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 collections
import functools
import six
import matplotlib.pyplot as plt
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 tensorflow as tf

#from object_detection.core import standard_fields as fields
import standard_fields as fields

# pylint: disable=too-many-arguments, too-many-nested-blocks, too-many-branches

_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 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 _visualize_boxes(image, boxes, classes, scores, category_index, **kwargs):
  return visualize_boxes_and_labels_on_image_array(
      image, boxes, classes, scores, category_index=category_index, **kwargs)


def _visualize_boxes_and_masks(image, boxes, classes, scores, masks,
                               category_index, **kwargs):
  return visualize_boxes_and_labels_on_image_array(
      image,
      boxes,
      classes,
      scores,
      category_index=category_index,
      instance_masks=masks,
      **kwargs)


def _visualize_boxes_and_keypoints(image, boxes, classes, scores, keypoints,
                                   category_index, **kwargs):
  return visualize_boxes_and_labels_on_image_array(
      image,
      boxes,
      classes,
      scores,
      category_index=category_index,
      keypoints=keypoints,
      **kwargs)


def _visualize_boxes_and_masks_and_keypoints(
    image, boxes, classes, scores, masks, keypoints, category_index, **kwargs):
  return visualize_boxes_and_labels_on_image_array(
      image,
      boxes,
      classes,
      scores,
      category_index=category_index,
      instance_masks=masks,
      keypoints=keypoints,
      **kwargs)


def draw_bounding_boxes_on_image_tensors(images,
                                         boxes,
                                         classes,
                                         scores,
                                         category_index,
                                         instance_masks=None,
                                         keypoints=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.
    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'}, ...}
    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.
    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.
  images = images[:, :, :, 0:3]
  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 instance_masks is not None and keypoints is None:
    visualize_boxes_fn = functools.partial(
        _visualize_boxes_and_masks,
        category_index=category_index,
        **visualization_keyword_args)
    elems = [images, boxes, classes, scores, instance_masks]
  elif instance_masks is None and keypoints is not None:
    visualize_boxes_fn = functools.partial(
        _visualize_boxes_and_keypoints,
        category_index=category_index,
        **visualization_keyword_args)
    elems = [images, boxes, classes, scores, keypoints]
  elif instance_masks is not None and keypoints is not None:
    visualize_boxes_fn = functools.partial(
        _visualize_boxes_and_masks_and_keypoints,
        category_index=category_index,
        **visualization_keyword_args)
    elems = [images, boxes, classes, scores, instance_masks, keypoints]
  else:
    visualize_boxes_fn = functools.partial(
        _visualize_boxes,
        category_index=category_index,
        **visualization_keyword_args)
    elems = [images, boxes, classes, scores]

  def draw_boxes(image_and_detections):
    """Draws boxes on image."""
    image_with_boxes = tf.py_func(visualize_boxes_fn, image_and_detections,
                                  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_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 [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()
  instance_masks = None
  if detection_fields.detection_masks in eval_dict:
    instance_masks = tf.cast(
        tf.expand_dims(eval_dict[detection_fields.detection_masks], axis=0),
        tf.uint8)
  keypoints = None
  if detection_fields.detection_keypoints in eval_dict:
    keypoints = tf.expand_dims(
        eval_dict[detection_fields.detection_keypoints], 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], axis=0),
        tf.uint8)
  images_with_detections = draw_bounding_boxes_on_image_tensors(
      eval_dict[input_data_fields.original_image],
      tf.expand_dims(eval_dict[detection_fields.detection_boxes], axis=0),
      tf.expand_dims(eval_dict[detection_fields.detection_classes], axis=0),
      tf.expand_dims(eval_dict[detection_fields.detection_scores], axis=0),
      category_index,
      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(
      eval_dict[input_data_fields.original_image],
      tf.expand_dims(eval_dict[input_data_fields.groundtruth_boxes], axis=0),
      tf.expand_dims(eval_dict[input_data_fields.groundtruth_classes], axis=0),
      tf.expand_dims(
          tf.ones_like(
              eval_dict[input_data_fields.groundtruth_classes],
              dtype=tf.float32),
          axis=0),
      category_index,
      instance_masks=groundtruth_instance_masks,
      keypoints=None,
      max_boxes_to_draw=None,
      min_score_thresh=0.0,
      use_normalized_coordinates=use_normalized_coordinates)
  return tf.concat([images_with_detections, images_with_groundtruth], axis=2)


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,
    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):
  """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
    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

  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)
  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 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]))
        box_to_display_str_map[box].append(display_str)
        if agnostic_mode:
          box_to_color_map[box] = 'DarkOrange'
        else:
          box_to_color_map[box] = STANDARD_COLORS[
              classes[i] % len(STANDARD_COLORS)]

  # Draw all boxes onto image.
  for box, color 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=color
      )
    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=color,
        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=color,
          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)