def preprocess_for_train(image,
labels,
bboxes,
out_shape,
data_format='NHWC',
scope='ssd_preprocess_train'):
"""
训练预处理
Args:
out_shape: 输出图片的大小
Returns:
经处理过的image
"""
fast_mode = False
with tf.name_scope(scope, 'ssd_preprocessing_train',
[image, labels, bboxes]):
if image.get_shape().ndims != 3:
raise ValueError('Input must be of size [height, width, C>0]')
if image.dtype != tf.float32:
image = tf.image.convert_image_dtype(image, dtype=tf.float32)
tf_summary_image(image, bboxes, 'image_with_bboxes')
# Distort image and bounding boxes.
dst_image = image
dst_image, labels, bboxes, distort_bbox = \
distorted_bounding_box_crop(image, labels, bboxes,
min_object_covered=MIN_OBJECT_COVERED,
aspect_ratio_range=CROP_RATIO_RANGE)
# Resize image to output size.
dst_image = tf_image.resize_image(
dst_image,
out_shape,
method=tf.image.ResizeMethod.BILINEAR,
align_corners=False)
tf_summary_image(dst_image, bboxes, 'image_shape_distorted')
# Randomly flip the image horizontally.
dst_image, bboxes = tf_image.random_flip_left_right(dst_image, bboxes)
# Randomly distort the colors. There are 4 ways to do it.
dst_image = apply_with_random_selector(
dst_image,
lambda x, ordering: distort_color(x, ordering, fast_mode),
num_cases=4)
tf_summary_image(dst_image, bboxes, 'image_color_distorted')
# Rescale to VGG input scale.
image = dst_image * 255.
image = tf_image_whitened(image, [_R_MEAN, _G_MEAN, _B_MEAN])
# Image data format.
if data_format == 'NCHW':
image = tf.transpose(image, perm=(2, 0, 1))
return image, labels, bboxes
def preprocess_for_face(image, images, labels, bboxes,
out_shape=EVAL_SIZE, 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([bboxes, bbox_img], axis=0)
label = tf.constant([[1]])
if labels is None:
labels = label
else:
labels = tf.concat([labels, label], axis=0)
# Warp resize of the image.
image = tf_image.resize_image(image, out_shape,
method=tf.image.ResizeMethod.BILINEAR,
align_corners=False)
image = tf.expand_dims(image, 0)
if images is None:
images = image
else:
images = tf.concat([images, image])
return images, labels, bboxes
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_train(image,
labels,
bboxes,
out_shape,
data_format='NHWC',
scope='ssd_preprocessing_train'):
"""Preprocesses the given image for training.
Note that the actual resizing scale is sampled from
[`resize_size_min`, `resize_size_max`].
Args:
image: A `Tensor` representing an image of arbitrary size.
output_height: The height of the image after preprocessing.
output_width: The width of the image after preprocessing.
resize_side_min: The lower bound for the smallest side of the image for
aspect-preserving resizing.
resize_side_max: The upper bound for the smallest side of the image for
aspect-preserving resizing.
Returns:
A preprocessed image.
"""
fast_mode = False
with tf.name_scope(scope, 'ssd_preprocessing_train',
[image, labels, bboxes]):
if image.get_shape().ndims != 3:
raise ValueError('Input must be of size [height, width, C>0]')
# Convert to float scaled [0, 1].
if image.dtype != tf.float32:
image = tf.image.convert_image_dtype(image, dtype=tf.float32)
tf_summary_image(image, bboxes, 'image_with_bboxes')
# # Remove DontCare labels.
# labels, bboxes = ssd_common.tf_bboxes_filter_labels(out_label,
# labels,
# bboxes)
# Distort image and bounding boxes.
dst_image = image
dst_image, labels, bboxes, distort_bbox = \
distorted_bounding_box_crop(image, labels, bboxes,
min_object_covered=MIN_OBJECT_COVERED,
aspect_ratio_range=CROP_RATIO_RANGE)
# Resize image to output size.
dst_image = tf_image.resize_image(
dst_image,
out_shape,
method=tf.image.ResizeMethod.BILINEAR,
align_corners=False)
tf_summary_image(dst_image, bboxes, 'image_shape_distorted')
# Randomly flip the image horizontally.
dst_image, bboxes = tf_image.random_flip_left_right(dst_image, bboxes)
# Randomly distort the colors. There are 4 ways to do it.
dst_image = apply_with_random_selector(
dst_image,
lambda x, ordering: distort_color(x, ordering, fast_mode),
num_cases=4)
tf_summary_image(dst_image, bboxes, 'image_color_distorted')
# Rescale to VGG input scale.
image = dst_image * 255.
image = tf_image_whitened(image, [_R_MEAN, _G_MEAN, _B_MEAN])
# Image data format.
if data_format == 'NCHW':
image = tf.transpose(image, perm=(2, 0, 1))
return image, labels, bboxes