如何为我的 COCO 格式数据集更改 TFRecords？

Question

I am currently trying to get the Caltech camera traps benchmark dataset into TFRecords but I am struggling quite a bit. https://lila.science/datasets/caltech-camera-traps . The annotations are displayed as follows:

"info": {"contributor": "Sara Beery", "date_created": "2018-07-03 18:34:36.573636", "version": "Caltech Camera Traps - ECCV18", "description": "Database of camera trap images collected from the NPS and the USGS with help from Justin Brown and Erin Boydston", "year": 2018}]

"categories": [{"id": 6, "name": "bobcat"}, ....] 

"images": [{"file_name": "59b93afb-23d2-11e8-a6a3-ec086b02610b.jpg", "rights_holder": "Justin Brown", "height": 1494, "width": 2048, "frame_num": 2, "date_captured": "2012-05-09 07:33:45", "location": 38, "seq_num_frames": 3, "seq_id": "6f04895c-5567-11e8-a3d6-dca9047ef277", "id": "59b93afb-23d2-11e8-a6a3-ec086b02610b"},...]

"annotations": [{"image_id": "59ffbd00-23d2-11e8-a6a3-ec086b02610b", "category_id": 1, "bbox": [1118.72, 570.88, 328.96000000000004, 180.48000000000002], "id": "36132"}

I am trying to use the create_coco_tf_record.py file and adapt it. I do not have the 'iscrowd' or 'segmentations' in my annotations and a lot of the images do not have bounding boxes. I was wondering if someone has done similar and would be able to help please. Thanks. Here is the file...

# 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.
# ==============================================================================
r"""Convert raw COCO dataset to TFRecord for object_detection.

This tool supports data generation for object detection (boxes, masks),
keypoint detection, and DensePose.

Please note that this tool creates sharded output files.

Example usage:
    python create_coco_tf_record.py --logtostderr \
      --train_image_dir="${TRAIN_IMAGE_DIR}" \
      --val_image_dir="${VAL_IMAGE_DIR}" \
      --test_image_dir="${TEST_IMAGE_DIR}" \
      --train_annotations_file="${TRAIN_ANNOTATIONS_FILE}" \
      --val_annotations_file="${VAL_ANNOTATIONS_FILE}" \
      --testdev_annotations_file="${TESTDEV_ANNOTATIONS_FILE}" \
      --output_dir="${OUTPUT_DIR}"
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import hashlib
import io
import json
import logging
import os
import contextlib2
import numpy as np
import PIL.Image

from pycocotools import mask
import tensorflow.compat.v1 as tf

from object_detection.dataset_tools import tf_record_creation_util
from object_detection.utils import dataset_util
from object_detection.utils import label_map_util

flags = tf.app.flags
tf.flags.DEFINE_boolean(
    'include_masks', False, 'Whether to include instance segmentations masks '
    '(PNG encoded) in the result. default: False.')
tf.flags.DEFINE_string('train_image_dir', '', 'Training image directory.')
tf.flags.DEFINE_string('val_image_dir', '', 'Validation image directory.')
tf.flags.DEFINE_string('test_image_dir', '', 'Test image directory.')
tf.flags.DEFINE_string('train_annotations_file', '',
                       'Training annotations JSON file.')
tf.flags.DEFINE_string('val_annotations_file', '',
                       'Validation annotations JSON file.')
tf.flags.DEFINE_string('testdev_annotations_file', '',
                       'Test-dev annotations JSON file.')
tf.flags.DEFINE_string('train_keypoint_annotations_file', '',
                       'Training annotations JSON file.')
tf.flags.DEFINE_string('val_keypoint_annotations_file', '',
                       'Validation annotations JSON file.')
# DensePose is only available for coco 2014.
tf.flags.DEFINE_string('train_densepose_annotations_file', '',
                       'Training annotations JSON file for DensePose.')
tf.flags.DEFINE_string('val_densepose_annotations_file', '',
                       'Validation annotations JSON file for DensePose.')
tf.flags.DEFINE_string('output_dir', '/tmp/', 'Output data directory.')
# Whether to only produce images/annotations on person class (for keypoint /
# densepose task).
tf.flags.DEFINE_boolean('remove_non_person_annotations', False, 'Whether to '
                        'remove all annotations for non-person objects.')
tf.flags.DEFINE_boolean('remove_non_person_images', False, 'Whether to '
                        'remove all examples that do not contain a person.')

