def _transform_boxes_to_feature_coordinates(self, provided_boxes,
true_image_shapes,
resized_image_shape,
instance_embedding):
"""Transforms normalzied boxes to feature map coordinates.
Args:
provided_boxes: A [batch, num_instances, 4] float tensor containing
normalized bounding boxes.
true_image_shapes: int32 tensor of shape [batch, 3] where each row is of
the form [height, width, channels] indicating the shapes of true images
in the resized images, as resized images can be padded with zeros.
resized_image_shape: A 4D int32 tensor containing shapes of the
preprocessed inputs (N, H, W, C).
instance_embedding: A [batch, output_height, output_width, embedding_size]
float tensor containing instance embeddings.
Returns:
A float tensor of size [batch, num_instances, 4] containing boxes whose
coordinates have been transformed to the absolute output space of the
feature extractor.
"""
# Input boxes must be normalized.
shape_utils.assert_box_normalized(provided_boxes)
# Transform the provided boxes to the absolute output space of the feature
# extractor.
height, width = (tf.shape(instance_embedding)[1],
tf.shape(instance_embedding)[2])
resized_image_height = resized_image_shape[1]
resized_image_width = resized_image_shape[2]
def transform_boxes(elems):
boxes_per_image, true_image_shape = elems
blist = box_list.BoxList(boxes_per_image)
# First transform boxes from image space to resized image space since
# there may have paddings in the resized images.
blist = box_list_ops.scale(
blist, true_image_shape[0] / resized_image_height,
true_image_shape[1] / resized_image_width)
# Then transform boxes from resized image space (normalized) to the
# feature map space (absolute).
blist = box_list_ops.to_absolute_coordinates(blist,
height,
width,
check_range=False)
return blist.get()
return tf.map_fn(transform_boxes, [provided_boxes, true_image_shapes],
dtype=tf.float32)
def merge_boxes_with_multiple_labels(boxes,
classes,
confidences,
num_classes,
quantization_bins=10000):
"""Merges boxes with same coordinates and returns K-hot encoded classes.
Args:
boxes: A tf.float32 tensor with shape [N, 4] holding N boxes. Only
normalized coordinates are allowed.
classes: A tf.int32 tensor with shape [N] holding class indices.
The class index starts at 0.
confidences: A tf.float32 tensor with shape [N] holding class confidences.
num_classes: total number of classes to use for K-hot encoding.
quantization_bins: the number of bins used to quantize the box coordinate.
Returns:
merged_boxes: A tf.float32 tensor with shape [N', 4] holding boxes,
where N' <= N.
class_encodings: A tf.int32 tensor with shape [N', num_classes] holding
K-hot encodings for the merged boxes.
confidence_encodings: A tf.float32 tensor with shape [N', num_classes]
holding encodings of confidences for the merged boxes.
merged_box_indices: A tf.int32 tensor with shape [N'] holding original
indices of the boxes.
"""
boxes_shape = tf.shape(boxes)
classes_shape = tf.shape(classes)
confidences_shape = tf.shape(confidences)
box_class_shape_assert = shape_utils.assert_shape_equal_along_first_dimension(
boxes_shape, classes_shape)
box_confidence_shape_assert = (
shape_utils.assert_shape_equal_along_first_dimension(
boxes_shape, confidences_shape))
box_dimension_assert = tf.assert_equal(boxes_shape[1], 4)
box_normalized_assert = shape_utils.assert_box_normalized(boxes)
with tf.control_dependencies([
box_class_shape_assert, box_confidence_shape_assert,
box_dimension_assert, box_normalized_assert
]):
quantized_boxes = tf.to_int64(boxes * (quantization_bins - 1))
ymin, xmin, ymax, xmax = tf.unstack(quantized_boxes, axis=1)
hashcodes = (
ymin + xmin * quantization_bins +
ymax * quantization_bins * quantization_bins +
xmax * quantization_bins * quantization_bins * quantization_bins)
unique_hashcodes, unique_indices = tf.unique(hashcodes)
num_boxes = tf.shape(boxes)[0]
num_unique_boxes = tf.shape(unique_hashcodes)[0]
merged_box_indices = tf.unsorted_segment_min(tf.range(num_boxes),
unique_indices,
num_unique_boxes)
merged_boxes = tf.gather(boxes, merged_box_indices)
def map_box_encodings(i):
"""Produces box K-hot and score encodings for each class index."""
box_mask = tf.equal(unique_indices,
i * tf.ones(num_boxes, dtype=tf.int32))
box_mask = tf.reshape(box_mask, [-1])
box_indices = tf.boolean_mask(classes, box_mask)
box_confidences = tf.boolean_mask(confidences, box_mask)
box_class_encodings = tf.sparse_to_dense(box_indices,
[num_classes],
1,
validate_indices=False)
box_confidence_encodings = tf.sparse_to_dense(
box_indices, [num_classes],
box_confidences,
validate_indices=False)
return box_class_encodings, box_confidence_encodings
class_encodings, confidence_encodings = tf.map_fn(
map_box_encodings,
tf.range(num_unique_boxes),
back_prop=False,
dtype=(tf.int32, tf.float32))
merged_boxes = tf.reshape(merged_boxes, [-1, 4])
class_encodings = tf.reshape(class_encodings, [-1, num_classes])
confidence_encodings = tf.reshape(confidence_encodings,
[-1, num_classes])
merged_box_indices = tf.reshape(merged_box_indices, [-1])
return (merged_boxes, class_encodings, confidence_encodings,
merged_box_indices)
K-hot encodings for the merged boxes.
confidence_encodings: A tf.float32 tensor with shape [N', num_classes]
holding encodings of confidences for the merged boxes.
merged_box_indices: A tf.int32 tensor with shape [N'] holding original
indices of the boxes.
