OS: Ubuntu 18.04
Lidar: Livox Mid 40
Hello,
im using livox mid 40 which export point cloud to .lvx files or rosbags. I am looking for a way to convert these files to a common pointcloud format like ply, las, laz so i can use it to programs like cloudcompare, meshlab or other. I tried some solutions like rosrun pcl_ros bag_to_pcd <input_file.bag> <output_directory> or cloud_assembler but the first one creates multiple pcds and not a single point cloud (it created one pcd for each frame) and the second one doesn't work (cloud_assembler_try).
Im really confused!!
Any help would be appreciated.
Related
I need to apply Dijkstra's algorithm using ROS and Opencv.
I have been given a png file and I need to convert it into an occupancy grid.
Map
I've tried searching online but didn't find anything that fits my case.
(Also, if anyone knows any good beginner tutorials on ROS then I would be very grateful)
Since you have the pic file (usually in png format), you create the yaml file
which may look like
image: map.png
resolution: 0.1
origin: [0.0, 0.0, 0.0]
occupied_thresh: 0.65
free_thresh: 0.196
negate: 0
You can read the details here
Then start roscore and try
rosrun map_server map_server mymap.yaml
Be careful with the paths (use the same folder for both)
i'm trying to practice transfer learning myself.
I'm trying to count the number of each cat and dog files (each 12500 pictures for cat and dog with the total of 25000 pictures).
Here is my code.Code
And here is my path for the picture folderenter image description here.
I thought this was a simple code, but still couldn't figure out why i keep getting (0,0) in my coding (supposed to be (12500 cat files,12500 dog files)):(.
Use os.path.join() inside glob.glob(). Also, if all your images are of a particular extension (say, jpg), you could replace '*.*' with '*.jpg*' for example.
Solution
import os, glob
files = glob.glob(os.path.join(path,'train/*.*'))
As a matter of fact, you might as well just do the following using os library alone, since you are not selecting any particular file extension type.
import os
files = os.listdir(os.path.join(path,'train'))
Some Explanation
The method os.path.join() here helps you join multiple folders together to create a path. This will work whether you are on a Windows/Mac/Linux system. But, for windows the path-separator is \ and for Mac/Linux it is /. So, not using os.path.join() could create an un-resolvable path for the OS. I would use glob.glob when I am interested in getting some specific types (extensions) of files. But glob.glob(path) requires a valid path to work with. In my solution, os.path.join() is creating that path from the path components and feeding it into glob.glob().
For more clarity, I suggest you see documentation for os.path.join and glob.glob.
Also, see pathlib module for path manipulation as an alternative to os.path.join().
I've been attempting to export boundary information from an OSM file. My process is nearly there however I have an issue with the polygon I'm generating drawing random lines.
I would appreciate some insight on where I may be going wrong.
Step 1: Export the OSM data into XML
osmfilter -v greater-london-latest.osm --keep="boundary= admin_level= place=" > b.txt
Step 2: Run a script to process the XML.
cycle each relation node
load the member ways
load the nodes from each specified way
record the lat/lon and build a poly set
This produces a series of lat/lon which when I build them as a polygon give the correct overall shape I'm looking for. However, there are issues with the connecting lines I assume..
My polygon output
I'm actually looking for this, which is similar but Im obviously missing something.
Actual Poly Im looking to generate
Again, thanks for any help.
Ways in relations are not necessarily sorted. See answers to this question on how to sort ways, especially the answer by user geocodezip.
Alternatively you can make use of various tools/libraries to do the sorting for you. Unfortunately I can't point you directly to one but there are various tools capable of sorting relation members, including the OSM website itself, JOSM, overpass turbo (I guess), some JS stuff, [...].
Maybe some other user can help out with pointing to some good examples?
It this Mapbox blog post, Lauren Budorick shares how they got working a routing engine with OSRM that uses elevation data in order to give cyclists better routes... AMAZING!
