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2013-03-08T12:37:04.497
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Here is the full code: <pre><code>import numpy from scipy import ndimage array = numpy.zeros((100, 100), dtype=np.uint8) x = np.random.randint(0, 100, 2000) y = np.random.randint(0, 100, 2000) array[x, y] = 1 pl.imshow(array, cmap="gray", interpolation="nearest") s = ndimage.generate_binary_structure(2,2) # iterate structure labeled_array, numpatches = ndimage.label(array,s) # labeling sizes = ndimage.sum(array,labeled_array,range(1,numpatches+1)) # To get the indices of all the min/max patches. Is this the correct label id? map = numpy.where(sizes==sizes.max())[0] + 1 mip = numpy.where(sizes==sizes.min())[0] + 1 # inside the largest, respecitively the smallest labeled patches with values max_index = np.zeros(numpatches + 1, np.uint8) max_index[map] = 1 max_feature = max_index[labeled_array] min_index = np.zeros(numpatches + 1, np.uint8) min_index[mip] = 1 min_feature = min_index[labeled_array] </code></pre> Notes: <ul> <li><code>numpy.where</code> returns a tuple</li> <li>the size of label 1 is sizes[0], so you need to add 1 to the result of <code>numpy.where</code></li> <li>To get a mask array with multiple labels, you can use <code>labeled_array</code> as the index of a label mask array.</li> </ul> The results: <img src="https://i.stack.imgur.com/pPmW5.png" alt="enter image description here"> <img src="https://i.stack.imgur.com/7S9Sn.png" alt="enter image description here"> <img src="https://i.stack.imgur.com/IoTr9.png" alt="enter image description here">
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1. COGREAT, thank your very much! This works very well. I tried indexing it before but i didn't get the trick of adding 1 to the size of label 1 before.
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