HELLO WORLD! Computation & Materiality Reseaerch Elective 2019 by Pieter-Jan Monserez, Bengüsu Cebeci, Didier El-Bacha, Cagatay Erbas lecturer: Corneel Cannaerts, Faculty of Architecture KU Leuven
Spy Cam Spy Cam is a series of different codes tries to work with computer vision which is the most common eye we are exposed today. The codes in this series aimed to detect person and / or motion and track them. In addition ,Spy Cam project also tries to use the output data of those trackings as the new input and transform it to another output. By doing so, Spy Cam creates series of filters which alters the real image simultaneously. Different types of codes whşich have been created during the project are: final code: People Scrambler exercise 1. Memory Tracking exercise 2. Face Swap exercise 3. Motion to Noise exercise 4. Movement Manipulation exercise 5. Movement Pixelation exercise 6. High Contrast to Noise exercise 7. Rotation Manipulation exercise 8. This Person Is Not Real exercise 9. Dancing Activator
Generative Adversarial Networks, Ian Goodfellow - https://thispersondoesnotexist.com/
OpenCv Face Detection Example Code
OpenCV Face Recognition
People Scrambler
final code
People Scrambler is an interactive installation which uses webcam as a tool to capture peoples faces, store and use them as an input data. By patching small pieces from different faces it generates a live mask with a scramble effect so the interacting person’s face cannot be recognized anymore. The code works with different stages; 1. Using opencv to track the scale and the x, y position of a face or multiple faces within the frame. 2. Capturing the detected faces and storing them on the computer temporarily. Note that in this process only a certain amount of pictures are stored in the data string. However the pictures can be edited or deleted later on. 3. Collecting multiple faces and scrambling them to get a new re-mixed face by taking random sections from the stored faces and combine them as a newly created face. 4. Creating a scrambled face effect by patching and gathering different pieces from stored faces on top of your face. The end result is always changing depending on how many faces are visible within the frame and how long they stay.
Memory Tracking
exercise 1
Memory Tracking works by comparing the difference in RGB values between the pixels in the consecutive frames and tracks the motion by this way. Tracked pixels filled with white color. As a second stage in the code, it captures the previous frame as a background when mouse clicked.
Face Swap
exercise 2
This is the earliest version of the final code and the inspiration for the idea. Face Swap works as a webcam filter with a catalogue of faces downloaded from “www.thispersondoesnotexist.com” (a website which AI generates high quality non-existing human faces by its abundant database of images).
Motion to Noise
exercise 3
Motşion to noşise is an example which uses contours to create motion and with tracking the change of these contours, code generates noise sound related to the difference.
Movement Manipulation
exercise 4
Movement manipulation uses base face detection code and by tracking the movement of the face, it translates into the 3D axis. The principle is basically using face as an motion slider through 3D environment.
Movement Pixelation
exercise 5
Movement pixelation gets the data from camera and working with the image on black and white scale. After creating grid of letters on top of the image it maps the picture with that letters based on the brightness of the pixels. In the string of letters it is important how the characters are organized and is going from white to black based on the character.
High Contrast to Noise
exercise 6
This code detects the contours of the motion and simplifies it with a threshold value. By doing that it gets an area of one continous countour line and translate it to noise sound. In addition, with the mouse controller threshold value changes and so does frequency of the output sound.
Rotation Manipulation
exercise 7
Rotation manipulation gets the data from face detection base code and rotates 3D object according to the location of the tracked face.
This Person Is Not Real
exercise 8
This is the previous version of the final code. It uses a database of faces generated by AI and through and creates a collection with array list on Processing. Then with the PImage and mapping tools, it gets different parts from the faces and creates a mixed face which is not recognizable.
Dancing Activator
exercise 9
Dancing activator is an interactive dancing encourager which is aimed to change the volume of the sound by the amount of motion within the frame. When there is no interaction the music stops and when there is more activity, the volume gradually goes up.
Qr code: video of the exercise codes