CCPi Show & Tell

Felipe Delestro

Senior Research Software Specialist @ QIM Center

Danmarks Tekniske Universitet (DTU)

 

fima@dtu.dk 

 

   

slides.com/delestro/ccpi

QIM Center

Center for Quantification of Imaging Data from MAX IV

supported by

qim.dk/about

Anders Bjorholm Dahl

Head of the QIM Center

Jon
Sporring

Deputy Head of the QIM Center

Stephen
Hall

Professor at Lund university

Rebecca
Engberg

Center Manager

open-source Python library, that focuses on 3D volumetric data

 Getting started is easy:
pip install qim3d

Documentation at platform.qim.dk/qim3d/

Data handling

Volumetric data can be stored in a variety of file formats.

This can make the very first step of importing the data more complex than it should be

load and save

Data handling

Data handling

Volumetric data can be stored in a variety of file formats.

This can make the very first step of importing the data more complex than it should be

load and save

qim3d.io.load and qim3d.io.save can handle the most common data formats:

  • Tiff (including file stacks)
  • HDF5
  • TXRM/TXM/XRM
  • NIfTI
  • PIL (including file stacks)
  • VOL/VGI
  • DICOM
import qim3d

vol = qim3d.io.load("path/to/image.tif", virtual_stack=True)

Data handling

We make a distinction between load/save and import/export when the operation includes transformations to the data.

import and export

Currently we're implementing a 3D import/export following the OME-Zarr standard, which
uses chunked data with a
ulti-scales scheme.

Visualization

Visualization

Easily exploring volumetric data may not be so straightforward. The library provides helper functions this task simple and easy, even within Jupyter Notebooks

import qim3d

vol = qim3d.examples.bone_128x128x128
qim3d.viz.slicer(vol)

Visualization

import qim3d

vol = qim3d.examples.shell_225x128x128
qim3d.viz.slices_grid(vol, num_slices=15)

Visualization

import qim3d

# Generate synthetic collection of objects
num_objects = 15
vol, labels = qim3d.generate.volume_collection(num_objects = num_objects)
# Visualize synthetic collection
qim3d.viz.volumetric(vol)

Visualization

import qim3d

vol = qim3d.generate.volume(base_shape=(128,128,128),
                          final_shape=(128,128,128),
                          noise_scale=0.03,
                          order=1,
                          gamma=1,
                          max_value=255,
                          threshold=0.5,
                          dtype='uint8'
                          )

mesh = qim3d.mesh.from_volume(vol, step_size=3)

qim3d.viz.mesh(mesh.vertices, mesh.faces)

Visualization

import qim3d

vol = qim3d.examples.cement_128x128x128
binary = qim3d.filters.gaussian(vol, sigma = 2) < 60
labeled_volume, num_labels = qim3d.segmentation.watershed(binary)

color_map = qim3d.viz.colormaps.segmentation(num_labels, style = 'bright')
qim3d.viz.slicer(labeled_volume, slice_axis = 1, color_map=color_map)

qim3d.viz.colormaps.segmentation creates an colormap designes specifically for segmentation tasks 

The parameter min_dist allows us to control how different neighbour colors are

+

=

❤️