visualization_utils.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.
# ==============================================================================

"""A set of functions that are used for visualization.

These functions often receive an image, perform some visualization on the image.
The functions do not return a value, instead they modify the image itself.

"""
import abc
import collections
# Set headless-friendly backend.
import matplotlib; matplotlib.use('Agg')  # pylint: disable=multiple-statements
import matplotlib.pyplot as plt  # pylint: disable=g-import-not-at-top
import numpy as np
import PIL.Image as Image
import PIL.ImageColor as ImageColor
import PIL.ImageDraw as ImageDraw
import PIL.ImageFont as ImageFont
import six
import tensorflow as tf

from object_detection.core import standard_fields as fields
from object_detection.utils import shape_utils

_TITLE_LEFT_MARGIN = 10
_TITLE_TOP_MARGIN = 10
STANDARD_COLORS = [
    'AliceBlue', 'Chartreuse', 'Aqua', 'Aquamarine', 'Azure', 'Beige', 'Bisque',
    'BlanchedAlmond', 'BlueViolet', 'BurlyWood', 'CadetBlue', 'AntiqueWhite',
    'Chocolate', 'Coral', 'CornflowerBlue', 'Cornsilk', 'Crimson', 'Cyan',
    'DarkCyan', 'DarkGoldenRod', 'DarkGrey', 'DarkKhaki', 'DarkOrange',
    'DarkOrchid', 'DarkSalmon', 'DarkSeaGreen', 'DarkTurquoise', 'DarkViolet',
    'DeepPink', 'DeepSkyBlue', 'DodgerBlue', 'FireBrick', 'FloralWhite',
    'ForestGreen', 'Fuchsia', 'Gainsboro', 'GhostWhite', 'Gold', 'GoldenRod',
    'Salmon', 'Tan', 'HoneyDew', 'HotPink', 'IndianRed', 'Ivory', 'Khaki',
    'Lavender', 'LavenderBlush', 'LawnGreen', 'LemonChiffon', 'LightBlue',
    'LightCoral', 'LightCyan', 'LightGoldenRodYellow', 'LightGray', 'LightGrey',
    'LightGreen', 'LightPink', 'LightSalmon', 'LightSeaGreen', 'LightSkyBlue',
    'LightSlateGray', 'LightSlateGrey', 'LightSteelBlue', 'LightYellow', 'Lime',
    'LimeGreen', 'Linen', 'Magenta', 'MediumAquaMarine', 'MediumOrchid',
    'MediumPurple', 'MediumSeaGreen', 'MediumSlateBlue', 'MediumSpringGreen',
    'MediumTurquoise', 'MediumVioletRed', 'MintCream', 'MistyRose', 'Moccasin',
    'NavajoWhite', 'OldLace', 'Olive', 'OliveDrab', 'Orange', 'OrangeRed',
    'Orchid', 'PaleGoldenRod', 'PaleGreen', 'PaleTurquoise', 'PaleVioletRed',
    'PapayaWhip', 'PeachPuff', 'Peru', 'Pink', 'Plum', 'PowderBlue', 'Purple',
    'Red', 'RosyBrown', 'RoyalBlue', 'SaddleBrown', 'Green', 'SandyBrown',
    'SeaGreen', 'SeaShell', 'Sienna', 'Silver', 'SkyBlue', 'SlateBlue',
    'SlateGray', 'SlateGrey', 'Snow', 'SpringGreen', 'SteelBlue', 'GreenYellow',
    'Teal', 'Thistle', 'Tomato', 'Turquoise', 'Violet', 'Wheat', 'White',
    'WhiteSmoke', 'Yellow', 'YellowGreen'
]


def _get_multiplier_for_color_randomness():
  """Returns a multiplier to get semi-random colors from successive indices.

  This function computes a prime number, p, in the range [2, 17] that:
  - is closest to len(STANDARD_COLORS) / 10
  - does not divide len(STANDARD_COLORS)

  If no prime numbers in that range satisfy the constraints, p is returned as 1.

  Once p is established, it can be used as a multiplier to select
  non-consecutive colors from STANDARD_COLORS:
  colors = [(p * i) % len(STANDARD_COLORS) for i in range(20)]
  """
  num_colors = len(STANDARD_COLORS)
  prime_candidates = [5, 7, 11, 13, 17]

  # Remove all prime candidates that divide the number of colors.
  prime_candidates = [p for p in prime_candidates if num_colors % p]
  if not prime_candidates:
    return 1

  # Return the closest prime number to num_colors / 10.
  abs_distance = [np.abs(num_colors / 10. - p) for p in prime_candidates]
  num_candidates = len(abs_distance)
  inds = [i for _, i in sorted(zip(abs_distance, range(num_candidates)))]
  return prime_candidates[inds[0]]


def save_image_array_as_png(image, output_path):
  """Saves an image (represented as a numpy array) to PNG.

  Args:
    image: a numpy array with shape [height, width, 3].
    output_path: path to which image should be written.
  """
  image_pil = Image.fromarray(np.uint8(image)).convert('RGB')
  with tf.gfile.Open(output_path, 'w') as fid:
    image_pil.save(fid, 'PNG')


def encode_image_array_as_png_str(image):
  """Encodes a numpy array into a PNG string.

