Computer Vision Mastery : 20+ Projects With Python & Ai

Master Computer Vision in 2025: Python, OpenCV, Deep Learning, YOLO, Tesseract OCR, Tkinter GUI & 20+ Real-Time Projects

What you'll learn

Understand the origins, evolution, and real-world impact of AI, with a focus on computer vision’s role in modern applications.

Install and configure Python and VS Code for seamless development of vision-based projects on any platform.

Apply OpenCV fundamentals—reading, writing, displaying, resizing, cropping, and color-space conversion of images and videos.

Implement image processing techniques such as thresholding, morphological transforms, bitwise operations, and histogram equalization.

Detect edges, corners, contours, and keypoints; match features across images to enable object recognition and scene analysis.

Leverage advanced methods—Canny edge detection, texture analysis, optical flow, object tracking, segmentation, and OCR with Tesseract.

Build a smart face‐attendance system: enroll faces, extract embeddings, train a model, and launch a Tkinter GUI for live recognition.

Create a driver-drowsiness detector using EAR/MAR metrics, integrate it into a Tkinter dashboard, and run real-time video inference.

Train YOLOv7-tiny for object and weapon detection, deploy in Colab, and build a GUI for live detection.

Implement a YOLOv8 people‐counting and entry/exit tracker, visualize counts with Tkinter, and manage line‐coordinate logic.

Develop license‐plate detection & recognition pipelines with Roboflow annotations, API integration, and live GUI display.

Craft a traffic‐sign recognition system: preprocess data, train EfficientNet-B0, and perform inference in real time.

Build AI-powered safety apps: accident detection with MQTT alerts, fall-detection APIs, and smart vehicle speed tracking.

Detect emotions, age, and gender from live video using pre-trained models and deploy via Tkinter interfaces.

Design a real-time mask detection application with YOLOv11, from dataset prep to GUI inference.

Create a hand-gesture recognition system with landmark annotation, MediaPipe pose estimation, and interactive GUI.

Train a wildlife identification model on EfficientNetB0, deploy in Flask/Ngrok, and recognize animals in live streams.

Integrate OCR via Tesseract for text extraction in images and build segmentation pipelines for robust scene parsing.

Requirements

Basic Python programming knowledge

Windows PC or Laptop with 4GB+ RAM is recommended. A GPU is optional but helpful for faster model training and processing large datasets or real-time tasks.

Description

Unlock the power of image- and video-based AI in 2025 with 20+ real-time projects that guide you from foundational theory to fully functional applications. Designed for engineering and science students, STEM graduates, and professionals switching into AI, this hands-on course equips you with end-to-end computer vision skills to build a standout portfolio.Key Highlights:Environment Setup & Basics: Install Python, configure VS Code, and master OpenCV operations—image I/O, color spaces, resizing, thresholding, filters, morphology, bitwise ops, and histogram equalization.Core & Advanced Techniques: Implement edge detection (Sobel, Canny), contour/corner/keypoint detection, texture analysis, optical flow, object tracking, segmentation, and OCR with Tesseract.Deep Learning Integration: Train and deploy TensorFlow/Keras models (EfficientNet-B0) alongside YOLOv7-tiny and YOLOv8 for robust detection tasks.GUI Development: Build interactive Tkinter interfaces to visualize live video feeds, detection results, and system dashboards.20+ Hands-On Projects Include:Smart Face Attendance with face enrollment, embedding extraction, model training, and GUI integration.Driver Drowsiness Detection using EAR/MAR algorithms and real-time alert dashboards.YOLO Object & Weapon Detection pipelines for live inference and visualization.People Counting & Entry/Exit Tracking with configurable line-coordinate logic.License-Plate & Traffic Sign Recognition leveraging Roboflow annotations and custom model training.Intrusion & PPE Detection for workplace safety monitoring.Accident & Fall Detection with MQTT alert systems.Mask, Emotion, Age/Gender & Hand-Gesture Recognition using custom-trained vision models.Wildlife Identification with EfficientNet-based classification in live streams.Vehicle Speed Tracking using calibration and object motion analysis.By course end, you’ll be able to:Develop, train, and fine-tune deep-learning vision models for diverse real-world tasks.Integrate CV pipelines into intuitive GUIs for live video applications.Execute industry-standard workflows: data annotation, training, evaluation, and deployment.Showcase a portfolio of 20+ complete projects to launch or advance your AI career.Join now to transform your STEM background into in-demand computer vision expertise—no prior CV experience required!

