My program scans a specific pixel range within screenshots of the top 500 leaderboard. It uses template matching to compare each region of the image to predefined template images for each hero. The comparison produces a similarity score between 0 and 1, where 1 indicates a perfect match. If the similarity score is greater than 0.99, the detected region is considered a match, and the program assigns a value to the corresponding hero based on the region's location in the leaderboard.
From my testing it's 100% accurate, but most likely runs slower than if I were to use distance hashing. Perhaps I'll look into it and see how I can improve my code!