  Args:
    image: a numpy array with shape [height, width, 3].

  Returns:
    PNG encoded image string.
  """
  image_pil = Image.fromarray(np.uint8(image))
  output = six.BytesIO()
  image_pil.save(output, format='PNG')
  png_string = output.getvalue()
  output.close()
  return png_string


def draw_bounding_box_on_image_array(image,
                                     ymin,
                                     xmin,
                                     ymax,
                                     xmax,
                                     color='red',
                                     thickness=4,
                                     display_str_list=(),
                                     use_normalized_coordinates=True):
  """Adds a bounding box to an image (numpy array).

  Bounding box coordinates can be specified in either absolute (pixel) or
  normalized coordinates by setting the use_normalized_coordinates argument.

  Args:
    image: a numpy array with shape [height, width, 3].
    ymin: ymin of bounding box.
    xmin: xmin of bounding box.
    ymax: ymax of bounding box.
    xmax: xmax of bounding box.
    color: color to draw bounding box. Default is red.
    thickness: line thickness. Default value is 4.
    display_str_list: list of strings to display in box
                      (each to be shown on its own line).
    use_normalized_coordinates: If True (default), treat coordinates
      ymin, xmin, ymax, xmax as relative to the image.  Otherwise treat
      coordinates as absolute.
  """
  image_pil = Image.fromarray(np.uint8(image)).convert('RGB')
  draw_bounding_box_on_image(image_pil, ymin, xmin, ymax, xmax, color,
                             thickness, display_str_list,
                             use_normalized_coordinates)
  np.copyto(image, np.array(image_pil))


def draw_bounding_box_on_image(image,
                               ymin,
                               xmin,
                               ymax,
                               xmax,
                               color='red',
                               thickness=4,
                               display_str_list=(),
                               use_normalized_coordinates=True):
  """Adds a bounding box to an image.

  Bounding box coordinates can be specified in either absolute (pixel) or
  normalized coordinates by setting the use_normalized_coordinates argument.

  Each string in display_str_list is displayed on a separate line above the
  bounding box in black text on a rectangle filled with the input 'color'.
  If the top of the bounding box extends to the edge of the image, the strings
  are displayed below the bounding box.

  Args:
    image: a PIL.Image object.
    ymin: ymin of bounding box.
    xmin: xmin of bounding box.
    ymax: ymax of bounding box.
    xmax: xmax of bounding box.
    color: color to draw bounding box. Default is red.
    thickness: line thickness. Default value is 4.
    display_str_list: list of strings to display in box
                      (each to be shown on its own line).
    use_normalized_coordinates: If True (default), treat coordinates
      ymin, xmin, ymax, xmax as relative to the image.  Otherwise treat
      coordinates as absolute.
  """
  draw = ImageDraw.Draw(image)
  im_width, im_height = image.size
  if use_normalized_coordinates:
    (left, right, top, bottom) = (xmin * im_width, xmax * im_width,
                                  ymin * im_height, ymax * im_height)
  else:
    (left, right, top, bottom) = (xmin, xmax, ymin, ymax)
  draw.line([(left, top), (left, bottom), (right, bottom),
             (right, top), (left, top)], width=thickness, fill=color)
  try:
    font = ImageFont.truetype('arial.ttf', 24)
  except IOError:
    font = ImageFont.load_default()

  # If the total height of the display strings added to the top of the bounding
  # box exceeds the top of the image, stack the strings below the bounding box
  # instead of above.
  display_str_heights = [font.getsize(ds)[1] for ds in display_str_list]
  # Each display_str has a top and bottom margin of 0.05x.
  total_display_str_height = (1 + 2 * 0.05) * sum(display_str_heights)

  if top > total_display_str_height:
    text_bottom = top
  else:
    text_bottom = bottom + total_display_str_height
  # Reverse list and print from bottom to top.
  for display_str in display_str_list[::-1]:
    text_width, text_height = font.getsize(display_str)
    margin = np.ceil(0.05 * text_height)
    draw.rectangle(
        [(left, text_bottom - text_height - 2 * margin), (left + text_width,
                                                          text_bottom)],
        fill=color)
    draw.text(
        (left + margin, text_bottom - text_height - margin),
        display_str,
        fill='black',
        font=font)
    text_bottom -= text_height - 2 * margin


def draw_bounding_boxes_on_image_array(image,
                                       boxes,
                                       color='red',
                                       thickness=4,
                                       display_str_list_list=()):
  """Draws bounding boxes on image (numpy array).

  Args:
    image: a numpy array object.
    boxes: a 2 dimensional numpy array of [N, 4]: (ymin, xmin, ymax, xmax).
           The coordinates are in normalized format between [0, 1].
    color: color to draw bounding box. Default is red.
    thickness: line thickness. Default value is 4.
    display_str_list_list: list of list of strings.
                           a list of strings for each bounding box.
                           The reason to pass a list of strings for a
                           bounding box is that it might contain
                           multiple labels.

  Raises:
    ValueError: if boxes is not a [N, 4] array
  """
  image_pil = Image.fromarray(image)
  draw_bounding_boxes_on_image(image_pil, boxes, color, thickness,
                               display_str_list_list)
  np.copyto(image, np.array(image_pil))


def draw_bounding_boxes_on_image(image,
                                 boxes,
                                 color='red',
                                 thickness=4,
                                 display_str_list_list=()):
  """Draws bounding boxes on image.

