Computer Vision
Learn Computer Vision and Image Analysis with Python and OpenCV
What you'll learn
- Understand the basics of computer vision
- Review the linear algebra, calculus and probability you need
- Understand images, colors and video
- Set up your programming environment
- Learn Python basics, conditionals and loops
- Read images and video with OpenCV
- Resize images, change the color system and crop images
- Draw shapes with NumPy
- Build a hands-on face recognition application
About this course
This course covers computer vision, which means enabling a computer to understand and analyze images and video, using the Python programming language and the OpenCV library. You start with an introduction to computer vision, then the mathematical foundation: linear algebra, vectors and matrices, calculus, probability and statistics, and the basics of geometry. Next you learn what images are, the color system, matrices and the concept of video, set up your programming environment, and learn Python basics including conditional statements and loops. Then you move to reading images and video with OpenCV, resizing and changing the color system, cropping images and videos, and drawing shapes with NumPy, and you finish with a hands-on face recognition project in two parts. It suits people interested in artificial intelligence and programming, and no previous experience is required because the course explains Python basics. The lessons go from the theoretical foundation to practice, so you understand the idea before you write the code. By the end, you will be able to write programs that read images and video, edit them and recognize faces.
Expected outcomes
- Deep understanding of computer vision concepts.
- Develop practical applications using Python libraries.
- Professionally process images and videos.
- Acquire practical skills in artificial intelligence projects.
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Course content
1Computer Vision
19 lectures
- Introduction to Computer VisionDefines computer vision, its importance and everyday impact, and how it differs from image processing. Explains that a computer sees an image as a matrix of numbers, with applications such as medicine and image generation.16:21
- Linear Algebra (Vectors and Matrices)Explains the linear algebra basics needed for computer vision: vectors as lists of numbers, matrices and matrix multiplication, and why they matter for image processing and neural networks.22:39
- CalculusExplains differentiation as a rate of change and the derivative rule, and integration in its definite and indefinite forms, with uses in computer vision such as lighting change and object motion.19:26
- Probability and StatisticsExplains probability with values between zero and one and the addition and multiplication rules, then statistics such as mean, median and standard deviation, and their use in evaluating model performance in computer vision.14:41
- Principles of GeometryExplains geometry basics useful in computer vision: points, the straight line and its equation, distance between two points, and geometric transformations such as translation, shearing, rotation and scaling in image processing.16:08
- Explanation of the Concept of ImagesExplains vector and raster image types, how an image is built from pixels, measuring its dimensions by width and height, that each pixel is a single-color square, and that video is made of images.14:49
- Color Systems and MatricesExplains how a computer represents image colors with red, green and blue values in a matrix, and how color values read from any design program become numbers the computer works with.20:58
- Explanation of the Concept of VideoExplains video as images shown in sequence, the frames-per-second term, how closely similar images create smooth motion, and common frame rates used in videos.10:58
- Setting Up the Programming EnvironmentCovers setting up the coding environment by installing Visual Studio Code and getting to know its menus, installing the needed extensions and libraries, and creating a new Python file to start coding.19:18
- Basics of Programming in PythonCovers Python basics: variables and naming rules, the difference between numbers and text, arithmetic operations, printing values with print, and why adding text to a number causes an error.16:06
- Conditional Statements in PythonExplains conditional statements in Python using if, else and elif, comparing values with the double equals sign, combining several conditions, and how code runs depending on whether a condition is true.17:10
- Loops in PythonExplains loops in Python: the for loop with range and setting start and end values, the while loop and its condition, and how to repeat code many times without rewriting it.18:11
- Image Reading Using openCVCovers reading and displaying an image with OpenCV: importing the library, passing the image path to imread, showing it with imshow and a window name, and using waitKey to keep the image on screen.20:15
- Video Reading Using openCVCovers reading video with OpenCV using VideoCapture and a while loop to show frames one by one with waitKey, controlling playback length, and opening the laptop camera by passing zero.25:44
- Changing Dimensions and Color SystemsExplains resizing images and videos in OpenCV and its effect on the aspect ratio, converting the color space to grayscale, and applying both to images, video and the laptop camera.24:05
- Cropping Images and VideosExplains cropping part of an image by setting the start and end point coordinates in pixels, clarifying the axis order, then performing the crop on an image and noting the same idea applies to video.16:07
- Drawing Shapes Using NumPyExplains drawing shapes with NumPy and OpenCV: creating a blank black image, drawing a rectangle with coordinates, color and thickness, drawing a circle, and using minus one to fill a shape.26:40
- Practical Application for Face Recognition (Part One)Starts the face recognition project: breaks a face down geometrically into a circle, ovals and lines to show how a computer can detect it, then opens the camera with OpenCV in preparation for detecting faces.12:40
- Practical Application for Face Recognition (Part Two)Completes the face project by loading the Cascade Classifier XML file, tuning the detection parameters, getting the face coordinates and size, and drawing a colored rectangle around the face in live video.19:25
Instructor
Eng. Mustafa Abul-Fadl
This course is part of the diploma
