2d floor plan (image) dataset for ML - annotations

Im looking for 2d floor plan dataset for annotation, but cannot find the big dataset. I already found site 2d floor plan dataset, but here does not have a lot dataset. Will be good if find around 500 of 2d floor images(.jpg) dataset

There are 500 floor plans (in the form of images) available here:
https://rit.rakuten.co.jp/data_release/
You need to make an application to get access to them.
Additionally, I've seen some open data-sets here:
http://dag.cvc.uab.es/resources/floorplans/
https://github.com/gesstalt/ROBIN/blob/master/ROBIN.zip
http://mathieu.delalandre.free.fr/projects/sesyd/symbols/floorplans.html
https://www.kaggle.com/finlay/renthop-image-extract-floorplan-img/data
Good luck!

Related

The Stanford bunny - tetrahedral mesh

I am doing some research on hex-mesh generation (via polycube deformation). I would like to test my algorithm on the 3D Stanford bunny geometry. I am using Matlab for my research. I need a good quality tetrahedral mesh of the bunny, i.e. list of vertices + list of indexes of tetrahedrons in some text file. Could anyone help me with this?
I tried to search through the internet, but I have not found the data I need, i.e. list of vertices + list of indexes of tetrahedrons in some text file.
A simple google turned up this website as the first result:
http://graphics.stanford.edu/data/3Dscanrep/
You can download the PLY file from there which has the data you need.

How to compute bounding boxes of specific roads from Overpass api

I have a high volume dataset with keys like this:
lat:6.897585,
long:52.785805,
speed:12,
bearing:144
Basically it is a dataset of records of various trips on cars. The data was stored every few seconds during each trip. The main goal of this project is to be able to visualize only u-turns (turn arounds) on a map. But for now, I am trying to at least show the data on specifc roads. For that, I am using Overpass API
With the help of Overpass Turbo, I can get a dataset with all the roads I need.
However, in the dataset, the road's geometry is represented with LineString type.
My question is, How can I get a bounding box(es) of the roads from Overpass API, so later on, I can display events that happened only on the given roads? Or maybe you have a better solution on how to achieve this?
A bounding box wouldn't be very helpful here, as using it to filter your points would show everything that falls within the box (which could include other nearby roads)
It sounds like getting a buffer around a linestring might get you closer, but could still include points that are within the buffer but not on the road you are inspecting.
The smarter way to do this would be to assign each event to a road segment using some logic based on their attributes/properties, so you don't have to depend on a spatial filter.

Extracting 2D surface from 3D STEP model

I'm trying to figure out a good way to programmatically generate contours describing a 2D surface, from a 3D STEP model. Application is generating NC code for a laser-cutting program from a 3D model.
Note: it's easy enough to do this in a wide variety of CAD systems. I am writing software that needs to do it automatically.
For example, this (a STEP model):
Needs to become this (a vector file, like an SVG or a DXF):
Perhaps the most obvious way of tackling the problem is to parse the STEP model and run some kind of algorithm to detect planes and select the largest as the cut surface, then generate the contour. Not a simple task!
I've also considered using a pre-existing SDK to render the model using an orthographic camera, capture a high-res image, and then operating on it to generate the appropriate contours. This method would work, but it will be CPU-heavy, and its accuracy will be limited to the pixel resolution of the rendered image - not ideal.
This is perhaps a long shot, but does anyone have thoughts about this? Cheers!
I would use a CAD library to load the STEP file (not a CAD API), look for the planar face with the higher number of edge curves in the face loop and transpose them on the XY plane. Afterward, finding 2D geometry min/max for centering etc. would be pretty easy.
Depending on the programming language you are using I would search for "CAD control" or "CAD component" on Google combining it with "STEP import".

Does using the Overpass polygon query have a computational advantage over a bounding box?

For a project on geospatial data analytics, we are currently extracting road type and speed limit data of certain roads along a track by using Overpass' polygon query (where we define the roads by a buffer zone around them). The problem is that in the case of separate tracks, we can end up with disconnected polygons which often lead to a significant increase in computation time. In this situation, we were wondering how Overpass' polygon query actually works. Does the algorithm actually query only the data inside this polygon/these polygons, or does it query inside a bounding box, after which it filters out the data inside the polygons?
The algorithm checks if nodes are inside the defined polygon, or if a way crosses the polygon. It's not based on bounding boxes as you mentioned.
From your description it's not quite clear why disconnected polygons pose an issue. You should get decent performance with a lz4-based backend and a reasonable number of lat/lon pairs in your (poly: ) filter (the more pairs you provide, the more expensive the computation gets).
BTW: The best approach to tackle this issue would be something I described in this blog post: https://www.openstreetmap.org/user/mmd/diary/42055 - unfortunately, this feature is not yet available in the official branch. If you see some use for it, please upvote here: https://github.com/drolbr/Overpass-API/issues/418

Image registration of two volume with different number of slices

I am trying to register two volumetric images from brain (PET and CT or even PET and MR). Each of these volumetric images contains different numbers of 2D images (slices).
For example, CT has 150 slices and PET has 100 slices. I was thinking of using an interpolation method to calculate and reduce the number of CT slices to 100. Is this a correct approach? Does anyone know of any resources that could be helpful for me? like a pseudo code, or steps that I should go through for registering two volumetric images.
Thank you :)
If you know the spacing information for the 150 CT slices and the 100 PET slices, you can look into MATLAB's interp1 function for interpolating along one axis to rescale the images to the same number of pixels. From here it might be possible to use MATLAB's imregister to perform registration.
If you are looking to learn how registration works under the hood (transforming between pixel and physical coordinates, transforming/resampling images, etc.), one resource I can direct you to is the ITK Software Guide pdf.
In particular, try reading Book 1 Section 4.1.4 (page 41 of the pdf) on image representation, and Book 2 Section 3.9 (page 532 of the pdf) on transforms.
In general, the problem of transforming and interpolating with 3D images in registration can be pretty cumbersome to write code for. You need to ask yourself about the spacing and orientation of pixels, how to transform and interpolate images so that their grids overlap, and you also need to decide what to do with pixels in your grid that lie outside the image boundary when evaluating the similarity metric.
While it's up to you to do what you think is best, I suggest you use existing registration programs if they are capable of doing what you want:
MATLAB's imregister (I have never used it so I can't comment on it)
simpleITK for Python
the ITK for C++ has a learning curve but gives full control over the registration process
elastix is a command line program that uses a text file of parameters to perform registration.
3D slicer has a graphical user interface for simple linear registration