Dive into real-time Computer Vision final-year projects developed using OpenCV, Python, TensorFlow, and deep learning. These projects include facial recognition, object detection, image classification, and video analysis—perfect for showcasing practical AI skills and solving real-world visual data problems.

1. Extract zip to project directory\n2. Create virtual environment:\n   python -m venv venv\n   source venv/bin/activate (Linux/Mac) or venv\\Scripts\\activate (Windows)\n3. Install dependencies:\n   pip install -r requirements.txt\n4. Start server:\n   python app.py\n5. Access at http://localhost:5000

<h2>Overview</h2>
<h6>Advanced Real-Time Object Detection Solution</h6>
<p>Our <strong>Flask-powered AI application</strong> leverages state-of-the-art YOLOv8 technology to deliver lightning-fast object detection capabilities. Perfect for developers, researchers, and businesses needing intelligent video analysis solutions.</p>

<h3>Key Features</h3>
<ul>
  <li>⚡ Real-Time Webcam Processing with MJPEG Streaming</li>
  <li>📁 Video File Support (MP4, AVI, MOV, MKV)</li>
  <li>📥 Download Annotated Video Files with Detection Boxes</li>
  <li>🔍 Class Filtering for Specific Object Recognition</li>
  <li>🎨 Modern Responsive UI with Dark Mode</li>
  <li>🔄 Background Processing Queue System</li>
</ul>

<h3>Technical Specifications</h3>
<p>Built with Python's <a href="https://flask.palletsprojects.com/ ">Flask</a> framework and <a href="https://docs.ultralytics.com/ ">Ultralytics YOLOv8</a>, this application delivers production-grade performance with minimal system requirements. The MJPEG streaming architecture ensures compatibility across all modern browsers.</p>

<h3>Industry Applications</h3>
<ul>
  <li>🛡️ Security Surveillance Systems</li>
  <li>🛒 Retail Customer Analytics</li>
  <li>🚗 Traffic Monitoring Solutions</li>
  <li>🏥 Medical Imaging Analysis</li>
  <li>🤖 Industrial Automation</li>
</ul>

<h3>System Requirements</h3>
<p>Python 3.8+ environment with OpenCV and Flask dependencies. Recommended minimum hardware: 4GB RAM, 2GHz CPU. Supports GPU acceleration through CUDA-enabled OpenCV builds.</p>

Download ready-to-use real-time object detection web app built with Flask and YOLOv8. Process webcam streams and videos with class filtering. Source code available from ₹99 with setup guide and documentation.

Real-Time Object Detection Web App | YOLOv8 Flask Project with Webcam Streaming ₹99

AI-powered Flask web application with YOLOv8 for real-time object detection from webcam streams and video files. Download processed videos with bounding boxes.

1. Install Required Dependencies:
   Run `pip install -r requirements.txt`
   
   Note: The 'face_recognition' library depends on 'dlib', which requires additional tools:
   - Windows: Install Visual C++ Build Tools
   - Linux: Run `sudo apt-get install cmake build-essential`

2. Prepare Dataset:
   - Create a folder named `dataset_faces` and add student face images as `student_name.jpg`
   - Place the uniform template image as `uniform_template.jpg` inside the `input` folder

3. Directory Structure:
   .
   ├── dataset_faces/
   │   ├── john_doe.jpg
   │   └── jane_doe.jpg
   ├── input/
   │   └── uniform_template.jpg
   ├── face_uniform_attendance.py
   ├── requirements.txt
   └── attendance.csv

4. Run the Program:
   Execute `python face_uniform_attendance.py` in the terminal.

5. Press 'q' to exit the application.

<h2>Overview</h2>
<p>The <strong>Face and Uniform Based Attendance System</strong> is a next-generation solution built using advanced computer vision techniques. It ensures secure and accurate marking of attendance by verifying both facial identity and uniform compliance simultaneously.</p>

<h2>Key Features</h2>
<ul>
  <li><strong>Real-Time Face Detection & Recognition:</strong> Uses OpenCV and dlib to detect and recognize faces in live webcam feed.</li>
  <li><strong>Uniform Verification via Image Comparison:</strong> Compares captured frames with a standard uniform template using histogram matching.</li>
  <li><strong>Automatic Attendance Logging:</strong> Stores attendance records in CSV format with name, timestamp, and status.</li>
  <li><strong>Duplicate Entry Prevention:</strong> Ensures each person is marked only once per session.</li>
  <li><strong>Live Video Feed with Overlay:</strong> Shows real-time results including name, uniform status, and final outcome.</li>
  <li><strong>Multi-Face Support:</strong> Capable of detecting and processing multiple faces within a single frame.</li>
</ul>

