visualization.py

# Visualization

import collections
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


_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).

  Args:
    image: a numpy array with shape [height, width, 3].
    ymin: ymin of bounding box in normalized coordinates (same below).
    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.

  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'.

  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()

  text_bottom = top
  # 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 visualize_boxes_and_labels_on_image_array(image,
                                              boxes,
                                              classes,
                                              scores,
                                              category_index,
                                              use_normalized_coordinates=True,
                                              max_boxes_to_draw=None,
                                              min_score_thresh=.35,
                                              line_thickness=4):
  """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 array in-place
  and does not return anything.

  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]
    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], 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.
  """

  # Collective of boxes/label
  obj_collective = []
  isDetected = False

  # 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)

  max_boxes_to_draw = boxes.shape[0]
  for i in range(max_boxes_to_draw):
    if scores is None or scores[i] > min_score_thresh:
      box = tuple(boxes[i].tolist())
      if scores is None:
        box_to_color_map[box] = 'black'
      else:
        if classes[i] in category_index.keys():
          class_name = category_index[classes[i]]['name']
        else:
          class_name = 'N/A'
        display_str = '{}: {}%'.format(class_name,int(100*scores[i]))

        box_to_display_str_map[box].append(display_str)
        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

    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)

    # Dissect matching object into name and probability
    v1,v2 = box_to_display_str_map[box][0].split(': ')
    obj_name = v1
    obj_prob = float(v2[:-1])/100

    # [object_name, object_probability, object_bbox_ymin, object_bbox_xmin, object_bbox_ymax, object_bbox_xmax]
    obj = [obj_name,obj_prob, box[0], box[1], box[2], box[3]]
    obj_collective.append(obj)
    isDetected = True

  return obj_collective, isDetected