  Args:
    image: a PIL.Image object.
    boxes: a 2 dimensional numpy array of [N, 4]: (ymin, xmin, ymax, xmax).
           The coordinates are in normalized format between [0, 1].
    color: color to draw bounding box. Default is red.
    thickness: line thickness. Default value is 4.
    display_str_list_list: list of list of strings.
                           a list of strings for each bounding box.
                           The reason to pass a list of strings for a
                           bounding box is that it might contain
                           multiple labels.

  Raises:
    ValueError: if boxes is not a [N, 4] array
  """
  boxes_shape = boxes.shape
  if not boxes_shape:
    return
  if len(boxes_shape) != 2 or boxes_shape[1] != 4:
    raise ValueError('Input must be of size [N, 4]')
  for i in range(boxes_shape[0]):
    display_str_list = ()
    if display_str_list_list:
      display_str_list = display_str_list_list[i]
    draw_bounding_box_on_image(image, boxes[i, 0], boxes[i, 1], boxes[i, 2],
                               boxes[i, 3], color, thickness, display_str_list)


def create_visualization_fn(category_index, include_masks=False,
                            include_keypoints=False, include_track_ids=False,
                            **kwargs):
  """Constructs a visualization function that can be wrapped in a py_func.

  py_funcs only accept positional arguments. This function returns a suitable
  function with the correct positional argument mapping. The positional
  arguments in order are:
  0: image
  1: boxes
  2: classes
  3: scores
  [4-6]: masks (optional)
  [4-6]: keypoints (optional)
  [4-6]: track_ids (optional)

  -- Example 1 --
  vis_only_masks_fn = create_visualization_fn(category_index,
    include_masks=True, include_keypoints=False, include_track_ids=False,
    **kwargs)
  image = tf.py_func(vis_only_masks_fn,
                     inp=[image, boxes, classes, scores, masks],
                     Tout=tf.uint8)

  -- Example 2 --
  vis_masks_and_track_ids_fn = create_visualization_fn(category_index,
    include_masks=True, include_keypoints=False, include_track_ids=True,
    **kwargs)
  image = tf.py_func(vis_masks_and_track_ids_fn,
                     inp=[image, boxes, classes, scores, masks, track_ids],
                     Tout=tf.uint8)

  Args:
    category_index: a dict that maps integer ids to category dicts. e.g.
      {1: {1: 'dog'}, 2: {2: 'cat'}, ...}
    include_masks: Whether masks should be expected as a positional argument in
      the returned function.
    include_keypoints: Whether keypoints should be expected as a positional
      argument in the returned function.
    include_track_ids: Whether track ids should be expected as a positional
      argument in the returned function.
    **kwargs: Additional kwargs that will be passed to
      visualize_boxes_and_labels_on_image_array.

  Returns:
    Returns a function that only takes tensors as positional arguments.
  """

  def visualization_py_func_fn(*args):
    """Visualization function that can be wrapped in a tf.py_func.

    Args:
      *args: First 4 positional arguments must be:
        image - uint8 numpy array with shape (img_height, img_width, 3).
        boxes - a numpy array of shape [N, 4].
        classes - a numpy array of shape [N].
        scores - a numpy array of shape [N] or None.
        -- Optional positional arguments --
        instance_masks - a numpy array of shape [N, image_height, image_width].
        keypoints - a numpy array of shape [N, num_keypoints, 2].
        track_ids - a numpy array of shape [N] with unique track ids.

    Returns:
      uint8 numpy array with shape (img_height, img_width, 3) with overlaid
      boxes.
    """
    image = args[0]
    boxes = args[1]
    classes = args[2]
    scores = args[3]
    masks = keypoints = track_ids = None
    pos_arg_ptr = 4  # Positional argument for first optional tensor (masks).
    if include_masks:
      masks = args[pos_arg_ptr]
      pos_arg_ptr += 1
    if include_keypoints:
      keypoints = args[pos_arg_ptr]
      pos_arg_ptr += 1
    if include_track_ids:
      track_ids = args[pos_arg_ptr]

    return visualize_boxes_and_labels_on_image_array(
        image,
        boxes,
        classes,
        scores,
        category_index=category_index,
        instance_masks=masks,
        keypoints=keypoints,
        track_ids=track_ids,
        **kwargs)
  return visualization_py_func_fn


def _resize_original_image(image, image_shape):
  image = tf.expand_dims(image, 0)
  image = tf.image.resize_images(
      image,
      image_shape,
      method=tf.image.ResizeMethod.NEAREST_NEIGHBOR,
      align_corners=True)
  return tf.cast(tf.squeeze(image, 0), tf.uint8)


def draw_bounding_boxes_on_image_tensors(images,
                                         boxes,
                                         classes,
                                         scores,
                                         category_index,
                                         original_image_spatial_shape=None,
                                         true_image_shape=None,
                                         instance_masks=None,
                                         keypoints=None,
                                         track_ids=None,
                                         max_boxes_to_draw=20,
                                         min_score_thresh=0.2,
                                         use_normalized_coordinates=True):
  """Draws bounding boxes, masks, and keypoints on batch of image tensors.

