A webcam-based 3x3x3 rubik's cube solver written in Python 3 and OpenCV.

Overview

Qbr

Qbr, pronounced as Cuber, is a webcam-based 3x3x3 rubik's cube solver written in Python 3 and OpenCV.

  • 🌈 Accurate color detection
  • 🔍 Accurate 3x3x3 rubik's cube detection
  • 🔠 Multilingual

Solve mode

solve mode

Calibrate mode

Isn't the default color detection working out for you? Use the calibrate mode to let Qbr be familiar with your cube's color scheme. If your room has proper lighting then this will give you a 99.9% guarantee that your colors will be detected properly.

Simply follow the on-screen instructions and you're ready to go.

calibrate mode calibrate mode success

Table of Contents

Introduction

The idea to create this came personally to mind when I started solving rubik's cubes. There were already so many professional programmers around the world who created robots that solve a rubik's cube in an ETA of 5 seconds and since 2016 in 1 second (link). That inspired me to create my own. I started using images only and eventually switched to webcam.

Installation

$ git clone --depth 1 https://github.com/kkoomen/qbr.git
$ cd qbr
$ python3 -m venv env
$ source ./env/bin/activate
$ pip3 install -r requirements.txt

Usage

Make sure you run source ./env/bin/activate every time you want to run the program.

Run Qbr:

$ ./src/qbr.py

This opens a webcam interface with the following things:

The first 9-sticker display (upper left corner)

This is preview mode. This will update immediately and display how Qbr has detected the colors.

The second 9-sticker display (upper left corner)

This is the snapshot state. When pressing SPACE it will create a snapshot in order to show you what state it has saved. You can press SPACE as many times as you'd like if it has been detected wrong.

Amount of sides scanned (bottom left corner)

The bottom left corner shows the amount of sides scanned. This is so you know if you've scanned in all sides before pressing ESC.

Interface language (top right corner)

In the top right corner you can see the current interface language. If you want to change the interface language you can press l to cycle through them. Continue to press l until you've found the right language.

Default language is set to English.

Available languages are:

  • English
  • Hungarian
  • Deutsch
  • French
  • Dutch
  • 简体中文

Full 2D cube state visualization (bottom right corner)

This visualization represents the whole cube state that is being saved and can be used to confirm whether the whole cube state has been scanned successfully.

Calibrate mode

The default color scheme contains the most prominent colors for white, yellow, red, orange, blue and green. If this can't detect your cube its colors properly then you can use calibrate mode.

Press c to go into calibrate mode in order to let Qbr be familiar with your cube's color scheme. Simply follow the on-screen instructions and you're ready to go.

Note: Your calibrated settings are automatically saved after you've calibrated your cube successfully. The next time you start Qbr it will automatically load it.

Tip: If you've scanned wrong, simple go out of calibrate mode by pressing c and go back into calibrate by pressing c again.

Getting the solution

Qbr checks if you have filled in all 6 sides when pressing ESC. If so, it'll calculate a solution if you've scanned it correctly.

You should now see a solution (or an error if you did it wrong).

How to scan your cube properly?

There is a strict way of scanning in the cube. Qbr will detect the side automatically, but the way you rotate the cube during the time you're scanning it is crucial in order for Qbr to properly calculate a solution. Make sure to follow the steps below properly:

  • Start off with the green side facing the camera and white on top, green being away from you. Start by scanning in the green side at this point.
  • After you've scanned in the green side, rotate the cube 90 or -90 degrees horizontally. It doesn't matter if you go clockwise or counter-clockwise. Continue to do this for the green, blue, red and orange sides until you are back at the green side.
  • You should now be in the same position like you started, having green facing the camera and white on top. Rotate the cube forward 90 degrees, resulting in green at the bottom and white facing the camera. Start scanning in the white side.
  • After you've scanned the white side, turn the cube back to how you started, having green in front again and white on top. Now rotate the cube backwards 90 degrees, resulting in green on top and yellow facing the camera. Now you can scan in the last yellow side.

If you've done the steps above correctly, you should have a solution from Qbr.

Keybindings

  • SPACE for saving the current state

  • ESC quit

  • c toggle calibrate mode

  • l switch interface language

Paramaters

You can use -n or --normalize to also output the solution in a "human-readable" format.

For example:

  • R will be: Turn the right side a quarter turn away from you.
  • F2 will be: Turn the front face 180 degrees.

Example runs

$ ./qbr.py
Starting position:
front: green
top: white

Moves: 20
Solution: U2 R D2 L2 F2 L U2 L F' U L U R2 B2 U' F2 D2 R2 D2 R2
$ ./qbr.py -n
Starting position:
front: green
top: white

Moves: 20
Solution: B2 U2 F' R U D' L' B' U L F U F2 R2 F2 D' F2 D R2 D2
1. Turn the back side 180 degrees.
2. Turn the top layer 180 degrees.
3. Turn the front side a quarter turn to the left.
4. Turn the right side a quarter turn away from you.
5. Turn the top layer a quarter turn to the left.
6. Turn the bottom layer a quarter turn to the left.
7. Turn the left side a quarter turn away from you.
8. Turn the back side a quarter turn to the right.
9. Turn the top layer a quarter turn to the left.
10. Turn the left side a quarter turn towards you.
11. Turn the front side a quarter turn to the right.
12. Turn the top layer a quarter turn to the left.
13. Turn the front side 180 degrees.
14. Turn the right side 180 degrees.
15. Turn the front side 180 degrees.
16. Turn the bottom layer a quarter turn to the left.
17. Turn the front side 180 degrees.
18. Turn the bottom layer a quarter turn to the right.
19. Turn the right side 180 degrees.
20. Turn the bottom layer 180 degrees.

