1": { 716 | "start_frame": 1, 717 | "end_frame": 3469, 718 | "folder_name": "uav1" 719 | } 720 | } 721 | } -------------------------------------------------------------------------------- /got10k/datasets/uav123.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import, print_function 2 | 3 | import os 4 | import glob 5 | import numpy as np 6 | import six 7 | import json 8 | 9 | 10 | class UAV123(object): 11 | """`UAV123 `_ Dataset. 12 | 13 | Publication: 14 | ``A Benchmark and Simulator for UAV Tracking``, 15 | M. Mueller, N. Smith and B. Ghanem, ECCV 2016. 16 | 17 | Args: 18 | root_dir (string): Root directory of dataset where sequence 19 | folders exist. 20 | version (integer or string): Specify the benchmark version, specify as one of 21 | ``UAV123`` and ``UAV20L``. 22 | """ 23 | def __init__(self, root_dir, version='UAV123'): 24 | super(UAV123, self).__init__() 25 | assert version.upper() in ['UAV20L', 'UAV123'] 26 | 27 | self.root_dir = root_dir 28 | self.version = version.upper() 29 | self._check_integrity(root_dir, version) 30 | 31 | # sequence meta information 32 | meta_file = os.path.join( 33 | os.path.dirname(__file__), 'uav123.json') 34 | with open(meta_file) as f: 35 | self.seq_metas = json.load(f) 36 | 37 | # sequence and annotation paths 38 | self.anno_files = sorted(glob.glob( 39 | os.path.join(root_dir, 'anno/%s/*.txt' % version))) 40 | self.seq_names = [ 41 | os.path.basename(f)[:-4] for f in self.anno_files] 42 | self.seq_dirs = [os.path.join( 43 | root_dir, 'data_seq/UAV123/%s' % \ 44 | self.seq_metas[version][n]['folder_name']) 45 | for n in self.seq_names] 46 | 47 | def __getitem__(self, index): 48 | r""" 49 | Args: 50 | index (integer or string): Index or name of a sequence. 51 | 52 | Returns: 53 | tuple: (img_files, anno), where ``img_files`` is a list of 54 | file names and ``anno`` is a N x 4 (rectangles) numpy array. 55 | """ 56 | if isinstance(index, six.string_types): 57 | if not index in self.seq_names: 58 | raise Exception('Sequence {} not found.'.format(index)) 59 | index = self.seq_names.index(index) 60 | 61 | # valid frame range 62 | start_frame = self.seq_metas[self.version][ 63 | self.seq_names[index]]['start_frame'] 64 | end_frame = self.seq_metas[self.version][ 65 | self.seq_names[index]]['end_frame'] 66 | img_files = [os.path.join( 67 | self.seq_dirs[index], '%06d.jpg' % f) 68 | for f in range(start_frame, end_frame + 1)] 69 | 70 | # load annotations 71 | anno = np.loadtxt(self.anno_files[index], delimiter=',') 72 | assert len(img_files) == len(anno) 73 | assert anno.shape[1] == 4 74 | 75 | return img_files, anno 76 | 77 | def __len__(self): 78 | return len(self.seq_names) 79 | 80 | def _check_integrity(self, root_dir, version): 81 | # sequence meta information 82 | meta_file = os.path.join( 83 | os.path.dirname(__file__), 'uav123.json') 84 | with open(meta_file) as f: 85 | seq_metas = json.load(f) 86 | seq_names = list(seq_metas[version].keys()) 87 | 88 | if os.path.isdir(root_dir) and len(os.listdir(root_dir)) > 3: 89 | # check each sequence folder 90 | for seq_name in seq_names: 91 | seq_dir = os.path.join( 92 | root_dir, 'data_seq/UAV123/%s' % \ 93 | seq_metas[version][seq_name]['folder_name']) 94 | if not os.path.isdir(seq_dir): 95 | print('Warning: sequence %s not exists.' % seq_name) 96 | else: 97 | # dataset not exists 98 | raise Exception('Dataset not found or corrupted.') 99 | -------------------------------------------------------------------------------- /got10k/datasets/vid.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import, print_function 2 | 3 | import os 4 | import glob 5 | import six 6 | import numpy as np 7 | import xml.etree.ElementTree as ET 8 | import json 9 | from collections import OrderedDict 10 | 11 | 12 | class ImageNetVID(object): 13 | r"""`ImageNet Video Image Detection (VID) `_ Dataset. 14 | 15 | Publication: 16 | ``ImageNet Large Scale Visual Recognition Challenge``, O. Russakovsky, 17 | J. deng, H. Su, etc. IJCV, 2015. 18 | 19 | Args: 20 | root_dir (string): Root directory of dataset where ``Data``, and 21 | ``Annotation`` folders exist. 22 | subset (string, optional): Specify ``train``, ``val`` or (``train``, ``val``) 23 | subset(s) of ImageNet-VID. Default is a tuple (``train``, ``val``). 24 | cache_dir (string, optional): Directory for caching the paths and annotations 25 | for speeding up loading. Default is ``cache/imagenet_vid``. 26 | """ 27 | def __init__(self, root_dir, subset=('train', 'val'), 28 | cache_dir='cache/imagenet_vid'): 29 | self.root_dir = root_dir 30 | self.cache_dir = cache_dir 31 | if isinstance(subset, str): 32 | assert subset in ['train', 'val'] 33 | self.subset = [subset] 34 | elif isinstance(subset, (list, tuple)): 35 | assert all([s in ['train', 'val'] for s in subset]) 36 | self.subset = subset 37 | else: 38 | raise Exception('Unknown subset') 39 | 40 | # cache filenames and annotations to speed up training 41 | self.seq_dict = self._cache_meta() 42 | self.seq_names = [n for n in self.seq_dict] 43 | 44 | def __getitem__(self, index): 45 | r""" 46 | Args: 47 | index (integer or string): Index or name of a sequence. 48 | 49 | Returns: 50 | tuple: (img_files, anno), where ``img_files`` is a list of 51 | file names and ``anno`` is a N x 4 (rectangles) numpy array. 52 | """ 53 | if isinstance(index, six.string_types): 54 | seq_name = index 55 | else: 56 | seq_name = self.seq_names[index] 57 | 58 | seq_dir, frames, anno_file = self.seq_dict[seq_name] 59 | img_files = [os.path.join( 60 | seq_dir, '%06d.JPEG' % f) for f in frames] 61 | anno = np.loadtxt(anno_file, delimiter=',') 62 | 63 | return img_files, anno 64 | 65 | def __len__(self): 66 | return len(self.seq_dict) 67 | 68 | def _cache_meta(self): 69 | cache_file = os.path.join(self.cache_dir, 'seq_dict.json') 70 | if os.path.isfile(cache_file): 71 | print('Dataset already cached.') 72 | with open(cache_file) as f: 73 | seq_dict = json.load(f, object_pairs_hook=OrderedDict) 74 | return seq_dict 75 | 76 | # image and annotation paths 77 | print('Gather sequence paths...') 78 | seq_dirs = [] 79 | anno_dirs = [] 80 | if 'train' in self.subset: 81 | seq_dirs_ = sorted(glob.glob(os.path.join( 82 | self.root_dir, 'Data/VID/train/ILSVRC*/ILSVRC*'))) 83 | anno_dirs_ = [os.path.join( 84 | self.root_dir, 'Annotations/VID/train', 85 | *s.split('/')[-2:]) for s in seq_dirs_] 86 | seq_dirs += seq_dirs_ 87 | anno_dirs += anno_dirs_ 88 | if 'val' in self.subset: 89 | seq_dirs_ = sorted(glob.glob(os.path.join( 90 | self.root_dir, 'Data/VID/val/ILSVRC2015_val_*'))) 91 | anno_dirs_ = [os.path.join( 92 | self.root_dir, 'Annotations/VID/val', 93 | s.split('/')[-1]) for s in seq_dirs_] 94 | seq_dirs += seq_dirs_ 95 | anno_dirs += anno_dirs_ 96 | seq_names = [os.path.basename(s) for s in seq_dirs] 97 | 98 | # cache paths and annotations 99 | print('Caching annotations to %s, ' % self.cache_dir + \ 100 | 'it may take a few minutes...') 101 | seq_dict = OrderedDict() 102 | cache_anno_dir = os.path.join(self.cache_dir, 'anno') 103 | if not os.path.isdir(cache_anno_dir): 104 | os.makedirs(cache_anno_dir) 105 | 106 | for s, seq_name in enumerate(seq_names): 107 | if s % 100 == 0 or s == len(seq_names) - 1: 108 | print('--Caching sequence %d/%d: %s' % \ 109 | (s + 1, len(seq_names), seq_name)) 110 | anno_files = sorted(glob.glob(os.path.join( 111 | anno_dirs[s], '*.xml'))) 112 | objects = [ET.ElementTree(file=f).findall('object') 113 | for f in anno_files] 114 | 115 | # find all track ids 116 | track_ids, counts = np.unique([ 117 | obj.find('trackid').text for group in objects 118 | for obj in group], return_counts=True) 119 | 120 | # fetch paths and annotations for each track id 121 | for t, track_id in enumerate(track_ids): 122 | if counts[t] < 2: 123 | continue 124 | frames = [] 125 | anno = [] 126 | for f, group in enumerate(objects): 127 | for obj in group: 128 | if not obj.find('trackid').text == track_id: 129 | continue 130 | frames.append(f) 131 | anno.append([ 132 | int(obj.find('bndbox/xmin').text), 133 | int(obj.find('bndbox/ymin').text), 134 | int(obj.find('bndbox/xmax').text), 135 | int(obj.find('bndbox/ymax').text)]) 136 | anno = np.array(anno, dtype=int) 137 | anno[:, 2:] -= anno[:, :2] - 1 138 | 139 | # store annotations 140 | key = '%s.%d' % (seq_name, int(track_id)) 141 | cache_anno_file = os.path.join(cache_anno_dir, key + '.txt') 142 | np.savetxt(cache_anno_file, anno, fmt='%d', delimiter=',') 143 | 144 | # store paths 145 | seq_dict.update([(key, [ 146 | seq_dirs[s], frames, cache_anno_file])]) 147 | 148 | # store seq_dict 149 | with open(cache_file, 'w') as f: 150 | json.dump(seq_dict, f) 151 | 152 | return seq_dict 153 | -------------------------------------------------------------------------------- /got10k/datasets/vot.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import, print_function, division 2 | 3 | import os 4 | import glob 5 | import numpy as np 6 | import six 7 | import json 8 | import hashlib 9 | 10 | from ..utils.ioutils import download, extract 11 | 12 | 13 | class VOT(object): 14 | r"""`VOT `_ Datasets. 15 | 16 | Publication: 17 | ``The Visual Object Tracking VOT2017 challenge results``, M. Kristan, A. Leonardis 18 | and J. Matas, etc. 2017. 19 | 20 | Args: 21 | root_dir (string): Root directory of dataset where sequence 22 | folders exist. 23 | version (integer, optional): Specify the benchmark version. Specify as 24 | one of 2013~2018. Default is 2017. 25 | anno_type (string, optional): Returned annotation types, chosen as one of 26 | ``rect`` and ``corner``. Default is ``rect``. 27 | download (boolean, optional): If True, downloads the dataset from the internet 28 | and puts it in root directory. If dataset is downloaded, it is not 29 | downloaded again. 30 | return_meta (string, optional): If True, returns ``meta`` 31 | of each sequence in ``__getitem__`` function, otherwise 32 | only returns ``img_files`` and ``anno``. 33 | list_file (string, optional): If provided, only read sequences 34 | specified by the file. 35 | """ 36 | __valid_versions = [2013, 2014, 2015, 2016, 2017, 2018, 'LT2018', 37 | 2019, 'LT2019', 'RGBD2019', 'RGBT2019'] 38 | 39 | def __init__(self, root_dir, version=2017, anno_type='rect', 40 | download=True, return_meta=False, list_file=None): 41 | super(VOT, self).__init__() 42 | assert version in self.__valid_versions, 'Unsupport VOT version.' 43 | assert anno_type in ['default', 'rect'], 'Unknown annotation type.' 44 | 45 | self.root_dir = root_dir 46 | self.version = version 47 | self.anno_type = anno_type 48 | if download: 49 | self._download(root_dir, version) 50 | self.return_meta = return_meta 51 | 52 | if list_file is None: 53 | list_file = os.path.join(root_dir, 'list.txt') 54 | self._check_integrity(root_dir, version, list_file) 55 | 56 | with open(list_file, 'r') as f: 57 | self.seq_names = f.read().strip().split('\n') 58 | self.seq_dirs = [os.path.join(root_dir, s) for s in self.seq_names] 59 | self.anno_files = [os.path.join(s, 'groundtruth.txt') 60 | for s in self.seq_dirs] 61 | 62 | def __getitem__(self, index): 63 | r""" 64 | Args: 65 | index (integer or string): Index or name of a sequence. 66 | 67 | Returns: 68 | tuple: (img_files, anno) if ``return_meta`` is False, otherwise 69 | (img_files, anno, meta), where ``img_files`` is a list of 70 | file names, ``anno`` is a N x 4 (rectangles) or N x 8 (corners) numpy array, 71 | while ``meta`` is a dict contains meta information about the sequence. 72 | """ 73 | if isinstance(index, six.string_types): 74 | if not index in self.seq_names: 75 | raise Exception('Sequence {} not found.'.format(index)) 76 | index = self.seq_names.index(index) 77 | 78 | img_files = sorted(glob.glob( 79 | os.path.join(self.seq_dirs[index], 'color', '*.jpg'))) 80 | anno = np.loadtxt(self.anno_files[index], delimiter=',') 81 | assert len(img_files) == len(anno), (len(img_files), len(anno)) 82 | assert anno.shape[1] in [4, 8] 83 | if self.anno_type == 'rect' and anno.shape[1] == 8: 84 | anno = self._corner2rect(anno) 85 | 86 | if self.return_meta: 87 | meta = self._fetch_meta( 88 | self.seq_dirs[index], len(img_files)) 89 | return img_files, anno, meta 90 | else: 91 | return img_files, anno 92 | 93 | def __len__(self): 94 | return len(self.seq_names) 95 | 96 | def _download(self, root_dir, version): 97 | assert version in self.__valid_versions 98 | 99 | if not os.path.isdir(root_dir): 100 | os.makedirs(root_dir) 101 | elif os.path.isfile(os.path.join(root_dir, 'list.txt')): 102 | with open(os.path.join(root_dir, 'list.txt')) as f: 103 | seq_names = f.read().strip().split('\n') 104 | if all([os.path.isdir(os.path.join(root_dir, s)) for s in seq_names]): 105 | print('Files already downloaded.') 