  Args:
    images: A 4D uint8 image tensor of shape [N, H, W, C]. If C > 3, additional
      channels will be ignored. If C = 1, then we convert the images to RGB
      images.
    boxes: [N, max_detections, 4] float32 tensor of detection boxes.
    classes: [N, max_detections] int tensor of detection classes. Note that
      classes are 1-indexed.
    scores: [N, max_detections] float32 tensor of detection scores.
    category_index: a dict that maps integer ids to category dicts. e.g.
      {1: {1: 'dog'}, 2: {2: 'cat'}, ...}
    original_image_spatial_shape: [N, 2] tensor containing the spatial size of
      the original image.
    true_image_shape: [N, 3] tensor containing the spatial size of unpadded
      original_image.
    instance_masks: A 4D uint8 tensor of shape [N, max_detection, H, W] with
      instance masks.
    keypoints: A 4D float32 tensor of shape [N, max_detection, num_keypoints, 2]
      with keypoints.
    track_ids: [N, max_detections] int32 tensor of unique tracks ids (i.e.
      instance ids for each object). If provided, the color-coding of boxes is
      dictated by these ids, and not classes.
    max_boxes_to_draw: Maximum number of boxes to draw on an image. Default 20.
    min_score_thresh: Minimum score threshold for visualization. Default 0.2.
    use_normalized_coordinates: Whether to assume boxes and kepoints are in
      normalized coordinates (as opposed to absolute coordiantes).
      Default is True.

  Returns:
    4D image tensor of type uint8, with boxes drawn on top.
  """
  # Additional channels are being ignored.
  if images.shape[3] > 3:
    images = images[:, :, :, 0:3]
  elif images.shape[3] == 1:
    images = tf.image.grayscale_to_rgb(images)
  visualization_keyword_args = {
      'use_normalized_coordinates': use_normalized_coordinates,
      'max_boxes_to_draw': max_boxes_to_draw,
      'min_score_thresh': min_score_thresh,
      'agnostic_mode': False,
      'line_thickness': 4
  }
  if true_image_shape is None:
    true_shapes = tf.constant(-1, shape=[images.shape.as_list()[0], 3])
  else:
    true_shapes = true_image_shape
  if original_image_spatial_shape is None:
    original_shapes = tf.constant(-1, shape=[images.shape.as_list()[0], 2])
  else:
    original_shapes = original_image_spatial_shape

  visualize_boxes_fn = create_visualization_fn(
      category_index,
      include_masks=instance_masks is not None,
      include_keypoints=keypoints is not None,
      include_track_ids=track_ids is not None,
      **visualization_keyword_args)

  elems = [true_shapes, original_shapes, images, boxes, classes, scores]
  if instance_masks is not None:
    elems.append(instance_masks)
  if keypoints is not None:
    elems.append(keypoints)
  if track_ids is not None:
    elems.append(track_ids)

  def draw_boxes(image_and_detections):
    """Draws boxes on image."""
    true_shape = image_and_detections[0]
    original_shape = image_and_detections[1]
    if true_image_shape is not None:
      image = shape_utils.pad_or_clip_nd(image_and_detections[2],
                                         [true_shape[0], true_shape[1], 3])
    if original_image_spatial_shape is not None:
      image_and_detections[2] = _resize_original_image(image, original_shape)

    image_with_boxes = tf.py_func(visualize_boxes_fn, image_and_detections[2:],
                                  tf.uint8)
    return image_with_boxes

  images = tf.map_fn(draw_boxes, elems, dtype=tf.uint8, back_prop=False)
  return images


def draw_side_by_side_evaluation_image(eval_dict,
                                       category_index,
                                       max_boxes_to_draw=20,
                                       min_score_thresh=0.2,
                                       use_normalized_coordinates=True):
  """Creates a side-by-side image with detections and groundtruth.

  Bounding boxes (and instance masks, if available) are visualized on both
  subimages.

  Args:
    eval_dict: The evaluation dictionary returned by
      eval_util.result_dict_for_batched_example() or
      eval_util.result_dict_for_single_example().
    category_index: A category index (dictionary) produced from a labelmap.
    max_boxes_to_draw: The maximum number of boxes to draw for detections.
    min_score_thresh: The minimum score threshold for showing detections.
    use_normalized_coordinates: Whether to assume boxes and kepoints are in
      normalized coordinates (as opposed to absolute coordiantes).
      Default is True.

  Returns:
    A list of [1, H, 2 * W, C] uint8 tensor. The subimage on the left
      corresponds to detections, while the subimage on the right corresponds to
      groundtruth.
  """
  detection_fields = fields.DetectionResultFields()
  input_data_fields = fields.InputDataFields()

  images_with_detections_list = []

