def preprocess_for_eval(image,
labels,
bboxes,
out_shape=EVAL_SIZE,
data_format='NHWC',
difficults=None,
resize=Resize.WARP_RESIZE,
scope='ssd_preprocessing_train'):
"""Preprocess an image for evaluation.
Args:
image: A `Tensor` representing an image of arbitrary size.
out_shape: Output shape after pre-processing (if resize != None)
resize: Resize strategy.
Returns:
A preprocessed image.
"""
with tf.name_scope(scope):
if image.get_shape().ndims != 3:
raise ValueError('Input must be of size [height, width, C>0]')
image = tf.to_float(image)
image = tf_image_whitened(image, [_R_MEAN, _G_MEAN, _B_MEAN])
# Add image rectangle to bboxes.
bbox_img = tf.constant([[0., 0., 1., 1.]])
if bboxes is None:
bboxes = bbox_img
else:
bboxes = tf.concat([bbox_img, bboxes], axis=0)
if resize == Resize.NONE:
# No resizing...
pass
elif resize == Resize.CENTRAL_CROP:
# Central cropping of the image.
image, bboxes = tf_image.resize_image_bboxes_with_crop_or_pad(
image, bboxes, out_shape[0], out_shape[1])
elif resize == Resize.PAD_AND_RESIZE:
# Resize image first: find the correct factor...
shape = tf.shape(image)
factor = tf.minimum(
tf.to_double(1.0),
tf.minimum(tf.to_double(out_shape[0] / shape[0]),
tf.to_double(out_shape[1] / shape[1])))
resize_shape = factor * tf.to_double(shape[0:2])
resize_shape = tf.cast(tf.floor(resize_shape), tf.int32)
image = tf_image.resize_image(
image,
resize_shape,
method=tf.image.ResizeMethod.BILINEAR,
align_corners=False)
# Pad to expected size.
image, bboxes = tf_image.resize_image_bboxes_with_crop_or_pad(
image, bboxes, out_shape[0], out_shape[1])
elif resize == Resize.WARP_RESIZE:
# Warp resize of the image.
image = tf_image.resize_image(
image,
out_shape,
method=tf.image.ResizeMethod.BILINEAR,
align_corners=False)
# Split back bounding boxes.
bbox_img = bboxes[0]
bboxes = bboxes[1:]
# Remove difficult boxes.
if difficults is not None:
mask = tf.logical_not(tf.cast(difficults, tf.bool))
labels = tf.boolean_mask(labels, mask)
bboxes = tf.boolean_mask(bboxes, mask)
# Image data format.
if data_format == 'NCHW':
image = tf.transpose(image, perm=(2, 0, 1))
return image, labels, bboxes, bbox_img
def preprocess_for_eval(image,
labels,
bboxes,
height,
width,
out_shape=EVAL_SIZE,
data_format='NHWC',
use_whiten=True,
difficults=None,
resize=Resize.WARP_RESIZE,
scope='ssd_preprocessing_train'):
"""Preprocess an image for evaluation.
Args:
image: A `Tensor` representing an image of arbitrary size.
labels : A Tensor inlcudes all labels
bboxes : A Tensor inlcudes cordinates of bbox in shape [N, 4]
out_shape : Image_size ,default is [300, 300]
Returns:
A preprocessed image.
"""
with tf.name_scope(scope):
if image.get_shape().ndims != 3:
raise ValueError('Input must be of size [height, width, C>0]')
image = tf.to_float(image)
num = 0
if labels is not None:
num = tf.reduce_sum(tf.cast(labels, tf.int32))
# Add image rectangle to bboxes.
bbox_img = tf.constant([[0., 0., 1., 1.]])
if bboxes is None:
bboxes = bbox_img
else:
bboxes = tf.concat([bbox_img, bboxes], axis=0)
if resize == Resize.NONE:
# No resizing...
pass
elif resize == Resize.CENTRAL_CROP:
# Central cropping of the image.
image, bboxes = tf_image.resize_image_bboxes_with_crop_or_pad(
image, bboxes, out_shape[0], out_shape[1])
elif resize == Resize.PAD_AND_RESIZE:
# Resize image first: find the correct factor...
shape = tf.shape(image)
factor = tf.minimum(
tf.to_double(1.0),
tf.minimum(tf.to_double(out_shape[0] / shape[0]),
tf.to_double(out_shape[1] / shape[1])))
resize_shape = factor * tf.to_double(shape[0:2])
resize_shape = tf.cast(tf.floor(resize_shape), tf.int32)
image = tf_image.resize_image(
image,
resize_shape,
method=tf.image.ResizeMethod.BILINEAR,
align_corners=False)
# Pad to expected size.
image, bboxes = tf_image.resize_image_bboxes_with_crop_or_pad(
image, bboxes, out_shape[0], out_shape[1])
elif resize == Resize.WARP_RESIZE:
# Warp resize of the image.
image = tf_image.resize_image(
image,
out_shape,
method=tf.image.ResizeMethod.BILINEAR,
align_corners=False)
# Split back bounding boxes.
bbox_img = bboxes[0]
bboxes = bboxes[1:]
# Remove difficult boxes.
if difficults is not None:
mask = tf.logical_not(tf.cast(difficults, tf.bool))
labels = tf.boolean_mask(labels, mask)
bboxes = tf.boolean_mask(bboxes, mask)
image = tf.clip_by_value(image, 0., 255.)
image = tf_image.tf_image_whitened(image, [_R_MEAN, _G_MEAN, _B_MEAN])
#image = image/255.
#image = tf.clip_by_value(image, 0., 255.)
#image = tf.subtract(image, 128.)
#image = tf.multiply(image, 2.0)
if data_format == 'NHWC':
image = image
else:
image = tf.transpose(image, perm=(2, 0, 1))
return image, labels, bboxes, bbox_img, num