I also want to explore the potential of OSRM's routing when plugging in external (user-generated) data, but I'm still having a hard time grasping how OSRM's profiles work. I think I get the main idea, that every way (or node?) is piped into a few functions that, all toghether, scores how good that path is.
But that's it, there are plenty of missing parts in my head, like what do each of the functions Lauren uses in her profile do. If anyone could point me to some more detailed information on how all of this works, you'd make my next week much, much easier :)
Also, in Lauren's post, inside source_function she loads a ./srtm_bayarea.asc file. What does that .asc file looks like? How would one generate a file like that one from, let's say, data stored in a pgsql database? Can we use some other format, like GeoJSON?
Then, when in segment_function she uses things like source.lon and target.lat, are those refered to the raw data stored in the asc file? Or is that file processed into some standard that maps everything to comply it?
As you can see, I'm a complete newbie on routing and maybe GIS in general, but I'd love to learn more about this standards and tools that circle around the OSRM ecosystem. Can you share some tips with me?
I think I get the main idea, that every way (or node?) is piped into a few functions that, all toghether, scores how good that path is.
Right, every way and every node are scored as they are read from an OSM dump to determine passability of a node and speed of a way (used as the scoring heuristic).
A basic description of the data format can be found here. As it reads, data immediately available in ArcInfo ASCII grids includes SRTM data. Currently plaintext ASCII grids are the only supported format. There are several great Python tools for GIS developers that may help in converting other data types to ASCII grids - check out rasterio, for example. Here's an example of a really simple python script to convert NED IMGs to ASCII grids:
import sys
import rasterio as rio
import numpy as np
args = sys.argv[1:]
with rio.drivers():
with rio.open(args[0]) as src:
elev = src.read()[0]
profile = src.profile
def shortify(x):
if x == profile['nodata']:
return -9999
elif x == np.finfo(x).tiny:
return 0
else:
return int(round(x))
out_elev = [map(shortify, row) for row in elev]
with open(args[0] + '.asc', 'a') as dst:
np.savetxt(dst, np.array(out_elev),fmt="%s",delimiter=" ")
source.lon and target.lat e.g: source and target are nodes provided as arguments by the extraction process. Their coordinates are used to look up data at each location during extraction.
Make sure to read thoroughly through the relevant wiki page (already linked).
Feel free alternately to open a Github issue in
https://github.com/Project-OSRM/osrm-backend/issues with OSRM
questions.
Apologies for tagging this just ImageJ - it's a problem regarding MicroManager, a microscopy plugin for it and I thought this would be best.
I'd recently taken images for an important experiment using MicroManager (a recent version, though I cannot recall the exact number). The IT services at my institution have recently been having some networking problems and my saved preferences for the software had been erased. I'd got half way through my experiment when I realised that I'd saved my images as separate image files (three greyscale TIFFs plus metadata text files) instead of OME-TIFF iamge stacks.
All of my ImageJ macros for image processing rely on having a multiple channel image stack, so this is a bit of a problem. Is there any easy way in MicroManager (or ImageJ) to bulk convert these single channel greyscale images into the OME-TIFF image stack after the images have already been taken?
Cheers.
You can start with a macro like this one:
// Convert your images to a stack
run("Images to Stack", "name=Stack title=[] use");
// The stack will default the images to time points. Convert to channels
run("Stack to Hyperstack...", "order=xyczt(default) channels=3 slices=1 frames=1 display=Color");
// Export as OME-TIFF
run("Bio-Formats Exporter");
This is designed to reconstruct one dataset at a time (open 3 images, run the macro and export the OME-TIFF).
If you don't want any dialogs to show you can pass an output directory to the Bio-Formats exporter:
run("Bio-Formats Exporter", "save=/path/to/image.ome.tif export compression=Uncompressed");
For the output file name you can get the original image name in the macro with getTitle()
There is also a template example on iterating over all the files in a directory, if you want to completely automate the macro. However this may take some tweaking since you want to operate on your images 3 at a time.
Hope that helps!