Python build_data.ImageReader方法代码示例

# 需要导入模块: import build_data [as 别名]
# 或者: from build_data import ImageReader [as 别名]
def _convert_dataset(dataset_split, dataset_dir, dataset_label_dir):
    """Converts the ADE20k dataset into into tfrecord format.

    Args:
      dataset_split: Dataset split (e.g., train, val).
      dataset_dir: Dir in which the dataset locates.
      dataset_label_dir: Dir in which the annotations locates.

    Raises:
      RuntimeError: If loaded image and label have different shape.
    """

    img_names = tf.gfile.Glob(os.path.join(dataset_dir, '*.jpg'))
    random.shuffle(img_names)
    seg_names = []
    for f in img_names:
        # get the filename without the extension
        basename = os.path.basename(f).split('.')[0]
        # cover its corresponding *_seg.png
        seg = os.path.join(dataset_label_dir, basename+'.png')
        seg_names.append(seg)

    num_images = len(img_names)
    num_per_shard = int(math.ceil(num_images / float(_NUM_SHARDS)))

    image_reader = build_data.ImageReader('jpeg', channels=3)
    label_reader = build_data.ImageReader('png', channels=1)

    for shard_id in range(_NUM_SHARDS):
        output_filename = os.path.join(
            FLAGS.output_dir,
            '%s-%05d-of-%05d.tfrecord' % (dataset_split, shard_id, _NUM_SHARDS))
        with tf.python_io.TFRecordWriter(output_filename) as tfrecord_writer:
            start_idx = shard_id * num_per_shard
            end_idx = min((shard_id + 1) * num_per_shard, num_images)
            for i in range(start_idx, end_idx):
                sys.stdout.write('\r>> Converting image %d/%d shard %d' % (
                    i + 1, num_images, shard_id))
                sys.stdout.flush()
                # Read the image.
                image_filename = img_names[i]
                image_data = tf.gfile.FastGFile(image_filename, 'rb').read()
                height, width = image_reader.read_image_dims(image_data)
                # Read the semantic segmentation annotation.
                seg_filename = seg_names[i]
                seg_data = tf.gfile.FastGFile(seg_filename, 'rb').read()
                seg_height, seg_width = label_reader.read_image_dims(seg_data)
                if height != seg_height or width != seg_width:
                    raise RuntimeError(
                        'Shape mismatched between image and label.')
                # Convert to tf example.
                example = build_data.image_seg_to_tfexample(
                    image_data, img_names[i], height, width, seg_data)
                tfrecord_writer.write(example.SerializeToString())
        sys.stdout.write('\n')
        sys.stdout.flush() 

# 需要导入模块: import build_data [as 别名]
# 或者: from build_data import ImageReader [as 别名]
def _convert_dataset(dataset_split):
    """Converts the specified dataset split to TFRecord format.

    Args:
      dataset_split: The dataset split (e.g., train, val).

    Raises:
      RuntimeError: If loaded image and label have different shape, or if the
        image file with specified postfix could not be found.
    """
    image_files = _get_files('image', dataset_split)
    label_files = _get_files('label', dataset_split)

    num_images = len(image_files)
    num_per_shard = int(math.ceil(num_images / float(_NUM_SHARDS)))

    image_reader = build_data.ImageReader('png', channels=3)
    label_reader = build_data.ImageReader('png', channels=1)

    for shard_id in range(_NUM_SHARDS):
        shard_filename = '%s-%05d-of-%05d.tfrecord' % (
            dataset_split, shard_id, _NUM_SHARDS)
        output_filename = os.path.join(FLAGS.output_dir, shard_filename)
        with tf.python_io.TFRecordWriter(output_filename) as tfrecord_writer:
            start_idx = shard_id * num_per_shard
            end_idx = min((shard_id + 1) * num_per_shard, num_images)
            for i in range(start_idx, end_idx):
                sys.stdout.write('\r>> Converting image %d/%d shard %d' % (
                    i + 1, num_images, shard_id))
                sys.stdout.flush()
                # Read the image.
                image_data = tf.gfile.FastGFile(image_files[i], 'rb').read()
                height, width = image_reader.read_image_dims(image_data)
                # Read the semantic segmentation annotation.
                seg_data = b''
                if label_files:
                    seg_data = tf.gfile.FastGFile(label_files[i], 'rb').read()
                    seg_height, seg_width = label_reader.read_image_dims(
                        seg_data)
                    if height != seg_height or width != seg_width:
                        raise RuntimeError(
                            'Shape mismatched between image and label.')
                # Convert to tf example.
                re_match = _IMAGE_FILENAME_RE.search(image_files[i])
                if re_match is None:
                    raise RuntimeError(
                        'Invalid image filename: ' + image_files[i])
                filename = os.path.basename(re_match.group(1))
                example = build_data.image_seg_to_tfexample(
                    image_data, filename, height, width, seg_data)
                tfrecord_writer.write(example.SerializeToString())
        sys.stdout.write('\n')
        sys.stdout.flush() 

