One of the best things about OpenCV is that it comes with a lot of built-in primitives for image processing and computer vision operations. If you have to start from scratch and write something, you will need to define things like an image, a point, a rectangle, and so on. Almost every computer vision algorithm needs these. All of these basic structures are already built into OpenCV. They are all in the core module. Another benefit is that these frameworks are already optimized for speed and memory, so users don't have to bother about the specifics of implementation.
The imgcodecs module is in charge of opening and saving image files. With a simple command, you can save the output image as either a jpg or a png file when you're done with it. When you work with cameras, you will have to deal with a lot of video files. There are different modules that take care of everything that has to do with putting and taking out video files. You can easily record a video from a webcam or read a video file in various formats. You can also set properties like frames per second, frame size, and so on to save a bunch of frames as a video file.
Processes for handling images
When you write a Computer Vision algorithm, you will use a lot of the same basic image processing steps over and over. The imgproc module has most of these functions. You can do things like image filtering, geometric transformations, morphological operations, drawing on images, color conversions, histograms, motion analysis, shape analysis, feature detection, and so on.
In OpenCV, we only need one line to do many of these manipulatinos, as you would see in this OpenCV course.
Author(s): Emenwa Global
Publisher: Independently published
Year: 2022
Language: English
Pages: 177
Introduction
What is OpenCV?
What can you do with OpenCV?
Chapter 1: Setting up OpenCV
Setting Up Windows
How to Install Pip
Setting Up OpenCV on Mac
Setting Up Linux
Chapter 2: Reading Images and Video
How OpenCV Displays Images With Colour Spaces
Reading Videos in OpenCV
Chapter 3 - Resizing and Rescaling Frames
Resizing Images
Rescaling a Video
Chapter 4 - Drawing Shapes & Putting Text on Images
Starting
Using Colours
Draw a line
Draw A Rectangle
Filling the Rectangle with Colour
Draw a Circle
Write Text on Image
Chapter 5 – Basic Functions You Must Use in OpenCV
Converting An Image to Greyscale
Blurring an image
Creating Edge Cascade
How to Dilate an Image
Erosion
Resize and Crop an Image
Rotation
Chapter 6 - Contour Detection
ADVANCED SECTION
Chapter 7 - Color Spaces
BGR to HSV
BGR to LAB
BGR to RGB
HSV to BGR
Chapter 8 - Color Channels
Splitting Channels
Merging Color Channels
Reconstructing Color Channels
Chapter 10 – The Magic of Blurring
Concepts of Blurring in OpenCV
Averaging
Blurring or Averaging an Image
Gaussian Blur
Median Blur
Bilateral Blurring
Chapter 11 – Bitwise Operations
Create A Rectangle and Circle
Bitwise AND
Bitwise OR
Bitwise XOR
Bitwise NOT
Chapter 12 - Masking
Image Masking with OpenCV
Chapter 13 - Histogram Computation
Working with CalcHist() Method
Histogram for Grayscale Images
Histogram Computation for RGB Images
Chapter 14 - Thresholding/Binarizing Images
Simple Thresholding
Adaptive Thresholding
Chapter 15 – Gradients and Edge Detection in OpenCV
How Do We Detect the Edges?
Laplacian Edge Detector
Sobel Edge Detection
Section #3 - Faces:
Chapter 16 - Face Detection with Haar Cascades
Face Detection
Haar Cascade Classifier
Integral Images
Detecting Faces
Chapter 17 - Object Recognition with OpenCV's built-in recognizer
OpenCV Built-in Face Recognizers
EigenFaces Face Recognizer
FisherFaces Face Recognizer
Local Binary Patterns Histograms (LBPH) Face Recognizer
Collecting Images
Preparing training data
Training The Face Recognizer
Face Recognition Testing
Chapter 17 – Capstone - Computer Vision Project: The Simpsons
Setting Up
Getting Data
Training Data
Features and Labels
Normalize FeatureSet
Create Training & Validation Data
Image Data Generator
Creating The Model
Training The Model
Testing and Predicting
End Game
