👁️ AI COMPUTER VISION PROJECTS

MY COMPUTER VISION JOURNEY WITH Python, OpenCV & YOLO

A PRACTICAL COMPUTER VISION PORTFOLIO repository documenting my journey from the fundamentals of image processing and computer vision to real-time AI applications using Python, OpenCV, NumPy, and YOLOv8.

THIS REPOSITORY includes hands-on exercises, experiments, MINI-PROJECTS, and real-time computer vision applications developed during my 5-Day Offline Computer Vision Hands-on Workshop by Innomatics Research Labs in July 2026, along with additional projects developed to strengthen MY PRACTICAL AI/ML skills.

THE GOAL OF THIS REPOSITORY is to demonstrate how computer vision concepts can be transformed into WORKING, REAL-WORLD APPLICATIONS.

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📌ABOUT THIS REPOSITORY

COMPUTER VISION IS ONE OF THE MOST PRACTICAL AREAS OF Artificial Intelligence, enabling computers to understand and interpret images and videos.

THIS REPOSITORY FOLLOWS A PROGRESSIVE LEARNING APPROACH :

Python & NumPy
      ↓
Image Processing
      ↓
OpenCV Fundamentals
      ↓
Video Processing
      ↓
Object Detection
      ↓
Object Tracking
      ↓
Real-Time Computer Vision
      ↓
YOLOv8
      ↓
AI-Based Applications

THE PROJECTS RANGE FROM basic OpenCV operational to REAL-TIME APPLICATIONS SUCH AS:

• 👤 FACE DETECTION
• 📝 FACE Attendance
• 🎯 Object Tracking
• 🚨 Motion Detection
• 🔍 REAL-TIME OBJECT DETECTION
• 🤖 YOLOv8-based AI Detection

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🎓 COMPLETED COMPUTER VISION WORKSHOP

5-DAY OFFLINE COMPUTER VISION HANDS-ON PROJECT

Organization: INNOMATICS RESEARCH LABS Duration: 5 Days Date: July 2026 Mode: Offline Hands-on Workshop

THE WORKSHOP PROVIDED PRACTICAL exposure to COMPUTER VISION CONCEPTS USING PYTHON and OpenCV.

THE LEARNING PROCESS FOCUSED ON implementing CONCEPTS THROUGH CODE RATHER THAN ONLY STUDYING THEORETICAL CONCEPTS.

KEYS AREAS COVERED

• PYTHON PROGRAMMING FOR COMPUTER VISION
• NumPy operations
• Image reading and writing
• Image resizing and transformations
• Image color spaces
• Image filtering
• Edge detection
• Contour detection
• Image thresholding
• Webcam and video processing
• Object detection concepts
• Object tracking
• Real-time computer vision
• YOLO-based OBJECT DETECTIONS

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🛠️ TECHNOLOGIES & TOOLS

TECHNOLOGY	PURPOSE
🐍 Python	CORE PROGRAMMING LANGUAGE
👁️ OpenCV	Image & video processing
🤖 YOLOv8	Real-time object detection
🔢 NumPy	Numerical and array operations
📊 Matplotlib	Visualization and image analysis
🧠 Ultralytics	YOLO implementation
📄 CSV	Attendance and event logging
💻 Jupyter Notebook	EXPERIMENTATION and learning

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📁 REPOSITORY STRUCTURE

AI-Computer-Vision-PROJECT/
│
├── README.md
│
├── Day-01/
│   ├── image_basics/
│   ├── image_reading/
│   └── image_processing/
│
├── Day-02/
│   ├── transformations/
│   ├── filtering/
│   └── edge_detection/
│
├── Day-03/
│   ├── contours/
│   ├── thresholding/
│   └── segmentation/
│
├── Day-04/
│   ├── video_processing/
│   ├── webcam/
│   └── object_tracking/
│
├── Day-05/
│   ├── object_detection/
│   ├── YOLO/
│   └── mini_projects/
│
├── mini-projects/
│   ├── face-attendance-system/
│   ├── color-object-tracker/
│   ├── motion-detection/
│   └── yolov8-object-detection/
│
├── data/
│
├── outputs/
│
├── requirements.txt
│
└── LICENSE

THE EXACT FOLDER STRUCTURE MAY EVOLVE AS Additional COMPUTER VISION PROJECTS ARE ADDED.

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🚀 MINI PROJECTS

1️⃣LIVE FACE ATTENDANCE SYSTEM

📌 OVERVIEW

The Live Face Attendance System is a real-time computer vision application designed to detect faces through a WEBCAM AND RECORD ATTENDANCE INFORMATION.

THE PROJECT demonstrated HOW COMPUTER VISION CAN BE USED to AUTOMATE a BASIC ATTENDANCE WORKFLOW.

