ELEE XXXXXXXXXXSpring XXXXXXXXXXLab 5 (worth 2 labs) Due 4/12, AoE (Reflection Due 4/17) The goal of this lab is to practice integrating computer vision. You will be using MediaPipe to detect...

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ELEE 2045 - Spring 2023 - Lab 5 (worth 2 labs) Due 4/12, AoE (Reflection Due 4/17) The goal of this lab is to practice integrating computer vision. You will be using MediaPipe to detect poses and count actions, as well as your M5StickCPlus to trigger events. You’ll also be using YOLOv5 to detect when a specific arrangement of objects is detected. All of your work should be found in your class GitHub repository under a folder, lab 5. Part 1. Repetition Counting (lab5_part1.py) The FaceMesh example was previously demonstrated. Another module is the mediapipe.solutions.pose.Pose. This is documented at https://google.github.io/mediapipe/solutions/pose.html In this example, they suggest that this library can be used to track the number of repetitions of a particular exercise. Your goal is to actually do this (in a limited fashion). The “user” is the person exercising. Let’s assume they are also wearing a smart watch (the M5StickCPlus), which will be used to initiate the repetition counting. You can use Bluetooth or MQTT to communicate. A complete repetition is defined by the user by hitting the M5StickCPlus main button. The first time, the user should be standing up straight. The second time, they should be squatting down at their target height. Once the user hits the button the second time, the workout begins. You can count repetitions by using the following three-state machine: • Start -> Top when the user is in state Start and their head gets near or above a target y value. • Top -> Bottom when the user is in state Top and their head gets near or below a target y value. • Bottom -> Top when the user is in state Bottom and their head gets near or above a target y value. This event also triggers a repetition count. During the workout, you should display the repetition count, repetitions per second, and display their pose targets. At any point, they should be able to hit the button on the M5StickCPlus again to finish the workout, which should be indicated on screen. Take a video of your completed repetition counter, and a add a link in your README.md file to it. Verify that this link works! Part 2. Arrangement Detection Using YOLOv5 in OpenCV was previous demonstrated. In this part, you’ll be detecting when a specific arrangement of three different types of objects in view of a camera. You can find the different object classes in the file classes.txt. If you don’t have 3 real objects, you may instead use pictures of objects (e.g. on your phone or another computer screen). Your program should determine when all three objects are in view at the same time, and when they are, it should save the image to a file (use the latest time as a filename) and then quit. https://google.github.io/mediapipe/solutions/pose.html Note, this requires very little modification to the example. Rather, you’ll need to get the example working, and then add logic to make the determination of the moment when the 3 required objects are present. The list of objects to be recognized should be at the top of your file. Take a video of your completed repetition counter, and a add a link in your README.md file to it. Verify that this link works! Submission (Due 4/12) As with previous labs, create a README.md file in the root of your lab5 folder. This should include links to your two videos. Reflection (Due 4/17) As with previous labs, update your README.md file in the root of your lab folder to include a reflection on the provided solution. This should compare and contrast your solution with the provided one. These reflections should not be shallow. There should be no doubt that you understand both the provided solution and your own solution based on what you write in the reflection. A good reflection will specifically call out parts of the code in each where there are differences. • Please use quality formatting in your README.md file. • Bullet point lists are appreciated.