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I think the problem with what you were trying to do is that the array data you're interested in (ie. <code>foo_np_view</code>) is actually only stored in one place i.e. <code>foo_np.data</code>, and the OpenCV <code>SetData</code> method doesn't provide any way to specify stride settings that would allow you to skip the bytes that are not part of <code>foo_np_view</code>. You can, however, get around this problem using Numpy’s <code>tostring()</code> method, which turns an array (or views therein) into a byte string: <pre><code>>>> import numpy as np >>> import cv >>> foo_np = np.array( 255 * np.random.rand( 200 , 300 , 3 ), dtype = 'uint8' ) >>> foo_np_view = foo_np [ 50:150:2 , 10:290:5 , : ] >>> h,w,d = foo_np_view.shape >>> foo_cv = cv.CreateMat( h , w , cv.CV_8UC3 ) </code></pre> Recreating the original problem: <pre><code>>>> cv.SetData( foo_cv , foo_np_view.data, foo_np_view.strides[0] ) Traceback (most recent call last): File "<stdin>", line 1, in <module> AttributeError: cannot get single-segment buffer for discontiguous array </code></pre> Using the <code>tostring()</code> method (see below for explanation of the stride setting): <pre><code>>>> cv.SetData( foo_cv , foo_np_view.tostring() , w * d * foo_np_view.dtype.itemsize ) >>> np.array_equal( np.asarray( foo_cv ) , foo_np_view ) True </code></pre> The value <code>w * d * foo_np_view.dtype.itemsize</code> gives us a stride value identical to that of <code>foo_np_view.copy()</code>, which is necessary as the string representations of the view and its copy are identical: <pre><code>>>> foo_np_view.copy().tostring() == foo_np_view.tostring() True >>> foo_np_view.copy().strides[0] == w * d * foo_np_view.dtype.itemsize True </code></pre>
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1. POHow to convert a numpy array view to opencv matrix?
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1. COthanks, that appears to work , but i don't think i can gain any performance over my current solution (making a copy of the array), because the tostring() method seems to return a copy anyway. correct me if i'm wrong..
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