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Introduction to OpenCV
This is a collection of Jupyter notebooks that is intended to provide an introduction to OpenCV's Python interface. All notebooks were initially developed and released by Hannah, with some minors changes, code update for python3, and some other customizations provided by me.
The target audience is broad and includes
- People who have done computer science (maybe to graduate level) but who have not looked at OpenCV before
- People who are studying other subjects and want to play with computer vision

Notebooks
The notebooks are divided by the following lessons.
I also provided the estimated time required to complete each lesson, a link to the source code, and the Google Colab link where anyone can use to follow the lessons and run the examples.
| Lesson | Estimated time needed | Source Code | Colab |
|---|
| OpenCV fundamentals | 20 min | Open | Open |
| Image stats and image processing | 20 min | Open | Open |
| Features in computer vision | 20 min | Open | Open |
| Cascade Classification |
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This repository contains both a collection of Jupyter Notebooks as well as other resources (e.g. presentations, links, ...) that are going to be used during the "Second quarter university extension courses" that the University of Oviedo is going to teach (online).
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Jupyter NotebookNo license#computer-vision#deep-learning
⑂ 10 forks◯ 0 issuesUpdated Jun 16, 2026
Facial recognition could soon jump from your smartphone to your workplace with employers using it to mark attendance and gauge the mood of the workforce.Every day, corporate offices and institutes are working to increase the productive working hours in a day. When the current system of clocking in daily using a fingerprint scanner is a time-consuming and inefficient use of time. I have planned to design a Voice Interactive Face Detection Based Smart Attendance management and behavior analysis to ensure a better work culture and environment,efficiency in a secure manner using Intel dev cloud. Currently, we have fingerprint and Smart-card Based entries in nearly all offices and a few schools and colleges. These system then automates the calculation of salary or attendance percentage.But fingerprint scanning and smart card barcode entries tend to take up time and prove to also be imperfect. In contrast, Face Recognition method provides a unique feature for every individual which is stored in a central database and can be retrieved during recognition and validation. The system includes an embedded application deployed in a SCB( Single Board Computer) which can interact with the users in real time. It will take down in and out time of every employee and monitor their working behavior(future scope) and notify the corresponding employee and the authority at times. We are aiming to analyze people's behavior,mood and emotions by monitoring and studying their actions in real time which in turn will help the organization know about the physical and mental status of the employees. This process of direct integration of physical world into computer vision based systems will indeed result in efficiency improvements, economic benefits and reduced human exertions. As of now I have developed a basic voice interactive attendance monitoring using Jupyter Notebook on Intel dev cloud. The in and out time (including mid in and out) will be monitored in Google spreadsheet and the system will calculate how many hours an employee has spent in office premises. The system won’t allow employees to step into the office after a certain time and won’t consider the attendance if the total hours spent is less than four hours. Everyday a mail will be sent to the admin containing the attendance details of the employees. In future, I would like to implement behavior and mood analysis of the employees and the staff on the office premises which in turn will help the concerned staff provide with solutions to get over the listless mood or erratic behavior.