Computer vision system to recognize Hand gestures

 Objectives for today: Finish software with output logic Finish setting up hardware Make a poster Add more functionalities (optional) We had run into a problem right from the start as our PCB was printed out wrong. We hope that our PCB will arrive by today, but if worse comes to the worst we will have to finish it in the second group's lab schedule or make the output only single-digit. We tested the one-digit PCB and it works well. This would be our final product if we don't manage to get the second PCB in time. We are still working on improving the software. In the end, we had to be satisfied with our one-digit output. 

 Objectives for today: Finish the basic structure of the software Put everything on PCB We started working on the code immediately and quite quickly finished the basic structure. Travis's method of using angles between finger landmarks was the most suitable to our system. We will also need to create output logic in the code to connect it to the hardware. The circuit design is also finished, we will solder after we are done with testing the final code output. The test circuit for 1-digit numbers was quickly connected to the Raspberry PI and worked wonderfully. We proceeded to build a two-digit test circuit right away. In the meantime, we are also debating about adding some functionalities to our code. The two-digit output test on the Raspberry PI caused us a lot of problems and we haven't managed to finish it this time. We will keep working on the software next week, and hopefully, it will take us little time as we would like to add some extra features an...

 Objectives for today: Finish the hardware circuit design Continue working on the code We all came to the labs today. Our objectives for this session are quite difficult to measure whether we have completed it or not. We kept working on the code and hardware. We kept tabs on each other, sharing our ideas and tested a few methods for finger detection. The methods included using Mediapipe, contour detection with high contrast between hand and background. Using Mediapipe proved to be the simplest as it automatically assigns landmark to a hand. We are working with the landmark relationships. We also made significant progress with circuit design and tested it's number output using a testing code that counts from 0 to 9. Ben also designed a potential PCB for our circuit.

Objectives for today Meet up in the lab Collect ordered supplies Set up Raspberry PI Make a simple test code for the Raspberry PI camera Timeline: All of us arrived at the lab and we went to collect the parts we ordered, which included: Raspberry 5 4GB RAM Seven-segment LED display ICs for the display (CD4511) Camera module for Raspberry PI Power supply for Raspberry PI 64 GB microSD card We set up the Raspberry PI which did not proceed without minor hiccups. We were missing a micro-HDMI to HDMI converter and could not connect to the university Wi-Fi network using the Raspberry PI device. All of these were fortunately resolved with the help of technicians and our own mobile hotspot. While Travis and Zhengyu worked on the code, Ben was researching the hardware connections and circuits. Abdulaziz and Huy Dat were working on the logbook and online blog. The members occasionally checked up on each other and tried to help if needed. The test code for Raspberry Pi cam...

Computer vision system to recognize Hand gestures Our goal in this project is to develop a program that is able to count fingers that are on the video feed from a camera and output it on a 7-segment LED display. We will be using Raspberry PI 5 with its camera module. As a programming language, we chose Python for its simplicity and libraries provided. The code will detect fingers from the camera module and create varying outputs depending on the number of fingers shown.  The varying outputs will be processed by decoders and the appropriate number will be displayed on the LED display.  We hope to achieve this within the scope of 4 weeks. Team members: Abdulaziz Almhmoud, Ben Cooke, Travis Johnson, Zhengyu Chen, Huy Dat Le