maybe somebody knows something, since I am not able to find anything that makes sense to me.
I have a dataset positions (lon, lat) and I want to snap them to the nearest road and calculate the distance between them.
So far I discovered OSM, however I can't find a working example on how to use the API using python.
If any of you could help, I am thankful for ever little detail.
Will try to find it out by myself in the meantime and publish the answer if successful (couldn't find any similar question so maybe it will help someone in the future)
Welcome! OSM is a wonderful resource, but is essentially a raw dataset that you have to download and do your own processing on. There are a number of ways to do this, if you need a relatively small extract of the data (as opposed to the full planet file) the Overpass API is the place to look. Overpass turbo (docs) is a useful tool to help with this API.
Once you have the road network data you need, you can use a library like Shapely to snap your points to the road network geometry, and then either calculate the distance between them (if you need "as the crow flies" distance), or split the road geometry by the snapped points and calculate the length of the line. If you need real-world distance that takes the curvature of the earth into consideration (as opposed to the distance as it appears on a projected map), you can use something like Geopy.
You may also want to look into the Map Matching API from Mapbox (full disclosure, I work there), which takes a set of coordinates, snaps them to the road network, and returns the snapped geometry as well as information about the route, including distance.
You might use KDTree of sklearn for this. You fill an array with coordinates of candidate roads (I downloaded this from openstreetmap). Then use KDTree to make a tree of this array. Finally, use KDTree.query(your_point, k=1) to get the nearest point of the tree (which is the nearest node of the coordinate roads). Since searching the tree is very fast (essentially log(N) for N points that form the tree), you can query lots of points.
Related
I have a dataset of georeferenced flickr posts (ca. 35k, picture below) and I have an unrelated dataset of georeferenced polygons (ca. 40k, picture below), both are currently panda dataframes. The polygons do not cover the entire area where flickr posts are possible. I am having trouble understanding how to sort many different points in many different polygons (or check if they are close). In the end I want a map with the points from the flickerdata in polygons colord to an attribute (Tag). I am trying to do this in Python. Do you have any ideas or recommendations?
Point dataframe Polygon dataframe
Since, you don't have any sample data to load and play with, my answer will be descriptive in nature, trying to explain some possible strategies to approach the problem you are trying to solve.
I assume that:
these polygons are probably some addresses and you essentially want to place the geolocated flickr posts to the nearest best-match among the polygons.
First of all, you need to identify or acquire information on the precision of those flickr geolocations. How off could they possibly be because of numerous sources of errors (the reason behind those errors is not your concern, but the amount of error is). This will give you an idea of a circle of confusion (2D) or more likely a sphere of confusion (3D). Why 3D? Well, you might have flickr post from a certain elevation on a high-rise apartment, and so, (x: latitude,y: longitude, z: altitude) all may be necessary to consider. But, you have to study the data and any other information available to you to determine the best option here (2D/3D space-of-confusion).
Once you have figured out the type of ND-space-of-confusion, you will need a distance metric (typically just a distance between two points) -- call this sigma. Just to be on the safe side, find all the addresses (geopolygons) within a radius of 1 sigma and additionally within 2 sigma -- these are your possible set of target addresses. For each of these addresses have a variable that calculates its distances of its centroid, and the four corners of its rectangular outer bounding box from the flickr geolocations.
You will then want to rank these addresses for each flickr geolocation, based on their distances for all the five points. You will need a way of identifying a flickr point that is far from a big building's center (distance from centroid could be way more than distance from the corners) but closer to it's edges vs. a different property with smaller area-footprint.
For each flickr point, thus you would have multiple predictions with different probabilities (convert the distance metric based scores into probabilities) using the distances, on which polygon they belong to.
Thus, if you choose any flickr location, you should be able to show top-k geopolygons that flickr location could belong to (with probabilities).
For visualizations, I would suggest you to use holoviews with datashader as that should be able to take care of curse of dimension in your data. Also, please take a look at leafmap (or, geemap).
References
holoviews: https://holoviews.org/
datshader: https://datashader.org/
leafmap: https://leafmap.org/
geemap: https://geemap.org/
I am using win10, python and c#. I want to calculate circumference of human parts (belly, biceps etc) with using point cloud or 3d scans like .stl .obj .ply. Now I can get the point cloud of human body with kinect v2. I have point cloud of human body, scanned 3d human body in .stl .obj .ply formats.
I need some ideas and infos about it. I don't know how to analyse the things I have and how to calculate what I want.
Here I found an example of what I am trying to do but It doesn't need to be perfectly stable like that, Its for a school homework. Maybe you can give me some ideas about how to achieve my goal. Thank you for your help.
https://www.youtube.com/watch?time_continue=48&v=jOvaZGloNRo
I get 3d scanned object with kinect v2 and use PCL to convert it into point cloud.
