OpenCV is an open source library for computer vision and machine learning. It contains 2500 optimized algorithms for real-time applications like face detection, recognition, and image processing. OpenCV supports C++, Python, and Java and runs on Linux, MacOS, Windows, iOS, and Android. Examples of uses include a smart face camera attendance system using face detection and recognition, separating color channels, finding histograms, filtering images, and detecting differences between images. OpenCV can be integrated with Processing using the OpenCV Java API.

1. OpenCV for Processing
By Procheta Nag

2. Open Source
Free “Open Source Computer Vision Library (OpenCV)”
Optimized
2500 optimized algorithms focusing real-time applications (ex: DIP)
Cross-Platform
C++, Python and Java interfaces support Linux, MacOS, Windows, iOS, and Android.
DIP =Digital Image Processing

3. Examples
Face Detection and Recognition
Facebook photo tag
Face lock
opencv.detect()

4. Ex:Smart Face-cam attendance system
Face detection
(Detect face in image)
Training images
Face recognition(live cam)

5. Examples
ColorChannels
Separate a color image into RGB and HSB.
work with the channels individually.
opencv.getR()
FindHistogram
Get and draw histograms for grayscale and individual color
channels.
opencv.findHistogram()

6. Examples
Filter Images
Basic filtering operations on images: threshold, blur,
and adaptive thresholds.
opencv.blur()
ImageDiff
Find the difference between two images
subtract the background or detect a new object in a scene.
opencv.diff()

7. Processing Integration
OpenCV JAVA API for processing
Installation ( Latest version 4.1.1)
Windows, Mac and Linux version
Import library
import gab.opencv.*;
OpenCV opencv;

8. References
• https://opencv.org
• http://www.willowgarage.com/pages/software/opencv
• https://github.com/atduskgreg/opencv-processing
• https://www.sciencealert.com/facebook-just-pushed-its-facial-recognition-
into-a-bold-new-future-tagged-privacy