Maze image solver

There are a number of different maze solving algorithmsthat is, automated methods for the solving of mazes. Mazes containing no loops are known as "simply connected", or "perfect" mazes, and are equivalent to a tree in graph theory. Thus many maze solving algorithms are closely related to graph theory. Intuitively, if one pulled and stretched out the paths in the maze in the proper way, the result could be made to resemble a tree.

This is a trivial method that can be implemented by a very unintelligent robot or perhaps a mouse. It is simply to proceed following the current passage until a junction is reached, and then to make a random decision about the next direction to follow.

Although such a method would always eventually find the right solutionthis algorithm can be extremely slow. The best-known rule for traversing mazes is the wall followeralso known as either the left-hand rule or the right-hand rule. If the maze is simply connectedthat is, all its walls are connected together or to the maze's outer boundary, then by keeping one hand in contact with one wall of the maze the solver is guaranteed not to get lost and will reach a different exit if there is one; otherwise, the algorithm will return to the entrance having traversed every corridor next to that connected section of walls at least once.

Maze solving algorithm

Another perspective into why wall following works is topological. If the walls are connected, then they may be deformed into a loop or circle. To further this idea, notice that by grouping together connected components of the maze walls, the boundaries between these are precisely the solutions, even if there is more than one solution see figures on the right. If the maze is not simply-connected i.

Another concern is that care should be taken to begin wall-following at the entrance to the maze. If the maze is not simply-connected and one begins wall-following at an arbitrary point inside the maze, one could find themselves trapped along a separate wall that loops around on itself and containing no entrances or exits. Should it be the case that wall-following begins late, attempt to mark the position in which wall-following began.

Because wall-following will always lead you back to where you started, if you come across your starting point a second time, you can conclude the maze is not simply-connected, and you should switch to an alternative wall not yet followed. See the Pledge Algorithmbelow, for an alternative methodology. Wall-following can be done in 3D or higher-dimensional mazes if its higher-dimensional passages can be projected onto the 2D plane in a deterministic manner.

For example, if in a 3D maze "up" passages can be assumed to lead Northwest, and "down" passages can be assumed to lead southeast, then standard wall following rules can apply. However, unlike in 2D, this requires that the current orientation is known, to determine which direction is the first on the left or right. Disjoint [ clarification needed ] mazes can be solved with the wall follower method, so long as the entrance and exit to the maze are on the outer walls of the maze.

If however, the solver starts inside the maze, it might be on a section disjoint from the exit, and wall followers will continually go around their ring. The Pledge algorithm named after Jon Pledge of Exeter can solve this problem.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. MazeSolver solves mazes, which are automatically read and interpreted from jpg images.

As input it takes a jpg image and the coordinates of the beginning and end of the maze. It outputs a file called "output. There are three example mazes included in the project. All are distributed under the creative commons license, the original images are available here: maze1. Below you can see the maze3.

MazeSolver is a console application, to run it you need to open the console and browse to the location of the app. The program can be ran with the following command:. So to run the app with one of the example mazes you need to write: MazeSolver. Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

Sign up. An app that can solve a maze based on its image. Branch: master. Find file. Sign in Sign up. Go back.

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Launching Xcode If nothing happens, download Xcode and try again. Latest commit. Latest commit 0fb4fac Dec 6, MazeSolver MazeSolver solves mazes, which are automatically read and interpreted from jpg images.This project aims at finding the solution of perfect mazes which is defined as a maze which has only one path from any point in the maze to any other point.

Also, maze has no inaccessibe sections, no circular paths and no open areas. Maze images should have dark walls on light background. I have included some sample images.

To generate mazes, this online maze generator can be used. This project idea is taken from this File exchange submission. OpenCV 2. For instructions regarding the installation of OpenCV refer documentation. Perfect mazes have a characteristic that their is only one possible path from start to end. This means that this image can be divided into two parts and this boundary of division will be the solution itself.

Therefore, morphological transformatons like erosion and dilation are just enough to find the solution of the maze. Dilation is one of the two basic operators in the area of mathematical morphology, the other being erosion.

It is typically applied to binary images, but there are versions that work on grayscale images.

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The basic effect of the operator on a binary image is to gradually enlarge the boundaries of regions of foreground pixels i. Thus areas of foreground pixels grow in size while holes within those regions become smaller.

Erosion is the second morphological operator. It is also applied to binary images. The basic effect of the operator on a binary image is to erode away the boundaries of regions of foreground pixels i. Thus areas of foreground pixels shrink in size, and holes within those areas become larger. Since, the maze can be divided into an open path seperated by two walls, finding contours will give us both the walls.

