Published 6/2025
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 12.63 GB | Duration: 16h 12m
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!
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