def rotate():
return tf_image.random_rotate90(image, bboxes, xs, ys)
def rotate():
return tf_image.random_rotate90(image, bboxes, xs, ys)
def preprocess_for_train(image,
labels,
bboxes,
xs,
ys,
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]')
# Randomly flip the image horizontally.
if FLIP:
image, bboxes, xs, ys = tf_image.random_flip_left_right_bbox(
image, bboxes, xs, ys)
if ROTATE:
# random rotate the image [-10, 10]
image, bboxes, xs, ys = tf_rotate_image(image, xs, ys)
# samples = tf.multinomial(tf.log([[0.25, 0.25, 0.25, 0.25]]), 1) # note log-prob
# scale=elems[tf.cast(samples[0][0], tf.int32)]
# if SCALE:
# image,bboxes,xs,ys=tf_scale_image(image,bboxes,xs,ys,640)
image_shape = tf.cast(tf.shape(image), dtype=tf.float32)
image_h, image_w = image_shape[0], image_shape[1]
if USE_NM_CROP:
mask = tf.greater_equal(labels, 1)
valid_bboxes = tf.boolean_mask(bboxes, mask)
# FIXME bboxes may is empty
# NOTE tf_func must return value must be numpy, or you will madding!!!!!!
crop_bbox = tf.py_func(generate_sample,
[image_shape, valid_bboxes], tf.float32)
else:
scales = tf.random_shuffle([0.5, 1.])
scales = tf.Print(scales, [crop])
target_h = tf.cast(640 / scales[0], dtype=tf.float32)
target_w = tf.cast(640 / scales[0], dtype=tf.float32)
bbox_begin_h_max = tf.maximum(image_h - target_h, 0)
bbox_begin_w_max = tf.maximum(image_w - target_w, 0)
bbox_begin_h = tf.random_uniform([],
minval=0,
maxval=bbox_begin_h_max,
dtype=tf.float32)
bbox_begin_w = tf.random_uniform([],
minval=0,
maxval=bbox_begin_w_max,
dtype=tf.float32)
crop_bbox = [bbox_begin_h/image_h, bbox_begin_w/image_w, \
(bbox_begin_h+target_h)/image_h, (bbox_begin_w+target_w)/image_w]
image = tf.image.crop_and_resize(tf.expand_dims(image, 0), [crop_bbox],
[0], (640, 640),
extrapolation_value=128)
image = tf.squeeze(image, 0)
bboxes, xs, ys = tfe.bboxes_resize(crop_bbox, bboxes, xs, ys)
labels, bboxes, xs, ys = tfe.bboxes_filter_overlap(
labels,
bboxes,
xs,
ys,
threshold=BBOX_CROP_OVERLAP,
assign_value=LABEL_IGNORE)
if ROTATE_90:
rnd = tf.random_uniform((), minval=0, maxval=1)
image, bboxes, xs, ys = tf.cond(
tf.less(rnd, 0.2),
lambda: tf_image.random_rotate90(image, bboxes, xs, ys),
lambda: (image, bboxes, xs, ys))
# tf_summary_image(tf.to_float(image), bboxes, 'crop_image')
# what is the enpand's meanoing?
# expand image
if MAX_EXPAND_SCALE > 1:
rnd2 = tf.random_uniform((), minval=0, maxval=1)
def expand():
scale = tf.random_uniform([],
minval=1.0,
maxval=MAX_EXPAND_SCALE,
dtype=tf.float32)
image_shape = tf.cast(tf.shape(image), dtype=tf.float32)
image_h, image_w = image_shape[0], image_shape[1]
target_h = tf.cast(image_h * scale, dtype=tf.int32)
target_w = tf.cast(image_w * scale, dtype=tf.int32)
tf.logging.info('expanded')
return tf_image.resize_image_bboxes_with_crop_or_pad(
image, bboxes, xs, ys, target_h, target_w)
def no_expand():
return image, bboxes, xs, ys
image, bboxes, xs, ys = tf.cond(tf.less(rnd2, config.expand_prob),
expand, no_expand)
# 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')
# Distort image and bounding boxes.
dst_image = image
# use tf.image.sample_distorted_bounding_box() random crop train patch, but can't control the scale
if False:
dst_image, labels, bboxes, xs, ys, distort_bbox = \
distorted_bounding_box_crop(image, labels, bboxes, xs, ys,
min_object_covered=MIN_OBJECT_COVERED,
aspect_ratio_range=CROP_ASPECT_RATIO_RANGE,
area_range=AREA_RANGE)
# Resize image to output size.
dst_image = tf_image.resize_image(
dst_image,
out_shape,
method=tf.image.ResizeMethod.BILINEAR,
align_corners=False)
# Filter bboxes using the length of shorter sides
if USING_SHORTER_SIDE_FILTERING:
xs = xs * out_shape[1]
ys = ys * out_shape[0]
labels, bboxes, xs, ys = tfe.bboxes_filter_by_shorter_side(
labels,
bboxes,
xs,
ys,
min_height=MIN_SHORTER_SIDE,
max_height=MAX_SHORTER_SIDE,
assign_value=LABEL_IGNORE)
xs = xs / out_shape[1]
ys = ys / out_shape[0]
# Randomly distort the colors. There are 4 ways to do it.
dst_image = apply_with_random_selector(
dst_image / 255.0,
lambda x, ordering: distort_color(x, ordering, fast_mode),
num_cases=4)
dst_image = dst_image * 255.
# tf_summary_image(dst_image, bboxes, 'image_color_distorted')
# FIXME: change the input value
# NOTE: resnet v1 use VGG data process, so we use the same way
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, xs, ys