🔍 KEY FEATURES

• REAL-TIME WEBCAMP INPUT
• Face detection
• Detection of multiple faces
• Attendance recording
• Timestamp generation
• CSV-based attendance logging
• Real-time visual feedback

🔄 WORKING PIPELINE

Webcam
   ↓
Capture Video Frame
   ↓
Face Detection
   ↓
Identify / Detect Face
   ↓
Check Attendance
   ↓
Record Name + Date + Time
   ↓
CSV Attendance File

💡PRACTICAL APPLICATION

THIS TYPE OF SYSTEM CAN BE EXTENDED FOR:

• CLASSROOM ATTEDANCE
• Office ATTENDANCE
• Training centers
• Workshops
• Laboratory ENVIRONMENTS

📈 FUTURE IMPROVEMENTS

• FACE RECOGNITION INSTEAD OF ONLY FACE DETECTION
• Database INTEGRATION
• Multiple-user recognition
• Anti-spoofing
• Web dashboard
• Cloud-based attendance records

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2️⃣ 🎨 REAL-TIME COLOR OBJECT TRACKER

📌 OVERVIEW

The Color Object Tracker is a real-time computer vision project that detects and tracks an object based on its color.

THE APPLICATION PROCESSES WEBCAM frames AND IDENTIFIES pixels BELONGING to a SELECTED COLOR RANGE.

🔍 KEY CONCEPTS

• HSV color space
• Color masking
• Thresholding
• Contour detection
• Object center CALCULATION
• Bounding boxes
• REAL-TIME TRACKING

🔄 WORKING PIPELINE

Webcam Frame
      ↓
Convert BGR → HSV
      ↓
Define Color Range
      ↓
Create Color Mask
      ↓
Find Contours
      ↓
Identify Object
      ↓
Draw Bounding Box
      ↓
Track Object

💡 PRACTICAL APPLICATIONS

THE CONCEPT CAN BE USED AS A FUNCTION FOR :

• Industrial OBJECT TRACKING
• Robotic vision
• Gesture-based systems
• Automated sorting
• Color-based quality inspection

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3️⃣ 🚨 MOTION DETECTION & ALERT SYSTEM

📌 OVERVIEWS

The Motion Detection & Alert System detects MOVEMENT in A VIDEO STREAM by COMPARING CONSECUTIVE frames.

WHEN SIGNIFICANT MOVEMENT IS DETECTED, the system can generate an alert and log the event.

🔍 KEY FEATURES

• Real-time video monitoring
• Background/frame comparison
• Motion detection
• Contour-based movement identification
• Alert generation
• Event logging
• Timestamp support

🔄 Working Pipeline

Video / Webcam
      ↓
Capture Frames
      ↓
Compare Consecutive Frames
      ↓
Calculate Difference
      ↓
Threshold
      ↓
Find Motion Regions
      ↓
Generate Alert
      ↓
Log Event

💡 Practical Applications

• Basic security monitoring
• Restricted-area monitoring
• Laboratory monitoring
• Smart surveillance
• Home monitoring
• Automated activity detection

📈 Future Improvements

• Email/SMS alerts
• Telegram notifications
• Person-specific detection
• Video recording
• Cloud storage
• YOLO integration

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4️⃣ 🤖 YOLOv8 REAL-TIME OBJECT DETECTION

📌 Overview

The YOLOv8 Object Detection project introduces deep-learning-based computer vision for detecting multiple objects in real time.

YOLO (You Only Look Once) is a popular object detection approach designed for fast object detection.

THIS PROJECT USES THE Ultralytics YOLOv8 framework to detect common objects from images, videos, or WEBCAM STREAMS.

🔍 KEY FEATURES

• Real-time object detection
• Webcam support
• Image detection
• Video detection
• Bounding boxes
• Class labels
• Confidence scores
• Multiple-object detection

🔄 Working Pipeline

Image / Video / Webcam
          ↓
        YOLOv8
          ↓
   Neural Network
          ↓
Object Detection
          ↓
Bounding Boxes
          ↓
Class Labels
          ↓
Confidence Scores
          ↓
Real-Time Output

🎯 Example Detection Classes

Depending on the selected pretrained model, the system can detect common objects such as:

• Person
• Car
• Bicycle
• Motorcycle
• Bus
• Truck
• Dog
• Cat
• Bottle
• Chair
• Laptop
• Cell phone

AND many OTHER PRETRAINED CLASSES.

💡 Practical Applications

YOLO-based systems can be extended to:

• Smart surveillance
• Traffic monitoring
• Retail analytics
• Industrial inspection
• Crowd monitoring
• Autonomous systems
• Safety monitoring
• Robotics

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🧠 Computer Vision Concepts Practiced

Throughout this repository, I have worked with several important computer vision concepts.

Image Processing

• Image loading
• Image saving
• Image resizing
• Image cropping
• Image rotation
• Image flipping
• Image enhancement

Color Processing

• RGB/BGR
• Grayscale
• HSV
• Color masking

Image Analysis

• Thresholding
• Edge detection
• Contours
• Morphological operations
• Segmentation

Video Processing

• Webcam capture
• Video frames
• Frame-by-frame processing
• Real-time visualization

Object Detection

• Bounding boxes
• Confidence scores
• Class prediction
• YOLO inference

Object Tracking

• Color-based tracking
• Contour-based tracking
• Real-time object movement

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📊 Learning Progress

This repository represents a progression from fundamental programming concepts to practical AI applications.