106 | return 107 | 108 | url = 'http://data.votchallenge.net/' 109 | if version in range(2013, 2015 + 1): 110 | # main challenge (2013~2015) 111 | homepage = url + 'vot{}/dataset/'.format(version) 112 | elif version in range(2015, 2019 + 1): 113 | # main challenge (2016~2019) 114 | homepage = url + 'vot{}/main/'.format(version) 115 | elif version.startswith('LT'): 116 | # long-term tracking challenge 117 | year = int(version[2:]) 118 | homepage = url + 'vot{}/longterm/'.format(year) 119 | elif version.startswith('RGBD'): 120 | # RGBD tracking challenge 121 | year = int(version[4:]) 122 | homepage = url + 'vot{}/rgbd/'.format(year) 123 | elif version.startswith('RGBT'): 124 | # RGBT tracking challenge 125 | year = int(version[4:]) 126 | url = url + 'vot{}/rgbtir/'.format(year) 127 | homepage = url + 'meta/' 128 | 129 | # download description file 130 | bundle_url = homepage + 'description.json' 131 | bundle_file = os.path.join(root_dir, 'description.json') 132 | if not os.path.isfile(bundle_file): 133 | print('Downloading description file...') 134 | download(bundle_url, bundle_file) 135 | 136 | # read description file 137 | print('\nParsing description file...') 138 | with open(bundle_file) as f: 139 | bundle = json.load(f) 140 | 141 | # md5 generator 142 | def md5(filename): 143 | hash_md5 = hashlib.md5() 144 | with open(filename, 'rb') as f: 145 | for chunk in iter(lambda: f.read(4096), b""): 146 | hash_md5.update(chunk) 147 | return hash_md5.hexdigest() 148 | 149 | # download all sequences 150 | seq_names = [] 151 | for seq in bundle['sequences']: 152 | seq_name = seq['name'] 153 | seq_names.append(seq_name) 154 | 155 | # download channel (color/depth/ir) files 156 | channels = seq['channels'].keys() 157 | seq_files = [] 158 | for cn in channels: 159 | seq_url = seq['channels'][cn]['url'] 160 | if not seq_url.startswith(('http', 'https')): 161 | seq_url = url + seq_url[seq_url.find('sequence'):] 162 | seq_file = os.path.join( 163 | root_dir, 164 | '{}_{}.zip'.format(seq_name, cn)) 165 | if not os.path.isfile(seq_file) or \ 166 | md5(seq_file) != seq['channels'][cn]['checksum']: 167 | print('\nDownloading %s...' % seq_name) 168 | download(seq_url, seq_file) 169 | seq_files.append(seq_file) 170 | 171 | # download annotations 172 | anno_url = homepage + '%s.zip' % seq_name 173 | anno_file = os.path.join(root_dir, seq_name + '_anno.zip') 174 | if not os.path.isfile(anno_file) or \ 175 | md5(anno_file) != seq['annotations']['checksum']: 176 | download(anno_url, anno_file) 177 | 178 | # unzip compressed files 179 | seq_dir = os.path.join(root_dir, seq_name) 180 | if not os.path.isfile(seq_dir) or len(os.listdir(seq_dir)) < 10: 181 | print('Extracting %s...' % seq_name) 182 | os.makedirs(seq_dir) 183 | for seq_file in seq_files: 184 | extract(seq_file, seq_dir) 185 | extract(anno_file, seq_dir) 186 | 187 | # save list.txt 188 | list_file = os.path.join(root_dir, 'list.txt') 189 | with open(list_file, 'w') as f: 190 | f.write(str.join('\n', seq_names)) 191 | 192 | return root_dir 193 | 194 | def _check_integrity(self, root_dir, version, list_file=None): 195 | assert version in self.__valid_versions 196 | if list_file is None: 197 | list_file = os.path.join(root_dir, 'list.txt') 198 | 199 | if os.path.isfile(list_file): 200 | with open(list_file, 'r') as f: 201 | seq_names = f.read().strip().split('\n') 202 | 203 | # check each sequence folder 204 | for seq_name in seq_names: 205 | seq_dir = os.path.join(root_dir, seq_name) 206 | if not os.path.isdir(seq_dir): 207 | print('Warning: sequence %s not exists.' % seq_name) 208 | else: 209 | # dataset not exists 210 | raise Exception('Dataset not found or corrupted. ' + 211 | 'You can use download=True to download it.') 212 | 213 | def _corner2rect(self, corners, center=False): 214 | cx = np.mean(corners[:, 0::2], axis=1) 215 | cy = np.mean(corners[:, 1::2], axis=1) 216 | 217 | x1 = np.min(corners[:, 0::2], axis=1) 218 | x2 = np.max(corners[:, 0::2], axis=1) 219 | y1 = np.min(corners[:, 1::2], axis=1) 220 | y2 = np.max(corners[:, 1::2], axis=1) 221 | 222 | area1 = np.linalg.norm(corners[:, 0:2] - corners[:, 2:4], axis=1) * \ 223 | np.linalg.norm(corners[:, 2:4] - corners[:, 4:6], axis=1) 224 | area2 = (x2 - x1) * (y2 - y1) 225 | scale = np.sqrt(area1 / area2) 226 | w = scale * (x2 - x1) + 1 227 | h = scale * (y2 - y1) + 1 228 | 229 | if center: 230 | return np.array([cx, cy, w, h]).T 231 | else: 232 | return np.array([cx - w / 2, cy - h / 2, w, h]).T 233 | 234 | def _fetch_meta(self, seq_dir, frame_num): 235 | meta = {} 236 | 237 | # attributes 238 | tag_files = glob.glob(os.path.join(seq_dir, '*.label')) + \ 239 | glob.glob(os.path.join(seq_dir, '*.tag')) 240 | for f in tag_files: 241 | tag = os.path.basename(f) 242 | tag = tag[:tag.rfind('.')] 243 | meta[tag] = np.loadtxt(f) 244 | 245 | # practical 246 | practical_file = os.path.join(seq_dir, 'practical') 247 | if os.path.isfile(practical_file + '.value'): 248 | meta['practical'] = np.loadtxt(practical_file + '.value') 249 | if os.path.isfile(practical_file + '.txt'): 250 | meta['practical_txt'] = np.loadtxt(practical_file + '.txt') 251 | 252 | # pad zeros if necessary 253 | for tag, val in meta.items(): 254 | if len(val) < frame_num: 255 | meta[tag] = np.pad( 256 | val, (0, frame_num - len(val)), 'constant') 257 | 258 | return meta 259 | -------------------------------------------------------------------------------- /got10k/experiments/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | 3 | from .got10k import ExperimentGOT10k 4 | from .otb import ExperimentOTB 5 | from .vot import ExperimentVOT 6 | from .dtb70 import ExperimentDTB70 7 | from .uav123 import ExperimentUAV123 8 | from .nfs import ExperimentNfS 9 | from .tcolor128 import ExperimentTColor128 10 | 11 | from .lasot import ExperimentLaSOT 12 | from .trackingnet import ExperimentTrackingNet 13 | -------------------------------------------------------------------------------- /got10k/experiments/dtb70.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | 3 | import os 4 | 5 | from .otb import ExperimentOTB 6 | from ..datasets import DTB70 7 | 8 | 9 | class ExperimentDTB70(ExperimentOTB): 10 | r"""Experiment pipeline and evaluation toolkit for DTB70 dataset. 11 | 12 | Args: 13 | root_dir (string): Root directory of DTB70 dataset. 14 | result_dir (string, optional): Directory for storing tracking 15 | results. Default is ``./results``. 16 | report_dir (string, optional): Directory for storing performance 17 | evaluation results. Default is ``./reports``. 18 | """ 19 | def __init__(self, root_dir, 20 | result_dir='results', report_dir='reports'): 21 | self.dataset = DTB70(root_dir) 22 | self.result_dir = os.path.join(result_dir, 'DTB70') 23 | self.report_dir = os.path.join(report_dir, 'DTB70') 24 | # as nbins_iou increases, the success score 25 | # converges to the average overlap (AO) 26 | self.nbins_iou = 21 27 | self.nbins_ce = 51 28 | -------------------------------------------------------------------------------- /got10k/experiments/got10k.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import, division, print_function 2 | 3 | import os 4 | import numpy as np 5 | import glob 6 | import ast 7 | import json 8 | import time 9 | import matplotlib.pyplot as plt 10 | import matplotlib 11 | from PIL import Image 12 | import cv2 13 | 14 | from ..datasets import GOT10k 15 | from ..utils.metrics import rect_iou 16 | from ..utils.viz import show_frame 17 | from ..utils.ioutils import compress 18 | 19 | 20 | class ExperimentGOT10k(object): 21 | r"""Experiment pipeline and evaluation toolkit for GOT-10k dataset. 22 | 23 | Args: 24 | root_dir (string): Root directory of GOT-10k dataset where 25 | ``train``, ``val`` and ``test`` folders exist. 26 | subset (string): Specify ``train``, ``val`` or ``test`` 27 | subset of GOT-10k. 28 | list_file (string, optional): If provided, only run experiments on 29 | sequences specified by this file. 30 | result_dir (string, optional): Directory for storing tracking 31 | results. Default is ``./results``. 32 | report_dir (string, optional): Directory for storing performance 33 | evaluation results. Default is ``./reports``. 34 | """ 35 | def __init__(self, root_dir, subset='val', list_file=None, 36 | result_dir='results', report_dir='reports', use_dataset=True): 37 | super(ExperimentGOT10k, self).__init__() 38 | assert subset in ['val', 'test'] 39 | self.subset = subset 40 | if use_dataset: 41 | self.dataset = GOT10k( 42 | root_dir, subset=subset, list_file=list_file) 43 | self.result_dir = os.path.join(result_dir, 'GOT-10k') 44 | self.report_dir = os.path.join(report_dir, 'GOT-10k') 45 | self.nbins_iou = 101 46 | self.repetitions = 3 47 | 48 | def run(self, tracker, visualize=False, save_video=False, overwrite_result=True): 49 | if self.subset == 'test': 50 | print('\033[93m[WARNING]:\n' \ 51 | 'The groundtruths of GOT-10k\'s test set is withholded.\n' \ 52 | 'You will have to submit your results to\n' \ 53 | '[http://got-10k.aitestunion.com/]' \ 54 | '\nto access the performance.\033[0m') 55 | time.sleep(2) 56 | 57 | print('Running tracker %s on GOT-10k...' % tracker.name) 58 | self.dataset.return_meta = False 59 | 60 | # loop over the complete dataset 61 | for s, (img_files, anno) in enumerate(self.dataset): 62 | seq_name = self.dataset.seq_names[s] 63 | print('--Sequence %d/%d: %s' % ( 64 | s + 1, len(self.dataset), seq_name)) 65 | 66 | # run multiple repetitions for each sequence 67 | for r in range(self.repetitions): 68 | # check if the tracker is deterministic 69 | if r > 0 and tracker.is_deterministic: 70 | break 71 | elif r == 3 and self._check_deterministic( 72 | tracker.name, seq_name): 73 | print(' Detected a deterministic tracker, ' + 74 | 'skipping remaining trials.') 75 | break 76 | print(' Repetition: %d' % (r + 1)) 77 | 78 | # skip if results exist 79 | record_file = os.path.join( 80 | self.result_dir, tracker.name, seq_name, 81 | '%s_%03d.txt' % (seq_name, r + 1)) 82 | if os.path.exists(record_file) and not overwrite_result: 83 | print(' Found results, skipping', seq_name) 84 | continue 85 | 86 | # tracking loop 87 | boxes, times = tracker.track( 88 | img_files, anno[0, :], visualize=visualize) 89 | 90 | # record results 91 | self._record(record_file, boxes, times) 92 | 93 | # save videos 94 | if save_video: 95 | video_dir = os.path.join(os.path.dirname(os.path.dirname(self.result_dir)), 96 | 'videos', 'GOT-10k', tracker.name) 97 | video_file = os.path.join(video_dir, '%s.avi' % seq_name) 98 | 99 | if not os.path.isdir(video_dir): 100 | os.makedirs(video_dir) 101 | image = Image.open(img_files[0]) 102 | img_W, img_H = image.size 103 | out_video = cv2.VideoWriter(video_file, cv2.VideoWriter_fourcc(*'MJPG'), 10, (img_W, img_H)) 104 | for ith, (img_file, pred) in enumerate(zip(img_files, boxes)): 105 | image = Image.open(img_file) 106 | if not image.mode == 'RGB': 107 | image = image.convert('RGB') 108 | img = np.array(image)[:, :, ::-1].copy() 109 | pred = pred.astype(int) 110 | cv2.rectangle(img, (pred[0], pred[1]), (pred[0] + pred[2], pred[1] + pred[3]), self.color['pred'], 2) 111 | if ith < anno.shape[0]: 112 | gt = anno[ith].astype(int) 113 | cv2.rectangle(img, (gt[0], gt[1]), (gt[0] + gt[2], gt[1] + gt[3]), self.color['gt'], 2) 114 | out_video.write(img) 115 | out_video.release() 116 | print(' Videos saved at', video_file) 117 | 118 | def report(self, tracker_names, plot_curves=True): 119 | assert isinstance(tracker_names, (list, tuple)) 120 | 121 | if self.subset == 'test': 122 | pwd = os.getcwd() 123 | 124 | # generate compressed submission file for each tracker 125 | for tracker_name in tracker_names: 126 | # compress all tracking results 127 | result_dir = os.path.join(self.result_dir, tracker_name) 128 | os.chdir(result_dir) 129 | save_file = '../%s' % tracker_name 130 | compress('.', save_file) 131 | print('Records saved at', save_file + '.zip') 132 | 133 | # print submission guides 134 | print('\033[93mLogin and follow instructions on') 135 | print('http://got-10k.aitestunion.com/submit_instructions') 136 | print('to upload and evaluate your tracking results\033[0m') 137 | 138 | # switch back to previous working directory 139 | os.chdir(pwd) 140 | 141 | return None 142 | elif self.subset == 'val': 143 | # meta information is useful when evaluation 144 | self.dataset.return_meta = True 145 | 146 | # assume tracker_names[0] is your tracker 147 | report_dir = os.path.join(self.report_dir, tracker_names[0]) 148 | if not os.path.exists(report_dir): 149 | os.makedirs(report_dir) 150 | report_file = os.path.join(report_dir, 'performance.json') 151 | 152 | # visible ratios of all sequences 153 | seq_names = self.dataset.seq_names 154 | covers = {s: self.dataset[s][2]['cover'][1:] for