Build Complete Webcam Security Camera | Python OpenCv & Pyqt - FTD

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