Tutorial Processing

BoofCV provides an easy to use interface tailored for Processing. Don't know what Processing is? Here is a summary taken from its website:

Processing is a programming language, development environment, and online community. Since 2001, Processing has promoted software literacy within the visual arts and visual literacy within technology. Initially created to serve as a software sketchbook and to teach computer programming fundamentals within a visual context, Processing evolved into a development tool for professionals. Today, there are tens of thousands of students, artists, designers, researchers, and hobbyists who use Processing for learning, prototyping, and production.

Installing

The Processing wrapper for BoofCV (BoofProcessing) has been pushed into it's own project, see below. BoofProcessing supports Processing 3.x.

BoofProcessing source code on GitHub

The latest stable version can be downloaded from GitHub, but it's easier to just install it with Processing.

• Sketch -> Import Library -> Add Library ...
• Type "boofcv" into filter
• Select and click Install button
• Wait for it to download and you're ready to go!

API and Usage Examples

Click below for a complete set of usage examples on GitHub:

* BoofProcessing/examples 

Browsing through those examples is the best way to learn how to use BoofCV in processing. The interface provided is more object oriented than general BoofCV API and allows for commands to be chained in a sequence. Consider the example below, contours are found by chaining several commands together; threshold, erode, and contour. Operations which don't have a nice easy to use interface can also be used. Just code them up as usual.

Consult the README.MD for instructions on how to build Processing support using Gradle.

import boofcv.processing.*;
import boofcv.struct.image.*;

PImage imgContour;
PImage imgBlobs;

void setup() {

  PImage input = loadImage("particles01.jpg");

  // Convert the image into a simplified BoofCV data type
  SimpleGray gray = Boof.gray(input,ImageDataType.F32);

  // Threshold the image using its mean value
  double threshold = gray.mean();

  // find blobs and contour of the particles
  ResultsBlob results = gray.threshold(threshold,true).erode8(1).contour();

  // Visualize the results
  imgContour = results.getContours().visualize();
  imgBlobs = results.getLabeledImage().visualize();

  surface.setSize(input.width, input.height);
}

void draw() {
  background(0);
  if( mousePressed ) {
    image(imgBlobs, 0, 0);
  } else {
    image(imgContour, 0, 0);
  }
}