Data Visualization and Technology

Name: Data Visualization and Technology
Author: Bill Hood

Learn about data visualization in technology, art, and machine learning

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Course: On-Demand

In this lesson, we're going to talk about image generation using Python. Process visualization or image generation is a hot field in the employment market. Tools on cameras to provide lenses, overlays, color shifts, and even thumbnails are all examples of generated images. We're going to use a new import, Pillow, from PIL, capital PIL is the usual way to use this. There's three primary tools we're going to use. Image, image enhance, and image filter. We're also going to, another new import is base 64, which is a binary text encoding scheme that will help us as we'll talk about in a minute, and I O which stands for input output. And what we'll do first here is read in an image, as you can see is named IMG-1012.jpg and we'll take a quick look at the bands we see from this. So we'll run it. We'll see that RGB, red, green, blue, which means color. So it is a color picture. Let's get a few more statistics off of this. And we'll go to size, run that. And we'll see that it is size 4032 by 3024. Alright, Now let's open that as a base64 image. And the reason we're doing that is because we need to encode this binary data to be stored and transferred over into an ASCII 2 format where we can manipulate it programmatically. So we're going to read this in as a base 64 image with this statement right here. Now that we've got it in, we can open it up, see what it looks like. And you'll see that does take some output or some horsepower and here's this image it's really quite large as you can see and it is Towski Valley the base of the mountain in the middle of summer. And what we can do too is go ahead we can save that now as a JPEG image. Now what we can do also just to make this a little easier to work with is that's a quite a large image big and beautiful but quite large so we're just going to make it smaller and make it instead of the 4000 by 3000 we're going to make it 500 by 500 and that's what we would do with this routine right here. All righty, and we also called that a thumbnail, tsv underscore thumb. You'll notice here too if we look in files and look under t for thumb. Now we can open that up and then let's go ahead and convert this now. So we've got a much smaller image for convenience. Let's do our first conversion here and let's convert this to black and white. And there it is now. It's saying it's a little bit smaller image but it's in black and white which you can see obviously a pretty big change. Let's enhance the sharpness of this. This routine right here That definitely looks more blurry and then a final one just an example There's really quite a few of these would be a contour. Run that. And you can see almost kind of an artistic version too of that. Thanks again.

Intermediate Provider : Bill Hood 3 Lessons · 12m in Arabic, German, English, Spanish, French, Portuguese, Chinese

Course Description

In these lessons, we begin with a quick exploration of the conversion of an image to a base64 format so we may manipulate the data programmatically. For example, you’ll see how a thumbnail can be manufactured by taking an original dense image that we can programmatically go through to produce a smaller, less dense thumbnail image. This could then be automated and incorporated into applications so that once a user uploads a new file with an image, an automatic process occurs to convert that image to base64, manipulate it, and produce a new image, all while leaving the original image unchanged.

We’ll also explore the world of artistic endeavors using Python, basic shapes, and random numbers to generate art. You’ll take a close look at a Python framework that can be easily manipulated in terms of shapes and number of iterations to create an amazingly varied collection of computer art. This is an emerging field with serious artists pushing this sort of technology to its limits.

What You'll Learn

Identify where art and pure technology intersect
Explain what base64 means and why it is necessary to alter a non-base64 image you wish to manipulate
Understand the basic process of producing an image thumbnail and how to automate it
Convert an image to base64 format in order to manipulate its data programmatically
Generate computer art using Python, basic shapes, and random numbers

Key Takeaways

Converting an image to base64 lets you manipulate its data programmatically.
A thumbnail can be produced by programmatically processing a dense original image into a smaller, less dense version while leaving the original unchanged.
The base64 conversion and image manipulation process can be automated and built into applications so it runs automatically when a user uploads a new image.
A Python framework can be adjusted in terms of shapes and number of iterations to generate a varied collection of computer art.
Generative art is an emerging field where serious artists are pushing this technology to its limits.

Frequently Asked Questions

What does this course cover?

It covers converting images to base64 format so they can be manipulated programmatically, producing and automating image thumbnails, and generating computer art using Python, basic shapes, and random numbers.

Why does the course focus on base64?

The course explains what base64 means and why it is necessary to convert any non-base64 image into base64 before you can manipulate it.

What will I learn about creating art with technology?

You will explore a Python framework that can be easily adjusted in terms of shapes and number of iterations to create a varied collection of computer art, an emerging field where serious artists push the technology to its limits.

What skills does this course build?

It builds skills in data visualization, graphical tools, graphics libraries, scientific visualization, statistical graphics, and visualization.

Does manipulating an image change the original file?

No. The automated process converts the uploaded image to base64, manipulates it, and produces a new image, all while leaving the original image unchanged.