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
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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
Description
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 qlabel in 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.
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