FLAGS = flags.FLAGS

logger = tf.get_logger()
logger.setLevel(logging.INFO)

_COCO_KEYPOINT_NAMES = [
    b'nose', b'left_eye', b'right_eye', b'left_ear', b'right_ear',
    b'left_shoulder', b'right_shoulder', b'left_elbow', b'right_elbow',
    b'left_wrist', b'right_wrist', b'left_hip', b'right_hip',
    b'left_knee', b'right_knee', b'left_ankle', b'right_ankle'
]

_COCO_PART_NAMES = [
    b'torso_back', b'torso_front', b'right_hand', b'left_hand', b'left_foot',
    b'right_foot', b'right_upper_leg_back', b'left_upper_leg_back',
    b'right_upper_leg_front', b'left_upper_leg_front', b'right_lower_leg_back',
    b'left_lower_leg_back', b'right_lower_leg_front', b'left_lower_leg_front',
    b'left_upper_arm_back', b'right_upper_arm_back', b'left_upper_arm_front',
    b'right_upper_arm_front', b'left_lower_arm_back', b'right_lower_arm_back',
    b'left_lower_arm_front', b'right_lower_arm_front', b'right_face',
    b'left_face',
]

_DP_PART_ID_OFFSET = 1


def clip_to_unit(x):
  return min(max(x, 0.0), 1.0)


def create_tf_example(image,
                      annotations_list,
                      image_dir,
                      category_index,
                      include_masks=False,
                      keypoint_annotations_dict=None,
                      densepose_annotations_dict=None,
                      remove_non_person_annotations=False,
                      remove_non_person_images=False):
  """Converts image and annotations to a tf.Example proto.

  Args:
    image: dict with keys: [u'license', u'file_name', u'coco_url', u'height',
      u'width', u'date_captured', u'flickr_url', u'id']
    annotations_list:
      list of dicts with keys: [u'segmentation', u'area', u'iscrowd',
        u'image_id', u'bbox', u'category_id', u'id'] Notice that bounding box
        coordinates in the official COCO dataset are given as [x, y, width,
        height] tuples using absolute coordinates where x, y represent the
        top-left (0-indexed) corner.  This function converts to the format
        expected by the Tensorflow Object Detection API (which is which is
        [ymin, xmin, ymax, xmax] with coordinates normalized relative to image
        size).
    image_dir: directory containing the image files.
    category_index: a dict containing COCO category information keyed by the
      'id' field of each category.  See the label_map_util.create_category_index
      function.
    include_masks: Whether to include instance segmentations masks
      (PNG encoded) in the result. default: False.
    keypoint_annotations_dict: A dictionary that maps from annotation_id to a
      dictionary with keys: [u'keypoints', u'num_keypoints'] represeting the
      keypoint information for this person object annotation. If None, then
      no keypoint annotations will be populated.
    densepose_annotations_dict: A dictionary that maps from annotation_id to a
      dictionary with keys: [u'dp_I', u'dp_x', u'dp_y', 'dp_U', 'dp_V']
      representing part surface coordinates. For more information see
      http://densepose.org/.
    remove_non_person_annotations: Whether to remove any annotations that are
      not the "person" class.
    remove_non_person_images: Whether to remove any images that do not contain
      at least one "person" annotation.

  Returns:
    key: SHA256 hash of the image.
    example: The converted tf.Example
    num_annotations_skipped: Number of (invalid) annotations that were ignored.
    num_keypoint_annotation_skipped: Number of keypoint annotations that were
      skipped.
    num_densepose_annotation_skipped: Number of DensePose annotations that were
      skipped.

