Skimming “Build OpenCV Mini-Projects” by PyImageSearch: Colors

I followed through PyImageSearch’s introductory Step 3: Learn OpenCV by Example (Beginner) line by line both to get a feel of using Python binding of OpenCV and also to learn this particular author’s style. Once I felt I had that, I started skimming at a faster pace just to get an idea of the resources available on this site. For Step 4: Build OpenCV Mini-Projects I only read through the instructions without actually following along with my own code.

I was impressed that the first part of Step 4 is dedicated to Python’s NoneType errors. The author is right — this is a very common thing to crop up for anyone experimenting with Python. It’s the inevitable downside of Python’s lack of static type checking. I understand the upsides of flexible runtime types and really enjoy the power it gives Python programmers, but when it goes bad it can go really bad and only at runtime. Certainly NoneType is not the only way it can manifest, but it is certainly going to be the most common and I’m glad there’s an overview of what beginners can do about it.

Which made the following section more puzzling. The topic was image rotation, and the author brought up the associated rotation matrix. I feel that anyone who would need an explanation of NoneType errors would not know how a mathematical matrix is involved in image rotation. Most people would only know image rotation from selecting a menu in Photoshop or, at most, grabbing the rotate handle with a mouse. Such beginners to image processing would need an explanation of how matrix math is involved.

The next few sections were focused on color, which I was happy to see because most of Step 3 dealt with gray scale images stripped of their color information. OpenCV enables some very powerful operations I want to revisit when I have a project that can make use of them. I am the most fascinated by the CIE L*a*b color space, something I had never heard of before. A color space focused on how humans perceived color rather than how computers represented it meant code working in that space will have more human-understandable results.

But operations like rotation, scaling, and color spaces are relatively common things shared with many other image manipulation libraries. The second half goes into operations that make OpenCV uniquely powerful: contours.

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