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.

"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from object_detection.utils import generate_xml
import os
from object_detection.utils import shape_utils
from object_detection.core import standard_fields as fields
import tensorflow as tf
from six.moves import zip
from six.moves import range
import six
import PIL.ImageFont as ImageFont
import PIL.ImageDraw as ImageDraw
import PIL.ImageColor as ImageColor
import PIL.Image as Image
import numpy as np
import matplotlib.pyplot as plt  # pylint: disable=g-import-not-at-top
import abc
import collections
# Set headless-friendly backend.
import matplotlib
matplotlib.use('Agg')  # pylint: disable=multiple-statements


_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
        # Positional argument for first optional tensor (masks).
        pos_arg_ptr = 4
        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 and key != input_data_fields.image_additional_channels:
                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_to_visualize = tf.concat([images_with_detections,
                                         images_with_groundtruth], axis=2)

        if input_data_fields.image_additional_channels in eval_dict:
            images_with_additional_channels_groundtruth = (
                draw_bounding_boxes_on_image_tensors(
                    tf.expand_dims(
                        eval_dict[input_data_fields.image_additional_channels][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_to_visualize = tf.concat(
                [images_to_visualize, images_with_additional_channels_groundtruth],
                axis=2)
        images_with_detections_list.append(images_to_visualize)

    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,
        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.
    new_xml = False

    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 six.viewkeys(category_index):
                            class_name = category_index[classes[i]]['name']
                            new_xml = True
                        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)]
                else:
                    box_to_color_map[box] = STANDARD_COLORS[
                        classes[i] % len(STANDARD_COLORS)]

    array_position = []
    im_height, im_width, shape = image.shape

    # Draw all boxes onto image.
    for box, color in box_to_color_map.items():
        ymin, xmin, ymax, xmax = box
        dict_position = {'xmin': 0, 'xmax': 0, 'ymin': 0, 'ymax': 0}

        dict_position['ymin'] = ymin * im_height
        dict_position['xmin'] = xmin * im_width
        dict_position['ymax'] = ymax * im_height
        dict_position['xmax'] = xmax * im_width

        array_position.append(dict_position)

        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)
    
    if new_xml != False:
      xml = generate_xml.GenerateXml(array_position, im_width, im_height, class_name)
      xml.gerenate_basic_structure()

    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(six.with_metaclass(abc.ABCMeta, 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`.
    """

    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)