contrib.image.single_image_random_dot_stereograms
tf.contrib.image.single_image_random_dot_stereograms
tf.contrib.image.single_image_random_dot_stereograms
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 )
Defined in tensorflow/contrib/image/python/ops/single_image_random_dot_stereograms.py
.
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 witin the image.
Based upon this paper.
This outputs a SIRDS image as picture_out.png:
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
: ATensor
. 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 optionalbool
. Defaults toTrue
. Activate hidden surface removal -
convergence_dots_size
: An optionalint
. Defaults to8
. Black dot size in pixels to help view converge image, drawn on bottom of the image -
dots_per_inch
: An optionalint
. Defaults to72
. Output device in dots/inch -
eye_separation
: An optionalfloat
. Defaults to2.5
. Separation between eyes in inches -
mu
: An optionalfloat
. Defaults to0.3333
. Depth of field, Fraction of viewing distance (eg. 1/3 = 0.3333) -
normalize
: An optionalbool
. Defaults toTrue
. Normalize input data to [0.0, 1.0] -
normalize_max
: An optionalfloat
. Defaults to-100
. Fix MAX value for Normalization (0.0) - if < MIN, autoscale -
normalize_min
: An optionalfloat
. Defaults to100
. Fix MIN value for Normalization (0.0) - if > MAX, autoscale -
border_level
: An optionalfloat
. Defaults to0
. Value of bord in depth 0.0 {far} to 1.0 {near} -
number_colors
: An optionalint
. Defaults to256
. 2 (Black & White), 256 (grayscale), and Numbers > 256 (Full Color) are supported -
output_image_shape
: An optionaltf.TensorShape
or list ofints
. 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 optionaltf.TensorShape
or list ofints
. Defaults to[1022, 757]
. Size of "DATA" window, must be equal to or smaller thanoutput_image_shape
, will be centered and useconvergence_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'
© 2017 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/contrib/image/single_image_random_dot_stereograms