does any of you guys know how i can decode a "neatly reconstructed square shaped data matrix"? and here is what I mean by neatly reconstructed:
assume that I have an image which contains a 26x26 data matrix(or 28x28 or whatever) . I have extracted and reconstructed the data matrix as a 26x26 Numpy array made up of zero's and one's . (out of 676 reconstructed points i might have gotten 1 or 2 wrong due to noise in image,etc.)
my questions is: how can i decode it? I mean feeding it AS AN IMAGE to a data matrix decoding library kinda defeats the whole point of reconstructing it in the first place.right?
any help, hint, piece of code(preferably python ;) ) highly appreciated.
now i know that i had the following options as well: using libraries such as pylibdmtx and zxing:
1-feed in the whole image, probably tweak a couple of parameters and hope that the data matrix would be found and decoded. 2-locate and crop the data matrix beforehand and then use the aforementioned libraries. 3-locate, crop and reconstruct the data matrix beforehand and then use some library to decode the reconstructed data matrix.
but neither of these 3 is what I'm looking for.