# 需要导入模块: import build_data [as 别名]
# 或者: from build_data import ImageReader [as 别名]
def _convert_dataset(dataset_split, dataset_dir, dataset_label_dir):
  """ Converts the ADE20k dataset into into tfrecord format (SSTable).

  Args:
    dataset_split: Dataset split (e.g., train, val).
    dataset_dir: Dir in which the dataset locates.
    dataset_label_dir: Dir in which the annotations locates.

  Raises:
    RuntimeError: If loaded image and label have different shape.
  """

  img_names = tf.gfile.Glob(os.path.join(dataset_dir, '*.jpg'))
  random.shuffle(img_names)
  seg_names = []
  for f in img_names:
    # get the filename without the extension
    basename = os.path.basename(f).split(".")[0]
    # cover its corresponding *_seg.png
    seg = os.path.join(dataset_label_dir, basename+'.png')
    seg_names.append(seg)

  num_images = len(img_names)
  num_per_shard = int(math.ceil(num_images / float(_NUM_SHARDS)))

  image_reader = build_data.ImageReader('jpeg', channels=3)
  label_reader = build_data.ImageReader('png', channels=1)

  for shard_id in range(_NUM_SHARDS):
    output_filename = os.path.join(
        FLAGS.output_dir,
        '%s-%05d-of-%05d.tfrecord' % (dataset_split, shard_id, _NUM_SHARDS))
    with tf.python_io.TFRecordWriter(output_filename) as tfrecord_writer:
      start_idx = shard_id * num_per_shard
      end_idx = min((shard_id + 1) * num_per_shard, num_images)
      for i in range(start_idx, end_idx):
        sys.stdout.write('\r>> Converting image %d/%d shard %d' % (
            i + 1, num_images, shard_id))
        sys.stdout.flush()
        # Read the image.
        image_filename = img_names[i]
        image_data = tf.gfile.FastGFile(image_filename, 'r').read()
        height, width = image_reader.read_image_dims(image_data)
        # Read the semantic segmentation annotation.
        seg_filename = seg_names[i]
        seg_data = tf.gfile.FastGFile(seg_filename, 'r').read()
        seg_height, seg_width = label_reader.read_image_dims(seg_data)
        if height != seg_height or width != seg_width:
          raise RuntimeError('Shape mismatched between image and label.')
        # Convert to tf example.
        example = build_data.image_seg_to_tfexample(
            image_data, img_names[i], height, width, seg_data)
        tfrecord_writer.write(example.SerializeToString())
    sys.stdout.write('\n')
    sys.stdout.flush() 

# 需要导入模块: import build_data [as 别名]
# 或者: from build_data import ImageReader [as 别名]
def _convert_dataset(dataset_split):
  """Converts the specified dataset split to TFRecord format.

  Args:
    dataset_split: The dataset split (e.g., train, test).

