Master Computer Vision Course in 2025 with Deep Learning, Python, OpenCV, YOLO, OCR & GUI through 20+ handson projects
Sub Category
- Data Science
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Objectives
- 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.
Pre Requisites
- 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. The projects are developed and tested on Windows systems.
FAQ
- Q. How long do I have access to the course materials?
- A. You can view and review the lecture materials indefinitely, like an on-demand channel.
- Q. Can I take my courses with me wherever I go?
- A. Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
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Coupon Code(s)