使用 numpy/scipy 将大图像与 python 中的内核相关联

Question

I have an image (10000x10000 pixels) and I have a kernel (5x5 pixels). I want to find the place(s) in the image that best matches the kernel.

I vaguely remember from my studies that I need to compute a correlation coefficient for each pixel in the large image with respect to the kernel. But having something like (10000 - 4) * (10000 - 4) pixels to go through, I expect to get a huge performance hit doing this in python .

Having only a very brief knowledge on the subject I was hoping to find something in either numpy or scipy that would do this relatively fast, but I haven't been able to find anything.

Does either numpy or scipy contain a method for doing this?

Answer 1

This is usually referred to as template matching in image processing and most image processing packages will have something for it. If you can use scikit-image then you probably want match_template . Of course, OpenCV can do template matching too .

If you need to stick with pure scipy, it's easy enough to implement yourself: just find the maximum pixel ( argmax ) of a normalized cross-correlation ( correlate2d ).