A while back, I wrote a blogpost about using computer vision techniques to play an automated game of pong. This was accomplished by scanning the screen for a white pixel and moving the mouse to this position.
In that particular blogpost, I didn’t really go deep into the optimization that can be done when using Java for computer vision. So this time, I’d like to try some techniques and benchmark them to find out how to shorten the execution time.
The benchmark is prepared as follows:
An image is loaded from disk
The benchmark timer starts
The image is converted to grayscale one hundred times
The benchmark timer stops
The image is written back to disk to manually verify the results
For each benchmark, a couple of variables are prepared:
imageO containing the original image as a BufferedImage object
imageN containing a blank target image as a BufferedImage object
Lets try some techniques, starting with a worst-case scenario:
Now that pong is fully automated, it is time to optimize.
First I switched to the game Doeo. The goal of the game is to move your mouse as fast over Doeos as possible. A fun game to use as an example for automation.
I also made a small performance enhancement (10 to 15 percent) by not using getRGB () from BufferdImage, but reading directly from an array. It does take some time to get everything into an integer array, but if that’s done, the for-loop is a lot faster.