  Raises:
    ValueError: if the image pointed to by data['filename'] is not a valid JPEG
  """
  image_height = image['height']
  image_width = image['width']
  filename = image['file_name']
  image_id = image['id']

  full_path = os.path.join(image_dir, filename)
  with tf.gfile.GFile(full_path, 'rb') as fid:
    encoded_jpg = fid.read()
  encoded_jpg_io = io.BytesIO(encoded_jpg)
  image = PIL.Image.open(encoded_jpg_io)
  key = hashlib.sha256(encoded_jpg).hexdigest()

  xmin = []
  xmax = []
  ymin = []
  ymax = []
  is_crowd = []
  category_names = []
  category_ids = []
  area = []
  encoded_mask_png = []
  keypoints_x = []
  keypoints_y = []
  keypoints_visibility = []
  keypoints_name = []
  num_keypoints = []
  include_keypoint = keypoint_annotations_dict is not None
  num_annotations_skipped = 0
  num_keypoint_annotation_used = 0
  num_keypoint_annotation_skipped = 0
  dp_part_index = []
  dp_x = []
  dp_y = []
  dp_u = []
  dp_v = []
  dp_num_points = []
  densepose_keys = ['dp_I', 'dp_U', 'dp_V', 'dp_x', 'dp_y', 'bbox']
  include_densepose = densepose_annotations_dict is not None
  num_densepose_annotation_used = 0
  num_densepose_annotation_skipped = 0
  for object_annotations in annotations_list:
    (x, y, width, height) = tuple(object_annotations['bbox'])
    if width <= 0 or height <= 0:
      num_annotations_skipped += 1
      continue
    if x + width > image_width or y + height > image_height:
      num_annotations_skipped += 1
      continue
    category_id = int(object_annotations['category_id'])
    category_name = category_index[category_id]['name'].encode('utf8')
    if remove_non_person_annotations and category_name != b'person':
      num_annotations_skipped += 1
      continue
    xmin.append(float(x) / image_width)
    xmax.append(float(x + width) / image_width)
    ymin.append(float(y) / image_height)
    ymax.append(float(y + height) / image_height)
    #is_crowd.append(object_annotations['iscrowd'])
    category_ids.append(category_id)
    category_names.append(category_name)
    #area.append(object_annotations['area'])

  #   if include_masks:
  #     run_len_encoding = mask.frPyObjects(object_annotations['segmentation'],
  #                                         image_height, image_width)
  #     binary_mask = mask.decode(run_len_encoding)
  #     if not object_annotations['iscrowd']:
  #       binary_mask = np.amax(binary_mask, axis=2)
  #     pil_image = PIL.Image.fromarray(binary_mask)
  #     output_io = io.BytesIO()
  #     pil_image.save(output_io, format='PNG')
  #     encoded_mask_png.append(output_io.getvalue())

  #   if include_keypoint:
  #     annotation_id = object_annotations['id']
  #     if annotation_id in keypoint_annotations_dict:
  #       num_keypoint_annotation_used += 1
  #       keypoint_annotations = keypoint_annotations_dict[annotation_id]
  #       keypoints = keypoint_annotations['keypoints']
  #       num_kpts = keypoint_annotations['num_keypoints']
  #       keypoints_x_abs = keypoints[::3]
  #       keypoints_x.extend(
  #           [float(x_abs) / image_width for x_abs in keypoints_x_abs])
  #       keypoints_y_abs = keypoints[1::3]
  #       keypoints_y.extend(
  #           [float(y_abs) / image_height for y_abs in keypoints_y_abs])
  #       keypoints_visibility.extend(keypoints[2::3])
  #       keypoints_name.extend(_COCO_KEYPOINT_NAMES)
  #       num_keypoints.append(num_kpts)
  #     else:
  #       keypoints_x.extend([0.0] * len(_COCO_KEYPOINT_NAMES))
  #       keypoints_y.extend([0.0] * len(_COCO_KEYPOINT_NAMES))
  #       keypoints_visibility.extend([0] * len(_COCO_KEYPOINT_NAMES))
  #       keypoints_name.extend(_COCO_KEYPOINT_NAMES)
  #       num_keypoints.append(0)

