The array interface is a yet another mechanism to communicate with other Python applications. This protocol, as its name suggests, is only applicable to array-like objects. A demonstration is in order. Let's use PIL again, but without saving files.
We will be reusing part of the code from the previous recipe, so the prerequisites are similar. We will skip the first step of the previous step here, and assume it is already known.
The following steps will let us explore the array interface:
- The PIL image array interface attribute.
The PIL image object has a
__array_interface__attribute. Let's inspect its contents. The value of this attribute is adictionary:array_interface = img.__array_interface__ print "Keys", array_interface.keys() print "Shape", array_interface['shape'] print "Typestr", array_interface['typestr']
This code prints the following information:
Keys ['shape', 'data', 'typestr'] Shape (512, 512, 4) Typestr |u1
- The NumPy array interface attributes.
The NumPy
ndarraymodule has a__array_interface__attribute as well. We can convert the PIL image to a NumPy array with theasarrayfunction:numpy_array = numpy.asarray(img) print "Shape", numpy_array.shape print "Data type", numpy_array.dtype
The shape and data type of the array:
Shape (512, 512, 4) Data type uint8
As you can see, the shape has not changed. The code for this recipe is as follows:
import numpy
import Image
import scipy.misc
lena = scipy.misc.lena()
data = numpy.zeros((lena.shape[0], lena.shape[1], 4), dtype=numpy.int8)
data[:,:,3] = lena.copy()
img = Image.frombuffer("RGBA", lena.shape, data)
array_interface = img.__array_interface__
print "Keys", array_interface.keys()
print "Shape", array_interface['shape']
print "Typestr", array_interface['typestr']
numpy_array = numpy.asarray(img)
print "Shape", numpy_array.shape
print "Data type", numpy_array.dtype