Hi,

I am using Anaconda on an OSX-64 machine. While I was trying to create the pyntcloud environment with the enviroment.yml, it prompted the following error:

NoPackagesFoundError: Package missing in current osx-64 channels: 
  - gst-plugins-base 1.8.0 0

Do you know how to solve this?

Thanks!

To be added in scalar_fields/k_neighbors.

Use Hugues Hoppe 92 to compute normals based on eigen values.

Use Scipy sparse matrix and graph routines. for build and transverse Riemannian graph.

I seem to have a problem with the .plot() method below is a screeshot of what I get running the Basic_Numpy_Plotting example

I tried Chrome and Firefox hoping it was browser-related but still no luck, any ideas?

I've noticed that the bounding box filter does not work for the z axis. I've got it work on x and y but for some reason it will not apply to the z direction. Any thoughts on why this is happening or what im doing wrong?

Hola! Great job with this library! I'm just getting started using it and it's definitely going to be really useful for my work.

I went through most of the issues and I see that there's been similar issues to what I'm getting with the .plot() function. I've tried those solutions but just haven't been able to figure it out yet.

I'm running your QuickStart file, and when I get to the first scene.plot() all I see is a black screen with the word 'screenshot' in an orange box in the top left and the logo in the middle of the screen. I'm using a Mac and Chrome. I have no clue where I should be looking and any help would be appreciated! Thanks!

When I attempt to load a obj file, which has e.g. rectangles as facet pyntcloud crashes with an AssertionError within pandas:

---------------------------------------------------------------------------
AssertionError                            Traceback (most recent call last)
<ipython-input-36-170af64711ff> in <module>()
----> 1 obj.read_obj("/Users/johannes/test.obj")

~/anaconda3/envs/suction/lib/python3.6/site-packages/pyntcloud/io/obj.py in read_obj(filename)
     50     f = [re.split(r'\D+', x) for x in f]
     51 
---> 52     mesh = pd.DataFrame(f, dtype='i4', columns=mesh_columns)
     53     # start index at 0
     54     mesh -= 1

~/anaconda3/envs/suction/lib/python3.6/site-packages/pandas/core/frame.py in __init__(self, data, index, columns, dtype, copy)
    367                     if is_named_tuple(data[0]) and columns is None:
    368                         columns = data[0]._fields
--> 369                     arrays, columns = _to_arrays(data, columns, dtype=dtype)
    370                     columns = _ensure_index(columns)
    371 

~/anaconda3/envs/suction/lib/python3.6/site-packages/pandas/core/frame.py in _to_arrays(data,     columns, coerce_float, dtype)
   6282     if isinstance(data[0], (list, tuple)):
   6283         return _list_to_arrays(data, columns, coerce_float=coerce_float,
-> 6284                                dtype=dtype)
   6285     elif isinstance(data[0], collections.Mapping):
   6286         return _list_of_dict_to_arrays(data, columns,

~/anaconda3/envs/suction/lib/python3.6/site-packages/pandas/core/frame.py in _list_to_arrays(data, columns, coerce_float, dtype)
   6361         content = list(lib.to_object_array(data).T)
   6362     return _convert_object_array(content, columns, dtype=dtype,
-> 6363                                  coerce_float=coerce_float)
   6364 
   6365 

~/anaconda3/envs/suction/lib/python3.6/site-packages/pandas/core/frame.py in _convert_object_array(content, columns, coerce_float, dtype)
   6418             # caller's responsibility to check for this...
   6419             raise AssertionError('%d columns passed, passed data had %s '
-> 6420                                  'columns' % (len(columns), len(content)))
   6421 
   6422     # provide soft conversion of object dtypes

AssertionError: 9 columns passed, passed data had 12 columns

Hi, This looks like it'd be the best solution to my problem but for the fact it requires Numba, which, if it is possible to install on the RPi, is very difficult. Any chance of creating a stripped down version? I've been on the hunt for a good while for a way to tessellate a point cloud.

