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Add dataset tools, pretrained models and documentatinos for open image dataset.
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‎research/object_detection/README.md‎

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@@ -21,6 +21,10 @@ Song Y, Guadarrama S, Murphy K, CVPR 2017
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\[[link](https://arxiv.org/abs/1611.10012)\]\[[bibtex](
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https://scholar.googleusercontent.com/scholar.bib?q=info:l291WsrB-hQJ:scholar.google.com/&output=citation&scisig=AAGBfm0AAAAAWUIIlnPZ_L9jxvPwcC49kDlELtaeIyU-&scisf=4&ct=citation&cd=-1&hl=en&scfhb=1)\]
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<p align="center">
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<img src="g3doc/img/tf-od-api-logo.png" width=140 height=195>
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</p>
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## Maintainers
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* Jonathan Huang, github: [jch1](https://github.com/jch1)
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Defining your own model architecture</a><br>
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* <a href='g3doc/using_your_own_dataset.md'>
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Bringing in your own dataset</a><br>
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* <a href='g3doc/oid_inference_and_evaluation.md'>
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Inference and evaluation on the Open Images dataset</a><br>
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* <a href='g3doc/evaluation_protocols.md'>
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Supported object detection evaluation protocols</a><br>
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## Getting Help
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[issue tracker](https://github.com/tensorflow/models/issues), prefixing the
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issue name with "object_detection".
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## Release information
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### November 17, 2017
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As a part of the Open Images V3 release we have released:
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* An implementation of the Open Images evaluation metric and the [protocol](g3doc/evaluation_protocols.md#open-images).
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* Additional tools to separate inference of detection and evaluation (see [this tutorial](g3doc/oid_inference_and_evaluation.md)).
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* A new detection model trained on the Open Images V2 data release (see [Open Images model](g3doc/detection_model_zoo.md#open-images-models)).
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See more information on the [Open Images website](https://github.com/openimages/dataset)!
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<b>Thanks to contributors</b>: Stefan Popov, Alina Kuznetsova
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### November 6, 2017
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<b>Thanks to contributors</b>: Jonathan Huang, Andrew Harp
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### June 15, 2017
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In addition to our base Tensorflow detection model definitions, this
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* Region-Based Fully Convolutional Networks (R-FCN) with Resnet 101,
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* Faster RCNN with Resnet 101,
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* Faster RCNN with Inception Resnet v2
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* Mask R-CNN with Resnet 101.
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* Frozen weights (trained on the COCO dataset) for each of the above models to
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be used for out-of-the-box inference purposes.
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* A [Jupyter notebook](object_detection_tutorial.ipynb) for performing
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<b>Thanks to contributors</b>: Jonathan Huang, Vivek Rathod, Derek Chow,
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Chen Sun, Menglong Zhu, Matthew Tang, Anoop Korattikara, Alireza Fathi, Ian Fischer, Zbigniew Wojna, Yang Song, Sergio Guadarrama, Jasper Uijlings,
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Viacheslav Kovalevskyi, Kevin Murphy
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‎research/object_detection/dataset_tools/BUILD‎

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"//tensorflow_models/object_detection/utils:dataset_util",
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],
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)
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py_test(
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name = "oid_tfrecord_creation_test",
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srcs = ["oid_tfrecord_creation_test.py"],
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deps = [
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":oid_tfrecord_creation",
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"//third_party/py/contextlib2",
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"//third_party/py/pandas",
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"//third_party/py/tensorflow",
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],
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)
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py_binary(
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name = "create_oid_tf_record",
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srcs = ["create_oid_tf_record.py"],
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deps = [
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":oid_tfrecord_creation",
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"//third_party/py/contextlib2",
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"//third_party/py/pandas",
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"//tensorflow",
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"//tensorflow_models/object_detection/utils:label_map_util",
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],
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)