Inspirational sources

Special thanks to HaginCodes for the main inspiration on how to improve my color detection.

https://github.com/HaginCodes/3x3x3-Rubiks-Cube-Solver

http://programmablebrick.blogspot.com/2017/02/rubiks-cube-tracker-using-opencv.html

https://gist.github.com/flyboy74/2cc3097f784c8c236a1a85278f08cddd

https://github.com/dwalton76/rubiks-color-resolver

License

Qbr is licensed under the MIT License.

Owner
Kim 金可明
Vim enthusiast; polyglot programmer; fullstack software engineer; QA engineer
Kim 金可明
Using computer vision method to recognize and calcutate the features of the architecture.

building-feature-recognition In this repository, we accomplished building feature recognition using traditional/dl-assisted computer vision method. Th

4 Aug 11, 2022
Official code for "Bridging Video-text Retrieval with Multiple Choice Questions", CVPR 2022 (Oral).

Bridging Video-text Retrieval with Multiple Choice Questions, CVPR 2022 (Oral) Paper | Project Page | Pre-trained Model | CLIP-Initialized Pre-trained

Applied Research Center (ARC), Tencent PCG 99 Jan 06, 2023
Crop regions in napari manually

napari-crop Crop regions in napari manually Usage Create a new shapes layer to annotate the region you would like to crop: Use the rectangle tool to a

Robert Haase 4 Sep 29, 2022
This is a repository to learn and get more computer vision skills, make robotics projects integrating the computer vision as a perception tool and create a lot of awesome advanced controllers for the robots of the future.

This is a repository to learn and get more computer vision skills, make robotics projects integrating the computer vision as a perception tool and create a lot of awesome advanced controllers for the

Elkin Javier Guerra Galeano 17 Nov 03, 2022
([email protected]) Boosting Co-teaching with Compression Regularization for Label Noise

Nested-Co-teaching ([email protected]) Pytorch implementation of paper "Boosting Co-tea

YINGYI CHEN 41 Jan 03, 2023
Deskewing images with slanted content

skew_correction De-skewing images with slanted content by finding the deviation using Canny Edge Detection. To Run: In python 3.6, from deskew import

13 Aug 27, 2022
Memory tests solver with using OpenCV

Human Benchmark project This project is OpenCV based programs which are puzzle solvers for 7 different games for https://humanbenchmark.com/. made as

Bahadır Araz 24 Dec 27, 2022
Corner-based Region Proposal Network

Corner-based Region Proposal Network CRPN is a two-stage detection framework for multi-oriented scene text. It employs corners to estimate the possibl

xhzdeng 140 Nov 04, 2022
Roboflow makes managing, preprocessing, augmenting, and versioning datasets for computer vision seamless.

Roboflow makes managing, preprocessing, augmenting, and versioning datasets for computer vision seamless. This is the official Roboflow python package that interfaces with the Roboflow API.

Roboflow 52 Dec 23, 2022
TextBoxes: A Fast Text Detector with a Single Deep Neural Network https://github.com/MhLiao/TextBoxes 基于SSD改进的文本检测算法,textBoxes_note记录了之前整理的笔记。

TextBoxes: A Fast Text Detector with a Single Deep Neural Network Introduction This paper presents an end-to-end trainable fast scene text detector, n

zhangjing1 24 Apr 28, 2022
A dataset handling library for computer vision datasets in LOST-fromat

A dataset handling library for computer vision datasets in LOST-fromat

8 Dec 15, 2022
Natural language detection

Detect the language of text. What’s so cool about franc? franc can support more languages(†) than any other library franc is packaged with support for

Titus 3.8k Jan 02, 2023
Text modding tools for FF7R (Final Fantasy VII Remake)

FF7R_text_mod_tools Subtitle modding tools for FF7R (Final Fantasy VII Remake) There are 3 tools I made. make_dualsub_mod.exe: Merges (or swaps) subti

10 Dec 19, 2022
Polaris is a Face recognition attendance system .

Support Me 🚀 About Polaris 📄 Polaris is a system based on facial recognition with a futuristic GUI design, Can easily find people informations store

XN3UR0N 215 Dec 26, 2022
Source Code for AAAI 2022 paper "Graph Convolutional Networks with Dual Message Passing for Subgraph Isomorphism Counting and Matching"

Graph Convolutional Networks with Dual Message Passing for Subgraph Isomorphism Counting and Matching This repository is an official implementation of

HKUST-KnowComp 13 Sep 08, 2022
Detect the mathematical formula from the given picture and the same formula is extracted and converted into the latex code

Mathematical formulae extractor The goal of this project is to create a learning based system that takes an image of a math formula and returns corres

6 May 22, 2022
Text page dewarping using a "cubic sheet" model

page_dewarp Page dewarping and thresholding using a "cubic sheet" model - see full writeup at https://mzucker.github.io/2016/08/15/page-dewarping.html

Matt Zucker 1.2k Dec 29, 2022
BoxToolBox is a simple python application built around the openCV library

BoxToolBox is a simple python application built around the openCV library. It is not a full featured application to guide you through the w

František Horínek 1 Nov 12, 2021
Code for CVPR 2022 paper "Bailando: 3D dance generation via Actor-Critic GPT with Choreographic Memory"

Bailando Code for CVPR 2022 (oral) paper "Bailando: 3D dance generation via Actor-Critic GPT with Choreographic Memory" [Paper] | [Project Page] | [Vi

Li Siyao 237 Dec 29, 2022
Basic functions manipulating images using the OpenCV library

OpenCV Basic functions manipulating images using the OpenCV library. Reading Ima

Shatha Siala 3 Feb 17, 2022