fps | R package for sparse PCA via Fantope Projection | Machine Learning library

The problem is that two numbers, pitch and yaw, provide insufficient degrees of freedom to represent consistent free rotation behavior in space without any “horizon”. Two numbers can represent a look-direction vector but they cannot represent the third component of camera orientation, called roll (rotation about the “depth” axis of the screen). As a consequence, no matter how you implement the controls, you will find that in some orientations the camera rolls strangely, because the effect of trying to do the math with this information is that every frame the roll is picked/reconstructed based on the pitch and yaw.

The minimal solution to this is to add a roll component to your camera state. However, this approach (“Euler angles”) is both tricky to compute with and has numerical stability issues (“gimbal lock”).

Instead, you should represent your camera/player orientation as a quaternion, a mathematical structure that is good for representing arbitrary rotations. Quaternions are used somewhat like rotation matrices, but have fewer components; you'll multiply quaternions by quaternions to apply player input, and convert quaternions to matrices to render with.

It is very common for general purpose game engines to use quaternions for describing objects' rotations. I haven't personally written quaternion camera code (yet!) but I'm sure the internet contains many examples and longer explanations you can work from.

TensorFlow Lite GPU delegate is majorly designed for mobile phone accelerations. See also https://www.tensorflow.org/lite/performance/gpu.

Experimentally, the OpenCL backend in TFLite GPU delegate can be supported through Linux platforms. However, we have not verified it on Windows yet.

See also https://github.com/tensorflow/tensorflow/issues/40325#issuecomment-642143623.

I found a book on the Papaja package. That link will bring you to the YAML section. I think this is what you were looking for.

First, there doesn't appear to be an association for APA 7, unless you have the development version of Papaja. That being said, there is a ticket on this in their main github.

If you set the output to apa6_docx, this "and &" error won't be there. (And even if it were, you could have deleted it.) You can save it as a PDF from there. I'm not sure if this is an option for you, but I thought I would mention it.

This is what it states about subsequent authors (and much more).

The problem with your approach is that it is recursive. beginning invokes game, game invokes game_over and game_over again invokes beginning:

This is not exactly what you are looking for, but it is possible to do it like this:

Set the model_complexity of mp.Pose to 0.

As the documentation states:

MODEL_COMPLEXITY Complexity of the pose landmark model: 0, 1 or 2. Landmark accuracy as well as inference latency generally go up with the model complexity. Default to 1.

This is the best solution I've found, also use this.

I'm a bit confused as how FFMPEG handles diverse framerates

It doesn't, which would cause a misalignment in your case. The vast majority of filters (any which deal with multiple sources and make use of frames, essentially), including the Concatenate filter require that be the sources have the same framerate.

For the concat filter to work, the inputs have to be of the same frame dimensions (e.g., 1920⨉1080 pixels) and should have the same framerate.

(emphasis added)

The documentation also adds:

Therefore, you may at least have to add a ​scale or ​scale2ref filter before concatenating videos. A handful of other attributes have to match as well, like the stream aspect ratio. Refer to the documentation of the filter for more info.

You should convert your sources to the same framerate first.

I've found the wurlitzer library, which can do exactly that, i.e., capture the streams written to by a C library:

Turns out that Windows 10 Update Assistant incorrectly reported it upgraded my OS to 21H2 on my laptop. Checking Windows version by running winver reports that my OS is still 21H1. Of course CUDA in WSL2 will not work in Windows 10 without 21H2.

After successfully installing 21H2 I can confirm CUDA works with WSL2 even for laptops with Optimus NVIDIA cards.