  #   if include_densepose:
  #     annotation_id = object_annotations['id']
  #     if (annotation_id in densepose_annotations_dict and
  #         all(key in densepose_annotations_dict[annotation_id]
  #             for key in densepose_keys)):
  #       dp_annotations = densepose_annotations_dict[annotation_id]
  #       num_densepose_annotation_used += 1
  #       dp_num_points.append(len(dp_annotations['dp_I']))
  #       dp_part_index.extend([int(i - _DP_PART_ID_OFFSET)
  #                             for i in dp_annotations['dp_I']])
  #       # DensePose surface coordinates are defined on a [256, 256] grid
  #       # relative to each instance box (i.e. absolute coordinates in range
  #       # [0., 256.]). The following converts the coordinates
  #       # so that they are expressed in normalized image coordinates.
  #       dp_x_box_rel = [
  #           clip_to_unit(val / 256.) for val in dp_annotations['dp_x']]
  #       dp_x_norm = [(float(x) + x_box_rel * width) / image_width
  #                    for x_box_rel in dp_x_box_rel]
  #       dp_y_box_rel = [
  #           clip_to_unit(val / 256.) for val in dp_annotations['dp_y']]
  #       dp_y_norm = [(float(y) + y_box_rel * height) / image_height
  #                    for y_box_rel in dp_y_box_rel]
  #       dp_x.extend(dp_x_norm)
  #       dp_y.extend(dp_y_norm)
  #       dp_u.extend(dp_annotations['dp_U'])
  #       dp_v.extend(dp_annotations['dp_V'])
  #     else:
  #       dp_num_points.append(0)

  # if (remove_non_person_images and
  #     not any(name == b'person' for name in category_names)):
  #   return (key, None, num_annotations_skipped,
  #           num_keypoint_annotation_skipped, num_densepose_annotation_skipped)
  feature_dict = {
      'image/height':
          dataset_util.int64_feature(image_height),
      'image/width':
          dataset_util.int64_feature(image_width),
      'image/filename':
          dataset_util.bytes_feature(filename.encode('utf8')),
      'image/source_id':
          dataset_util.bytes_feature(str(image_id).encode('utf8')),
      'image/key/sha256':
          dataset_util.bytes_feature(key.encode('utf8')),
      'image/encoded':
          dataset_util.bytes_feature(encoded_jpg),
      'image/format':
          dataset_util.bytes_feature('jpeg'.encode('utf8')),
      'image/object/bbox/xmin':
          dataset_util.float_list_feature(xmin),
      'image/object/bbox/xmax':
          dataset_util.float_list_feature(xmax),
      'image/object/bbox/ymin':
          dataset_util.float_list_feature(ymin),
      'image/object/bbox/ymax':
          dataset_util.float_list_feature(ymax),
      'image/object/class/text':
          dataset_util.bytes_list_feature(category_names),
      'image/object/is_crowd':
          dataset_util.int64_list_feature(is_crowd),
      'image/object/area':
          dataset_util.float_list_feature(area),
  }
  # if include_masks:
  #   feature_dict['image/object/mask'] = (
  #       dataset_util.bytes_list_feature(encoded_mask_png))
  # if include_keypoint:
  #   feature_dict['image/object/keypoint/x'] = (
  #       dataset_util.float_list_feature(keypoints_x))
  #   feature_dict['image/object/keypoint/y'] = (
  #       dataset_util.float_list_feature(keypoints_y))
  #   feature_dict['image/object/keypoint/num'] = (
  #       dataset_util.int64_list_feature(num_keypoints))
  #   feature_dict['image/object/keypoint/visibility'] = (
  #       dataset_util.int64_list_feature(keypoints_visibility))
  #   feature_dict['image/object/keypoint/text'] = (
  #       dataset_util.bytes_list_feature(keypoints_name))
  #   num_keypoint_annotation_skipped = (
  #       len(keypoint_annotations_dict) - num_keypoint_annotation_used)
  # if include_densepose:
  #   feature_dict['image/object/densepose/num'] = (
  #       dataset_util.int64_list_feature(dp_num_points))
  #   feature_dict['image/object/densepose/part_index'] = (
  #       dataset_util.int64_list_feature(dp_part_index))
  #   feature_dict['image/object/densepose/x'] = (
  #       dataset_util.float_list_feature(dp_x))
  #   feature_dict['image/object/densepose/y'] = (
  #       dataset_util.float_list_feature(dp_y))
  #   feature_dict['image/object/densepose/u'] = (
  #       dataset_util.float_list_feature(dp_u))
  #   feature_dict['image/object/densepose/v'] = (
  #       dataset_util.float_list_feature(dp_v))
  #   num_densepose_annotation_skipped = (
  #       len(densepose_annotations_dict) - num_densepose_annotation_used)

