My current project is information extraction on unlabeled and unstructured data. After some 'homework', AutoSlog looks good. However, the source code cannot be found on Github. Does anybody know how to apply AutoSlog? Do I have to start from scratch by coding myself?
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I am looking for a way to extract images from within another image. For example:
Here is a picture taken of a paper. It includes text, an image of a camera, and an image of a qr code. Is there an API that can possibly extract those two(camera and qr code) from this larger image and separate them into their own individual images. I know this is doable with the text(OCR), but I need to find some way to do Image Recognition if that even exists. For now, I cant find any reference to doing this besides extracting images from pdf's, which none of those softwares have the capability to extract them from a non-perfect pdf.
Price for the API(node.js prefered, but i can adapt to use any language) is not a big concern, I'm just not sure this is even possible to due without programming a legitable artificial intelligence using machine learning, which I would no doubt cause a global internet shutdown from breaking everything if I attempted to do so.
Anyway, any suggestions would be great and much appreciated. Thanks!
EDIT: the images aren't always those, it can be an image of anything, from potatoes to flags
For the QR code, you can simply use a QR code scanner library and convert the output back into a QR code. As for the camera, you are going to need an image recognition service like Google Cloud Vision or train your own neural network with something like TensorFlow to recognize pictures of cameras.
QR detectors abound around the web and some are on github but for single objects you could try hotpot API https://hotpot.ai/docs/api
your code example linked into https://hotpot.ai/remove-background
for striping back you may need a secondary autocrop task
I am working on a project to generate G-Code for 3D Model in STL Format.
Suggest me how I can do it
Basically this generated G-Code will be used with Milling CNC.
You need CAM Software, a program that has an algorithm that extracts the path of the STL drawing and based in the type of your machine, generate the correct CNC commands.
There are several proprietary programs for this on the market and it really depends on your machine type and tools. Maybe this link will help you get some ideas.
If you want to develop your own CAM, I warn you that this is not a trivial job...
I'm doing sentiment analysis on users' transcripts for UX website testing. I get the transcript from the testing session and then I analyze the transcript for sentiment analysis - what's the user's opinion about the website, what problems did the user encounter, whether he had any problems, got stuck, lost. Since this is quite domain-specific, I'm testing both TextBlob and Vader and see which gives better results. My issue is at the beginning of the process - the speech-to-text API's transcript isn't perfect. Sentences (periods) are not captured or are minimal. I'm not sure on what level the analysis should be since I was hoping I could do it on sentence-level. I tried making n-grams and analyzing those short chunks of text, but it isn't ideal and the results are slightly difficult to read - because there will be some parts that are repeated. Apart from this, I do classical text cleaning, tokenization, pos tagging, lemmatization and feed it to TextBlob and Vader.
Transcript example: okay so if I go just back over here it has all the information I need it seems like which is great so I'm pretty impressed with it similar to how a lot of government websites are set up over here it looks like I have found all the information I need it's a great website it has everything overall though it had more than enough information...
I did:
ngram_object = TextBlob(lines)
ngrams = ngram_object.ngrams(n=4)
which gives me something like (actually a WordList): [okay so if I, so if I go, if I go just...]
Then the results look like:
62 little bit small -0.21875 Negative
61 like little bit -0.18750 Negative
0 information hard find not see -0.291666667 Negative
1 hard find not see information -0.291666667 Negative
Is there a better way to analyze unstructured text in chunks rather than a full transcript?
This makes it difficult to capture what was the issue with the website. Changing the API isn't really an option since I'm working with something that was given to me to use as data collection for this particular sentiment analysis problem.
Any tips or suggestions would be highly appreciated, couldn't find anyone doing something similar to this.
I am not sure about what you really want but maybe you could take a look on speech sentiment analysis? I have read about RAVDESS, a database useful for sentiment classification. Take a look: https://smartlaboratory.org/ravdess/
Here is my problem:
I must match two images. One image from the project folder and this folder have over 20.000 images. The other one is from a camera.
What I have done?
I can compare images with basic OpenCV example codes that I found in the documentation. OpenCV Doc I can also compare and find an image by using the hash of my image data set. It is so fast and it is only suitable for 2 exact images. One for query the other one is the target. But they are the same exact image.
So, I need something as reliable as feature matching and as fast as hash methods. But I can't use machine learning or anything on that level. It should be basic. Plus, I'm new to these stuff. So, my term project is on risk.
Example scenario:
If I ever take a picture of an image in my image data set from my computer's screen. This would change many features of the original image. In the case of defining what's in that image, a human won't struggle much but a comparison algorithm will struggle. Such a case leaves lot's of basic comparison algorithm out of the game. But, a machine-learning algorithm could solve the problem but it's forbidden to use in my project.
Needs:
It must be fast.
It must be accurate.
It must be easy to understand.
Any help is okay. A piece of code, maybe an article or a tutorial. Even an advice or a topic title might be really helpful to me.
Once saw this camera model identification challenge on kaggle. This notebook discusses about noise pattern changes with changing devices. May be you should look in to this and other notebooks in that challenge. Thanks!
I am doing one experiment in which I need to capture skeleton data from kinect and then apply that data to a model, I have captured data from kinect and have stored it in a file, i.e in a file i have location of each joint in each frame,
Now I want my model in blender to take the joint position from file, and move accordingly. But I dont have any idea on how to start.
I also have written a small script in python to read position from file and update the position of one bone:
obj.channels['head'].location = Vector((float(xs),float(ys),float(zs)))
but it does not move anything. Am I doing it in wrong way, or we cannot move the armature by just updating the position??
Please guide me on this topic, as i am completely new to python and blender
I don't think that this is the best solution, you can simply export your data to a bvh file and save yourself from a lot of headaches.
You can find a lot of Kinect-sdk to bvh tutorials on the net and the bvh is the de-facto standard to store data from motion capture events, there are no reasons why you should re-invent the wheel and doing extra work.
To use your bvh file in Blender you can simply follow one of the many tutorial on the subject.