  # Add the batch dimension if the eval_dict is for single example.
  if len(eval_dict[detection_fields.detection_classes].shape) == 1:
    for key in eval_dict:
      if key != input_data_fields.original_image:
        eval_dict[key] = tf.expand_dims(eval_dict[key], 0)

  for indx in range(eval_dict[input_data_fields.original_image].shape[0]):
    instance_masks = None
    if detection_fields.detection_masks in eval_dict:
      instance_masks = tf.cast(
          tf.expand_dims(
              eval_dict[detection_fields.detection_masks][indx], axis=0),
          tf.uint8)
    keypoints = None
    if detection_fields.detection_keypoints in eval_dict:
      keypoints = tf.expand_dims(
          eval_dict[detection_fields.detection_keypoints][indx], axis=0)
    groundtruth_instance_masks = None
    if input_data_fields.groundtruth_instance_masks in eval_dict:
      groundtruth_instance_masks = tf.cast(
          tf.expand_dims(
              eval_dict[input_data_fields.groundtruth_instance_masks][indx],
              axis=0), tf.uint8)

    images_with_detections = draw_bounding_boxes_on_image_tensors(
        tf.expand_dims(
            eval_dict[input_data_fields.original_image][indx], axis=0),
        tf.expand_dims(
            eval_dict[detection_fields.detection_boxes][indx], axis=0),
        tf.expand_dims(
            eval_dict[detection_fields.detection_classes][indx], axis=0),
        tf.expand_dims(
            eval_dict[detection_fields.detection_scores][indx], axis=0),
        category_index,
        original_image_spatial_shape=tf.expand_dims(
            eval_dict[input_data_fields.original_image_spatial_shape][indx],
            axis=0),
        true_image_shape=tf.expand_dims(
            eval_dict[input_data_fields.true_image_shape][indx], axis=0),
        instance_masks=instance_masks,
        keypoints=keypoints,
        max_boxes_to_draw=max_boxes_to_draw,
        min_score_thresh=min_score_thresh,
        use_normalized_coordinates=use_normalized_coordinates)
    images_with_groundtruth = draw_bounding_boxes_on_image_tensors(
        tf.expand_dims(
            eval_dict[input_data_fields.original_image][indx], axis=0),
        tf.expand_dims(
            eval_dict[input_data_fields.groundtruth_boxes][indx], axis=0),
        tf.expand_dims(
            eval_dict[input_data_fields.groundtruth_classes][indx], axis=0),
        tf.expand_dims(
            tf.ones_like(
                eval_dict[input_data_fields.groundtruth_classes][indx],
                dtype=tf.float32),
            axis=0),
        category_index,
        original_image_spatial_shape=tf.expand_dims(
            eval_dict[input_data_fields.original_image_spatial_shape][indx],
            axis=0),
        true_image_shape=tf.expand_dims(
            eval_dict[input_data_fields.true_image_shape][indx], axis=0),
        instance_masks=groundtruth_instance_masks,
        keypoints=None,
        max_boxes_to_draw=None,
        min_score_thresh=0.0,
        use_normalized_coordinates=use_normalized_coordinates)
    images_with_detections_list.append(
        tf.concat([images_with_detections, images_with_groundtruth], axis=2))
  return images_with_detections_list


def draw_keypoints_on_image_array(image,
                                  keypoints,
                                  color='red',
                                  radius=2,
                                  use_normalized_coordinates=True):
  """Draws keypoints on an image (numpy array).

  Args:
    image: a numpy array with shape [height, width, 3].
    keypoints: a numpy array with shape [num_keypoints, 2].
    color: color to draw the keypoints with. Default is red.
    radius: keypoint radius. Default value is 2.
    use_normalized_coordinates: if True (default), treat keypoint values as
      relative to the image.  Otherwise treat them as absolute.
  """
  image_pil = Image.fromarray(np.uint8(image)).convert('RGB')
  draw_keypoints_on_image(image_pil, keypoints, color, radius,
                          use_normalized_coordinates)
  np.copyto(image, np.array(image_pil))


def draw_keypoints_on_image(image,
                            keypoints,
                            color='red',
                            radius=2,
                            use_normalized_coordinates=True):
  """Draws keypoints on an image.

  Args:
    image: a PIL.Image object.
    keypoints: a numpy array with shape [num_keypoints, 2].
    color: color to draw the keypoints with. Default is red.
    radius: keypoint radius. Default value is 2.
    use_normalized_coordinates: if True (default), treat keypoint values as
      relative to the image.  Otherwise treat them as absolute.
  """
  draw = ImageDraw.Draw(image)
  im_width, im_height = image.size
  keypoints_x = [k[1] for k in keypoints]
  keypoints_y = [k[0] for k in keypoints]
  if use_normalized_coordinates:
    keypoints_x = tuple([im_width * x for x in keypoints_x])
    keypoints_y = tuple([im_height * y for y in keypoints_y])
  for keypoint_x, keypoint_y in zip(keypoints_x, keypoints_y):
    draw.ellipse([(keypoint_x - radius, keypoint_y - radius),
                  (keypoint_x + radius, keypoint_y + radius)],
                 outline=color, fill=color)


def draw_mask_on_image_array(image, mask, color='red', alpha=0.4):
  """Draws mask on an image.

