1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
|
# Copyright 2016 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.
# ==============================================================================
"""Python layer for image_ops."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.contrib.image.ops import gen_single_image_random_dot_stereograms_ops
from tensorflow.contrib.util import loader
from tensorflow.python.framework import ops
from tensorflow.python.platform import resource_loader
_sirds_ops = loader.load_op_library(
resource_loader.get_path_to_datafile(
"_single_image_random_dot_stereograms.so"))
def single_image_random_dot_stereograms(depth_values,
hidden_surface_removal=None,
convergence_dots_size=None,
dots_per_inch=None,
eye_separation=None,
mu=None,
normalize=None,
normalize_max=None,
normalize_min=None,
border_level=None,
number_colors=None,
output_image_shape=None,
output_data_window=None):
"""Output a RandomDotStereogram Tensor for export via encode_PNG/JPG OP.
Given the 2-D tensor 'depth_values' with encoded Z values, this operation
will encode 3-D data into a 2-D image. The output of this Op is suitable
for the encode_PNG/JPG ops. Be careful with image compression as this may
corrupt the encode 3-D data within the image.
Based upon [this
paper](http://www.learningace.com/doc/4331582/b6ab058d1e206d68ab60e4e1ead2fe6e/sirds-paper).
This outputs a SIRDS image as picture_out.png:
```python
img=[[1,2,3,3,2,1],
[1,2,3,4,5,2],
[1,2,3,4,5,3],
[1,2,3,4,5,4],
[6,5,4,4,5,5]]
session = tf.InteractiveSession()
sirds = single_image_random_dot_stereograms(
img,
convergence_dots_size=8,
number_colors=256,normalize=True)
out = sirds.eval()
png = tf.image.encode_png(out).eval()
with open('picture_out.png', 'wb') as f:
f.write(png)
```
Args:
depth_values: A `Tensor`. Must be one of the following types:
`float64`, `float32`, `int64`, `int32`. Z values of data to encode
into 'output_data_window' window, lower further away {0.0 floor(far),
1.0 ceiling(near) after norm}, must be 2-D tensor
hidden_surface_removal: An optional `bool`. Defaults to `True`.
Activate hidden surface removal
convergence_dots_size: An optional `int`. Defaults to `8`.
Black dot size in pixels to help view converge image, drawn on bottom
of the image
dots_per_inch: An optional `int`. Defaults to `72`.
Output device in dots/inch
eye_separation: An optional `float`. Defaults to `2.5`.
Separation between eyes in inches
mu: An optional `float`. Defaults to `0.3333`.
Depth of field, Fraction of viewing distance (eg. 1/3 = 0.3333)
normalize: An optional `bool`. Defaults to `True`.
Normalize input data to [0.0, 1.0]
normalize_max: An optional `float`. Defaults to `-100`.
Fix MAX value for Normalization (0.0) - if < MIN, autoscale
normalize_min: An optional `float`. Defaults to `100`.
Fix MIN value for Normalization (0.0) - if > MAX, autoscale
border_level: An optional `float`. Defaults to `0`.
Value of bord in depth 0.0 {far} to 1.0 {near}
number_colors: An optional `int`. Defaults to `256`. 2 (Black &
White), 256 (grayscale), and Numbers > 256 (Full Color) are
supported
output_image_shape: An optional `tf.TensorShape` or list of `ints`.
Defaults to shape `[1024, 768, 1]`. Defines output shape of returned
image in '[X,Y, Channels]' 1-grayscale, 3 color; channels will be
updated to 3 if number_colors > 256
output_data_window: An optional `tf.TensorShape` or list of `ints`.
Defaults to `[1022, 757]`. Size of "DATA" window, must be equal to or
smaller than `output_image_shape`, will be centered and use
`convergence_dots_size` for best fit to avoid overlap if possible
Returns:
A `Tensor` of type `uint8` of shape 'output_image_shape' with encoded
'depth_values'
"""
result = gen_single_image_random_dot_stereograms_ops.single_image_random_dot_stereograms( # pylint: disable=line-too-long
depth_values=depth_values,
hidden_surface_removal=hidden_surface_removal,
convergence_dots_size=convergence_dots_size,
dots_per_inch=dots_per_inch,
eye_separation=eye_separation,
mu=mu,
normalize=normalize,
normalize_max=normalize_max,
normalize_min=normalize_min,
border_level=border_level,
number_colors=number_colors,
output_image_shape=output_image_shape,
output_data_window=output_data_window)
return result
ops.NotDifferentiable("SingleImageRandomDotStereograms")
|
