Patrick introduces the most commonly used data formats with image processing, and how to use the Numpy library to generate uniform-colored images. It is important to stay aware of what data-type variables are functions at all times. Often, there are performance impacts of storing unnecessary image variables.
- [Instructor] As we work with image processing,…it is important to understand how to operate…and create matrices, or NumPy arrays.…It is also important to keep track of scalar operations.…Let's review these image formats and how to operate on them…with efficiency.…To begin, we'll be looking at generating initialized arrays.…For this, I've opened the Chapter 2, Module 3 script file.…Note that we're going to be writing our script in this file,…and then running it on the command line.…To begin, we will import numpy as np,…and import cv2.…
The first command we're going to be using…is the zeroes command from numpy.…This creates an array of zero values.…As we've seen from previous modules,…zeroes in an image will appear as black, therefore,…when a name or variable black,…then say = np.zeros,…and then pass the arguments required for the zeroes command.…The first input is the size, or the shape of the image.…For this, we're going to indicate an image…that is 150 pixels tall, 200 pixels wide,…and only has one channel.…
AuthorPatrick W. Crawford
- Installing and configuring OpenCV
- Data types and structures
- Image types
- Manipulating pixels
- Scaling and rotating images
- Using video inputs
- Creating custom interfaces
- Object detection
- Face and feature detection
- Template matching
Skill Level Intermediate
Python: Programming Efficientlywith Michele Vallisneri2h 15m Intermediate
NumPy Data Science Essential Trainingwith Charles Kelly3h 54m Intermediate
1. Install and Configure OpenCV
2. Basic Image Operations
3. Object Detection
4. Face and Feature Detection
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