def _mosaic_crop_image(self, image, boxes, classes, is_crowd, area):
"""Process a patched image in preperation for final output."""
if self._mosaic_crop_mode != 'crop':
shape = tf.cast(preprocessing_ops.get_image_shape(image),
tf.float32)
center = shape * self._mosaic_center
# shift the center of the image by applying a translation to the whole
# image
ch = tf.math.round(
preprocessing_ops.random_uniform_strong(-center[0],
center[0],
seed=self._seed))
cw = tf.math.round(
preprocessing_ops.random_uniform_strong(-center[1],
center[1],
seed=self._seed))
# clip the boxes to those with in the image
image = tfa.image.translate(image, [cw, ch],
fill_value=self._pad_value)
boxes = box_ops.denormalize_boxes(boxes, shape[:2])
boxes = boxes + tf.cast([ch, cw, ch, cw], boxes.dtype)
boxes = box_ops.clip_boxes(boxes, shape[:2])
inds = box_ops.get_non_empty_box_indices(boxes)
boxes = box_ops.normalize_boxes(boxes, shape[:2])
boxes, classes, is_crowd, area = self._select_ind(
inds,
boxes,
classes, # pylint:disable=unbalanced-tuple-unpacking
is_crowd,
area)
# warp and scale the fully stitched sample
image, _, affine = preprocessing_ops.affine_warp_image(
image, [self._output_size[0], self._output_size[1]],
scale_min=self._aug_scale_min,
scale_max=self._aug_scale_max,
translate=self._aug_rand_translate,
degrees=self._aug_rand_angle,
perspective=self._aug_rand_perspective,
random_pad=self._random_pad,
seed=self._seed)
height, width = self._output_size[0], self._output_size[1]
image = tf.image.resize(image, (height, width))
# clip and clean boxes
boxes, inds = preprocessing_ops.transform_and_clip_boxes(
boxes,
None,
affine=affine,
area_thresh=self._area_thresh,
seed=self._seed)
classes, is_crowd, area = self._select_ind(inds, classes, is_crowd,
area) # pylint:disable=unbalanced-tuple-unpacking
return image, boxes, classes, is_crowd, area, area
def _parse_eval_data(self, data):
"""Parses data for training and evaluation."""
classes = data['groundtruth_classes']
boxes = data['groundtruth_boxes']
is_crowd = data['groundtruth_is_crowd']
# Gets original image and its size.
image = data['image']
# Normalizes image with mean and std pixel values.
image = preprocess_ops.normalize_image(image)
scales = tf.constant([self._resize_scales[-1]], tf.float32)
image_shape = tf.shape(image)[:2]
boxes = box_ops.denormalize_boxes(boxes, image_shape)
gt_boxes = boxes
short_side = scales[0]
image, image_info = preprocess_ops.resize_image(
image, short_side, max(self._output_size))
boxes = preprocess_ops.resize_and_crop_boxes(boxes, image_info[2, :],
image_info[1, :],
image_info[3, :])
boxes = box_ops.normalize_boxes(boxes, image_info[1, :])
# Filters out ground truth boxes that are all zeros.
indices = box_ops.get_non_empty_box_indices(boxes)
boxes = tf.gather(boxes, indices)
classes = tf.gather(classes, indices)
is_crowd = tf.gather(is_crowd, indices)
boxes = box_ops.yxyx_to_cycxhw(boxes)
image = tf.image.pad_to_bounding_box(image, 0, 0, self._output_size[0],
self._output_size[1])
labels = {
'classes':
preprocess_ops.clip_or_pad_to_fixed_size(classes,
self._max_num_boxes),
'boxes':
preprocess_ops.clip_or_pad_to_fixed_size(boxes,
self._max_num_boxes)
}
labels.update({
'id':
int(data['source_id']),
'image_info':
image_info,
'is_crowd':
preprocess_ops.clip_or_pad_to_fixed_size(is_crowd,
self._max_num_boxes),
'gt_boxes':
preprocess_ops.clip_or_pad_to_fixed_size(gt_boxes,
self._max_num_boxes),
})
return image, labels
def scale_boxes(self, patch, ishape, boxes, classes, xs, ys):
"""Scale and translate the boxes for each image prior to patching."""
