PyDIP User Manual
Currently, most functionality in the PyDIP module is directly mirrored from the DIPlib library. That is, function names and signatures are mostly identical to those in DIPlib. Please see the documentation for DIPlib to learn how to use these functions. Type t here to bring up a search dialog box where you can find functions by name.
To install the package from PyPI, use
pip install diplib
To read images through the Bio-Formats library, you will need to download it separately:
python -m diplib download_bioformats
This user manual discusses the differences with the DIPlib library.
Most classes defined in DIPlib and used as input arguments to functions have a Python binding, with the following exceptions:
dip::: pass a string such as
dip::Sample: pass a scalar (a regular Python number).
dip::Pixel: pass a list of scalars.
dip::: pass a slice (
slice(0, 3, 1)). Note that the second argument, the end value, is interpreted differently by DIPlib: it is included in the range. You can also pass a scalar here.
dip::, or similar: pass a Python list:
[5, 5]. A scalar is accepted as a one-element list.
dip::: pass a list of strings (
dip::: pass a dictionary (
By using named arguments, it is quite simple to set only needed arguments, and leave all others with their default values. All arguments that have a default value in C++ also have a default value in Python.
import diplib as dip img = dip.ImageReadTIFF('cameraman') img.Show()
By default, the image intensities are mapped to the full display range (i.e. the minimum image intensity is black and the maximum is white). This can be changed for example as follows:
img.Show('unit') # maps [0,1] to the display range img.Show('8bit') # maps [0,255] to the display range img.Show('orientation') # maps [0,pi] to the display range img.Show('base') # keeps 0 to the middle grey level, and uses a divergent color map img.Show('log') # uses logarithmic mapping
help(dip.Show) in Python to learn about many more options.
If DIPviewer is installed, its functionality will be in the
diplib.viewer namespace. Use
dip.viewer.Show(img). Depending on the backend used, it will be necessary to do
dip.viewer.Spin() to interact with the created window.
Spin() interrupts the interactive session until all DIPviewer windows have been closed. Even when
Spin() is not needed to interact with the windows, it should be run before closing the Python session to avoid a series of error messages. Alternatively, periodically call
dip.viewer.Show() has additional parameters that can be used to set viewing options; type
help(dip.viewer.Show) for details. It also returns an object that can be used for further interaction.
Indexing into a
dip.Image object works as it does for other array types in Python:
img img[0:10] img[0:-1:2, 0:-1:2]
Note that dimensions are ordered in reverse from how NumPy stores them (the first dimension is horizontal, or x).
It is possible to assign to a subset of the image pixels using indexing:
img = 0 img[0:10] = img[20:30] img[0:-1:2, 0:-1:2] = 255
Unlike in DIPlib, the square brackets index into spatial dimensions. To index into tensor dimensions, use round brackets (parenthesis):
img(0) img(0, 2) img(slice(0, 3))
The output of any of these indexing operations shares data with the original image, so writing to that output also changes the original image:
img2 = img(0) # this copy shares data with img img2.Fill(100) # same as img(0).Fill(100) img(1).Copy(img(0)) img(2)[:,:] = img(0) img2 = img(0).Copy() # this copy does not share data with img img2.Fill(100) # does not affect img
Irregular indexing using a mask image is also supported. This indexing returns a copy of the data, but an assignment form is also available:
img2 = img[mask] # this copy does not share data with img img2.Fill(0) # does not affect img img[mask] = 0 # sets all pixels in mask to 0
IsEmpty() to test if an image is forged.
Functions that expect an image interpret
None as an empty (non-forged) image.