Teaching Hands-On Digital Image Processing with morph.py: Methods and Comprehensive Results

Teaching Digital Image Processing (DIP) presents significant challenges due to mathematical and algorithmic complexities. Although the field has experienced recent growth, there is a lack of comprehensive resources to support effective DIP education. To address this gap, this paper introduces a practical course utilizing a Python library named morph.py, which is designed for beginners and accessible on Google Colab. This interactive course features illustrative examples and hands-on exercises to help learners grasp fundamental DIP concepts and operators. It starts with basic concepts (e.g., image representation) and gradually advances to more complex topics, including image transformations and feature extraction. This work demonstrates how to use morph.py in Colab for teaching materials to tackle different computer vision problems. It also discusses the integration with MCTest and Moodle for assessments with automatic grading, enhancing the reproducibility of the presented method. Additionally, the paper details the use of Safe Exam Browser (SEB) for securing exams. We conducted an exploratory case study in one group (N=15) and gathered their perception through a voluntary survey. Our quantitative analysis provides strong support for the effectiveness of our teaching method based on the morph.py library, successfully addressing the difficulties of teaching DIP to beginners.