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install-python-bqapi.md

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description
Use the BQAPI to upload and download data from BisQue

Install Python BQAPI

PyPi Install

pip install bqapi-ucsb

Tutorials

We have two tutorials to get you started with using the BQAPI.

{% content-ref url="download-an-array.md" %} download-an-array.md {% endcontent-ref %}

{% content-ref url="upload-an-image.md" %} upload-an-image.md {% endcontent-ref %}

BQAPI

BQAPI provides bisque users with a means to extract features from resources using the feature service. Below is information on the Python API and a few example on how to use it.

FeatureResource(image=None, mask=None, gobject=None)

Named tuple to make it easier to organize resources. Of course, one can always just make a simple list of tuples for the resource list.

class Feature()

The Feature class is the base implementation of the feature service API for when one need to extract a set of features on a small set of resources. BQSession is used as the communication layer for bqfeatures, therefore before making any requests a local BQSession has to be instantiated to pass to any feature request.

fetch(session, name, resource_list, path=None)

Requests the feature server to calculate features on provided resources.

Input

  • session - a local instantiated BQSession attached to a MEX.
  • name - the name of the feature one wishes to extract.
  • resource_list - list of the resources to extract. Format: [(image_url, mask_url, gobject_url),...] if a parameter is not required just provided None.
  • path - the location were the HDF5 file is stored. If None is set, the file will be placed in a temporary file and a Pytables file handle will be returned. (default: None)

Output

  • Returns either a Pytables file handle or the file name when the path is provided

Lets upload an image an calculate a single feature on it.

import os
from bqapi.comm import BQSession
from bqapi.bqfeature import Feature, FeatureResource

# initialize local session
session = BQSession().init_local(user, pwd, bisque_root='http://bisque.ece.ucsb.edu') 

#post image to bisque and get the response
response_xml = session.postblob('myimage.jpg') 

#construct resource list of the image just uploaded
resource_list = [FeatureResource(image='http://bisque.ece.ucsb.edu/image_service/%s' % response_xml.attrib['resource_uniq'])]

#fetch features on image resource
pytables_response = Feature().fetch(session, 'HTD', resource_list)

#get a numpy list of features for the downloaded HDF5 file.
feature = pytables_response.root.values[:]['feature']

#close and remove the HDF5 file since it is stored in a tempfile
pytables_response.close()
os.remove(pytables_response.filename)

fetch_vector(session, name, resource_list)

Requests the feature server to calculate features on provided resources. Designed more for requests of very few features since all the features will be loaded into memory.

Input

  • session - a local instantiated BQSession attached to a MEX.
  • name - the name of the feature one wishes to extract
  • resource_list - list of the resources to extract. format: [(image_url, mask_url, gobject_url),...] if a parameter is not required just provided None

Output

  • return - a list of features as numpy array

length(session, name)

Static method that returns the length of the feature requested.

Input

  • session - a local instantiated BQSession attached to a MEX.
  • name - the name of the feature one wishes to extract

Output

  • return feature length

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