Python API for bob.ip.tensorflow_extractor

Classes

bob.ip.tensorflow_extractor.Extractor(...[, ...])	Feature extractor using tensorflow
bob.ip.tensorflow_extractor.FaceNet([...])	Wrapper for the free FaceNet variant:

Detailed API

bob.ip.tensorflow_extractor.scratch_network(inputs, end_point='fc1', reuse=False)

bob.ip.tensorflow_extractor.download_file(url, out_file)

Downloads a file from a given url

Parameters:

• url (str) – The url to download form.
• out_file (str) – Where to save the file.

bob.ip.tensorflow_extractor.get_config()

Returns a string containing the configuration information.

class bob.ip.tensorflow_extractor.Extractor(checkpoint_filename, input_tensor, graph, debug=False)

Bases: object

Feature extractor using tensorflow

__call__(data)

Forward the data with the loaded neural network

Parameters:image (numpy.array) – Input Data Returns:The features. Return type:numpy.array

__init__(checkpoint_filename, input_tensor, graph, debug=False)

Loads the tensorflow model

Parameters:

• checkpoint_filename (str) – Path of your checkpoint. If the .meta file is providede the last checkpoint will be loaded.
• model – input_tensor: tf.Tensor used as a data entrypoint. It can be a tf.placeholder, the result of tf.train.string_input_producer, etc
• graph – A tf.Tensor containing the operations to be executed

class bob.ip.tensorflow_extractor.FaceNet(model_path=None, image_size=160, **kwargs)

Bases: object

Wrapper for the free FaceNet variant: https://github.com/davidsandberg/facenet

To use this class as a bob.bio.base extractor:

from bob.bio.base.extractor import Extractor
class FaceNetExtractor(FaceNet, Extractor):
    pass
extractor = FaceNetExtractor()

static download_model()

Download and extract the FaceNet files in bob/ip/tensorflow_extractor

static get_modelpath()

load_model()