Python package for multiple object tracking research with focus on laboratory animals tracking.

Related tags

Deep Learningmotutils
Overview

Build Status

motutils is a Python package for multiple object tracking research with focus on laboratory animals tracking.

Features

  • loads:
  • saves: MOTChallenge CSV
  • Mot, BboxMot and PoseMot classes backed by xarray dataset with frame and id coordinates
  • export to Pandas DataFrame
  • oracle detector: fake all knowing detector based on ground truth with configurable inaccuracies
  • different classes of tracked objects: point, bounding box, pose
  • interpolation of missing positions
  • find mapping between MOT results and ground truth
  • visualization:
    • tracked positions / objects overlaid on a video
    • montage of multiple videos with results and/or ground truth
  • cli
    • visualization
    • evaluation ()
    • mot format conversion

visualization montage

Video comparison of multiple tracking methods and the ground truth.

Installation

pip install git+https://github.com/smidm/motutils

Usage

$ motutils --help
Usage: motutils [OPTIONS] COMMAND [ARGS]...

Options:
--load-mot FILENAME             load a MOT challenge csv file(s)
--load-gt FILENAME              load ground truth from a MOT challenge csv
file
--load-idtracker FILENAME       load IdTracker trajectories (e.g.,
trajectories.txt)
--load-idtrackerai FILENAME     load idtracker.ai trajectories (e.g.,
trajectories_wo_gaps.npy)
--load-sleap-analysis FILENAME  load SLEAP analysis trajectories (exported
from sleap-label File -> Export Analysis
HDF5)
--load-toxtrac FILENAME         load ToxTracker trajectories (e.g.,
Tracking_0.txt)
--toxtrac-topleft-xy 
   
    ...
position of the arena top left corner, see
first tuple in the Arena line in Stats_1.txt
--help                          Show this message and exit.

Commands:
convert    Convert any format to MOT Challenge format.
eval       Evaluate a single MOT file against the ground truth.
visualize  Visualize MOT file(s) overlaid on a video.

   
$ motutils convert --help

Usage: motutils convert [OPTIONS] OUTPUT_MOT

  Convert any format to MOT Challenge format.


$ motutils eval --help

Usage: motutils eval [OPTIONS]

  Evaluate a single MOT file against the ground truth.

Options:
  --write-eval FILENAME  write evaluation results as a CSV file
  --keypoint INTEGER     keypoint to use when evaluating pose MOT results
                         against point ground truth

$ motutils visualize --help

Usage: motutils visualize [OPTIONS] VIDEO_IN VIDEO_OUT
                          [SOURCE_DISPLAY_NAME]...

  Visualize MOT file(s) overlaid on a video.

Options:
  --limit-duration INTEGER  visualization duration limit in s
  --help                    Show this message and exit.

Python API Quickstart

>> mot.ds Dimensions: (frame: 4500, id: 5) Coordinates: * frame (frame) int64 0 1 2 3 4 5 6 ... 4494 4495 4496 4497 4498 4499 * id (id) int64 1 2 3 4 5 Data variables: x (frame, id) float64 434.5 277.7 179.2 ... 185.3 138.6 420.2 y (frame, id) float64 279.0 293.6 407.9 ... 393.3 387.2 294.7 width (frame, id) float64 nan nan nan nan nan ... nan nan nan nan nan height (frame, id) float64 nan nan nan nan nan ... nan nan nan nan nan confidence (frame, id) float64 1.0 1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0 1.0 >>> mot.num_ids() 5 >>> mot.count_missing() 0 >>> mot.get_object(frame=1, obj_id=2) Dimensions: () Coordinates: frame int64 1 id int64 2 Data variables: x float64 278.2 y float64 293.7 width float64 nan height float64 nan confidence float64 1.0 >>> mot.match_xy(frame=1, xy=(300, 300), maximal_match_distance=40) Dimensions: () Coordinates: frame int64 1 id int64 2 Data variables: x float64 278.2 y float64 293.7 width float64 nan height float64 nan confidence float64 1.0 >>> mot.to_dataframe() frame id x y width height confidence 0 1 1 434.5 279.0 -1.0 -1.0 1.0 1 1 2 277.7 293.6 -1.0 -1.0 1.0 2 1 3 179.2 407.9 -1.0 -1.0 1.0 3 1 4 180.0 430.0 -1.0 -1.0 1.0 4 1 5 155.0 397.0 -1.0 -1.0 1.0 ... .. ... ... ... ... ... 22495 4500 1 90.3 341.9 -1.0 -1.0 1.0 22496 4500 2 187.9 431.9 -1.0 -1.0 1.0 22497 4500 3 185.3 393.3 -1.0 -1.0 1.0 22498 4500 4 138.6 387.2 -1.0 -1.0 1.0 22499 4500 5 420.2 294.7 -1.0 -1.0 1.0 [22500 rows x 7 columns]"> 
>>> from motutils import Mot
>>> mot = Mot("tests/data/Sowbug3_cut.csv")

