dapi | A decentralized API for the Dash network | REST library

I thought MATLAB's imfindcircles might be useful here. It overcounts #14, but seems to do better than what you have right now.

What is going on?

Think it is not a question of request vs selenium for web-scraping cause all that you are doing is requesting an api and getting a json response.

And your code works fine the way it is written, but what you expect to do is something other, so you have to deal with iterating dict in another way.

This will do the trick:

So you map the port of 80 on your container to 5001 on the host right? If you then want to connect to the nginx-container over the same network, you should try to connect to port 80 (that' presumably the only "exposed" port that container has).

So this should work from your risk-analysis-XXXX1 container:

http://ngnix-server:80

This line:

I reproduced your scenario on my cluster and basically the same logic will works for deleting it.

You were close, just need to subset to the DAPI column.

Here's the man page, help(wilcox.test).

wilcox.test(x, y = NULL, alternative = c("two.sided", "less", "greater"), mu = 0, paired = FALSE, exact = NULL, correct = TRUE, conf.int = FALSE, conf.level = 0.95, ...)

Arguments

x numeric vector of data values. Non-finite (e.g., infinite or missing) values will be omitted.

y an optional numeric vector of data values: as with x non-finite values will be omitted.

So to compare two groups with wilcox.test, x and y should be vectors of data.

The main issue is that the laplacian kernel size is too small.

You are using kernel_size = 3, and it's too small for the above scene.
In the above images, kernel_size = 3 is affected mostly by noise, because the edges (in the image that shows more details) are much larger than 3x3 pixels.

In other words, the "special frequency" of the details is low frequency, and the 3x3 kernel emphasizes much higher special frequency.

Possible solutions:

• You may increase the kernel size - set kernel_size = 11 for example.
• As an alternative, you may resize (shrink) the source image by a factor of say 0.25 in each axis. You may also compute the weighted sum of std before and after resizing (in case the shrunk image is not accurate enough when focus is good).

There is a small issue in your code:
Core.convertScaleAbs(lplImage, absLplImage) computes absolute value of the laplacian result, and as a result the computed STD is incorrect.

I suggest the following fix:

• Set Laplacian depth to CvType.CV_16S (instead of CvType.CV_64F):

you should use traits

Traits are a mechanism for code reuse in single inheritance languages such as PHP. A Trait is intended to reduce some limitations of single inheritance by enabling a developer to reuse sets of methods freely in several independent classes living in different class hierarchies. The semantics of the combination of Traits and classes is defined in a way which reduces complexity, and avoids the typical problems associated with multiple inheritance and Mixins.

for example:

in your traite:

You might be looking for something like that :