Level	Skills
🟢 Beginner	Python, NumPy, basic image operations
🟡 Intermediate	OpenCV, transformations, filtering
🟠 Advanced	Contours, segmentation, video processing
🔴 AI/ML	YOLOv8, object detection
🚀 Application	Attendance, tracking, monitoring

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⚙️ Installation

Clone the repository:

git clone https://github.com/shreekanthashokg-lang/AI-Computer-Vision-PROJECT.git

Move into the project:

cd AI-Computer-Vision-PROJECT

Create a virtual environment:

python -m venv venv

Activate it on Windows:

venv\Scripts\activate

Install dependencies:

pip install -r requirements.txt

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📦 Main Dependencies

Typical dependencies used throughout the projects include:

Python
opencv-python
numpy
matplotlib
ultralytics

Additional dependencies may be added as individual projects evolve.

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▶️ Running the Projects

Each mini-project contains its own implementation and instructions.

For example:

python face_attendance.py

or:

python color_tracker.py

or:

python motion_detection.py

For YOLO-based detection:

python yolo_detection.py

Camera-based projects require a working webcam and appropriate camera permissions.

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🎯 What I Learned

Through these projects, I developed practical experience in:

• Understanding how images are represented digitally
• Processing images using Python
• Working with OpenCV
• Handling real-time webcam streams
• Extracting useful information from images
• Detecting and tracking objects
• Understanding computer vision pipelines
• Working with pretrained YOLO models
• Building real-time AI applications
• Structuring computer vision projects for GitHub

More importantly, this journey helped me move from learning computer vision concepts to implementing them as working applications.

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🚀 Future Roadmap

My next goal is to continue expanding this repository from traditional OpenCV projects toward more advanced AI-powered computer vision systems.

🔹 Phase 1 — Advanced OpenCV

• Advanced image processing
• Perspective transformation
• Image segmentation
• Background removal
• Advanced tracking

🔹 Phase 2 — YOLO

• Custom dataset creation
• Custom YOLO training
• Object detection
• Object tracking
• Model evaluation
• Model optimization

🔹 Phase 3 — MediaPipe

• Hand tracking
• Face landmarks
• Pose estimation
• Gesture recognition
• Human activity analysis

🔹 Phase 4 — AI Applications

• Smart attendance
• PPE detection
• Vehicle detection
• Number plate recognition
• People counting
• Crowd monitoring
• Safety monitoring

🔹 Phase 5 — Deployment

• Flask
• Streamlit
• REST APIs
• Web dashboards
• Docker
• Cloud deployment

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🌟 Vision for This Repository

This repository is not intended to be just a collection of tutorial programs.

My objective is to continuously transform the projects into practical, portfolio-level Computer Vision applications.

LEARN
  ↓
PRACTICE
  ↓
BUILD
  ↓
IMPROVE
  ↓
DEPLOY
  ↓
SOLVE REAL-WORLD PROBLEMS

I will continue adding projects, experiments, datasets, trained models, and deployment examples as I progress in my AI/ML and Computer Vision journey.

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📚 Learning Journey

July 2026

Completed a 5-Day Offline Computer Vision Hands-on Workshop by Innomatics Research Labs, gaining practical exposure to Python, OpenCV, image processing, video processing, object detection, and real-time computer vision.

The workshop served as the foundation for this repository, which I am continuing to expand through independent projects and real-world applications.

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🔮 Future Projects

Some planned projects include:

• 🤖 Custom YOLO Object Detector
• 👷 PPE / Safety Helmet Detection
• 🚗 Vehicle Detection & Counting
• 🔢 Automatic Number Plate Recognition
• 🧍 People Counting System
• ✋ Hand Gesture Recognition
• 🧑‍💻 Face Recognition Attendance
• 🏃 Human Activity Recognition
• 🏭 Industrial Object Detection
• 🚦 Traffic Monitoring System
• 🏥 Healthcare Computer Vision Applications

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👨‍💻 About Me

SHREEKANTH A GUTTEDAR

MCA — AI/ML & Data Science

Interested in:

• Artificial Intelligence
• Machine Learning
• Data Science
• Computer Vision
• Deep Learning
• Python
• Real-Time AI Applications

I am continuously building practical projects to strengthen my technical skills and develop a strong AI/ML portfolio.

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📫 Connect With Me

LinkedIn: https://www.linkedin.com/in/shreekanthashokg/

GitHub: https://github.com/shreekanthashokg-lang

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⭐ Support

If you find this repository useful or interesting, consider giving it a ⭐ on GitHub.

More Computer Vision projects will be added as I continue learning, experimenting, and building.

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❤️ Built with Python, OpenCV & AI

Started during the Innomatics Research Labs Computer Vision Workshop — July 2026

Learn → Build → Experiment → Deploy → Repeat.