====== 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. Attendance at the labs is **recommended but not mandatory**. However, if you do not attend, you will not get points for the quizzes. See information about the [[courses:zmo:semestral:start|semester work]]. ===== Schedule ===== ^ Week ^ Date ^ Topic ^ Quiz topics ^ | 1 | 27. 9. | {{ :courses:zmo:tutorials:week01.zip | Introduction }} | //no quiz// | | 2 | 4. 10. | {{ :courses:zmo:tutorials:week02.zip | Active contours }} | 1.1, 1.3 | | 3 | 11. 10. | {{ :courses:zmo:tutorials:week03.zip | Point distribution models }} | 2.1, 2.2 | | 4 | 18. 10. | {{ :courses:zmo:tutorials:week04.zip | SLIC }} | 3.1, 3.3 | | 5 | 25. 10. | {{ :courses:zmo:tutorials:week05.zip | Texture features & Graphcut}} | 4.1, 4.2 | | 6 | 1. 11. | {{ :courses:zmo:tutorials:week06.zip | Deep learning: segmentation (U-Net)}} | 5.1, 5.2 | | 7 | 8. 11. | {{ :courses:zmo:tutorials:week07.zip | Cell detection}} | 6.1, 6.2 | | 8 | 15. 11. | {{ :courses:zmo:tutorials:week08.zip | Frangi vesselness filter}} | //no quiz// | | 9 | 22. 11. | {{ :courses:zmo:tutorials:week09.zip | Nodule detection}} ([[https://drive.google.com/file/d/1aj9S67UdxCTNxQq-BdOfhHpHiT3j7gXv/view?usp=sharing|data]]) | 8.1, 8.2 | | 10 | 29. 11. | {{ :courses:zmo:tutorials:week10.zip | B-splines }} | 9.1, 9.2 | | 11 | 6. 12. | {{ :courses:zmo:tutorials:week11.zip | Registration }} | 10.1, 10.2 | | 12 | 13. 12. | {{ :courses:zmo:tutorials:week12.zip | Registration (Simple ITK)}} | 11.1, 11.2 + B-splines from lecture | | 13 | 20. 12. | Semester work: consultation | //no quiz// | | 14 | 10. 1. | Semester work: presentation | //no quiz// | ===== Required software ===== Although the students can use the lab computers, they are 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://conda.io/projects/conda|Conda]] system. Use the [[https://docs.conda.io/en/latest/miniconda.html|Miniconda]] installer to obtain a Conda installation. After installing Conda, create a new environment and install the required dependencies: conda create -n zmo python=3 jupyter matplotlib numpy To verify the installation, activate the fresh environment conda activate zmo and check that the following commands exit successfuly: python -c "import numpy; import matplotlib" jupyter notebook --version It is necessary to activate the Conda environment before using Jupyter, so it is required to run conda activate zmo before working on the assignments.