Python datasets.dataset_utils.int64_feature() Examples

The following are 7 code examples of datasets.dataset_utils.int64_feature(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module datasets.dataset_utils , or try the search function .
Example #1
Source File: convert_celeba.py    From TwinGAN with Apache License 2.0 5 votes vote down vote up 
def _convert_to_example(filename, image_data, height, width, current_file_info, common_info):
    colorspace = 'RGB'
    channels = 3
    image_format = 'JPEG'

    example = tf.train.Example(features=tf.train.Features(feature={
      'image/height': dataset_utils.int64_feature(height),
      'image/width': dataset_utils.int64_feature(width),
      'image/colorspace': dataset_utils.bytes_feature(colorspace),
      'image/channels': dataset_utils.int64_feature(channels),
      'image/format': dataset_utils.bytes_feature(image_format),
      'image/filename': dataset_utils.bytes_feature(os.path.basename(filename)),
      'image/encoded': dataset_utils.bytes_feature(image_data)}))
    return example
Example #2
Source File: convert_danbooru_data.py    From TwinGAN with Apache License 2.0 5 votes vote down vote up 
def _convert_to_example(filename, image_buffer, height, width, current_file_info, common_info):
    """Build an Example proto for an example.

    Args:
      filename: string, path to an image file, e.g., '/path/to/example.JPG'
      image_buffer: string, JPEG encoding of RGB image
      height: integer, image height in pixels
      width: integer, image width in pixels
      current_file_info:  equivalent to label: integer, identifier for the ground truth for the network
      common_info: a list of tags with format: ('type', 'ambiguous', 'count', 'name', 'id')

    Returns:
      Example proto
    """
    colorspace = 'RGB'
    channels = 3
    image_format = 'JPEG'
    human_readable_tags = DanbooruDataConverter._tag_to_human_readable(current_file_info,common_info)

    example = tf.train.Example(features=tf.train.Features(feature={
      'image/height': dataset_utils.int64_feature(height),
      'image/width': dataset_utils.int64_feature(width),
      'image/colorspace': dataset_utils.bytes_feature(colorspace),
      'image/channels': dataset_utils.int64_feature(channels),
      'image/class/label': dataset_utils.int64_feature(current_file_info),
      'image/class/text': dataset_utils.bytes_feature(human_readable_tags),
      'image/format': dataset_utils.bytes_feature(image_format),
      'image/filename': dataset_utils.bytes_feature(os.path.basename(filename)),
      'image/encoded': dataset_utils.bytes_feature(image_buffer)}))
    return example
Example #3
Source File: convert_image_only.py    From TwinGAN with Apache License 2.0 5 votes vote down vote up 
def _convert_to_example(filename, image_data, height, width, current_file_info, common_info):
    colorspace = 'RGB'
    channels = 3
    image_format = 'JPEG'

    example = tf.train.Example(features=tf.train.Features(feature={
      'image/colorspace': dataset_utils.bytes_feature(colorspace),
      'image/channels': dataset_utils.int64_feature(channels),
      'image/format': dataset_utils.bytes_feature(image_format),
      'image/filename': dataset_utils.bytes_feature(os.path.basename(filename)),
      'image/encoded': dataset_utils.bytes_feature(image_data),
    }))
    return example
Example #4
Source File: pascalvoc_to_tfrecords.py    From MobileNet with Apache License 2.0 4 votes vote down vote up 
def _convert_to_example(image_data, labels, labels_text, bboxes, shape,
                        difficult, truncated):
    """Build an Example proto for an image example.

    Args:
      image_data: string, JPEG encoding of RGB image;
      labels: list of integers, identifier for the ground truth;
      labels_text: list of strings, human-readable labels;
      bboxes: list of bounding boxes; each box is a list of integers;
          specifying [xmin, ymin, xmax, ymax]. All boxes are assumed to belong
          to the same label as the image label.
      shape: 3 integers, image shapes in pixels.
    Returns:
      Example proto
    """
    xmin = []
    ymin = []
    xmax = []
    ymax = []
    for b in bboxes:
        assert len(b) == 4
        # pylint: disable=expression-not-assigned
        [l.append(point) for l, point in zip([ymin, xmin, ymax, xmax], b)]
        # pylint: enable=expression-not-assigned

    image_format = b'JPEG'
    example = tf.train.Example(features=tf.train.Features(feature={
            'image/height': int64_feature(shape[0]),
            'image/width': int64_feature(shape[1]),
            'image/channels': int64_feature(shape[2]),
            'image/shape': int64_feature(shape),
            'image/object/bbox/xmin': float_feature(xmin),
            'image/object/bbox/xmax': float_feature(xmax),
            'image/object/bbox/ymin': float_feature(ymin),
            'image/object/bbox/ymax': float_feature(ymax),
            'image/object/bbox/label': int64_feature(labels),
            'image/object/bbox/label_text': bytes_feature(labels_text),
            'image/object/bbox/difficult': int64_feature(difficult),
            'image/object/bbox/truncated': int64_feature(truncated),
            'image/format': bytes_feature(image_format),
            'image/encoded': bytes_feature(image_data)}))
    return example
Example #5
Source File: kitti_object_to_tfrecords.py    From MobileNet with Apache License 2.0 4 votes vote down vote up 
def _process_image(directory, split, name):
    # Read the image file.
    filename = os.path.join(directory, 'image_2', name + '.png')
    image_data = tf.gfile.FastGFile(filename, 'r').read()

