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POHow to improve efficiency in this numpy iterating?
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  1. POHow to improve efficiency in this numpy iterating?
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    copied!<p>I'm working on an assignment about converting a grayscale image to 1-bit binary image by dithering. I'm trying a simple 4x4 matrix that will make the image 16 times larger than original.</p> <pre><code>dithering_matrix = array([[ 0, 8, 2, 10], [12, 4, 14, 6], [ 3, 11, 1, 9], [15, 7, 13, 5]], dtype=uint8) split_num = dithering_matrix.size + 1 </code></pre> <p>I read a 512x512 image to <code>im</code> ndarray and did following things:</p> <pre><code>output = list() for row in im: row_output = list() for pixel in row: pixel_matrix = ((pixel / (256 / split_num)) &gt; dithering_matrix) * 255 row_output.append(pixel_matrix) output.append( hstack( tuple(row_output) ) ) output_matrix = vstack( tuple(output) ) </code></pre> <p>I found it took 8-10s to output and I think the loop of <code>im</code> above spent much time. In some software the same operation was usually done in a flash. So is it possible to improve the efficiency? </p> <hr> <p>UPDATE: @Ignacio Vazquez-Abrams I'm not vert fimiliar with profiler:( I tried cProfile and the result is strange.</p> <pre><code> 1852971 function calls (1852778 primitive calls) in 9.127 seconds Ordered by: internal time List reduced from 561 to 20 due to restriction &lt;20&gt; ncalls tottime percall cumtime percall filename:lineno(function) 1 6.404 6.404 9.128 9.128 a1.1.py:10(&lt;module&gt;) 513 0.778 0.002 0.778 0.002 {numpy.core.multiarray.concatenate } 262144 0.616 0.000 1.243 0.000 D:\Python27\lib\site-packages\nump y\core\shape_base.py:6(atleast_1d) 262696 0.260 0.000 0.261 0.000 {numpy.core.multiarray.array} 262656 0.228 0.000 0.487 0.000 D:\Python27\lib\site-packages\nump y\core\numeric.py:237(asanyarray) 515 0.174 0.000 1.419 0.003 {map} 527019 0.145 0.000 0.145 0.000 {method 'append' of 'list' objects } </code></pre> <p>The line 10 of a1.1.py is the first line <code>from numpy import *</code> (all comments before that) which really puzzles me up.</p>
 

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