Overview

Section 1: Introduction

Lecture 1 Introduction

Section 2: Meet Your Instructor

Lecture 2 Introduction to Your Instructor and Course Overview

Section 3: Understanding AI – From Origins to Impact

Lecture 3 What is AI and How It Has Evolved

Section 4: Overview of computer vision

Lecture 4 Introduction to Computer Vision and Its Applications

Section 5: Environment Setup for Python Development

Lecture 5 Installing Python

Lecture 6 VS Code Setup for Python Development

Section 6: Computer Vision Basic Techniques

Lecture 7 OpenCv Fundamental's Overview

Lecture 8 Reading and Writing Images

Lecture 9 Color Space Conversion

Lecture 10 Displaying Images and Videos

Lecture 11 Image Resizing, Cropping, and Rotation

Lecture 12 Drawing Functions

Lecture 13 Image Thresholding

Lecture 14 Morphological Operations

Lecture 15 Contour Detection

Lecture 16 Mask Image Generation

Lecture 17 Background Subtraction

Lecture 18 Image Bitwise Operations

Lecture 19 Histogram Equalization, Gamma Correction

Lecture 20 Smoothing Filters

Lecture 21 Sharpening Filters

Lecture 22 Edge Detection Sobel

Lecture 23 Contrast Adjustment

Section 7: Computer Vision Advanced Techniques

Lecture 24 Computer Vision Advanced Introduction Video

Lecture 25 Edge Detection Canny

Lecture 26 Corner Detection

Lecture 27 Keypoint Detection & Matching

Lecture 28 Texture Analysis

Lecture 29 Optical Flow and Motion Analysis

Lecture 30 Object Tracking

Lecture 31 Image Segmentation

Lecture 32 Image object detection

Lecture 33 Live streaming

Lecture 34 Tesseract OCR Engine

Section 8: Project #1: Smart Face Attendance System with Python & Computer Vision

Lecture 35 Course Overview and Features

Lecture 36 Installing Required Packages (Dilib, OpenCV, etc.)

Lecture 37 Face Enrollment

Lecture 38 Extracting Face Embeddings and Identifying Landmark

Lecture 39 Training the Facial Recognition Model

Lecture 40 Real-Time Face Recognition and Attendance

Lecture 41 Building the Attendance Management GUI

Section 9: Project #2: Driver Drowsiness Detection System with Python & Computer Vision

Lecture 42 Introduction of the Driver Drowsiness Detection System

Lecture 43 Driver Drowsiness Detection Project Overview

Lecture 44 Understanding Key Packages for Driver Drowsiness Detection

Lecture 45 Implementing Drowsiness Detection Logic Using EAR and MAR

Lecture 46 Integrating Drowsiness Detection with Tkinter GUI

Lecture 47 Real-Time Driver Drowsiness Detection with Live Video Streaming

Lecture 48 Real-Time Model Inference for Driver Drowsiness Detection

Section 10: Project #3: Object Detection Using Yolov7 with Python & Computer Vision

Lecture 49 Introduction of the Object Detection using yolov7

Lecture 50 Object Detection Project Overview

Lecture 51 Understanding Key Packages for Object Detection

Lecture 52 Understanding the YOLOv7-tiny Model Weights

Lecture 53 Real-Time Object Detection with YOLOv7-tiny

Lecture 54 Building a Tkinter GUI for Real-Time Object Detection

Lecture 55 Executing Real-Time Model Inference for Object Detection

Section 11: Project #4: AI-Powered Weapon Detection for Enhanced Security with Python & CV

Lecture 56 Introduction of the Weapon Detection using YOLOv7

Lecture 57 Weapon Detection Project Overview

Lecture 58 Setup in Google Colab for Weapon Detection Model Training

Lecture 59 Mounting Google Drive on Google Colab

Lecture 60 Utilizing Sohas Weapon Detection Dataset for Weapon Detection

Lecture 61 Cloning YOLOv7 Repository and Installing Required Packages

Lecture 62 Visualizing the Weapon Detection Dataset

Lecture 63 Splitting the Weapon Detection Dataset

Lecture 64 Detailed Walkthrough of YOLOv7 Code for Weapon Detection

Lecture 65 Training the YOLOv7 Model for Weapon Detection

Lecture 66 Model Inference for Weapon Detection

Section 12: Project #5: Real-Time Entry/Exit Occupancy Tracker using Python & OpenCV

