Recent changes to Using mia from Python

Using mia from Python modified by Gert Wollny

Gert Wollny — Fri, 09 Jan 2015 17:32:30 -0000

--- v2
+++ v3
@@ -1,9 +1,9 @@
 ## Using mia from Python

-The name of the module is simply *mia*, hence the is is loaded as 
+The name of the module is simply *pymia* (from pymia >= 1.0.7 on), hence the is is loaded as 

     :::python 
-    import mia
+    import pymia

 The functionality currently exposed to Python  is limited to loading images, running 2D and 3D filters on gray scale image, and running image registration. Within Python the images are represented as numpy arrays, in fact no MIA objects are exposed directly to Python.

Using mia from Python modified by Gert Wollny

Gert Wollny — Thu, 12 Dec 2013 14:46:27 -0000

--- v1
+++ v2
@@ -8,29 +8,6 @@
 The functionality currently exposed to Python  is limited to loading images, running 2D and 3D filters on gray scale image, and running image registration. Within Python the images are represented as numpy arrays, in fact no MIA objects are exposed directly to Python.

-### Filtering 
-
-The *mia.filter* function transparently distinguishes between 2D and 3D images. Filters are specified as strings like they are given on the [mia-2dimagefilter](http://mia.sourceforge.net/userref/Secmia2dimagefilter.html) and [mia-3dimagefilter](http://mia.sourceforge.net/userref/Secmia3dimagefilter.html) command line.
-
-Filters can be run one at a time, or as a pipeline. To run a single filter, e.g. a *median* filter of *half filter width* 3 on an image *input* run 
-
-    :::python 
-    filtered = mia.filter(input, "median:w=3")
-
-To run a filter chain, e.g. the above median, followed by a *mean-least-variance* filter with a filter width parameter 5, provide the filters as a list: 
-
-    :::python 
-    filtered = mia.filter(input, ["median:w=3", "mlv:w=5"])
-
-Note that the filters function converts the *input* to the internal MIA image format, creates and runs the filters, and then converts the images back to *numpy* arrays. Therefore, for performance reasons, it is best to run the filter chains instead of one filter at a time. 
-
-In order to avoid re-creating filters one can enable the filter cache by calling 
-
-    :::python 
-    mia.set_filter_plugin_cache(True)
-
-Then, filters with the same description are re-used  in subsequent calls to the *mia.filter* function. The filters in MIA normally work like functions, i.e. no state changes when it is called. *However, in some rare cases, when external data is loaded by means of a filter parameter, this may actually not work like expected (cf. [tickets:#134]).*
-
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-
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+* [Python, loading images]
+* [Python, filtering]
+* [Python, image registration]

Using mia from Python modified by Gert Wollny

Gert Wollny — Thu, 12 Dec 2013 12:48:17 -0000

Using mia from Python

The name of the module is simply mia, hence the is is loaded as

import mia

The functionality currently exposed to Python is limited to loading images, running 2D and 3D filters on gray scale image, and running image registration. Within Python the images are represented as numpy arrays, in fact no MIA objects are exposed directly to Python.

Filtering

The mia.filter function transparently distinguishes between 2D and 3D images. Filters are specified as strings like they are given on the mia-2dimagefilter and mia-3dimagefilter command line.

Filters can be run one at a time, or as a pipeline. To run a single filter, e.g. a median filter of half filter width 3 on an image input run

filtered = mia.filter(input, "median:w=3")

To run a filter chain, e.g. the above median, followed by a mean-least-variance filter with a filter width parameter 5, provide the filters as a list:

filtered = mia.filter(input, ["median:w=3", "mlv:w=5"])

Note that the filters function converts the input to the internal MIA image format, creates and runs the filters, and then converts the images back to numpy arrays. Therefore, for performance reasons, it is best to run the filter chains instead of one filter at a time.

In order to avoid re-creating filters one can enable the filter cache by calling

mia.set_filter_plugin_cache(True)

Then, filters with the same description are re-used in subsequent calls to the mia.filter function. The filters in MIA normally work like functions, i.e. no state changes when it is called. However, in some rare cases, when external data is loaded by means of a filter parameter, this may actually not work like expected (cf. [tickets:#134]).

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