I want to extract CU/PU/TU partitioning data from HEVC HM encoder.
I could extract CU/PU partitioning information using getHeight/getWidth/getPartitionSize functions in TComDataCU class. But, don't know how to access TU partitioning information. Please help.
You can do it easily on the decoder side.
You need to find the function parseCoeffNxN and then access the TU size with uiWidth and uiHeight.
Start with this simple test and then give feedbacks here. If there's a problem, we will figure out.
On the encoder side, you should do the same thing as the decoder side, but in the codeCoeffNxN function instead of parseCoeffNxN. The only difficulty is that you should differentiate between the times you call this function during RDO and encoding. This problem and its solution are explained here.
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I don't know if this is the right place to ask this but, i am trying to build a bot in Python that will read incoming messages on a Slack channel where customer post their issues such as 'unable to connect to VPN', 'can someone reply to my ticket' etc…
The bot will analyze the message, determine if the customer is angry or not, and then propose a solution until an agent is free to actually check the issue.
Now, I was experimenting with TextBlob for the sentiment analysis part, but I don't know which technologies to actually use to determine the issue based on specific keywords and provide a solution to the user. Can someone propose me some python libraries/technologies that I could use to achieve this ?
To be honest your question is to generic to answer in one go.
Nontheless, you first have to clearly define the scope of your project. In doing so, you might want to first do a quick literaty review (Google Scholar) to familiarize with the state of the art technologies and methods.
From my little experience, a common (maybe simple) technique (lexicon-based approach) used to determine the sentiment of a word, is to use a pre-compiled dictionary (you can create your own though) that contains words - sentiment mappings. For example:
word:tired, sentiment:negative, score:5
So each time the bot finds the keyword "tired" in a sentence it will assign its corresponding negative value (polarity) to the sentence.
You might want to consider applying POS tags in the input text, as sometimes nouns or ``verbs carry significant meaning, compared to adjectives for example.
Keep in mind though, that negative comments can be written in the form of sarcasm. Sarcasm detectioin is a more difficult task though.
Alternatively, you could try using a pre-trained model such as bert-base-multilingual-uncased-sentiment that can be found here in Hugging Face.
For more information on the matter you have a look at this post.
Again as I mentioned, you have to clearly define your goals. This will enable you to specify the libraries or methodology available to solve your problem. Hope my answer helps.
I want to use dataset at https://ingmec.ual.es/datasets/lidar3d-pf-benchmark/ in my project. The available map is .simplemap. What I understand is it stores both map and the robot poses as well. I want to get the point cloud representation of this map (which later I can convert into octomap) as well as vehicles ground truth pose in the map.
I have been able to get the CPose3DPDF from which I obtained CPose3d which I believe is the desired vehicle's ground truth pose. Please correct me if I am wrong. Now I have two problems. First the length of trajectory is just 97 which makes me suspicious about my code to obtain it. Second is about the CSensoryFrame which I obtain along with CPose3DPDF. When I get CObservation by doing CSensorFrame->getObservationByIndex and write to a file, it gives me idea that it stores velodyne readings. But I am unable to recover point cloud from it. Could anyone please guide me to a tool which can convert a .simplemap into a point cloud or an octomap representation and obtain vehicle's pose out of it as well. Many thanks in advance.
For the records: this one was answered here:
Your assumptions were all correct.
I realized the full UAL campus map was not included into the downloads. It's now available to download inside 2018-02-26-ual-campus-map.zip, at the bottom of this dataset page.
You can also regenerate the pointcloud, octomap from the .simplemap using the app application-observations2map.
Example .ini files can be found under MRPT/share/mrpt/config_files/*
You can also visually inspect .simplemap files with the robot-map-gui app.
I have some basic knowledge in ISO 8583 message. I have seen data-elements and parsing them. Now I came across a message like DE48 SE62 SF4. I'm curious to know about the spceification of SE and SF. I tried to find what SE and SF are. But I could only end up finding their abbreviation and could not get much information about them. Could anyone please elaborate on this? It will be of great help to me.
