This is part of my obj file. The goal is to create indices in the order of the vertices.
The coordinates of the index of the 0th vertex of the blender do not match the 0th vertex of the obj file
It was confirmed that the vertex of the first line in the obj file is assigned an index of 523 in the blender. Is there a way to generate the indices in the order of the vertices of the obj file? Or is there a way to specify the vertex indices in the obj file format?
The solution with Blender 2.9 is quite easy :
Import your .obj, that is File -> Import -> Wavefront (.obj)
In the Blender file View, click on the file you want to import
Open the Geometry panel, click on Keep Vert Order;
click Import OBJ
Now the vertices ID should match the .obj indices.
Related
I have a folium.Map that contains custom HTML Popups with clickable URLs. These Popups open when clicking on the polygons of the map. This is a feature that doesn't seem to be possible to achieve using holoviews.
My ideal example of the final application that I want to build with holoviews/geoviews is here with the source code here, but I would like to exchange the main map with my folium Map and plot polygons instead of rasterized points. Now when I would like to create the holoviews.DynamicMap from the folium.Map, holoviews complains (of course) that the data type "map" is not accepted. Is this somehow still possible?
I have found some notebook on GitHub where a holoviews plot in embedded in a folium map using a workaround that writes and reads again HTML, but it seems impossible to embed a folium map into holoviews such that other plots can be updated from this figure using Streams!?
Here is some toy data (from here) for the datasets that I use. For simplicity, let's assume I just had point data instead of polygons:
import folium as fn
def make_map():
m = fm.Map(location=[20.59,78.96], zoom_start=5)
green_p1 = fm.map.FeatureGroup()
green_p1.add_child(
fm.CircleMarker(
[row.Latitude, row.Longitude],
radius=10,
fill=True,
fill_color=fill_color,
fill_opacity=0.7
)
)
map.add_child(green_p1)
return map
If I understand it correctly, this needs to be tweaked now in the fashion that it can passed as the first argument to a holoviews.DynamicMap:
hv.DynamicMap(make_map, streams=my_streams)
where my_streams are some other plots that should be updated with the extent of the folium map.
Is that somehow possible or is my strategy wrong?
I have a set of files that contain data that I want to produce a set of box plots for in order to compare them. I can get the data into gnuplot, but I don't know the correct format to separate each file into its own plot.
I have tried reading all the required files into a variable, which does work, however when the plot is produced, all the boxplots are on top of each other. I need to get gnuplot to index each plot along one space for each new data file.
For example, this produces the output with overlaying plots:
FILES = system("ls -1 /path/to/files/*")
plot for [data in FILES] data using (1):($4) with boxplot notitle
I know the X position is being stated explicitly there with the (1), but I'm not sure what to replace it with to get the position to move for each plot. This isn't a problem with other chart types, since they don't have the same field locating them.
You can try the following.
You can access the file in your file list by index via word(FILES,i). Check help word and help words. The code below assumes that you have some datafiles Data0*.dat in your directory. Maybe there is a smarter/shorter way to implement the xtic labels.
Code:
### boxplots from a list of files
reset session
# get a list of files (Windows)
FILES = system('dir /B "C:\Data\Data0*.dat"')
# set tics as filenames
set xtics () # remove xtics
set yrange [-2:27]
do for [i=1:words(FILES)] {
set xtics add (word(FILES,i) i) rotate by 45 right
}
plot for [i=1:words(FILES)] word(FILES,i) u (i):2 w boxplot notitle
### end of code
Result:
I'm trying to use the imageai python library, and more particularly this function:
detector.detectObjectsFromImage()
The doc says it should be used with a Numpy array of file stream of any image.
https://imageai.readthedocs.io/en/latest/detection/index.html
When I pass it a Numpy array, like this:
detections = detector.detectObjectsFromImage(input_image=anumpyarray,input_type = "array")
I get the error:
detections =
detector.detectObjectsFromImage(input_image=anumpyarray,input_type =
"array") File
"/usr/local/lib/python3.6/site-packages/imageai/Detection/init.py",
line 517, in detectObjectsFromImage raise ValueError("Ensure you
specified correct input image, input type, output type and/or output
image path ") ValueError: Ensure you specified correct input image,
input type, output type and/or output image path
Is it because a Numpy array and a Numpy array of a stream of an image are different things?
I know it's old, but for anyone who needs help:
Try to set 2 additional params:
minimum_percentage_probability=0, output_type='array'
For more info, go into imageai\Detection\__init__.py -> detectObjectsFromImage
I have an (n, m) array that I've been visualizing with matplotlib.pyplot.imshow. I'd like to save this data in some type of raster graphics file (e.g. a png) so that:
The colors are the ones shown with imshow
Each element of the underlying array is exactly one pixel in the saved image -- meaning that if the underlying array is (n, m) elements, the image is NxM pixels. (I'm not interested in interpolation='nearest' in imshow.)
There is nothing in the saved image except for the pixels corresponding to the data in the array. (I.e. there's no white space around the edges, axes, etc.)
How can I do this?
I've seen some code that can kind of do this by using interpolation='nearest' and forcing matplotlib to (grudgingly) turn off axes, whitespace, etc. However, there must be some way to do this more directly -- maybe with PIL? After all, I have the underlying data. If I can get an RGB value for each element of the underlying array, then I can save it with PIL. Is there some way to extract the RGB data from imshow? I can write my own code to map the array values to RGB values, but I don't want to reinvent the wheel, since that functionality already exists in matplotlib.
As you already guessed there is no need to create a figure. You basically need three steps. Normalize your data, apply the colormap, save the image. matplotlib provides all the necessary functionality:
import numpy as np
import matplotlib.pyplot as plt
# some data (512x512)
import scipy.misc
data = scipy.misc.lena()
# a colormap and a normalization instance
cmap = plt.cm.jet
norm = plt.Normalize(vmin=data.min(), vmax=data.max())
# map the normalized data to colors
# image is now RGBA (512x512x4)
image = cmap(norm(data))
# save the image
plt.imsave('test.png', image)
While the code above explains the single steps, you can also let imsave do all three steps (similar to imshow):
plt.imsave('test.png', data, cmap=cmap)
Result (test.png):
I am having a hard time understanding how to access the data in a carray.
http://carray.pytables.org/docs/manual/index.html
I have a carray that I can view in a group structure using vitables - but how to open it and retrieve the data it beyond me.
The data are a point cloud that is 3 levels down that I want to make a scatter plot of and extract as a .obj file..
I then have to loop through (many) clouds and do the same thing..
Is there anyone that can give me a simple example of how to do this?
This was my attempt:
import carray as ca
fileName = 'hdf5_example_db.h5'
a = ca.open(rootdir=fileName)
print a
I managed to solve my issue.. I wasn't treating the carray differently to the rest of the hierarchy. I needed to first load the entire db, then refer to the data I needed. I ended up not having to use carray, and just stuck to h5py:
from __future__ import print_function
import h5py
import numpy as np
# read the hdf5 format file
fileName = 'hdf5_example_db.h5'
f = h5py.File(fileName, 'r')
# full path of carry type data (which is in ply format)
dataspace = '/objects/object_000/object_model'
# view the data
print(f[dataspace])
# print to ply file
with open('object_000.ply', 'w') as fo:
for line in f[dataspace]:
fo.write(line+'\n')