Faster/Better way to modify (tuple defined) parts of a Numpy array in Python?

Question

I have a numpy pixel array (either 0 or 255), using .where I pull the tuples of where it is > 0. I now want to use those tuples to add 1 to a separate 2D numpy array. Is the best way to just use a for loop as shown below or is there a better numpy-like way?

changedtuples = np.where(imageasarray > 0)

#If there's too much movement change nothing.
if frame_size[0]*frame_size[1]/2 < changedtuples[0].size:
    print "No change- too much movement."
elif changedtuples[0].size == 0:
    print "No movement detected."
else:
    for x in xrange(changedtuples[0].size):
        movearray[changedtuples[1][x],changedtuples[0][x]] = movearray[changedtuples[1][x],changedtuples[0][x]]+1

user2357112 · Accepted Answer · 2014-08-03 05:53:41Z

1

movearray[imageasarray.T > 0] += 1

where is redundant. You can index an array with a boolean mask, like that produced by imageasarray.T > 0, to select all array cells where the mask has a True. The += then adds 1 to all those cells. Finally, the T is a transpose, since it looks like you're switching the indices around when you increment the cells of movearray.

answered Aug 3, 2014 at 5:53

user2357112

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2 Comments

DTC Over a year ago

That's perfect, thanks. If I remove the where I can still check for no movement with all but how do I check the first condition, if over 50% (or whatever threshold) is greater than 0 to disregard the movement?

user2357112 Over a year ago

@DTC: Save the mask, so you don't have to compute it twice (mask = imageasarray.T > 0). Then, mask.sum() is the number of True entries.

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