Rebuilding the OpenCV Library from Scratch: A Technical Deep Dive with Rebecca Stone

Rebecca Stone is an expert in computer vision and has been working on the OpenCV library for many years. She has recently embarked on a project to rebuild the entire library from scratch, and this blog post will provide a technical deep dive into her process.

The Need for Rebuilding

OpenCV is one of the most widely used computer vision libraries in the world. It provides a wide range of functions for image and video processing, feature detection, object recognition, and more. However, over time, the library has become increasingly complex and difficult to maintain. Rebecca Stone has decided to rebuild the entire library from scratch as a way to simplify its architecture and improve its performance.

The Process of Rebuilding

Rebecca Stone began by studying the current OpenCV codebase in depth. She spent several months analyzing every line of code, identifying areas that could be improved or removed, and creating a list of tasks to complete during the rebuilding process.

Next, she started writing new code from scratch. This involved implementing each function one by one, making sure they were correct and tested thoroughly before moving on to the next one. She also made sure to follow best practices for coding and commenting her code so that others could understand what it was doing.

Another important aspect of rebuilding the library was deciding which parts of it to remove or modify. Rebecca Stone identified areas where functionality had been duplicated or where code was no longer needed, and she removed them entirely. She also made changes to the API to make it more consistent and easier to use.

Practical Examples

To give you a better idea of how this process works, let’s take a look at an example from Rebecca Stone’s own blog post on rebuilding OpenCV:

### Example: Rebuilding the `cv::Mat` Class

The `cv::Mat` class is one of the most fundamental classes in OpenCV. It represents a matrix of pixels and provides various methods for manipulating it.

In the current version of OpenCV, the `cv::Mat` class has many dependencies on other parts of the library. This makes it difficult to use independently or modify without affecting other parts of the codebase.

Rebecca Stone decided to rebuild the `cv::Mat` class from scratch using a different approach. Instead of relying on other parts of the library, she implemented all the necessary methods directly in the `cv::Mat` class itself.

Here is an example of how this might look:

```cpp
#include "opencv2/core.hpp"
#include "opencv2/utils.hpp"

namespace cv {

class Mat {
public:
    // Constructor
    Mat(int rows, int cols);

    // Destructor
    ~Mat();

    // Copy constructor
    Mat(const Mat& m);

    // Assignment operator
    Mat& operator=(const Mat& m);

    // Get the number of rows
    int rows() const;

    // Get the number of columns
    int cols() const;

    // Set a pixel value
    void set(int row, int col, uchar val);

    // Get a pixel value
    uchar get(int row, int col) const;
};

}  // namespace cv

This new version of cv::Mat is much simpler and easier to use than the original. It can be used independently without relying on other parts of the library.

Conclusion

Rebuilding OpenCV from scratch has been a challenging but rewarding experience for Rebecca Stone. By simplifying its architecture and removing unnecessary dependencies, she has created a more maintainable and efficient library that is better suited for modern computer vision tasks.

If you are interested in learning more about this process, I recommend checking out Rebecca’s blog post on the subject. She provides detailed explanations of how she rebuilt each part of the library, as well as practical examples and code snippets to help illustrate her approach.

Rebuilding OpenCV from scratch is a complex task that requires extensive knowledge of computer vision and programming languages like C++. However, by following best practices for coding and commenting your code, you can make it easier for others to understand what you are doing.