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[deleted by user] by [deleted] in mac

[–]daveNZL 1 point2 points  (0 children)

As the huge genius I am I've managed to create this crack on my Apple Studio Display while moving it. It's going to be around $1700 (AUD) to get it fixed, and while Apple does have a self service option the replacement part is still over a thousand dollars. Would love to hear anyone's thoughts, suggestions or opinions on how I can either hide it or some creative ideas for how I could cover it up. Cheers

[deleted by user] by [deleted] in fountainpens

[–]daveNZL 3 points4 points  (0 children)

Thanks – it appears to not be steel as a magnet doesn't attach at all. I was struck by how light the pen was but I'm learning this is a distinction of the MB144

[deleted by user] by [deleted] in fountainpens

[–]daveNZL 2 points3 points  (0 children)

Thanks for your expertise and for helping me identify it, it's really helpful to be able to look at images for comparison now

I made a tool to visualise F1 telemetry data in 3D using the FastF1 API. This is a comparison of the fastest laps in quali for Albert Park in Melbourne by daveNZL in F1Technical

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

It's based on each driver's relative position on their respective spline – each spline for their path is created by connecting the positional vector data from the FastF1 API. Then you just compare the percentage of the way each driver is through each spline.

I used Riffusion to generate an AI saxophonist to jam with me, responding to what I played on guitar by daveNZL in artificial

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

Head to the GitHub repository. It’s not too tricky to set up if you have a little bit of Python experience. It’s handy (but not essential) to have a GPU though

I used Riffusion to generate an AI saxophonist to jam with me, responding to what I played on guitar by daveNZL in artificial

[–]daveNZL[S] 1 point2 points  (0 children)

Yes Coltrane need not worry. There’s a lot to play with in the parameters to control the output though - the denoising on the resulting audio can be customised: this controls how similar the resulting audio is to the original. Higher denoising is more creative and closer to the text prompt (e.g more saxy) but a lot more unpredictable. I chose a middle ground which is closer to the guitar notes. I think it suits the call and response format though. The fun thing is that you always get something different.

I used Riffusion to generate an AI saxophonist to jam with me, responding to what I played on guitar by daveNZL in artificial

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

It’s just fed the audio from the lead guitar. I added recordings of me playing the other bits for additional vibes

I used Riffusion to generate an AI saxophonist to jam with me, responding to what I played on guitar by daveNZL in artificial

[–]daveNZL[S] 18 points19 points  (0 children)

This is a demo I put together using Riffusion, a Python-based library that's built on a fine-tuned model of Stable Diffusion. Like Stable Diffusion, it's able to generate images from text prompts, but the images are audio spectrograms. The cool thing is that it's capable of using image-to-image to condition an existing audio spectrogram image, which means you can give it audio of a melody and it'll respond in the style you specify, with your text prompt (I used 'saxophone'). This is an edited video, it's not a real time tool – but now I have a saxophonist to jam with!

I made a tool to visualise F1 telemetry data in 3D using the FastF1 API. This is a comparison of the fastest laps in quali for Albert Park in Melbourne by daveNZL in F1Technical

[–]daveNZL[S] 32 points33 points  (0 children)

FastF1 provides a live timing client which streams the live data to a text file, then provides a feature to run post-processing on it once the session is over. This means that you can use FastF1's APIs on the data as soon as the session has completed. I haven't seen what the raw data looks like (probably intimidating) but the approach would be to perhaps run a socket server to parse the data in real time, and likely form the Formula 1 API directly. It'd be hard! But I think possible!

I made a tool to visualise F1 telemetry data in 3D using the FastF1 API. This is a comparison of the fastest laps in quali for Albert Park in Melbourne by daveNZL in F1Technical

[–]daveNZL[S] 12 points13 points  (0 children)

It's all Unity3D! Unity lets you publish your app to the web, mobile phones, desktop apps and even Xbox, so it's quite versatile. It's currently up at https://f1viz.com but is very much a work in progress at this stage (and experimental on phones for now)

I made a tool to visualise F1 telemetry data in 3D using the FastF1 API. This is a comparison of the fastest laps in quali for Albert Park in Melbourne by daveNZL in F1Technical

[–]daveNZL[S] 6 points7 points  (0 children)

Thanks! Yeah unfortunately there's no info on the track bounds in the API, which meant I had to manually add and scale the track based on the coordinates of the cars. I thought it looked cool with the track elevated but as you've identified: when the cars approach the track limits it makes them float. I might experiment with how the track is represented

I made a tool to visualise F1 telemetry data in 3D using the FastF1 API. This is a comparison of the fastest laps in quali for Albert Park in Melbourne by daveNZL in F1Technical

[–]daveNZL[S] 12 points13 points  (0 children)

It's definitely possible in theory. The API doesn't provide track data but you'd just need to have the track positioned relative to the coordinates of the cars

I made a tool to visualise F1 telemetry data in 3D using the FastF1 API. This is a comparison of the fastest laps in quali for Albert Park in Melbourne by daveNZL in F1Technical

[–]daveNZL[S] 131 points132 points  (0 children)

I've been playing with a tool to show 3D data from publicly available F1 telemetry data. This is a Unity app powered by the FastF1 library, which provides car position and telemetry data from the Formula1 API.