‎research/object_detection/dataset_tools/create_oid_tf_record.py‎

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# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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r"""Creates TFRecords of Open Images dataset for object detection.
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Example usage:
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./create_oid_tf_record \
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--input_annotations_csv=/path/to/input/annotations-human-bbox.csv \
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--input_images_directory=/path/to/input/image_pixels_directory \
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--input_label_map=/path/to/input/labels_bbox_545.labelmap \
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--output_tf_record_path_prefix=/path/to/output/prefix.tfrecord
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CSVs with bounding box annotations and image metadata (including the image URLs)
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can be downloaded from the Open Images GitHub repository:
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https://github.com/openimages/dataset
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This script will include every image found in the input_images_directory in the
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output TFRecord, even if the image has no corresponding bounding box annotations
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in the input_annotations_csv.
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"""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import os
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import contextlib2
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import pandas as pd
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import tensorflow as tf
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from object_detection.dataset_tools import oid_tfrecord_creation
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from object_detection.utils import label_map_util
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tf.flags.DEFINE_string('input_annotations_csv', None,
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'Path to CSV containing image bounding box annotations')
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tf.flags.DEFINE_string('input_images_directory', None,
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'Directory containing the image pixels '
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'downloaded from the OpenImages GitHub repository.')
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tf.flags.DEFINE_string('input_label_map', None, 'Path to the label map proto')
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tf.flags.DEFINE_string(
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'output_tf_record_path_prefix', None,
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'Path to the output TFRecord. The shard index and the number of shards '
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'will be appended for each output shard.')
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tf.flags.DEFINE_integer('num_shards', 100, 'Number of TFRecord shards')
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FLAGS = tf.flags.FLAGS
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def main(_):
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tf.logging.set_verbosity(tf.logging.INFO)
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required_flags = [
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'input_annotations_csv', 'input_images_directory', 'input_label_map',
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'output_tf_record_path_prefix'
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]
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for flag_name in required_flags:
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if not getattr(FLAGS, flag_name):
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raise ValueError('Flag --{} is required'.format(flag_name))
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label_map = label_map_util.get_label_map_dict(FLAGS.input_label_map)
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all_annotations = pd.read_csv(FLAGS.input_annotations_csv)
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all_images = tf.gfile.Glob(
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os.path.join(FLAGS.input_images_directory, '*.jpg'))
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all_image_ids = [os.path.splitext(os.path.basename(v))[0] for v in all_images]
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all_image_ids = pd.DataFrame({'ImageID': all_image_ids})
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all_annotations = pd.concat([all_annotations, all_image_ids])
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tf.logging.log(tf.logging.INFO, 'Found %d images...', len(all_image_ids))
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with contextlib2.ExitStack() as tf_record_close_stack:
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output_tfrecords = oid_tfrecord_creation.open_sharded_output_tfrecords(
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tf_record_close_stack, FLAGS.output_tf_record_path_prefix,
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FLAGS.num_shards)
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for counter, image_data in enumerate(all_annotations.groupby('ImageID')):
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tf.logging.log_every_n(tf.logging.INFO, 'Processed %d images...', 1000,
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counter)
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image_id, image_annotations = image_data
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# In OID image file names are formed by appending ".jpg" to the image ID.
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image_path = os.path.join(FLAGS.input_images_directory, image_id + '.jpg')
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with tf.gfile.Open(image_path) as image_file:
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encoded_image = image_file.read()
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tf_example = oid_tfrecord_creation.tf_example_from_annotations_data_frame(
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image_annotations, label_map, encoded_image)
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if tf_example:
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shard_idx = long(image_id, 16) % FLAGS.num_shards
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output_tfrecords[shard_idx].write(tf_example.SerializeToString())
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if __name__ == '__main__':
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tf.app.run()

