I cannot display a vtk file using python. Spyder Code Analysis prompts me that vtkDataSetMapper is an undefined name.
I know the vtk file is in order because I have already displayed it using Paraview.
My vtk file looks like this:
# vtk DataFile Version 2.0
velocity field
ASCII
DATASET STRUCTURED_POINTS
DIMENSIONS 108 103 31
ORIGIN 0.0000000000000000 0.0000000000000000 -297.50000000000000
SPACING 15.465818554775332 12.565027060488859 10.000000000000000
POINT_DATA 344844
SCALARS scalars float
LOOKUP_TABLE default
8.4405251
8.4405251
...
...
...
After the last shown line, the vtk file contains the rest of information, which are merely numbers (~ 300000 values)
My code looks like this:
import vtk
# Read vtk file data
reader = vtk.vtkDataSetReader()
reader.SetFileName("seaust.vtk")
reader.ReadAllScalarsOn() # Activate the reading of all scalars
reader.ReadAllVectorsOn() # Activate the reading of all vectors
reader.ReadAllTensorsOn() # Activate the reading of all tensors
reader.Update()
data = reader.GetOutput()
scalar_range = data.GetScalarRange()
# Create the mapper that corresponds the objects of the vtk file
# into graphics elements
mapper = vtkDataSetMapper()
mapper.SetInput(data)
When trying to compile the code, python prompts me this error:
AttributeError: 'vtkRenderingCorePython.vtkDataSetMapper' object has no attribute 'SetInput'
I'm expecting a 3D visualisation of my data.
Can you please help me to get it?
I guess you are missing the vtk. in
mapper = vtk.vtkDataSetMapper()
and you probably need to use
mapper.SetInputData(data)
Related
How to export Stanza to ONNX format?
It seems impossible to just simply train the model.
There is an explanation here: https://pytorch.org/tutorials/advanced/super_resolution_with_onnxruntime.html
I created a fork from stanza for this experiment here https://github.com/vivkvv/stanza. See also my commits https://github.com/vivkvv/stanza/commits?author=vivkvv.
I used pipeline_demo.py for testing. The main thing I added is code just inside models/tokanization/trainer.py below the line 77
pred = self.model(units, features)
Due to explanation I added
torch.onnx.export(
self.model,
(units, features),
onnx_export_file_name,
opset_version=9,
export_params=True,
do_constant_folding=True,
input_names=['input'],
output_names=['output'],
dynamic_axes={
'input': {0: 'batch_size'},
'output': {0: 'batch_size'}
}
)
and it works for tokenization. But the same does not work for e.g. pos or lemmatizer (see my commit for PartOfSpeech). And I get different errors for different opset_version.
I created a question on github/stanza and you could see there https://github.com/stanfordnlp/stanza/issues/893
Open Cascade has glTF writer in their current development branch - RWGltf_CafWriter
I am trying to convert STP to glTF using it and got starting point from this question - Any Open source Libraries to Convert STEP files to glTF file format?
It looks doable, but I am new to Open Cascade technology and have few questions
While calculating triangulation for shapes using BRepMesh_IncrementalMesh, it needs line deflection and angle deflection, what are these and what should be its values?
RWGltf_CafWriter requires TDocStd_Document and TDF_LabelSequence, how do we get these from Shapes?
Thank You
While calculating triangulation for shapes using BRepMesh_IncrementalMesh,
it needs line deflection and angle deflection,
what are these and what should be its values?
Deflection parameters define the mesh quality. Within specific domain / algorithm, you should probably know in advance applicable deviation of your geometry (like no more than 1 mm). However, in context of visualization and arbitrary CAD model, linear deflection is usually defined relatively to the bounding box of the document.
RWGltf_CafWriter requires TDocStd_Document and TDF_LabelSequence, how do we get these from Shapes?
TDocStd_Document is an XDE document supported by various file format translators - including STEP and glTF. If at that point you have a single TopoDS_Shape from STEP file, then you probably used a simplified STEP translator STEPControl_Reader. To preserve the structure of original document, it is better using STEPCAFControl_Reader filling in an XDE document.
Within XDE document, shapes (and not only shapes) are stored as Labels, so that TDF_LabelSequence collection is used to pass through the information like a sequence of root shapes (model tree roots in the document), which are called Free Shapes:
// read / create / fill in the document
Handle(TDocStd_Document) theXdeDoc; // created in advance
STEPCAFControl_Reader aStepReader;
if (!aStepReader.ReadFile ("myStep.stp") != IFSelect_RetDone) { // parse error }
if (!aStepReader.Transfer (theXdeDoc)) { // translation error }
...