Lecture 67 Intro to Real-Time Entry/Exit Tracking for Smart Occupancy Management

Lecture 68 Understanding the YOLOv8 Algorithm

Lecture 69 Setting Up and Exploring Essential Packages

Lecture 70 Key Variables and Their Role in YOLOv8

Lecture 71 People Counting Logic and Function Implementation

Lecture 72 Accessing and Using Line Coordinates for Tracking

Lecture 73 Implementing People Counting with YOLOv8 Model Inference

Lecture 74 Tkinter Implementation for Real-Time People Counting

Lecture 75 Package Installation for People Counting System

Lecture 76 Drawing Lines on Images for People Counting

Lecture 77 Getting Line Coordinates Using Roboflow for People Counting

Lecture 78 People In and Out Counting Code Execution

Section 13: Project #6: Facial Emotion Detection & Recognition using Python & OpenCV

Lecture 79 Introduction of the Facial Emotion Detection

Lecture 80 Facial Emotion Detection Project Overview

Lecture 81 Set up Google Colab

Lecture 82 Facial Emotion Detection Dataset Download

Lecture 83 Dataset Visualization

Lecture 84 Pre-trained yolov9 model weight file download

Lecture 85 yolov9 model info

Lecture 86 yolov9 model code explanation

Lecture 87 yolov9 model training

Lecture 88 Model Inference Explanation

Lecture 89 Code Execution

Section 14: Project #7: LLM-Powered License Plate Detection & Recognition with Python & CV

Lecture 90 Introduction to Real-Time License Plate Detection and Recognition

Lecture 91 License Plate Detection and Recognition System Overview

Lecture 92 Managing Folders and Files of the Project

Lecture 93 Setting Up and Exploring Essential Packages

Lecture 94 Setting Up API Access for Vehicle Recognition

Lecture 95 Key Variables and Their Role in License Plate Detection and Recognition

Lecture 96 Implementing License Plate Detection and Recognition

Lecture 97 Vision-Language Model Integration

Lecture 98 Tkinter Implementation for Real-Time License Plate Detection and Recognition

Lecture 99 Package Installation for License Plate Detection and Recognition

Lecture 100 Getting Polygon Coordinates Using Roboflow for License Plate Detection

Lecture 101 Obtaining the NVIDIA NIM API Key for Vehicle License Plate Detection

Lecture 102 Vehicle License Plate Detection and Tracking Code Execution

Section 15: Project #8: Driving with AI – Real-Time Traffic Sign Detection with Python & CV

Lecture 103 Introduction of theTraffic Sign Detection And Recognition System

Lecture 104 Google Colab Setup

Lecture 105 Packages Installation

Lecture 106 Dataset Preparation

Lecture 107 Implementing Utility Functions for Traffic Sign Detection

Lecture 108 Implementing Loss Function for Traffic Sign Detection

Lecture 109 EfficientNet-B0 Model Implementation Info

Lecture 110 Model Training configuration

Lecture 111 Training the EfficientNet-B0 Model

Lecture 112 Model Inference

Section 16: Project #9: Smart Human Intrusion Detection System with Python & Computer Vision

Lecture 113 Launching VS Code from the Command Line

Lecture 114 Managing Folders and Files of the Project

Lecture 115 Understanding and Setting Up Required Packages

Lecture 116 Accessing and Using Polygon Coordinates for Tracking

Lecture 117 Key Variables and Their Role in YOLOv8

Lecture 118 Model Inference Code Explanation for Intrusion Detection

Lecture 119 Tkinter Implementation for Real-Time Intrusion Detection

Lecture 120 Getting Polygon Coordinates Using Roboflow for Intrusion Detection

Lecture 121 Intrusion Detection Code Execution

Section 17: Project #10: AI-Powered PPE Detection: Ensuring Workplace Safety in Real Time

