Build Complete Webcam Security Camera | Python OpenCv & Pyqt

Build Complete Webcam Security Camera | Python OpenCv & Pyqt, available at $34.99, has an average rating of 4, with 26 lectures, based on 1 reviews, and has 21 subscribers.

You will learn about How to detect and recognize objects in webcam captured images using OpenCV Python code. Learn to convert images to gray scale, difference between tow images, gaussian blur in opencv python Learn to get contours of detected objects in a 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 alarm sound if any object found This course is ideal for individuals who are Developers who want to learn opencv and develop a complete project using open cv or Students who want to develop a complete project using opencv and pyqt for final year submission or Students or developers who want to build their own security camera software using webcam or Python learners who want to increase their skills and enter into Artificial Intelligence programming It is particularly useful for Developers who want to learn opencv and develop a complete project using open cv or Students who want to develop a complete project using opencv and pyqt for final year submission or Students or developers who want to build their own security camera software using webcam or Python learners who want to increase their skills and enter into Artificial Intelligence programming.

Enroll now: Build Complete Webcam Security Camera | Python OpenCv & Pyqt

Summary

Title: Build Complete Webcam Security Camera | Python OpenCv & Pyqt

Price: $34.99

Average Rating: 4

Number of Lectures: 26

Number of Published Lectures: 26

Number of Curriculum Items: 26

Number of Published Curriculum Objects: 26

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

How to detect and recognize objects in webcam captured images using OpenCV Python code.
Learn to convert images to gray scale, difference between tow images, gaussian blur in opencv python
Learn to get contours of detected objects in a 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 alarm sound if any object found

Who Should Attend

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 own security camera software using webcam
Python learners who want to increase their skills and enter into Artificial Intelligence programming

Target Audiences

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 own security camera software using webcam
Python learners who want to increase their skills and enter into Artificial Intelligence programming

Hello Students

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

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

1. Installation and configuration

First we are going to install the required software to start our project from the internet. Learn to install Python, pyqt5, pyqt5-tools and opencv library. Then you are going to 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 the beautiful user interface using Qt Designer. In this interface we are going to use basic controls like QPushButton, QLabel, QSlider and how to use style sheets to the controls to look good. Then you will learn how to provide the hover effects to the QPushButtons and how to dynamically change the change the images in the qlabels.

3. Camera Capture and display in window

Then we are going to implement the camera using 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 contours in the images. We will first convert the images to grayscale image using opencv. Then we will blur the images using gaussian blur in opencv python. Then we are going to dilate images using opencv. Then we are going to collect all the contours in the images using opencv python.

5. Object Detection

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

6. Display captured objects

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

By doing this project you will learn lot of basic functions in opencv library and how to use basic controls using qt designer and how to process the GUI controls using python code.

Thank you for your interest in this course…

I will see you in the course.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction – How to execute this project from Python code. Explains how opencv

Chapter 2: Software installation for coding and designing and processing

Lecture 1: Learn how to install Python software

Lecture 2: Learn how to install pyqt5 and pyqt5-tools libraries

Lecture 3: Learn how to configure Qt Designer to desktop

Lecture 4: Learn how to install opencv library

Lecture 5: Learn how to install Visual Studio Code and configure to python

Chapter 3: Image resources

Lecture 1: Image resources for project

Chapter 4: Creating form design using Qt Designer

Lecture 1: Create the new window size and set the back ground using Qt Designer

Lecture 2: Add QPushButton and provide styles like hover effects and icon size setting

Lecture 3: Create QPushButton for volume control, exit the project and copy previous style

Lecture 4: Create volume indicator label and volume control slider using Qt Designer

Lecture 5: Create image display using QLabel in the computer window using Qt Designer

Chapter 5: Download user interface file

Lecture 1: Students can download the user interface file source code from this lecture

Chapter 6: Connecting Pyqt – Qt Designer ui file to the python code

Lecture 1: Creating new python file and loading the pyqt – qt designer generated ui file

Lecture 2: Creating New class from python file and opening the pyqt – qt designer ui file

Chapter 7: Connecting all the buttons with required functions

Lecture 1: Connecting all qpushbuttons with required Python functions

Lecture 2: Setting the volume level control works properly with volume slider

Lecture 3: Setting wait time and hides the volume slider and showing the volume level

Lecture 4: Creating volume variable and collect data from the volume slider

Chapter 8: Monitoring process

Lecture 1: We are going to capture camera using OpcnCV and showing it in the screeen.

Lecture 2: Finding difference between the images and creating gaussian blur using OpenCv

Lecture 3: Finding difference between the images and creating contours using OpenCv

Lecture 4: Creating sound if any objects found using OpenCv

Lecture 5: Creating the captured image in the Qt Designer window using python

Chapter 9: Download Source codes

Lecture 1: Students can download the python file and user interface file source codes

Chapter 10: Thank you

Lecture 1: Thank you for joining this course

Instructors

Muthu Manavandi
Teacher in Sanyu academy from 2006 & trained 1000+ students

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