xs = tf.cast(xs, boxes.dtype)
ys = tf.cast(ys, boxes.dtype)
pshape = tf.cast(tf.shape(patch), boxes.dtype)
ishape = tf.cast(ishape, boxes.dtype)
translate = tf.cast((ishape - pshape), boxes.dtype)
boxes = box_ops.denormalize_boxes(boxes, pshape[:2])
boxes = boxes + tf.cast([
translate[0] * ys, translate[1] * xs, translate[0] * ys,
translate[1] * xs
], boxes.dtype)
boxes = box_ops.normalize_boxes(boxes, ishape[:2])
return boxes, classes
def visualize_images_with_bounding_boxes(images, box_outputs, step,
summary_writer):
"""Records subset of evaluation images with bounding boxes."""
if not isinstance(images, list):
logging.warning(
'visualize_images_with_bounding_boxes expects list of '
'images but received type: %s and value: %s', type(images), images)
return
image_shape = tf.shape(images[0])
image_height = tf.cast(image_shape[0], tf.float32)
image_width = tf.cast(image_shape[1], tf.float32)
normalized_boxes = box_ops.normalize_boxes(box_outputs,
[image_height, image_width])
bounding_box_color = tf.constant([[1.0, 1.0, 0.0, 1.0]])
image_summary = tf.image.draw_bounding_boxes(
tf.cast(images, tf.float32), normalized_boxes, bounding_box_color)
with summary_writer.as_default():
tf.summary.image('bounding_box_summary', image_summary, step=step)
summary_writer.flush()
def undo_info(boxes: tf.Tensor,
num_detections: int,
info: tf.Tensor,
expand: bool = True) -> tf.Tensor:
"""Clip and normalize boxes for serving."""
mask = tf.sequence_mask(num_detections, maxlen=tf.shape(boxes)[1])
boxes = tf.cast(tf.expand_dims(mask, axis=-1), boxes.dtype) * boxes
if expand:
info = tf.cast(tf.expand_dims(info, axis=0), boxes.dtype)
inshape = tf.expand_dims(info[:, 1, :], axis=1)
ogshape = tf.expand_dims(info[:, 0, :], axis=1)
scale = tf.expand_dims(info[:, 2, :], axis=1)
offset = tf.expand_dims(info[:, 3, :], axis=1)
boxes = box_ops.denormalize_boxes(boxes, inshape)
boxes += tf.tile(offset, [1, 1, 2])
boxes /= tf.tile(scale, [1, 1, 2])
boxes = box_ops.clip_boxes(boxes, ogshape)
boxes = box_ops.normalize_boxes(boxes, ogshape)
return boxes
def serve(self, images: tf.Tensor):
"""Cast image to float and run inference.
Args:
images: uint8 Tensor of shape [batch_size, None, None, 3]
Returns:
Tensor holding detection output logits.
"""
# Skip image preprocessing when input_type is tflite so it is compatible
# with TFLite quantization.
if self._input_type != 'tflite':
images, anchor_boxes, image_info = self.preprocess(images)
else:
with tf.device('cpu:0'):
anchor_boxes = self._build_anchor_boxes()
# image_info is a 3D tensor of shape [batch_size, 4, 2]. It is in the
# format of [[original_height, original_width],
# [desired_height, desired_width], [y_scale, x_scale],
# [y_offset, x_offset]]. When input_type is tflite, input image is
# supposed to be preprocessed already.
image_info = tf.convert_to_tensor([[
self._input_image_size, self._input_image_size, [1.0, 1.0],
[0, 0]
]],
dtype=tf.float32)
input_image_shape = image_info[:, 1, :]
# To overcome keras.Model extra limitation to save a model with layers that
# have multiple inputs, we use `model.call` here to trigger the forward
# path. Note that, this disables some keras magics happens in `__call__`.
detections = self.model.call(images=images,
image_shape=input_image_shape,
anchor_boxes=anchor_boxes,
training=False)
if self.params.task.model.detection_generator.apply_nms:
