Merge branch 'main' of https://github.com/tanjeffreyz/auto-maple into…

… dev

README.md

@@ -16,7 +16,7 @@ Community-created resources, such as **command books** for each class and **rout
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-Auto Maple uses OpenCV template matching to determine the bounds of the minimap as well as the various elements within it, allowing it to accurately track the player's in-game position. If <code>record_layout</code> is set to <code>True</code>, Auto Maple will record the player's previous positions in a quadtree-based Layout object, which is periodically saved to a file in the "layouts" directory. Every time a new routine is loaded, its corresponding layout file, if it exists, will also be loaded. This Layout object uses the A* search algorithm on its stored points to calculate the shortest path from the player to any target location, which can dramatically improve the accuracy and speed at which routines are executed.
+Auto Maple uses <b>OpenCV template matching</b> to determine the bounds of the minimap as well as the various elements within it, allowing it to accurately track the player's in-game position. If <code>record_layout</code> is set to <code>True</code>, Auto Maple will record the player's previous positions in a <b>quadtree-based</b> Layout object, which is periodically saved to a file in the "layouts" directory. Every time a new routine is loaded, its corresponding layout file, if it exists, will also be loaded. This Layout object uses the <b>A* search algorithm</b> on its stored points to calculate the shortest path from the player to any target location, which can dramatically improve the accuracy and speed at which routines are executed.
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@@ -129,7 +129,7 @@ Below is a summary of the most commonly used routine components:
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-Auto Maple has the ability to automatically solve "runes", or in-game arrow key puzzles. It first uses OpenCV's color filtration and Canny edge detection algorithms to isolate the arrow keys and reduce as much background noise as possible. Then, it runs multiple inferences on the preprocessed frames using a custom-trained TensorFlow model until two inferences agree. Because of this preprocessing, Auto Maple is extremely accurate at solving runes in all kinds of (often colorful and chaotic) environments.
+Auto Maple has the ability to automatically solve "runes", or in-game arrow key puzzles. It first uses OpenCV's color filtration and <b>Canny edge detection</b> algorithms to isolate the arrow keys and reduce as much background noise as possible. Then, it runs multiple inferences on the preprocessed frames using a custom-trained <b>TensorFlow</b> model until two inferences agree. Because of this preprocessing, Auto Maple is extremely accurate at solving runes in all kinds of (often colorful and chaotic) environments.
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