Hi,

Is it possible to refresh the point cloud plot? i.e. displaying a video of point clouds whilst still being able to interact in the browser and changing views? Alternatively is it possible to take PNGs rather than HTMLs ?

Thanks!

I know this must be frustrating or I might have done something very stupid but I am not seeing any error message and when I run the plot() function I see

Renderer(camera=PerspectiveCamera(aspect=1.6, fov=90.0, position=(135.3456573486328, 9146.374328613281, 41812.…

HBox(children=(Label(value='Background color:'), ColorPicker(value='black'), Label(value='Point size:'), Float…

instead of the actual render. I tried staring a simple server too. I am working on Chrome for Ubuntu 16.04 and python 3.5.

Is there a way to get a single, fixed, 2D snapshot of the point-cloud? I'm having some trouble embedding the IFrame created by plot in Google's Collaboratory - localhost refused to connect. A static snapshot would help, even if it is not interactive.

https://storage.googleapis.com/nvidia-dev/113620.ply At my end I either get the points lost or the color lost with conversion `[GCC 8.4.0] on linux Type "help", "copyright", "credits" or "license" for more information.

from pyntcloud import PyntCloud diamond = PyntCloud.from_file("cloud.ply") convex_hull_id = diamond.add_structure("convex_hull") convex_hull = diamond.structures[convex_hull_id] diamond.to_file("diamond_hull.obj", also_save=["mesh"])`

Describe the bug When running radius search on my point cloud, I get an error:

File "D:\repos\scripts\venv\lib\site-packages\pyntcloud\core_class.py", line 590, in get_neighbors return r_neighbors(kdtree, r) File "D:\repos\scripts\venv\lib\site-packages\pyntcloud\neighbors\r_neighbors.py", line 21, in r_neighbors return np.array(kdtree.query_ball_tree(kdtree, r)) ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (5342,) + inhomogeneous part.`

k-neighbor search works as expected.

To Reproduce My code to reproduce the bug:

cloud = PyntCloud.from_file(pc_path) kdtree_id = cloud.add_structure("kdtree") r_neighbors = cloud.get_neighbors( r=5, kdtree=kdtree_id)

Desktop (please complete the following information):

• OS: Win 10
• pyntcloud: 0.3.1
• python 3.9

Hi daavoo/pyntcloud!

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FAQ

Hello everyone. I am trying to download nltk into my notebook but get an error message everytime.

Here is what I input conda install -c conda-forge nltk

Here is the error message I see everytime. Note: you may need to restart the kernel to use updated packages. usage: conda-script.py [-h] [-V] command ... conda-script.py: error: unrecognized arguments: nltk

I have tried reseting the Kernal, closing the program and trying on a blank notebook but nothing seems to work. Let me know what I can do to fix this!!! Thanks

Hello. Thanks for the nice library! I think I may have found a bug, could you please check?

Describe the bug The logic of io.las.read_las_with_laspy() may not meet the las data specification. https://github.com/daavoo/pyntcloud/blob/c9dcf59eacbec33de0279899a43fe73c5c094b09/pyntcloud/io/las.py#L46

To Reproduce Steps to reproduce the behavior:

• Download point cloud data (.las) of Kakegawa Castle.
  • https://www.geospatial.jp/ckan/dataset/kakegawacastle/resource/61d02b61-3a44-4a5b-b263-814c6aa23551
  • Please note that the data is 2GB in zip file and 5GB after unzipping.
  • The file name is in Japanese, so please be careful of the character encoding.
  • Kakegawa Castle is https://en.wikipedia.org/wiki/Kakegawa_Castle
• Feel free to rename the file as you wish. Here, the file name is KakegawaCastle.las.
• Execute the following code to get the xyz coordinates.
  • You will find 190 million points.

