Measures input lag without dedicated hardware, performing motion detection on recorded or live video

Overview

What is InputLagTimer?

This tool can measure input lag by analyzing a video where both the game controller and the game screen can be seen on a webcam or a video file.

Here's how it looks in action:

Usage demo

Even though the typical usage is game latency, InputLagTimer can measure any latency so long as it's captured on video. For example, if you point a camera at both your car key and its door lock, you can measure how fast that remote unlocks your car.

How does it measure input lag?

You first mark two rectangles in the video you provide:

  • 🟦 Input rectangle (blue): where the input motion happens. Such as a gamepad stick.
  • 🟪 Output rectangle (purple): where the response will be visible. Such as the middle left of your TV screen, where the front wheels can be seen turning in your car simulator.

InputLagTimer will detect motion on the input area, and time how long it takes to detect motion on the output area.

Things should work for latencies of up to 700ms; if you need to measure slower events, the limit can be trivially edited in code.

How to use it:

  1. Download InputLagTimer (some windows binaries are available on github if you prefer that)
  2. Open InputLagTimer:
    • Plug your webcam then run the program.
    • Or drag-and-drop your video file to the program.
    • Or, from command line, type InputLagTimer 2 to open the 3rd webcam, or InputLagTimer file.mp4 to open a file.
  3. Press S then follow screen instructions to select the 🟦 input and 🟪 output rectangles.
  4. Observe the input and output motion bars at the top, and press 1/2 and 3/4 to adjust the motion detection thresholds (white indicator). Latency timing will start when the input motion passes the threshold, and stop when the output motion does.

Note: a .cfg file will be created for each video, allowing to reproduce the same latency analysis.

Tips and gotchas

  • Use a tripod to hold the camera. The InputLagTimer is based on motion detection, therefore hand-held footage is doomed to spam false positives.
  • Disable gamepad vibration and put the gamepad in a table (unless you want to measure vibration-latency!): in other words,reduce unnecessary motion from both the input and output rectangles.
  • Select the 🟦 input and 🟪 output rectangles as accurately as possible. E.g. to measure keyboard key travel time, draw an input rectangle including the entire key height. If you don't want to include key travel latency, draw the input rectangle as close to the key activation point as possible.
  • If using certain artificial lights, enable camera's anti-flicker feature when available (press C in InputLagTimer when using a webcam), or choose a recording framerate different than the powerline frequency used in your country (often 50Hz or 60Hz). This removes video flicker, vastly improving motion detection.
  • Prefer higher recording framerate, this provides finer-grained latency measurements:
    • Some phones and actioncams can reach hundreds of FPS.
    • Recording equipment may not reach its advertised framerate if it's not bright enough. If in doubt, add more lighting.
  • If your camera cannot reach the requested framerate (e.g. it only manages to capture 120FPS out of 240FPS, due to lack of light), consider recording directly at the reachable framerate. This eliminates the useless filler frames your camera was forced to duplicate, making it easier to tune the motion detection thresholds in InputLagTimer.
  • Prefer global shutter over rolling shutter cameras. Rolling shutter can slightly skew latency measurements, as one corner of the image is recorded earlier than the oposite corner.

Rolling Shutter example

(source: Axel1963 - CC BY-SA 3.0)

  • Screens normally refresh pixels from the top earlier than pixels from the bottom (or left before right, etc). The location of 🟦 input/ 🟪 output rectangles in a screen can slightly skew latency measurements.
  • The pixels on a screen can take longer or shorter to update, depending on:
    • Pixel color. E.g. white-to-black response time might be longer than black-to-white.
    • Panel type. E.g. OLED will normally be much quicker than LCD panels.
    • Screen configuration. E.g. enabling 'overdrive', enabling 'game mode', etc.
  • Press A (Advanced mode) to see more keys and additional information.

Advanced Mode screenshot

Dependencies

To run the EXE, you don't need anythig else. So move along, nothing to see in this section :)

To run the python code directly, you'll need opencv for python, numpy, and whichever python interpreter you prefer.

To build the binary (with compile.py), you'll need PyInstaller.

Credits and licenses

InputLagTimer software:

Copyright 2021 Bruno Gonzalez Campo | [email protected] | @stenyak

Distributed under MIT license (see license.txt)

InputLagTimer icon:

Copyright 2021 Bruno Gonzalez Campo | [email protected] | @stenyak

Distributed under CC BY 3.0 license (see license_icon.txt)

Icon derived from:

You might also like...
Python scripts for performing object detection with the 1000 labels of the ImageNet dataset in ONNX.
Python scripts for performing object detection with the 1000 labels of the ImageNet dataset in ONNX.

