juherask / l-system-installation

Code for an L-system visualziation & installation

l-system-installation

Source code for an L-system visualziation & installation / demo. It uses physical blocks with symbols and OCR to define the L-system and a PC and a TV screen to display the plant/fractal generated by those rules.

To build one you need to get and build following things:

1. At least 24 blocks with following symbols F, X, +, -, [, and ]. I used plastic 40x40mm blocks from kids puzzle with letters < and > instead of [ and ] to make OCR to work better.
2. A custom made table with a part of the table top being transparent. Preferably there are slots where the blocks fit with some slack.
3. 4 potentiometers and one on/off type switch and an Arduino to read the values and send the readings to the PC via USB-serial. Source code for the Arduino is in lsystem_pots.
4. First pot adjusts how many rewrite iterations are applied with the current set of rules.
5. Second pot adjusts the length of each F draw command.
6. Third pot adjusts the angle of - and + commands.
7. Fourth pot adjusts the strength of the wind effect.
8. A PC with Python 2.7, PyGame, OpenCV and Arduino IDE installed. I used Ubuntu Linux 16.04 LTS.
9. A monitor or a flat TV and some way of fixing it in portrait mode.
10. USB webcam and a light source and a adjustable mount to hold it them position under the table. I used PS3 PlayStation Eye camera and an 220V LED spot.

Unfortunately I have no detailed plans, but feel free to consult following photo.

There are three Python scripts in the repository:

• l-system.py which is the main application. It opens a PyGame window to display the plant, reads the parameter values from the USB-serial and can be used to control the application.
  • Keys [ESC] and [Q] exit the application.
  • Keys [1] to [7] intialize one of the predefined L-Systems.
  • Key [M] reads a L-System from a file manual_rules.txt. Can be used to override the recognized system if OCR fails.
  • Key [P] takes a picture using the webcam, saves it on the disk and and detects the symbols on the table using the funcionality in read_ocr.py.
  • Also toggling the button connected to the Arduino will send a command which takes a picture and tries to recognize the system from it.
  • Key [S] reintializes the serial communication.
• read_cam.py can be used to position the camera. Just continuously grabs images from the webcam.
• train_ocr.py is used to label the data in trainingdata folder for training OCR classifiers. Please take care to adjust MAX_CHAR_SIZE_IN_PIX, MIN_CHAR_NARROW_SIZE_IN_PIX, and MIN_CHAR_WIDE_SIZE_IN_PIX constants to fit your setup. [ESC] stops training and stores the data files in the working directory, here they can be transferred to a labeled_data folder. Use following keys for the block symbols:
  • n = not a symbol (glitch)
  • 9 = > or ]
  • 8 = < or [
  • i = |/-
  • p = +
  • f = F
  • e = upsidedown F
  • x = X
  • z = X rotated 90 degrees
• read_ocr.py offers the OCR recognition funcitionality. It builds a classifier model and applies it when asked to detect symbols from an image. If run as a script OCRs the images in the img_source_folder. The recognized characters can then be interpreted as starting condition and rules.

Example Plants/Systems

1. Plant

X : F<+X>-X

F : FF

2. Plant

X : F-<+X>+F<+FX>-X

F : FF

3. Plant

F : F<+F>F<-F>F

4. Plant

F : F<+F>F<-F>

5. Plant

F : FF-<-F+F+F>+<+F-F-F>

6. Plant

X : F<+X>F<-X>+X

F : FF

7. Plant

X : F<+X><-X>FX

F : FF