s in seq_names} 155 | 156 | performance = {} 157 | for name in tracker_names: 158 | print('Evaluating', name) 159 | ious = {} 160 | times = {} 161 | performance.update({name: { 162 | 'overall': {}, 163 | 'seq_wise': {}}}) 164 | 165 | for s, (_, anno, meta) in enumerate(self.dataset): 166 | seq_name = self.dataset.seq_names[s] 167 | record_files = glob.glob(os.path.join( 168 | self.result_dir, name, seq_name, 169 | '%s_[0-9]*.txt' % seq_name)) 170 | if len(record_files) == 0: 171 | raise Exception('Results for sequence %s not found.' % seq_name) 172 | 173 | # read results of all repetitions 174 | boxes = [np.loadtxt(f, delimiter=',') for f in record_files] 175 | assert all([b.shape == anno.shape for b in boxes]) 176 | 177 | # calculate and stack all ious 178 | bound = ast.literal_eval(meta['resolution']) 179 | seq_ious = [rect_iou(b[1:], anno[1:], bound=bound) for b in boxes] 180 | # only consider valid frames where targets are visible 181 | seq_ious = [t[covers[seq_name] > 0] for t in seq_ious] 182 | seq_ious = np.concatenate(seq_ious) 183 | ious[seq_name] = seq_ious 184 | 185 | # stack all tracking times 186 | times[seq_name] = [] 187 | time_file = os.path.join( 188 | self.result_dir, name, seq_name, 189 | '%s_time.txt' % seq_name) 190 | if os.path.exists(time_file): 191 | seq_times = np.loadtxt(time_file, delimiter=',') 192 | seq_times = seq_times[~np.isnan(seq_times)] 193 | seq_times = seq_times[seq_times > 0] 194 | if len(seq_times) > 0: 195 | times[seq_name] = seq_times 196 | 197 | # store sequence-wise performance 198 | ao, sr, speed, _ = self._evaluate(seq_ious, seq_times) 199 | performance[name]['seq_wise'].update({seq_name: { 200 | 'ao': ao, 201 | 'sr': sr, 202 | 'speed_fps': speed, 203 | 'length': len(anno) - 1}}) 204 | 205 | ious = np.concatenate(list(ious.values())) 206 | times = np.concatenate(list(times.values())) 207 | 208 | # store overall performance 209 | ao, sr, speed, succ_curve = self._evaluate(ious, times) 210 | performance[name].update({'overall': { 211 | 'ao': ao, 212 | 'sr': sr, 213 | 'speed_fps': speed, 214 | 'succ_curve': succ_curve.tolist()}}) 215 | 216 | # save performance 217 | with open(report_file, 'w') as f: 218 | json.dump(performance, f, indent=4) 219 | # plot success curves 220 | if plot_curves: 221 | self.plot_curves([report_file], tracker_names) 222 | 223 | return performance 224 | 225 | def show(self, tracker_names, seq_names=None, play_speed=1): 226 | if seq_names is None: 227 | seq_names = self.dataset.seq_names 228 | elif isinstance(seq_names, str): 229 | seq_names = [seq_names] 230 | assert isinstance(tracker_names, (list, tuple)) 231 | assert isinstance(seq_names, (list, tuple)) 232 | 233 | play_speed = int(round(play_speed)) 234 | assert play_speed > 0 235 | self.dataset.return_meta = False 236 | 237 | for s, seq_name in enumerate(seq_names): 238 | print('[%d/%d] Showing results on %s...' % ( 239 | s + 1, len(seq_names), seq_name)) 240 | 241 | # load all tracking results 242 | records = {} 243 | for name in tracker_names: 244 | record_file = os.path.join( 245 | self.result_dir, name, seq_name, 246 | '%s_001.txt' % seq_name) 247 | records[name] = np.loadtxt(record_file, delimiter=',') 248 | 249 | # loop over the sequence and display results 250 | img_files, anno = self.dataset[seq_name] 251 | for f, img_file in enumerate(img_files): 252 | if not f % play_speed == 0: 253 | continue 254 | image = Image.open(img_file) 255 | boxes = [anno[f]] + [ 256 | records[name][f] for name in tracker_names] 257 | show_frame(image, boxes, 258 | legends=['GroundTruth'] + tracker_names, 259 | colors=['w', 'r', 'g', 'b', 'c', 'm', 'y', 260 | 'orange', 'purple', 'brown', 'pink']) 261 | 262 | def _record(self, record_file, boxes, times): 263 | # record bounding boxes 264 | record_dir = os.path.dirname(record_file) 265 | if not os.path.isdir(record_dir): 266 | os.makedirs(record_dir) 267 | np.savetxt(record_file, boxes, fmt='%.3f', delimiter=',') 268 | while not os.path.exists(record_file): 269 | print('warning: recording failed, retrying...') 270 | np.savetxt(record_file, boxes, fmt='%.3f', delimiter=',') 271 | print(' Results recorded at', record_file) 272 | 273 | # record running times 274 | time_file = record_file[:record_file.rfind('_')] + '_time.txt' 275 | times = times[:, np.newaxis] 276 | if os.path.exists(time_file): 277 | exist_times = np.loadtxt(time_file, delimiter=',') 278 | if exist_times.ndim == 1: 279 | exist_times = exist_times[:, np.newaxis] 280 | times = np.concatenate((exist_times, times), axis=1) 281 | np.savetxt(time_file, times, fmt='%.8f', delimiter=',') 282 | 283 | def _check_deterministic(self, tracker_name, seq_name): 284 | record_dir = os.path.join( 285 | self.result_dir, tracker_name, seq_name) 286 | record_files = sorted(glob.glob(os.path.join( 287 | record_dir, '%s_[0-9]*.txt' % seq_name))) 288 | 289 | if len(record_files) < 3: 290 | return False 291 | 292 | records = [] 293 | for record_file in record_files: 294 | with open(record_file, 'r') as f: 295 | records.append(f.read()) 296 | 297 | return len(set(records)) == 1 298 | 299 | def _evaluate(self, ious, times): 300 | # AO, SR and tracking speed 301 | ao = np.mean(ious) 302 | sr = np.mean(ious > 0.5) 303 | if len(times) > 0: 304 | # times has to be an array of positive values 305 | speed_fps = np.mean(1. / times) 306 | else: 307 | speed_fps = -1 308 | 309 | # success curve 310 | # thr_iou = np.linspace(0, 1, 101) 311 | thr_iou = np.linspace(0, 1, self.nbins_iou) 312 | bin_iou = np.greater(ious[:, None], thr_iou[None, :]) 313 | succ_curve = np.mean(bin_iou, axis=0) 314 | 315 | return ao, sr, speed_fps, succ_curve 316 | 317 | def plot_curves(self, report_files, tracker_names, extension='.png'): 318 | assert isinstance(report_files, list), \ 319 | 'Expected "report_files" to be a list, ' \ 320 | 'but got %s instead' % type(report_files) 321 | 322 | # assume tracker_names[0] is your tracker 323 | report_dir = os.path.join(self.report_dir, tracker_names[0]) 324 | if not os.path.exists(report_dir): 325 | os.makedirs(report_dir) 326 | 327 | performance = {} 328 | for report_file in report_files: 329 | with open(report_file) as f: 330 | performance.update(json.load(f)) 331 | 332 | succ_file = os.path.join(report_dir, 'success_plot'+extension) 333 | key = 'overall' 334 | 335 | # filter performance by tracker_names 336 | performance = {k:v for k,v in performance.items() if k in tracker_names} 337 | 338 | # sort trackers by AO 339 | tracker_names = list(performance.keys()) 340 | aos = [t[key]['ao'] for t in performance.values()] 341 | inds = np.argsort(aos)[::-1] 342 | tracker_names = [tracker_names[i] for i in inds] 343 | 344 | # markers 345 | markers = ['-', '--', '-.'] 346 | markers = [c + m for m in markers for c in [''] * 10] 347 | 348 | # plot success curves 349 | thr_iou = np.linspace(0, 1, self.nbins_iou) 350 | fig, ax = plt.subplots() 351 | lines = [] 352 | legends = [] 353 | for i, name in enumerate(tracker_names): 354 | line, = ax.plot(thr_iou, 355 | performance[name][key]['succ_curve'], 356 | markers[i % len(markers)]) 357 | lines.append(line) 358 | legends.append('%s: [%.3f]' % ( 359 | name, performance[name][key]['ao'])) 360 | matplotlib.rcParams.update({'font.size': 7.4}) 361 | legend = ax.legend(lines, legends, loc='lower left', 362 | bbox_to_anchor=(0., 0.)) 363 | 364 | matplotlib.rcParams.update({'font.size': 9}) 365 | ax.set(xlabel='Overlap threshold', 366 | ylabel='Success rate', 367 | xlim=(0, 1), ylim=(0, 1), 368 | title='Success plots on GOT-10k') 369 | ax.grid(True) 370 | fig.tight_layout() 371 | 372 | # control ratio 373 | # ax.set_aspect('equal', 'box') 374 | 375 | print('Saving success plots to', succ_file) 376 | fig.savefig(succ_file, 377 | bbox_extra_artists=(legend,), 378 | bbox_inches='tight', 379 | dpi=300) 380 | -------------------------------------------------------------------------------- /got10k/experiments/lasot.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | 3 | import os 4 | import json 5 | import numpy as np 6 | import matplotlib.pyplot as plt 7 | import matplotlib 8 | 9 | from .otb import ExperimentOTB 10 | from ..datasets import LaSOT 11 | from ..utils.metrics import rect_iou, center_error, normalized_center_error 12 | 13 | 14 | class ExperimentLaSOT(ExperimentOTB): 15 | r"""Experiment pipeline and evaluation toolkit for LaSOT dataset. 16 | 17 | Args: 18 | root_dir (string): Root directory of LaSOT dataset. 19 | subset (string, optional): Specify ``train`` or ``test`` 20 | subset of LaSOT. Default is ``test``. 21 | return_meta (bool, optional): whether to fetch meta info 22 | (occlusion or out-of-view). Default is ``False``. 23 | result_dir (string, optional): Directory for storing tracking 24 | results. Default is ``./results``. 25 | report_dir (string, optional): Directory for storing performance 26 | evaluation results. Default is ``./reports``. 27 | """ 28 | def __init__(self, root_dir, subset='test', return_meta=False, 29 | result_dir='results', report_dir='reports'): 30 | # assert subset.upper() in ['TRAIN', 'TEST'] 31 | self.dataset = LaSOT(root_dir, subset, return_meta=return_meta) 32 | self.result_dir = result_dir 33 | self.report_dir = report_dir 34 | 35 | # as nbins_iou increases, the success score 36 | # converges to the average overlap (AO) 37 | self.nbins_iou = 21 38 | self.nbins_ce = 51 39 | self.nbins_nce = 51 40 | 41 | def report(self, tracker_names): 42 | assert isinstance(tracker_names, (list, tuple)) 43 | 44 | # assume tracker_names[0] is your tracker 45 | report_dir = os.path.join(self.report_dir, tracker_names[0]) 46 | if not os.path.isdir(report_dir): 47 | os.makedirs(report_dir) 48 | report_file = os.path.join(report_dir, 'performance.json') 49 | 50 | performance = {} 51 | for name in tracker_names: 52 | print('Evaluating', name) 53 | seq_num = len(self.dataset) 54 | succ_curve = np.zeros((seq_num, self.nbins_iou)) 55 | prec_curve = np.zeros((seq_num, self.nbins_ce)) 56 | norm_prec_curve = np.zeros((seq_num, self.nbins_nce)) 57 | speeds = np.zeros(seq_num) 58 | 59 | performance.update({name: { 60 | 'overall': {}, 61 | 'seq_wise': {}}}) 62 | 63 | for s, (_, anno) in enumerate(self.dataset): 64 | seq_name = self.dataset.seq_names[s] 65 | record_file = os.path.join( 66 | self.result_dir, name, '%s.txt' % seq_name) 67 | boxes = np.loadtxt(record_file, delimiter=',') 68 | boxes[0] = anno[0] 69 | if not (len(boxes) == len(anno)): 70 | # from IPython import embed;embed() 71 | print('warning: %s anno donnot match boxes'%seq_name) 72 | len_min = min(len(boxes),len(anno)) 73 | boxes = boxes[:len_min] 74 | anno = anno[:len_min] 75 | assert len(boxes) == len(anno) 76 | 77 | ious, center_errors, norm_center_errors = self._calc_metrics(boxes, anno) 78 | succ_curve[s], prec_curve[s], norm_prec_curve[s] = self._calc_curves(ious, center_errors, norm_center_errors) 79 | 80 | # calculate average tracking speed 81 | time_file = os.path.join( 82 | self.result_dir, name, 'times/%s_time.txt' % seq_name) 83 | if os.path.isfile(time_file): 84 | times = np.loadtxt(time_file) 85 | times = times[times > 0] 86 | if len(times) > 0: 87 | speeds[s] = np.mean(1. / times) 88 | 89 | # store sequence-wise performance 90 | performance[name]['seq_wise'].update({seq_name: { 91 | 'success_curve': succ_curve[s].tolist(), 92 | 'precision_curve': prec_curve[s].tolist(), 93 | 'normalized_precision_curve': norm_prec_curve[s].tolist(), 94 | 'success_score': np.mean(succ_curve[s]), 95 | 'precision_score': prec_curve[s][20], 96 | 'normalized_precision_score': np.mean(norm_prec_curve[s]), 97 | 'success_rate': succ_curve[s][self.nbins_iou // 2], 98 | 'speed_fps': speeds[s] if speeds[s] > 0 else -1}}) 99 | 100 | succ_curve = np.mean(succ_curve, axis=0) 101 | prec_curve = np.mean(prec_curve, axis=0) 102 | norm_prec_curve = np.mean(norm_prec_curve, axis=0) 103 | succ_score = np.mean(succ_curve) 104 | prec_score = prec_curve[20] 105 | norm_prec_score = np.mean(norm_prec_curve) 106 | succ_rate = succ_curve[self.nbins_iou // 2] 107 | if np.count_nonzero(speeds) > 0: 108 | avg_speed = np.sum(speeds) / np.count_nonzero(speeds) 109 | else: 110 | avg_speed = -1 111 | 112 | # store overall performance 113 | performance[name]['overall'].update({ 114 | 'success_curve': succ_curve.tolist(), 115 | 'precision_curve': prec_curve.tolist(), 116 | 'normalized_precision_curve': norm_prec_curve.tolist(), 117 | 'success_score': succ_score, 118 | 'precision_score': prec_score, 119 | 'normalized_precision_score': norm_prec_score, 120 | 'success_rate': succ_rate, 121 | 'speed_fps': avg_speed}) 122 | 123 | # report the performance 124 | with open(report_file, 'w') as f: 125 | json.dump(performance, f, indent=4) 126 | # plot precision and success curves 127 | self.plot_curves(tracker_names) 128 | 129 | return performance 130 | 131 | def _calc_metrics(self, boxes, anno): 132 | valid = ~np.any(np.isnan(anno), axis=1) 133 | if len(valid) == 0: 134 | print('Warning: no valid annotations') 135 | return None, None, None 136 | else: 137 | ious = rect_iou(boxes[valid, :], anno[valid, :]) 138 | center_errors = center_error( 139 | boxes[valid, :], anno[valid, :]) 140 | norm_center_errors = normalized_center_error( 141 | boxes[valid, :], anno[valid, :]) 142 | return ious, center_errors, norm_center_errors 143 | 144 | def _calc_curves(self, ious, center_errors, norm_center_errors): 145 | ious = np.asarray(ious, float)[:, np.newaxis] 146 | center_errors = np.asarray(center_errors, float)[:, np.newaxis] 147 | norm_center_errors = np.asarray(norm_center_errors, float)[:, np.newaxis] 148 | 149 | thr_iou = np.linspace(0, 1, self.nbins_iou)[np.newaxis, :] 150 | thr_ce = np.arange(0, self.nbins_ce)[np.newaxis, :] 151 | thr_nce = np.linspace(0, 0.5, self.nbins_nce)[np.newaxis, :] 152 | 153 | bin_iou = np.greater(ious, thr_iou) 154 | bin_ce = np.less_equal(center_errors, thr_ce) 155 | bin_nce = np.less_equal(norm_center_errors, thr_nce) 156 | 157 | succ_curve = np.mean(bin_iou, axis=0) 158 | prec_curve = np.mean(bin_ce, axis=0) 159 | norm_prec_curve = np.mean(bin_nce, axis=0) 160 | 161 | return succ_curve, prec_curve, norm_prec_curve 162 | 163 | def plot_curves(self, tracker_names, extension='.png'): 164 | # assume tracker_names[0] is your tracker 165 | report_dir = os.path.join(self.report_dir, tracker_names[0]) 166 | assert os.path.exists(report_dir), \ 167 | 'No reports found. Run "report" first' \ 168 | 'before plotting curves.' 