Commit 1beb281d by Emmanuel Roubin

### Generalize covariance function to any image shapes

parent dac97218
Pipeline #51811 passed with stages
in 25 minutes and 12 seconds
 ... ... @@ -63,3 +63,6 @@ ttkvenv # latex *.aux *.log # code OSS .vscode
 ... ... @@ -84,7 +84,7 @@ def covarianceAlongAxis(im, d, mask=None, axis=[0, 1, 2]): print('spam.measurements.covariance.covarianceAlongAxis: d={}. Should be a list of integers.'.format(type(d[0]))) print('exit function.') return -1 if max(d) >= im.shape[0] or max(d) >= im.shape[1] or max(d) >= im.shape[2]: if any([max(d) >= im.shape[i] for i in axis]): print('spam.measurements.covariance.covarianceAlongAxis: max(d)={}. Should be smaller than the image.'.format(max(d))) print('exit function.') return -1 ... ... @@ -115,8 +115,7 @@ def covarianceAlongAxis(im, d, mask=None, axis=[0, 1, 2]): im_multi = numpy.multiply(im_multi, mask_eff, dtype=numpy.float32) else: # Step 2.1: Compute the pairs of numbers size = im.shape[a] n = (size - x) * size**2 n = (im.shape[a] - x) * numpy.prod([s for i, s in enumerate(im.shape) if i != a]) # # Step 2.2: Multiply the image im_multi = numpy.multiply(im, spam.helpers.singleShift(im, x, a, sub=0), dtype=numpy.float32) # n_multi = numpy.sum(im_multi) ... ...
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