Objectives:
This lab revisits important concepts covered in the Week 1 and Week 2 lectures and aims to make you familiar with implementing specific algorithms.
Preliminaries:
As mentioned in the first lecture, we assume you are familiar with programming in Python or are willing to learn it independently. You do not need to be an expert, as you will further develop your skills during the course, but you should at least know the basics. If you do not yet know Python, we assume you are familiar with at least one other programming language such as C, in which case it should be relatively easy to learn Python.
To learn or brush up your Python skills, see several free online resources listed at the end of this document. Especially if you already know C or similar languages, there is no need to go through all the linked resources in detail. Just quickly learn the syntax and the main features of the language. The rest will follow as you go.
For implementing and testing computer vision algorithms, we use OpenCV in this course.
OpenCV is a library of programming functions mainly for computer vision. The library is crossplatform and licensed as free and open-source software under Apache License 2. It also supports training and execution of machine/deep learning models. Originally written in C, with new algorithms developed in C++, it has wrappers for languages such as Python and Java. As stated above, in this course we will focus on programming in Python. See the links below for OpenCV tutorials and documentation.
Software:
You are required to use OpenCV 3+ with Python 3+ and submit your code as a Jupyter notebook (see coding and submission requirements below). In the first tutor consultation session this week, your tutors will give a demo of the software to be used, and you can ask any questions you may have about this.
Materials:
The sample images to be used in this lab are available via WebCMS3.
Submission:
All code and requested results are assessable after the lab. Submit your source code as a Jupyter notebook (.ipynb) which includes all output and answers to all questions (see coding requirements at the end of this document) by the above deadline. The submission link will be announced in due time.
1. Contrast Stretching
Contrast is a measure of the range of intensity values in an image and is defined as the difference between the maximum pixel value and minimum pixel value. The maximum possible contrast of an 8-bit image is 255 (max) – 0 (min) = 255. Any value less than that means the image has lower contrast than possible. Contrast stretching attempts to improve the contrast of the image by stretching the range of intensity values using linear scaling.