# For RetinaNet model, apply export_config.
# TODO(huizhongc): Add export_config to fasterrcnn and maskrcnn as needed.
if isinstance(self.params.task.model, configs.retinanet.RetinaNet):
export_config = self.params.task.export_config
# Normalize detection box coordinates to [0, 1].
if export_config.output_normalized_coordinates:
detection_boxes = (
detections['detection_boxes'] /
tf.tile(image_info[:, 2:3, :], [1, 1, 2]))
detections['detection_boxes'] = box_ops.normalize_boxes(
detection_boxes, image_info[:, 0:1, :])
# Cast num_detections and detection_classes to float. This allows the
# model inference to work on chain (go/chain) as chain requires floating
# point outputs.
if export_config.cast_num_detections_to_float:
detections['num_detections'] = tf.cast(
detections['num_detections'], dtype=tf.float32)
if export_config.cast_detection_classes_to_float:
detections['detection_classes'] = tf.cast(
detections['detection_classes'], dtype=tf.float32)
final_outputs = {
'detection_boxes': detections['detection_boxes'],
'detection_scores': detections['detection_scores'],
'detection_classes': detections['detection_classes'],
'num_detections': detections['num_detections']
}
else:
final_outputs = {
'decoded_boxes': detections['decoded_boxes'],
'decoded_box_scores': detections['decoded_box_scores']
}
if 'detection_masks' in detections.keys():
final_outputs['detection_masks'] = detections['detection_masks']
final_outputs.update({'image_info': image_info})
return final_outputs
def _parse_train_data(self, data):
"""Parses data for training and evaluation."""
classes = data['groundtruth_classes'] + self._class_offset
boxes = data['groundtruth_boxes']
is_crowd = data['groundtruth_is_crowd']
# Gets original image.
image = data['image']
# Normalizes image with mean and std pixel values.
image = preprocess_ops.normalize_image(image)
image, boxes, _ = preprocess_ops.random_horizontal_flip(image, boxes)
do_crop = tf.greater(tf.random.uniform([]), 0.5)
if do_crop:
# Rescale
boxes = box_ops.denormalize_boxes(boxes, tf.shape(image)[:2])
index = tf.random.categorical(tf.zeros([1, 3]), 1)[0]
scales = tf.gather([400.0, 500.0, 600.0], index, axis=0)
short_side = scales[0]
image, image_info = preprocess_ops.resize_image(image, short_side)
boxes = preprocess_ops.resize_and_crop_boxes(
boxes, image_info[2, :], image_info[1, :], image_info[3, :])
boxes = box_ops.normalize_boxes(boxes, image_info[1, :])
# Do croping
shape = tf.cast(image_info[1], dtype=tf.int32)
h = tf.random.uniform([],
384,
tf.math.minimum(shape[0], 600),
dtype=tf.int32)
w = tf.random.uniform([],
384,
tf.math.minimum(shape[1], 600),
dtype=tf.int32)
i = tf.random.uniform([], 0, shape[0] - h + 1, dtype=tf.int32)
j = tf.random.uniform([], 0, shape[1] - w + 1, dtype=tf.int32)
image = tf.image.crop_to_bounding_box(image, i, j, h, w)
boxes = tf.clip_by_value(
(boxes[..., :] *
tf.cast(tf.stack([shape[0], shape[1], shape[0], shape[1]]),
dtype=tf.float32) -
tf.cast(tf.stack([i, j, i, j]), dtype=tf.float32)) /
tf.cast(tf.stack([h, w, h, w]), dtype=tf.float32), 0.0, 1.0)
scales = tf.constant(self._resize_scales, dtype=tf.float32)
index = tf.random.categorical(tf.zeros([1, 11]), 1)[0]
scales = tf.gather(scales, index, axis=0)
image_shape = tf.shape(image)[:2]
boxes = box_ops.denormalize_boxes(boxes, image_shape)
short_side = scales[0]
image, image_info = preprocess_ops.resize_image(
image, short_side, max(self._output_size))
boxes = preprocess_ops.resize_and_crop_boxes(boxes, image_info[2, :],
image_info[1, :],
image_info[3, :])
boxes = box_ops.normalize_boxes(boxes, image_info[1, :])
