OpenCV stands for Open Source Computer Vision Library, it is an open-source computer vision and machine learning software library primarily designed for real-time computer vision applications.
Key features of OpenCV:
a. Image processing
b. Video analysis
c. Object detection and recognition
d. OpenCV can be integrated with machine learning libraries like TensorFlow and PyTorch etc.,
e. You can stitch multiple images into one to create Panorama kind of effect.
f. OpenCV has lot of pre-built computer vision algorithms such as edge detection, image segmentation etc.,
g. It can be used to develop Augmented reality
h. OpenCV is available in several programming languages like Python, Java, C, JavaScript, Ruby, Perl, MATLAB, R etc.,
Prerequisites to this course
a. Python should be installed in your system. You can confirm this by executing one of the below commands from your terminal.
python –version (or) python3 --version
$python3 --version
Python 3.9.6
If you get the output like above, then you are good to go with Python installation.
b. Install open cv module by executing either of below commands.
pip install opencv-contrib-python (OR) pip3 install opencv-contrib-python
opencv-contrib-python vs opencv-python packages
opencv-python package contains core functionality of OpenCV.
Opencv-contrib-python is an extended package that includes additional modules and features beyond the core functionality of OpenCV. These additional modules are used to perform text detection, depth sensing, augmented reality, and more.
If your task is just to work with standard computer vision tasks, then you can go with opencv-python package. If you want additional functionalities like text detection, augmented reality then go with opencv-contrib-python module.
Note
opencv-contrib-python is larger in size, so it may take up more storage space, especially if you only need core functionality.
You can download all the examples of this tutorial from this link.
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