// collect document roots into temporary compound
Handle(XCAFDoc_ShapeTool) aShapeTool = XCAFDoc_DocumentTool::ShapeTool (myXdeDoc->Main());
TDF_LabelSequence aRootLabels;
aShapeTool->GetFreeShapes (aRootLabels);
TopoDS_Compound aCompound;
BRep_Builder aBuildTool;
aBuildTool.MakeCompound (aCompound);
for (TDF_LabelSequence::Iterator aRootIter (aRootLabels); aRootIter.More(); aRootIter.Next())
{
const TDF_Label& aRootLabel = aRootIter.Value();
TopoDS_Shape aRootShape;
if (XCAFDoc_ShapeTool::GetShape (aRootLabel, aRootShape))
{
aBuildTool.Add (aCompound, aRootShape);
}
}
// perform meshing
Handle(Prs3d_Drawer) aDrawer = new Prs3d_Drawer(); // holds visualization defaults
BRepMesh_IncrementalMesh anAlgo;
anAlgo.ChangeParameters().Deflection = Prs3d::GetDeflection (aCompound, aDrawer);
anAlgo.ChangeParameters().Angle = 20.0 * M_PI / 180.0; // 20 degrees
anAlgo.ChangeParameters().InParallel = true;
anAlgo.SetShape (aCompound);
anAlgo.Perform();
...
// write or export the document
TColStd_IndexedDataMapOfStringString aMetadata;
RWGltf_CafWriter aGltfWriter ("exported.glb", true);
// STEP reader translates into mm units by default
aGltfWriter.ChangeCoordinateSystemConverter().SetInputLengthUnit (0.001);
aGltfWriter.ChangeCoordinateSystemConverter().SetInputCoordinateSystem (RWMesh_CoordinateSystem_Zup);
if (!aGltfWriter.Perform (theXdeDoc, aMetadata, Handle(Message_ProgressIndicator)())) { // export error }
In Draw Harness the conversion may look like this (the source code of commands can be used as a helpful reference of working code using related OCCT algorithms):
pload XDE OCAF VISUALIZATION MODELING
# read STEP file into XDE document
ReadStep D myStep.stp
# display the document in 3D viewer (will also compute default triangulation)
vinit
XDisplay -dispMode 1 D
vfit
# export XDE document into glTF file
WriteGltf D myGltf.glb
I have many HDF5 datasets containing complex number arrays, which I have created using Python and h5py. For example:
import numpy, h5py
with h5py.File("test.h5", "w") as f:
f["mat"] = numpy.array([1.0 + .5j, 2.0 - 1.0j], dtype=complex)
HDF5 has no native concept of complex numbers, so h5py stores them as a compound data type, with fields "r" and "i" for the real and imaginary parts.
How can I load such arrays of complex numbers in Julia, using HDF5.jl?
EDIT: The obvious attempt
using HDF5
h5open("test.h5", "r") do fd
println(read(fd, "mat"))
end
returns a cryptic response:
HDF5Compound(Uint8[0,0,0,0,0,0,240,63,0,0,0,0,0,0,224,63,0,0,0,0,0,0,0,64,0,0,0,0,0,0,240,191],Type[Float64,Float64],ASCIIString["r","i"],Uint64[0,8])
As #simonster pointed out, there is a fast and safe way to do this.
If you had written:
a = read(fd, "mat"))
then the complex vector that you want is simply:
cx_vec = reinterpret(Complex{Float64}, a.data)
I hadn't thought of this before, but one solution is simply to use h5py with PyCall:
using PyCall
#pyimport h5py
f = h5py.File("test.h5", "r")
mat = get(get(f, "mat"), pybuiltin("Ellipsis"))
f[:close]()
println(mat)
In Julia 0.6 you can do the following. As long as you have the HDF5 module and DataFrames already installed this example is immediately executable because the example HDF5 file comes with HDF5.jl. In all likelihood it only works on common types. I haven't tested it beyond the example file as I'm still trying to figure out how to write/create compound tables from Julia.
using HDF5
using DataFrames
# Compound Table Read
d = h5read(joinpath(Pkg.dir("HDF5"),"test","test_files","compound.h5"),"/data")
# Convert d to a dataframe, D
types = [typeof(i) for i in d[1].data] # Data type list
names_HDF5 = [Symbol(i) for i in d[1].membername] # Column name list
D = DataFrame(types,names_HDF5,length(d)) # Preallocate the array
rows = length(d) # Number of rows
cols = length(d[1].data) # Number of columns
for i=1:rows
for j=1:cols
D[i,j] = d[i].data[j] # Save each element to the preallocated dataframe
end
end
d is a vector of table rows. Each element is of type HD5FCompound which each have three fields: data, membername, and membertype.
