OpenCV + Python Introduction and installation process

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This course covers the introduction to OpenCV using Python. OpenCV is a representative library that can easily implement computer vision. Computer vision is a technology that analyzes and processes images, and can be used efficiently when used with Python. This course is designed for beginners to easily follow, and focuses on practice rather than theory. You will learn various basic functions from the OpenCV installation process, and complete interesting projects. Through practice, you will learn the core principles of computer vision and learn how to apply them to solving real-life problems.

OpenCV logo (source: wikipedia)

 

1. What is computer vision?

Computer vision refers to the technology that allows computers to understand and process the process that humans perceive visually. In other words, it is a technology that analyzes images and helps computers ‘understand’ the information contained within them. In this process, the computer collects image or video data, and recognizes objects or analyzes patterns based on the data to derive meaningful results. For example, self-driving cars use computer vision to detect lanes, pedestrians, and vehicles on the road to support safe driving. Computer vision technology is also widely used in facial recognition, object tracking, document scanning, medical image analysis, and factory automation.

Pedestrian, vehicle and bicycle detection

Computer vision is actively used in various fields, and OpenCV is an essential tool for its implementation. OpenCV provides functions necessary for image and video processing, and helps to implement complex algorithms simply. When used with Python, even beginners can easily start a computer vision project.

 

2. OpenCV Basic Concepts

The most important basic unit in computer vision is the 'image'. The image is made up of numerous 'pixels', and each pixel has a different color and brightness, and the image we see is formed by their combination. For example, a black and white (binary) image consists of only black (0) and white (255), and is called a grayscale image, and can express various levels of gray using values ​​from 0 to 255.

Grayscale Image

On the other hand, color images are created by combining three colors: red (R), green (G), and blue (B). When these RGB values are combined, they become the color images we commonly see. The resolution of an image is defined by the number of pixels, and refers to resolutions such as VGA (640x480), HD (1280x720), Full HD (1920x1080), and 4K (3840x2160). Higher resolutions contain more pixels, resulting in more detailed images. OpenCV can be used to effectively process and analyze this pixel data.

The three primary colors of light and the mixing of light (source: flaticon)

 

3. Installation Process

To use OpenCV, you must first install Python and the OpenCV library. In this process, you will install Python in the Anaconda virtual environment and install the PyCharm code editor. PyCharm is a tool that helps you write and debug code easily. Create a new project and install the OpenCV-Python library. Search for OpenCV in the PyCharm settings menu, install it, and use the `import cv2` command to load the OpenCV library. Alternatively, you can install it using the Terminal, and you can install it using the pip install python-opencv command. Once this process is complete, you will be ready to create a Python file and write code. For detailed installation process, please refer to the post below.

2024.10.23 - [Programming/Python] - Anaconda Installation Guide (feat. JupyterLab)

2024.10.23 - [Programming/Python] - PyCharm Installation Guide (feat. Anaconda Interpreter)

 

4. Practice Content

Let's start with how to load images and videos using OpenCV. For example, you can load an image with cv2.imread() and display the image on the screen with cv2.imshow(). After loading an image, you can easily resize or crop it. OpenCV provides a function to change the size of an image with the cv2.resize() function and crop only the desired part.

You can also draw shapes or add text on an image using OpenCV. You can draw various shapes with functions such as cv2.line() and cv2.circle(), and you can also add text that describes the image with the cv2.putText() function. After learning these basic tasks, you can learn more advanced functions such as geometric transformation, color detection, and contour detection.

Geometric transformation is the task of rotating or moving a specific part of an image, and can be implemented with the cv2.warpPerspective() function. Color detection is a technique to filter an image based on a specific color, and the cv2.inRange() function is used to detect a specific range of colors. Edge detection is a technique for finding the boundaries of objects in an image and is used to recognize objects or shapes.

 

5. Hands-on Projects

You can use the skills you have learned in this course to connect them to real projects. Hands-on projects will be added in the future and will be revised as we reach the end of this course.

 

6. Conclusion

This course covers a wide range of computer vision from basics to advanced techniques, with a focus on applying what you have learned through hands-on projects. Computer vision plays a vital role in modern technology, and is used in a variety of fields such as facial recognition, autonomous driving, and industrial automation. Using OpenCV and Python, anyone can easily start a computer vision project. Based on what you have learned in this post, you will be able to learn computer vision techniques that can be applied usefully in real life.

 

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★ All contents are referenced from the link below. ★

OpenCV: OpenCV-Python Tutorials