I am trying to build an outdoor smoke detection from the neighbor chimneys.
I live in a neighborhood where a couple of houses are still using wood-burning fireplaces and cause lots of smoke and they do during the day time. when it is smoky outside, the kid's room sometime has windows open and smoke get in and very hard to get smoke out. The worst part is it is not illegal (yet) so I found little help apart from talking to them and react to it quickly, in vain.
I am thinking to have an outdoor camera looking at chimneys and detect smoke. Then a program sends a text message for alerting. Most time, the image is pretty still and not a lot of variations. It shouldn't be a too hard problem for classification I imagine? I have little experience with Tensorflow or machine learning but I am a good programmer. So given some direction and some existing model, I hope I can get this working...
I know this sounds desperate, nevertheless, for a good deed. Please help.
For fire and smoke classification, you can check the following tutorial: https://www.pyimagesearch.com/2019/11/18/fire-and-smoke-detection-with-keras-and-deep-learning/.
PyImageSearch is a very good website for image processing, you can find there many articles which can help you (even deployment of neural networks on RaspberryPi and so on).
Related
I am currently failing into find an easy and modular framework to link openAI gym or tensorflow or keras with omnet++ in such a way I can produce communication between each tool and have online learning.
There are tools like omnetpy and veins-gym, however one is very strict and not trustworthy (and no certainty into bridge with openAI, for example) and the other is really poor documented in such a way one person can’t taper how it is supposed to be incorporated into a project.
Being omnet so big project, how is it possible that it is so disconnected to ML world like this?
On top of that, I still will need to use federated learning, so a custom scrappy solution would be even more difficult.
I found various articles that say “we have used omnet++ and keras or tensorflow”, etc, but none of them shared their code, so it is kinda misterious how they did it.
Alternatively, I could use NS3, but as far as I know, it is very steeped to learn it. Some ML tools are well documented, apparently, for NS3. But since I didn’t tried to implement something in NS3 with those tools, I can’t know for sure. Omnet++ was easy to learn for what I need, changing to NS3 still seems a burden with no clear guarantees.
I would like to ask help in both senses:
if u have links regarding good middleware between omnetpp and openai-gym or keras or such, and you have used them, please share with me.
if u have experience with NS3 and ML using ML middleware to link NS3 with openai-gym and keras and so on, please share with me.
I will only be able to finish my POC if I manage to use Reinforcement Learning tooling online a omnet++ simulation (i.e., agent is deciding on simulation runtime which actions to take).
My project is actually complex, but the POC may be simple. I am relying in these tools because I have no sufficient experience to build a complex system translating a domain to another. So a help will be nice.
Thank You.
What am trying to do is, count the revving("vroom" sound) of a physical car, through my app. Am coding in ReactNative. And I don't plan to create something complex, like communicating with the Car's inbuilt computer or anything to do this.
But instead, I was planning to create the app to listen to the nearby sounds. So if the nearby sound is that of a revving, then the app will simply count it.
I have done other features in my app, but listening to the sound and detect if it's a "vroom" sound is what am stuck with.
Based on my research, I can see that I have to make use of the Fast Fourier Transform algorithm. But am confused at how I can implement it in my ReactNative app. Am still searching for a package that has an implementation.
I have seen some apps that can be used to tune the sounds of Violin, Guitar, etc. What am trying to do is similar to this, but pretty simple. Once I get a basic idea, I will be able to get going. In my case, my app will be listening to the high decibel sound.
Any inputs would be highly appreciated.
This is known as Acoustic Event Detection. Possibly you can use an Audio Classification approach. The best way to solve it is using supervised machine learning. For example a CNN on mel-spectrograms. Here is an introduction. You can do the same in JavaScript using Tensorflow.JS. The official documentation contains a tutorial.
One of the first steps is to collect a small dataset of examples of "vroom" sounds versus other loud non-vroom sounds.
A co-worker and I had an idea to create a little web game where a user enters a chunk of data about themselves and then the application would write for them to sound like them in certain structures. (Trying to leave the idea a little vague.) We are both new to ML and thought this could be a fun first dive.
We have a decent bit of background with PHP, JavaScript (FE and Node), Ruby a little bit of other languages, and have had interest in learning Python for ML. Curious if you can run a cost efficient ML library for text well with a web app, being most servers lack GPUs?