Select ay contour for further operations. Now create a mask by inverting the diff image. Bitwise and r and g components of original maze using the mask created in the last step.This ible will teach you how to automate solving mazes - to solve any maze in well under a minute. You will learn to solve simple digital maze as well as photos of printed out maze puzzles this one is a bit more complex but I promised solving mazes in under a minute so Yesthat is it.

So let's get started! And don't forget to vote for me in how to play a game contest and automation contest if you like it :. Did you use this instructable in your classroom? Add a Teacher Note to share how you incorporated it into your lesson.

Microsoft Paint might be useless when it comes to image processing but it has all the necessary tools for solving mazes.

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For small ones like the one on above pictures this is the best option. No preparations needed whatsoever - two quick steps and you are done. The part where you follow colour gap may get harder when overall maze size and complexity increase. To avoid headaches or just support your laziness : I prepared a tutorial that does that step for you step 2 if you have Photoshop, step 3 if you prefer Gimp. You have photoshop, you are lazy and you want to solve a maze. If this description fits you, you came to the right place!

You solved another maze in well under a minute! This method is the the best as it can be used unchanged for solving infinitely large mazes in seconds. You are lazy, you want to solve a maze and you don't have photoshop. I know I almost repeated introduction from previous step but there is a reason: there are no differences except for where functions are and how they are named!

You solved yet another maze in well under a minute!

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Ready for something a bit more serious? Go to next step lazy ones as well - it will be fun. It requires a bit more work sorry lazy ones, I tricked you into coming here with "fun" : but trust me, It is worth it! This process might sound difficult but it takes you less than two minutes when you get a gang of it. The idea behind all techniques found in this Ible come from this: every maze is nothing but two walls next to each other, each with some dead allies and turns.

The two walls are not connected see image above. Most common computer generated mazes follow this criteria though I may have lied when I said you will be able to solve any maze. Be aware however, that this method does require some trial and error.

In most cases cutting outside wall does the job but in some cases, you will need to experiment. If enough of you show support I might write an app for solving mazes. Do not hesitate to leave any questions and feedback in comments.

For a maze with start or end at the center, split the boundary around the center ending. If you have multiple starts or ends in the middle, split them all. You get multiple connected boundaries, red, blue, green, purple. Each color leads from one start or end to another. Reply 5 years ago on Introduction.

Sometimes around centre like you saidsometimes around outside border. After you try a few mazes, you will begin to see where to cut it without trying a few cuts first.

Follow the gap between black and newly coloured border and you are done. Reply 4 years ago on Introduction.This ible will teach you how to automate solving mazes - to solve any maze in well under a minute. You will learn to solve simple digital maze as well as photos of printed out maze puzzles this one is a bit more complex but I promised solving mazes in under a minute so Yesthat is it.

So let's get started! And don't forget to vote for me in how to play a game contest and automation contest if you like it :. Did you use this instructable in your classroom? Add a Teacher Note to share how you incorporated it into your lesson.

Microsoft Paint might be useless when it comes to image processing but it has all the necessary tools for solving mazes. For small ones like the one on above pictures this is the best option.

No preparations needed whatsoever - two quick steps and you are done. The part where you follow colour gap may get harder when overall maze size and complexity increase.

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To avoid headaches or just support your laziness : I prepared a tutorial that does that step for you step 2 if you have Photoshop, step 3 if you prefer Gimp. You have photoshop, you are lazy and you want to solve a maze. If this description fits you, you came to the right place! You solved another maze in well under a minute! This method is the the best as it can be used unchanged for solving infinitely large mazes in seconds. You are lazy, you want to solve a maze and you don't have photoshop.

I know I almost repeated introduction from previous step but there is a reason: there are no differences except for where functions are and how they are named! You solved yet another maze in well under a minute! Ready for something a bit more serious? Go to next step lazy ones as well - it will be fun.

It requires a bit more work sorry lazy ones, I tricked you into coming here with "fun" : but trust me, It is worth it!

This process might sound difficult but it takes you less than two minutes when you get a gang of it. The idea behind all techniques found in this Ible come from this: every maze is nothing but two walls next to each other, each with some dead allies and turns. The two walls are not connected see image above. Most common computer generated mazes follow this criteria though I may have lied when I said you will be able to solve any maze. Be aware however, that this method does require some trial and error.

In most cases cutting outside wall does the job but in some cases, you will need to experiment. If enough of you show support I might write an app for solving mazes.

