I'm currently trying to create a bot for Minesweeper with computer vision. However using scipy.signal.correlate2d only yields noise. My test code is down below, why is the output just noise and not the heatmap I would expect?
from scipy import signal
import numpy as np
from cv2 import cv2
from PIL import Image
image = cv2.imread('MinesweeperTest.png',0)
template = cv2.imread('Mine2.png',0)
corr = signal.correlate2d(image,template,mode="same")
Image.fromarray(corr).save("correlation.png")
All the images involved can be found here:
MinesweeperTest.png: https://i.sstatic.net/Gxqtq.jpg
Mine2.png: https://i.sstatic.net/atV7T.jpg
Correlation.png: https://i.sstatic.net/aelY6.jpg
Preprocessing the images so that their mean value is 0 before invoking correlate2d should help get a more meaningful 2D cross-correlation:
image = image - image.mean()
template = template - template.mean()
A reproducible example reads:
from imageio.v3 import imread
from matplotlib import pyplot as plt
from scipy import signal
import numpy as np
image = imread('https://i.imgur.com/PpLLOW7.png', pilmode='L')
template = imread('https://i.imgur.com/ApIIs1Z.png', pilmode='L')
# fixed these
image = image - image.mean()
template = template - template.mean()
corr = signal.correlate2d(image, template, mode="same")
plt.imshow(corr, cmap='hot')