I have 'n' number of slices, is it possible to convert them to a single file, (that has correct slice arrangement), and parse them using ImageIO or any other python package ?
I'm not sure what ImageIO is, however for parsing a set of slices (which I assume you mean a single CT or MR type series, that's meant to be a single 3D volume) check out simpleITK.
I think it will do exactly what you want: it's a very complete "3d aware" dicom library (and very fast as it's wrapped around C libraries). In particular it will read a complete multi-file series, and create a single 3D representation of it.
It's representation is based on extended numpy objects - so in particular it will have a 3D numpy array for the series, but in addition knows about the 3D location/orientation of the series relative to the dicom patient coordinate system.
So once you have that, you've got all the spatial/3D info you need to be able to use with any other python libraries.
Related
I am an absolute beginner in PostgreSQL and PostGIS (databases in general) but have a fairly good working experience in R. I have two multi-polygon data sets of vulnerable areas of India from two different sources - one is around 12gb and it's in .gdb format (let's call it mygdb) and the other is a shapefile around 2gb (let's call it myshp). I want to compare the two sets of vulnerability maps and generate some state-wise measures of fit using intersection (I), difference (D), and union (U) between the maps.
I would like to make use of PostGIS functionalities (via R) as neither R (crashes!) nor qgis (too slow) is efficient for this. To start with, I have uploaded both data sets in my PostGIS database. I used ogr2ogr in R to upload mygdb. But I am kind of stuck at this point. My idea is to split both polygon files by states and then apply other functions to get I, U and D. From my search, I think I can use sf functions like st_split, st_intersect, st_difference, and st_union. However, even after splitting, I would imagine that the file sizes will be still too large for r to process, so my questions are
Is my approach the best way forward?
How can I use sf::st_ functions (e.g. st_split, st_intersection) without importing the data from database into R
There are some useful answers to previous relevant questions, like this one for example. But I find it hard to put the steps together from different links and any help with a dummy example would be great. Many thanks in advance.
Maybe you could try loading it as a stars proxy. It doesn't load the file to the memory, it applies it directly to the hard drive.
https://r-spatial.github.io/stars/articles/stars2.html
Not answer for question sensu stricte, however in response to request in comment, an example of postgresql/postgis query for ST_Intersection. Based on OSM data in postgresql database imported with osm2pgsql:
WITH
highway AS (
select osm_id, way from planet_osm_line where osm_id = 332054927),
dln AS (
select osm_id, way from planet_osm_polygon where "boundary" = 'administrative'
and "admin_level" = '4' and "ref" = 'DS')
SELECT ST_Intersection(dln.way, highway.way) FROM highway, dln
This is my first post, I hope I have followed convention.
I've found a lot of success with pydicom, but am stuck on one particular application. I would like to do the following:
Read in dicom to numpy array
Reshape to (frames, rows, columns, pixels)
Do some processing including cropping and converting to grayscale
Output as new dicom file
I use
r = ds.Rows
c = ds.Columns
f = ds.NumberOfFrames
s = ds.SamplesPerPixel
imageC = np.reshape(img,(f,r,c,s), order='C')
to get the initial numpy matrix I want and do the processing. I have confirmed that these steps look reasonable.
Prior to saving the new dicom, I update the ds Rows and Columns with the new correct dimensions and set SamplesPerPixels to 1. I then reshape the numpy matrix before reassigning to PixelData with .tostring().
np.reshape(mat, (p, f, r, c), order='C')
The resulting image is nonsensical (green) in my dicom viewer. Are there any obvious logical mistakes? I can provide more code if it would be of use.
I am rather guessing, as I have not used pydicom for witing files. Anyway, if the original image is an RGB one and you convert it to grayscale, than you should change the Media Storage SOP Class UID of the image so that the viewer can interpret it properly. Can you check the value? It is under tag (0002,0002). Here is the list.
It is possible that there are more tags to change. Can you dump both files and show us differences?
By the way, from your post it seems that you import the image by ds.PixelData. Why don't you use ds.pixel_array? Then you wouldn't need to reshape.
