what would be the best (fastest) way to check if a small picture is inside a big picture?
(Zoomed picture:)
Want to Find:I have a solution, but it is very slow:
- i iterate through every single pixel (x,y) in the big picture and compare the pixel (0,0) of the small picture (color value). 开发者_如何学C
- if the pixel is the same, I iterate through the small picture and compare it with the bigger one.. if it fails, it goes back to the big picture scanning loop..
this method needs like ~7 seconds to find a 50x50 pic on 1600x1200 photo.
maybe you know a better algorithm? i know a software which can do this in under a second.
The mathematical operation convolution (which can be efficiently implemented with the Fast Fourier Transform) can be used for this.
the other answer describes cross-correlation via convolution of images (implemented by multiplying ffts). but sometimes you want to use normalized cross-correlation - see http://scribblethink.org/Work/nvisionInterface/nip.html for a full discussion and details of a fast implementation.
If you know the pixel values will be exact, this just becomes a special case of a string matching problem. There are lots of fast string matching algorithms, I'd start with Boyer-Moore or Knuth-Morris-Pratt.
You're algo has a worst case of O(hA*wA*hB*wB)
where hA
,wA
,hB
,wB
are height and width of the big image A
and the small image B
.
This algo should instead have a worst case of O((wA+wB)*hA*hB)
It's based on string matching and this is how it works:
- Find each row of the image
B
in each row of imageA
using string matching each time.- Every time you have a match, store in the array
matched_row
a triple(rA, cA, rB)
where(rA, cA)
represents the starting point in the imageA
of therB
row of the fileB
.
- Every time you have a match, store in the array
- Now you sort
matched_row
first according tocA
, then torA
and then torB
. Now you iterate the array and if you matched an image B of 5 row you will have something like this:
(12, 5, 0), (13, 5, 1), (14, 5, 2), (15, 5, 3), (15, 5, 4)
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