<h2>Applications</h2>
<p>This system is ideal for use in educational institutions, corporate offices, coaching centers, or any environment where maintaining discipline through proper identification and dress code is crucial. It enhances security, eliminates manual errors, and speeds up the attendance process.</p>

<h2>Why Choose This Project?</h2>
<ul>
  <li><strong>Built with Industry-Standard Libraries:</strong> Developed using Python, OpenCV, NumPy, Pandas, and face_recognition for high accuracy.</li>
  <li><strong>Customizable Thresholds:</strong> Adjust face and uniform comparison thresholds based on lighting and environmental conditions.</li>
  <li><strong>Easy Integration:</strong> Can be easily integrated into existing systems without requiring expensive hardware.</li>
  <li><strong>Cost-Effective:</strong> Eliminates the need for traditional biometric devices like fingerprint scanners.</li>
</ul>

<h2>Who Should Buy This?</h2>
<p>This project is perfect for students working on AI/ML-based final year projects, developers building smart campus solutions, startups aiming for tech-driven education tools, or organizations looking to digitize their attendance system securely.</p>

<h2>Project Benefits</h2>
<ul>
  <li>Highly accurate facial recognition using deep learning models</li>
  <li>Ensures proper adherence to uniform standards</li>
  <li>Logs attendance data in clean, exportable CSV format</li>
  <li>Supports batch processing and real-time monitoring</li>
  <li>Lightweight and easy to deploy on any Windows/Linux machine</li>
</ul>

<h2>Buy Now from codeAj Marketplace</h2>
<p>Get the complete source code starting at just ₹99. You can also opt for additional services like <strong>Project Setup</strong>, <strong>Source Code Explanation</strong>, and a ready-to-submit <strong>Project Report</strong>.</p>

Download the Face and Uniform Based Attendance System with real-time face recognition and uniform verification. Get the full source code starting at ₹99 only at codeAj Marketplace.

Face & Uniform Based Attendance System | AI Project Source Code | codeAj Marketplace

A smart attendance system that uses computer vision for face recognition and uniform verification to mark attendance in real-time. Ideal for schools, colleges, and offices.

An AI-powered attendance system that uses face recognition and uniform verification to mark attendance in real-time.

Face and Uniform Based Attendance System

codeAj Marketplace

Installation Steps
Install Python 3.8 or Higher
Ensure you have Python installed on your system.
Verify installation with:
python --version

Install Required Dependencies
pip install -r requirements.txt
This installs essential packages such as opencv-python, torch, flask, and numpy.
Download YOLOv8 Weights
The weights will be downloaded automatically upon first run of main.py.
Alternatively, you can manually download them from the official Ultralytics repository.

Run the Main Application
python main.py

Optional: Start the Web Dashboard
python dashboard.py

Access the Dashboard
Open a browser and navigate to:

http://localhost:5000

<h2>Overview</h2>
<p>The <strong>Classroom Cheat Detection System</strong> is a cutting-edge solution designed to maintain academic integrity by detecting suspicious behavior during exams using real-time video analytics.</p>

<h3>Key Features</h3>
<ul>
    <li><strong>Real-Time Head Orientation Detection:</strong> Monitors student head movements to identify signs of cheating.</li>
    <li><strong>Mobile Phone Detection:</strong> Detects unauthorized phone usage during examinations using YOLOv8 model.</li>
    <li><strong>Student Tracking & Movement Monitoring:</strong> Tracks movement patterns to flag unusual behavior.</li>
    <li><strong>Suspicious Behavior Alerts:</strong> Sends instant alerts when potential cheating is detected.</li>
    <li><strong>Log and Snapshot Storage:</strong> Automatically captures snapshots and logs incidents for later review.</li>
    <li><strong>Web Dashboard:</strong> User-friendly interface to monitor live feeds and access recorded events.</li>
</ul>

<h3>Applications</h3>
<ul>
    <li>Academic institutions for maintaining exam integrity.</li>
    <li>Online proctoring systems with physical classroom integration.</li>
    <li>Behavioral analysis tools for educational research.</li>
    <li>Surveillance systems in controlled environments.</li>
</ul>

<h3>Why Choose This System?</h3>
<p>This system leverages modern computer vision techniques and machine learning models to offer an automated, scalable, and accurate approach to monitoring classrooms without human supervision. It’s ideal for universities, colleges, and competitive exam centers aiming to prevent malpractice.</p>

AI-powered Classroom Cheating Detection System using Computer Vision and YOLOv8 for real-time surveillance and alert generation.

