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README.rst

cjio, or CityJSON/io

License: MIT image1

Python CLI to process and manipulate CityJSON files. The different operators can be chained to perform several processing operations in one step, the CityJSON model goes through them and different versions of the CityJSON model can be saved as files along the pipeline.

Documentation

cjio.readthedocs.io

Installation

It uses Python 3.5+ only.

To install the latest release:

pip install cjio

To install the development branch, and still develop with it:

git checkout develop
virtualenv venv
. venv/bin/activate
pip install --editable .

Alternatively, you can use the included Pipfile to manage the virtual environment with pipenv.

Note for Windows users

If your installation fails based on a pyproj or pyrsistent error there is a small hack to get around it. Based on the python version you have installed you can download a wheel (binary of a python package) of the problem package/s. A good website to use is here. You then run:

pip install [name of wheel file]

You can then continue with:

pip install cjio

Usage of the CLI

After installation, you have a small program called cjio, to see its possibities:

cjio --help

Commands:
  assign_epsg                Assign a (new) EPSG.
  clean                      Clean = remove_duplicate_vertices +...
  compress                   Compress a CityJSON file, ie stores its...
  decompress                 Decompress a CityJSON file, ie remove the...
  export                     Export the CityJSON to another format.
  extract_lod                Extract only one LoD for a dataset.
  info                       Output info in simple JSON.
  locate_textures            Output the location of the texture files.
  merge                      Merge the current CityJSON with others.
  remove_duplicate_vertices  Remove duplicate vertices a CityJSON file.
  remove_materials           Remove all materials from a CityJSON file.
  remove_orphan_vertices     Remove orphan vertices a CityJSON file.
  remove_textures            Remove all textures from a CityJSON file.
  reproject                  Reproject the CityJSON to a new EPSG.
  save                       Save the city model to a CityJSON file.
  subset                     Create a subset of a CityJSON file.
  translate                  Translate the file by its (-minx, -miny,...
  update_bbox                Update the bbox of a CityJSON file.
  update_textures            Update the location of the texture files.
  upgrade_version            Upgrade the CityJSON to the latest version.
  validate                   Validate the CityJSON file: (1) against its...

Or see the command-specific help by calling --help after a command:

cjio subset --help

Usage: cjio subset [OPTIONS]

  Create a subset of a CityJSON file. One can select City Objects by (1) IDs
  of City Objects; (2) bbox; (3) City Object type; (4) randomly.

  These can be combined, except random which overwrites others.

  Option '--exclude' excludes the selected objects, or "reverse" the
  selection.

Options:
  --id TEXT                       The ID of the City Objects; can be used
                                  multiple times.
  --bbox FLOAT...                 2D bbox: (minx miny maxx maxy).
  --random INTEGER                Number of random City Objects to select.
  --cotype [Building|Bridge|Road|TransportSquare|LandUse|Railway|TINRelief|WaterBody|PlantCover|SolitaryVegetationObject|CityFurniture|GenericCityObject|Tunnel]
                                  The City Object type
  --exclude                       Excludes the selection, thus delete the
                                  selected object(s).
  --help                          Show this message and exit.

Pipelines of operators

The 3D city model opened is passed through all the operators, and it gets modified by some operators. Operators like info and validate output information in the console and just pass the 3D city model to the next operator.

cjio example.json subset --id house12 info remove_materials info save out.json
cjio example.json remove_textures compress info
cjio example.json upgrade_version save new.json
cjio myfile.json merge '/home/elvis/temp/*.json' save all_merged.json

Validation of CityJSON files against the schema

To validate a CityJSON file against the schemas of CityJSON (this will automatically fetch the schemas for the version of CityJSON):

cjio myfile.json validate

If the errors are too many, you can save the validation output to a file:

cjio myfile.json validate > /path/to/report.txt

If the file is too large (and thus validation is slow), an option is to crop a subset and just validate it:

cjio myfile.json subset --random 2 validate

If you want to use your own schemas, give the folder where the master schema file cityjson.schema.json is located:

cjio example.json validate --folder_schemas /home/elvis/temp/myschemas/

Generating Binary glTF

Convert the CityJSON example.json to a glb file /home/elvis/gltfs/example.glb

cjio example.json export --format glb /home/elvis/gltfs

Convert the CityJSON example.json to a glb file /home/elvis/test.glb

cjio example.json export --format glb /home/elvis/test.glb

Usage of the API

cjio.readthedocs.io/en/stable/tutorials.html

Docker

If docker is the tool of your choice, please read the following hints.

To run cjio via docker simply call:

docker run --rm  -v <local path where your files are>:/data tudelft3d/cjio:latest cjio --help

To give a simple example for the following lets assume you want to create a geojson which represents the bounding boxes of the files in your directory. Lets call this script gridder.py. It would look like this:

from cjio import cityjson
import glob
import ntpath
import json
import os
from shapely.geometry import box, mapping

def path_leaf(path):
    head, tail = ntpath.split(path)
    return tail or ntpath.basename(head)

files = glob.glob('./*.json')

geo_json_dict = {
    "type": "FeatureCollection",
    "features": []
}

for f in files:
    cj_file = open(f, 'r')
    cm = cityjson.reader(file=cj_file)
    theinfo = json.loads(cm.get_info())
    las_polygon = box(theinfo['bbox'][0], theinfo['bbox'][1], theinfo['bbox'][3], theinfo['bbox'][4])
    feature = {
        'properties': {
            'name': path_leaf(f)
        },
        'geometry': mapping(las_polygon)
    }
    geo_json_dict["features"].append(feature)
    geo_json_dict["crs"] = {
        "type": "name",
        "properties": {
            "name": "EPSG:{}".format(theinfo['epsg'])
        }
    }
geo_json_file = open(os.path.join('./', 'grid.json'), 'w+')
geo_json_file.write(json.dumps(geo_json_dict, indent=2))
geo_json_file.close()

This script will produce for all files with postfix ".json" in the directory a bbox polygon using cjio and save the complete geojson result in grid.json in place.

If you have a python script like this, simply put it inside your local data and call docker like this:

docker run --rm  -v <local path where your files are>:/data tudelft3d/cjio:latest python gridder.py

This will execute your script in the context of the python environment inside the docker image.

Example CityJSON datasets

There are a few example files on the CityJSON webpage.

Alternatively, any CityGML file can be automatically converted to CityJSON with the open-source project citygml-tools (based on citygml4j).

Acknowledgements

The glTF exporter is adapted from Kavisha's CityJSON2glTF.