  Args:
    image: uint8 numpy array with shape (img_height, img_height, 3)
    mask: a uint8 numpy array of shape (img_height, img_height) with
      values between either 0 or 1.
    color: color to draw the keypoints with. Default is red.
    alpha: transparency value between 0 and 1. (default: 0.4)

  Raises:
    ValueError: On incorrect data type for image or masks.
  """
  if image.dtype != np.uint8:
    raise ValueError('`image` not of type np.uint8')
  if mask.dtype != np.uint8:
    raise ValueError('`mask` not of type np.uint8')
  if np.any(np.logical_and(mask != 1, mask != 0)):
    raise ValueError('`mask` elements should be in [0, 1]')
  if image.shape[:2] != mask.shape:
    raise ValueError('The image has spatial dimensions %s but the mask has '
                     'dimensions %s' % (image.shape[:2], mask.shape))
  rgb = ImageColor.getrgb(color)
  pil_image = Image.fromarray(image)

  solid_color = np.expand_dims(
      np.ones_like(mask), axis=2) * np.reshape(list(rgb), [1, 1, 3])
  pil_solid_color = Image.fromarray(np.uint8(solid_color)).convert('RGBA')
  pil_mask = Image.fromarray(np.uint8(255.0*alpha*mask)).convert('L')
  pil_image = Image.composite(pil_solid_color, pil_image, pil_mask)
  np.copyto(image, np.array(pil_image.convert('RGB')))


def visualize_boxes_and_labels_on_image_array(
    image,
    boxes,
    classes,
    scores,
    category_index,
    instance_masks=None,
    instance_boundaries=None,
    keypoints=None,
    track_ids=None,
    color=None,
    use_normalized_coordinates=False,
    max_boxes_to_draw=20,
    min_score_thresh=.5,
    agnostic_mode=False,
    line_thickness=4,
    groundtruth_box_visualization_color='black',
    skip_scores=False,
    skip_labels=False,
    skip_track_ids=False):
  """Overlay labeled boxes on an image with formatted scores and label names.

  This function groups boxes that correspond to the same location
  and creates a display string for each detection and overlays these
  on the image. Note that this function modifies the image in place, and returns
  that same image.

  Args:
    image: uint8 numpy array with shape (img_height, img_width, 3)
    boxes: a numpy array of shape [N, 4]
    classes: a numpy array of shape [N]. Note that class indices are 1-based,
      and match the keys in the label map.
    scores: a numpy array of shape [N] or None.  If scores=None, then
      this function assumes that the boxes to be plotted are groundtruth
      boxes and plot all boxes as black with no classes or scores.
    category_index: a dict containing category dictionaries (each holding
      category index `id` and category name `name`) keyed by category indices.
    instance_masks: a numpy array of shape [N, image_height, image_width] with
      values ranging between 0 and 1, can be None.
    instance_boundaries: a numpy array of shape [N, image_height, image_width]
      with values ranging between 0 and 1, can be None.
    keypoints: a numpy array of shape [N, num_keypoints, 2], can
      be None
    track_ids: a numpy array of shape [N] with unique track ids. If provided,
      color-coding of boxes will be determined by these ids, and not the class
      indices.
    use_normalized_coordinates: whether boxes is to be interpreted as
      normalized coordinates or not.
    max_boxes_to_draw: maximum number of boxes to visualize.  If None, draw
      all boxes.
    min_score_thresh: minimum score threshold for a box to be visualized
    agnostic_mode: boolean (default: False) controlling whether to evaluate in
      class-agnostic mode or not.  This mode will display scores but ignore
      classes.
    line_thickness: integer (default: 4) controlling line width of the boxes.
    groundtruth_box_visualization_color: box color for visualizing groundtruth
      boxes
    skip_scores: whether to skip score when drawing a single detection
    skip_labels: whether to skip label when drawing a single detection
    skip_track_ids: whether to skip track id when drawing a single detection

  Returns:
    uint8 numpy array with shape (img_height, img_width, 3) with overlaid boxes.
  """
  # Create a display string (and color) for every box location, group any boxes
  # that correspond to the same location.
  box_to_display_str_map = collections.defaultdict(list)
  box_to_color_map = collections.defaultdict(str)
  box_to_instance_masks_map = {}
  box_to_instance_boundaries_map = {}
  box_to_keypoints_map = collections.defaultdict(list)
  box_to_track_ids_map = {}
  if not max_boxes_to_draw:
    max_boxes_to_draw = boxes.shape[0]
  for i in range(min(max_boxes_to_draw, boxes.shape[0])):
    if scores is None or scores[i] > min_score_thresh:
      box = tuple(boxes[i].tolist())
      if instance_masks is not None:
        box_to_instance_masks_map[box] = instance_masks[i]
      if instance_boundaries is not None:
        box_to_instance_boundaries_map[box] = instance_boundaries[i]
      if keypoints is not None:
        box_to_keypoints_map[box].extend(keypoints[i])
      if track_ids is not None:
        box_to_track_ids_map[box] = track_ids[i]
      if scores is None:
        box_to_color_map[box] = groundtruth_box_visualization_color
      else:
        display_str = ''
        if not skip_labels:
          if not agnostic_mode:
            if classes[i] in category_index.keys():
              class_name = category_index[classes[i]]['name']
            else:
              class_name = 'N/A'
            display_str = str(class_name)
        if not skip_scores:
          if not display_str:
            display_str = '{}%'.format(int(100*scores[i]))
          else:
            display_str = '{}: {}%'.format(display_str, int(100*scores[i]))
        if not skip_track_ids and track_ids is not None:
          if not display_str:
            display_str = 'ID {}'.format(track_ids[i])
          else:
            display_str = '{}: ID {}'.format(display_str, track_ids[i])
        box_to_display_str_map[box].append(display_str)
        if agnostic_mode:
          box_to_color_map[box] = 'DarkOrange'
        elif track_ids is not None:
          prime_multipler = _get_multiplier_for_color_randomness()
          box_to_color_map[box] = STANDARD_COLORS[
              (prime_multipler * track_ids[i]) % len(STANDARD_COLORS)]
        elif color is not None:
          box_to_color_map[box] = color[i % len(color)]
        else:
          box_to_color_map[box] = STANDARD_COLORS[
              classes[i] % len(STANDARD_COLORS)]