Smart Classroom Surveillance System – Real-Time Cheating Detection with AI

An advanced AI-driven system that detects cheating behaviors in real-time using computer vision and YOVA-based object detection.

<h2>Face Recognition and Attendance System</h2>
<p>The <strong>Face Recognition and Attendance System</strong> is an innovative project that integrates facial recognition technology with an automated attendance system. This system uses a webcam to detect individuals' faces and automatically records their attendance in an Excel sheet, making the entire process seamless and highly efficient. This system can be employed in educational institutions, workplaces, and other environments where attendance management is necessary.</p>

<h3>Project Features:</h3>
<ul>
    <li><strong>Real-Time Face Detection:</strong> Uses a webcam to capture and detect faces in real-time.</li>
    <li><strong>Automatic Attendance Tracking:</strong> Records attendance automatically in an Excel sheet without manual intervention.</li>
    <li><strong>High Accuracy:</strong> Utilizes advanced facial recognition algorithms for high accuracy in detection.</li>
    <li><strong>User-Friendly Interface:</strong> Easy to use with minimal setup required.</li>
    <li><strong>Customizable:</strong> The system can be tailored to the needs of educational institutions, offices, or any attendance-based system.</li>
</ul>

<h3>Applications:</h3>
<ul>
    <li><strong>Educational Institutions:</strong> Perfect for managing student attendance in schools and colleges.</li>
    <li><strong>Workplaces:</strong> Efficient attendance tracking for employees in any company.</li>
    <li><strong>Events:</strong> Ideal for event management, where attendance needs to be tracked for participants.</li>
    <li><strong>Smart Homes/Offices:</strong> Can be integrated into smart home or office systems for added convenience and security.</li>
</ul>

<h3>How It Works:</h3>
<p>The system captures the image of individuals through a webcam. It then compares the detected face with a pre-existing database to verify identity and records the attendance if the match is successful. The attendance data is then automatically saved into an Excel file, providing a hassle-free and paperless solution for attendance management.</p>

<h3>Technology Used:</h3>
<ul>
    <li>Python</li>
    <li>OpenCV</li>
    <li>Face Recognition </li>
    <li>Excel File Handling (Pandas)</li>
</ul>

Discover the Face Recognition and Attendance System project, which uses webcam technology to detect faces and automatically records attendance in an Excel sheet. Perfect for schools, offices, and events.

Face Recognition and Attendance System | Real-Time Attendance Tracking

A real-time attendance system that uses facial recognition to detect faces via a webcam and records attendance automatically in an Excel sheet.

CodeAJ

A real-time face recognition system that uses a webcam to detect faces and record attendance in an Excel sheet, designed for schools, offices, and event management.

Face Recognition and Attendance Project

<p class="paraa">
                
 Emotion Detection is an innovative project that utilizes Python, the Deepface library, and CV2 (OpenCV) to accurately detect emotions from facial expressions. With the aim of offering valuable insights into human sentiment analysis, the project excels in facial emotion recognition. Through the powerful combination of Deepface and OpenCV, it can analyze facial expressions captured in images or real-time video feed, accurately identifying emotions like happiness, sadness, anger, surprise, fear, and more.
<br><br>
One of the key features of Emotion Detection is its real-time emotion analysis capability, providing instantaneous feedback on the emotional state of the person in front of the camera. This feature has diverse applications, including interactive interfaces, customer feedback systems, and entertainment platforms. Powered by cutting-edge deep learning algorithms trained on extensive datasets, Emotion Detection ensures high accuracy and reliability in its predictions.
<br><br>
The project is designed with a user-friendly interface, making it accessible to both developers and end-users. The intuitive GUI allows users to interact with the application effortlessly, enhancing the overall experience. Moreover, Emotion Detection is highly customizable, empowering developers to seamlessly integrate it into existing applications or tailor it to specific requirements.
<br><br>
Complementing its technical prowess, the project also includes comprehensive documentation, enabling developers to understand the code, make modifications, and explore potential enhancements with ease.
<br><br>
Emotion Detection finds practical applications across various domains. In market research, it aids in gauging customers' reactions to products, advertisements, or services. In education and training, it assists educators in understanding students' engagement levels during online classes and training sessions. In the healthcare sector, Emotion Detection supports emotional analysis for patients, enabling healthcare professionals to monitor mental well-being and response to treatments.
<br><br>
Beyond these, the project opens up possibilities in human-computer interaction by enhancing interactive applications, virtual assistants, and chatbots. Furthermore, it can revolutionize the entertainment industry by creating personalized experiences in gaming, virtual reality, and video content.
<br><br>
In conclusion, Emotion Detection is a groundbreaking project that leverages the power of computer vision and deep learning to accurately interpret and analyze human emotions. With its diverse applications and potential for understanding human sentiment and emotional responses, this project holds promise in shaping the future of emotion analysis technology.
            </p>