‎research/object_detection/dataset_tools/oid_tfrecord_creation.py‎

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# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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r"""Utilities for creating TFRecords of TF examples for the Open Images dataset.
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"""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import tensorflow as tf
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from object_detection.core import standard_fields
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from object_detection.utils import dataset_util
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def tf_example_from_annotations_data_frame(annotations_data_frame, label_map,
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encoded_image):
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"""Populates a TF Example message with image annotations from a data frame.
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Args:
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annotations_data_frame: Data frame containing the annotations for a single
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image.
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label_map: String to integer label map.
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encoded_image: The encoded image string
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Returns:
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The populated TF Example, if the label of at least one object is present in
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label_map. Otherwise, returns None.
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"""
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filtered_data_frame = annotations_data_frame[
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annotations_data_frame.LabelName.isin(label_map)]
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image_id = annotations_data_frame.ImageID.iloc[0]
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feature_map = {
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standard_fields.TfExampleFields.object_bbox_ymin:
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dataset_util.float_list_feature(filtered_data_frame.YMin.as_matrix()),
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standard_fields.TfExampleFields.object_bbox_xmin:
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dataset_util.float_list_feature(filtered_data_frame.XMin.as_matrix()),
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standard_fields.TfExampleFields.object_bbox_ymax:
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dataset_util.float_list_feature(filtered_data_frame.YMax.as_matrix()),
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standard_fields.TfExampleFields.object_bbox_xmax:
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dataset_util.float_list_feature(filtered_data_frame.XMax.as_matrix()),
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standard_fields.TfExampleFields.object_class_text:
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dataset_util.bytes_list_feature(
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filtered_data_frame.LabelName.as_matrix()),
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standard_fields.TfExampleFields.object_class_label:
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dataset_util.int64_list_feature(
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filtered_data_frame.LabelName.map(lambda x: label_map[x])
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.as_matrix()),
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standard_fields.TfExampleFields.filename:
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dataset_util.bytes_feature('{}.jpg'.format(image_id)),
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standard_fields.TfExampleFields.source_id:
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dataset_util.bytes_feature(image_id),
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standard_fields.TfExampleFields.image_encoded:
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dataset_util.bytes_feature(encoded_image),
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}
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if 'IsGroupOf' in filtered_data_frame.columns:
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feature_map[standard_fields.TfExampleFields.
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object_group_of] = dataset_util.int64_list_feature(
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filtered_data_frame.IsGroupOf.as_matrix().astype(int))
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if 'IsOccluded' in filtered_data_frame.columns:
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feature_map[standard_fields.TfExampleFields.
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object_occluded] = dataset_util.int64_list_feature(
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filtered_data_frame.IsOccluded.as_matrix().astype(int))
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if 'IsTruncated' in filtered_data_frame.columns:
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feature_map[standard_fields.TfExampleFields.
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object_truncated] = dataset_util.int64_list_feature(
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filtered_data_frame.IsTruncated.as_matrix().astype(int))
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if 'IsDepiction' in filtered_data_frame.columns:
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feature_map[standard_fields.TfExampleFields.
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object_depiction] = dataset_util.int64_list_feature(
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filtered_data_frame.IsDepiction.as_matrix().astype(int))
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return tf.train.Example(features=tf.train.Features(feature=feature_map))
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def open_sharded_output_tfrecords(exit_stack, base_path, num_shards):
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"""Opens all TFRecord shards for writing and adds them to an exit stack.
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Args:
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exit_stack: A context2.ExitStack used to automatically closed the TFRecords
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opened in this function.
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base_path: The base path for all shards
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num_shards: The number of shards
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Returns:
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The list of opened TFRecords. Position k in the list corresponds to shard k.
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"""
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tf_record_output_filenames = [
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'{}-{:05d}-of-{:05d}'.format(base_path, idx, num_shards)
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for idx in xrange(num_shards)
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]
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tfrecords = [
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exit_stack.enter_context(tf.python_io.TFRecordWriter(file_name))
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for file_name in tf_record_output_filenames
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]
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return tfrecords

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