  # Draw all boxes onto image.
  for box, colormap in box_to_color_map.items():
    ymin, xmin, ymax, xmax = box
    if instance_masks is not None:
      draw_mask_on_image_array(
          image,
          box_to_instance_masks_map[box],
          color=colormap
      )
    if instance_boundaries is not None:
      draw_mask_on_image_array(
          image,
          box_to_instance_boundaries_map[box],
          color='red',
          alpha=1.0
      )
    draw_bounding_box_on_image_array(
        image,
        ymin,
        xmin,
        ymax,
        xmax,
        color=colormap,
        thickness=line_thickness,
        display_str_list=box_to_display_str_map[box],
        use_normalized_coordinates=use_normalized_coordinates)
    if keypoints is not None:
      draw_keypoints_on_image_array(
          image,
          box_to_keypoints_map[box],
          color=colormap,
          radius=line_thickness / 2,
          use_normalized_coordinates=use_normalized_coordinates)

  return image


def add_cdf_image_summary(values, name):
  """Adds a tf.summary.image for a CDF plot of the values.

  Normalizes `values` such that they sum to 1, plots the cumulative distribution
  function and creates a tf image summary.

  Args:
    values: a 1-D float32 tensor containing the values.
    name: name for the image summary.
  """
  def cdf_plot(values):
    """Numpy function to plot CDF."""
    normalized_values = values / np.sum(values)
    sorted_values = np.sort(normalized_values)
    cumulative_values = np.cumsum(sorted_values)
    fraction_of_examples = (np.arange(cumulative_values.size, dtype=np.float32)
                            / cumulative_values.size)
    fig = plt.figure(frameon=False)
    ax = fig.add_subplot('111')
    ax.plot(fraction_of_examples, cumulative_values)
    ax.set_ylabel('cumulative normalized values')
    ax.set_xlabel('fraction of examples')
    fig.canvas.draw()
    width, height = fig.get_size_inches() * fig.get_dpi()
    image = np.fromstring(fig.canvas.tostring_rgb(), dtype='uint8').reshape(
        1, int(height), int(width), 3)
    return image
  cdf_plot = tf.py_func(cdf_plot, [values], tf.uint8)
  tf.summary.image(name, cdf_plot)


def add_hist_image_summary(values, bins, name):
  """Adds a tf.summary.image for a histogram plot of the values.

  Plots the histogram of values and creates a tf image summary.

  Args:
    values: a 1-D float32 tensor containing the values.
    bins: bin edges which will be directly passed to np.histogram.
    name: name for the image summary.
  """

  def hist_plot(values, bins):
    """Numpy function to plot hist."""
    fig = plt.figure(frameon=False)
    ax = fig.add_subplot('111')
    y, x = np.histogram(values, bins=bins)
    ax.plot(x[:-1], y)
    ax.set_ylabel('count')
    ax.set_xlabel('value')
    fig.canvas.draw()
    width, height = fig.get_size_inches() * fig.get_dpi()
    image = np.fromstring(
        fig.canvas.tostring_rgb(), dtype='uint8').reshape(
            1, int(height), int(width), 3)
    return image
  hist_plot = tf.py_func(hist_plot, [values, bins], tf.uint8)
  tf.summary.image(name, hist_plot)


class EvalMetricOpsVisualization(object):
  """Abstract base class responsible for visualizations during evaluation.

  Currently, summary images are not run during evaluation. One way to produce
  evaluation images in Tensorboard is to provide tf.summary.image strings as
  `value_ops` in tf.estimator.EstimatorSpec's `eval_metric_ops`. This class is
  responsible for accruing images (with overlaid detections and groundtruth)
  and returning a dictionary that can be passed to `eval_metric_ops`.
  """
  __metaclass__ = abc.ABCMeta

  def __init__(self,
               category_index,
               max_examples_to_draw=5,
               max_boxes_to_draw=20,
               min_score_thresh=0.2,
               use_normalized_coordinates=True,
               summary_name_prefix='evaluation_image'):
    """Creates an EvalMetricOpsVisualization.

    Args:
      category_index: A category index (dictionary) produced from a labelmap.
      max_examples_to_draw: The maximum number of example summaries to produce.
      max_boxes_to_draw: The maximum number of boxes to draw for detections.
      min_score_thresh: The minimum score threshold for showing detections.
      use_normalized_coordinates: Whether to assume boxes and kepoints are in
        normalized coordinates (as opposed to absolute coordiantes).
        Default is True.
      summary_name_prefix: A string prefix for each image summary.
    """

    self._category_index = category_index
    self._max_examples_to_draw = max_examples_to_draw
    self._max_boxes_to_draw = max_boxes_to_draw
    self._min_score_thresh = min_score_thresh
    self._use_normalized_coordinates = use_normalized_coordinates
    self._summary_name_prefix = summary_name_prefix
    self._images = []

  def clear(self):
    self._images = []

  def add_images(self, images):
    """Store a list of images, each with shape [1, H, W, C]."""
    if len(self._images) >= self._max_examples_to_draw:
      return

    # Store images and clip list if necessary.
    self._images.extend(images)
    if len(self._images) > self._max_examples_to_draw:
      self._images[self._max_examples_to_draw:] = []

  def get_estimator_eval_metric_ops(self, eval_dict):
    """Returns metric ops for use in tf.estimator.EstimatorSpec.

