Viewing file:  im_util.py (4.16 KB) -rw-r--r--
Select action/file-type:
 (+) |  (+) |  (+) | Code (+) | Session (+) |  (+) | SDB (+) |  (+) |  (+) |  (+) |  (+) |  (+) |
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import numpy as np
import tensorflow as tf
from tensorboard import util
from tensorboard.plugins.beholder import colormaps
# pylint: disable=not-context-manager
def global_extrema(arrays):
return min([x.min() for x in arrays]), max([x.max() for x in arrays])
def scale_sections(sections, scaling_scope):
'''
input: unscaled sections.
returns: sections scaled to [0, 255]
'''
new_sections = []
if scaling_scope == 'layer':
for section in sections:
new_sections.append(scale_image_for_display(section))
elif scaling_scope == 'network':
global_min, global_max = global_extrema(sections)
for section in sections:
new_sections.append(scale_image_for_display(section,
global_min,
global_max))
return new_sections
def scale_image_for_display(image, minimum=None, maximum=None):
image = image.astype(float)
minimum = image.min() if minimum is None else minimum
image -= minimum
maximum = image.max() if maximum is None else maximum
if maximum == 0:
return image
else:
image *= 255 / maximum
return image.astype(np.uint8)
def pad_to_shape(array, shape, constant=245):
padding = []
for actual_dim, target_dim in zip(array.shape, shape):
start_padding = 0
end_padding = target_dim - actual_dim
padding.append((start_padding, end_padding))
return np.pad(array, padding, mode='constant', constant_values=constant)
def apply_colormap(image, colormap='magma'):
if colormap == 'grayscale':
return image
cm = getattr(colormaps, colormap)
return image if cm is None else cm[image]
class PNGDecoder(util.PersistentOpEvaluator):
def __init__(self):
super(PNGDecoder, self).__init__()
self._image_placeholder = None
self._decode_op = None
def initialize_graph(self):
self._image_placeholder = tf.placeholder(dtype=tf.string)
self._decode_op = tf.image.decode_png(self._image_placeholder)
# pylint: disable=arguments-differ
def run(self, image):
return self._decode_op.eval(feed_dict={
self._image_placeholder: image,
})
class Resizer(util.PersistentOpEvaluator):
def __init__(self):
super(Resizer, self).__init__()
self._image_placeholder = None
self._size_placeholder = None
self._resize_op = None
def initialize_graph(self):
self._image_placeholder = tf.placeholder(dtype=tf.float32)
self._size_placeholder = tf.placeholder(dtype=tf.int32)
self._resize_op = tf.image.resize_nearest_neighbor(self._image_placeholder,
self._size_placeholder)
# pylint: disable=arguments-differ
def run(self, image, height, width):
if len(image.shape) == 2:
image = image.reshape([image.shape[0], image.shape[1], 1])
resized = np.squeeze(self._resize_op.eval(feed_dict={
self._image_placeholder: [image],
self._size_placeholder: [height, width]
}))
return resized
decode_png = PNGDecoder()
resize = Resizer()
def read_image(filename):
with tf.gfile.Open(filename, 'rb') as image_file:
return np.array(decode_png(image_file.read()))
def write_image(array, filename):
with tf.gfile.Open(filename, 'w') as image_file:
image_file.write(util.encode_png(array))
def get_image_relative_to_script(filename):
script_directory = os.path.dirname(__file__)
filename = os.path.join(script_directory, 'resources', filename)
return read_image(filename)
|