169 | report_file = os.path.join(report_dir, 'performance.json') 170 | assert os.path.exists(report_file), \ 171 | 'No reports found. Run "report" first' \ 172 | 'before plotting curves.' 173 | 174 | # load pre-computed performance 175 | with open(report_file) as f: 176 | performance = json.load(f) 177 | 178 | succ_file = os.path.join(report_dir, 'success_plots'+extension) 179 | prec_file = os.path.join(report_dir, 'precision_plots'+extension) 180 | norm_prec_file = os.path.join(report_dir, 'norm_precision_plots'+extension) 181 | key = 'overall' 182 | 183 | # markers 184 | markers = ['-', '--', '-.'] 185 | markers = [c + m for m in markers for c in [''] * 10] 186 | 187 | # filter performance by tracker_names 188 | performance = {k:v for k,v in performance.items() if k in tracker_names} 189 | 190 | # sort trackers by success score 191 | tracker_names = list(performance.keys()) 192 | succ = [t[key]['success_score'] for t in performance.values()] 193 | inds = np.argsort(succ)[::-1] 194 | tracker_names = [tracker_names[i] for i in inds] 195 | 196 | # plot success curves 197 | thr_iou = np.linspace(0, 1, self.nbins_iou) 198 | fig, ax = plt.subplots() 199 | lines = [] 200 | legends = [] 201 | for i, name in enumerate(tracker_names): 202 | line, = ax.plot(thr_iou, 203 | performance[name][key]['success_curve'], 204 | markers[i % len(markers)]) 205 | lines.append(line) 206 | legends.append('%s: [%.3f]' % (name, performance[name][key]['success_score'])) 207 | matplotlib.rcParams.update({'font.size': 7.4}) 208 | # legend = ax.legend(lines, legends, loc='center left', bbox_to_anchor=(1, 0.5)) 209 | legend = ax.legend(lines, legends, loc='lower left', bbox_to_anchor=(0., 0.)) 210 | 211 | matplotlib.rcParams.update({'font.size': 9}) 212 | ax.set(xlabel='Overlap threshold', 213 | ylabel='Success rate', 214 | xlim=(0, 1), ylim=(0, 1), 215 | title='Success plots on LaSOT') 216 | ax.grid(True) 217 | fig.tight_layout() 218 | 219 | # control ratio 220 | # ax.set_aspect('equal', 'box') 221 | 222 | print('Saving success plots to', succ_file) 223 | fig.savefig(succ_file, 224 | bbox_extra_artists=(legend,), 225 | bbox_inches='tight', 226 | dpi=300) 227 | 228 | # sort trackers by precision score 229 | tracker_names = list(performance.keys()) 230 | prec = [t[key]['precision_score'] for t in performance.values()] 231 | inds = np.argsort(prec)[::-1] 232 | tracker_names = [tracker_names[i] for i in inds] 233 | 234 | # plot precision curves 235 | thr_ce = np.arange(0, self.nbins_ce) 236 | fig, ax = plt.subplots() 237 | lines = [] 238 | legends = [] 239 | for i, name in enumerate(tracker_names): 240 | line, = ax.plot(thr_ce, 241 | performance[name][key]['precision_curve'], 242 | markers[i % len(markers)]) 243 | lines.append(line) 244 | legends.append('%s: [%.3f]' % (name, performance[name][key]['precision_score'])) 245 | matplotlib.rcParams.update({'font.size': 7.4}) 246 | # legend = ax.legend(lines, legends, loc='center left', bbox_to_anchor=(1, 0.5)) 247 | legend = ax.legend(lines, legends, loc='lower right', bbox_to_anchor=(1., 0.)) 248 | 249 | matplotlib.rcParams.update({'font.size': 9}) 250 | ax.set(xlabel='Location error threshold', 251 | ylabel='Precision', 252 | xlim=(0, thr_ce.max()), ylim=(0, 1), 253 | title='Precision plots on LaSOT') 254 | ax.grid(True) 255 | fig.tight_layout() 256 | 257 | # control ratio 258 | # ax.set_aspect('equal', 'box') 259 | 260 | print('Saving precision plots to', prec_file) 261 | fig.savefig(prec_file, dpi=300) 262 | 263 | # added by user 264 | # sort trackers by normalized precision score 265 | tracker_names = list(performance.keys()) 266 | prec = [t[key]['normalized_precision_score'] for t in performance.values()] 267 | inds = np.argsort(prec)[::-1] 268 | tracker_names = [tracker_names[i] for i in inds] 269 | 270 | # plot normalized precision curves 271 | thr_nce = np.arange(0, self.nbins_nce) 272 | fig, ax = plt.subplots() 273 | lines = [] 274 | legends = [] 275 | for i, name in enumerate(tracker_names): 276 | line, = ax.plot(thr_nce, 277 | performance[name][key]['normalized_precision_curve'], 278 | markers[i % len(markers)]) 279 | lines.append(line) 280 | legends.append('%s: [%.3f]' % (name, performance[name][key]['normalized_precision_score'])) 281 | matplotlib.rcParams.update({'font.size': 7.4}) 282 | # legend = ax.legend(lines, legends, loc='center left', bbox_to_anchor=(1, 0.5)) 283 | legend = ax.legend(lines, legends, loc='lower right', bbox_to_anchor=(1., 0.)) 284 | 285 | matplotlib.rcParams.update({'font.size': 9}) 286 | ax.set(xlabel='Normalized location error threshold', 287 | ylabel='Normalized precision', 288 | xlim=(0, thr_ce.max()), ylim=(0, 1), 289 | title='Normalized precision plots on LaSOT') 290 | ax.grid(True) 291 | fig.tight_layout() 292 | 293 | # control ratio 294 | # ax.set_aspect('equal', 'box') 295 | 296 | print('Saving normalized precision plots to', norm_prec_file) 297 | fig.savefig(norm_prec_file, dpi=300) 298 | 299 | -------------------------------------------------------------------------------- /got10k/experiments/nfs.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | 3 | import os 4 | 5 | from .otb import ExperimentOTB 6 | from ..datasets import NfS 7 | 8 | 9 | class ExperimentNfS(ExperimentOTB): 10 | r"""Experiment pipeline and evaluation toolkit for NfS dataset. 11 | 12 | Args: 13 | root_dir (string): Root directory of NfS dataset. 14 | result_dir (string, optional): Directory for storing tracking 15 | results. Default is ``./results``. 16 | report_dir (string, optional): Directory for storing performance 17 | evaluation results. Default is ``./reports``. 18 | """ 19 | def __init__(self, root_dir, fps=240, 20 | result_dir='results', report_dir='reports'): 21 | self.dataset = NfS(root_dir, fps) 22 | self.result_dir = os.path.join(result_dir, 'NfS/%d' % fps) 23 | self.report_dir = os.path.join(report_dir, 'NfS/%d' % fps) 24 | # as nbins_iou increases, the success score 25 | # converges to the average overlap (AO) 26 | self.nbins_iou = 21 27 | self.nbins_ce = 51 28 | -------------------------------------------------------------------------------- /got10k/experiments/otb.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import, division, print_function 2 | 3 | import os 4 | import numpy as np 5 | import matplotlib.pyplot as plt 6 | import matplotlib 7 | import json 8 | from PIL import Image 9 | 10 | from ..datasets import OTB 11 | from ..utils.metrics import rect_iou, center_error 12 | from ..utils.viz import show_frame 13 | 14 | 15 | class ExperimentOTB(object): 16 | r"""Experiment pipeline and evaluation toolkit for OTB dataset. 17 | 18 | Args: 19 | root_dir (string): Root directory of OTB dataset. 20 | version (integer or string): Specify the benchmark version, specify as one of 21 | ``2013``, ``2015``, ``tb50`` and ``tb100``. Default is ``2015``. 22 | result_dir (string, optional): Directory for storing tracking 23 | results. Default is ``./results``. 24 | report_dir (string, optional): Directory for storing performance 25 | evaluation results. Default is ``./reports``. 26 | """ 27 | def __init__(self, root_dir, version=2015, 28 | result_dir='results', report_dir='reports'): 29 | super(ExperimentOTB, self).__init__() 30 | self.dataset = OTB(root_dir, version, download=True) 31 | self.result_dir = os.path.join(result_dir, 'OTB' + str(version)) 32 | self.report_dir = os.path.join(report_dir, 'OTB' + str(version)) 33 | # as nbins_iou increases, the success score 34 | # converges to the average overlap (AO) 35 | self.nbins_iou = 21 36 | self.nbins_ce = 51 37 | 38 | def run(self, tracker, visualize=False): 39 | print('Running tracker %s on %s...' % ( 40 | tracker.name, type(self.dataset).__name__)) 41 | 42 | # loop over the complete dataset 43 | for s, (img_files, anno) in enumerate(self.dataset): 44 | seq_name = self.dataset.seq_names[s] 45 | print('--Sequence %d/%d: %s' % (s + 1, len(self.dataset), seq_name)) 46 | 47 | # skip if results exist 48 | record_file = os.path.join( 49 | self.result_dir, tracker.name, '%s.txt' % seq_name) 50 | if os.path.exists(record_file): 51 | print(' Found results, skipping', seq_name) 52 | continue 53 | 54 | # tracking loop 55 | boxes, times = tracker.track( 56 | img_files, anno[0, :], visualize=visualize) 57 | assert len(boxes) == len(anno) 58 | 59 | # record results 60 | self._record(record_file, boxes, times) 61 | 62 | def report(self, tracker_names, plot_curves=True): 63 | assert isinstance(tracker_names, (list, tuple)) 64 | 65 | # assume tracker_names[0] is your tracker 66 | report_dir = os.path.join(self.report_dir, tracker_names[0]) 67 | if not os.path.isdir(report_dir): 68 | os.makedirs(report_dir) 69 | report_file = os.path.join(report_dir, 'performance.json') 70 | 71 | performance = {} 72 | for name in tracker_names: 73 | print('Evaluating', name) 74 | seq_num = len(self.dataset) 75 | succ_curve = np.zeros((seq_num, self.nbins_iou)) 76 | prec_curve = np.zeros((seq_num, self.nbins_ce)) 77 | speeds = np.zeros(seq_num) 78 | 79 | performance.update({name: { 80 | 'overall': {}, 81 | 'seq_wise': {}}}) 82 | 83 | for s, (_, anno) in enumerate(self.dataset): 84 | seq_name = self.dataset.seq_names[s] 85 | record_file = os.path.join( 86 | self.result_dir, name, '%s.txt' % seq_name) 87 | boxes = np.loadtxt(record_file, delimiter=',') 88 | boxes[0] = anno[0] 89 | if not (len(boxes) == len(anno)): 90 | print('warning: %s anno donnot match boxes'%seq_name) 91 | len_min = min(len(boxes),len(anno)) 92 | boxes = boxes[:len_min] 93 | anno = anno[:len_min] 94 | assert len(boxes) == len(anno) 95 | 96 | ious, center_errors = self._calc_metrics(boxes, anno) 97 | succ_curve[s], prec_curve[s] = self._calc_curves(ious, center_errors) 98 | 99 | # calculate average tracking speed 100 | time_file = os.path.join( 101 | self.result_dir, name, 'times/%s_time.txt' % seq_name) 102 | if os.path.isfile(time_file): 103 | times = np.loadtxt(time_file) 104 | times = times[times > 0] 105 | if len(times) > 0: 106 | speeds[s] = np.mean(1. / times) 107 | 108 | # store sequence-wise performance 109 | performance[name]['seq_wise'].update({seq_name: { 110 | 'success_curve': succ_curve[s].tolist(), 111 | 'precision_curve': prec_curve[s].tolist(), 112 | 'success_score': np.mean(succ_curve[s]), 113 | 'precision_score': prec_curve[s][20], 114 | 'success_rate': succ_curve[s][self.nbins_iou // 2], 115 | 'speed_fps': speeds[s] if speeds[s] > 0 else -1}}) 116 | 117 | succ_curve = np.mean(succ_curve, axis=0) 118 | prec_curve = np.mean(prec_curve, axis=0) 119 | succ_score = np.mean(succ_curve) 120 | prec_score = prec_curve[20] 121 | succ_rate = succ_curve[self.nbins_iou // 2] 122 | if np.count_nonzero(speeds) > 0: 123 | avg_speed = np.sum(speeds) / np.count_nonzero(speeds) 124 | else: 125 | avg_speed = -1 126 | 127 | # store overall performance 128 | performance[name]['overall'].update({ 129 | 'success_curve': succ_curve.tolist(), 130 | 'precision_curve': prec_curve.tolist(), 131 | 'success_score': succ_score, 132 | 'precision_score': prec_score, 133 | 