  example = tf.train.Example(features=tf.train.Features(feature=feature_dict))
  return (key, example, num_annotations_skipped,
          num_keypoint_annotation_skipped, num_densepose_annotation_skipped)


def _create_tf_record_from_coco_annotations(annotations_file, image_dir,
                                            output_path, include_masks,
                                            num_shards,
                                            keypoint_annotations_file='',
                                            densepose_annotations_file='',
                                            remove_non_person_annotations=False,
                                            remove_non_person_images=False):
  """Loads COCO annotation json files and converts to tf.Record format.

  Args:
    annotations_file: JSON file containing bounding box annotations.
    image_dir: Directory containing the image files.
    output_path: Path to output tf.Record file.
    include_masks: Whether to include instance segmentations masks
      (PNG encoded) in the result. default: False.
    num_shards: number of output file shards.
    keypoint_annotations_file: JSON file containing the person keypoint
      annotations. If empty, then no person keypoint annotations will be
      generated.
    densepose_annotations_file: JSON file containing the DensePose annotations.
      If empty, then no DensePose annotations will be generated.
    remove_non_person_annotations: Whether to remove any annotations that are
      not the "person" class.
    remove_non_person_images: Whether to remove any images that do not contain
      at least one "person" annotation.
  """
  with contextlib2.ExitStack() as tf_record_close_stack, \
      tf.gfile.GFile(annotations_file, 'r') as fid:
    output_tfrecords = tf_record_creation_util.open_sharded_output_tfrecords(
        tf_record_close_stack, output_path, num_shards)
    groundtruth_data = json.load(fid)
    images = groundtruth_data['images']
    category_index = label_map_util.create_category_index(
        groundtruth_data['categories'])

    annotations_index = {}
    if 'annotations' in groundtruth_data:
      logging.info('Found groundtruth annotations. Building annotations index.')
      for annotation in groundtruth_data['annotations']:
        image_id = annotation['image_id']
        if image_id not in annotations_index:
          annotations_index[image_id] = []
        annotations_index[image_id].append(annotation)
    missing_annotation_count = 0
    for image in images:
      image_id = image['id']
      if image_id not in annotations_index:
        missing_annotation_count += 1
        annotations_index[image_id] = []
    logging.info('%d images are missing annotations.',
                 missing_annotation_count)

    keypoint_annotations_index = {}
    if keypoint_annotations_file:
      with tf.gfile.GFile(keypoint_annotations_file, 'r') as kid:
        keypoint_groundtruth_data = json.load(kid)
      if 'annotations' in keypoint_groundtruth_data:
        for annotation in keypoint_groundtruth_data['annotations']:
          image_id = annotation['image_id']
          if image_id not in keypoint_annotations_index:
            keypoint_annotations_index[image_id] = {}
          keypoint_annotations_index[image_id][annotation['id']] = annotation

    densepose_annotations_index = {}
    if densepose_annotations_file:
      with tf.gfile.GFile(densepose_annotations_file, 'r') as fid:
        densepose_groundtruth_data = json.load(fid)
      if 'annotations' in densepose_groundtruth_data:
        for annotation in densepose_groundtruth_data['annotations']:
          image_id = annotation['image_id']
          if image_id not in densepose_annotations_index:
            densepose_annotations_index[image_id] = {}
          densepose_annotations_index[image_id][annotation['id']] = annotation

