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 test_resize_image(self, input_shape, size, max_size, expected_shape):
resized_img, image_info = preprocess_ops.resize_image(
tf.zeros((input_shape)), size, max_size)
self.assertAllEqual(tf.shape(resized_img), expected_shape)
self.assertAllEqual(image_info[0], input_shape[:-1])
self.assertAllEqual(image_info[1], expected_shape[:-1])
self.assertAllEqual(
image_info[2],
np.array(expected_shape[:-1]) / np.array(input_shape[:-1]))
self.assertAllEqual(image_info[3], [0, 0])
def _build_inputs(self, image):
"""Builds classification model inputs for serving."""
# Normalizes image with mean and std pixel values.
image = preprocess_ops.normalize_image(image,
offset=MEAN_RGB,
scale=STDDEV_RGB)
if self.params.task.train_data.preserve_aspect_ratio:
image, image_info = preprocess_ops.resize_and_crop_image(
image,
self._input_image_size,
padded_size=self._input_image_size,
aug_scale_min=1.0,
aug_scale_max=1.0)
else:
image, image_info = preprocess_ops.resize_image(
image, self._input_image_size)
return image, image_info
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