'success_rate': succ_rate, 134 | 'speed_fps': avg_speed}) 135 | 136 | # report the performance 137 | with open(report_file, 'w') as f: 138 | json.dump(performance, f, indent=4) 139 | # plot precision and success curves 140 | if plot_curves: 141 | self.plot_curves(tracker_names) 142 | 143 | return performance 144 | 145 | def show(self, tracker_names, seq_names=None, play_speed=1): 146 | if seq_names is None: 147 | seq_names = self.dataset.seq_names 148 | elif isinstance(seq_names, str): 149 | seq_names = [seq_names] 150 | assert isinstance(tracker_names, (list, tuple)) 151 | assert isinstance(seq_names, (list, tuple)) 152 | 153 | play_speed = int(round(play_speed)) 154 | assert play_speed > 0 155 | 156 | for s, seq_name in enumerate(seq_names): 157 | print('[%d/%d] Showing results on %s...' % ( 158 | s + 1, len(seq_names), seq_name)) 159 | 160 | # load all tracking results 161 | records = {} 162 | for name in tracker_names: 163 | record_file = os.path.join( 164 | self.result_dir, name, '%s.txt' % seq_name) 165 | records[name] = np.loadtxt(record_file, delimiter=',') 166 | 167 | # loop over the sequence and display results 168 | img_files, anno = self.dataset[seq_name] 169 | for f, img_file in enumerate(img_files): 170 | if not f % play_speed == 0: 171 | continue 172 | image = Image.open(img_file) 173 | boxes = [anno[f]] + [ 174 | records[name][f] for name in tracker_names] 175 | show_frame(image, boxes, 176 | legends=['GroundTruth'] + tracker_names, 177 | colors=['w', 'r', 'g', 'b', 'c', 'm', 'y', 178 | 'orange', 'purple', 'brown', 'pink']) 179 | 180 | def _record(self, record_file, boxes, times): 181 | # record bounding boxes 182 | record_dir = os.path.dirname(record_file) 183 | if not os.path.isdir(record_dir): 184 | os.makedirs(record_dir) 185 | np.savetxt(record_file, boxes, fmt='%.3f', delimiter=',') 186 | while not os.path.exists(record_file): 187 | print('warning: recording failed, retrying...') 188 | np.savetxt(record_file, boxes, fmt='%.3f', delimiter=',') 189 | print(' Results recorded at', record_file) 190 | 191 | # record running times 192 | time_dir = os.path.join(record_dir, 'times') 193 | if not os.path.isdir(time_dir): 194 | os.makedirs(time_dir) 195 | time_file = os.path.join(time_dir, os.path.basename( 196 | record_file).replace('.txt', '_time.txt')) 197 | np.savetxt(time_file, times, fmt='%.8f') 198 | 199 | def _calc_metrics(self, boxes, anno): 200 | # can be modified by children classes 201 | ious = rect_iou(boxes, anno) 202 | center_errors = center_error(boxes, anno) 203 | return ious, center_errors 204 | 205 | def _calc_curves(self, ious, center_errors): 206 | ious = np.asarray(ious, float)[:, np.newaxis] 207 | center_errors = np.asarray(center_errors, float)[:, np.newaxis] 208 | 209 | thr_iou = np.linspace(0, 1, self.nbins_iou)[np.newaxis, :] 210 | thr_ce = np.arange(0, self.nbins_ce)[np.newaxis, :] 211 | 212 | bin_iou = np.greater(ious, thr_iou) 213 | bin_ce = np.less_equal(center_errors, thr_ce) 214 | 215 | succ_curve = np.mean(bin_iou, axis=0) 216 | prec_curve = np.mean(bin_ce, axis=0) 217 | 218 | return succ_curve, prec_curve 219 | 220 | def plot_curves(self, tracker_names): 221 | # assume tracker_names[0] is your tracker 222 | report_dir = os.path.join(self.report_dir, tracker_names[0]) 223 | assert os.path.exists(report_dir), \ 224 | 'No reports found. Run "report" first' \ 225 | 'before plotting curves.' 226 | report_file = os.path.join(report_dir, 'performance.json') 227 | assert os.path.exists(report_file), \ 228 | 'No reports found. Run "report" first' \ 229 | 'before plotting curves.' 230 | 231 | # load pre-computed performance 232 | with open(report_file) as f: 233 | performance = json.load(f) 234 | 235 | succ_file = os.path.join(report_dir, 'success_plots.png') 236 | prec_file = os.path.join(report_dir, 'precision_plots.png') 237 | key = 'overall' 238 | 239 | # markers 240 | markers = ['-', '--', '-.'] 241 | markers = [c + m for m in markers for c in [''] * 10] 242 | 243 | # sort trackers by success score 244 | tracker_names = list(performance.keys()) 245 | succ = [t[key]['success_score'] for t in performance.values()] 246 | inds = np.argsort(succ)[::-1] 247 | tracker_names = [tracker_names[i] for i in inds] 248 | 249 | # plot success curves 250 | thr_iou = np.linspace(0, 1, self.nbins_iou) 251 | fig, ax = plt.subplots() 252 | lines = [] 253 | legends = [] 254 | for i, name in enumerate(tracker_names): 255 | line, = ax.plot(thr_iou, 256 | performance[name][key]['success_curve'], 257 | markers[i % len(markers)]) 258 | lines.append(line) 259 | legends.append('%s: [%.3f]' % (name, performance[name][key]['success_score'])) 260 | matplotlib.rcParams.update({'font.size': 7.4}) 261 | legend = ax.legend(lines, legends, loc='center left', 262 | bbox_to_anchor=(1, 0.5)) 263 | 264 | matplotlib.rcParams.update({'font.size': 9}) 265 | ax.set(xlabel='Overlap threshold', 266 | ylabel='Success rate', 267 | xlim=(0, 1), ylim=(0, 1), 268 | title='Success plots of OPE') 269 | ax.grid(True) 270 | fig.tight_layout() 271 | 272 | print('Saving success plots to', succ_file) 273 | fig.savefig(succ_file, 274 | bbox_extra_artists=(legend,), 275 | bbox_inches='tight', 276 | dpi=300) 277 | 278 | # sort trackers by precision score 279 | tracker_names = list(performance.keys()) 280 | prec = [t[key]['precision_score'] for t in performance.values()] 281 | inds = np.argsort(prec)[::-1] 282 | tracker_names = [tracker_names[i] for i in inds] 283 | 284 | # plot precision curves 285 | thr_ce = np.arange(0, self.nbins_ce) 286 | fig, ax = plt.subplots() 287 | lines = [] 288 | legends = [] 289 | for i, name in enumerate(tracker_names): 290 | line, = ax.plot(thr_ce, 291 | performance[name][key]['precision_curve'], 292 | markers[i % len(markers)]) 293 | lines.append(line) 294 | legends.append('%s: [%.3f]' % (name, performance[name][key]['precision_score'])) 295 | matplotlib.rcParams.update({'font.size': 7.4}) 296 | legend = ax.legend(lines, legends, loc='center left', 297 | bbox_to_anchor=(1, 0.5)) 298 | 299 | matplotlib.rcParams.update({'font.size': 9}) 300 | ax.set(xlabel='Location error threshold', 301 | ylabel='Precision', 302 | xlim=(0, thr_ce.max()), ylim=(0, 1), 303 | title='Precision plots of OPE') 304 | ax.grid(True) 305 | fig.tight_layout() 306 | 307 | print('Saving precision plots to', prec_file) 308 | fig.savefig(prec_file, dpi=300) 309 | -------------------------------------------------------------------------------- /got10k/experiments/tcolor128.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | 3 | import os 4 | 5 | from .otb import ExperimentOTB 6 | from ..datasets import TColor128 7 | 8 | 9 | class ExperimentTColor128(ExperimentOTB): 10 | r"""Experiment pipeline and evaluation toolkit for TColor128 dataset. 11 | 12 | Args: 13 | root_dir (string): Root directory of TColor128 dataset. 14 | result_dir (string, optional): Directory for storing tracking 15 | results. Default is ``./results``. 16 | report_dir (string, optional): Directory for storing performance 17 | evaluation results. Default is ``./reports``. 18 | """ 19 | def __init__(self, root_dir, 20 | result_dir='results', report_dir='reports'): 21 | self.dataset = TColor128(root_dir) 22 | self.result_dir = os.path.join(result_dir, 'TColor128') 23 | self.report_dir = os.path.join(report_dir, 'TColor128') 24 | # as nbins_iou increases, the success score 25 | # converges to the average overlap (AO) 26 | self.nbins_iou = 21 27 | self.nbins_ce = 51 28 | -------------------------------------------------------------------------------- /got10k/experiments/trackingnet.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | 3 | import os 4 | import numpy as np 5 | 6 | from .otb import ExperimentOTB 7 | from ..datasets import TrackingNet 8 | from ..utils.metrics import rect_iou, center_error 9 | 10 | 11 | class ExperimentTrackingNet(ExperimentOTB): 12 | r"""Experiment pipeline and evaluation toolkit for TrackingNet dataset. 13 | Only the TEST subset part implemented. 14 | 15 | Args: 16 | root_dir (string): Root directory of LaSOT dataset. 17 | subset (string, optional): Specify ``train`` or ``test`` 18 | subset of LaSOT. Default is ``test``. 19 | return_meta (bool, optional): whether to fetch meta info 20 | (occlusion or out-of-view). Default is ``False``. 21 | result_dir (string, optional): Directory for storing tracking 22 | results. Default is ``./results``. 23 | report_dir (string, optional): Directory for storing performance 24 | evaluation results. Default is ``./reports``. 25 | """ 26 | def __init__(self, root_dir, subset='test', return_meta=False, 27 | result_dir='results', report_dir='reports'): 28 | assert subset.upper() in ['TRAIN', 'TEST'] 29 | self.dataset = TrackingNet(root_dir, subset, return_meta=return_meta) 30 | self.result_dir = result_dir 31 | self.report_dir = report_dir 32 | 33 | # as nbins_iou increases, the success score 34 | # converges to the average overlap (AO) 35 | self.nbins_iou = 21 36 | self.nbins_ce = 51 37 | 38 | # def _calc_metrics(self, boxes, anno): 39 | # valid = ~np.any(np.isnan(anno), axis=1) 40 | # if len(valid) == 0: 41 | # print('Warning: no valid annotations') 42 | # return None, None 43 | # else: 44 | # ious = rect_iou(boxes[valid, :], anno[valid, :]) 45 | # center_errors = center_error( 46 | # boxes[valid, :], anno[valid, :]) 47 | # return ious, center_errors 48 | -------------------------------------------------------------------------------- /got10k/experiments/uav123.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | 3 | import os 4 | import numpy as np 5 | 6 | from .otb import ExperimentOTB 7 | from ..datasets import UAV123 8 | from ..utils.metrics import rect_iou, center_error 9 | 10 | 11 | class ExperimentUAV123(ExperimentOTB): 12 | r"""Experiment pipeline and evaluation toolkit for UAV123 dataset. 13 | 14 | Args: 15 | root_dir (string): Root directory of UAV123 dataset. 16 | result_dir (string, optional): Directory for storing tracking 17 | results. Default is ``./results``. 18 | report_dir (string, optional): Directory for storing performance 19 | evaluation results. Default is ``./reports``. 20 | """ 21 | def __init__(self, root_dir, version='UAV123', 22 | result_dir='results', report_dir='reports'): 23 | assert version.upper() in ['UAV123', 'UAV20L'] 24 | self.dataset = UAV123(root_dir, version) 25 | self.result_dir = os.path.join(result_dir, version.upper()) 26 | self.report_dir = os.path.join(report_dir, version.upper()) 27 | # as nbins_iou increases, the success score 28 | # converges to the average overlap (AO) 29 | self.nbins_iou = 21 30 | self.nbins_ce = 51 31 | 32 | def _calc_metrics(self, boxes, anno): 33 | valid = ~np.any(np.isnan(anno), axis=1) 34 | if len(valid) == 0: 35 | print('Warning: no valid annotations') 36 | return None, None 37 | else: 38 | ious = rect_iou(boxes[valid, :], anno[valid, :]) 39 | center_errors = center_error( 40 | boxes[valid, :], anno[valid, :]) 41 | return ious, center_errors 42 | -------------------------------------------------------------------------------- /got10k/experiments/vot.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import, division, print_function 2 | 3 | import time 4 | import numpy as np 5 | import os 6 | import glob 7 | import warnings 8 | import json 9 | from PIL import Image 10 | 11 | from ..datasets import VOT 12 | from ..utils.metrics import poly_iou 13 | from ..utils.viz import show_frame 14 | 15 | 16 | class ExperimentVOT(object): 17 | r"""Experiment pipeline and evaluation toolkit for VOT dataset. 18 | 19 | Notes: 20 | - The tracking results of three types of experiments ``supervised`` 21 | ``unsupervised`` and ``realtime`` are compatible with the official 22 | VOT toolkit `. 23 | - TODO: The evaluation function for VOT tracking results is still 24 | under development. 25 | 26 | Args: 27 | root_dir (string): Root directory of VOT dataset where sequence 28 | folders exist. 29 | version (integer, optional): Specify the VOT dataset version. Specify as 30 | one of 2013~2018. Default is 2017. 31 | list_file (string, optional): If provided, only run experiments over 32 | sequences specified by the file. 33 | read_image (boolean, optional): If True, return the read PIL image in 34 | each frame. Otherwise only return the image path. Default is True. 35 | experiments (string or tuple): Specify the type(s) of experiments to run. 36 | Default is a tuple (``supervised``, ``unsupervised``, ``realtime``). 37 | result_dir (string, optional): Directory for storing tracking 38 | results. Default is ``./results``. 39 | report_dir (string, optional): Directory for storing performance 40 | evaluation results. Default is ``./reports``. 