Laser Tracker Slingshot

Slingshot

Developed by : Siddhant Betrabet, Selena Vaz, Deepika Parsharam.

Developed at Nestwell Technologies.

The Project: A projector connected to a Linux computer projects images on a wall. A hand held device looking like a wooden slingshot has a phone, a laser pointer, an Esp-Wifi Module/ RF-Module(both have been used) and a switch connected to the sling is operated by the user. The user types in a message and a selfie into an app running on the phone. The pulls the sling which activates a laser pointer onto the same wall with the projector. Upon release, a paint splash image with a the message appears on the wall.

Calibrated for a Dell monitor and Lighting conditions at close range.

The object tracking code was recalibrated for the different lighting conditions of the projector and the room

This video is an implementation of the Slingshot at the TATA Communications WeConnect event.

The software:

• I used the code from https://github.com/jrosebr1/imutils/blob/master/bin/range-detector program. The program let us use simple trackbars to pin down the correct HSV values of the lasere pointer by pointing the webcam on the intended projection surface/TV. I added some of my own code in to save these values in an Sqlite database
• Secondly i used the Balltracking.py code from GitHub by hksdun https://gist.github.com/hkdsun/2825a1b0916cb74a455dbe3e31b06db5 and added my own code to read the HSV values from the SQLite database into itself as its parameters.
• Then i added some Mean-shift Cluster recognition code to the previous code. The idea was to have to point thelaser pointer at four vertices of the screen/monitor, as the ball tracking program would output its values depending on its own vertices. The laser would be pointed at the these fourpoints for 20seconds , 15 seconds, 10 seconds, and 5 second so the x,y values of the four vertices would be stored in a text file. Then it would run a 1-D cluster recognition on the x-coordinates and a separate 1-D cluster recognition on the Y-coordinates and know where the vertices were locatedas those would be those four clusters of points. This numbers were then used for mapping the output of the tracking program with its relevant point on the screen.
• At the end of the calibration a similar tracking program would run. That is, every time the slingshot sling was pulled the balltracking algorithm tracking the Laser Red color would update the values. The moment the sling was released, the Linux laptop would get a Tcp message/ RF signal (Raspberry PI with RF connected to it's GPIOs) would then display an image in HTML through a websocket to create a new image element on the screen with the message.
• The Android app developed by Dipika Parsharam embedded in the wooden slingshot would accept message and name fields as well as text fields. A change that was made for the event was disabling the laser tracker and adding a photo that was transferred over tcp to the Raspberry PI/Linux Laptop and was then displayed on the screen with a message