{"id":37,"date":"2023-02-10T00:54:04","date_gmt":"2023-02-10T00:54:04","guid":{"rendered":"https:\/\/blogs.oregonstate.edu\/schaefkr\/?p=37"},"modified":"2023-02-10T00:54:04","modified_gmt":"2023-02-10T00:54:04","slug":"the-best-python-libraries-for-working-with-lidar-data","status":"publish","type":"post","link":"https:\/\/blogs.oregonstate.edu\/schaefkr\/2023\/02\/10\/the-best-python-libraries-for-working-with-lidar-data\/","title":{"rendered":"The Best Python Libraries for Working with Lidar Data"},"content":{"rendered":"\n

Obsessed with point clouds and Python? Me too! <\/p>\n\n\n\n

Recently I discovered that the USGS has tons of free lidar survey data available on The National Map<\/a>. Free lidar data is great, but what’s the best way to extract the data in these files to suit your needs? There’s open source software available such as CloudCompare<\/a> to help you visualize your point cloud data, but if you’re a coder and you’re not interested in visualization then it can be tedious and frustrating to extract the right data or file type. Thankfully there are plenty of libraries out there for process lidar data. Check out below to see my top 3 picks for Python libraries.<\/p>\n\n\n\n

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A quick experiment visualizing USGS lidar data of SLU Seattle which I made with Laspy and Rhino<\/figcaption><\/figure>\n<\/figure>\n\n\n\n

<\/p>\n\n\n\n

Laspy<\/h2>\n\n\n\n

Laspy<\/a> is my favorite Python library to use for working with .las<\/em> and .laz<\/em> files. If you’re a fan of Numpy<\/a> then you’ll be off and running in no time since Laspy works directly with Numpy arrays. I like Laspy because it’s a simple, straight forward library which allows you to do all of your basic reading and writing operations with great examples<\/a> for support. I also had no installation problems with Laspy on Mac or Windows whereas with libLAS<\/a> I dealt with a lot of frustration trying to get it working on Windows. <\/p>\n\n\n\n

Here’s an example of some code I wrote to read a .las file from the USGS TNM then write each point to a Rhino<\/a> .3dm point cloud organized by classification code. Note I did use a constants file to specify which classification codes I wanted to use.<\/p>\n\n\n\n

import laspy\nimport numpy as np\nimport rhino3dm\nimport constants\n\nif __name__ == '__main__':\n    # Get Data from Las File with Laspy\n    las_filepath = r'filename.las'\n    with laspy.open(las_filepath) as f:\n        for points in f.chunk_iterator(1000000000):\n            x, y, z, classification_codes = points.x.copy(), points.y.copy(), points.z.copy(), points.classification.copy()\n    pts = zip(x.tolist(), y.tolist(), z.tolist(), classification_codes.tolist())\n    classification_codes = np.unique(np.array(classification_codes.tolist()))\n    \n    # Create Rhino 3dm file\n    model = rhino3dm.File3dm()\n    model.Settings.ModelUnitSystem = rhino3dm.UnitSystem.Feet\n    layers_by_classification_codes = {}\n\n    for code in classification_codes:\n        name = f'{code}-{constants.LAS_CLASSIFICATION_CODES[str(code)]}'\n        point_cloud = rhino3dm.PointCloud()\n        props = {\n            'name': name,\n            'idx': 0,\n            'point_cloud': point_cloud,\n        }\n        layers_by_classification_codes[str(code)] = props\n    \n    for x, y, z, code in pts:\n        pt = create_rhino3dm_point(x, y, z)\n        layers_by_classification_codes[str(code)]['point_cloud'].Add(pt)\n    \n    for code, props in layers_by_classification_codes.items():\n        layer = rhino3dm.Layer()\n        layer.Name = props['name']\n        layer_idx = model.Layers.Add(layer)\n        attrs = rhino3dm.ObjectAttributes()\n        attrs.LayerIndex = layer_idx\n        model.Objects.Add(props['point_cloud'], attrs)\n    \n    model.Write(f'las_to_rhino_test.3dm', 7)<\/code><\/pre>\n\n\n\n
<\/p>\n\n\n\n
WhiteboxTools<\/h2>\n\n\n\n
Although lesser known, WhiteboxTools<\/a> is a library that is “written in Rust and has extensive support for\u00a0Python-based scripting<\/a>“.<\/p>\n\n\n\n
Where Laspy is great for it’s no nonsense read and write functions, WhiteboxTools offers a great range of extra functions for working with .las<\/em> or .laz<\/em> files.<\/p>\n\n\n\n
For example, you can color a lidar point set with an image (code courtesy of WhiteboxTools<\/a>): <\/p>\n\n\n\n
from whitebox_tools import WhiteboxTools\n\nwbt = WhiteboxTools()\nwbt.work_dir = \"\/path\/to\/data\/\"\nin_lidar = \"lidar_data.las\"\nin_image = \"airphoto.tif\"\nout_lidar = \"colourized_lidar.las\"\nwbt.lidar_colourize(in_lidar, in_image, out_lidar) <\/code><\/pre>\n\n\n\n
Here’s the full list of the additional functions WhiteboxTools offers<\/a>:<\/p>\n\n\n\n