# Filters out ground truth boxes that are all zeros.
indices = box_ops.get_non_empty_box_indices(boxes)
boxes = tf.gather(boxes, indices)
classes = tf.gather(classes, indices)
is_crowd = tf.gather(is_crowd, indices)
boxes = box_ops.yxyx_to_cycxhw(boxes)
image = tf.image.pad_to_bounding_box(image, 0, 0, self._output_size[0],
self._output_size[1])
labels = {
'classes':
preprocess_ops.clip_or_pad_to_fixed_size(classes,
self._max_num_boxes),
'boxes':
preprocess_ops.clip_or_pad_to_fixed_size(boxes,
self._max_num_boxes)
}
return image, labels
def preprocess(self, inputs):
"""Preprocess COCO for DETR."""
image = inputs['image']
boxes = inputs['objects']['bbox']
classes = inputs['objects']['label'] + 1
is_crowd = inputs['objects']['is_crowd']
image = preprocess_ops.normalize_image(image)
if self._params.is_training:
image, boxes, _ = preprocess_ops.random_horizontal_flip(image, boxes)
do_crop = tf.greater(tf.random.uniform([]), 0.5)
if do_crop:
# Rescale
boxes = box_ops.denormalize_boxes(boxes, tf.shape(image)[:2])
index = tf.random.categorical(tf.zeros([1, 3]), 1)[0]
scales = tf.gather([400.0, 500.0, 600.0], index, axis=0)
short_side = scales[0]
image, image_info = preprocess_ops.resize_image(image, short_side)
boxes = preprocess_ops.resize_and_crop_boxes(boxes,
image_info[2, :],
image_info[1, :],
image_info[3, :])
boxes = box_ops.normalize_boxes(boxes, image_info[1, :])
# Do croping
shape = tf.cast(image_info[1], dtype=tf.int32)
h = tf.random.uniform(
[], 384, tf.math.minimum(shape[0], 600), dtype=tf.int32)
w = tf.random.uniform(
[], 384, tf.math.minimum(shape[1], 600), dtype=tf.int32)
i = tf.random.uniform([], 0, shape[0] - h + 1, dtype=tf.int32)
j = tf.random.uniform([], 0, shape[1] - w + 1, dtype=tf.int32)
image = tf.image.crop_to_bounding_box(image, i, j, h, w)
boxes = tf.clip_by_value(
(boxes[..., :] * tf.cast(
tf.stack([shape[0], shape[1], shape[0], shape[1]]),
dtype=tf.float32) -
tf.cast(tf.stack([i, j, i, j]), dtype=tf.float32)) /
tf.cast(tf.stack([h, w, h, w]), dtype=tf.float32), 0.0, 1.0)
scales = tf.constant(
self._params.resize_scales,
dtype=tf.float32)
index = tf.random.categorical(tf.zeros([1, 11]), 1)[0]
scales = tf.gather(scales, index, axis=0)
else:
scales = tf.constant([self._params.resize_scales[-1]], tf.float32)
image_shape = tf.shape(image)[:2]
boxes = box_ops.denormalize_boxes(boxes, image_shape)
gt_boxes = boxes
short_side = scales[0]
image, image_info = preprocess_ops.resize_image(
image,
short_side,
max(self._params.output_size))
boxes = preprocess_ops.resize_and_crop_boxes(boxes,
image_info[2, :],
image_info[1, :],
image_info[3, :])
boxes = box_ops.normalize_boxes(boxes, image_info[1, :])
