Computer Vision is according to Wikipedia and other sources a field of study that seeks to develop techniques to help computers to see and understand the content of digital images such as photographs and videos.
It is a multidisciplinary field that can be considered as a subfield of Artificial Intelligence and Machine Learning where specialized methods and general learning algorithms are used.
According to Computer Vision: Models, Learning, and Inference (2012):
The goal of computer vision is to extract useful information from images. This has proved a surprisingly challenging task; it has occupied thousands of intelligent and creative minds over the last four decades, and despite this, we are still far from being able to build a general-purpose "seeing machine".
With this in mind and after working on some computer vision projects, I realize the importance to enter and deep into this field, as long with the passion that I discover for it. Based on this, I decided to become proficient in the field. As a first step, I decided to create these self-made tutorials series and projects regarding Computer Vision, Machine Learning, and Deep Learning based on the wonderful courses developed by Adrian Rosebrock "PyImageSeach Gurus".
The main work of these was based on the course materials and my work. Since I am a strong believer in open source, I hope these series help you on starting in Computer Vision with the powerful OpenCV, as it has being helping me.
In this tutorial (script and Jupyter Notebook) a simple exercise is carried out with OpenCV to load, display on the screen, and save the image in a different format. Using Colab.
With the script you can:
Load an image with:
cv2.imread()
Display the image with:
cv2.imshow()
Write the image back to the disk and save it with a different format using:
cv2.imwrite()