41 | """ 42 | def __init__(self, root_dir, version=2017, 43 | read_image=True, list_file=None, 44 | experiments=('supervised', 'unsupervised', 'realtime'), 45 | result_dir='results', report_dir='reports'): 46 | super(ExperimentVOT, self).__init__() 47 | if isinstance(experiments, str): 48 | experiments = (experiments,) 49 | assert all([e in ['supervised', 'unsupervised', 'realtime'] 50 | for e in experiments]) 51 | self.dataset = VOT( 52 | root_dir, version, anno_type='default', 53 | download=True, return_meta=True, list_file=list_file) 54 | self.experiments = experiments 55 | if version == 'LT2018': 56 | version = '-' + version 57 | self.read_image = read_image 58 | self.result_dir = os.path.join(result_dir, 'VOT' + str(version)) 59 | self.report_dir = os.path.join(report_dir, 'VOT' + str(version)) 60 | self.skip_initialize = 5 61 | self.burnin = 10 62 | self.repetitions = 15 63 | self.sensitive = 100 64 | self.nbins_eao = 1500 65 | self.tags = ['camera_motion', 'illum_change', 'occlusion', 66 | 'size_change', 'motion_change', 'empty'] 67 | 68 | def run(self, tracker, visualize=False): 69 | print('Running tracker %s on %s...' % ( 70 | tracker.name, type(self.dataset).__name__)) 71 | 72 | # run all specified experiments 73 | if 'supervised' in self.experiments: 74 | self.run_supervised(tracker, visualize) 75 | if 'unsupervised' in self.experiments: 76 | self.run_unsupervised(tracker, visualize) 77 | if 'realtime' in self.experiments: 78 | self.run_realtime(tracker, visualize) 79 | 80 | def run_supervised(self, tracker, visualize=False): 81 | print('Running supervised experiment...') 82 | 83 | # loop over the complete dataset 84 | for s, (img_files, anno, _) in enumerate(self.dataset): 85 | seq_name = self.dataset.seq_names[s] 86 | print('--Sequence %d/%d: %s' % (s + 1, len(self.dataset), seq_name)) 87 | 88 | # rectangular bounding boxes 89 | anno_rects = anno.copy() 90 | if anno_rects.shape[1] == 8: 91 | anno_rects = self.dataset._corner2rect(anno_rects) 92 | 93 | # run multiple repetitions for each sequence 94 | for r in range(self.repetitions): 95 | # check if the tracker is deterministic 96 | if r > 0 and tracker.is_deterministic: 97 | break 98 | elif r == 3 and self._check_deterministic('baseline', tracker.name, seq_name): 99 | print(' Detected a deterministic tracker, ' + 100 | 'skipping remaining trials.') 101 | break 102 | print(' Repetition: %d' % (r + 1)) 103 | 104 | # skip if results exist 105 | record_file = os.path.join( 106 | self.result_dir, tracker.name, 'baseline', seq_name, 107 | '%s_%03d.txt' % (seq_name, r + 1)) 108 | if os.path.exists(record_file): 109 | print(' Found results, skipping', seq_name) 110 | continue 111 | 112 | # state variables 113 | boxes = [] 114 | times = [] 115 | failure = False 116 | next_start = -1 117 | 118 | # tracking loop 119 | for f, img_file in enumerate(img_files): 120 | image = Image.open(img_file) 121 | if self.read_image: 122 | frame = image 123 | else: 124 | frame = img_file 125 | 126 | start_time = time.time() 127 | if f == 0: 128 | # initial frame 129 | tracker.init(frame, anno_rects[0]) 130 | boxes.append([1]) 131 | elif failure: 132 | # during failure frames 133 | if f == next_start: 134 | failure = False 135 | tracker.init(frame, anno_rects[f]) 136 | boxes.append([1]) 137 | else: 138 | start_time = np.NaN 139 | boxes.append([0]) 140 | else: 141 | # during success frames 142 | box = tracker.update(frame) 143 | iou = poly_iou(anno[f], box, bound=image.size) 144 | if iou <= 0.0: 145 | # tracking failure 146 | failure = True 147 | next_start = f + self.skip_initialize 148 | boxes.append([2]) 149 | else: 150 | # tracking succeed 151 | boxes.append(box) 152 | 153 | # store elapsed time 154 | times.append(time.time() - start_time) 155 | 156 | # visualize if required 157 | if visualize: 158 | if len(boxes[-1]) == 4: 159 | show_frame(image, boxes[-1]) 160 | else: 161 | show_frame(image) 162 | 163 | # record results 164 | self._record(record_file, boxes, times) 165 | 166 | def run_unsupervised(self, tracker, visualize=False): 167 | print('Running unsupervised experiment...') 168 | 169 | # loop over the complete dataset 170 | for s, (img_files, anno, _) in enumerate(self.dataset): 171 | seq_name = self.dataset.seq_names[s] 172 | print('--Sequence %d/%d: %s' % (s + 1, len(self.dataset), seq_name)) 173 | 174 | # skip if results exist 175 | record_file = os.path.join( 176 | self.result_dir, tracker.name, 'unsupervised', seq_name, 177 | '%s_001.txt' % seq_name) 178 | if os.path.exists(record_file): 179 | print(' Found results, skipping', seq_name) 180 | continue 181 | 182 | # rectangular bounding boxes 183 | anno_rects = anno.copy() 184 | if anno_rects.shape[1] == 8: 185 | anno_rects = self.dataset._corner2rect(anno_rects) 186 | 187 | # tracking loop 188 | boxes, times = tracker.track( 189 | img_files, anno_rects[0], visualize=visualize) 190 | assert len(boxes) == len(anno) 191 | 192 | # re-formatting 193 | boxes = list(boxes) 194 | boxes[0] = [1] 195 | 196 | # record results 197 | self._record(record_file, boxes, times) 198 | 199 | def run_realtime(self, tracker, visualize=False): 200 | print('Running real-time experiment...') 201 | 202 | # loop over the complete dataset 203 | for s, (img_files, anno, _) in enumerate(self.dataset): 204 | seq_name = self.dataset.seq_names[s] 205 | print('--Sequence %d/%d: %s' % (s + 1, len(self.dataset), seq_name)) 206 | 207 | # skip if results exist 208 | record_file = os.path.join( 209 | self.result_dir, tracker.name, 'realtime', seq_name, 210 | '%s_001.txt' % seq_name) 211 | if os.path.exists(record_file): 212 | print(' Found results, skipping', seq_name) 213 | continue 214 | 215 | # rectangular bounding boxes 216 | anno_rects = anno.copy() 217 | if anno_rects.shape[1] == 8: 218 | anno_rects = self.dataset._corner2rect(anno_rects) 219 | 220 | # state variables 221 | boxes = [] 222 | times = [] 223 | next_start = 0 224 | failure = False 225 | failed_frame = -1 226 | total_time = 0.0 227 | grace = 3 - 1 228 | offset = 0 229 | 230 | # tracking loop 231 | for f, img_file in enumerate(img_files): 232 | image = Image.open(img_file) 233 | if self.read_image: 234 | frame = image 235 | else: 236 | frame = img_file 237 | 238 | start_time = time.time() 239 | if f == next_start: 240 | # during initial frames 241 | tracker.init(frame, anno_rects[f]) 242 | boxes.append([1]) 243 | 244 | # reset state variables 245 | failure = False 246 | failed_frame = -1 247 | total_time = 0.0 248 | grace = 3 - 1 249 | offset = f 250 | elif not failure: 251 | # during success frames 252 | # calculate current frame 253 | if grace > 0: 254 | total_time += 1000.0 / 25 255 | grace -= 1 256 | else: 257 | total_time += max(1000.0 / 25, last_time * 1000.0) 258 | current = offset + int(np.round(np.floor(total_time * 25) / 1000.0)) 259 | 260 | # delayed/tracked bounding box 261 | if f < current: 262 | box = boxes[-1] 263 | elif f == current: 264 | box = tracker.update(frame) 265 | 266 | iou = poly_iou(anno[f], box, bound=image.size) 267 | if iou <= 0.0: 268 | # tracking failure 269 | failure = True 270 | failed_frame = f 271 | next_start = current + self.skip_initialize 272 | boxes.append([2]) 273 | else: 274 | # tracking succeed 275 | boxes.append(box) 276 | else: 277 | # during failure frames 278 | if f < current: 279 | # skipping frame due to slow speed 280 | boxes.append([0]) 281 | start_time = np.NaN 282 | elif f == current: 283 | # current frame 284 | box = tracker.update(frame) 285 | iou = poly_iou(anno[f], box, bound=image.size) 286 | if iou <= 0.0: 287 | # tracking failure 288 | boxes.append([2]) 289 | boxes[failed_frame] = [0] 290 | times[failed_frame] = np.NaN 291 | else: 292 | # tracking succeed 293 | boxes.append(box) 294 | elif f < next_start: 295 | # skipping frame due to failure 296 | boxes.append([0]) 297 | start_time = np.NaN 298 | 299 | # store elapsed time 300 | last_time = time.time() - start_time 301 | times.append(last_time) 302 | 303 | # visualize if required 304 | if visualize: 305 | if len(boxes[-1]) == 4: 306 | show_frame(image, boxes[-1]) 307 | else: 308 | show_frame(image) 309 | 310 | # record results 311 | self._record(record_file, boxes, times) 312 | 313 | def report(self, tracker_names): 314 | assert isinstance(tracker_names, (list, tuple)) 315 | 316 | # function for loading results 317 | def read_record(filename): 318 | with open(filename) as f: 319 | record = f.read().strip().split('\n') 320 | record = [[float(t) for t in line.split(',')] 321 | for line in record] 322 | return record 323 | 324 | # assume tracker_names[0] is your tracker 325 | report_dir = os.path.join(self.report_dir, tracker_names[0]) 326 | if not os.path.exists(report_dir): 327 | os.makedirs(report_dir) 328 | report_file = os.path.join(report_dir, 'performance.json') 329 | 330 | performance = {} 331 | for name in tracker_names: 332 | print('Evaluating', name) 333 | ious = {} 334 | ious_full = {} 335 | failures = {} 336 | times = {} 337 | masks = {} # frame masks for attribute tags 338 | 339 | for s, (img_files, anno, meta) in enumerate(self.dataset): 340 | seq_name = self.dataset.seq_names[s] 341 | 342 | # initialize frames scores 343 | frame_num = len(img_files) 344 | ious[seq_name] = np.full( 345 | (self.repetitions, frame_num), np.nan, dtype=float) 346 | ious_full[seq_name] = np.full( 347 | (self.repetitions, frame_num), np.nan, dtype=float) 348 | failures[seq_name] = np.full( 349 | (self.repetitions, frame_num), np.nan, dtype=float) 350 | times[seq_name] = np.full( 351 | (self.repetitions, frame_num), np.nan, dtype=float) 352 | 353 | # read results of all repetitions 354 | record_files = sorted(glob.glob(os.path.join( 355 | self.result_dir, name, 'baseline', seq_name, 356 | '%s_[0-9]*.txt' % seq_name))) 357 | boxes = [read_record(f) for f in record_files] 358 | assert all([len(b) == len(anno) for b in boxes]) 359 | 360 | # calculate frame ious with burnin 361 | bound = Image.open(img_files[0]).size 362 | seq_ious = [self._calc_iou(b, anno, bound, burnin=True) 363 | for b in boxes] 364 | ious[seq_name][:len(seq_ious), :] = seq_ious 365 | 366 | # calculate frame ious without burnin 367 | seq_ious_full = [self._calc_iou(b, anno, bound) 368 | for b in boxes] 369 | ious_full[seq_name][:len(seq_ious_full), :] = seq_ious_full 370 | 371 | # calculate frame failures 372 | seq_failures = [ 373 | [len(b) == 1 and b[0] == 2 for b in boxes_per_rep] 374 | for boxes_per_rep in boxes] 375 | failures[seq_name][:len(seq_failures), :] = seq_failures 376 | 377 | # collect frame runtimes 378 | time_file = os.path.join( 379 | self.result_dir, name, 'baseline', seq_name, 380 | '%s_time.txt' % seq_name) 381 | if os.path.exists(time_file): 382 | seq_times = np.loadtxt(time_file, delimiter=',').T 383 | times[seq_name][:len(seq_times), :] = seq_times 384 | 385 | # collect attribute masks 386 | tag_num = len(self.tags) 387 | masks[seq_name] = np.zeros((tag_num, frame_num), bool) 388 | for i, tag in enumerate(self.tags): 389 | if tag in meta: 390 | masks[seq_name][i, :] = meta[tag] 391 | # frames with no tags 392 | if 'empty' in self.tags: 393 | tag_frames = np.array([ 394 | v for k, v in meta.items() 395 | if not 'practical' in k], dtype=bool) 396 | ind = self.tags.index('empty') 397 | masks[seq_name][ind, :] = \ 398 | ~np.logical_or.reduce(tag_frames, axis=0) 399 | 400 | # concatenate frames 401 | seq_names = self.dataset.seq_names 402 | masks = np.concatenate( 403 | [masks[s] for s in seq_names], axis=1) 404 | ious = np.concatenate( 405 | [ious[s] for s in seq_names], axis=1) 406 | failures = np.concatenate( 407 | [failures[s] for s in seq_names], axis=1) 408 | 409 | with warnings.catch_warnings(): 410 | # average over repetitions 411 | warnings.simplefilter('ignore', category=RuntimeWarning) 412 | ious = np.nanmean(ious, axis=0) 413 | failures = np.nanmean(failures, axis=0) 414 | 415 | # calculate average overlaps and failures for each tag 416 | tag_ious = np.array( 417 | [np.nanmean(ious[m]) for m in masks]) 418 | tag_failures = np.array( 419 | [np.nansum(failures[m]) for m in masks]) 420 | tag_frames = masks.sum(axis=1) 421 | 422 | # remove nan values 423 | tag_ious[np.isnan(tag_ious)] = 0.0 424 | tag_weights = tag_frames / tag_frames.sum() 425 | 426 | # calculate weighted accuracy and robustness 427 | accuracy = np.sum(tag_ious * tag_weights) 428 | robustness = np.sum(tag_failures * tag_weights) 429 | 430 | # calculate tracking speed 431 | times = np.concatenate([ 432 | t.reshape(-1) for t in times.values()]) 433 | # remove invalid values 434 | times = times[~np.isnan(times)] 435 | times = times[times > 0] 436 | if len(times) > 0: 437 | speed = np.mean(1. / times) 438 | else: 439 | speed = -1 440 | 441 | performance.update({name: { 442 | 'accuracy': accuracy, 443 | 'robustness': robustness, 444 | 