fast conversion of IplImage to Numpy array

Question

The newer OpenCV documentation here says you can convert an IplImage to a Numpy array just like this:

arr = numpy.asarray( im )

but that doesn't work for my needs, because it apparently doesn't support math:

x = arr/0.01
TypeError: unsupported operand type(s) for /: 'cv2.cv.iplimage' and 'float'

If I try to specify data type, I can't even get that far:

arr = numpy.asarray( im, dtype=num.float32 )
TypeError: float() argument must be a string or a number

So I'm using the code provided in the older documentation here. Basically, it does this:

arr = numpy.fromstring( im.tostring(), dtype=numpy.float32 )

But the tostring call is really slow, perhaps because it's copying the data? I need this conversion to be really fast and not copy any buffers it doesn't need to. I don't think the data are inherently incompatible; I'm creating my IplImage with cv.fromarray in the first place, which is extremely fast and accepted by the OpenCV functions.

Is there a way I can make the newer asarray method work for me, or else can I get direct access to the data pointer in the IplImage in a way that numpy.fromstring will accept it? I'm using OpenCV 2.3.1 prepackaged for Ubuntu Precise.

Answer 1

Fun Fact:
Say you call:

import cv2.cv as cv    #Just a formality!

Capture = cv.CaptureFromCAM(0)
Img = cv.QueryFrame(Capture)

The object Img is an ipimage, and numpy.asarray(Img) is erratic at best. However! Img[:,:] is a cvmat type, and numpy.asarray(Img[:,:]) works fantastically, and more important: quickly!

This is by far the fastest way I've found to grab a frame and make it an ndarray for numpy processing.