Readme is under construction, cleaner details to come...
A path finding visualizer that implements BFS, Multisource BFS, and A* algorithms to find the shortest path between the two blue points.
Press i to spawn pdf images where a sobel filter is applied and the images are converted to maze like arrays.
Press f (fast) or s (slow) to adjust program's frame rate.
Greediness of the A* algorithm is an adjustable parameter, in the path_finder_2.py version the optimal greed is automatically estimated by a machine learning model.
Grids can be randomly generated using the random grid button.
The density of the grids can be adjusted by the grid density buttoms
Use the reset button to clear the previous solution before running a new solution.
Grey pixels are obstacles.
Black pixels are unvisited open spaces.
Blue pixels mark the start point, target point, and optimal path.
Orange pixels are visited nodes.
Green pixels are nodes that are currenlty in the queue to be visited.
Method 1: Click the Gitpod button below - wait a minute - pop the window out by clicking the expand button circled in the image below.
Method 2: Clone this repo and run path_finder_2.py in python3, dependencies are listed in requirements.txt, if you do not already have TensorFlow installed, it may be simpler to just use path_finder_1. It has all of the same features except that it does not predict the optimal greediness for A*.
Multisource Breadth First Search
Single Source Breadth First Search
A*