  Raises:
    RuntimeError: If loaded image and label have different shape.
  """
  dataset = os.path.basename(dataset_split)[:-4]
  sys.stdout.write('Processing ' + dataset)
  filenames = [x.strip('\n') for x in open(dataset_split, 'r')]
  num_images = len(filenames)
  num_per_shard = int(math.ceil(num_images / float(_NUM_SHARDS)))

  image_reader = build_data.ImageReader('jpeg', channels=3)
  label_reader = build_data.ImageReader('png', channels=1)

  for shard_id in range(_NUM_SHARDS):
    output_filename = os.path.join(
        FLAGS.output_dir,
        '%s-%05d-of-%05d.tfrecord' % (dataset, shard_id, _NUM_SHARDS))
    with tf.python_io.TFRecordWriter(output_filename) as tfrecord_writer:
      start_idx = shard_id * num_per_shard
      end_idx = min((shard_id + 1) * num_per_shard, num_images)
      for i in range(start_idx, end_idx):
        sys.stdout.write('\r>> Converting image %d/%d shard %d' % (
            i + 1, len(filenames), shard_id))
        sys.stdout.flush()
        # Read the image.
        image_filename = os.path.join(
            FLAGS.image_folder, filenames[i] + '.' + FLAGS.image_format)
        image_data = tf.gfile.FastGFile(image_filename, 'rb').read()
        height, width = image_reader.read_image_dims(image_data)
        # Read the semantic segmentation annotation.
        seg_filename = os.path.join(
            FLAGS.semantic_segmentation_folder,
            filenames[i] + '.' + FLAGS.label_format)
        seg_data = tf.gfile.FastGFile(seg_filename, 'rb').read()
        seg_height, seg_width = label_reader.read_image_dims(seg_data)
        if height != seg_height or width != seg_width:
          raise RuntimeError('Shape mismatched between image and label.')
        # Convert to tf example.
        example = build_data.image_seg_to_tfexample(
            image_data, filenames[i], height, width, seg_data)
        tfrecord_writer.write(example.SerializeToString())
    sys.stdout.write('\n')
    sys.stdout.flush() 

# 需要导入模块: import build_data [as 别名]
# 或者: from build_data import ImageReader [as 别名]
def _convert_dataset(dataset_split):
  """Converts the specified dataset split to TFRecord format.

  Args:
    dataset_split: The dataset split (e.g., train, val).

  Raises:
    RuntimeError: If loaded image and label have different shape, or if the
      image file with specified postfix could not be found.
  """
  image_files = _get_files('image', dataset_split)
  label_files = _get_files('label', dataset_split)

  num_images = len(image_files)
  num_per_shard = int(math.ceil(num_images / float(_NUM_SHARDS)))

  image_reader = build_data.ImageReader('png', channels=3)
  label_reader = build_data.ImageReader('png', channels=1)

  for shard_id in range(_NUM_SHARDS):
    shard_filename = '%s-%05d-of-%05d.tfrecord' % (
        dataset_split, shard_id, _NUM_SHARDS)
    output_filename = os.path.join(FLAGS.output_dir, shard_filename)
    with tf.python_io.TFRecordWriter(output_filename) as tfrecord_writer:
      start_idx = shard_id * num_per_shard
      end_idx = min((shard_id + 1) * num_per_shard, num_images)
      for i in range(start_idx, end_idx):
        sys.stdout.write('\r>> Converting image %d/%d shard %d' % (
            i + 1, num_images, shard_id))
        sys.stdout.flush()
        # Read the image.
        image_data = tf.gfile.FastGFile(image_files[i], 'rb').read()
        height, width = image_reader.read_image_dims(image_data)
        # Read the semantic segmentation annotation.
        seg_data = tf.gfile.FastGFile(label_files[i], 'rb').read()
        seg_height, seg_width = label_reader.read_image_dims(seg_data)
        if height != seg_height or width != seg_width:
          raise RuntimeError('Shape mismatched between image and label.')
        # Convert to tf example.
        re_match = _IMAGE_FILENAME_RE.search(image_files[i])
        if re_match is None:
          raise RuntimeError('Invalid image filename: ' + image_files[i])
        filename = os.path.basename(re_match.group(1))
        example = build_data.image_seg_to_tfexample(
            image_data, filename, height, width, seg_data)
        tfrecord_writer.write(example.SerializeToString())
    sys.stdout.write('\n')
    sys.stdout.flush() 

# 需要导入模块: import build_data [as 别名]
# 或者: from build_data import ImageReader [as 别名]
def _convert_dataset(dataset_split, dataset_dir, dataset_label_dir):
  """Converts the ADE20k dataset into into tfrecord format.