I don't know about using PCL with Python or C#. In general you are looking at the following steps:
Filtering the points to the interested region
Segmenting the shape
Extracting the parameters
If you're interested in only Python, then OpenCV might be the best option. You can also develop the core logic in C++ and wrap it for Python or C#. C++ also has some nice UI libaries (Qt, nanogui), please see the following details for achieving the objective with PCL
Filtering
CropBox or PassThrough can be used for this. It'll result in similar results as shown in the image assuming that the frame has been chosen properly. If not, the points cloud can be easily transformed
Segmenting the shape
Assuming you want an average circumference, you might need to experiment with Circle 2D, Circle 3D and Cylinder models. More details regarding usage and API are here. The method chosen can be simple SAC (Sample Consensus) like RANSAC (Random SAC) or advanced method like LMEDS (Least Median of Squares) or MLESAC (Max Likelihood Estimation SAC)
Extracting the parameters
All models have a radius field which can be used to find the circumference using standard formula (2*pi*r)
Disclaimer: Please take note that the shape is circular, not ellipse and the cylinder are right angled cylinders. So if the object measured (arm, or bicep) is not circular, the computed value might not be close to ground truth in extreme cases
I would like to implement a Maya plugin (this question is independent from Maya) to create 3D Voronoi patterns, Something like
I just know that I have to start from point sampling (I implemented the adaptive poisson sampling algorithm described in this paper).
I thought that, from those points, I should create the 3D wire of the mesh applying Voronoi but the result was something different from what I expected.
Here are a few example of what I get handling the result i get from scipy.spatial.Voronoi like this (as suggested here):
vor = Voronoi(points)
for vpair in vor.ridge_vertices:
for i in range(len(vpair) - 1):
if all(x >= 0 for x in vpair):
v0 = vor.vertices[vpair[i]]
v1 = vor.vertices[vpair[i+1]]
create_line(v0.tolist(), v1.tolist())
The grey vertices are the sampled points (the original shape was a simple sphere):
Here is a more complex shape (an arm)
I am missing something? Can anyone suggest the proper pipeline and algorithms I have to implement to create such patterns?
I saw your question since you posted it but didn’t have a real answer for you, however as I see you still didn’t get any response I’ll at least write down some ideas from me. Unfortunately it’s still not a full solution for your problem.
For me it seems you’re mixing few separate problems in this question so it would help to break it down to few pieces:
Voronoi diagram:
The diagram is by definition infinite, so when you draw it directly you should expect a similar mess you’ve got on your second image, so this seems fine. I don’t know how the SciPy does that, but the implementation I’ve used flagged some edge ends as ‘infinite’ and provided me the edges direction, so I could clip it at some distance by myself. You’ll need to check the exact data you get from SciPy.
In the 3D world you’ll almost always want to remove such infinite areas to get any meaningful rendering, or at least remove the area that contains your camera.
Points generation:
The Poisson disc is fine as some sample data or for early R&D but it’s also the most boring one :). You’ll need more ways to generate input points.
I tried to imagine the input needed for your ball-like example and I came up with something like this:
Create two spheres of points, with the same center but different radius.
When you create a Voronoi diagram out of it and remove infinite areas you should end up with something like a football ball.
If you created both spheres randomly you’ll get very irregular boundaries of the ‘ball’, but if you scale the points of one sphere, to use for the 2nd one you should get a regular mesh, similar to ball. You can also use similar points, but add some random offset to control the level of surface irregularity.
Get your computed diagram and for each edge create few points along this edge - this will give you small areas building up the edges of bigger areas. Play with random offsets again. Try to ignore edges, that doesn't touch any infinite region to get result similar to your image.
Get the points from both stages and compute the diagram once more.
Mesh generation:
Up to now it didn’t look like your target images. In fact it may be really hard to do it with production quality (for a Maya plugin) but I see some tricks that may help.
What I would try first would be to get all my edges and extrude some circle along them. You may modulate circle size to make it slightly bigger at the ends. Then do Boolean ‘OR’ between all those meshes and some Mesh Smooth at the end.
This way may give you similar results but you’ll need to be careful at mesh intersections, they can get ugly and need some special treatment.
This is the first time I'm using GeoDjango with postGIS. After installation and some tests with everything running fine I am concerned about query performance when table rows will grow.
I'm saving in a geometry point longitudes and latitudes that I get from Google geocoding (WGS84, or SRID 4326). My problem is that distance operations are very common in my application. I often need to get near spots from a landmark. Geometry maths are very complex, so even if I have an spatial index, it will probably take too long in the future having more than 1000 spots in a nearby area.
So is there any way to project this geometry type to do distance operations faster? does anyone know a Django library that can render a Google map containing some of these points?
Any advices on how to speed up spatial queries on GeoDjango?
If you can fit your working area into a map projection, that will always be faster, as there are fewer math calls necessary for things like distance calculations. However, if you have truly global data, suck it up: use geography. If you only have continental USA data, use something like EPSG:2163 http://spatialreference.org/ref/epsg/2163/
The more constrained your working area, the more accurate results you can get in a map projection. See the state plane projections for highly constrained, accurate projections for regional areas in the USA. Or UTM projections for larger sub-national regions.
I'm researching on this topic. As far as I have found, coordinates that you get from geopy library are in SRID 4326 format, so you can store them in a geometry field type without problems. This would be an example of a GeoDjango model using geometry:
class Landmark(models.Model):
point = models.PointField(spatial_index = True,
srid = 4326,
geography = True)
objects = models.GeoManager()
By the way, be very careful to pass longitude / latitude to the PointField, in that exact order. geopy returns latitude / longitude coordinates, so you will need to reverse them.
For transforming points in one coordinate system to another we can use GEOS with GeoDjango. In the example I will transform a point in 4326 to the famous Google projection 900913:
from django.contrib.gis.geos import Point
punto = Point(40,-3)
punto.set_srid(900913)
punto.transform(4326)
punto.wkt
Out[5]: 'POINT (0.0003593261136478 -0.0000269494585230)'
This way we can store coordinates in projection systems, which will have better performance maths.
For showing points in a Google map in the admin site interface. We can use this great article.
I have decided to go on with geography types, and I will convert them in the future, in case I need to improve performance.
Generally, GeoDjango will create and use spatial indexes on geometry columns where appropriate.
For an application dealing primarily with distances between points, the Geography type (introduced in PostGIS 1.5, and supported by GeoDjango) may be a good fit. GeoDjango says it gives "much better performance on WGS84 distance queries" [link].
How do I calculate distance between 2 coordinates by sea? I also want to be able to draw a route between the two coordinates.
Only solution I found so far is to split a map into pixels, identify each pixel as LAND or SEA and then try to find the path using A* algorithm. Then transform pixels to relative coordinates.
There are some software packages I could buy but none have online extensions. A service that calculates distances between sea ports and plots the path on a map is searates.com
Beware of the fact that maps can distort distances. For example, in a Mercator projections segments far away from the equator represent less actual distance than segments near the equator of equal length. If you just assign uniform cost to your pixels/squares/etc, you will end up with non-optimal routing and erroneous distance calculations.
If you project a grid on your map (pixels being just one particular grid out of many possible ones) and search for the optimal path using A*, all you need to do to get the search algorithm to behave properly is set the edge weight according to the real distance along the surface of the sphere (earth) and not the distance on the map.
Beware that simply saying "sea or not-sea" is not enough to determine navigability. There are also issues of depth, traffic routing (e.g. shipping traffic thought the English Channel is split into lanes) and political considerations (territorial waters etc). You also want to add routes manually for channels that are too small to show up on the map (Panama, Suez) and adjust their cost to cover for any overhead incurred.
Pretty much you'll need to split the sea into pixels and do something like A*. You could optimize it a bit by coalescing contiguous pixels into larger areas, but if you keep everything squares it'll probably make the search easier. The search would no longer be Manhattan-style, but if you had large enough squares, the additional connection decision time would be more than made up for.
Alternatively, you could iteratively "grow" polygons from all of your ports, building up convex polygons (so that any point within the polygon is reachable from any other without going outside, you want to avoid the PacMan shape, for instance), although this is a refinement/complication/optimization of the "squares" approach I first mentioned. The key is that you know once you're in an area that you can get to anywhere else in that area.
I don't know if this helps, sorry. It's been a long day. Good luck, though. It sounds like a fun problem!
Edit: Forgot to mention, you could also preprocess your area into a quadtree. That is, take your entire map and split it in half vertically and horizontally (you don't need to do both splits at the same time, and if you want to spend some time making "better" splits, you can do that later), and do that recursively until each node is entirely land or sea. From this you can trivially make a network of connections (just connect neighboring leaves), and the A* should be easy enough to implement from there. This'll probably be the easiest way to implement my first suggestion anyway. :)
I reached a satisfactory solution. It is along the lines of what you suggested and what I had in mind initially but it took me a while to figure out the software and GIS concepts, I am a GIS newbie. If someone bumps into something similar again here's my setup: PostGIS for PostgreSQL, maps from Natural Earth, GIS editing software qGis and OpenJUmp, routing algorithms pgRouting.
The Natural Earth maps needed some processing to be useful, I joined the marine polys and the rivers to be able to get some accurate paths to the most inland points. Then I used the 1 degree graticules to get paths from one continent to another (I need to find a more elegant solution than this because some paths look like chess cubes). All these operations can be done from command line by using PostGIS, I found it easier to use the desktop software (next, next). An alternative to Natural Earth maps might be the OpenStreetMap but the planet.osm dump is aroung 200Gb and that discouraged me.
I think this setup also solves the distance accuracy problem, PostGIS takes into account the Earth's actual form and distances should be pretty accurate.
I still need to do some testing and fine tunings but I can say it can calculate and draw a route from any 2 points on the world's coastlines (no small isolated islands yet) and display the routing points names (channels, seas, rivers, oceans).