Oscar Smith Oscar Smith -4 years ago 137
Python Question

Speed up numpy filtering

I am making a music recognition program, and as part of it, I need to find the largest connected areas of a numpy array from a png (2200x1700 pixels). My current solution is the following.

labels, nlabels = ndimage.label(blobs)
cutoff = len(blobs)*len(blobs[0]) / nlabels
blobs_found = 0
x = []
t1 = time()
for n in range(1, nlabels+1):
squares = np.where(labels==n)
if len(squares[0]) < cutoff:
blobs[squares] = 0
blobs_found += 1
blobs[squares] = blobs_found
x.append(squares - np.amin(squares, axis=0, keepdims=True))
nlabels = blobs_found
print(time() - t1)

This works, but it takes ~6.5 seconds to run. Is there a way I could remove the loop from this code (or otherwise speed it up)?

Answer Source

You can get the size (in pixels) of each labelled region with:

unique_labels = numpy.unique(labels)
label_sizes = scipy.ndimage.measurement.sum(numpy.ones_like(blobs), labels, unique_labels)

The largest will then be:

unique_labels[label_size == numpy.max(label_size)]
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