I have an HDF5 file containing arrays that are saved with Python/numpy. When I read them into Julia using HDF5.jl, the axes are in the reverse of the order in which they appear in Python. To reduce the mental gymnastics involved in moving between the Python and Julia codebases, I reverse the axis order when I read the data into Julia. I have written my own function to do this:
function reversedims(ary::Array)
permutedims(ary, [ ndims(ary):-1:1 ])
end
data = HDF5.read(someh5file, somekey) |> reversedims
This is not ideal because (1) I always have to import reversedims to use this; (2) I have to remember to do this for each Array I read. I am wondering if it is possible to either:
instruct HDF5.jl to read in the arrays with a numpy-style axis order, either through a keyword argument or some kind of global configuration parameter
use a builtin single argument function to reverse the axes
The best approach would be to create a H5py.jl package, modeled on MAT.jl (which reads and writes .mat files created by Matlab). See also https://github.com/timholy/HDF5.jl/issues/180.
It looks to me like permutedims! does what you're looking for, however it does do an array copy. If you can rewrite the hdf5 files in python, numpy.asfortranarray claims to return your data stored in column-major format, though the numpy internals docs seem to suggest that the data isn't altered, simply the stride is, so I don't know if the hdf5 file output would be any different
Edit: Sorry, I just saw you are already using permutedims in your function. I couldn't find anything else on the Julia side, but I would still try the numpy.asfortranarray and see if that helps.
I have 10+ files that I want to add to ArcMap then do some spatial analysis in an automated fashion. The files are in csv format which are located in one folder and named in order as "TTS11_path_points_1" to "TTS11_path_points_13". The steps are as follows:
Make XY event layer
Export the XY table to a point shapefile using the feature class to feature class tool
Project the shapefiles
Snap the points to another line shapfile
Make a Route layer - network analyst
Add locations to stops using the output of step 4
Solve to get routes between points based on a RouteName field
I tried to attach a snapshot of the model builder to show the steps visually but I don't have enough points to do so.
I have two problems:
How do I iterate this procedure over the number of files that I have?
How to make sure that every time the output has a different name so it doesn't overwrite the one form the previous iteration?
Your help is much appreciated.
Once you're satisfied with the way the model works on a single input CSV, you can batch the operation 10+ times, manually adjusting the input/output files. This easily addresses your second problem, since you're controlling the output name.
You can use an iterator in your ModelBuilder model -- specifically, Iterate Files. The iterator would be the first input to the model, and has two outputs: File (which you link to other tools), and Name. The latter is a variable which you can use in other tools to control their output -- for example, you can set the final output to C:\temp\out%Name% instead of just C:\temp\output. This can be a little trickier, but once it's in place it tends to work well.
For future reference, gis.stackexchange.com is likely to get you a faster response.
Trying to create an array from an xyz data file. The data file is arranged so that x,y,z of each atom is on a new line and I want the array to reflect this.
Then to use this array to find find the distance from each atom in the list with all the others.
To do this the array has been copied such that atom1 & atom2 should be identical to the input file.
length is simply the number of atoms in the list.
The write statement: WRITE(20,'(3F12.9)') atom1 actually gives the matrix wanted but when I try to find individual elements they're all wrong!
Any help would be really appreciated!
Thanks guys.
DOUBLE PRECISION, DIMENSION(:,:), ALLOCATABLE ::atom1,atom2'
ALLOCATE(atom1(length,3),atom2(length,3))
READ(10,*) ((atom1(i,j), i=1,length), j=1,3)
atom2=atom1
distn=0
distc=0
DO n=1,length
x1=atom1(n,1)
y1=atom1(n,2) !1st atom
z1=atom1(n,3)
DO m=1,length
x2=atom2(m,1)
y2=atom2(m,2) !2nd atom
z2=atom2(m,3)`
Your READ statement reads all the x coordinates for all atoms from however many records, then all the y coordinates, then all the z coordinates. That's inconsistent with your description of the input file. You have the nesting of the io-implied-do's in the READ statement around the wrong way - it should be ((atom1(i,j),j=1,3),i=1,length).
Similarly, as per the comment, your diagnostic write mislead you - you were outputting all x ordinates, followed by all y ordinates, etc. Array element order of a whole array reference varies the first (leftmost) dimension fastest (colloquially known as column major order).
(There are various pitfalls associated with list directed formatting that mean I wouldn't recommend it for production code (or perhaps for input specifically written with the knowledge of and defence against those pitfalls). One of those pitfalls is that the READ under list directed formatting will pull in as many records as it requires to satisfy the input list. You may have detected the problem earlier if you were using an explicit format that nominated the number of fields per record.)