'speed_fps': speed}}) 445 | 446 | # save performance 447 | with open(report_file, 'w') as f: 448 | json.dump(performance, f, indent=4) 449 | print('Performance saved at', report_file) 450 | 451 | return performance 452 | 453 | def show(self, tracker_names, seq_names=None, play_speed=1, 454 | experiment='supervised'): 455 | if seq_names is None: 456 | seq_names = self.dataset.seq_names 457 | elif isinstance(seq_names, str): 458 | seq_names = [seq_names] 459 | assert isinstance(tracker_names, (list, tuple)) 460 | assert isinstance(seq_names, (list, tuple)) 461 | assert experiment in ['supervised', 'unsupervised', 'realtime'] 462 | 463 | play_speed = int(round(play_speed)) 464 | assert play_speed > 0 465 | 466 | # "supervised" experiment results are stored in "baseline" folder 467 | if experiment == 'supervised': 468 | experiment = 'baseline' 469 | 470 | # function for loading results 471 | def read_record(filename): 472 | with open(filename) as f: 473 | record = f.read().strip().split('\n') 474 | record = [[float(t) for t in line.split(',')] 475 | for line in record] 476 | for i, r in enumerate(record): 477 | if len(r) == 4: 478 | record[i] = np.array(r) 479 | elif len(r) == 8: 480 | r = np.array(r)[np.newaxis, :] 481 | r = self.dataset._corner2rect(r) 482 | record[i] = r[0] 483 | else: 484 | record[i] = np.zeros(4) 485 | return record 486 | 487 | for s, seq_name in enumerate(seq_names): 488 | print('[%d/%d] Showing results on %s...' % ( 489 | s + 1, len(seq_names), seq_name)) 490 | 491 | # load all tracking results 492 | records = {} 493 | for name in tracker_names: 494 | record_file = os.path.join( 495 | self.result_dir, name, experiment, seq_name, 496 | '%s_001.txt' % seq_name) 497 | records[name] = read_record(record_file) 498 | 499 | # loop over the sequence and display results 500 | img_files, anno, _ = self.dataset[seq_name] 501 | if anno.shape[1] == 8: 502 | anno = self.dataset._corner2rect(anno) 503 | for f, img_file in enumerate(img_files): 504 | if not f % play_speed == 0: 505 | continue 506 | image = Image.open(img_file) 507 | boxes = [anno[f]] + [ 508 | records[name][f] for name in tracker_names] 509 | show_frame(image, boxes, 510 | legends=['GroundTruth'] + tracker_names, 511 | colors=['w', 'r', 'g', 'b', 'c', 'm', 'y', 512 | 'orange', 'purple', 'brown', 'pink']) 513 | 514 | def _record(self, record_file, boxes, times): 515 | # convert boxes to string 516 | lines = [] 517 | for box in boxes: 518 | if len(box) == 1: 519 | lines.append('%d' % box[0]) 520 | else: 521 | lines.append(str.join(',', ['%.4f' % t for t in box])) 522 | 523 | # record bounding boxes 524 | record_dir = os.path.dirname(record_file) 525 | if not os.path.isdir(record_dir): 526 | os.makedirs(record_dir) 527 | with open(record_file, 'w') as f: 528 | f.write(str.join('\n', lines)) 529 | print(' Results recorded at', record_file) 530 | 531 | # convert times to string 532 | lines = ['%.4f' % t for t in times] 533 | lines = [t.replace('nan', 'NaN') for t in lines] 534 | 535 | # record running times 536 | time_file = record_file[:record_file.rfind('_')] + '_time.txt' 537 | if os.path.exists(time_file): 538 | with open(time_file) as f: 539 | exist_lines = f.read().strip().split('\n') 540 | lines = [t + ',' + s for t, s in zip(exist_lines, lines)] 541 | with open(time_file, 'w') as f: 542 | f.write(str.join('\n', lines)) 543 | 544 | def _check_deterministic(self, exp, tracker_name, seq_name): 545 | record_dir = os.path.join( 546 | self.result_dir, tracker_name, exp, seq_name) 547 | record_files = sorted(glob.glob(os.path.join( 548 | record_dir, '%s_[0-9]*.txt' % seq_name))) 549 | 550 | if len(record_files) < 3: 551 | return False 552 | 553 | records = [] 554 | for record_file in record_files: 555 | with open(record_file, 'r') as f: 556 | records.append(f.read()) 557 | 558 | return len(set(records)) == 1 559 | 560 | def _calc_iou(self, boxes, anno, bound, burnin=False): 561 | # skip initialization frames 562 | if burnin: 563 | boxes = boxes.copy() 564 | init_inds = [i for i, box in enumerate(boxes) 565 | if box == [1.0]] 566 | for ind in init_inds: 567 | boxes[ind:ind + self.burnin] = [[0]] * self.burnin 568 | # calculate polygon ious 569 | ious = np.array([poly_iou(np.array(a), b, bound) 570 | if len(a) > 1 else np.NaN 571 | for a, b in zip(boxes, anno)]) 572 | return ious 573 | -------------------------------------------------------------------------------- /got10k/trackers/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | 3 | import numpy as np 4 | import time 5 | from PIL import Image 6 | 7 | from ..utils.viz import show_frame 8 | 9 | 10 | class Tracker(object): 11 | 12 | def __init__(self, name, is_deterministic=False): 13 | self.name = name 14 | self.is_deterministic = is_deterministic 15 | 16 | def init(self, image, box): 17 | raise NotImplementedError() 18 | 19 | def update(self, image): 20 | raise NotImplementedError() 21 | 22 | def track(self, img_files, box, visualize=False): 23 | frame_num = len(img_files) 24 | boxes = np.zeros((frame_num, 4)) 25 | boxes[0] = box 26 | times = np.zeros(frame_num) 27 | 28 | for f, img_file in enumerate(img_files): 29 | image = Image.open(img_file) 30 | if not image.mode == 'RGB': 31 | image = image.convert('RGB') 32 | 33 | start_time = time.time() 34 | if f == 0: 35 | self.init(image, box) 36 | else: 37 | boxes[f, :] = self.update(image) 38 | times[f] = time.time() - start_time 39 | 40 | if visualize: 41 | show_frame(image, boxes[f, :]) 42 | 43 | return boxes, times 44 | 45 | 46 | from .identity_tracker import IdentityTracker 47 | -------------------------------------------------------------------------------- /got10k/trackers/identity_tracker.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | 3 | from . import Tracker 4 | 5 | 6 | class IdentityTracker(Tracker): 7 | 8 | def __init__(self): 9 | super(IdentityTracker, self).__init__( 10 | name='IdentityTracker', 11 | is_deterministic=True) 12 | 13 | def init(self, image, box): 14 | self.box = box 15 | 16 | def update(self, image): 17 | return self.box 18 | -------------------------------------------------------------------------------- /got10k/utils/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/got-10k/toolkit/956e7286fdf209cbb125adac9a46376bd8297ffb/got10k/utils/__init__.py -------------------------------------------------------------------------------- /got10k/utils/ioutils.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import, division 2 | 3 | import wget 4 | import os 5 | import shutil 6 | import zipfile 7 | import sys 8 | 9 | 10 | def download(url, filename): 11 | r"""Download file from the internet. 12 | 13 | Args: 14 | url (string): URL of the internet file. 15 | filename (string): Path to store the downloaded file. 16 | """ 17 | return wget.download(url, out=filename) 18 | 19 | 20 | def extract(filename, extract_dir): 21 | r"""Extract zip file. 22 | 23 | Args: 24 | filename (string): Path of the zip file. 25 | extract_dir (string): Directory to store the extracted results. 26 | """ 27 | if os.path.splitext(filename)[1] == '.zip': 28 | if not os.path.isdir(extract_dir): 29 | os.makedirs(extract_dir) 30 | with zipfile.ZipFile(filename) as z: 31 | z.extractall(extract_dir) 32 | else: 33 | raise Exception('Unsupport extension {} of the compressed file {}.'.format( 34 | os.path.splitext(filename)[1]), filename) 35 | 36 | 37 | def compress(dirname, save_file): 38 | """Compress a folder to a zip file. 39 | 40 | Arguments: 41 | dirname {string} -- Directory of all files to be compressed. 42 | save_file {string} -- Path to store the zip file. 43 | """ 44 | shutil.make_archive(save_file, 'zip', dirname) 45 | -------------------------------------------------------------------------------- /got10k/utils/metrics.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import, division 2 | 3 | import numpy as np 4 | from shapely.geometry import box, Polygon 5 | 6 | 7 | def center_error(rects1, rects2): 8 | r"""Center error. 9 | 10 | Args: 11 | rects1 (numpy.ndarray): An N x 4 numpy array, each line represent a rectangle 12 | (left, top, width, height). 13 | rects2 (numpy.ndarray): An N x 4 numpy array, each line represent a rectangle 14 | (left, top, width, height). 15 | """ 16 | centers1 = rects1[..., :2] + (rects1[..., 2:] - 1) / 2 17 | centers2 = rects2[..., :2] + (rects2[..., 2:] - 1) / 2 18 | errors = np.sqrt(np.sum(np.power(centers1 - centers2, 2), axis=-1)) 19 | 20 | return errors 21 | 22 | def normalized_center_error(rects1, rects2): 23 | r"""Center error normalized by the size of ground truth. 24 | 25 | Args: 26 | rects1 (numpy.ndarray): prediction box. An N x 4 numpy array, each line represent a rectangle 27 | (left, top, width, height). 28 | rects2 (numpy.ndarray): groudn truth box. An N x 4 numpy array, each line represent a rectangle 29 | (left, top, width, height). 30 | """ 31 | centers1 = rects1[..., :2] + (rects1[..., 2:] - 1) / 2 32 | centers2 = rects2[..., :2] + (rects2[..., 2:] - 1) / 2 33 | errors = np.sqrt(np.sum(np.power((centers1 - centers2)/np.maximum(np.array([[1.,1.]]), rects2[:, 2:]), 2), axis=-1)) 34 | 35 | return errors 36 | 37 | 38 | def rect_iou(rects1, rects2, bound=None): 39 | r"""Intersection over union. 40 | 41 | Args: 42 | rects1 (numpy.ndarray): An N x 4 numpy array, each line represent a rectangle 43 | (left, top, width, height). 44 | rects2 (numpy.ndarray): An N x 4 numpy array, each line represent a rectangle 45 | (left, top, width, height). 46 | bound (numpy.ndarray): A 4 dimensional array, denotes the bound 47 | (min_left, min_top, max_width, max_height) for ``rects1`` and ``rects2``. 48 | """ 49 | assert rects1.shape == rects2.shape 50 | if bound is not None: 51 | # bounded rects1 52 | rects1[:, 0] = np.clip(rects1[:, 0], 0, bound[0]) 53 | rects1[:, 1] = np.clip(rects1[:, 1], 0, bound[1]) 54 | rects1[:, 2] = np.clip(rects1[:, 2], 0, bound[0] - rects1[:, 0]) 55 | rects1[:, 3] = np.clip(rects1[:, 3], 0, bound[1] - rects1[:, 1]) 56 | # bounded rects2 57 | rects2[:, 0] = np.clip(rects2[:, 0], 0, bound[0]) 58 | rects2[:, 1] = np.clip(rects2[:, 1], 0, bound[1]) 59 | rects2[:, 2] = np.clip(rects2[:, 2], 0, bound[0] - rects2[:, 0]) 60 | rects2[:, 3] = np.clip(rects2[:, 3], 0, bound[1] - rects2[:, 1]) 61 | 62 | rects_inter = _intersection(rects1, rects2) 63 | areas_inter = np.prod(rects_inter[..., 2:], axis=-1) 64 | 65 | areas1 = np.prod(rects1[..., 2:], axis=-1) 66 | areas2 = np.prod(rects2[..., 2:], axis=-1) 67 | areas_union = areas1 + areas2 - areas_inter 68 | 69 | eps = np.finfo(float).eps 70 | ious = areas_inter / (areas_union + eps) 71 | ious = np.clip(ious, 0.0, 1.0) 72 | 73 | return ious 74 | 75 | 76 | def _intersection(rects1, rects2): 77 | r"""Rectangle intersection. 78 | 79 | Args: 80 | rects1 (numpy.ndarray): An N x 4 numpy array, each line represent a rectangle 81 | (left, top, width, height). 82 | rects2 (numpy.ndarray): An N x 4 numpy array, each line represent a rectangle 83 | (left, top, width, height). 84 | """ 85 | assert rects1.shape == rects2.shape 86 | x1 = np.maximum(rects1[..., 0], rects2[..., 0]) 87 | y1 = np.maximum(rects1[..., 1], rects2[..., 1]) 88 | x2 = np.minimum(rects1[..., 0] + rects1[..., 2], 89 | rects2[..., 0] + rects2[..., 2]) 90 | y2 = np.minimum(rects1[..., 1] + rects1[..., 3], 91 | rects2[..., 1] + rects2[..., 3]) 92 | 93 | w = np.maximum(x2 - x1, 0) 94 | h = np.maximum(y2 - y1, 0) 95 | 96 | return np.stack([x1, y1, w, h]).T 97 | 98 | 99 | def poly_iou(polys1, polys2, bound=None): 100 | r"""Intersection over union of polygons. 101 | 102 | Args: 103 | polys1 (numpy.ndarray): An N x 4 numpy array, each line represent a rectangle 104 | (left, top, width, height); or an N x 8 numpy array, each line represent 105 | the coordinates (x1, y1, x2, y2, x3, y3, x4, y4) of 4 corners. 106 | polys2 (numpy.ndarray): An N x 4 numpy array, each line represent a rectangle 107 | (left, top, width, height); or an N x 8 numpy array, each line represent 108 | the coordinates (x1, y1, x2, y2, x3, y3, x4, y4) of 4 corners. 109 | bound (numpy.ndarray, optional): A 2 dimensional array, denotes the image bound 110 | (width, height) for ``rects1`` and ``rects2``. 111 | """ 112 | assert polys1.ndim in [1, 2] 113 | if polys1.ndim == 1: 114 | polys1 = np.array([polys1]) 115 | polys2 = np.array([polys2]) 116 | assert len(polys1) == len(polys2) 117 | 118 | polys1 = _to_polygon(polys1) 119 | polys2 = _to_polygon(polys2) 120 | if bound is not None: 121 | bound = box(0, 0, bound[0], bound[1]) 122 | polys1 = [p.intersection(bound) for p in polys1] 123 | polys2 = [p.intersection(bound) for p in polys2] 124 | 125 | eps = np.finfo(float).eps 126 | ious = [] 127 | for poly1, poly2 in zip(polys1, polys2): 128 | area_inter = poly1.intersection(poly2).area 129 | area_union = poly1.union(poly2).area 130 | ious.append(area_inter / (area_union + eps)) 131 | ious = np.clip(ious, 0.0, 1.0) 132 | 133 | return ious 134 | 135 | 136 | def _to_polygon(polys): 137 | r"""Convert 4 or 8 dimensional array to Polygons 138 | 139 | Args: 140 | polys (numpy.ndarray): An N x 4 numpy array, each line represent a rectangle 141 | (left, top, width, height); or an N x 8 numpy array, each line represent 142 | the coordinates (x1, y1, x2, y2, x3, y3, x4, y4) of 4 corners. 