    Args:
      eval_dict: A dictionary that holds an image, groundtruth, and detections
        for a batched example. Note that, we use only the first example for
        visualization. See eval_util.result_dict_for_batched_example() for a
        convenient method for constructing such a dictionary. The dictionary
        contains
        fields.InputDataFields.original_image: [batch_size, H, W, 3] image.
        fields.InputDataFields.original_image_spatial_shape: [batch_size, 2]
          tensor containing the size of the original image.
        fields.InputDataFields.true_image_shape: [batch_size, 3]
          tensor containing the spatial size of the upadded original image.
        fields.InputDataFields.groundtruth_boxes - [batch_size, num_boxes, 4]
          float32 tensor with groundtruth boxes in range [0.0, 1.0].
        fields.InputDataFields.groundtruth_classes - [batch_size, num_boxes]
          int64 tensor with 1-indexed groundtruth classes.
        fields.InputDataFields.groundtruth_instance_masks - (optional)
          [batch_size, num_boxes, H, W] int64 tensor with instance masks.
        fields.DetectionResultFields.detection_boxes - [batch_size,
          max_num_boxes, 4] float32 tensor with detection boxes in range [0.0,
          1.0].
        fields.DetectionResultFields.detection_classes - [batch_size,
          max_num_boxes] int64 tensor with 1-indexed detection classes.
        fields.DetectionResultFields.detection_scores - [batch_size,
          max_num_boxes] float32 tensor with detection scores.
        fields.DetectionResultFields.detection_masks - (optional) [batch_size,
          max_num_boxes, H, W] float32 tensor of binarized masks.
        fields.DetectionResultFields.detection_keypoints - (optional)
          [batch_size, max_num_boxes, num_keypoints, 2] float32 tensor with
          keypoints.

    Returns:
      A dictionary of image summary names to tuple of (value_op, update_op). The
      `update_op` is the same for all items in the dictionary, and is
      responsible for saving a single side-by-side image with detections and
      groundtruth. Each `value_op` holds the tf.summary.image string for a given
      image.
    """
    if self._max_examples_to_draw == 0:
      return {}
    images = self.images_from_evaluation_dict(eval_dict)

    def get_images():
      """Returns a list of images, padded to self._max_images_to_draw."""
      images = self._images
      while len(images) < self._max_examples_to_draw:
        images.append(np.array(0, dtype=np.uint8))
      self.clear()
      return images

    def image_summary_or_default_string(summary_name, image):
      """Returns image summaries for non-padded elements."""
      return tf.cond(
          tf.equal(tf.size(tf.shape(image)), 4),
          lambda: tf.summary.image(summary_name, image),
          lambda: tf.constant(''))

    if tf.executing_eagerly():
      update_op = self.add_images([[images[0]]])
      image_tensors = get_images()
    else:
      update_op = tf.py_func(self.add_images, [[images[0]]], [])
      image_tensors = tf.py_func(
          get_images, [], [tf.uint8] * self._max_examples_to_draw)
    eval_metric_ops = {}
    for i, image in enumerate(image_tensors):
      summary_name = self._summary_name_prefix + '/' + str(i)
      value_op = image_summary_or_default_string(summary_name, image)
      eval_metric_ops[summary_name] = (value_op, update_op)
    return eval_metric_ops

  @abc.abstractmethod
  def images_from_evaluation_dict(self, eval_dict):
    """Converts evaluation dictionary into a list of image tensors.

    To be overridden by implementations.

    Args:
      eval_dict: A dictionary with all the necessary information for producing
        visualizations.

    Returns:
      A list of [1, H, W, C] uint8 tensors.
    """
    raise NotImplementedError


class VisualizeSingleFrameDetections(EvalMetricOpsVisualization):
  """Class responsible for single-frame object detection visualizations."""

  def __init__(self,
               category_index,
               max_examples_to_draw=5,
               max_boxes_to_draw=20,
               min_score_thresh=0.2,
               use_normalized_coordinates=True,
               summary_name_prefix='Detections_Left_Groundtruth_Right'):
    super(VisualizeSingleFrameDetections, self).__init__(
        category_index=category_index,
        max_examples_to_draw=max_examples_to_draw,
        max_boxes_to_draw=max_boxes_to_draw,
        min_score_thresh=min_score_thresh,
        use_normalized_coordinates=use_normalized_coordinates,
        summary_name_prefix=summary_name_prefix)

  def images_from_evaluation_dict(self, eval_dict):
    return draw_side_by_side_evaluation_image(
        eval_dict, self._category_index, self._max_boxes_to_draw,
        self._min_score_thresh, self._use_normalized_coordinates)