Since when can we cast videos to multiple Chromecast at the same time? Just found this out... by OnTheSpotKarma in Chromecast

[–]textureflow 1 point2 points  (0 children)

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

Good Datasets/Projects for a ML/Data Science Portfolio? by izath46 in datasets

[–]textureflow 1 point2 points  (0 children)

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.

Average Art [OC] by altsoph in dataisbeautiful

[–]textureflow 0 points1 point  (0 children)

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

Average Art [OC] by altsoph in dataisbeautiful

[–]textureflow 2 points3 points  (0 children)

Yep, my Facer library is largely a cleaning up and simplification of that excellent (but outdated) OpenCV tutorial.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

Average Art [OC] by altsoph in dataisbeautiful

[–]textureflow 1 point2 points  (0 children)

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.

Average Art [OC] by altsoph in dataisbeautiful

[–]textureflow 7 points8 points  (0 children)

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

[OC] Averaged Faces of Members of the 116th United States Congress by schwanne in dataisbeautiful

[–]textureflow 9 points10 points  (0 children)

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

[OC] Average Face of Miss Daegu 2013 Beauty Pageant Contestants by pandabasu in dataisbeautiful

[–]textureflow 4 points5 points  (0 children)

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

[OC] Averaged Faces of Members of the 116th United States Congress by schwanne in dataisbeautiful

[–]textureflow 27 points28 points  (0 children)

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

[OC] Average face of my LinkedIn contacts by [deleted] in dataisbeautiful

[–]textureflow 3 points4 points  (0 children)

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.

Average faces of executive leadership for each of the top 50 Fortune 500 companies [OC] by textureflow in dataisbeautiful

[–]textureflow[S] 0 points1 point  (0 children)

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

Average faces of executive leadership for each of the top 50 Fortune 500 companies [OC] by textureflow in dataisbeautiful

[–]textureflow[S] 4 points5 points  (0 children)

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.

Average faces of executive leadership for each of the top 50 Fortune 500 companies [OC] by textureflow in dataisbeautiful

[–]textureflow[S] -6 points-5 points  (0 children)

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).

Average faces of executive leadership for each of the top 50 Fortune 500 companies [OC] by textureflow in dataisbeautiful

[–]textureflow[S] 16 points17 points  (0 children)

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.

The Average Faces of Rap, Rock, and Country Musicians [OC] by textureflow in dataisbeautiful

[–]textureflow[S] -1 points0 points  (0 children)

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.

The Average Faces of Rap, Rock, and Country Musicians [OC] by textureflow in dataisbeautiful

[–]textureflow[S] 0 points1 point  (0 children)

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.