def draw_side_by_side_evaluation_image(eval_dict,
category_index,
max_boxes_to_draw=20,
min_score_thresh=0.2,
use_normalized_coordinates=True):
"""Creates a side-by-side image with detections and groundtruth.
Bounding boxes (and instance masks, if available) are visualized on both
subimages.
Args:
eval_dict: The evaluation dictionary returned by
eval_util.result_dict_for_batched_example() or
eval_util.result_dict_for_single_example().
category_index: A category index (dictionary) produced from a labelmap.
max_boxes_to_draw: The maximum number of boxes to draw for detections.
min_score_thresh: The minimum score threshold for showing detections.
use_normalized_coordinates: Whether to assume boxes and kepoints are in
normalized coordinates (as opposed to absolute coordiantes).
Default is True.
Returns:
A list of [1, H, 2 * W, C] uint8 tensor. The subimage on the left
corresponds to detections, while the subimage on the right corresponds to
groundtruth.
"""
detection_fields = fields.DetectionResultFields()
input_data_fields = fields.InputDataFields()
images_with_detections_list = []
# Add the batch dimension if the eval_dict is for single example.
if len(eval_dict[detection_fields.detection_classes].shape) == 1:
for key in eval_dict:
if key != input_data_fields.original_image:
eval_dict[key] = tf.expand_dims(eval_dict[key], 0)
for indx in range(eval_dict[input_data_fields.original_image].shape[0]):
instance_masks = None
if detection_fields.detection_masks in eval_dict:
instance_masks = tf.cast(
tf.expand_dims(
eval_dict[detection_fields.detection_masks][indx], axis=0),
tf.uint8)
keypoints = None
if detection_fields.detection_keypoints in eval_dict:
keypoints = tf.expand_dims(
eval_dict[detection_fields.detection_keypoints][indx], axis=0)
groundtruth_instance_masks = None
if input_data_fields.groundtruth_instance_masks in eval_dict:
groundtruth_instance_masks = tf.cast(
tf.expand_dims(eval_dict[
input_data_fields.groundtruth_instance_masks][indx],
axis=0), tf.uint8)
images_with_detections = draw_bounding_boxes_on_image_tensors(
tf.expand_dims(eval_dict[input_data_fields.original_image][indx],
axis=0),
tf.expand_dims(eval_dict[detection_fields.detection_boxes][indx],
axis=0),
tf.expand_dims(eval_dict[detection_fields.detection_classes][indx],
axis=0),
tf.expand_dims(eval_dict[detection_fields.detection_scores][indx],
axis=0),
category_index,
original_image_spatial_shape=tf.expand_dims(eval_dict[
input_data_fields.original_image_spatial_shape][indx],
axis=0),
true_image_shape=tf.expand_dims(
eval_dict[input_data_fields.true_image_shape][indx], axis=0),
instance_masks=instance_masks,
keypoints=keypoints,
max_boxes_to_draw=max_boxes_to_draw,
min_score_thresh=min_score_thresh,
use_normalized_coordinates=use_normalized_coordinates)
images_with_groundtruth = draw_bounding_boxes_on_image_tensors(
tf.expand_dims(eval_dict[input_data_fields.original_image][indx],
axis=0),
tf.expand_dims(
eval_dict[input_data_fields.groundtruth_boxes][indx], axis=0),
tf.expand_dims(
eval_dict[input_data_fields.groundtruth_classes][indx],
axis=0),
tf.expand_dims(tf.ones_like(
eval_dict[input_data_fields.groundtruth_classes][indx],
dtype=tf.float32),
axis=0),
category_index,
original_image_spatial_shape=tf.expand_dims(eval_dict[
input_data_fields.original_image_spatial_shape][indx],
axis=0),
true_image_shape=tf.expand_dims(
eval_dict[input_data_fields.true_image_shape][indx], axis=0),
instance_masks=groundtruth_instance_masks,
keypoints=None,
max_boxes_to_draw=None,
min_score_thresh=0.0,
use_normalized_coordinates=use_normalized_coordinates)
images_with_detections_list.append(
tf.concat([images_with_detections, images_with_groundtruth],
axis=2))
return images_with_detections_list
def draw_side_by_side_evaluation_image(eval_dict,
category_index,
max_boxes_to_draw=20,
min_score_thresh=0.2):
"""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.
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)
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)
return tf.concat([images_with_detections, images_with_groundtruth], axis=2)