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    <p>I'm using python and using numpy so can do:</p> <pre><code>&gt;&gt;&gt; import cv &gt;&gt;&gt; import numpy as np &gt;&gt;&gt; im = cv.LoadImageM("aaa.jpg") &gt;&gt;&gt; np.sin(im) array([[[ 0.36319944, 0.46771851, 0.99646664], [ 0.98024565, -0.49104786, 0.46771851], [ 0.69605851, -0.9983471 , -0.49104786], ..., [-0.58777064, -0.79041475, 0.60906792], [-0.79041475, -0.94252455, 0.36319944], [ 0.08839871, -0.58777064, -0.94252455]], etc... </code></pre> <p>Otherwise, it should be easy to adapt the below python code to create and use a look up table, <a href="http://opencv.willowgarage.com/documentation/python/core_operations_on_arrays.html#lut" rel="nofollow">cv.LUT</a>:</p> <pre><code>import cv import math im = cv.LoadImageM("aaa.jpg") dst = cv.CreateMat(im.height,im.width, cv.CV_32FC3) lut_sin = cv.CreateMat(256, 1, cv.CV_32FC3) for i in xrange(256): s = math.sin(i) cv.Set1D(lut_sin, i, (s,s,s,s)) cv.LUT(im,dst,lut_sin) print cv.Get2D(dst,0,0) #output, matches above: (0.36319944262504578, 0.46771851181983948, 0.99646663665771484, 0.0) </code></pre> <p>For floating point data, you can use <a href="http://opencv.willowgarage.com/documentation/python/core_operations_on_arrays.html?highlight=polartocart#PolarToCart" rel="nofollow">cv.PolarToCart</a>:</p> <pre><code>&gt;&gt;&gt; import cv &gt;&gt;&gt; im = cv.LoadImageM("aaa.jpg") &gt;&gt;&gt; im2 = cv.CreateMat(im.height, im.width, cv.CV_32FC3) &gt;&gt;&gt; cv.Convert(im,im2) &gt;&gt;&gt; sin_of_angle = cv.CreateMat(im.height, im.width, cv.CV_32FC3) &gt;&gt;&gt; cv.PolarToCart(None,im2,None,sin_of_angle,0) &gt;&gt;&gt; cv.Get1D(sin_of_angle,0) (0.36319947242736816, 0.46771851181983948, 0.99646669626235962, 0.0) &gt;&gt;&gt; cv.Get1D(sin_of_angle,1) (0.98024564981460571, -0.49104788899421692, 0.46771851181983948, 0.0) </code></pre> <p>You can use this to easily get the cos values as well. You can switch the last parameter to <code>True</code> for Radians.</p>
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    1. POhow to apply sin() to all elements in an opencv Mat
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    1. COBut if you don't have numpy, then you have to loop over all the elements.
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    2. CO@Martin Beckett - Perhaps that is a bit python-centric, I've added a `LUT` approach which should be quick.
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    3. COexcellent answer, LUT is a very good approach (assuming an 8bit image)
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