Luminosity | web application for visualizing astronomical data | Computer Vision library

I'm currently running with a little bit of trouble with regards to setting a range of colors for my errors bars. Apparently, it looks like there are two error bars imposed to one another. One is color Orange while the other is Red. For reference, I followed the steps from this post: Setting Different error bar colors in bar plot in matplotlib and tweaked to fit in. Is there a way to resolve the small issue?

I'll also include the .csv file for usage. At the moment, the code runs like this:

I have a Xamarin.Forms app being built for iOS and Android.

I'm having some difficulty in Android updating the icon colors when setting the status bar color. I have this working for API levels below 30 using the following code:

I have a client and we are moving their site from Squarespace to Wordpress. The export of posts from Squarespace produces tons of unnecessary code that I am trying to remove.

If I run this Regex in an online tester like regex101 it highlights exactly what I am looking for:

I'm trying to read in a CSV file into a pandas dataframe and select a column, but keep getting a key error.

This is a snippet of the input file I am working with. I want to skip the first 5 lines, use the sitxh line with model_number,star_age, etc. as the headers, and collected the columns of data:

This is a general question regarding ImageAnalysis use case of camera-x but I will use a slightly modified version of this codelab as an example to illustrate the issue I'm seeing. I'm seeing a mismatch between the image dimensions (image.height * image.width) and the associated ByteBuffer size as measured by its limit and or capacity. I would expect them to be the same and mapping one pixel of the image to a single value in the ByteBuffer. This does not appear to be the case. Hoping someone can clarify if this is a bug, and if not, how to interpret this mismatch.

On step 6 (Image Analysis) of the codelab they provide a subclass for their luminosity analyzer:

I'm trying to declare a function that returns TFPColor type but I got an error message:

Error: Identifier not found "TFPColor"

I have a pie chart that is created using d3.js. I can click on any slice of the pie and it works as expected. However I have tried adding an unordered list and trying to make it behave the same way as the slice. So if you click on a list item the pie should rotate the same as if you click on a slice.

In order to make this happen I need to get d.startAngle and d.endAngle. This works perfectly when clicking the slice

one of the problems that I am having is that when I click on the list item I get an error code "Uncaught ReferenceError: d is not defined"

Here is the code I am using

after spending some time learning basic computer vision concepts and techniques I started to notice how unreliable simple scripts can get when the luminosity or scale changes and how resource consuming is to use more advanced solutions like creating a well-made HAAR cascade or HOG-feature based svm. Furthermore, some even more advanced methods involving machine learning usually take a lot of time and GPUhours when a high quality model is created.

Recently while looking through YouTube I've found a lot of so called VTubers who use various software to control virtual avatars with somewhat precise motion tracking and what seems to be no errors whatsoever. While not something unimaginable, the amount of people using the software and the amount of software itself seems to be rather large.

Planning to investigate even further I looked into different ways similar technology works, but so far I only found a complex solutions involving either AI driven models or assistance from some sort of positional sensors attached to the body of the user. Still its hard to believe all of those people go through such measures, so I realised that perhaps this is accomplishable with some cv solution which is relatively easy on resource consumption. So far I looked into different ways to "map" model joints to human ones. On my own I tried basic counter matching, and greenscreen filtering to avoid errors. while I successfully managed to remove almost all errors, there still were moments when mapping snapped arm for example to elbow and etc.

How exactly is object recognition and motion tracking of such quality is achieved using only computer vision?

I've updated my gradle plugins for CameraX kotlin and all of a sudden I've got an error that I don't know how to fix.

This is my camera fragment