freeze | An immutability helper for Go | Runtime Evironment library

For the AMD process, we need to make clear to the visual studio that we use the AMD process and it should change the emulator behavior to our process.

First of all like I said we need to make sure the Virtual Machine Platform and Hyper-V are enabled because it’s necessary to run an android emulator in a visual studio.

Second, We need to make sure that Android Emulator Hypervisor Driver for AMD Processors is selected in the visual studio.

Android Emulator Hypervisor Driver for the AMD Processors:

The long execution time comes from the fact that each child of a Parent registers a listener with the disabled and treeVisible properties of that Parent. The way JavaFX is currently implemented, these listeners are stored in an array (i.e. a list structure). Adding the listeners is relatively low cost because the new listener is simply inserted at the end of the array, with an occasional resize of the array. However, when you remove a child from its Parent and the listeners are removed, the array needs to be linearly searched so that the correct listener is found and removed. This happens for each removed child individually.

So, when you clear the children list of the Group you are triggering 1,000,000 linear searches for both properties, resulting in a total of 2,000,000 linear searches. And to make things worse, the listener to be removed is either--depending on the order the children are removed--always at the end of the array, in which case there's 2,000,000 worst case linear searches, or always at the start of the array, in which case there's 2,000,000 best case linear searches, but where each individual removal results in all remaining elements having to be shifted over by one.

There are at least two solutions/workarounds:

1. Don't display 1,000,000 nodes. If you can, try to only display nodes for the data that can actually be seen by the user. For example, the virtualized controls such as ListView and TableView only display about 1-20 cells at any given time.

2. Don't clear the children of the Group. Instead, just replace the old Group with a new Group. If needed, you can prepare the new Group in a background thread.

   Doing it that way, it took 3.5 seconds on my computer to create another Group with 1,000,000 children and then replace the old Group with the new Group. However, there was still a bit of a lag spike due to all the new nodes that needed to be rendered at once.

   If you don't need to populate the new Group then you don't even need a thread. In that case, the swap took about 0.27 seconds on my computer.

You misunderstood what is meant by the async void that is to be avoided.

It doesn't mean you should never use a task with no result attached. It just says the asynchronous methods that invoke them should return a Task, not void.

Simply take the signature of your async method from

The video has frame rate of 25fps, and not 24fps:

After putting the correct value it works ok: demo
The VideoFrame api heavily relies on FPS provided by you. You can find FPS of your videos offline and send as metadata along with stop frames from server.

The site videoplayer.handmadeproductions.de uses window.requestAnimationFrame() to get the callback.

There is a new better alternative to requestAnimationFrame. The requestVideoFrameCallback(), allows us to do per-video-frame operations on video.
The same functionality, you domed in OP, can be achieved like this:

Generally, Tensorflow is the only network with the shape NHWC while most others use NCHW. Thus, the OpenVINO Inference Engine satisfies the majority of networks and uses the NCHW layout. Model must be converted to NCHW layout in order to work with Inference Engine.

The conversion of the native model format into IR involves the process where the Model Optimizer performs the necessary transformation to convert the shape to the layout required by the Inference Engine (N,C,H,W). Using the --input_shape parameter with the correct input shape of the model should suffice.

Besides, most TensorFlow models are trained with images in RGB order. In this case, inference results using the Inference Engine samples may be incorrect. By default, Inference Engine samples and demos expect input with BGR channels order. If you trained your model to work with RGB order, you need to manually rearrange the default channels order in the sample or demo application or reconvert your model using the Model Optimizer tool with --reverse_input_channels argument.

I suggest you validate this by inferring your model with the Hello Classification Python Sample instead since this is one of the official samples provided to test the model's functionality.

You may refer to this "Intel Math Kernel Library for Deep Neural Network" for deeper explanation regarding the input shape.

Instead of chaining animations off one another, have all the animations begin="0s; 3005.click". Then they will all reset when you click.

You can use keyPoints and keyTimes, to control when each of the animations starts. So you can still have your staggered starts.

Note that I was unable to replicate the errors you were having with destroying the subwindows. However, you are potentially having the issue as you are trying to destroy the individual windows, when you could just destroy the main window (as said by @furas). Just call self.parent.destroy() in your Destroy_subwindow function.

According to this comment in a Github thread for a similar issue if the sliver being replaced is not the first it should work fine.

So a possible workaround is to add empty SliverToBoxAdapter() as first sliver before BodyContent().

There is more information and possible solutions in the Github thread, I recommend taking a look at it.

You are returning anonymous functions from useSnackbar hook, which creates a new function every time a re-render happens

Using useCallback on showSnackbarVariant function does the trick for me

Please find the updated useSnackbar hook below

useSnackbar.js