Bioimage Analysis Wiki

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====== Python Image Processing Cookbook ======

===== Loading Image File =====

matplotlib has matplotlib.image.imread, and PIL also has similar file input methods. The former is limited to png files and the latter is limited to 8-bit images. For this reason, we focus on openCV and TiffFile modules. 

==== openCV ====

=== Tiff file to numpy.ndarray ===

<code python>
>>> import cv
>>> img = cv.imread('/Users/miura/img/blobs.tif')
>>> type(img)
>>> <type 'numpy.ndarray'>
</code>

  * cv.imread reads only the first frame in tiff stack. 
  * 8bit image becomes RGB-like format, triplet of 8 bits per pixel. 

=== Tiff file to Iplimage ===

<code python>
In [40]: img = cv.LoadImage('/Users/miura/img/blobs.tif')
In [41]: img
Out[41]: <iplimage(nChannels=3 width=256 height=254 widthStep=768 )>
</code>


=== Tiff file to cvMat ===


==== Tifffile.py ====

=== Tiff to numpy.ndarray, Single Image ===

<code python>
In [44]: import tifffile as tff
In [50]: tiffimg = tff.TIFFfile('/Users/miura/img/blobs.tif')
In [51]: type(tiffimg)
Out[51]: <class 'tifffile.TIFFfile'>
In [52]: img = tiffimg.asarray()
In [53]: type(img)
Out[53]: <type 'numpy.ndarray'>
</code>

=== Tiff to numpy.ndarray, Stack Image ===

<code python>
In [54]: tiffimg = tff.TIFFfile('/Users/miura/img/flybrainG.tif')
In [55]: img = tiffimg.asarray()
In [57]: tiffimg
Out[57]: <tifffile.TIFFfile object at 0x1299ac910>
In [65]: type(img)
Out[65]: <type 'numpy.ndarray'>
In [61]: img10 = tiffimg[10].asarray()
In [62]: type(img10)
Out[62]: <type 'numpy.ndarray'>
</code>
  * if the image file is a single frame image, not so different from the others
  * Stack tiff file is loaded peroperly. Single frame is extractable by indexing. In above case, 11th frame is extracted. 

===== Conversion between types =====

==== OpenCV Mat object to numpy.ndarray object ====

  * tested with python2.6, openCV 2.2, numpy 1.6.1, OSX10.6.8

<code python>
>>> import cv
>>> import numpy as np
>>> mat = cv.CreateMat( 3 , 5 , cv.CV_32FC1 )
>>> cv.Set( mat , 7 )
>>> a = np.asarray( mat[:,:] )
>>> a
array([[ 7.,  7.,  7.,  7.,  7.],
       [ 7.,  7.,  7.,  7.,  7.],
       [ 7.,  7.,  7.,  7.,  7.]], dtype=float32)

</code>
<[[http://stackoverflow.com/questions/5762440/how-to-transform-a-opencv-cvmat-back-to-ndarray-in-numpy|link]]>

==== OpenCV Image object to numpy.ndarray object ====

  * tested with python2.6, openCV 2.2, numpy 1.6.1, OSX10.6.8

<code python>
>>> im = cv.CreateImage( ( 5 , 5 ) , 8 , 1 )
>>> cv.Set( im , 100 )
>>> im_array = np.asarray( im )
>>> im_array
array(<iplimage(nChannels=1 width=5 height=5 widthStep=8 )>, dtype=object)
>>> im_array = np.asarray( im[:,:] )
>>> im_array
array([[100, 100, 100, 100, 100],
       [100, 100, 100, 100, 100],
       [100, 100, 100, 100, 100],
       [100, 100, 100, 100, 100],
       [100, 100, 100, 100, 100]], dtype=uint8)

</code>
<[[http://stackoverflow.com/questions/5762440/how-to-transform-a-opencv-cvmat-back-to-ndarray-in-numpy|link]]>

===== Data Visualization =====

==== plot 3D coordinates using Mayavi ====

An example script for loading (x, y, y) coordinates data from tab-delimited text file and plot them in 3D using [[http://github.enthought.com/mayavi/mayavi/|mayavi2]]. 

<code python linenums:1>
from matplotlib import mlab as matp
filename = '/Users/miura/data.txt'
x1, y1, z1, x2, y2, z2 = matp.load(filename, usecols=[0, 1, 2, 3, 4, 5], unpack=True)

from mayavi.mlab import points3d
from mayavi.mlab import plot3d
from mayavi import mlab as maya

p1s = points3d(x1, y1, z1, scale_factor=.25, color=(0, 1, 1))
p2s = points3d(x2, y2, z2, scale_factor=.25, color=(1, 0, 0))

for idx, xval in enumerate(x1):
    plin1 = plot3d([x1[idx], x2[idx]], [y1[idx], y2[idx]], [z1[idx], z2[idx]], tube_radius=0.1, colormap='Spectral', color=(0, 0, 1))
maya.show()
</code>

[{{ :documents:figure20110923:mayaviexample.png?250| plotting data with mayavi}}]

In this example, we assume the follwoing data structure in the file: a pair of coordinates per line, so 6 numbers are in one line separated by tab. It should look like

<code>
1.0 3.6 4.8 5.1 6.12 7.14
...  
</code>

To run this script, the best is to run it from ipython with thread so first you start ipython by
<code>
ipython -wthread
</code>
or incase of newer ipython (version >= 1.1)
<code>
ipython --i
</code>
then in the ipython interface, 
<code>
run exampleMayavi.py
</code>
A new window pops up, and after drawing of the scene is finished, you could control the scene by such as
<code>
maya.view(100, 40)
</code>
or to animate the scene
<code>
for i in range (1, 360, 3): maya.view(i, i)
</code>
to close the scene, 
<code>
maya.close()
</code>

Note that in this example, 
<code python>
from mayavi import mlab as maya
</code>

since the namespace "mlab" overlaps with matplotlib.mlab. 

==== plot 3D trajectory using Mayavi ====

To evaluate 3D particle tracking results, trajectories could be plotted by color coding the time. 

[{{ :documents:figure20111219:snapshot3.png?300| Plotting 3D trajectory. Trajectories are colored so that the beginning of frame is blue and gradually become red towards the end of the sequence}}]