https://github.com/evetion/lazio.jl

Extends LasIO with Laszip integration
https://github.com/evetion/lazio.jl

hacktoberfest io julia lasio lastools laszip laz lidar pointcloud

Last synced: about 2 months ago
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Extends LasIO with Laszip integration

• Host: GitHub
• URL: https://github.com/evetion/lazio.jl
• Owner: evetion
• License: mit
• Created: 2017-09-26T07:08:57.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2024-04-08T06:18:30.000Z (9 months ago)
• Last Synced: 2024-10-02T14:13:59.219Z (3 months ago)
• Topics: hacktoberfest, io, julia, lasio, lastools, laszip, laz, lidar, pointcloud
• Language: Julia
• Homepage:
• Size: 1.72 MB
• Stars: 10
• Watchers: 5
• Forks: 2
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • Changelog: CHANGELOG.md
  • License: LICENSE

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README

        
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# LazIO

Extends LasIO with LASzip integration.

Uses the [LASzip](https://github.com/LASzip/LASzip/) shared library to read compressed las files (\*.laz) into the uncompressed format that [LasIO](https://github.com/visr/LasIO.jl) reads natively.

```julia

julia> using LazIO

# Open file and iterate over points

julia> ds = LazIO.open("test/libLAS_1.2.laz")

LazIO Dataset of test/libLAS_1.2.laz with 497536 points of version 0.

# Each point is correctly scaled and has its return_number and classification widened

julia> p = ds[1]

LazIO.Point0(1.44013394e6, 375000.23, 846.66, 0x00fa, 0x00, 0x00, 0x00, false, 2, false, false, false, 0x00, 0x001d)

# This results in accessible attributes, such as edge_of_flightline and withheld

julia> fieldnames(typeof(p))

(:x, :y, :z, :intensity, :return_number, :number_of_returns, :scan_direction, :edge_of_flight_line, :classification, :synthetic, :key_point, :withheld, :user_data, :point_source_id)

# LazIO implements the GeoInterface

julia> using GeoInterface

julia> GeoInterface.coordinates(p)

3-element Vector{Float64}:

      1.44013394e6

 375000.23

    846.66

julia> GeoInterface.extent(ds)

Extent(X = (1.44e6, 1.44499996e6), Y = (375000.03, 379999.99), Z = (832.1800000000001, 972.6700000000001))

# Or one can use the Tables interface

julia> using DataFrames

julia> DataFrame(ds)

497536×14 DataFrame

    Row │ x          y               z        intensity  return_number  number ⋯

        │ Float64    Float64         Float64  UInt16     UInt8          UInt8  ⋯

────────┼───────────────────────────────────────────────────────────────────────

      1 │ 1.44013e6       3.75e5      846.66        250              0         ⋯

      2 │ 1.44012e6       3.75e5      846.55        245              0

      3 │ 1.44011e6       3.75001e5   846.44        239              0

      4 │ 1.4401e6   375001.0         846.32        251              0

      5 │ 1.44009e6       3.75001e5   846.21        229              0         ⋯

      6 │ 1.44009e6       3.75002e5   846.1         249              0

      7 │ 1.44008e6       3.75002e5   846.0         189              0

      8 │ 1.44007e6       3.75002e5   845.9         250              0

```

Plotting is done via either the Plots, or Makie ecosystem. The latter is recommended for large datasets.

```julia

julia> # using Plots

julia> using GLMakie

julia> plot(ds)

```

![](plot-fs8.png)