Creating an "array_like" QImage subclass for numpy.array()

Question

I want to create an "array_like" QImage subclass that can be passed to numpy.array(). I'd like to avoid using PIL as a substitute; the whole point of this is to avoid the dependency on PIL. Besides, constantly converting between QImage and the PIL Image is impractical for my program.

I find the documentation cryptic, and after reading it I'm still confused about how to emulate the array interface. As the numpy documentation states, to qualify as an "array_like" object, it needs the __array_interface__ attribute, which is a dictionary with five keys. However, I've never dealt with types, buffers, and memory before; if someone could explain how to solve this problem it would be much appreciated.

I'm using Python 3.3 and PySide 1.1.2. Thanks to all who reply!

Robert Kern · Accepted Answer · 2013-01-27 14:55:16Z

3

It's easier to just use the buffer object returned from QImage.bits() and np.frombuffer().

def qimage2array(q_image):
    width = q_image.width()
    height = q_image.height()
    arr = np.frombuffer(q_image.bits(), dtype=np.uint8).reshape([height, width, -1])
    return arr

answered Jan 27, 2013 at 14:55

Robert Kern

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3 Comments

midrare Over a year ago

How do you determine the appropriate dtype? What's the relation between QImage.format() and the dtype?

Robert Kern Over a year ago

I don't recommend using an image format for which uint8 is not an appropriate dtype. numpy won't be able to do much with any of the other packed formats. Use QImage.convertToFormat() to convert the QImage to one of the RGB32 formats.

Oleh Prypin Over a year ago

np.frombuffer(img.bits(), dtype=np.uint32).reshape(img.height(), img.width()) with qRgba, qRed etc. worked best for me.

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