  Args:
    dataset_split: Dataset split (e.g., train, val).
    dataset_dir: Dir in which the dataset locates.
    dataset_label_dir: Dir in which the annotations locates.

  Raises:
    RuntimeError: If loaded image and label have different shape.
  """

  img_names = tf.gfile.Glob(os.path.join(dataset_dir, '*.jpg'))
  random.shuffle(img_names)
  seg_names = []
  for f in img_names:
    # get the filename without the extension
    basename = os.path.basename(f).split('.')[0]
    # cover its corresponding *_seg.png
    seg = os.path.join(dataset_label_dir, basename+'.png')
    seg_names.append(seg)

  num_images = len(img_names)
  num_per_shard = int(math.ceil(num_images / float(_NUM_SHARDS)))

  image_reader = build_data.ImageReader('jpeg', channels=3)
  label_reader = build_data.ImageReader('png', channels=1)

  for shard_id in range(_NUM_SHARDS):
    output_filename = os.path.join(
        FLAGS.output_dir,
        '%s-%05d-of-%05d.tfrecord' % (dataset_split, shard_id, _NUM_SHARDS))
    with tf.python_io.TFRecordWriter(output_filename) as tfrecord_writer:
      start_idx = shard_id * num_per_shard
      end_idx = min((shard_id + 1) * num_per_shard, num_images)
      for i in range(start_idx, end_idx):
        sys.stdout.write('\r>> Converting image %d/%d shard %d' % (
            i + 1, num_images, shard_id))
        sys.stdout.flush()
        # Read the image.
        image_filename = img_names[i]
        image_data = tf.gfile.FastGFile(image_filename, 'r').read()
        height, width = image_reader.read_image_dims(image_data)
        # Read the semantic segmentation annotation.
        seg_filename = seg_names[i]
        seg_data = tf.gfile.FastGFile(seg_filename, 'r').read()
        seg_height, seg_width = label_reader.read_image_dims(seg_data)
        if height != seg_height or width != seg_width:
          raise RuntimeError('Shape mismatched between image and label.')
        # Convert to tf example.
        example = build_data.image_seg_to_tfexample(
            image_data, img_names[i], height, width, seg_data)
        tfrecord_writer.write(example.SerializeToString())
    sys.stdout.write('\n')
    sys.stdout.flush() 

# 需要导入模块: import build_data [as 别名]
# 或者: from build_data import ImageReader [as 别名]
def _convert_dataset(dataset_split):
  """Converts the specified dataset split to TFRecord format.

  Args:
    dataset_split: The dataset split (e.g., train, test).

  Raises:
    RuntimeError: If loaded image and label have different shape.
  """
  dataset = os.path.basename(dataset_split)[:-4]
  sys.stdout.write('Processing ' + dataset)
  filenames = [x.strip('\n') for x in open(dataset_split, 'r')]
  num_images = len(filenames)
  num_per_shard = int(math.ceil(num_images / float(_NUM_SHARDS)))

  image_reader = build_data.ImageReader('jpeg', channels=3)
  label_reader = build_data.ImageReader('jpeg', channels=1)

  for shard_id in range(_NUM_SHARDS):
    output_filename = os.path.join(
        FLAGS.output_dir,
        '%s-%05d-of-%05d.tfrecord' % (dataset, shard_id, _NUM_SHARDS))
    with tf.python_io.TFRecordWriter(output_filename) as tfrecord_writer:
      start_idx = shard_id * num_per_shard
      end_idx = min((shard_id + 1) * num_per_shard, num_images)
      for i in range(start_idx, end_idx):
        sys.stdout.write('\r>> Converting image %d/%d shard %d' % (
            i + 1, len(filenames), shard_id))
        sys.stdout.flush()
        # Read the image.
        image_filename = os.path.join(
            FLAGS.image_folder, filenames[i] + '.' + FLAGS.image_format)
        image_data = tf.gfile.FastGFile(image_filename, 'rb').read()
        height, width = image_reader.read_image_dims(image_data)
        # Read the semantic segmentation annotation.
        seg_filename = os.path.join(
            FLAGS.semantic_segmentation_folder,
           str(int(filenames[i])) + '.jpg')
        seg_data = tf.gfile.FastGFile(seg_filename, 'rb').read()
        seg_height, seg_width = label_reader.read_image_dims(seg_data)
        if height != seg_height or width != seg_width:
          raise RuntimeError('Shape mismatched between image and label.')
        # Convert to tf example.
        example = build_data.image_seg_to_tfexample(
            image_data, filenames[i], height, width, seg_data)
        tfrecord_writer.write(example.SerializeToString())
    sys.stdout.write('\n')
    sys.stdout.flush() 

# 需要导入模块: import build_data [as 别名]
# 或者: from build_data import ImageReader [as 别名]
def _convert_dataset(dataset_split, image_format, label_format):
  """Converts the specified dataset split to TFRecord format.

