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Installation and Usage of OpenCV Library in Python Programming

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In this Article, We'll Discuss about the most important library named as "OpenCV" in Python Programming. So, in out last article's about NumPy, Matplotlib, Pandas - ahead I thought to write more on libraries in Python Programming.
OpenCV is a Python open-source library, which is used for computer vision in Artificial intelligence, Machine Learning, face recognition, etc.
OpenCV is a library of Python Programming functions mainly aimed at real-time computer vision. OpenCV is originally developed by Intel and later on supported by Willow Garage then Itseez.
This Library is Cross-Platform and free to use under the open source Apache 2 License.

OpenCV was released in June 2000.

How to Install OpenCV?

pip install opencv-python

Installation and Usage

If you have previous/other manually installed (= not installed via pip) version of OpenCV installed (e.g. cv2 module in the root of Python's site-packages), remove it before installation to avoid conflicts.

Make sure that your pip version is up-to-date (19.3 is the minimum supported version): pip install --upgrade pip. Check version with pip -V. For example Linux distributions ship usually with very old pip versions which cause a lot of unexpected problems especially with the many Linux format.

Select the correct package for your environment:

There are four different packages (see options 1, 2, 3 and 4 below) and you should SELECT ONLY ONE OF THEM. Do not install multiple different packages in the same environment. There is no plugin architecture: all the packages use the same namespace (cv2). If you installed multiple different packages in the same environment, uninstall them all with pip uninstall and reinstall only one package.

a. Packages for standard desktop environments (Windows, macOS, almost any GNU/Linux distribution)

Option 1 - Main modules package: pip install opencv-python
Option 2 - Full package (contains both main modules and contrib/extra modules): pip install opencv-contrib-python (check contrib/extra modules listing from OpenCV documentation)
b. Packages for server (headless) environments (such as Docker, cloud environments etc.), no GUI library dependencies

These packages are smaller than the two other packages above because they do not contain any GUI functionality (not compiled with Qt / other GUI components). This means that the packages avoid a heavy dependency chain to X11 libraries and you will have for example smaller Docker images as a result. You should always use these packages if you do not use cv2.imshow et al. or you are using some other package (such as PyQt) than OpenCV to create your GUI.

Option 3 - Headless main modules package: pip install opencv-python-headless
Option 4 - Headless full package (contains both main modules and contrib/extra modules): pip install opencv-contrib-python-headless (check contrib/extra modules listing from OpenCV documentation)

Import the package:

import cv2

All packages contain Haar cascade files. cv2.data.haarcascades can be used as a shortcut to the data folder. For example:

      cv2.CascadeClassifier(cv2.data.haarcascades +
      "haarcascade_frontalface_default.xml")
OpenCV is the huge open-source library for the computer vision, machine learning, and image processing and now it plays a major role in real-time operation which is very important in today’s systems. By using it, one can process images and videos to identify objects, faces, or even handwriting of a human. When it integrated with various libraries, such as NumPy, python is capable of processing the OpenCV array structure for analysis. To Identify image pattern and its various features we use vector space and perform mathematical operations on these features. 



The first OpenCV version was 1.0. OpenCV is released under a BSD license and hence it’s free for both academic and commercial use. It has C++, C, Python and Java interfaces and supports Windows, Linux, Mac OS, iOS and Android. When OpenCV was designed the main focus was real-time applications for computational efficiency. All things are written in optimized C/C++ to take advantage of multi-core processing.

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