OpenCV Python Development: Working with Images

Name: OpenCV Python Development: Working with Images
Author: Tom Rosenwinkel

Learn how to process images and videos in OpenCV Python

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Course: On-Demand

Hello, my name is Tom Rosenwinkle. In this series of lessons, we are going to see examples of opening images and processing them in OpenCV. We will see how to manipulate the colors of an image, transform images with algebraic geometric and convolution transformations. This will include blurring images, eroding images, and ultimately we will also open and work with processing videos and their frames. In this lesson, we will open an image and look at the color channels. I've specified the path to an image in my project directory in the data directory and a sub-directory called raw. To find the file at that path, we will use OpenCV's samples module to find file function. And with that file, we will open the image using OpenCV's IMRead function. We can write that image to a file using OpenCV's IMWrite function, specifying the file location for our output image and the image that we want to write. So we will see that this sample image of fruits has color and a few pieces of fruit in it. We will remember that if we look at the way the image is represented in OpenCV, we will find it is a NumPy array that has three dimensions to the shape. It has rows and columns of pixels and at each pixel is three color channels. The particular shape of this image is 480 rows, 512 columns, and the three color channels. We can split the color channels in OpenCV and create three different grayscale images. Remember the order of the color channels in OpenCV is blue, green, and red. And we can get each color channel split from the original image using OpenCV's split function. So we can look at the shape of any one of those color channels and we can write all three of them to output files. So we will see the grayscale representations. So the shape are 480 rows and 512 columns. And we will see the blue component of the fruits in the green component. We can see the Kiwi is a lot more green to it and the red component. In the next lesson, we will learn how to create some drawings or annotations onto our images in OpenCV. Thank you for watching.

Intermediate Provider : Tom Rosenwinkel 12 Lessons · 59m in English

Course Description

How can you read images and videos and process them to reveal features of interest? How can you transform the pixels and colors to satisfy specific requirements? The NumPy array structure of images and video frames, as well as OpenCV’s image processing module, provide the necessary foundation to deconstruct and analyze images and videos. This course contains several lessons showing these structures and transformations. Learning these tools is an essential skill in any computer vision project. The topics discussed in these lessons will provide a foundation for future work in object detection and feature extraction.

In these lessons on OpenCV Python Development, you will learn how to read and process images and videos in OpenCV. We will explore color channels, pixels, and geometric and algebraic transformations of images in OpenCV. By the end of these lessons, you will be able to construct and deconstruct images and videos in OpenCV.

What You'll Learn

Read and process images and videos in OpenCV using the NumPy array structure
Manipulate pixels, regions, and color channels including splitting and merging channels
Apply geometric transformations such as translations, rotations, affine, and perspective transforms
Perform algebraic transformations including filtering, morphological transformations, and image blending
Draw annotations, add padding and border types, and work with video frames in OpenCV
Build custom interfaces for image and video work using Streamlit

Key Takeaways

The NumPy array structure of images and video frames, together with OpenCV's image processing module, provides the foundation to deconstruct and analyze images and videos.
The course covers color channels, pixels, and geometric and algebraic transformations of images in OpenCV.
By the end of the lessons, learners can construct and deconstruct images and videos in OpenCV.
These image processing tools are an essential skill in any computer vision project and lay a foundation for future work in object detection and feature extraction.

Frequently Asked Questions

What will I be able to do after completing this course?

By the end of the lessons, you will be able to construct and deconstruct images and videos in OpenCV, including reading and processing them and applying geometric and algebraic transformations.

What topics does this course cover?

It covers image types and color channels, drawing and annotations, pixel and region manipulations, splitting and merging color channels, padding and border types, translations and rotations, affine and perspective transforms, filtering, morphological transformations, image blending, working with videos, and building custom interfaces with Streamlit.

What skills will I gain from this course?

You will build skills in Computer Vision, Digital Image Processing, Image Analysis, Image Recognition, OpenCV, and Video Processing.

Why are these skills important?

Learning these image processing tools is an essential skill in any computer vision project and provides a foundation for future work in object detection and feature extraction.