Perhaps you have to pay for one of the cloud based systems, but wanted to find the best entry point for this idea without racking up too much cost. (So far I have been reading about running Pytorch or TensorFlow, but it sounds like you lose a lot of efficiency running with CPUs.)
Thank you!
(My other thought is doing it via an iOS app and trying Apple's ML setup.)
It sounds like you are looking for something like Tensorflow JS
Yes, before jumping into training something with Deep Learning; (this might even be un-necessary for your purpose) try to build a nice and simple baseline for this.
Before Deep Learning (just a few yrs ago) people did similar tasks using n-gram feature based language models. https://web.stanford.edu/~jurafsky/slp3/3.pdf
Essentially you try to predict the next few words probabilistically given a small context(of n-words; typically n is small like 5 or 6)
This should be a lot of fun to work out and will certainly do quite well with a small amount of data. Also such a model will run blazingly fast; so you don't have to worry about GPUs and compute .
To improve on these results with Deep Learning, you'll need to collect a ton of data first; and it will be work to get it to be fast on a web based platform
I have a task: to determine the sound source location.
I had some experience working with tensorflow, creating predictions on some simple features and datasets. I assume that for this task, there would be necessary to analyze the sound frequences and probably other related data on training and then prediction steps. The sound goes from the headset, so human ear is able to detect the direction.
1) Did somebody already perform that? (unfortunately couldn't find any similar project)
2) What kind of caveats could I meet while trying to achieve that?
3) Am I able to do that using this technology approach? Are there any other sound processing frameworks / technologies / open source projects that could help me ?
I am asking that here, since my research on google, github, stackoverflow didn't show me any relevant results on that specific topic, so any help is highly appreciated!
This is typically done with more traditional DSP with multiple sensors. You might want to look into time difference of arrival(TDOA) and direction of arrival(DOA). Algorithms such as GCC-PHAT and MUSIC will be helpful.
Issues that you might encounter are: DOA accuracy is function of the direct to reverberant ratio of the source, i.e. the more reverberant the environment the harder it is to determine the source location.
Also you might want to consider the number of location dimensions you want to resolve. A point in 3D space is much more difficult than a direction relative to the sensors
Using ML as an approach to this is not entirely without merit but you will have to consider what it is you would be learning, i.e. you probably don't want to learn the test rooms reverberant properties but instead the sensors spatial properties.
I mean posts per sentence, not per letter. Such a doctor's prescription handwriting which hard to read. Not just a normal handwriting.
In example :
I use a data mining or machine learning for doing a training from
paper handwrited.
User scanning a paper with hard to read writing.
The application doing an image processing.
And the output is some sentence from paper.
And what device to use? (Scanner or webcam)
I am newbie. If could i need some example in vb.net with emguCV/openCV and researches journals.
Any help would be appreciated.
Welcome to stack overflow! The answer to your question is twofold:
a. If you want to recognize handwriting that has already happened i.e. it is presented to you as an image you are in trouble. Computer Vision is still not good enough to provide you with reasonable accuracy.
b. If you have a chance to recognize handwriting “as it's happening” - you are in luck. Download, for example, a Gesture Search app from Android play store and you are in business.
The difference between the two scenarios is subtle but significant. In the second case you have an extra piece of information that makes handwriting recognition possible. This piece is timing of each stroke. In other words, instead of an image with handwriting you have a bunch of strokes that are all labeled with their time stamps. You can think about it as a sequence of lines and curves or as image segmentation - in any way this provides a big hint for the system. Additional help comes from the dictionary on your phone but this is typically used by any handwriting system.
Android of course has an open source library for stroke recognition (find more on your own). If you still want to go for recognizing images though, you have to first detect text (e.g. as a bounding box) and second use any of the existing engines to process detected regions. For text detection I can recommend MSER. But be careful trying to implement even text detection on your own - you are entering a world of pain here ;). Here is an article that can help.
As for learning how to recognize text from images on the Internet - this can be your plan B or C or Z when you master above mentioned stages. Don’t try to abuse learning methods and make them do hard work for you - you will hit a wall if you don’t understand what’s going on under the hood.