Python scripts for performing object detection with the 1000 labels of the ImageNet dataset in ONNX. The repository combines a class agnostic object localizer to first detect the objects in the image, and next a ResNet50 model trained on ImageNet is used to label each box.

Python scripts for performing road segemtnation and car detection using the HybridNets multitask model in ONNX.
Python scripts for performing road segemtnation and car detection using the HybridNets multitask model in ONNX.

ONNX-HybridNets-Multitask-Road-Detection Python scripts for performing road segemtnation and car detection using the HybridNets multitask model in ONN

This project uses reinforcement learning on stock market and agent tries to learn trading. The goal is to check if the agent can learn to read tape. The project is dedicated to hero in life great Jesse Livermore.

Reinforcement-trading This project uses Reinforcement learning on stock market and agent tries to learn trading. The goal is to check if the agent can

NeuralCompression is a Python repository dedicated to research of neural networks that compress data

NeuralCompression is a Python repository dedicated to research of neural networks that compress data. The repository includes tools such as JAX-based entropy coders, image compression models, video compression models, and metrics for image and video evaluation.

SCAAML is a deep learning framwork dedicated to side-channel attacks run on top of TensorFlow 2.x.
SCAAML is a deep learning framwork dedicated to side-channel attacks run on top of TensorFlow 2.x.

SCAAML (Side Channel Attacks Assisted with Machine Learning) is a deep learning framwork dedicated to side-channel attacks. It is written in python and run on top of TensorFlow 2.x.

NeoPlay is the project dedicated to ESport events.

NeoPlay is the project dedicated to ESport events. On this platform users can participate in tournaments with prize pools as well as create their own tournaments.

This program was designed to detect whether someone is wearing a facemask through a live video stream.

This program was designed to detect whether someone is wearing a facemask through a live video stream. A custom lightweight CNN trained with TensorFlow on a public dataset provided by Kaggle is used to detect whether each face detected by the cv2 face detection dnn is wearing a mask

Video-Captioning - A machine Learning project to generate captions for video frames indicating the relationship between the objects in the video
Video-Captioning - A machine Learning project to generate captions for video frames indicating the relationship between the objects in the video

Video-Captioning - A machine Learning project to generate captions for video frames indicating the relationship between the objects in the video

Official implementation of the network presented in the paper
Official implementation of the network presented in the paper "M4Depth: A motion-based approach for monocular depth estimation on video sequences"

M4Depth This is the reference TensorFlow implementation for training and testing depth estimation models using the method described in M4Depth: A moti

Releases(v1.2)
  • v1.2(Mar 29, 2022)

    • Display summary of measured latencies: min/avg/max latencies and a histogram
    • Added display with the current framerate
    • Fixed incorrect timing when a webcam dropped below the advertised framerate
    • The 'a' key will now cycle between varying amounts of detail (more detail can lead to lower framerates)
    • Add CC license links on readme
    • Minor cleanups here and there

    Full Changelog: https://github.com/stenyak/inputLagTimer/compare/v1.1...v1.2

    Source code(tar.gz)
    Source code(zip)
    InputLagTimer.exe(50.81 MB)
  • v1.1(Jan 8, 2022)

    • Fix safety timeout kicking in too soon if using a custom maxLatency
    • Fix first webcam being ignored when running the program without arguments
    • Rename compiled file from camelCase to CamelCase

    Full Changelog: https://github.com/stenyak/inputLagTimer/compare/v1.0...v1.1

    Source code(tar.gz)
    Source code(zip)
    InputLagTimer.exe(49.22 MB)
  • v1.0(Jan 8, 2022)

Data augmentation for NLP, accepted at EMNLP 2021 Findings

AEDA: An Easier Data Augmentation Technique for Text Classification This is the code for the EMNLP 2021 paper AEDA: An Easier Data Augmentation Techni

Akbar Karimi 81 Dec 09, 2022
Attention-based CNN-LSTM and XGBoost hybrid model for stock prediction

Attention-based CNN-LSTM and XGBoost hybrid model for stock prediction Requirements The code has been tested running under Python 3.7.4, with the foll

zshicode 84 Jan 01, 2023
A curated list of Generative Deep Art projects, tools, artworks, and models

Generative Deep Art A curated list of Generative Deep Art projects, tools, artworks, and models Inbox Get started with making AI art in 2022 – deeplea

Filipe Calegario 251 Jan 03, 2023
Eth brownie struct encoding example

eth-brownie struct encoding example Overview This repository contains an example of encoding a struct, so that it can be used in a function call, usin

Ittai Svidler 2 Mar 04, 2022
PyDeepFakeDet is an integrated and scalable tool for Deepfake detection.

