shape | The future of collaborative work | Frontend Framework library

1. Get the Edges

2. Dilate to close the lines

3. Hough line to detect the lines

4. Filter out the non horizontal lines

5. Inpaint the mask

6. Getting the Edges

Have you tried FLANN?

This code doesn't solve your problem completely. It simply finds the nearest 50 neighbors to each point in your 500000 point dataset:

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.

This seems to be a bug in Tensorflow 2.7 when using model.save combined with the parameter save_format="tf", which is set by default. The layers RandomFlip, RandomRotation, RandomZoom, and RandomContrast are causing the problems, since they are not serializable. Interestingly, the Rescaling layer can be saved without any problems. A workaround would be to simply save your model with the older Keras H5 format model.save("test", save_format='h5'):

As mentioned in the comments of the question, the described behavior is a bug in Go 1.18beta1 and is being tracked by issue 50419.

I have confirmed that the bug in question is fixed in 1.18beta2, which was released on 31 January, 2022.

You can do calculations within a function for the x and y values to construct the ggplot which extends the circle all the way round and gives labels correct heights.

I've adapted a function to work with other datasets. This takes a dataset in a tidy format, with:

• a 'year' column
• one row per 'event'
• a grouping variable (such as country)

I've used Nobel laurate data from here as an example dataset to show the function in practice. Data setup:

I think you cannot achieve this with BottomAppBar without working some hacks around it. I can suggest you use 2 FABs, an invisible one to get the BottomAppBar curved the way you wish, and another one (the actual one) and place it at the position you need it to be placed at, here is an example

Delete learning_rate='auto' solved my problem.

Thanks @FlaviaGiammarino comment!!

Well, since you explicitly say macros may be ok: