I use geojson files to display layers on maps with openlayers3. Each geojson file contains one feature collection representing a layer. What I would like to do is to gather those geojson files in single one. So, I should have several feature collections in single geojson. Is it possible to do that and can I read this kind of geojson in openlayers3? Thank you very much for you help.
Regards.
Related
I created .arobject file from apple's object scanning sample code.
Now I am wondering is there any way to convert this .arobject file to .usdz file?
No, in ARKit 5.0 and earlier you can't convert .arobject file into .usdz file format (and vice versa). That's because .arobject file contains only the spatial feature information needed to recognize a scanned real-world object, it is not a displayable 3D reconstruction mesh of that object. In other words, .arobject contains a sparse point cloud, not a dense point cloud.
If you want to create a 3D model from a dense point cloud you need a special RealityKit's API for that. Look at this post and this post for further details.
So I am working on a project for school and what we are trying to do is to teach a neural network to recognize buildings from non-buildings. The problem I am having right now is representing the data in a form, that would be "readable" by the classifier function.
The training data is a bunch of pictures + .wkt file with coordinates of buildings on a picture. So far we have been able to rescale the polygons, but kinda got stuck there.
Can you give any hints or ideas of how to bring this all to an appropriate form?
Edit: I do not need the code written for me, a link to an article on a similar subject or a book is more of stuff I am looking for.
You did not mention what framework you are using, but I will give an answer for caffe.
Your problem is very close to detecting objects within an image. You have full images with object (building in your case) bounding boxes.
The easiest way of doing this is through a python data layer which reads an image and a file with stored coordinates for that image and feeds that into your network. A tutorial on how to use it can be found here: https://github.com/NVIDIA/DIGITS/tree/master/examples/python-layer
To accelerate the process you may want to store image, coordinate pairs in your custom lmdb database.
Finally a good working example with complete caffe implementation can be found within Faster-RCNN library here: https://github.com/rbgirshick/caffe-fast-rcnn/
You should check roi_pooling_layer.cpp in their custom caffe branch and roi_data_layer on how the data is fed into the network.
I am new to Rapidminer. I have many XML files and I want to classify these files manually based on keywords. Then I would like to train a classifier like Naive Bayer and SVM on these data and calculate their performances using cross- validator.
Could you please let me know different steps for this?
Should I need to use text processing activities like tokenising, TFIDF etc.?
The steps would go something like this
Loop over files - i.e. iterate over all files in a folder and read each one in turn.
For each file
read it in as a document.
tokenize it using operators like Extract Information or Cut Document containing suitable XPath queries to output a row corresponding to the extracted information in the document.
Create a document vector with all the rows. This is where TF-IDF or other approaches would be used. The choice depends on the problem at hand with TF-IDF being a usual choice where it is important to give more weight to tokens that appear often in a relatively small number of the documents.
Build the model and use cross validation to get an estimate of the performance on unseen data.
I have included a link to a process that you could use as the basis for this. It reads the RapidMiner repository which contains XML files so is a good example of processing XML documents using text processing techniques. Obviously, you would have to make some large modifications for your case.
Hope it helps.
Probably, it is too late to reply. But it could help to other people. There is an extension called 'text mining extension', I am using version 6.1.0 . So you may go to RapidMiner > help>update and install this extension. It will get all the files from one directory. It has various text mining algorithms that you may use
Also, I found this tutorial video which could be of some help to you as well
https://www.youtube.com/watch?v=oXrUz5CWM4E
i'm currently trying to extract information, e.g. sender or recipient from business documents like bills. The documents were processed with ocr software into xml files, so they are annotated with formatting characteristics. I want to extract specific information from a new document after annotated one similar document manually with features like sender and recipient.
So my question is, if there is a learning or matching algorithm which is able to extract specific data by comparing with only one or two examples of similar documents. If yes: is there somehow a java framework capable of that?
Yours thankfully
maggu
If the XML structure is always the same (using the same template):
Just save the XML parent nodes of the selected nodes where the information is located so you know the path to the information. Shouldn't be a problem - trivial task.
If you have to search for the information:
It could work by creating certain feature extraction rules and then use that features to train a Support Vector Machine for detecting the areas where the information is located.
I once asked a similar question Algorithm to match natural text in mail.
But that is far from trivial, and definitely needs more than one or two training documents.
I'm thinking to stick for a particular framework to work for my academic course but only based on results I should prove. I want to plot the graph for all the three frameworks where No.of Vertices is one axis and FPS (threshold is 60) is on other axis. Will that be good enough to take single predefined model in formats like obj, collada, json etc and load it in three frameworks? Then log the frame rate and number of vertices to some external file and thereafter use the data for plotting a graph to report the best framework among three based on Performance parameter. But I'm looking for some boilerplate codes for all these frameworks to load different models (can be used for number of vertices dimension in my graph) and log the frame rates for every second to external file. This is the approach I've been thinking. But couldn't find much help on this on internet. I wish someone could help me?
You can get FPS histogram data using stats.js library which is bundled with all Three.js examples
https://github.com/mrdoob/stats.js
Exporting the collected data to a file can be done using HTML5 File System API.
http://www.html5rocks.com/en/tutorials/file/filesystem/