  Args:
    dataset_split: The dataset split (e.g., train, test).

  Raises:
    RuntimeError: If loaded image and label have different shape.
  """
  dataset = os.path.basename(dataset_split)[:-4]
  sys.stdout.write('Processing ' + dataset)
  filenames = [x.strip('\n') for x in open(dataset_split, 'r')]
  num_images = len(filenames)
  num_per_shard = int(math.ceil(num_images / float(_NUM_SHARDS)))

  image_reader = build_data.ImageReader('jpeg', channels=3)
  label_reader = build_data.ImageReader('png', channels=1)

  # will create output directory if specified directory does not exist (GS, 6/3)
  if not os.path.exists(FLAGS.output_dir):
    os.mkdir(FLAGS.output_dir)

  for shard_id in range(_NUM_SHARDS):
    output_filename = os.path.join(
        FLAGS.output_dir,
        '%s-%05d-of-%05d.tfrecord' % (dataset, shard_id, _NUM_SHARDS))
    with tf.python_io.TFRecordWriter(output_filename) as tfrecord_writer:
      start_idx = shard_id * num_per_shard
      end_idx = min((shard_id + 1) * num_per_shard, num_images)
      for i in range(start_idx, end_idx):
        sys.stdout.write('\r>> Converting image %d/%d shard %d' % (
            i + 1, len(filenames), shard_id))
        sys.stdout.flush()
        # Read the image.
        image_filename = os.path.join(
            FLAGS.image_folder, filenames[i] + '.' + image_format)
        image_data = tf.gfile.FastGFile(image_filename, 'rb').read()
        height, width = image_reader.read_image_dims(image_data)
        # Read the semantic segmentation annotation.
        seg_filename = os.path.join(
            FLAGS.semantic_segmentation_folder,
            filenames[i] + '.' + label_format)
        seg_data = tf.gfile.FastGFile(seg_filename, 'rb').read()
        seg_height, seg_width = label_reader.read_image_dims(seg_data)
        if height != seg_height or width != seg_width:
          raise RuntimeError('Shape mismatched between image and label.')
        # Convert to tf example.
        example = build_data.image_seg_to_tfexample(
            image_data, filenames[i], height, width, seg_data)
        tfrecord_writer.write(example.SerializeToString())
    sys.stdout.write('\n')
    sys.stdout.flush() 

# 需要导入模块: import build_data [as 别名]
# 或者: from build_data import ImageReader [as 别名]
def _convert_dataset(dataset_split):
  """Converts the specified dataset split to TFRecord format.

  Args:
    dataset_split: The dataset split (e.g., train, test).

  Raises:
    RuntimeError: If loaded image and label have different shape.
  """
  dataset = os.path.basename(dataset_split)[:-4]
  filenames = [x.strip('\n') for x in open(dataset_split, 'r')]
  num_images = len(filenames)
  sys.stdout.write('Processing %s, #samples: %d\n' % (dataset, num_images))
  num_per_shard = int(math.ceil(num_images / float(_NUM_SHARDS)))

  image_reader = build_data.ImageReader(FLAGS.image_format, channels=3)
  label_reader = build_data.ImageReader(FLAGS.label_format, channels=1)