Lecture 122 Introduction of the PPE Detection Detection System

Lecture 123 PPE Detection Project Overview

Lecture 124 File Uploaded on Google Colab

Lecture 125 Dataset Visualization

Lecture 126 PPE Model Information

Lecture 127 PPE Code Execution

Lecture 128 VS Code Open

Lecture 129 Packages and Module Import

Lecture 130 NVIDIA Nim Information

Lecture 131 API Information

Lecture 132 File Format

Lecture 133 Predict API

Lecture 134 Get API

Lecture 135 Code Execution

Section 18: Project #11: AI Vision – Age & Gender Detection with Python & Computer Vision

Lecture 136 Introduction to Age and Gender Detection

Lecture 137 Age and Gender Detection Project Overview

Lecture 138 Packages Information for Age and Gender Detection

Lecture 139 Variable Initialization and System Configuration

Lecture 140 Folder Creation

Lecture 141 Model Inference

Lecture 142 TKinter Implementation

Lecture 143 Package Installation

Lecture 144 Code Execution

Section 19: Project #12: AI Accident Detection & Real-Time Monitoring with Python & CV

Lecture 145 Intro to AI-Powered Accident Detection & Real-Time Monitoring & Alert System

Lecture 146 AI Accident Detection & Real-Time Monitoring & Alert System: Project Overview

Lecture 147 Configuring the environment on Google Colab

Lecture 148 Setting Up and Exploring Essential Packages

Lecture 149 Dataset Acquisition: Downloading & Understanding

Lecture 150 Dataset Visualization & Analysis

Lecture 151 Dataset Preprocessing: Normalization & Resizing

Lecture 152 Label Encoding & Data Preparation

Lecture 153 Training & Validation Data Visualization

Lecture 154 CNN Model Implementation & Training

Lecture 155 Downloading and Saving Trained Model Weights

Lecture 156 Understanding MQTT Protocol & Package Requirements

Lecture 157 Model Inference Code Walkthrough

Lecture 158 Final Code Execution & Live Demonstration

Section 20: Project #13: Smart Vehicle Speed Tracking System with Python & Computer Vision

Lecture 159 Introduction of Smart Vehicle Speed Tracking System

Lecture 160 Vehicle Detection and Speed Tracking System Overview

Lecture 161 Setting Up and Exploring Essential Packages

Lecture 162 Calibration for Real-World Measurements

Lecture 163 Implementing Vehicle Speed Tracking with YOLOv8 Model Inference

Lecture 164 Vehicle Speed Calculation Logic and Function Implementation

Lecture 165 Tkinter Implementation for Real-Time Vehicle Speed Tracking

Lecture 166 Vehicle Speed Tracking Code Execution

Section 21: Project #14: Real-Time Vehicle Parking Management with Python & Computer Vision

Lecture 167 Introduction to Real-Time Vehicle Tracking for Effective Parking Management

Lecture 168 Managing Folders and Files of the Project

Lecture 169 Vehicle Detection and Parking Slot Tracking System Overview

Lecture 170 Setting Up and Exploring Essential Packages

Lecture 171 Implementing Vehicle Parking Management with Flask

Lecture 172 Building the Backend for Vehicle Parking Management with Flask

Lecture 173 Implementing Vehicle Parking Detection and Occupancy Tracking

Lecture 174 Package Installation for Vehicle Parking Management system

Lecture 175 Get Polygon Coordinates with Roboflow to Calculate Available Parking Spaces

Lecture 176 Vehicle Parking Space Detection and Occupancy Tracking Code Execution

Section 22: Project #15: Real-Time Mask Detection with AI using Python & Computer Vision

Lecture 177 Introduction to Face Mask Detection and Recognition

Lecture 178 Face Mask Detection System Project Overview

Lecture 179 Google Drive Mount

Lecture 180 Face Mask Detection Dataset Download

Lecture 181 Dataset Visualization

Lecture 182 Ultralytics Installation & Setting Up YOLOv11 for Mask Detection

Lecture 183 YOLOv11 Model Training for Mask Detection

Lecture 184 Packages Explanation

Lecture 185 Model Inference Code Explanation

Lecture 186 Tkinter Implementation

Lecture 187 Code Execution

Section 23: Project #16: Real-Time Hand Gesture Detection & Recognition with Python & CV