>>> mot.ds
<xarray.Dataset>
Dimensions:     (frame: 4500, id: 5)
Coordinates:
  * frame       (frame) int64 0 1 2 3 4 5 6 ... 4494 4495 4496 4497 4498 4499
  * id          (id) int64 1 2 3 4 5
Data variables:
    x           (frame, id) float64 434.5 277.7 179.2 ... 185.3 138.6 420.2
    y           (frame, id) float64 279.0 293.6 407.9 ... 393.3 387.2 294.7
    width       (frame, id) float64 nan nan nan nan nan ... nan nan nan nan nan
    height      (frame, id) float64 nan nan nan nan nan ... nan nan nan nan nan
    confidence  (frame, id) float64 1.0 1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0 1.0

>>> mot.num_ids()
5

>>> mot.count_missing()
0

>>> mot.get_object(frame=1, obj_id=2)
<xarray.Dataset>
Dimensions:     ()
Coordinates:
    frame       int64 1
    id          int64 2
Data variables:
    x           float64 278.2
    y           float64 293.7
    width       float64 nan
    height      float64 nan
    confidence  float64 1.0

>>> mot.match_xy(frame=1, xy=(300, 300), maximal_match_distance=40)
<xarray.Dataset>
Dimensions:     ()
Coordinates:
    frame       int64 1
    id          int64 2
Data variables:
    x           float64 278.2
    y           float64 293.7
    width       float64 nan
    height      float64 nan
    confidence  float64 1.0

>>> mot.to_dataframe()
       frame  id      x      y  width  height  confidence
0          1   1  434.5  279.0   -1.0    -1.0         1.0
1          1   2  277.7  293.6   -1.0    -1.0         1.0
2          1   3  179.2  407.9   -1.0    -1.0         1.0
3          1   4  180.0  430.0   -1.0    -1.0         1.0
4          1   5  155.0  397.0   -1.0    -1.0         1.0
      ...  ..    ...    ...    ...     ...         ...
22495   4500   1   90.3  341.9   -1.0    -1.0         1.0
22496   4500   2  187.9  431.9   -1.0    -1.0         1.0
22497   4500   3  185.3  393.3   -1.0    -1.0         1.0
22498   4500   4  138.6  387.2   -1.0    -1.0         1.0
22499   4500   5  420.2  294.7   -1.0    -1.0         1.0
[22500 rows x 7 columns]

Documentation

See the quickstart and tests for now.

Write me if you would like to use the package, but the lack of documentation is hindering you. You can easily reorder my priorities on this simply just by letting me know that there is an interest.

Owner
Matěj Šmíd
Matěj Šmíd
GitHub Repository
Rethinking Portrait Matting with Privacy Preserving

Rethinking Portrait Matting with Privacy Preserving This is the official repository of the paper Rethinking Portrait Matting with Privacy Preserving.

184 Jan 03, 2023
A Fast and Stable GAN for Small and High Resolution Imagesets - pytorch

A Fast and Stable GAN for Small and High Resolution Imagesets - pytorch The official pytorch implementation of the paper "Towards Faster and Stabilize

Bingchen Liu 455 Jan 08, 2023
Abstractive opinion summarization system (SelSum) and the largest dataset of Amazon product summaries (AmaSum). EMNLP 2021 conference paper.