  for shard_id in range(_NUM_SHARDS):
    output_filename = os.path.join(
        FLAGS.output_dir,
        '%s-%05d-of-%05d.tfrecord' % (dataset, shard_id, _NUM_SHARDS))
    with tf.python_io.TFRecordWriter(output_filename) as tfrecord_writer:
      start_idx = shard_id * num_per_shard
      end_idx = min((shard_id + 1) * num_per_shard, num_images)
      for i in range(start_idx, end_idx):
        sys.stdout.write('\r>> Converting image %d/%d shard %d' % (
            i + 1, len(filenames), shard_id))
        sys.stdout.flush()
        # Read the image.
        image_filename = os.path.join(
            FLAGS.image_folder, filenames[i] + '.' + FLAGS.image_format)
        image_data = tf.gfile.GFile(image_filename, 'rb').read()
        height, width = image_reader.read_image_dims(image_data)
        # Read the semantic segmentation annotation.
        seg_filename = os.path.join(
            FLAGS.semantic_segmentation_folder,
            filenames[i] + '.' + FLAGS.label_format)
        seg_data = tf.gfile.FastGFile(seg_filename, 'rb').read()
        seg_height, seg_width = label_reader.read_image_dims(seg_data)
        if height != seg_height or width != seg_width:
          raise RuntimeError('Shape mismatched between image and label.')
        # Convert to tf example.
        example = build_data.image_seg_to_tfexample(
            image_data, filenames[i], height, width, seg_data)
        tfrecord_writer.write(example.SerializeToString())
    sys.stdout.write('\n')
    sys.stdout.flush() 

# 需要导入模块: import build_data [as 别名]
# 或者: from build_data import ImageReader [as 别名]
def _convert_dataset(dataset_split, dataset_dir, dataset_label_dir):
  """Converts the ADE20k dataset into into tfrecord format.

  Args:
    dataset_split: Dataset split (e.g., train, val).
    dataset_dir: Dir in which the dataset locates.
    dataset_label_dir: Dir in which the annotations locates.

  Raises:
    RuntimeError: If loaded image and label have different shape.
  """

  img_names = tf.gfile.Glob(os.path.join(dataset_dir, '*.jpg'))
  random.shuffle(img_names)
  seg_names = []
  for f in img_names:
    # get the filename without the extension
    basename = os.path.basename(f).split('.')[0]
    # cover its corresponding *_seg.png
    seg = os.path.join(dataset_label_dir, basename+'.png')
    seg_names.append(seg)

  num_images = len(img_names)
  num_per_shard = int(math.ceil(num_images / _NUM_SHARDS))

  image_reader = build_data.ImageReader('jpeg', channels=3)
  label_reader = build_data.ImageReader('png', channels=1)

  for shard_id in range(_NUM_SHARDS):
    output_filename = os.path.join(
        FLAGS.output_dir,
        '%s-%05d-of-%05d.tfrecord' % (dataset_split, shard_id, _NUM_SHARDS))
    with tf.python_io.TFRecordWriter(output_filename) as tfrecord_writer:
      start_idx = shard_id * num_per_shard
      end_idx = min((shard_id + 1) * num_per_shard, num_images)
      for i in range(start_idx, end_idx):
        sys.stdout.write('\r>> Converting image %d/%d shard %d' % (
            i + 1, num_images, shard_id))
        sys.stdout.flush()
        # Read the image.
        image_filename = img_names[i]
        image_data = tf.gfile.FastGFile(image_filename, 'rb').read()
        height, width = image_reader.read_image_dims(image_data)
        # Read the semantic segmentation annotation.
        seg_filename = seg_names[i]
        seg_data = tf.gfile.FastGFile(seg_filename, 'rb').read()
        seg_height, seg_width = label_reader.read_image_dims(seg_data)
        if height != seg_height or width != seg_width:
          raise RuntimeError('Shape mismatched between image and label.')
        # Convert to tf example.
        example = build_data.image_seg_to_tfexample(
            image_data, img_names[i], height, width, seg_data)
        tfrecord_writer.write(example.SerializeToString())
    sys.stdout.write('\n')
    sys.stdout.flush() 

# 需要导入模块: import build_data [as 别名]
# 或者: from build_data import ImageReader [as 别名]
def _convert_dataset(dataset_split):
  """Converts the specified dataset split to TFRecord format.

  Args:
    dataset_split: The dataset split (e.g., train, test).

