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Usairim Kamal

Grade 7 | Points: 13140 | Rank: 3116

I am Usairim Kamal. I'm 9-years old from Pakistan. I love making new advance AI-Powered Project including: Games, Website and Images. My Brother Uwaimir Profile: https://www.codingal.com/en-pk/@pridefulCordial41/

Hobby: Building Apps, Websites and Games Using HTML, CSS, JAVASCRIPT, Python and WordPress

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Classes completed

41

Quizzes submitted

37

Projects submitted

37

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Activities

Gesture-Based Scrolling Control

Students will build a gesture-controlled scroll system that detects hand gestures in real-time and controls the system’s scrolling behavior based on whether the hand is open or closed.

Gesture-Based Volume and Brightness Control

Students will build a real-time gesture control system that adjusts the system volume and screen brightness using hand gestures. By calculating the distance between the thumb and index finger, students will manipulate the volume and brightness on their computer in real-time.

Real-Time Hand Gesture Detection

Build a complete, working gesture detection system that uses a webcam to track hands in real-time, identifies all finger positions, and classifies gestures as Open, Closed Fist, or Partial.

Real-Time Hand Gesture Detection

Build a complete, working gesture detection system that uses a webcam to track hands in real-time, identifies all finger positions, and classifies gestures as Open, Closed Fist, or Partial.

Real-Time Color Filters and Edge Detection

Students will experiment with applying real-time color filters and edge detection effects to a sample image or webcam feed. They will toggle between filters and edge detection techniques using keyboard input and observe the effects instantly.

Real-Time Face Tracking and Counting People

Students will implement real-time face tracking and counting using their webcam. They will detect faces, count the number of people in the frame, and display the count dynamically.

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Projects

Gesture-Controlled Scrolling System Using Hand Gestures

In this assignment, you will build a real-time gesture control system that allows you to scroll up and down using hand gestures. Using MediaPipe for hand tracking, PyAutoGUI for controlling scrolling, and OpenCV for real-time image processing, you will create an interactive gesture-based controller that tracks your hand movements and scrolls based on specific gestures (open palm for scroll up, closed fist for scroll down).

Implementing Gesture-Based Volume and Brightness Control

In this assignment, you will extend your understanding of gesture control by building a system that adjusts your computer’s volume and screen brightness using hand gestures. By calculating the distance between the thumb and index finger, you will control the volume and brightness of your device in real-time, providing interactive feedback on the screen.

Implementing Gesture Control with OpenCV

In this assignment, you will extend your knowledge of gesture control by building a simple real-time gesture interaction system. Using OpenCV, you will track hand gestures using color filtering and contour detection techniques. You will experiment with capturing specific gestures and triggering interactive actions based on those gestures, such as moving shapes or drawing lines.

Enhancing Your Image Processing with Color Filters and Edge Detection

In this assignment, you will continue experimenting with the image processing techniques covered in the lesson, focusing on applying color filters and performing edge detection on images. You will dynamically toggle between filters and edge detection methods using keyboard input and observe the effects on real-time image processing.

Image Manipulation Challenge

In this project, you will apply fundamental image manipulation techniques, including rotation, cropping, and brightness adjustments, using OpenCV. This project will deepen their understanding of how these operations work and how they affect images in the context of computer vision and AI applications.

Real-Time Face Detection and Emotion Detection

In this project, students will extend real-time face detection by adding emotion recognition. Using the OpenCV face detection technique, students will integrate a pre-trained emotion recognition model to classify facial expressions.

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