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# 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.
# ==============================================================================
"""Utility functions."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import StringIO
import matplotlib
matplotlib.use('Agg')
from matplotlib import pylab as p
# axes3d is being used implictly for visualization.
from mpl_toolkits.mplot3d import axes3d as p3 # pylint:disable=unused-import
import numpy as np
from PIL import Image
from skimage import measure
from six.moves import xrange
import tensorflow as tf
def save_image(inp_array, image_file):
"""Function that dumps the image to disk."""
inp_array = np.clip(inp_array, 0, 255).astype(np.uint8)
image = Image.fromarray(inp_array)
buf = StringIO.StringIO()
image.save(buf, format='JPEG')
with open(image_file, 'w') as f:
f.write(buf.getvalue())
return None
def image_flipud(images):
"""Function that flip (up-down) the np image."""
quantity = images.get_shape().as_list()[0]
image_list = []
for k in xrange(quantity):
image_list.append(tf.image.flip_up_down(images[k, :, :, :]))
outputs = tf.stack(image_list)
return outputs
def resize_image(inp_array, new_height, new_width):
"""Function that resize the np image."""
inp_array = np.clip(inp_array, 0, 255).astype(np.uint8)
image = Image.fromarray(inp_array)
# Reverse order
image = image.resize((new_width, new_height))
return np.array(image)
def display_voxel(points, vis_size=128):
"""Function to display 3D voxel."""
try:
data = visualize_voxel_spectral(points, vis_size)
except ValueError:
data = visualize_voxel_scatter(points, vis_size)
return data
def visualize_voxel_spectral(points, vis_size=128):
"""Function to visualize voxel (spectral)."""
points = np.rint(points)
points = np.swapaxes(points, 0, 2)
fig = p.figure(figsize=(1, 1), dpi=vis_size)
verts, faces = measure.marching_cubes_classic(points, 0, spacing=(0.1, 0.1, 0.1))
ax = fig.add_subplot(111, projection='3d')
ax.plot_trisurf(
verts[:, 0], verts[:, 1], faces, verts[:, 2], cmap='Spectral_r', lw=0.1)
ax.set_axis_off()
fig.tight_layout(pad=0)
fig.canvas.draw()
data = np.fromstring(
fig.canvas.tostring_rgb(), dtype=np.uint8, sep='').reshape(
vis_size, vis_size, 3)
p.close('all')
return data
def visualize_voxel_scatter(points, vis_size=128):
"""Function to visualize voxel (scatter)."""
points = np.rint(points)
points = np.swapaxes(points, 0, 2)
fig = p.figure(figsize=(1, 1), dpi=vis_size)
ax = fig.add_subplot(111, projection='3d')
x = []
y = []
z = []
(x_dimension, y_dimension, z_dimension) = points.shape
for i in range(x_dimension):
for j in range(y_dimension):
for k in range(z_dimension):
if points[i, j, k]:
x.append(i)
y.append(j)
z.append(k)
ax.scatter3D(x, y, z)
ax.set_axis_off()
fig.tight_layout(pad=0)
fig.canvas.draw()
data = np.fromstring(
fig.canvas.tostring_rgb(), dtype=np.uint8, sep='').reshape(
vis_size, vis_size, 3)
p.close('all')
return data
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