Visual Hulls Recon¶
Description¶
A Plugin to reconstruct an image by filter back projection using the inverse radon transform from scikit-image.
Parameters
in_datasets:
visibility: datasets
dtype: "[list[],list[str]]"
description:
summary: A list of the dataset(s) to process.
verbose: A list of strings, where each string gives the name of a dataset that was either specified by a loader plugin or created as output to a previous plugin. The length of the list is the number of input datasets requested by the plugin. If there is only one dataset and the list is left empty it will default to that dataset.
default: "[]"
out_datasets:
visibility: datasets
dtype: "[list[],list[str]]"
description:
summary: A list of the dataset(s) to create.
verbose: A list of strings, where each string is a name to be assigned to a dataset output by the plugin. If there is only one input dataset and one output dataset and the list is left empty, the output will take the name of the input dataset. The length of the list is the number of output datasets created by the plugin.
default: "[]"
centre_of_rotation:
visibility: basic
dtype: "[float, str, dict{int:float}]"
description: Centre of rotation to use for the reconstruction.
default: "0.0"
example: It could be a fixed value, a dictionary of (sinogram number, value) pairs for a polynomial fit of degree 1, or a dataset name.
init_vol:
visibility: hidden
dtype: "[None, str]"
description: Dataset to use as volume initialiser (does not currently work with preview)
default: None
example: "Type the name of the initialised dataset e.g. ['tomo']"
log:
visibility: intermediate
dtype: bool
description:
summary: Option to take the log of the data before reconstruction.
verbose: Should be set to false if you use PaganinFilter
default: "True"
example: Set to True to take the log of the data before reconstruction.
preview:
visibility: intermediate
dtype: preview
description: A slice list of required frames.
default: "[]"
force_zero:
visibility: intermediate
dtype: "[list[float,float],list[None,None]]"
description: Set any values in the reconstructed image outside of this range to zero.
default: "['None', 'None']"
example: "[0, 1]"
ratio:
visibility: intermediate
dtype: "[float, list[float, float]]"
description: Ratio of the masks diameter in pixels to the smallest edge size along given axis. If a list of two floats is given, the second value is used to fill up the area outside the mask.
default: "0.95"
log_func:
visibility: advanced
dtype: str
description: Override the default log function with a numpy statement
default: np.nan_to_num(-np.log(sino))
vol_shape:
visibility: intermediate
dtype: "[str, int]"
description:
summary: Override the size of the reconstruction volume with an integer value.
verbose: When fixed, you get the dimension of the horizontal detector or you can specify any reconstruction size you like with an integer.
default: fixed
threshold:
visibility: basic
dtype: float
description: Threshold to binarize the input sinogram.
default: "0.5"
Key
visibility: The visibility level of the parameter
dtype: The type of data
description: A short explanation of the parameter
default: The default value
options: A list of permitted values
dependency: The name of the parameter and value which this parameter depends upon
range: A guide for the range of the parameter
Citations
The visual hull concept for silhouette-based image understanding by Laurentini, Aldo et al.
Bibtex
@article{laurentini1994visual,
title={The visual hull concept for silhouette-based image understanding},
author={Laurentini, Aldo},
journal={IEEE Transactions on pattern analysis and machine intelligence},
volume={16},
number={2},
pages={150--162},
year={1994},
publisher={IEEE}
}
Endnote
%0 Journal Article
%T The visual hull concept for silhouette-based image understanding
%A Laurentini, Aldo
%J IEEE Transactions on pattern analysis and machine intelligence
%V 16
%N 2
%P 150-162
%@ 0162-8828
%D 1994
%I IEEE
API
-
class
VisualHullsRecon
[source] -
get_max_frames
()[source] Number of data frames to pass to each instance of process_frames func
- Returns
- “single”, “multiple” or integer (only to be used if the number of
frames MUST be fixed.)
- Return type
str or int
-
process_frames
(data)[source] This method is called after the plugin has been created by the pipeline framework and forms the main processing step
- Parameters
data (list(np.array)) – A list of numpy arrays for each input dataset.
-