    total_num_annotations_skipped = 0
    total_num_keypoint_annotations_skipped = 0
    total_num_densepose_annotations_skipped = 0
    for idx, image in enumerate(images):
      if idx % 100 == 0:
        logging.info('On image %d of %d', idx, len(images))
      annotations_list = annotations_index[image['id']]
      keypoint_annotations_dict = None
      if keypoint_annotations_file:
        keypoint_annotations_dict = {}
        if image['id'] in keypoint_annotations_index:
          keypoint_annotations_dict = keypoint_annotations_index[image['id']]
      densepose_annotations_dict = None
      if densepose_annotations_file:
        densepose_annotations_dict = {}
        if image['id'] in densepose_annotations_index:
          densepose_annotations_dict = densepose_annotations_index[image['id']]
      (_, tf_example, num_annotations_skipped, num_keypoint_annotations_skipped,
       num_densepose_annotations_skipped) = create_tf_example(
           image, annotations_list, image_dir, category_index, include_masks,
           keypoint_annotations_dict, densepose_annotations_dict,
           remove_non_person_annotations, remove_non_person_images)
      total_num_annotations_skipped += num_annotations_skipped
      total_num_keypoint_annotations_skipped += num_keypoint_annotations_skipped
      total_num_densepose_annotations_skipped += (
          num_densepose_annotations_skipped)
      shard_idx = idx % num_shards
      if tf_example:
        output_tfrecords[shard_idx].write(tf_example.SerializeToString())
    logging.info('Finished writing, skipped %d annotations.',
                 total_num_annotations_skipped)
    if keypoint_annotations_file:
      logging.info('Finished writing, skipped %d keypoint annotations.',
                   total_num_keypoint_annotations_skipped)
    if densepose_annotations_file:
      logging.info('Finished writing, skipped %d DensePose annotations.',
                   total_num_densepose_annotations_skipped)


def main(_):
  assert FLAGS.train_image_dir, '`train_image_dir` missing.'
  assert FLAGS.val_image_dir, '`val_image_dir` missing.'
  assert FLAGS.test_image_dir, '`test_image_dir` missing.'
  assert FLAGS.train_annotations_file, '`train_annotations_file` missing.'
  assert FLAGS.val_annotations_file, '`val_annotations_file` missing.'
  assert FLAGS.testdev_annotations_file, '`testdev_annotations_file` missing.'

  if not tf.gfile.IsDirectory(FLAGS.output_dir):
    tf.gfile.MakeDirs(FLAGS.output_dir)
  train_output_path = os.path.join(FLAGS.output_dir, 'coco_train.record')
  val_output_path = os.path.join(FLAGS.output_dir, 'coco_val.record')
  testdev_output_path = os.path.join(FLAGS.output_dir, 'coco_testdev.record')

  _create_tf_record_from_coco_annotations(
      FLAGS.train_annotations_file,
      FLAGS.train_image_dir,
      train_output_path,
      FLAGS.include_masks,
      num_shards=100,
      keypoint_annotations_file=FLAGS.train_keypoint_annotations_file,
      densepose_annotations_file=FLAGS.train_densepose_annotations_file,
      remove_non_person_annotations=FLAGS.remove_non_person_annotations,
      remove_non_person_images=FLAGS.remove_non_person_images)
  _create_tf_record_from_coco_annotations(
      FLAGS.val_annotations_file,
      FLAGS.val_image_dir,
      val_output_path,
      FLAGS.include_masks,
      num_shards=50,
      keypoint_annotations_file=FLAGS.val_keypoint_annotations_file,
      densepose_annotations_file=FLAGS.val_densepose_annotations_file,
      remove_non_person_annotations=FLAGS.remove_non_person_annotations,
      remove_non_person_images=FLAGS.remove_non_person_images)
  _create_tf_record_from_coco_annotations(
      FLAGS.testdev_annotations_file,
      FLAGS.test_image_dir,
      testdev_output_path,
      FLAGS.include_masks,
      num_shards=50)


if __name__ == '__main__':
  tf.app.run()

Answer 1

You can leave the iscrowd and segmentations as empty if you are doing object detection or classification or... tasks. But You wouldn't make use of the data with missing bounding boxes for such tasks.