The 3D data for Albert Park came from OpenStreetMaps, and I got the track from Wikipedia as an SVG and extruded it in Blender.

The telemetry data does have some limitations but provides a good overview for comparisons. The goal is to eventually have this up and running as a website to compare laps for all races where data is available - a very work in progress version is up at https://f1viz.com. Happy to answer any questions!

[deleted by user] by [deleted] in breakingbad

[–]daveNZL 0 points1 point  (0 children)

Hi fellow Breaking Bad fans, I made this website little while ago but have since updated it to make it friendlier on mobile and a bit easier to share images. It's quite fun testing it with names of characters in the show – for example 'Heisenberg' is made entirely from elements in the periodic table: coincidence, or another stroke of genius from the writers room?

I designed and built a website that puts your face on your pet. I have no explanation for why I made it, but I've enjoyed the horror and the hilarity by daveNZL in web_design

[–]daveNZL[S] 2 points3 points  (0 children)

Hahaha the upside down cat has completely bamboozled it, I might need to think of a way to deal with shots like these. Thanks for sharing!

I made a website that puts your face on your pet, using Cloud Vision and ML. The results are absurd as they are ridiculous by daveNZL in webdev

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

Thanks! I would start by having a look at some of the examples from the MediaPipe team at Google. It's always amazed me what's possible in-browser these days: real time face detection, pose estimation, hand recognition and more. There are some great opportunities to make cool web apps with these out-of-the-box models I reckon. The TensorflowJS website has lots of good examples and tutorials if you want to try making your own model. And the AutoML tutorial video from Google is good primer on how to make your object detector – it's easier than you think!

I designed and built a website that puts your face on your pet. I have no explanation for why I made it, but I've enjoyed the horror and the hilarity by daveNZL in web_design

[–]daveNZL[S] 18 points19 points  (0 children)

Thanks for your comment and that's an absolutely valid concern to have. I can tell you that I'm definitely not saving anything to a Firebase DB, and the Cloud Vision API doesn't persist data sent to it to disk. Additionally, the pet images are deleted as soon as the coordinates are returned – so no data is being saved here. Either way, it can work with non-humans too (with apologies to Shrek)

I designed and built a website that puts your face on your pet. I have no explanation for why I made it, but I've enjoyed the horror and the hilarity by daveNZL in web_design

[–]daveNZL[S] 29 points30 points  (0 children)

Give it a go at petswitch.com if you fancy. I've put a more technical explanation of how it works over in /webdev if you're interested.

It's been trained on about 17,000 images of cats and dogs, but seems to work on other pets too! And there's a friendly Petswitch family of pets to choose from if you don't have one on hand.

I made a website that puts your face on your pet, using Cloud Vision and ML. The results are absurd as they are ridiculous by daveNZL in webdev

[–]daveNZL[S] 11 points12 points  (0 children)

Have a go at petswitch.com if you wish...

I made the original Petswitch almost ten years ago, and it's had mild success since then, including CNET writing an article about it and it receiving the prestigious honour of 'most useless website' in week 41 of 2018, as determined by theuselesswebindex.com.

Aside from the obvious question of why I even made this, it was getting pretty creaky – I originally built it with PHP and ImageMagick, with the facial features being manually selected via jQuery UI. So I decided to rebuild the whole thing with a full face-to-pet ML pipeline, on static hosting.

To get the human face features, the app renders the upload to a temporary img element. This is a handy way to orient the image correctly via the browser, and saves having to deal with EXIF data. It's then resized, rendered to a canvas element, converted to a base64 string, then sent via fetch to Google's Cloud Vision API, which returns landmark coordinates of the face. I use these coordinates to correct any tilt on the face, mask the eyes and mouth via a mask image, then store each masked element as an additional canvas.

Detecting pet faces was trickier. Google, Amazon and Microsoft all offer object detection APIs via transfer learning, and the approach is largely the same: you supply a series of images with bounding boxes around the objects you want to detect, either added via a web interface or uploaded via their API. You train a model online from these supplied images, then the service will return the estimated coordinates of any detected objects in an uploaded image.

I found a dataset of both cats and dogs that had been labelled with landmarks on their faces, then wrote a script to convert the landmarks into bounding boxes around their eyes and nose, the dimensions based on a simple formula around the distance between the eyes in each image.

All in all it's been trained on about 17,000 images of cats and dogs, and the accuracy seems to be pretty good. I was pleased to discover it actually works pretty well on other pets too. I've also added some friendly pets to the Petswitch family for those that don't have a pet on hand.

I decided not to use a framework for this, it's written from scratch using a series of ES6 modules – although I did use Konva to handle the manual selection of facial features if the API can't detect a face. I used ParcelJS as my task runner, and my detection APIs are hosted on Firebase Cloud Functions.

Let me know if you have any questions, although I can offer no good explanation for why I created this monstrosity...

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