Maze Solver (shortest path finder)

Do not hesitate to leave any questions and feedback in comments. For a maze with start or end at the center, split the boundary around the center ending. If you have multiple starts or ends in the middle, split them all. You get multiple connected boundaries, red, blue, green, purple.

Each color leads from one start or end to another. Reply 5 years ago on Introduction.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service. The dark mode beta is finally here. Change your preferences any time. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. Given an JPEG image as seen abovewhat's the best way to read it in, parse it into some data structure and solve the maze?

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My first instinct is to read the image in pixel by pixel and store it in a list array of boolean values: True for a white pixel, and False for a non-white pixel the colours can be discarded. The issue with this method, is that the image may not be "pixel perfect". By that I simply mean that if there is a white pixel somewhere on a wall it may create an unintended path.

BFS Maze Solver using OpenCV - Python Intermediate Tutorial

Another method which came to me after a bit of thought is to convert the image to an SVG file - which is a list of paths drawn on a canvas. This way, the paths could be read into the same sort of list boolean values where True indicates a path or wall, False indicating a travel-able space.

Also an issue with converting to SVG is that the lines are not "perfectly" straight. This results in the paths being cubic bezier curves. With a list array of boolean values indexed by integers, the curves would not transfer easily, and all the points that line on the curve would have to be calculated, but won't exactly match to list indices. I assume that while one of these methods may work though probably not that they are woefully inefficient given such a large image, and that there exists a better way.

Is there even a best way? Then comes the solving of the maze. If I use either of the first two methods, I will essentially end up with a matrix. How would one create a tree from the image? Any ideas? TL;DR Best way to parse? Into what data structure? I feel that the algorithm is correct, but it's not working as hoped. Code below. It is really very simple and standard, there should not be difficulties on implementing this in Python or whatever.

Note: Marks a white visited pixel grey. This removes the need for a visited list, but this requires a second load of the image file from disk before drawing a path if you don't want a composite image of the final path and ALL paths taken.

I tried myself implementing A-Star search for this problem. Followed closely the implementation by Joseph Kern for the framework and the algorithm pseudocode given here :. As A-Star is a heuristic search algorithm you need to come up with a function that estimates the remaining cost here: distance until the goal is reached. Unless you're comfortable with a suboptimal solution it should not overestimate the cost. A conservative choice would here be the manhattan or taxicab distance as this represents the straight-line distance between two points on the grid for the used Von Neumann neighborhood.

Which, in this case, wouldn't ever overestimate the cost. This would however significantly underestimate the actual cost for the given maze at hand.

maze image solver

Therefore I've added two other distance metrics squared euclidean distance and the manhattan distance multiplied by four for comparison. These however might overestimate the actual cost, and might therefore yield suboptimal results.

maze image solver

Here are some images for a visualization of the results inspired by the one posted by Joseph Kern. The animations show a new frame each after iterations of the main while-loop.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again.

maze image solver

These are the python files associated with the computerphile video on maze solving. Feel free to use, alter, redistribute the code as you see fit. I'm not actively developing this project, simply for time reasons. Mostly I want the code to be as it was at least in general function at the time of the video.

If you're interested in improving the code, then you can fork it into your own repository and make any changes you wish. If you come up with something good, feel free to share it on the wikithanks to Jacob Mitchell for starting that. Some example mazes are included in the repository.

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These were generated either by hand, or using the software Daedalus. Once exported I edited the mazes to ensure that the following rules are adhered to:. This was just a side project I did for fun over a couple of evenings, I'm sure there are many improvements and extensions you could make if you wanted to.

Some things to note:. Skip to content.

maze image solver

Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. Sign up. A variety of algorithms to solve mazes from an input image. Python Branch: master. Find file.

Sign in Sign up. Go back. Launching Xcode If nothing happens, download Xcode and try again. Latest commit. Latest commit e81d36b Feb 28, About These are the python files associated with the computerphile video on maze solving.

Input Some example mazes are included in the repository. Once exported I edited the mazes to ensure that the following rules are adhered to: Each maze is black and white. White represents paths, black represents walls. All mazes are surrounded entirely by black walls. One white square exists on the top row of the image, and is the start of the maze.

One white square exists on the bottom row of the image, and is the end of the maze. Notes This was just a side project I did for fun over a couple of evenings, I'm sure there are many improvements and extensions you could make if you wanted to.

Some things to note: The data structures and representations can probably be improved for speed - I only focused a little on efficiency. Mostly I wanted to keep memory usage down, to allow the use of very large mazes.


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