PyDeepFakeDet An integrated and scalable library for Deepfake detection research. Introduction PyDeepFakeDet is an integrated and scalable Deepfake de

Junke, Wang 49 Dec 11, 2022
Codebase for Inducing Causal Structure for Interpretable Neural Networks

Interchange Intervention Training (IIT) Codebase for Inducing Causal Structure for Interpretable Neural Networks Release Notes 12/01/2021: Code and Pa

Zen 6 Oct 10, 2022
Reimplement of SimSwap training code

SimSwap-train Reimplement of SimSwap training code Instructions 1.Environment Preparation (1)Refer to the README document of SIMSWAP to configure the

seeprettyface.com 111 Dec 31, 2022
PyTorch code for EMNLP 2021 paper: Don't be Contradicted with Anything! CI-ToD: Towards Benchmarking Consistency for Task-oriented Dialogue System

Don’t be Contradicted with Anything!CI-ToD: Towards Benchmarking Consistency for Task-oriented Dialogue System This repository contains the PyTorch im

Libo Qin 25 Sep 06, 2022
Real-time Object Detection for Streaming Perception, CVPR 2022

StreamYOLO Real-time Object Detection for Streaming Perception Jinrong Yang, Songtao Liu, Zeming Li, Xiaoping Li, Sun Jian Real-time Object Detection

Jinrong Yang 237 Dec 27, 2022
Repo público onde postarei meus estudos de Python, buscando aprender por meio do compartilhamento do aprendizado!

Seja bem vindo à minha repo de Estudos em Python 3! Este é um repositório criado por um programador amador que estuda tópicos de finanças, estatística

32 Dec 24, 2022
A Unified Framework and Analysis for Structured Knowledge Grounding

UnifiedSKG 📚 : Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models Code for paper UnifiedSKG: Unifying and Mu

HKU NLP Group 370 Dec 21, 2022
Eff video representation - Efficient video representation through neural fields

Neural Residual Flow Fields for Efficient Video Representations 1. Download MPI

41 Jan 06, 2023
This program will stylize your photos with fast neural style transfer.

Neural Style Transfer (NST) Using TensorFlow Demo TensorFlow TensorFlow is an end-to-end open source platform for machine learning. It has a comprehen

Ismail Boularbah 1 Aug 08, 2022
Explainer for black box models that predict molecule properties

Explaining why that molecule exmol is a package to explain black-box predictions of molecules. The package uses model agnostic explanations to help us

White Laboratory 172 Dec 19, 2022
Resilience from Diversity: Population-based approach to harden models against adversarial attacks

Resilience from Diversity: Population-based approach to harden models against adversarial attacks Requirements To install requirements: pip install -r

0 Nov 23, 2021
StyleMapGAN - Official PyTorch Implementation

StyleMapGAN - Official PyTorch Implementation StyleMapGAN: Exploiting Spatial Dimensions of Latent in GAN for Real-time Image Editing Hyunsu Kim, Yunj

NAVER AI 425 Dec 23, 2022
Rendering Point Clouds with Compute Shaders

Compute Shader Based Point Cloud Rendering This repository contains the source code to our techreport: Rendering Point Clouds with Compute Shaders and

Markus Schütz 460 Jan 05, 2023
torchsummaryDynamic: support real FLOPs calculation of dynamic network or user-custom PyTorch ops

torchsummaryDynamic Improved tool of torchsummaryX. torchsummaryDynamic support real FLOPs calculation of dynamic network or user-custom PyTorch ops.

Bohong Chen 1 Jan 07, 2022
zeus is a Python implementation of the Ensemble Slice Sampling method.

zeus is a Python implementation of the Ensemble Slice Sampling method. Fast & Robust Bayesian Inference, Efficient Markov Chain Monte Carlo (MCMC), Bl

Minas Karamanis 197 Dec 04, 2022
An SMPC companion library for Syft

SyMPC A library that extends PySyft with SMPC support SyMPC /ˈsɪmpəθi/ is a library which extends PySyft ≥0.3 with SMPC support. It allows computing o

Arturo Marquez Flores 0 Oct 13, 2021