KNIME Image Processing Python Bindings to work with images in the KNIME Python Extension.
You can install the KNIME Python Bindings from the stable community contributions update-site (see /wiki/install-knime-image-processing 2.2 how to activate it).
In order to use the Python Bindings, you have to install the KNIME Python Integration, which can be found on the default KNIME Update-Site. In addition to that, your local Python installation must comprise the following packages:
- Python 2.7, NumPy, Pandas 0.7, Protobuf 2.5, JEDI
See also: /blog/how-to-setup-the-python-extension
An example workflow can be downloaded
In order to access the image data from e.g. an "Image Reader" node, a "Python Script" node must contain the following lines:
from KNIPImage import KNIPImage from scipy import ndimage # Copy structure of incoming KNIME table output_table = input_table.copy() # Create empty output_column output_column =  # Loop over every cell in the 'Img' column for index,input_cell in input_table['Img'].iteritems(): # get image from cell image = input_cell.array # apply some operation of image, here a Gaussian filtering filtered = ndimage.gaussian_filter(image, 3) # Write result back into a KNIPImage output_cell = KNIPImage(filtered) # Append output_cell to output array output_column.append(output_cell) # Set output_column in output_table output_table['Img'] = output_column
- In the above script, `input_cell` and `output_cell` are instances of KNIPImage. Further information/metadata could be defined in this class.
- Copying `output_table` from `input_table` will keep the table structure that KNIME expects intact. Since we are only interested in manipulating the `"Img"` column, we can do so without having to worry about creating a new table.
- In the above copy operation, arrays are not copied. This is good, because (a) we save memory and (b) we loaded the image from a byte-stream and could not possibly change anything in the previous node.
- Keep in mind that instead of creating an output_column and setting it as the Img column of the output_table, we can just edit the input_cell.array in-place with e.g. input_cell.array[0,:,:] = 1. This hackish approach saves memory and reduces the amount of scripting code.
KNIME is written in Java while NumPy is a CPython extension.
We use a patched version of tifffile.py by Christoph Goehlke to byte-stream images from and to Python. This results in an increased memory footprint and additional processing time. See our GitHub repository https://github.com/knime-ip/knip-python-bindings for details.