How to use OpenCV for Python

Build Complete Webcam Security Camera | Python OpenCV & Pyqt

Step-by-step guide| Build your own webcam security camera alert system using Python Pyqt OpenCv QtDesigner from scratch

What you’ll learn

Build Complete Webcam Security Camera | Python OpenCV & Pyqt

How to detect and recognize objects in webcam-captured images using OpenCV Python code.

Learn to convert images to greyscale, the difference between two images, and gaussian blur in opencv python.

Learn to get contours of detected objects in webcam-captured video frames and draw rectangles of detected objects.

How to find the area of contours detected by the opencv in the camera-captured images and provide an alarm sound if any object

Requirements

Basic Python programming

A computer or laptop with an internet connection

Description

Hello Students

Welcome to the course “Build Complete Webcam Security Camera | Python OpenCv & Pyqt.”

You will learn how to create a beautiful user interface for the project using Pyqt Library and the Qt Designer.

1. Installation and configuration

First, we will install the required software to start our project from the internet. Learn to install Python, pyqt5, pyqt5-tools and OpenCV library. Then you will learn how to install the vs code and configure vs code to python programming through this course.

2. Design the user interface

Then we are going to design a beautiful user interface using Qt Designer. In this interface, we will use basic controls like QPushButton, QLabel, and QSlider and learn how to use style sheets to make the controls look good. Then you will learn how to provide the hover effects to the QPushButtons and change the images in the dynamic labels.

3. Camera Capture and display in the window

Then we will implement the camera using the cv2 library and capture the images in the camera. Then we show the captured images in the cv2 window.

4. Image processing

Then we will convert the images to our required formats to identify the contours in the images. We will first convert the images to grayscale images using OpenCV. Then we are going to dilate images using OpenCV. Then we will collect all the contours in the images using opencv python.

5. Object Detection

Then will find the contour area greater than 5000 and draw a rectangle using the cv2 library for the captured objects. This shows the captured objects in green colour to identify them easily.

6. Display captured objects

Then we are going to save the captured objects in an image file. The captured image file is then labelled in the pyqt window. This is used to identify the object even if the object passes the cam area.

This project will teach you many basic functions in the OpenCV library and how to use basic controls using qt designer and process the GUI controls using python code.

Thank you for your interest in this course…

I will see you on the course.

Who this course is for:

Developers who want to learn OpenCV and develop a complete project using open cv

Students who want to develop a complete project using opencv and pyqt for final-year submission

Students or developers who want to build their security camera software using a webcam

Python learners who want to increase their skills and enter into Artificial Intelligence programming