  Raises:
    RuntimeError: If loaded image and label have different shape.
  """
  dataset = os.path.basename(dataset_split)[:-4]
  sys.stdout.write('Processing ' + dataset)
  filenames = [x.strip('\n') for x in open(dataset_split, 'r')]
  num_images = len(filenames)
  num_per_shard = int(math.ceil(num_images / _NUM_SHARDS))

  image_reader = build_data.ImageReader('jpeg', channels=3)
  label_reader = build_data.ImageReader('png', channels=1)

  for shard_id in range(_NUM_SHARDS):
    output_filename = os.path.join(
        FLAGS.output_dir,
        '%s-%05d-of-%05d.tfrecord' % (dataset, shard_id, _NUM_SHARDS))
    with tf.python_io.TFRecordWriter(output_filename) as tfrecord_writer:
      start_idx = shard_id * num_per_shard
      end_idx = min((shard_id + 1) * num_per_shard, num_images)
      for i in range(start_idx, end_idx):
        sys.stdout.write('\r>> Converting image %d/%d shard %d' % (
            i + 1, len(filenames), shard_id))
        sys.stdout.flush()
        # Read the image.
        image_filename = os.path.join(
            FLAGS.image_folder, filenames[i] + '.' + FLAGS.image_format)
        image_data = tf.gfile.GFile(image_filename, 'rb').read()
        height, width = image_reader.read_image_dims(image_data)
        # Read the semantic segmentation annotation.
        seg_filename = os.path.join(
            FLAGS.semantic_segmentation_folder,
            filenames[i] + '.' + FLAGS.label_format)
        seg_data = tf.gfile.GFile(seg_filename, 'rb').read()
        seg_height, seg_width = label_reader.read_image_dims(seg_data)
        if height != seg_height or width != seg_width:
          raise RuntimeError('Shape mismatched between image and label.')
        # Convert to tf example.
        example = build_data.image_seg_to_tfexample(
            image_data, filenames[i], height, width, seg_data)
        tfrecord_writer.write(example.SerializeToString())
    sys.stdout.write('\n')
    sys.stdout.flush() 

# 需要导入模块: import build_data [as 别名]
# 或者: from build_data import ImageReader [as 别名]
def _convert_dataset(dataset_split):
  """Converts the specified dataset split to TFRecord format.

  Args:
    dataset_split: The dataset split (e.g., train, val).

  Raises:
    RuntimeError: If loaded image and label have different shape, or if the
      image file with specified postfix could not be found.
  """
  image_files = _get_files('image', dataset_split)
  label_files = _get_files('label', dataset_split)

  num_images = len(image_files)
  num_per_shard = int(math.ceil(num_images / _NUM_SHARDS))

  image_reader = build_data.ImageReader('png', channels=3)
  label_reader = build_data.ImageReader('png', channels=1)

  for shard_id in range(_NUM_SHARDS):
    shard_filename = '%s-%05d-of-%05d.tfrecord' % (
        dataset_split, shard_id, _NUM_SHARDS)
    output_filename = os.path.join(FLAGS.output_dir, shard_filename)
    with tf.python_io.TFRecordWriter(output_filename) as tfrecord_writer:
      start_idx = shard_id * num_per_shard
      end_idx = min((shard_id + 1) * num_per_shard, num_images)
      for i in range(start_idx, end_idx):
        sys.stdout.write('\r>> Converting image %d/%d shard %d' % (
            i + 1, num_images, shard_id))
        sys.stdout.flush()
        # Read the image.
        image_data = tf.gfile.FastGFile(image_files[i], 'rb').read()
        height, width = image_reader.read_image_dims(image_data)
        # Read the semantic segmentation annotation.
        seg_data = tf.gfile.FastGFile(label_files[i], 'rb').read()
        seg_height, seg_width = label_reader.read_image_dims(seg_data)
        if height != seg_height or width != seg_width:
          raise RuntimeError('Shape mismatched between image and label.')
        # Convert to tf example.
        re_match = _IMAGE_FILENAME_RE.search(image_files[i])
        if re_match is None:
          raise RuntimeError('Invalid image filename: ' + image_files[i])
        filename = os.path.basename(re_match.group(1))
        example = build_data.image_seg_to_tfexample(
            image_data, filename, height, width, seg_data)
        tfrecord_writer.write(example.SerializeToString())
    sys.stdout.write('\n')
    sys.stdout.flush()