I'm currently using MeshLab for 3D modeling and was wondering how to convert a regular OBJ file, like
v X Y Z
f 1 2 3
to a file that has all the Half Edge Data Structure information.
I haven't found any digestible documentation online about how to do this with MeshLab and i feel like i'm missing a step somewhere. I thought it'd be as easy as exporting the OBJ file to another type, but I guess not. I need to extract info from it using OpenMesh and some functions but I can't even seem to be able to get a half edge data structure file to begin with. Any help is appreciated, thanks!
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I want to output a pcl point cloud from an istream (ply file read directly from server and can't be saved locally). Can anyone help if there is a way to achieve this?
I am familiar with pcl::io::loadPLYFile<pcl::PointXYZRGBA>(std::string fname, *cloud); but the function I am looking for is of the form xxxx(std::istream, *cloud); I tried looking at pcl's codebase in the github but couldn't get anywhere! :(
I don't think PCL has one function to do what you're asking. But looking at the .ply file format, I'd suggest you do something like following
Go through the file line by line
Once you find element vertex X, keep the number X
Once you find end_header, start reading vertex values (I'm guessing in the order of x y z r g b a) from the next line to the next Xth line
Assign the read values to a pointcloud you created
I am concerned with, I guess, a relatively simple problem.
I currently conduct a simulation where I track time, position and orientation of one particle for a given number of simulation steps.
The task is: Simply write this data to a h5-file on the fly.
So far, I did this using Jupyter. With the h5py-package it is very simple to create a data set of predefined structure (columns x rows) via
outfile = h5py.File("outfile.h5", "w")
dset = outfile.create_dataset("dsetname", (number_of_lines, number_of_columns))
and then write the data line by line for each simulation time step to the data set with
dset[time_step] = np.array([t, x, phi])
Now, I moved to C++, implemented the simulation there and would like to store the data in the same way I used to do it with Python.
However, from basic examples like this, one would have to store the whole set of data in an array during the simulation run and then write its content to the h5 file afterwards.
This is not very elegant. As I did with Python, I would like to just write the data line by line to the HDF5 data set on the fly – and not store the (sometimes up to several GB) large amounts of data in an array.
Unfortunately, so far, I did not find a way how I can implement the procedure I used with Python into C++.
Has anybody ever encountered a similar problem and could show me a way how to solve this it?
Thank you!
Best,
Sven
I'm trying to do binary LSTM classification using theano.
I have gone through the example code however I want to build my own.
I have a small set of "Hello" & "Goodbye" recordings that I am using. I preprocess these by extracting the MFCC features for them and saving these features in a text file. I have 20 speech files(10 each) and I am generating a text file for each word, so 20 text files that contains the MFCC features. Each file is a 13x56 matrix.
My problem now is: How do I use this text file to train the LSTM?
I am relatively new to this. I have gone through some literature on it as well but not found really good understanding of the concept.
Any simpler way using LSTM's would also be welcome.
There are many existing implementation for example Tensorflow Implementation, Kaldi-focused implementation with all the scripts, it is better to check them first.
Theano is too low-level, you might try with keras instead, as described in tutorial. You can run tutorial "as is" to understand how things goes.
Then, you need to prepare a dataset. You need to turn your data into sequences of data frames and for every data frame in sequence you need to assign an output label.
Keras supports two types of RNNs - layers returning sequences and layers returning simple values. You can experiment with both, in code you just use return_sequences=True or return_sequences=False
To train with sequences you can assign dummy label for all frames except the last one where you can assign the label of the word you want to recognize. You need to place input and output labels to arrays. So it will be:
X = [[word1frame1, word1frame2, ..., word1framen],[word2frame1, word2frame2,...word2framen]]
Y = [[0,0,...,1], [0,0,....,2]]
In X every element is a vector of 13 floats. In Y every element is just a number - 0 for intermediate frames and word ID for final frame.
To train with just labels you need to place input and output labels to arrays and output array is simpler. So the data will be:
X = [[word1frame1, word1frame2, ..., word1framen],[word2frame1, word2frame2,...word2framen]]
Y = [[0,0,1], [0,1,0]]
Note that output is vectorized (np_utils.to_categorical) to turn it to vectors instead of just numbers.
