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|
| """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 matplotlib; matplotlib.use('Agg')
|
| 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 six
|
| import tensorflow as tf
|
|
|
| import standard_fields as fields
|
|
|
|
|
| _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()
|
|
|
|
|
|
|
|
|
| display_str_heights = [font.getsize(ds)[1] for ds in display_str_list]
|
|
|
| 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
|
|
|
| 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.
|
| """
|
|
|
| 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.
|
| """
|
|
|
|
|
| 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)]
|
|
|
|
|
| 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)
|
|
|
