GOT-10k Python Toolkit

UPDATE:

The toolkit can be directly installed via pip install got10k !

Check our light-weighted repository SiamFC for a minimal example of training and evaluation using GOT-10k toolkit!

This repository contains the official python toolkit for running experiments and evaluate performance on GOT-10k 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.

For convenience, the toolkit also provides unofficial implementation of dataset interfaces and tracking pipelines for OTB (2013/2015), VOT (2013~2018), DTB70, TColor128, NfS and UAV123 benchmarks.

GOT-10k 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:

"GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild."
L. Huang, X. Zhao and K. Huang,
arXiv:1810.11981, 2018.

[Project][PDF][Bibtex]

Installation
Quick Start: A Concise Example
Quick Start: Jupyter Notebook for Off-the-Shelf Usage
How to Define a Tracker?
How to Run Experiments on GOT-10k?
How to Evaluate Performance?
How to Loop Over GOT-10k Dataset?
Issues

Installation

Install the toolkit using pip (recommended):

pip install --upgrade got10k

Or, alternatively, clone the repository and install dependencies:

git clone https://github.com/got-10k/toolkit.git
cd toolkit
pip install -r requirements.txt

Then directly copy got10k folder to your workspace to use it.

Quick Start: A Concise Example

Here is a simple example on how to use the toolkit to define a tracker, run experiments on GOT-10k and evaluate performance.

from got10k.trackers import Tracker
from got10k.experiments import ExperimentGOT10k

class IdentityTracker(Tracker):
    def __init__(self):
        super(IdentityTracker, self).__init__(name='IdentityTracker')
    
    def init(self, image, box):
        self.box = box

    def update(self, image):
        return self.box

if __name__ == '__main__':
    # setup tracker
    tracker = IdentityTracker()

    # run experiments on GOT-10k (validation subset)
    experiment = ExperimentGOT10k('data/GOT-10k', subset='val')
    experiment.run(tracker, visualize=True)

    # report performance
    experiment.report([tracker.name])

To run experiments on OTB, VOT or other benchmarks, simply change ExperimentGOT10k, e.g., to ExperimentOTB or ExperimentVOT, and root_dir to their corresponding paths for this purpose.

Quick Start: Jupyter Notebook for Off-the-Shelf Usage

Open quick_examples.ipynb in Jupyter Notebook to see more examples on toolkit usage.

How to Define a Tracker?

To define a tracker using the toolkit, simply inherit and override init and update methods from the Tracker class. Here is a simple example:

from got10k.trackers import Tracker

class IdentityTracker(Tracker):
    def __init__(self):
        super(IdentityTracker, self).__init__(
            name='IdentityTracker',  # tracker name
            is_deterministic=True    # stochastic (False) or deterministic (True)
        )
    
    def init(self, image, box):
        self.box = box

    def update(self, image):
        return self.box

How to Run Experiments on GOT-10k?

Instantiate an ExperimentGOT10k object, and leave all experiment pipelines to its run method:

from got10k.experiments import ExperimentGOT10k

# ... tracker definition ...

# instantiate a tracker
tracker = IdentityTracker()

# setup experiment (validation subset)
experiment = ExperimentGOT10k(
    root_dir='data/GOT-10k',    # GOT-10k's root directory
    subset='val',               # 'train' | 'val' | 'test'
    result_dir='results',       # where to store tracking results
    report_dir='reports'        # where to store evaluation reports
)
experiment.run(tracker, visualize=True)

The tracking results will be stored in result_dir.

How to Evaluate Performance?

Use the report method of ExperimentGOT10k for this purpose:

# ... run experiments on GOT-10k ...

# report tracking performance
experiment.report([tracker.name])

When evaluated on the validation subset, the scores and curves will be directly generated in report_dir.

However, when evaluated on the test subset, since all groundtruths are withholded, you will have to submit your results to the evaluation server for evaluation. The report function will generate a .zip file which can be directly uploaded for submission. For more instructions, see submission instruction.

See public evaluation results on GOT-10k's leaderboard.

How to Loop Over GOT-10k Dataset?

The got10k.datasets.GOT10k provides an iterable and indexable interface for GOT-10k's sequences. Here is an example:

from PIL import Image
from got10k.datasets import GOT10k
from got10k.utils.viz import show_frame

dataset = GOT10k(root_dir='data/GOT-10k', subset='train')

# indexing
img_file, anno = dataset[10]

# for-loop
for s, (img_files, anno) in enumerate(dataset):
    seq_name = dataset.seq_names[s]
    print('Sequence:', seq_name)

    # show all frames
    for f, img_file in enumerate(img_files):
        image = Image.open(img_file)
        show_frame(image, anno[f, :])

To loop over OTB or VOT datasets, simply change GOT10k to OTB or VOT for this purpose.

Issues

Please report any problems or suggessions in the Issues page.

chenqi1997/toolkit