Discover Emotion Detection, a Python-based project using Deepface and OpenCV for real-time facial emotion analysis. Identify emotions like happiness, anger, and surprise with high accuracy. Explore its diverse applications in healthcare, education, and AI.

Emotion Detection | Real-Time Facial Emotion Recognition Using Deep Learning

Emotion Detection is an innovative project utilizing Python, Deepface, and CV2 to accurately detect emotions from facial expressions.

Emotion Detection is a Python-based project that leverages Deepface and OpenCV to perform real-time facial emotion recognition. It identifies emotions like happiness, sadness, anger, and more, offering insights for diverse applications such as healthcare, education, and entertainment.

Emotion Detection

<p class="paraa">
                
Emotion is Everything is an exciting and innovative project that blends the power of Python Django, Deepface, and CV2 for face detection and emotion analysis. The platform goes beyond traditional movie recommendation systems by suggesting movies based on users' emotional states, identified through facial expressions.
<br><br>
<b>Key Features:</b>
<br><br>
<b>1. Facial Emotion Analysis:</b> Emotion is Everything employs the Deepface library and CV2 to analyze facial expressions in real-time. By detecting emotions such as joy, sadness, anger, fear, and more, the platform gains valuable insights into users' emotional states.
<br><br>
<b>2. Personalized Movie Recommendations:</b> Leveraging the emotions detected, the platform offers personalized movie recommendations to users. Whether they seek heartwarming comedies, thrilling adventures, or thought-provoking dramas, Emotion is Everything has the perfect movie in store.
<br><br>
<b>3. User-Friendly Interface:</b> The platform boasts a user-friendly interface with a seamless user experience. Navigating through movie recommendations based on emotions becomes intuitive and engaging.
<br><br>
<b>4. Extensive Movie Database:</b> Emotion is Everything houses an extensive movie database, ensuring a diverse selection of movies catering to various emotional preferences.
<br><br>
<b>5. Privacy and Security:</b> Privacy and data security are paramount. Emotion is Everything prioritizes safeguarding users' facial data and emotions, providing a safe and trustworthy experience.
<br><br>
<b>6. Real-Time Updates:</b> The platform continuously adapts to users' evolving emotional states, ensuring movie recommendations remain relevant and enjoyable.
<br><br>
<b>Applications:</b>
<br><br>
<b>1. Movie Enthusiasts:</b> Movie enthusiasts can explore a new dimension of movie recommendations tailored to their current emotional states, enhancing their cinematic experiences.
<br><br>
<b>2. Movie Streaming Platforms:</b> Emotion is Everything can be integrated with movie streaming platforms, offering users a unique and personalized recommendation feature.
<br><br>
<b>3. Entertainment Industry:</b> In the entertainment industry, the platform can be leveraged to analyze audience reactions during movie screenings and gauge emotional responses for marketing purposes.
<br><br>
<b>4. Psychological Research:</b> Emotion is Everything can find applications in psychological research, helping professionals understand emotional triggers and responses to movies.
<br><br>
Emotion is Everything is a groundbreaking project that taps into the realm of emotions and movie preferences. Through the fusion of Python Django, Deepface, and CV2, the platform creates an immersive movie recommendation experience based on users' facial expressions and emotions. Experience the magic of emotions and movies with Emotion is Everything!
            </p>

an innovative project using Python Django, Deepface, and CV2 for real-time facial emotion analysis. It suggests personalized movie recommendations based on users' emotions..

Computer vision Projects

Real-Time Object Detection Web App Using YOLOv8 & Flask | AI-Powered Webcam & Video Processing

AI-Powered Face & Uniform-Based Attendance System with Real-Time Recognition

Smart Classroom Surveillance: AI-Powered Cheating Detection System

Face Recognition and Attendance Project

Emotion Detection

Emotion-Based Movie Recommender with DeepFace & Django