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#!/usr/bin/python

import SimpleITK as sitk
import vtk
import numpy as np
import sys

from vtk.util.vtkConstants import *

filename = sys.argv[1]

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#!/usr/bin/python
"""
http://www.it.uu.se/edu/course/homepage/avgrafik/vt06/ComputerExercises/vtkexamples.shtml
"""

import sys
import vtk
import numpy as np
import math
from math import cos, sin

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#!/usr/bin/env jython

from ij import IJ
import sys

from mpicbg.ij.clahe import Flat

# http://fiji.sc/wiki/index.php/Enhance_Local_Contrast_(CLAHE)
# http://fiji.sc/cgi-bin/gitweb.cgi?p=mpicbg.git;a=blob;f=mpicbg/ij/clahe/PlugIn.java;h=663153764493547de560c08ee11f2e6b1e7e1a32;hb=HEAD

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#!/usr/bin/env jython

#  \begin{enumerate}
#    \item Process $\rightarrow$ Gaussian Blur
#    \item Process $\rightarrow$ Enhance Contrast (normalize \& equalize histogram)
#    \item Image $\rightarrow$ Type $\rightarrow$ HSB stack
#    \item Image $\rightarrow$ Stacks $\rightarrow$ Stack to images
#    \item keep only the Hue image
#    \item Process $\rightarrow$ Find maxima... (noise tolerance 60)
# \end{enumerate}

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import numpy as np

def dr_toolbox(D,n_components=None):
    """
    Landmark isomap dimensionality reduction method
    This is the version implemented in
    http://homepage.tudelft.nl/19j49/Matlab_Toolbox_for_Dimensionality_Reduction.html
    
    Parameters
    ----------

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Note: this page was originally written in pmWiki for the wiki of the Biomedia group.

It was converted to markdown using an HTML-to-markdown converter and a markdown editor.

See the official documentation for more info about SLURM.

Example 1: looping over subjects

the --wrap option allows you to pass one-liners to sbatch (see sbatch -h):

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import dicom
import numpy as np

def load_dcm(filename):
    dcm = dicom.ReadFile(filename)

    # http://www.creatis.insa-lyon.fr/pipermail/dcmlib/2005-September/002141.html
    # http://www.dabsoft.ch/dicom/3/C.7.6.2.1.1/
    # http://xmedcon.sourceforge.net/Docs/OrientationDicomStandard
    img0 = dcm[(0x5200, 0x9230)][0] # Per-frame Functional Groups Sequence 

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# http://stackoverflow.com/questions/9924135/fast-cartesian-to-polar-to-cartesian-in-python

import cv2
import numpy as np
import scipy.ndimage as nd

import irtk

def polar2cart(r, theta, center):
    x = r * np.cos(theta) + center[0]

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Building SimpleElastix

Swig can be installed separately:

brew install swig

Then run the SuperBuild:

git clone https://github.com/kaspermarstal/SimpleElastix

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#!/usr/bin/python

import numpy as np
import cv2
import densecrf as dcrf
from skimage.segmentation import relabel_sequential
import sys

# Usage:
# python dense_inference.py image annotations output