# Filters out ground truth boxes that are all zeros.
indices = box_ops.get_non_empty_box_indices(boxes)
boxes = tf.gather(boxes, indices)
classes = tf.gather(classes, indices)
is_crowd = tf.gather(is_crowd, indices)
boxes = box_ops.yxyx_to_cycxhw(boxes)
image = tf.image.pad_to_bounding_box(
image, 0, 0, self._params.output_size[0], self._params.output_size[1])
labels = {
'classes':
preprocess_ops.clip_or_pad_to_fixed_size(
classes, self._params.max_num_boxes),
'boxes':
preprocess_ops.clip_or_pad_to_fixed_size(
boxes, self._params.max_num_boxes)
}
if not self._params.is_training:
labels.update({
'id':
inputs['image/id'],
'image_info':
image_info,
'is_crowd':
preprocess_ops.clip_or_pad_to_fixed_size(
is_crowd, self._params.max_num_boxes),
'gt_boxes':
preprocess_ops.clip_or_pad_to_fixed_size(
gt_boxes, self._params.max_num_boxes),
})
return image, labels
def _parse_train_data(self, data):
"""Parses data for training.
Args:
data: the decoded tensor dictionary from TfExampleDecoder.
Returns:
image: image tensor that is preproessed to have normalized value and
dimension [output_size[0], output_size[1], 3]
labels: a dictionary of tensors used for training. The following describes
{key: value} pairs in the dictionary.
image_info: a 2D `Tensor` that encodes the information of the image and
the applied preprocessing. It is in the format of
[[original_height, original_width], [scaled_height, scaled_width],
anchor_boxes: ordered dictionary with keys
[min_level, min_level+1, ..., max_level]. The values are tensor with
shape [height_l, width_l, 4] representing anchor boxes at each level.
rpn_score_targets: ordered dictionary with keys
[min_level, min_level+1, ..., max_level]. The values are tensor with
shape [height_l, width_l, anchors_per_location]. The height_l and
width_l represent the dimension of class logits at l-th level.
rpn_box_targets: ordered dictionary with keys
[min_level, min_level+1, ..., max_level]. The values are tensor with
shape [height_l, width_l, anchors_per_location * 4]. The height_l and
width_l represent the dimension of bounding box regression output at
l-th level.
gt_boxes: Groundtruth bounding box annotations. The box is represented
in [y1, x1, y2, x2] format. The coordinates are w.r.t the scaled
image that is fed to the network. The tennsor is padded with -1 to
the fixed dimension [self._max_num_instances, 4].
gt_classes: Groundtruth classes annotations. The tennsor is padded
with -1 to the fixed dimension [self._max_num_instances].
gt_masks: groundtrugh masks cropped by the bounding box and
resized to a fixed size determined by mask_crop_size.
"""
classes = data['groundtruth_classes']
boxes = data['groundtruth_boxes']
if self._include_mask:
masks = data['groundtruth_instance_masks']
is_crowds = data['groundtruth_is_crowd']
# Skips annotations with `is_crowd` = True.
if self._skip_crowd_during_training:
num_groundtruths = tf.shape(classes)[0]
with tf.control_dependencies([num_groundtruths, is_crowds]):
indices = tf.cond(
tf.greater(tf.size(is_crowds), 0),
lambda: tf.where(tf.logical_not(is_crowds))[:, 0],
lambda: tf.cast(tf.range(num_groundtruths), tf.int64))
classes = tf.gather(classes, indices)
boxes = tf.gather(boxes, indices)
if self._include_mask:
masks = tf.gather(masks, indices)