143 | """ 144 | def to_polygon(x): 145 | assert len(x) in [4, 8] 146 | if len(x) == 4: 147 | return box(x[0], x[1], x[0] + x[2], x[1] + x[3]) 148 | elif len(x) == 8: 149 | return Polygon([(x[2 * i], x[2 * i + 1]) for i in range(4)]) 150 | 151 | if polys.ndim == 1: 152 | return to_polygon(polys) 153 | else: 154 | return [to_polygon(t) for t in polys] 155 | -------------------------------------------------------------------------------- /got10k/utils/viz.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | 3 | import numpy as np 4 | import matplotlib 5 | import matplotlib.pyplot as plt 6 | import matplotlib.patches as patches 7 | import matplotlib.colors as mcolors 8 | from PIL import Image 9 | 10 | 11 | fig_dict = {} 12 | patch_dict = {} 13 | 14 | 15 | def show_frame(image, boxes=None, fig_n=1, pause=0.001, 16 | linewidth=3, cmap=None, colors=None, legends=None): 17 | r"""Visualize an image w/o drawing rectangle(s). 18 | 19 | Args: 20 | image (numpy.ndarray or PIL.Image): Image to show. 21 | boxes (numpy.array or a list of numpy.ndarray, optional): A 4 dimensional array 22 | specifying rectangle [left, top, width, height] to draw, or a list of arrays 23 | representing multiple rectangles. Default is ``None``. 24 | fig_n (integer, optional): Figure ID. Default is 1. 25 | pause (float, optional): Time delay for the plot. Default is 0.001 second. 26 | linewidth (int, optional): Thickness for drawing the rectangle. Default is 3 pixels. 27 | cmap (string): Color map. Default is None. 28 | color (tuple): Color of drawed rectanlge. Default is None. 29 | """ 30 | if isinstance(image, np.ndarray): 31 | image = Image.fromarray(image[..., ::-1]) 32 | 33 | if not fig_n in fig_dict or \ 34 | fig_dict[fig_n].get_size() != image.size[::-1]: 35 | fig = plt.figure(fig_n) 36 | plt.axis('off') 37 | fig.tight_layout() 38 | fig_dict[fig_n] = plt.imshow(image, cmap=cmap) 39 | else: 40 | fig_dict[fig_n].set_data(image) 41 | 42 | if boxes is not None: 43 | if not isinstance(boxes, (list, tuple)): 44 | boxes = [boxes] 45 | 46 | if colors is None: 47 | colors = ['r', 'g', 'b', 'c', 'm', 'y'] + \ 48 | list(mcolors.CSS4_COLORS.keys()) 49 | elif isinstance(colors, str): 50 | colors = [colors] 51 | 52 | if not fig_n in patch_dict: 53 | patch_dict[fig_n] = [] 54 | for i, box in enumerate(boxes): 55 | patch_dict[fig_n].append(patches.Rectangle( 56 | (box[0], box[1]), box[2], box[3], linewidth=linewidth, 57 | edgecolor=colors[i % len(colors)], facecolor='none', 58 | alpha=0.7 if len(boxes) > 1 else 1.0)) 59 | for patch in patch_dict[fig_n]: 60 | fig_dict[fig_n].axes.add_patch(patch) 61 | else: 62 | for patch, box in zip(patch_dict[fig_n], boxes): 63 | patch.set_xy((box[0], box[1])) 64 | patch.set_width(box[2]) 65 | patch.set_height(box[3]) 66 | 67 | if legends is not None: 68 | fig_dict[fig_n].axes.legend( 69 | patch_dict[fig_n], legends, loc=1, 70 | prop={'size': 8}, fancybox=True, framealpha=0.5) 71 | 72 | plt.pause(pause) 73 | plt.draw() 74 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | cycler==0.10.0 2 | kiwisolver==1.0.1 3 | matplotlib==3.0.2 4 | numpy==1.15.4 5 | Pillow==6.2.0 6 | pyparsing==2.3.0 7 | python-dateutil==2.7.5 8 | Shapely==1.6.4.post2 9 | six==1.11.0 10 | -------------------------------------------------------------------------------- /resources/sample_batch_run.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/got-10k/toolkit/956e7286fdf209cbb125adac9a46376bd8297ffb/resources/sample_batch_run.jpg -------------------------------------------------------------------------------- /setup.cfg: -------------------------------------------------------------------------------- 1 | [metadata] 2 | description-file = README.md 3 | -------------------------------------------------------------------------------- /setup.py: -------------------------------------------------------------------------------- 1 | from setuptools import setup, find_packages 2 | 3 | 4 | setup(name='got10k', 5 | version='0.1.3', 6 | description='GOT-10k benchmark official API', 7 | author='Lianghua Huang', 8 | author_email='lianghua.huang.cs@gmail.com', 9 | url='https://github.com/got-10k/toolkit', 10 | license='MIT', 11 | install_requires=[ 12 | 'numpy', 'matplotlib', 'Pillow', 'Shapely', 'fire', 'wget'], 13 | packages=find_packages(), 14 | include_package_data=True, 15 | keywords=[ 16 | 'GOT-10k', 17 | 'Generic Object Tracking', 18 | 'Benchmark',]) 19 | -------------------------------------------------------------------------------- /tests/test_datasets.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | 3 | import unittest 4 | import os 5 | import random 6 | 7 | from got10k.datasets import GOT10k, OTB, VOT, DTB70, TColor128, \ 8 | UAV123, NfS, LaSOT, TrackingNet, ImageNetVID 9 | 10 | 11 | class TestDatasets(unittest.TestCase): 12 | 13 | def setUp(self): 14 | self.data_dir = os.path.expanduser('~/data') 15 | 16 | def tearDown(self): 17 | pass 18 | 19 | def test_got10k(self): 20 | root_dir = os.path.join(self.data_dir, 'GOT-10k') 21 | # without meta 22 | for subset in ['train', 'val', 'test']: 23 | dataset = GOT10k(root_dir, subset=subset) 24 | self._check_dataset(dataset) 25 | # with meta 26 | for subset in ['train', 'val', 'test']: 27 | dataset = GOT10k(root_dir, subset=subset, return_meta=True) 28 | self._check_dataset(dataset) 29 | 30 | def test_otb(self): 31 | root_dir = os.path.join(self.data_dir, 'OTB') 32 | dataset = OTB(root_dir) 33 | self._check_dataset(dataset) 34 | 35 | def test_vot(self): 36 | root_dir = os.path.join(self.data_dir, 'vot2018') 37 | # without meta 38 | dataset = VOT(root_dir, anno_type='rect') 39 | self._check_dataset(dataset) 40 | # with meta 41 | dataset = VOT(root_dir, anno_type='rect', return_meta=True) 42 | self._check_dataset(dataset) 43 | 44 | def test_dtb70(self): 45 | root_dir = os.path.join(self.data_dir, 'DTB70') 46 | dataset = DTB70(root_dir) 47 | self._check_dataset(dataset) 48 | 49 | def test_tcolor128(self): 50 | root_dir = os.path.join(self.data_dir, 'Temple-color-128') 51 | dataset = TColor128(root_dir) 52 | self._check_dataset(dataset) 53 | 54 | def test_uav123(self): 55 | root_dir = os.path.join(self.data_dir, 'UAV123') 56 | for version in ['UAV123', 'UAV20L']: 57 | dataset = UAV123(root_dir, version) 58 | self._check_dataset(dataset) 59 | 60 | def test_nfs(self): 61 | root_dir = os.path.join(self.data_dir, 'nfs') 62 | for fps in [30, 240]: 63 | dataset = NfS(root_dir, fps) 64 | self._check_dataset(dataset) 65 | 66 | def test_lasot(self): 67 | root_dir = os.path.join(self.data_dir, 'LaSOTBenchmark') 68 | for subset in ['train', 'test']: 69 | dataset = LaSOT(root_dir, subset) 70 | self._check_dataset(dataset) 71 | 72 | def test_trackingnet(self): 73 | root_dir = os.path.join(self.data_dir, 'TrackingNet') 74 | for subset in ['train', 'test']: 75 | dataset = TrackingNet(root_dir, subset) 76 | self._check_dataset(dataset) 77 | 78 | def test_vid(self): 79 | root_dir = os.path.join(self.data_dir, 'ILSVRC') 80 | dataset = ImageNetVID(root_dir, subset=('train', 'val')) 81 | self._check_dataset(dataset) 82 | 83 | def _check_dataset(self, dataset): 84 | n = len(dataset) 85 | self.assertGreater(n, 0) 86 | inds = random.sample(range(n), min(n, 100)) 87 | for i in inds: 88 | img_files, anno = dataset[i][:2] 89 | self.assertEqual(len(img_files), len(anno)) 90 | 91 | 92 | if __name__ == '__main__': 93 | unittest.main() 94 | -------------------------------------------------------------------------------- /tests/test_experiments.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | 3 | import unittest 4 | import os 5 | 6 | from got10k.trackers import IdentityTracker 7 | from got10k.experiments import ExperimentGOT10k, ExperimentOTB, \ 8 | ExperimentVOT, ExperimentDTB70, ExperimentTColor128, \ 9 | ExperimentUAV123, ExperimentNfS 10 | 11 | 12 | class TestExperiments(unittest.TestCase): 13 | 14 | def setUp(self): 15 | self.data_dir = 'data' 16 | self.tracker = IdentityTracker() 17 | 18 | def tearDown(self): 19 | pass 20 | 21 | def test_got10k(self): 22 | root_dir = os.path.join(self.data_dir, 'GOT-10k') 23 | # run experiment 24 | experiment = ExperimentGOT10k(root_dir) 25 | experiment.run(self.tracker, visualize=False) 26 | # report performance 27 | experiment.report([self.tracker.name]) 28 | 29 | def test_otb(self): 30 | root_dir = os.path.join(self.data_dir, 'OTB') 31 | # run experiment 32 | experiment = ExperimentOTB(root_dir) 33 | experiment.run(self.tracker, visualize=False) 34 | # report performance 35 | experiment.report([self.tracker.name]) 36 | 37 | def test_vot(self): 38 | root_dir = os.path.join(self.data_dir, 'vot2018') 39 | # run experiment 40 | experiment = ExperimentVOT(root_dir) 41 | experiment.run(self.tracker, visualize=False) 42 | # report performance 43 | experiment.report([self.tracker.name]) 44 | 45 | def test_dtb70(self): 46 | root_dir = os.path.join(self.data_dir, 'DTB70') 47 | # run experiment 48 | experiment = ExperimentDTB70(root_dir) 49 | experiment.run(self.tracker, visualize=False) 50 | # report performance 51 | experiment.report([self.tracker.name]) 52 | 53 | def test_uav123(self): 54 | root_dir = os.path.join(self.data_dir, 'UAV123') 55 | for version in ['UAV123', 'UAV20L']: 56 | # run experiment 57 | experiment = ExperimentUAV123(root_dir, version) 58 | experiment.run(self.tracker, visualize=False) 59 | # report performance 60 | experiment.report([self.tracker.name]) 61 | 62 | def test_nfs(self): 63 | root_dir = os.path.join(self.data_dir, 'nfs') 64 | for fps in [30, 240]: 65 | # run experiment 66 | experiment = ExperimentNfS(root_dir, fps) 67 | experiment.run(self.tracker, visualize=False) 68 | # report performance 69 | experiment.report([self.tracker.name]) 70 | 71 | def test_tcolor128(self): 72 | root_dir = os.path.join(self.data_dir, 'Temple-color-128') 73 | # run experiment 74 | experiment = ExperimentTColor128(root_dir) 75 | experiment.run(self.tracker, visualize=False) 76 | # report performance 77 | experiment.report([self.tracker.name]) 78 | 79 | 80 | if __name__ == '__main__': 81 | unittest.main() 82 | -------------------------------------------------------------------------------- /tests/test_trackers.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | 3 | import unittest 4 | import os 5 | import random 6 | 7 | from got10k.trackers import IdentityTracker 8 | from got10k.datasets import GOT10k 9 | 10 | 11 | class TestTrackers(unittest.TestCase): 12 | 13 | def setUp(self): 14 | self.data_dir = 'data' 15 | self.tracker = IdentityTracker() 16 | 17 | def tearDown(self): 18 | pass 19 | 20 | def test_identity_tracker(self): 21 | # setup dataset 22 | root_dir = os.path.join(self.data_dir, 'GOT-10k') 23 | dataset = GOT10k(root_dir, subset='val') 24 | # run experiment 25 | img_files, anno = random.choice(dataset) 26 | boxes, times = self.tracker.track( 27 | img_files, anno[0], visualize=True) 28 | self.assertEqual(boxes.shape, anno.shape) 29 | self.assertEqual(len(times), len(anno)) 30 | 31 | 32 | if __name__ == '__main__': 33 | unittest.main() 34 | -------------------------------------------------------------------------------- /tests/test_utils.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | 3 | import unittest 4 | import numpy as np 5 | 6 | from got10k.utils.metrics import rect_iou, poly_iou 7 | 8 | 9 | class TestUtils(unittest.TestCase): 10 | 11 | def setUp(self): 12 | pass 13 | 14 | def tearDown(self): 15 | pass 16 | 17 | def test_iou(self): 18 | rects1 = np.random.rand(1000, 4) * 100 19 | rects2 = np.random.rand(1000, 4) * 100 20 | bound = (50, 100) 21 | ious1 = rect_iou(rects1, rects2, bound=bound) 22 | ious2 = poly_iou(rects1, rects2, bound=bound) 23 | self.assertTrue((ious1 - ious2).max() < 1e-14) 24 | 25 | polys1 = self._rect2corner(rects1) 26 | polys2 = self._rect2corner(rects2) 27 | ious3 = poly_iou(polys1, polys2, bound=bound) 28 | self.assertTrue((ious1 - ious3).max() < 1e-14) 29 | 30 | def _rect2corner(self, rects): 31 | x1, y1, w, h = rects.T 32 | x2, y2 = x1 + w, y1 + h 33 | corners = np.array([x1, y1, x1, y2, x2, y2, x2, y1]).T 34 | 35 | return corners 36 | 37 | 38 | if __name__ == '__main__': 39 | unittest.main() 40 | --------------------------------------------------------------------------------