"""
boxes_shape = tf.shape(boxes)
classes_shape = tf.shape(classes)
confidences_shape = tf.shape(confidences)
box_class_shape_assert = shape_utils.assert_shape_equal_along_first_dimension(
boxes_shape, classes_shape)
box_confidence_shape_assert = (
shape_utils.assert_shape_equal_along_first_dimension(
boxes_shape, confidences_shape))
box_dimension_assert = tf.assert_equal(boxes_shape[1], 4)
box_normalized_assert = shape_utils.assert_box_normalized(boxes)
with tf.control_dependencies(
[box_class_shape_assert, box_confidence_shape_assert,
box_dimension_assert, box_normalized_assert]):
quantized_boxes = tf.to_int64(boxes * (quantization_bins - 1))
ymin, xmin, ymax, xmax = tf.unstack(quantized_boxes, axis=1)
hashcodes = (
ymin +
xmin * quantization_bins +
ymax * quantization_bins * quantization_bins +
xmax * quantization_bins * quantization_bins * quantization_bins)
unique_hashcodes, unique_indices = tf.unique(hashcodes)
num_boxes = tf.shape(boxes)[0]
num_unique_boxes = tf.shape(unique_hashcodes)[0]
merged_box_indices = tf.unsorted_segment_min(
def merge_boxes_with_multiple_labels(boxes,
classes,
confidences,
num_classes,
quantization_bins=10000):
"""Merges boxes with same coordinates and returns K-hot encoded classes.
Args:
boxes: A tf.float32 tensor with shape [N, 4] holding N boxes. Only
normalized coordinates are allowed.
classes: A tf.int32 tensor with shape [N] holding class indices.
The class index starts at 0.
confidences: A tf.float32 tensor with shape [N] holding class confidences.
num_classes: total number of classes to use for K-hot encoding.
quantization_bins: the number of bins used to quantize the box coordinate.
Returns:
merged_boxes: A tf.float32 tensor with shape [N', 4] holding boxes,
where N' <= N.
class_encodings: A tf.int32 tensor with shape [N', num_classes] holding
K-hot encodings for the merged boxes.
confidence_encodings: A tf.float32 tensor with shape [N', num_classes]
holding encodings of confidences for the merged boxes.
merged_box_indices: A tf.int32 tensor with shape [N'] holding original
indices of the boxes.
"""
boxes_shape = tf.shape(boxes)
classes_shape = tf.shape(classes)
confidences_shape = tf.shape(confidences)
box_class_shape_assert = shape_utils.assert_shape_equal_along_first_dimension(
boxes_shape, classes_shape)
box_confidence_shape_assert = (
shape_utils.assert_shape_equal_along_first_dimension(
boxes_shape, confidences_shape))
box_dimension_assert = tf.assert_equal(boxes_shape[1], 4)
box_normalized_assert = shape_utils.assert_box_normalized(boxes)
with tf.control_dependencies(
[box_class_shape_assert, box_confidence_shape_assert,
box_dimension_assert, box_normalized_assert]):
quantized_boxes = tf.to_int64(boxes * (quantization_bins - 1))
ymin, xmin, ymax, xmax = tf.unstack(quantized_boxes, axis=1)
hashcodes = (
ymin +
xmin * quantization_bins +
ymax * quantization_bins * quantization_bins +
xmax * quantization_bins * quantization_bins * quantization_bins)
unique_hashcodes, unique_indices = tf.unique(hashcodes)
num_boxes = tf.shape(boxes)[0]
num_unique_boxes = tf.shape(unique_hashcodes)[0]
merged_box_indices = tf.unsorted_segment_min(
tf.range(num_boxes), unique_indices, num_unique_boxes)
merged_boxes = tf.gather(boxes, merged_box_indices)
def map_box_encodings(i):
"""Produces box K-hot and score encodings for each class index."""
box_mask = tf.equal(
unique_indices, i * tf.ones(num_boxes, dtype=tf.int32))
box_mask = tf.reshape(box_mask, [-1])
box_indices = tf.boolean_mask(classes, box_mask)
box_confidences = tf.boolean_mask(confidences, box_mask)
box_class_encodings = tf.sparse_to_dense(
box_indices, [num_classes], 1, validate_indices=False)
box_confidence_encodings = tf.sparse_to_dense(
box_indices, [num_classes], box_confidences, validate_indices=False)
return box_class_encodings, box_confidence_encodings
class_encodings, confidence_encodings = tf.map_fn(
map_box_encodings,
tf.range(num_unique_boxes),
back_prop=False,
dtype=(tf.int32, tf.float32))
merged_boxes = tf.reshape(merged_boxes, [-1, 4])
class_encodings = tf.reshape(class_encodings, [-1, num_classes])
confidence_encodings = tf.reshape(confidence_encodings, [-1, num_classes])
merged_box_indices = tf.reshape(merged_box_indices, [-1])
return (merged_boxes, class_encodings, confidence_encodings,
merged_box_indices)
