Page Comparison

Here you will find all the necessary resources for the computer vision input.

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Watch the presentation and write down questions and thoughts that come to your mind! We can discuss them later in the Zoom conference.

Video: https://vimeo.com/401186852/1b174179e8

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📼IAD-CV-Introduction Video

• Microsoft - Training deep control policies for the real world

• Deep Vision - Understanding machine perception

• Kollision - Urban Future

• Moovelab - Who want’s to be a self driving car?

• Philipp Schmitt - Camera Restricta

• ClearView AI - Brüssel - Facial Recognition NZZ

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https://zhdk.zoom.us/j/4710337215

Topcis

• What do you expect of today?

• How was it to watch the presentation?

• Specific ideas for your group project?

• How is the input structured?

• Use Slack / Zoom for help

Processing & Media (10:30 - 12:00)

Do your own first steps with processing and computer vision by creating a paint tool with your own interaction method.

• Video Library 2.0

• PImage (Processing)

• Filter (Processing)

• Computer Vision Wiki IAD

• How do common virtual reality tracking systems work?

Task 1

Create a new processing sketch which loads this image and displays it. How can we find the position of the moon?

int w = img.width;
int h = img.height;

for(int y = 0; y < h; y++) {
  for(int x = 0; x < w; x++) {
    
    // do something
    img.set(x, y, color(0)):
  }
}

Task 2

Install the Video Library 2.0 and display the webcam image!

Task 3 a)

Combine brightest point tracking with the webcam capture to track the brightest point in your webcam video.

Task 3 b)

Create an own drawing software that is controlled by the light of your smartphone flashlight.

• Think about additional features you could implement!

Zoom Conference (13:00)

Discuss the morning & share what everyone has created.

https://zhdk.zoom.us/j/4710337215

• Technical Input

• Was it possible to follow?

OpenCV (

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15:00 -

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16:

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30)

Getting deeper into computer vision by using OpenCV, a framework to analyse images with traditional computer vision algorithms.

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Task 4

Use opencv to track the brightest spot.

• opencv.blur(10);

• PVector location = opencv.max();

opencv.loadImage(cam);

// process
opencv.blur(10);
PVector location = opencv.max();

image(opencv.getSnapshot(), 0, 0);

noFill();
stroke(100, 255, 80);
circle(location.x, location.y, 10);

import processing.video.*;
import gab.opencv.*;
import org.opencv.core.*;

Capture cam;
OpenCV opencv;

void setup() {
  size(500, 500, FX2D);

  String[] inputDevices = Capture.list();
  cam = new Capture(this, 640, 480, inputDevices[0]);
  cam.start();

  opencv = new OpenCV(this, 640, 480);
}

void draw() {
  background(0);

  if (cam.available())
    cam.read();

  opencv.loadImage(cam);

  // do something

  image(opencv.getSnapshot(), 0, 0);
}

Task 5

Write your own sketch that is able to detect motion! Use either background subtraction, image difference or your own algorithm.

How will you notify yourself?

// import the following at the top of your sketch:
import org.opencv.core.*;

// run this after you have loaded the image to opencv
int count = Core.countNonZero(opencv.getGray());

import processing.video.*;
import gab.opencv.*;
import org.opencv.core.*;

Capture cam;
OpenCV opencv;

PImage last = null;

void setup() {
  size(640, 480);

  String[] inputDevices = Capture.list();
  cam = new Capture(this, 640, 480, inputDevices[0]);
  opencv = new OpenCV(this, 640, 480);
  
  cam.start();
}

void draw() {
  background(0);
  
  if(cam.available())
    cam.read();
  
  opencv.loadImage(cam);
  
  if(last != null) {
    opencv.diff(last);
    opencv.threshold(40);
  }
  
  last = cam.copy();
  
  float count = Core.countNonZero(opencv.getGray());
  float difference = count / (cam.width * cam.height) * 100;
  
  if(difference > 10.0) {
    println(millis() + "WARNING!");
  }

  image(opencv.getSnapshot(), 0, 0);
  
  fill(0, 255, 255);
  text("Difference: " + difference, 20, 20);
}

Task 6

Use Contour Detection to implement a multi-user drawing system.

import processing.video.*;
import gab.opencv.*;

Capture cam;
OpenCV opencv;

PImage last = null;

void setup() {
  size(640, 480);

  String[] inputDevices = Capture.list();
  cam = new Capture(this, 640, 480, inputDevices[0]);
  cam.start();

  opencv = new OpenCV(this, 640, 480);
}

void draw() {
  background(0);

  if (cam.available())
    cam.read();

  opencv.loadImage(cam);
  opencv.threshold(240);

  blendMode(BLEND);
  image(cam, 0, 0);

  noFill();
  stroke(255, 0, 0);
  strokeWeight(3);
  for (Contour contour : opencv.findContours()) {
    contour.draw();
  }
}

Zoom Conference (15:30)

Discuss the current state and mood.

https://zhdk.zoom.us/j/4710337215

Tuesday 30.03.2020

Depth Sensing (Tuesday 9:00 - 10:00)

Watch the video about depth sensing and on how machines are able to perceive our world.

📼IAD-CV-DepthSensing Video

• DenseDepth - High Quality Monocular Depth Estimation via Transfer Learning [paper][code]

Zoom Conference (Tuesday 10:00 - 10:30)

Questions, notes and comments.

Deep Vision Input (Tuesday 10:30 - 11:45)

This input is about computer vision combined with modern machine learning techniques. It is also an introduction about the topic of machine learning in general. How does a machine learn? How does it see?

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Task 7

Download the Deep Vision Library (direct download link) for Processing and install it into your library folder. Try out the examples and see how the library works.

Task 8

Use the FaceDetector Webcam Example and try to combine it with the emotion classifier. To create an emotion detector, use the following code sample:

FERPlusEmotionNetwork emotionNetwork = vision.createFERPlusEmotionClassifier();
emotionNetwork.setup();

// run on a single face
emotionNetwork.run(face)

// run on multiple detected images
emotions = emotionNetwork.runByDetections(testImage, detections);

A Java example on how to combine these two methods you can find here, it is Processing as well, but you may have to adapt it slightly.

Furthermore: Have a look at the Gender & Age prediction example and try to add it to the sketch as well.

Data Analysis and Connection (Friday 13:00-17:00)

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• Spatial Interaction Examples

• MQTT / shiftr.io

• Open Sound Control (OSC)

• Wekinator

Task 9

Try to analyze your own set of images and think about what you could

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use this information for. Use the example provided here.

Task 10

Try out the mouse example to train your own classifier.

• What else could you classify?

• Where do you get the data from?

• How can you include this into your main project

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• ?

Content

Please do not share the slides and the content from this page. The images and videos in the presentations are sourced but not licensed.

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