    # Get shape
    img = cv2.imread(filename)
    shape = np.shape(img)

    label_list = []
    type_list = []

    bbox_x1_list = []
    bbox_y1_list = []
    bbox_x2_list = []
    bbox_y2_list = []


    # If 'test' split, skip annotations
    if re.findall(r'train', split):
      # Read the txt annotation file.
      filename = os.path.join(directory, 'label_2', name + '.txt')
      with open(filename) as anno_file:
        objects = anno_file.readlines()

      for object in objects:
          obj_anno = object.split(' ')
          type_txt = obj_anno[0].encode('ascii')
          if type_txt in CLASSES:
            label_list.append(CLASSES[type_txt])
            type_list.append(type_txt)

            # Bounding Box
            bbox_x1 = float(obj_anno[4])
            bbox_y1 = float(obj_anno[5])
            bbox_x2 = float(obj_anno[6])
            bbox_y2 = float(obj_anno[7])
            bbox_x1_list.append(bbox_x1)
            bbox_y1_list.append(bbox_y1)
            bbox_x2_list.append(bbox_x2)
            bbox_y2_list.append(bbox_y2)

    image_format = b'PNG'
    example = tf.train.Example(features=tf.train.Features(feature={
            'image/encoded': bytes_feature(image_data),
            'image/height': int64_feature(shape[0]),
            'image/width': int64_feature(shape[1]),
            'image/channels': int64_feature(shape[2]),
            'image/shape': int64_feature(shape),
            'image/object/bbox/xmin': float_feature(bbox_x1_list),
            'image/object/bbox/xmax': float_feature(bbox_x2_list),
            'image/object/bbox/ymin': float_feature(bbox_y1_list),
            'image/object/bbox/ymax': float_feature(bbox_y2_list),
            'image/object/bbox/label': int64_feature(label_list),
            'image/object/bbox/label_text': bytes_feature(type_list),
    }))
    return example
Example #6
Source File: convert_anime_faces_from_object_detection.py    From TwinGAN with Apache License 2.0 4 votes vote down vote up 
def _convert_to_example(filename, image_data, height, width, current_file_info, shared_info):
    colorspace = 'RGB'
    channels = 3
    image_format = 'JPEG'
    (x_expanded, y_expanded, w_expanded, h_expanded, image_w, image_h, tags_id, original_image,
     face_xywh) = current_file_info

    feature = {
      'image/x': dataset_utils.int64_feature(x_expanded),
      'image/y': dataset_utils.int64_feature(y_expanded),
      'image/height': dataset_utils.int64_feature(h_expanded),
      'image/width': dataset_utils.int64_feature(w_expanded),

      'image/face_xywh': dataset_utils.float_feature(face_xywh),
      # 'image/left_eye_xywh': dataset_utils.float_feature(left_eye_xywh),
      # 'image/right_eye_xywh': dataset_utils.float_feature(right_eye_xywh),
      # 'image/mouth_xywh': dataset_utils.float_feature(mouth_xywh),

      'image/colorspace': dataset_utils.bytes_feature(colorspace),
      'image/channels': dataset_utils.int64_feature(channels),
      'image/format': dataset_utils.bytes_feature(image_format),
      'image/filename': dataset_utils.bytes_feature(os.path.basename(filename)),
      'image/encoded': dataset_utils.bytes_feature(image_data),
      # Encoding original takes up too much space. Not recommended.
      # 'image/original': dataset_utils.bytes_feature(original_image),
    }
    example = tf.train.Example(features=tf.train.Features(feature=feature))
    return example

  ###########################
  # Other utility functions #
  ###########################
  # Inherits from parent class.

  ########
  # Main #
  ########
  # Inherits from parent class.


################
# Helper class #
################
Example #7
Source File: pascalvoc_to_tfrecords.py    From SSD_tensorflow_VOC with Apache License 2.0 4 votes vote down vote up 
def _convert_to_example(image_data, labels, labels_text, bboxes, shape,
                        difficult, truncated,name):
    """Build an Example proto for an image example.

    Args:
      image_data: string, JPEG encoding of RGB image;
      labels: list of integers, identifier for the ground truth;
      labels_text: list of strings, human-readable labels;
      bboxes: list of bounding boxes; each box is a list of integers;
          specifying [xmin, ymin, xmax, ymax]. All boxes are assumed to belong
          to the same label as the image label.
      shape: 3 integers, image shapes in pixels.
    Returns:
      Example proto
    """
    xmin = []
    ymin = []
    xmax = []
    ymax = []
    for b in bboxes:
        assert len(b) == 4
        # pylint: disable=expression-not-assigned
        [l.append(point) for l, point in zip([ymin, xmin, ymax, xmax], b)]
        # pylint: enable=expression-not-assigned

    image_format = b'JPEG'
    example = tf.train.Example(features=tf.train.Features(feature={
            'image/height': int64_feature(shape[0]),
            'image/width': int64_feature(shape[1]),
            'image/channels': int64_feature(shape[2]),
            'image/shape': int64_feature(shape),
            'image/object/bbox/xmin': float_feature(xmin),
            'image/object/bbox/xmax': float_feature(xmax),
            'image/object/bbox/ymin': float_feature(ymin),
            'image/object/bbox/ymax': float_feature(ymax),
            'image/object/bbox/label': int64_feature(labels),
            'image/object/bbox/label_text': bytes_feature(labels_text),
            'image/object/bbox/difficult': int64_feature(difficult),
            'image/object/bbox/truncated': int64_feature(truncated),
            'image/format': bytes_feature(image_format),
            'image/filename': bytes_feature(name.encode('utf-8')),
            'image/encoded': bytes_feature(image_data)}))
    return example