Then you create network architecture. You can have 13 floats for input, a vector for output. In the middle you might have one fully connected layer followed by one lstm layer. Do not use too big layers, start with small ones.
Then you feed this dataset into model.fit and it trains you the model. You can estimate model quality on heldout set after training.
You will have a problem with convergence since you have just 20 examples. You need way more examples, preferably thousands to train LSTM, you will only be able to use very small models.
I have about 30 rasters with 4 bands each that I am trying to create composites so that I can eventually bring all of the rasters together into 1 large raster. But the first step is to create composite rasters. I would like to do this all at once and I found a few examples on various sites on how to do it, including ESRI's. I've pieced them together to create my own code, unfortunately I keep getting error 000271: Cannot open the input datasets. I know the path is correct because arcpy.ListRasters() returns the files in the folder in a large list, so the problem is definitely with the CompositeBands tool. I've looked up possible solutions to this problem, but I did not understand the solutions or how they worked, so if you do have an answer or suggestion, could you comment on your code (if you write one) or answer so I know what is going on and why? About the data - they are all ERDAS Imagine image rasters with 4 image color bands : R, G, B, and whatever N is. All but a few rasters have bands named Layer_1, Layer_2 and so on. The few are called Band_1, Band_2 and so on. Here is my code:
arcpy.env.workspace = r'\\network\folder\subfolder1\subfolder2\All_RGBN'
ws = arcpy.env.workspace
outws = r'\\network\folder\subfolder1\subfolder2\RGBN_Composit'
for ras in arcpy.ListRasters("*.img"):
name = outws+"\\"+ras
try:
arcpy.CompositeBands_management("Layer_1.img;Layer_2.img;Layer_3.img,Layer_4.img", name)
except:
arcpy.CompositeBands_management("Band_1.img;Band_2.img;Band_3.img,Band_4.img", name)
Thanks!
If your rasters have multiple bands, they are already composite. Composite Bands should be used when your bands are distinct raster datasets that you want to merge into one raster.
If you want to merge all your rasters (composite or not) into one single dataset, you should create a Mosaic Dataset or a Raster Catalog and load your rasters into it.
And FYI, you get an error message in the Composite Band tool because your raster bands (inputs) are not correctly referenced, you should write something like:
ras + "\\Layer_x" instead of "Layer_x.img"
But doing this will output the exact same raster as the original one.
I'm working on a tool to visualize RNA secondary structure, for this purpose I have implemented Nussinov's algorithm which generates the RNA secondary structure as list with the corresponding indices, the code can be found here [0]
[0] http://dpaste.com/596262/
But I really stuck with understanding how I should visualize it (as a planar graph), the code above gives me a sequential list of the secondary structure, so can someone please suggest me as to how I can visualize the structure.An example of such tool can be found here [1]
[1] http://rna.tbi.univie.ac.at/cgi-bin/RNAfold.cgi
and I know there are better algorithms but for now I would just want to visualize with this and once I understand visualization, I will go for a better algorithm.
Visualizing the secondary structure of RNA (or any graph, for that matter) algorithmically is a difficult problem. You need to take care that there are as few overlaps as possible while maintaining consistent link lengths. As the other answers have pointed out, there are a number of existing implementations that you can already use. I'll just throw in another one that's quite easy to use and requires no downloads:
forna - nibiru.tbi.univie.ac.at/forna
Here you just need to enter a dotbracket string:
>molecule_name
CGCUUCAUAUAAUCCUAAUGAUAUGGUUUGGGAGUUUCUACCAAGAGCCUUAAACUCUUGAUUAUGAAGUG
((((((((((..((((((.........))))))......).((((((.......))))))..)))))))))
This will give you a visualization that looks something like this:
This is computed using a combination of the ViennaRNA RNAplot program and d3's force-directed graph algorithm.
You could do this with jmol . Jmol allows you to add arbitrary bonds / atoms to a coordinate space using its java or I believe its javascript api also.
In general, of course, PDB file formats would be used for such data.
RNAviz is old but still commonly used. JalView apparently was supposed to get RNA secondary structure rendering thru a GSoC project last year, but I'm not sure what the status in the program is.