Simple Feature Computation with ColorDescriptor

The following is a concrete example of performing feature computation for a set of ten butterfly images using the CSIFT descriptor from the ColorDescriptor software package. It assumes you have set up the colordescriptor executable and python library in your PATH and PYTHONPATH. Once set up, the following code will compute a CSIFT descriptor:

# Import some butterfly data
urls = ["http://www.comp.leeds.ac.uk/scs6jwks/dataset/leedsbutterfly/examples/{:03d}.jpg".format(i) for i in range(1,11)]
from smqtk.representation.data_element.url_element import DataUrlElement
el = [DataUrlElement(d) for d in urls]

# Create a model. This assumes you have set up the colordescriptor executable.
from smqtk.algorithms.descriptor_generator import get_descriptor_generator_impls
cd = get_descriptor_generator_impls()['ColorDescriptor_Image_csift'](model_directory='data', work_directory='work')
cd.generate_model(el)

# Set up a factory for our vector (here in-memory storage)
from smqtk.representation.descriptor_element_factory import DescriptorElementFactory
from smqtk.representation.descriptor_element.local_elements import DescriptorMemoryElement
factory = DescriptorElementFactory(DescriptorMemoryElement, {})

# Compute features on the first image
result = cd.compute_descriptor(el[0], factory)
result.vector()

# array([ 0.        ,  0.01254855,  0.        , ...,  0.0035853 ,
#         0.        ,  0.00388408])