====== Labs ======

The labs cover selected image processing methods. For each method, the students are provided with a Jupyter notebook that contains a brief explanation and a template for the implementation. The students are tasked to follow the template and complete the implementation.

Some of the labs include a short quiz (see [[courses:zmo:start#organization | Organization]]). The quizzes target selected material from the lecture as indicated in the schedule below. The quizzes are submitted using the [[https://cw.felk.cvut.cz/brute/|BRUTE]] web application.

See information about the [[courses:zmo:semestral:start|semester work]].

The points awarded for the quizzes and the semester work are listed in [[https://docs.google.com/spreadsheets/d/e/2PACX-1vQV_2QBh5WkkAHObfpUQ66LetjBbvKwV9kJC0qHHleYs5tf8AdKyP_qJV7fxzfPXPP_FwXewEWJX4bp/pubhtml|a table]]. Ask the lab instructor to tell you your (randomly assigned) ID.
===== Schedule =====

^ Week ^ Date     ^ Topic                                                                         ^ Quiz topics                            ^
|    1 |  24.  9. | {{ :courses:zmo:tutorials:week01.zip | Introduction }}                        | //no quiz//                            |
|    2 |   1. 10. | {{ :courses:zmo:tutorials:week02.zip | Active contours }}                     | 1.1, 1.3                               |
|    3 |   8. 10. | {{ :courses:zmo:tutorials:week03.zip | Point distribution models }}           | 2.1, 2.2                               |
|    4 |  15. 10. | {{ :courses:zmo:tutorials:week04.zip | SLIC }}                                | 3.1, 3.2                               |
|    5 |  22. 10. | {{ :courses:zmo:tutorials:week05.zip | Texture features & Graphcut }}         | 4.1, 4.2                               |
|    6 |  29. 10. | {{ :courses:zmo:tutorials:week06.zip | Deep learning: segmentation (U-Net) }} | 5.1, 5.2                               |
|    7 |   5. 11. | {{ :courses:zmo:tutorials:week07.zip | Cell detection }}                      | 7.1, 7.2                               |
|    8 |  12. 11. | {{ :courses:zmo:tutorials:week08.zip | Frangi vesselness filter }}            | //no quiz//                            |
|    9 |  19. 11. | {{ :courses:zmo:tutorials:week09.zip | Nodule detection }}                    | 8.1, 9.1                               |
|   10 |  26. 11. | {{ :courses:zmo:tutorials:week10.zip | Region proposal networks }}            | 10.1, 10.2                             |
|   11 |   3. 12. | {{ :courses:zmo:tutorials:week11.zip | B-splines }}                           | 11.1, 11.2                             |
|   12 |  10. 12. | {{ :courses:zmo:tutorials:week12.zip | Registration }}                        | 12.1, 12.2                             |
|   13 |  17. 12. | Semester work: consultation                                                   |                                        |
|   14 |   7.  1. | Semester work: presentation                                                   |                                        |
===== Required software =====

Although the students can use the lab computers, they are strongly encouraged to use their own devices, which will allow them to work on the assignments at home. The easiest way to install all required software is through the [[https://mamba.readthedocs.io/|Mamba]] system. Follow the [[https://mamba.readthedocs.io/en/latest/installation/micromamba-installation.html|instructions]] to get a Micromamba installation.

After installing Mamba, create a new environment and install the required dependencies:

  micromamba create -n zmo python=3 jupyter matplotlib numpy -c conda-forge

To verify the installation, activate the fresh environment

  micromamba activate zmo

and check that the following commands exit successfuly:

  python -c "import numpy; import matplotlib"
  jupyter notebook --version

<note important>
It is necessary to always activate the Mamba environment before using Jupyter:

  micromamba activate zmo

</note>