When I tried to use drawMatchesKnn function as mentioned in this tutorial for FLANN feature matching, I get the following error
AttributeError: 'module' object has no attribute 'drawMatchesKnn'
I checked with other resources that drawMatchesKnn method is present in opencv.
Why am I getting this error?
Thanks in advance
The functions cv2.drawMatches and cv2.drawMatchesKnn are not available in newer versions of OpenCV 2.4. #rayryeng provided a lightweight alternative which works as is for the output of DescriptorMatcher.match. The difference with DescriptorMatcher.knnMatch is that the matches are returned as a list of lists. To use the #rayryeng alternative, the matches must be extracted into a 1-D list.
For example, the Brute-Force Matching with SIFT Descriptors and Ratio Test tutorial could be amended as such:
# BFMatcher with default params
bf = cv2.BFMatcher()
matches = bf.knnMatch(des1,des2, k=2)
# Apply ratio test
good = []
for m,n in matches:
if m.distance < 0.75*n.distance:
# Removed the brackets around m
good.append(m)
# Invoke #rayryeng's drawMatches alternative, note it requires grayscale images
gray1 = cv2.cvtColor(img1,cv2.COLOR_BGR2GRAY)
gray2 = cv2.cvtColor(img2,cv2.COLOR_BGR2GRAY)
drawMatches(gray1,kp1,gray2,kp2,good)
You need to use OpenCV version 3. drawMatchesKnn() is present in 3.0.0-alpha but not in 2.4.11
That error is there, because you are using an old version of OpenCV.
Instead of doing good.append(m) try good.append([m])
I have a large dataset of x,y coordinates in "NAD 1983 StatePlane Michigan South FIPS 2113 Feet" (aka ESRI 102690). I'd like to convert them to lat-lng points.
In theory, this is something proj is built to handle, but the documentation hasn't given me a clue -- it seems to describe much more complicated cases.
I've tried using a python interface, like so:
from pyproj import Proj
p = Proj(init='esri:102690')
sx = 13304147.06410000000 #sample points
sy = 288651.94040000000
x2, y2 = p(sx, sy, inverse=True)
But that gives wildly incorrect output.
There's a Javascript library, but I have ~50,000 points to handle, so that doesn't seem appropriate.
What worked for me:
I created a file called ptest with each pair on its own line, x and y coordinates separated by a space, like so:
13304147.06410000000 288651.94040000000
...
Then I fed that file into the command and piped the results to an output file:
$>cs2cs -f %.16f +proj=lcc +lat_1=42.1 +lat_2=43.66666666666666
+lat_0=41.5 +lon_0=-84.36666666666666 +x_0=4000000 +y_0=0 +ellps=GRS80
+datum=NAD83 +to_meter=0.3048006096012192 +no_defs +zone=20N +to
+proj=latlon ptest > out.txt
If you only need to reproject and can do some data-mining on your text files use whatever you like and use http://spatialreference.org/ref/esri/102690/ as reference.
For example use the Proj4 and store it in a shell/cmd file and call out your input file with proj4 (linux/windows version available) no problem with the size your dataset.
cs2cs +proj=latlong +datum=NAD83 +to +proj=utm +zone=10 +datum=NAD27 -r <<EOF
cs2cs -f %.16f +proj=utm +zone=20N +to +proj=latlon - | awk '{print $1 " " $2}
so in your case something like this:
cs2cs -f %.16f +proj=lcc +lat_1=42.1 +lat_2=43.66666666666666 +lat_0=41.5 +lon_0=-84.36666666666666 +x_0=4000000 +y_0=0 +ellps=GRS80 +datum=NAD83 +to_meter=0.3048006096012192 +no_defs +zone=20N +to +proj=latlon
http://trac.osgeo.org/proj/wiki/man_cs2cs
http://trac.osgeo.org/proj/
If you have coordinates in TXT, CSV or XLS file, you can do CTRL+C and insert them to http://cs2cs.mygeodata.eu where you can set appropriate input and desired output coordinate system. It is possible to insert thousands of coordinates in various formats...