Lecture 188 Hand Gesture Detection and Recognition Overview

Lecture 189 Setting Up and Exploring Essential Packages

Lecture 190 Key Variables and Their Role in Hand Gesture Recognition

Lecture 191 Annotating Frames with Detected Gestures and Landmarks

Lecture 192 Real-Time Gesture Recognition and Frame Processing

Lecture 193 Integrating Real-Time Gesture Recognition with Tkinter GUI

Lecture 194 Tkinter Implementation for Real-Time Hand Gesture Recognition

Lecture 195 Package Installation for Hand Gesture Recognition System

Lecture 196 Hand Gesture Recognition Code Execution

Section 24: Project #17: Smart Vehicle Traffic Monitoring System with Python & CV

Lecture 197 Intro to Real-Time Vehicle Traffic Monitoring for Efficient Traffic Management

Lecture 198 Vehicle Detection and Traffic Monitoring System Overview

Lecture 199 Setting Up and Exploring Essential Packages

Lecture 200 User Input and Video File Selection

Lecture 201 Implementing Vehicle Monitoring with YOLOv8 Model Inference

Lecture 202 Tkinter Implementation for Real-Time Vehicle Monitoring

Lecture 203 Vehicle Traffic Monitoring Code Execution

Section 25: Project #18: Smart Fitness – Real-Time Exercise Counter using AI, Python & CV

Lecture 204 Introduction of the Human Fitness Tracking System

Lecture 205 Project Overview & Purpose

Lecture 206 Packages Overview & MediaPipe Initialization

Lecture 207 Calculating Angles in Pose Estimation

Lecture 208 Logic Behind Repetition Counting

Lecture 209 Tkinter Log Window & Variable Initialization

Lecture 210 Model Inference and Code Explanation

Lecture 211 Tkinter Implementation for UI

Lecture 212 Package Installation Guide

Lecture 213 Code Execution Workflow

Section 26: Project #19: SafeFall – AI-Powered Fall Detection & Alerts with Python & CV

Lecture 214 Introduction to AI-Powered Fall Down Detection & Alert System

Lecture 215 Fall Down Detection System Project Overview

Lecture 216 Dependency & Package Overview

Lecture 217 Installation & MQTT Setup

Lecture 218 User Registration & Login API

Lecture 219 MQTT & Flask Integration

Lecture 220 Fall Detection Logic

Lecture 221 Prediction API Workflow

Lecture 222 Code Execution & Testing

Section 27: Project #20: Wildlife Tracking – Real-Time Animal ID with Python & CV

Lecture 223 Introduction to Animal Detection System

Lecture 224 Animal Detection System Project Overview

Lecture 225 Setting Up Google Colab and Mounting Google Drive

Lecture 226 Dataset Download and Exploration

Lecture 227 Dataset Preprocessing and Augmentation

Lecture 228 Splitting the Dataset for Training, Validation, and Testing

Lecture 229 Visualizing the Animal Dataset and Augmented Data

Lecture 230 EfficientNetB0 Model Implementation

Lecture 231 Training the EfficientNetB0 Model and Monitoring Progress

Lecture 232 Model Inference using Flask and Ngrok

Lecture 233 Code Execution

Section 28: Project #21: AI-Powered Driver Monitoring – Distraction Detection with Python

Lecture 234 Introduction of the Driver Distraction System

Lecture 235 Driver Distraction Project Overview

Lecture 236 Google Colab Setup & Google Drive Mount

Lecture 237 Dataset Download & Exploration

Lecture 238 Data Visualization & Insights

Lecture 239 Data Preprocessing & Augmentation

Lecture 240 ResNet-50 Model Architecture & Implementation

Lecture 241 Model Training & Optimization

Lecture 242 Model Inference Code Explanation

Lecture 243 Code Execution

Section 29: Wrapping Up

Lecture 244 Course Wrap-Up

Undergraduate and Graduate Students in engineering, computer science, electronics or related fields seeking hands-on CV projects to complement their studies.,Recent Graduates with STEM degrees who want to build practical AI skills and showcase real-world projects on their résumé.,Working Professionals in software, electronics, robotics or data roles aiming to pivot into AI/ML and leverage vision applications in industry.,Career-Switchers from STEM Fields (e.g., physics, mathematics, biotech) looking for a structured path into computer vision without starting from scratch.,R&D Engineers & IoT Developers who need to integrate vision analytics on edge devices like Jetson, Raspberry Pi or in cloud pipelines.,Self-Learners & Hobbyists with a science/engineering mindset who want to master end-to-end CV workflows—from algorithm basics to GUI deployment and model inference.