from pyntcloud import PyntCloud
cloud = PyntCloud.from_file(". /KakegawaCastle.las")
cloud.points
# x y z intensity bit_fields raw_classification scan_angle_rank user_data point_source_id red green blue
# 0 37.910053 71.114777 28.936932 513 0 1 0 0 29 138 122 127
# 1 37.690052 75.975777 28.918930 2309 0 1 0 0 29 15 5 14
# 2 38.465054 71.277779 33.523930 64149 0 1 0 0 29 44 15 35
# 3 32.406052 78.586777 30.808931 19758 0 1 0 0 29 99 54 59
# 4 30.372051 86.346779 30.809931 257 0 1 0 0 29 107 56 55
# ...	...	...	...	...	...	...	...	...	...	...	...	...
# 192366074 151.807999 172.604996 17.660999 50886 0 1 0 0 29 198 198 190
# 192366075 152.425003 173.162994 16.458000 25186 0 1 0 0 29 101 96 96
# 192366076 152.126007 172.781998 16.620001 30840 0 1 0 0 29 121 120 116
# 192366077 152.085007 172.682999 17.497000 40863 0 1 0 0 29 166 157 146
# 192366078 151.832993 173.360001 16.886000 31868 0 1 0 0 29 132 121 115
# 192366079 rows × 12 columns

• At this time, the first point in column x is 37.910053
• If you run the following command, the data should look like this.
  • pdal info: https://pdal.io/apps/info.html

% pdal info /KakegawaCastle.las -p 0
{
  "file_size": 5001518281,
  "filename": "KakegawaCastle.las",
  "now": "2022-06-14T09:39:43+0900",
  "pdal_version": "2.4.0 (git-version: Release)",
  "points":
  {
    "point":
    {
      "Blue": 32640,
      "Classification": 1,
      "EdgeOfFlightLine": 0,
      "Green": 31365,
      "Intensity": 513,
      "NumberOfReturns": 0,
      "PointId": 0,
      "PointSourceId": 29,
      "Red": 35445,
      "ReturnNumber": 0,
      "ScanAngleRank": 0,
      "ScanDirectionFlag": 0,
      "UserData": 0,
      "X": -44490.84295,
      "Y": -135781.1752,
      "Z": 54.58493098
    }
  }
  "reader": "readers.las"
}

• The value of x is -44490.84295, which is different from the value output by pyntcloud!
• The above value can be calculated from the data output when using the following laspy.

import laspy
las = laspy.read(". /KakegawaCastle.las")
header = las.header

# first x point value: 531578298
x_point = las.X[0]

# x scale: 7.131602618438667e-08 -> 0.0000007
x_scale = header.x_scale

# x offset: -44528.753
x_offset = header.x_offset

# x_coordinate output from above variables: -44490.842948180776
real_coordinate_x = (x_point * x_scale) + x_offset

• The value calculated from laspy based on EPSG:6676 is indeed at Kakegawa Castle!
  • https://www.google.co.jp/maps/place/34%C2%B046'30.3%22N+138%C2%B000'50.1%22E/@34.775077,138.0117243,17z/data=!3m1!4b1!4m5!3m4!1s0x0:0x2ce21e9ef0b19341!8m2!3d34.775077!4d138.013913?hl=ja
• But in read_las_with_laspy(), the logic is as follows, and the offset values are not added https://github.com/daavoo/pyntcloud/blob/c9dcf59eacbec33de0279899a43fe73c5c094b09/pyntcloud/io/las.py#L55

Expected behavior Offset values are taken into account for the xyz coordinates of the DataFrame.

Screenshots Does not exist.

Desktop (please complete the following information):

• OS: macOS Monterey v12.4
• Browser: Does not used.
• Version

Conda -V
conda 4.12.0
❯ conda list | grep pyntcloud
pyntcloud 0.3.0 pyhd8ed1ab_0 conda-forge

Additional context If the above context looks OK, shall I create a PullRequest?