Learning Opinion Summarizers by Selecting Informative Reviews This repository contains the codebase and the dataset for the corresponding EMNLP 2021

Arthur Bražinskas 39 Jan 01, 2023
Hyper-parameter optimization for sklearn

hyperopt-sklearn Hyperopt-sklearn is Hyperopt-based model selection among machine learning algorithms in scikit-learn. See how to use hyperopt-sklearn

1.4k Jan 01, 2023
Flappy bird automation using Neuroevolution of Augmenting Topologies (NEAT) in Python

FlappyAI Flappy bird automation using Neuroevolution of Augmenting Topologies (NEAT) in Python Everything Used Genetic Algorithm especially NEAT conce

Eryawan Presma Y. 2 Mar 24, 2022
基于YoloX目标检测+DeepSort算法实现多目标追踪Baseline

项目简介： 使用YOLOX+Deepsort实现车辆行人追踪和计数，代码封装成一个Detector类，更容易嵌入到自己的项目中。 代码地址（欢迎star）： https://github.com/Sharpiless/yolox-deepsort/ 最终效果： 运行demo： python demo

114 Dec 30, 2022
AITUS - An atomatic notr maker for CYTUS

AITUS an automatic note maker for CYTUS. 利用AI根据指定乐曲生成CYTUS游戏谱面。 效果展示：https://www

GradiusTwinbee 6 Feb 24, 2022
Live Hand Tracking Using Python

Live-Hand-Tracking-Using-Python Project Description: In this project, we will be

Hassan Shahzad 2 Jan 06, 2022
Projecting interval uncertainty through the discrete Fourier transform

Projecting interval uncertainty through the discrete Fourier transform This repo

1 Mar 02, 2022
nn_builder lets you build neural networks with less boilerplate code

nn_builder lets you build neural networks with less boilerplate code. You specify the type of network you want and it builds it. Install pip install n

Petros Christodoulou 157 Nov 20, 2022
Official repository for Hierarchical Opacity Propagation for Image Matting

HOP-Matting Official repository for Hierarchical Opacity Propagation for Image Matting 🚧 🚧 🚧 Under Construction 🚧 🚧 🚧 🚧 🚧 🚧   Coming Soon   

Li Yaoyi 54 Dec 30, 2021
EMNLP 2021 paper Models and Datasets for Cross-Lingual Summarisation.

This repository contains data and code for our EMNLP 2021 paper Models and Datasets for Cross-Lingual Summarisation. Please contact me at

9 Oct 28, 2022
Yolov5-lite - Minimal PyTorch implementation of YOLOv5

Yolov5-Lite: Minimal YOLOv5 + Deep Sort Overview This repo is a shortened versio

Kadir Nar 57 Nov 28, 2022
Simple, but essential Bayesian optimization package

BayesO: A Bayesian optimization framework in Python Simple, but essential Bayesian optimization package. http://bayeso.org Online documentation Instal

Jungtaek Kim 74 Dec 05, 2022
This example implements the end-to-end MLOps process using Vertex AI platform and Smart Analytics technology capabilities

MLOps with Vertex AI This example implements the end-to-end MLOps process using Vertex AI platform and Smart Analytics technology capabilities. The ex

Google Cloud Platform 238 Dec 21, 2022
The codes I made while I practiced various TensorFlow examples

TensorFlow_Exercises The codes I made while I practiced various TensorFlow examples About the codes I didn't create these codes by myself, but re-crea

Terry Taewoong Um 614 Dec 08, 2022
PyTorch code of "SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks"

SLAPS-GNN This repo contains the implementation of the model proposed in SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks

60 Dec 22, 2022
Visualize Camera's Pose Using Extrinsic Parameter by Plotting Pyramid Model on 3D Space

extrinsic2pyramid Visualize Camera's Pose Using Extrinsic Parameter by Plotting Pyramid Model on 3D Space Intro A very simple and straightforward modu

JEONG HYEONJIN 106 Dec 28, 2022
Official implementation of TMANet.

Temporal Memory Attention for Video Semantic Segmentation, arxiv Introduction We propose a Temporal Memory Attention Network (TMANet) to adaptively in

wanghao 94 Dec 02, 2022
Exploiting a Zoo of Checkpoints for Unseen Tasks

Exploiting a Zoo of Checkpoints for Unseen Tasks This repo includes code to reproduce all results in the above Neurips paper, authored by Jiaji Huang,

Baidu Research 8 Sep 06, 2022