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

Note that Bio-Formats also requires a working Java installation.

This user manual discusses the differences with the DIPlib library.

Type correspondences

Most classes defined in DIPlib and used as input arguments to functions have a Python binding, with the following exceptions:

  • dip::DataType: pass a string such as 'UINT8' or 'SFLOAT'.

  • dip::Sample: pass a scalar (a regular Python number).

  • dip::Pixel: pass a list of scalars.

  • dip::Range: 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::UnsignedArray, dip::FloatArray, or similar: pass a Python list: [5, 5]. A scalar is accepted as a one-element list.

  • dip::StringArray: pass a list of strings (['foo','bar']).

  • dip::StringSet: pass a dictionary ({'foo','bar'}).

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.

Displaying images

The class dip.Image has a method Show(). There is an identical function dip.Show(). They display an image to the current matplotlib window, if matplotlib is installed:

import diplib as dip
img = dip.ImageReadTIFF('cameraman')

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

Type help(dip.Show) in Python to learn about many more options.

If DIPviewer is installed, its functionality will be in the dip.viewer namespace. Use img.ShowSlice() for convenience. 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.Draw().

dip.Image.ShowSlice() and dip.viewer.Show() have additional parameters that can be used to set viewing options. They also return an object that can be used for further interaction:

wdw = img.ShowSlice('Window title', mapping='unit', lut='sequential')

Type help(dip.viewer.Show) for details.

Indexing into images

Indexing into a dip.Image object works as it does for other array types in Python:

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] = 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, 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(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

Testing image validity

You can use either IsForged() or IsEmpty() to test if an image is forged. IsEmpty() is the opposite of IsForged(), and returns True if this image is not forged.

Functions that expect an image interpret None as an empty (non-forged) image.

Mixing NumPy arrays and DIPlib images

A NumPy array can be passed instead of an image to any DIPlib function. In fact, any Python object that uses the buffer interface implicitly casts to an image. The reverse is also true: NumPy treats DIPlib images as an array, you can call any NumPy function on an image. However, some code that accepts a NumPy array calls methods of the array, which would not be defined for a DIPlib image. For example,

array = np.zeros((10, 11))
dip.Gauss(array)          # OK
img = dip.Image((11, 10))
np.amax(img)              # OK
img.max()                 # error! np.array method not defined for dip.Image
img.shape                 # error! np.array property not defined for dip.Image

One can “cast” from a NumPy array to a DIPlib image and back:

x = np.asarray(img)
y = dip.Image(array)

The image and the array point to the same memory in these two cases: modifying values in the one cause the other to see the modified values as well

The mapping from images to arrays causes the indexes to be reversed: The first array index corresponds to the last image index, and vice versa. If an image is indexed as img[x,y,z], the corresponding array is indexed as array[z,y,x]. 2D NumPy arrays are typically interpreted with the first dimension being vertical (y) and the second horizontal (x). This is how they are printed to the console, and how pyplot.imshow displays them as images. Preserving the indexing order between DIPlib and NumPy would therefore cause 2D images to be shown transposed by other Python tools.

Thus, the following indexing operations are identical:

array = np.zeros((10, 11, 12))
img = dip.Image(array)
array[1, 2, 3] == img[3, 2, 1]

Furthermore, by reversing the indexing, we map an image with normal strides to an array in NumPy’s standard C-ordering. The following evaluates to True:

dip.Image( np.zeros((10,11,5,7)) ).HasNormalStrides()

When using a NumPy array as an image in a DIPlib function, it is implicitly cast to a dip.Image object as above, and passed to the DIPlib function. This means that, whether the input is a NumPy array or a DIPlib image, other function parameters that identify dimensions are always interpreted in the same way. For example, the filter sizes are ordered (x, y, z), not (z, y, x) as they would be ordered in scikit-image or other Python imaging libraries.

By calling dip.ReverseDimensions() (which one should do only directly after loading the diplib module to avoid confusing results), PyDIP is configured to reverse dimensions of all DIPlib images. This means that the NumPy indexing order will be preserved, images will be indexed as img[z,y,x]. This has several surprising results, for example the direction of all angles is reversed, with positive angles being counter-clockwise instead of clockwise. This option is intended to make it easier to mix DIPlib functions into code that also uses e.g. scikit-image.

When casting a tensor image to a NumPy array, the tensor dimension will become the last array dimension. When casting a NumPy array to a DIPlib image, there is no information about which dimension, if any, is the tensor dimension. By default the following heuristic is used: if the array has more than two dimensions, and if the smaller of the last or the first array dimension has no more than 4 elements, then that dimension will be the tensor dimension. The tensor will have a column vector shape (this is the default tensor shape in DIPlib). The threshold of 4 was picked because it will handle correctly all color images. This threshold can be adjusted using dip.SetTensorConversionThreshold(). If set to 0, all arrays will be converted to a scalar image.