object_detection/dataset_tools/create_coco_tf_record_test.py

# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Test for create_coco_tf_record.py."""

import io
import json
import os

import numpy as np
import PIL.Image
import tensorflow as tf

from object_detection.dataset_tools import create_coco_tf_record


class CreateCocoTFRecordTest(tf.test.TestCase):

  def _assertProtoEqual(self, proto_field, expectation):
    """Helper function to assert if a proto field equals some value.

    Args:
      proto_field: The protobuf field to compare.
      expectation: The expected value of the protobuf field.
    """
    proto_list = [p for p in proto_field]
    self.assertListEqual(proto_list, expectation)

  def test_create_tf_example(self):
    image_file_name = 'tmp_image.jpg'
    image_data = np.random.rand(256, 256, 3)
    tmp_dir = self.get_temp_dir()
    save_path = os.path.join(tmp_dir, image_file_name)
    image = PIL.Image.fromarray(image_data, 'RGB')
    image.save(save_path)

    image = {
        'file_name': image_file_name,
        'height': 256,
        'width': 256,
        'id': 11,
    }

    annotations_list = [{
        'area': .5,
        'iscrowd': False,
        'image_id': 11,
        'bbox': [64, 64, 128, 128],
        'category_id': 2,
        'id': 1000,
    }]

    image_dir = tmp_dir
    category_index = {
        1: {
            'name': 'dog',
            'id': 1
        },
        2: {
            'name': 'cat',
            'id': 2
        },
        3: {
            'name': 'human',
            'id': 3
        }
    }

    (_, example,
     num_annotations_skipped) = create_coco_tf_record.create_tf_example(
         image, annotations_list, image_dir, category_index)

    self.assertEqual(num_annotations_skipped, 0)
    self._assertProtoEqual(
        example.features.feature['image/height'].int64_list.value, [256])
    self._assertProtoEqual(
        example.features.feature['image/width'].int64_list.value, [256])
    self._assertProtoEqual(
        example.features.feature['image/filename'].bytes_list.value,
        [image_file_name])
    self._assertProtoEqual(
        example.features.feature['image/source_id'].bytes_list.value,
        [str(image['id'])])
    self._assertProtoEqual(
        example.features.feature['image/format'].bytes_list.value, ['jpeg'])
    self._assertProtoEqual(
        example.features.feature['image/object/bbox/xmin'].float_list.value,
        [0.25])
    self._assertProtoEqual(
        example.features.feature['image/object/bbox/ymin'].float_list.value,
        [0.25])
    self._assertProtoEqual(
        example.features.feature['image/object/bbox/xmax'].float_list.value,
        [0.75])
    self._assertProtoEqual(
        example.features.feature['image/object/bbox/ymax'].float_list.value,
        [0.75])
    self._assertProtoEqual(
        example.features.feature['image/object/class/text'].bytes_list.value,
        ['cat'])

  def test_create_tf_example_with_instance_masks(self):
    image_file_name = 'tmp_image.jpg'
    image_data = np.random.rand(8, 8, 3)
    tmp_dir = self.get_temp_dir()
    save_path = os.path.join(tmp_dir, image_file_name)
    image = PIL.Image.fromarray(image_data, 'RGB')
    image.save(save_path)

    image = {
        'file_name': image_file_name,
        'height': 8,
        'width': 8,
        'id': 11,
    }

    annotations_list = [{
        'area': .5,
        'iscrowd': False,
        'image_id': 11,
        'bbox': [0, 0, 8, 8],
        'segmentation': [[4, 0, 0, 0, 0, 4], [8, 4, 4, 8, 8, 8]],
        'category_id': 1,
        'id': 1000,
    }]

    image_dir = tmp_dir
    category_index = {
        1: {
            'name': 'dog',
            'id': 1
        },
    }

    (_, example,
     num_annotations_skipped) = create_coco_tf_record.create_tf_example(
         image, annotations_list, image_dir, category_index, include_masks=True)

    self.assertEqual(num_annotations_skipped, 0)
    self._assertProtoEqual(
        example.features.feature['image/height'].int64_list.value, [8])
    self._assertProtoEqual(
        example.features.feature['image/width'].int64_list.value, [8])
    self._assertProtoEqual(
        example.features.feature['image/filename'].bytes_list.value,
        [image_file_name])
    self._assertProtoEqual(
        example.features.feature['image/source_id'].bytes_list.value,
        [str(image['id'])])
    self._assertProtoEqual(
        example.features.feature['image/format'].bytes_list.value, ['jpeg'])
    self._assertProtoEqual(
        example.features.feature['image/object/bbox/xmin'].float_list.value,
        [0])
    self._assertProtoEqual(
        example.features.feature['image/object/bbox/ymin'].float_list.value,
        [0])
    self._assertProtoEqual(
        example.features.feature['image/object/bbox/xmax'].float_list.value,
        [1])
    self._assertProtoEqual(
        example.features.feature['image/object/bbox/ymax'].float_list.value,
        [1])
    self._assertProtoEqual(
        example.features.feature['image/object/class/text'].bytes_list.value,
        ['dog'])
    encoded_mask_pngs = [
        io.BytesIO(encoded_masks) for encoded_masks in example.features.feature[
            'image/object/mask'].bytes_list.value
    ]
    pil_masks = [
        np.array(PIL.Image.open(encoded_mask_png))
        for encoded_mask_png in encoded_mask_pngs
    ]
    self.assertTrue(len(pil_masks) == 1)
    self.assertAllEqual(pil_masks[0],
                        [[1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0, 0, 0],
                         [1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
                         [0, 0, 0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 0, 1, 1],
                         [0, 0, 0, 0, 0, 1, 1, 1], [0, 0, 0, 0, 1, 1, 1, 1]])

  def test_create_sharded_tf_record(self):
    tmp_dir = self.get_temp_dir()
    image_paths = ['tmp1_image.jpg', 'tmp2_image.jpg']
    for image_path in image_paths:
      image_data = np.random.rand(256, 256, 3)
      save_path = os.path.join(tmp_dir, image_path)
      image = PIL.Image.fromarray(image_data, 'RGB')
      image.save(save_path)

    images = [{
        'file_name': image_paths[0],
        'height': 256,
        'width': 256,
        'id': 11,
    }, {
        'file_name': image_paths[1],
        'height': 256,
        'width': 256,
        'id': 12,
    }]

    annotations = [{
        'area': .5,
        'iscrowd': False,
        'image_id': 11,
        'bbox': [64, 64, 128, 128],
        'category_id': 2,
        'id': 1000,
    }]

    category_index = [{
        'name': 'dog',
        'id': 1
    }, {
        'name': 'cat',
        'id': 2
    }, {
        'name': 'human',
        'id': 3
    }]
    groundtruth_data = {'images': images, 'annotations': annotations,
                        'categories': category_index}
    annotation_file = os.path.join(tmp_dir, 'annotation.json')
    with open(annotation_file, 'w') as annotation_fid:
      json.dump(groundtruth_data, annotation_fid)

    output_path = os.path.join(tmp_dir, 'out.record')
    create_coco_tf_record._create_tf_record_from_coco_annotations(
        annotation_file,
        tmp_dir,
        output_path,
        False,
        2)
    self.assertTrue(os.path.exists(output_path + '-00000-of-00002'))
    self.assertTrue(os.path.exists(output_path + '-00001-of-00002'))


if __name__ == '__main__':
  tf.test.main()