Thanks in advance!
Data Element (DE) 48 used for Private Additional data in all original specifications from 1987, 1993 and 2003 years. The definition of Sub-Elements (SE) vary from one customized specification to another as well the format and structure of data inside this data element.
Without having full message dump it is not possible to detect the specification you need to follow.
Feel free to post your test ISO 8583 message here and get additional explanation or use our online service to browse message data definitions, parse the financial messages to sub-elements and sub-fields for known messages.
I am using spatstat package in R to read my road network shapefile which also has some additional attributes.
When i am reading my shapefiles and converting them to as.psp(before I make them an object of linnet), I am getting n columns of data frame discarded. I do not understand why? The columns being discarded are my covariates for a linear network, so I am not able to bring them into my analysis.
Could someone give me an idea why this happens and how to correct it?
Why it happens:
I would guess that we (spatstat authors) need to spend a bit of time discussing with the maptools guys how to handle the additional info in the SpatialLinesDataFrame object, and we haven't done that yet.
How to correct it:
You have to write some code on your own at the moment. You can extract the data from SpatialLinesDataFrame object by accessing the #data slot. Please provide specific data and how you need to use the additional data (what format do you need it in) if you need more help. You can find a few helpful commands here: https://cran.r-project.org/web/packages/spatstat/vignettes/shapefiles.pdf
Don't know where to start on this one so hopefully you guys can clear up my question. I have project where email will be searched for specific words/patterns and stored in a structured manner. Something that is done with Trip it.
The article states that they developed a DataMapper
The DataMapper is responsible for taking inbound email messages
addressed to plans [at] tripit.com and transforming them from the
semi-structured format you see in your mail reader into a highly
structured XML document.
There is a comment that also states
If you're looking to build this yourself, reading a little bit about
Wrappers and Wrapper Induction might be helpful
I Googled and read about wrapper induction but it was just too broad of a definition and didn't help me understand how one would go about solving such problem.
Is there some open source project out there that does similar things?
There are a couple of different ways and things you can do to accomplish this.
The first part, which involves getting access to the email content I'll not answer here. Basically, I'll assume that you have access to the text of emails, and if you don't there are some libraries that allow you to connect java to an email box like camel (http://camel.apache.org/mail.html).
So now you've got the email so then what?
A handy thing that could help is that lingpipe (http://alias-i.com/lingpipe/) has an entity recognizer that you can populate with your own terms. Specifically, look at some of their extraction tutorials and their dictionary extractor (http://alias-i.com/lingpipe/demos/tutorial/ne/read-me.html) So inside of the lingpipe dictionary extractor (http://alias-i.com/lingpipe/docs/api/com/aliasi/dict/ExactDictionaryChunker.html) you'd simply import the terms you're interested in and use that to associate labels with an email.
You might also find the following question helpful: Dictionary-Based Named Entity Recognition with zero edit distance: LingPipe, Lucene or what?
Really a very broad question, but I can try to give you some general ideas, which might be enough to get started. Basically, it sounds like you're talking about an elaborate parsing problem - scanning through the text and looking to apply meaning to specific chunks. Depending on what exactly you're looking for, you might get some good mileage out of a few regular expressions to start - things like phone numbers, email addresses, and dates have fairly standard structures that should be matchable. Other data points might benefit from some indicator words - the phrase "departing from" might indicate that what follows is an address. The natural language processing community also has a large tool set available for text processing - check out things like parts of speech taggers and semantic analyzers if they're appropriate to what you're trying to do.
Armed with those techniques, you can follow a basic iterative development process: For each data point in your expected output structure, define some simple rules for how to capture it. Then, run the application over a batch of test data and see which samples didn't capture that datum. Look at the samples and revise your rules to catch those samples. Repeat until the extractor reaches an acceptable level of accuracy.
Depending on the specifics of your problem, there may be machine learning techniques that can automate much of that process for you.