def preprocess_for_train(image, labels, bboxes, out_shape,
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,
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])
return image, labels, bboxes
def preprocess_for_train(image, labels, bboxes, out_shape,
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,
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])
return image, labels, bboxes
def preprocess_for_train(image, labels, bboxes,
out_shape, data_format='NHWC',
scope='ssd_preprocessing_train'):
"""Preprocesses the given image for training.
"""
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')
# 上面保证了图片是3维的tf.float32格式
# （有条件的）随机裁剪，labels(n,)、bboxes(n, 4)，裁剪图片对应原图坐标(4,)
dst_image, labels, bboxes, distort_bbox = \
distorted_bounding_box_crop(image, labels, bboxes,
min_object_covered=MIN_OBJECT_COVERED, # 0.25
aspect_ratio_range=CROP_RATIO_RANGE) # (0.6, 1.67)
# 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])
# mean = tf.constant(means, dtype=image.dtype)
# image = image - mean
# Image data format.
if data_format == 'NCHW':
image = tf.transpose(image, perm=(2, 0, 1))
# 'NHWC' (n,) (n, 4)
return image, labels, bboxes
def preprocess_for_train(image,
labels,
bboxes,
out_shape=EVAL_SIZE,
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, '0_original_image')
# # 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(dst_image, labels, bboxes)
tf_summary_image(image, tf.reshape(distort_bbox, (1, -1)),
'1_cropped_position')
# 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, '2_resized_image')
# Randomly flip the image horizontally.
dst_image, bboxes = tf_image.random_flip_left_right(dst_image, bboxes)
# Randomly flip the image vertically.
# dst_image, bboxes = tf_image.random_flip_up_down(dst_image, bboxes)
# Randomly rotate the image 90 degrees counterclockwise.
# dst_image, bboxes = tf_image.random_rot90(dst_image, bboxes)
tf_summary_image(dst_image, bboxes, '3_flipped_image')
# 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, '4_color_distorted_image')
# 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 ron_preprocess_for_train(image,
labels,
bboxes,
out_shape,
data_format='NHWC',
scope='ron_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, 'ron_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_0')
image, bboxes = control_flow_ops.cond(
tf.random_uniform([1], minval=0., maxval=1., dtype=tf.float32)[0] <
0.5, lambda: (image, bboxes),
lambda: tf_image.ssd_random_expand(image, bboxes, 2))
tf_summary_image(image, bboxes, 'image_on_canvas_1')
# Distort image and bounding boxes.
random_sample_image, labels, bboxes = tf_image.ssd_random_sample_patch(
image, labels, bboxes)
tf_summary_image(random_sample_image, bboxes,
'image_shape_distorted_2')
# Randomly flip the image horizontally.
random_sample_flip_image, bboxes = tf_image.random_flip_left_right(
random_sample_image, bboxes)
random_sample_flip_resized_image = tf_image.resize_image(
random_sample_flip_image,
out_shape,
method=tf.image.ResizeMethod.BILINEAR,
align_corners=False)
tf_summary_image(random_sample_flip_resized_image, bboxes,
'image_fliped_and_resized_3')
# Randomly distort the colors. There are 4 ways to do it.
dst_image = apply_with_random_selector(
random_sample_flip_resized_image,
lambda x, ordering: distort_color(x, ordering, fast_mode),
num_cases=4)
tf_summary_image(dst_image, bboxes, 'image_color_distorted_4')
dst_image = random_sample_flip_resized_image
# Rescale to VGG input scale.
dst_image.set_shape([None, None, 3])
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_train(image,
labels,
bboxes,
out_shape=EVAL_SIZE,
data_format='NHWC',
scope='ssd_preprocessing_train'):
'''
Pre-processes the given image for training.
:param image: images
:param labels: labels for faces
:param bboxes: bounding boxes
:param out_shape: output shape
:param data_format:
:param scope:
:return:
'''
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, 'original_image')
#print(tfe.get_shape(image),image.get_shape().as_list(),'debug')
# # Remove DontCare labels.
# labels, bboxes = ssd_common.tf_bboxes_filter_labels(out_label,
# labels,
# bboxes)
# Distort image and bounding boxes.
dst_image = image
debug_image = image
bboxes = tf.clip_by_value(bboxes, 0., 1.)
dst_image, labels, bboxes, distort_bbox = distorted_bounding_box_crop(
dst_image, labels, bboxes)
#tf_summary_image(image, tf.reshape(distort_bbox, (1,-1)), 'cropped_position')
# Resize image to output size.
dst_image = tf_image.resize_image(
dst_image,
out_shape,
method=tf.image.ResizeMethod.BILINEAR,
align_corners=False)
# Randomly flip the image horizontally.
dst_image, bboxes = tf_image.random_flip_left_right(dst_image, bboxes)
tf_summary_image(dst_image, bboxes, 'resized_image')
# 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, 'color_distorted_image')
# 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_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`].
＃注意底下所给的参数和上面提供的参数不一致，因此我们在程序中关注它的实际参数就可以了！
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.
#因为distorted_bounding_box_crop返回的图像我们都已经set_shape为了[None,None,3]，我们需要将其调整为网络所需要的输入大小，
#所以统一resize为out_shape大小！！！
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.
#随机左右翻转图像
dst_image, bboxes = tf_image.random_flip_left_right(dst_image, bboxes)
# Randomly distort the colors. There are 4 ways to do it.
# 使用一种随机的顺序调整图像的色彩！！！
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.
# 注意dst_image的输出为0~1.0之间，我们需要进行调整恢复为０~255.0作为VGG网络的输入！
image = dst_image * 255.
#对图像进行白化操作！
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
