keras-rcnn

Keras package for region-based convolutional neural networks (RCNNs)

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Keras-RCNN

.. image:: https://travis-ci.org/broadinstitute/keras-rcnn.svg?branch=master :target: https://travis-ci.org/broadinstitute/keras-rcnn

.. image:: https://codecov.io/gh/broadinstitute/keras-rcnn/branch/master/graph/badge.svg :target: https://codecov.io/gh/broadinstitute/keras-rcnn

keras-rcnn is the Keras package for region-based convolutional neural networks.

Requirements

Python 3

keras-resnet==0.2.0

numpy==1.16.2

tensorflow==1.13.1

Keras==2.2.4

scikit-image==0.15.0

Getting Started

Let’s read and inspect some data:

.. code:: python

training_dictionary, test_dictionary = keras_rcnn.datasets.shape.load_data()

categories = {"circle": 1, "rectangle": 2, "triangle": 3}

generator = keras_rcnn.preprocessing.ObjectDetectionGenerator()

generator = generator.flow_from_dictionary(
    dictionary=training_dictionary,
    categories=categories,
    target_size=(224, 224)
)

validation_data = keras_rcnn.preprocessing.ObjectDetectionGenerator()

validation_data = validation_data.flow_from_dictionary(
    dictionary=test_dictionary,
    categories=categories,
    target_size=(224, 224)
)

target, _ = generator.next()

target_bounding_boxes, target_categories, target_images, target_masks, target_metadata = target

target_bounding_boxes = numpy.squeeze(target_bounding_boxes)

target_images = numpy.squeeze(target_images)

target_categories = numpy.argmax(target_categories, -1)

target_categories = numpy.squeeze(target_categories)

keras_rcnn.utils.show_bounding_boxes(target_images, target_bounding_boxes, target_categories)

Let’s create an RCNN instance:

.. code:: python

model = keras_rcnn.models.RCNN((224, 224, 3), ["circle", "rectangle", "triangle"])

and pass our preferred optimizer to the compile method:

.. code:: python

optimizer = keras.optimizers.Adam(0.0001)

model.compile(optimizer)

Finally, let’s use the fit_generator method to train our network:

.. code:: python

model.fit_generator(    
    epochs=10,
    generator=generator,
    validation_data=validation_data
)

External Data

The data is made up of a list of dictionaries corresponding to images.

  • For each image, add a dictionary with keys 'image', 'objects'
    • 'image' is a dictionary, which contains keys 'checksum', 'pathname', and 'shape'
      • 'checksum' is the md5 checksum of the image
      • 'pathname' is the pathname of the image, put in full pathname
      • 'shape' is a dictionary with keys 'r', 'c', and 'channels'
        • 'c': number of columns
        • 'r': number of rows
        • 'channels': number of channels
    • 'objects' is a list of dictionaries, where each dictionary has keys 'bounding_box', 'category'
      • 'bounding_box' is a dictionary with keys 'minimum' and 'maximum'
        • 'minimum': dictionary with keys 'r' and 'c'
          • 'r': smallest bounding box row
          • 'c': smallest bounding box column
        • 'maximum': dictionary with keys 'r' and 'c'
          • 'r': largest bounding box row
          • 'c': largest bounding box column
      • 'category' is a string denoting the class name

Suppose this data is save in a file called training.json. To load data,

.. code:: python

import json

with open('training.json') as f:
    d = json.load(f)

Slack

We’ve been meeting in the #keras-rcnn channel on the keras.io Slack server.

You can join the server by inviting yourself from the following website:

https://keras-slack-autojoin.herokuapp.com/

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