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I want to create a music player with Python which uses OpenGL for visualizing the audio spectrum.
I already have the 3d engine set up and all I need is to get the spectrum data and feed it to the renderer.
I'd imagine it would be a list of numbers updated each few miliseconds or so.
I've heard you can get that info with FMOD and there's pyfmodex Python wrapper for it, but I can't access the FMOD documentation and the pyfmodex is almost undocumented. Can't find what I need by just browsing the class/function names.
If there's another library which can get that info that will also work.
numpy has an FFT function that will compute a fast fourier transform on a block of input data. You can use its output to obtain your spectral information.
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I'm trying to clean the line noises from this captcha, so I can implement an algorithm to read them. However, I'm finding some difficulties to make it readable to an AI using some techniques, such as Open CV threshold combined with some resources from pil.Image. I also tried an algorithm to "chop" the image, which gave me a better results, but stil far from the expected. I want to know if there is an alternative to remove noises from captchas like this one effectively.
(I'm using python)
Initially, the Captcha looks like this:
Once processed using OpenCV + Pillow, I've got this:
Later, using the "chop method" this what we have:
However, I need a better final image, but I think this methods combination is not appropriate. Is there a better alternative?
I think you could try minisom: https://github.com/JustGlowing/minisom
SOM (Self organizes maps) are a type of neural networks that group clusters of points in data, with an appropiate threshold it could help you removing those lines that are not surrounding the numbers/letters, combining that with chop method could do the job.
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I have a few lists of movement tracking data, which looks something like this
I want to create a list of outputs where I mark these large spikes, essentially telling that there is a movement at that point.
I applied a rolling standard deviation on the data with a window size of two and got this result
Now I can see the spikes which mark the point of interest, but I am not sure how to do it in code. A statistical tool to measure these spikes, which can be used to flag these spikes.
There are several approaches that you can use for an anomaly detection task.
The choice depends on your data.
If you want to use a statistical approach, you can use some measures like z-score or IQR.
Here you can find a tutorial for these measures.
Here instead, you can find another tutorial for a statistical approach which uses mean and variance.
Last but not least, I suggest you also to check how to use a control chart, because in some cases it's enough.
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I'm wondering how I can calculate powers of a complex number without using the complex numbers data type. So I have a function
def Power_complex(re, im, n):
How can I calculate (re + im * i)^n with this? Thank you!
You can use the Biniomial theorem for arbitrary exponents, although positive integers is the easiest case.
Or you can treat the problem in polar coordinates (this link simply gives you the answer, only click if you really don't want to figure it out on your own)
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My data was modelled with a Cox-regression, using R, however I would like to use this model into a python GUI. As my knowledge of R is very limited. This way non-coders would be able to 'predict' survival rates based on our model.
What is the best way that I could use this model (combination of 3 different regressions) in python?
Do you want to predict values based on your estimates?
In this case you can just copy the R outputs into python and apply to
respective procedures.
Do you want the user to be able to run "your R regression pipeline" from within Python?
There are python libraries that help with that. I find this
source a useful start.
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I am working on pattern recognition program using R/python. What would be the best way to compare two or more figures and identify/recognize the similar or duplicate figures based on pattern recognition?
There are lots of papers on the internet, we can try to get the idea how to extract and process feature in a fingerprint. For instance, http://www.cse.unr.edu/~bebis/CS790Q/PaperPresentations/MinutiaeDetection.pdf
Then you can use whatever classifier you want such as support vector machine.
If you need more idea you can visit http://dermatoglyphics.org/11-basic-patterns-of-fingerprint/ to generalize