# Gets original image and its size.
image = data['image']
if self._augmenter is not None:
image = self._augmenter.distort(image)
image_shape = tf.shape(image)[0:2]
# Normalizes image with mean and std pixel values.
image = preprocess_ops.normalize_image(image)
# Flips image randomly during training.
if self._aug_rand_hflip:
if self._include_mask:
image, boxes, masks = preprocess_ops.random_horizontal_flip(
image, boxes, masks)
else:
image, boxes, _ = preprocess_ops.random_horizontal_flip(
image, boxes)
# Converts boxes from normalized coordinates to pixel coordinates.
# Now the coordinates of boxes are w.r.t. the original image.
boxes = box_ops.denormalize_boxes(boxes, image_shape)
# Resizes and crops image.
image, image_info = preprocess_ops.resize_and_crop_image(
image,
self._output_size,
padded_size=preprocess_ops.compute_padded_size(
self._output_size, 2 ** self._max_level),
aug_scale_min=self._aug_scale_min,
aug_scale_max=self._aug_scale_max)
image_height, image_width, _ = image.get_shape().as_list()
# Resizes and crops boxes.
# Now the coordinates of boxes are w.r.t the scaled image.
image_scale = image_info[2, :]
offset = image_info[3, :]
boxes = preprocess_ops.resize_and_crop_boxes(
boxes, image_scale, image_info[1, :], offset)
# Filters out ground truth boxes that are all zeros.
indices = box_ops.get_non_empty_box_indices(boxes)
boxes = tf.gather(boxes, indices)
classes = tf.gather(classes, indices)
if self._include_mask:
masks = tf.gather(masks, indices)
# Transfer boxes to the original image space and do normalization.
cropped_boxes = boxes + tf.tile(tf.expand_dims(offset, axis=0), [1, 2])
cropped_boxes /= tf.tile(tf.expand_dims(image_scale, axis=0), [1, 2])
cropped_boxes = box_ops.normalize_boxes(cropped_boxes, image_shape)
num_masks = tf.shape(masks)[0]
masks = tf.image.crop_and_resize(
tf.expand_dims(masks, axis=-1),
cropped_boxes,
box_indices=tf.range(num_masks, dtype=tf.int32),
crop_size=[self._mask_crop_size, self._mask_crop_size],
method='bilinear')
masks = tf.squeeze(masks, axis=-1)
# Assigns anchor targets.
# Note that after the target assignment, box targets are absolute pixel
# offsets w.r.t. the scaled image.
input_anchor = anchor.build_anchor_generator(
min_level=self._min_level,
max_level=self._max_level,
num_scales=self._num_scales,
aspect_ratios=self._aspect_ratios,
anchor_size=self._anchor_size)
anchor_boxes = input_anchor(image_size=(image_height, image_width))
anchor_labeler = anchor.RpnAnchorLabeler(
self._rpn_match_threshold,
self._rpn_unmatched_threshold,
self._rpn_batch_size_per_im,
self._rpn_fg_fraction)
rpn_score_targets, rpn_box_targets = anchor_labeler.label_anchors(
anchor_boxes, boxes,
tf.cast(tf.expand_dims(classes, axis=-1), dtype=tf.float32))
# Casts input image to self._dtype
image = tf.cast(image, dtype=self._dtype)
# Packs labels for model_fn outputs.
labels = {
'anchor_boxes':
anchor_boxes,
'image_info':
image_info,
'rpn_score_targets':
rpn_score_targets,
'rpn_box_targets':
rpn_box_targets,
'gt_boxes':
preprocess_ops.clip_or_pad_to_fixed_size(boxes,
self._max_num_instances,
-1),
'gt_classes':
preprocess_ops.clip_or_pad_to_fixed_size(classes,
self._max_num_instances,
-1),
}
if self._include_mask:
labels['gt_masks'] = preprocess_ops.clip_or_pad_to_fixed_size(
masks, self._max_num_instances, -1)
return image, labels
def transform_and_clip_boxes(boxes,
infos,
affine=None,
shuffle_boxes=False,
area_thresh=0.1,
seed=None,
filter_and_clip_boxes=True):
"""Clips and cleans the boxes.
