tf.contrib.image.single_image_random_dot_stereograms

Output a RandomDotStereogram Tensor for export via encode_PNG/JPG OP.

tf.contrib.image.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
)

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.

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.compat.v1.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'

tf.contrib.image.single_image_random_dot_stereograms

Args

Returns