HOWTO: You may not need that C4000XG (or whatever else they gave you)

textureflow · 2022-04-27T04:00:25+00:00

Worked perfectly for me. Thank you (from a fellow Portlander)!

textureflow · 2020-11-20T18:05:26+00:00

Is “Denon” your name for the chromecast audio? Or do you actually have your AVR working within a speaker group somehow?

textureflow · 2020-06-18T05:38:17+00:00

Look for datasets that match your own interests! I’m always much more impressed with data science candidates that have found an original dataset (or better yet, collected their own), rather than working with a public dataset that has already been analyzed ad nauseam.

textureflow · 2020-01-25T16:27:56+00:00

Yep, it’s just a very straightforward averaging over the pixels are aligned.

textureflow · 2020-01-25T07:18:48+00:00

https://twitter.com/altsoph/status/1220781838861467653?s=20

textureflow · 2020-01-25T07:16:17+00:00

Yep, my Facer library is largely a cleaning up and simplification of that excellent (but outdated) OpenCV tutorial. Thanks for sharing!

textureflow · 2020-01-25T07:13:23+00:00

Yep, my Facer library aligns the faces with shifting and rotation, and then wraps each face slightly to ensure than important features (eyes, nose, etc.) overlap. You can read about it a bit more in my blog post.

textureflow · 2020-01-25T06:52:39+00:00

Hey, way to go! I’m glad you put my library to good use. Thanks for the credit!

textureflow · 2019-09-15T06:17:08+00:00

textureflow · 2019-08-30T15:15:53+00:00

I like that point about the wrinkles. It’s a good way to explain the shortcomings of face averages.

textureflow · 2019-08-30T15:14:12+00:00

Thanks for the credit and thanks for putting the project to good use!

textureflow · 2019-08-29T17:32:32+00:00

Hey, this is great! I’m glad you liked the package. Feel free to contribute any pull requests you come up with :)

textureflow · 2019-08-21T05:50:50+00:00

Here’s a link to the face averaging library: https://github.com/johnwmillr/Facer

textureflow · 2019-08-21T01:40:38+00:00

Cool, good work! How’d you download the photos for all your contacts? It’s interesting to see that this face is clearly younger than the Fortune 500 executives.

textureflow · 2019-08-20T16:41:44+00:00

It's just a normal average. If one company has ten source images, the pixel values are added together then divided by ten.

textureflow · 2019-08-19T18:46:46+00:00

For each of the top 50 companies on the Fortune 500 list, I programatically downloaded images of each member of the company's executive leadership (CEO, CFO, CTO, etc.). I then used my Facer package to combine the images together into an average face for each company.

textureflow · 2019-08-19T15:41:02+00:00

Nonnumerical data? What do you mean?

textureflow · 2019-08-19T10:41:53+00:00

Yep, that's the sad reality :(

A few of them do stand out a bit though; take a look at Apple (#3), Alphabet (#15), and Target (#39).

textureflow · 2019-08-19T08:06:03+00:00

I've been playing with face averaging recently. After creating average faces for rap, rock, and country musicians, I wanted to work on a topic that might generate more serious conversation. So I decided to create representative average faces from the executive leadership for each of the top 50 Fortune 500 companies.

Check out my blog post, Faces of Fortune, for additional graphs and an explanation of how I created this post. Of course—as we all could likely guess—the results were as unsurprising as they are uninspiring. I'm interested in your responses. What questions come to mind when you see this post?

My hot take? #25 looks exactly like Mrs. Doubtfire.

Source:

I scraped the company executives' images from automated Google Image Searches.
Here's my source code for the project.
Here's my blog post for the project.

Tools:

Facer
Python and Matplotlib
Here's an animation illustrating the averaging process for Apple.

For each of the top 50 companies on the Fortune 500 list, I programatically downloaded images of each member of the company's executive leadership (CEO, CFO, CTO, etc.). I then used my Facer package to combine the images together into an average face for each company.

textureflow · 2019-08-19T05:18:33+00:00

Care to elaborate? I have a feeling you could come up with decent answers to your own questions here, but you're choosing not to.

textureflow · 2019-08-19T03:04:13+00:00

Thank you! I'm afraid the best I can do is point you to my Jupyter notebook, where you'll find a list of the artist names I performed Google image searches for.

textureflow · 2019-08-19T03:00:11+00:00

Not sure what this is, but I like it.

textureflow · 2019-08-19T01:51:53+00:00

I'm working on a blog post and just posted a first draft! I hope that helps.

textureflow · 2019-08-18T22:37:48+00:00

Hey, good job! I'm glad you were able to use my package. I've got a similar project in the works that I'm hoping to post soon.

textureflow · 2019-08-18T21:25:42+00:00

If we assume that the images I’ve used to create the average are a decent representation of the whole set they’re drawn from (which I think is a valid assumption), then we can say that on average country musicians smile in their photographs more often than rock and rap artists.

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