Args:
boxes: A `Tensor` for the boxes.
infos: A `list` that contains the image infos.
affine: A `list` that contains parameters for resize and crop.
shuffle_boxes: A `bool` for shuffling the boxes.
area_thresh: An `int` for the area threshold.
seed: seed for random number generation.
filter_and_clip_boxes: A `bool` for filtering and clipping the boxes to
[0, 1].
Returns:
boxes: A `Tensor` representing the augmented boxes.
ind: A `Tensor` valid box indices.
"""
# Clip and clean boxes.
def get_valid_boxes(boxes):
"""Get indices for non-empty boxes."""
# Convert the boxes to center width height formatting.
height = boxes[:, 2] - boxes[:, 0]
width = boxes[:, 3] - boxes[:, 1]
base = tf.logical_and(tf.greater(height, 0), tf.greater(width, 0))
return base
# Initialize history to track operation applied to boxes
box_history = boxes
# Make sure all boxes are valid to start, clip to [0, 1] and get only the
# valid boxes.
output_size = None
if filter_and_clip_boxes:
boxes = tf.math.maximum(tf.math.minimum(boxes, 1.0), 0.0)
cond = get_valid_boxes(boxes)
if infos is None:
infos = []
for info in infos:
# Denormalize the boxes.
boxes = bbox_ops.denormalize_boxes(boxes, info[0])
box_history = bbox_ops.denormalize_boxes(box_history, info[0])
# Shift and scale all boxes, and keep track of box history with no
# box clipping, history is used for removing boxes that have become
# too small or exit the image area.
(boxes, box_history) = resize_and_crop_boxes(
boxes, info[2, :], info[1, :], info[3, :], box_history=box_history)
# Get all the boxes that still remain in the image and store
# in a bit vector for later use.
cond = tf.logical_and(get_valid_boxes(boxes), cond)
# Normalize the boxes to [0, 1].
output_size = info[1]
boxes = bbox_ops.normalize_boxes(boxes, output_size)
box_history = bbox_ops.normalize_boxes(box_history, output_size)
if affine is not None:
# Denormalize the boxes.
boxes = bbox_ops.denormalize_boxes(boxes, affine[0])
box_history = bbox_ops.denormalize_boxes(box_history, affine[0])
# Clipped final boxes.
(boxes, box_history) = affine_warp_boxes(
affine[2], boxes, affine[1], box_history=box_history)
# Get all the boxes that still remain in the image and store
# in a bit vector for later use.
cond = tf.logical_and(get_valid_boxes(boxes), cond)
# Normalize the boxes to [0, 1].
output_size = affine[1]
boxes = bbox_ops.normalize_boxes(boxes, output_size)
box_history = bbox_ops.normalize_boxes(box_history, output_size)
# Remove the bad boxes.
boxes *= tf.cast(tf.expand_dims(cond, axis=-1), boxes.dtype)
# Threshold the existing boxes.
if filter_and_clip_boxes:
if output_size is not None:
boxes_ = bbox_ops.denormalize_boxes(boxes, output_size)
box_history_ = bbox_ops.denormalize_boxes(box_history, output_size)
inds = boxes_candidates(boxes_, box_history_, area_thr=area_thresh)
else:
inds = boxes_candidates(
boxes, box_history, wh_thr=0.0, area_thr=area_thresh)
# Select and gather the good boxes.
if shuffle_boxes:
inds = tf.random.shuffle(inds, seed=seed)
else:
inds = bbox_ops.get_non_empty_box_indices(boxes)
boxes = tf.gather(boxes, inds)
return boxes, inds
