Tips and typical application examples for using OpenCV in Python

• A time-lapse photography app
• A screen capture tool for videos
• An image data augmentation gadget
• An object detection annotation tool

6.1 Introduction to OpenCV

OpenCV is one of the most widely used open-source computer vision libraries in the world. Originally developed in C/C++, it supports multiple platforms including Linux, Windows, macOS, Android, and iOS. Its popularity stems from its rich set of interfaces and high performance, making it accessible to developers using languages like Python, MATLAB, and Java. OpenCV’s permissive licensing has made it a favorite both in academic research and industrial applications.

The project originated in 1998 as a research initiative by Intel. Gary Bradski, a computer vision engineer at Intel, noticed that many students were working on similar vision algorithms but had limited access to shared code. This inspired the creation of OpenCV as a general-purpose library to help researchers and developers build upon existing work efficiently.

The first alpha version was released in 2000 at CVPR. Over the next few years, several beta versions were released before the official version 1.0 came out in 2006. In 2009, after Gary joined Willow Garage, OpenCV received more active support and saw the release of version 1.1. Version 2.0 followed in 2010, introducing a comprehensive C++ interface. Since then, OpenCV has continued to evolve, with version 3.0 officially launched in 2015. This version introduced improved architecture, enhanced performance, and more concise APIs, along with better GPU support.

Today, OpenCV is used across many research institutions and commercial companies, serving as a cornerstone for computer vision applications worldwide.

6.1.1 Structure of OpenCV

Currently, OpenCV comes in two major versions: OpenCV2 and OpenCV3. While OpenCV3 offers more features and better usability, this section will focus on OpenCV2 for ease of use and compatibility with deep learning frameworks.

OpenCV is organized into various modules based on functionality. Some of the most commonly used ones include:

• core: The core module contains fundamental data structures such as matrices, points, and shapes, along with basic operations.
• imgproc: This module handles image processing tasks, including filtering, resizing, edge detection, and more advanced techniques like histogram analysis and morphological operations.
• highgui: Provides user interface functions, such as image display and file I/O for images and videos.

For video and specialized visual tasks, OpenCV includes additional modules:

• video: Contains tools for optical flow and object tracking.
• calib3d: Handles 3D reconstruction and camera calibration.
• features2d: Offers feature detection and matching algorithms like ORB.
• objdetect: Includes object detection methods such as cascade classifiers and Latent SVM.
• ml: A machine learning module with traditional vision algorithms.
• flann: A fast library for approximate nearest neighbor searches, often used with keypoint-based algorithms.
• gpu: Provides GPU-accelerated functions using CUDA.
• photo: Focuses on computational photography, mainly image restoration and noise reduction.
• stitching: Enables image stitching for panoramic photos.
• nonfree: Contains patented algorithms like SIFT and SURF.
• contrib: Experimental algorithms that may be included in future versions.
• legacy: Older interfaces maintained for backward compatibility.
• ocl: Interfaces that utilize OpenCL for parallel computing.
• superres: Implements super-resolution algorithms like BTV-L1.
• viz: A 3D rendering module built on top of VTK.

From a user perspective, OpenCV3 introduces more features and a more granular modular structure compared to OpenCV2.

6.1.2 Installing and Using OpenCV

Installing OpenCV on Linux is straightforward. Most distributions have it available through the package manager. For example, on Ubuntu 16.04 LTS, you can run the following commands in the terminal:

sudo apt install libopencv-dev python-opencv

Alternatively, you can download the source code from the official website and compile it manually. First, install the necessary dependencies:

sudo apt install build-essential cmake git libgtk2.0-dev pkg-config libavcodec-dev libavformat-dev libswscale-dev

sudo apt-get install python-dev python-numpy libtbb2 libtbb-dev libjpeg-dev libpng-dev libtiff-dev libjasper-dev libdc1394-22-dev

Next, clone the OpenCV repository:

git clone https://github.com/opencv/opencv.git

Then create a build directory and configure the build:

mkdir release && cd release

cmake -D CMAKE_BUILD_TYPE=RELEASE -D CMAKE_INSTALL_PREFIX=/usr/local

Finally, compile and install:

make

sudo make install

On Windows, installation is also simple. Visit the OpenCV official website to download the installer. After installation, you’ll find a file named `cv2.pyd` in the `/build/python/2.7` folder. Copy this file to `\Lib\site-packages` to complete the setup.

If you want to try OpenCV in Python on Windows without compiling, you can download the appropriate wheel file from Christoph Gohlke's site and install it via pip:

~gohlke/pythonlibs/#opencv

To use OpenCV in Python, simply import it with:

import cv2

Once imported, you're ready to start exploring the powerful features of OpenCV.

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