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mapillary.interface

mapillary.interface#

This module implements the basic functionalities of the Mapillary Python SDK, a Python implementation of the Mapillary API v4. For more information, please check out https://www.mapillary.com/developer/api-documentation/

  • Copyright: (c) 2021 Facebook
  • License: MIT LICENSE

mapillary.interface.feature_from_key(key: str, fields: list = [])#

Gets a map feature for the given key argument

  • Parameters

    • key (int) – The map feature ID to which will be used to get the feature
* **fields** (*list*) – The fields to include. The field ‘geometry’ will always be includedso you do not need to specify it, or if you leave it off, it will still be returned.
Fields:
```1. first_seen_at - timestamp, timestamp of the least recent    detection contributing to this feature2. last_seen_at - timestamp, timestamp of the most recent    detection contributing to this feature3. object_value - string, what kind of map feature it is4. object_type - string, either a traffic_sign or point5. geometry - GeoJSON Point geometry6. images - list of IDs, which images this map feature was derivedfrom```
Refer to [https://www.mapillary.com/developer/api-documentation/#map-feature](https://www.mapillary.com/developer/api-documentation/#map-feature) for more details
  • Returns

    A GeoJSON string that represents the queried feature

  • Return type

    str

Usage:

>>> import mapillary as mly>>> mly.interface.set_access_token('MLY|XXX')>>> mly.interface.feature_from_key(...     key='VALID_MAP_FEATURE_KEY',...     fields=['object_value']... )

mapillary.interface.get_detections_with_image_id(image_id: int, fields: list = None)#

Extracting all the detections within an image using an image key

  • Parameters

    • image_id (int) – The image key as the argument
* **fields** (*list*) – The fields possible for the detection endpoint. Please see[https://www.mapillary.com/developer/api-documentation](https://www.mapillary.com/developer/api-documentation) for more information
  • Returns

    The GeoJSON in response

  • Return type

    dict

Usage:

>>> import mapillary as mly>>> mly.interface.set_access_token('CLIENT_TOKEN_HERE')>>> mly.interface.get_detections_with_image_id(image_id=1933525276802129)... {"data":[{"created_at":"2021-05-20T17:49:01+0000","geometry":... "GjUKBm1weS1vchIVEgIAABgDIg0JhiekKBoqAABKKQAPGgR0eXBlIgkKB3BvbHlnb24ogCB4AQ==","image"... :{"geometry":{"type":"Point","coordinates":[-97.743279722222,30.270651388889]},"id":... "1933525276802129"},"value":"regulatory--no-parking--g2","id":"1942105415944115"},... {"created_at":"2021-05-20T18:40:21+0000","geometry":... "GjYKBm1weS1vchIWEgIAABgDIg4J7DjqHxpWAADiAVUADxoEdHlwZSIJCgdwb2x5Z29uKIAgeAE=",... "image":{"geometry":{"type":"Point","coordinates":[-97.743279722222,30.270651388889]},... "id":"1933525276802129"},"value":"information--parking--g1","id":"1942144785940178"},... , ...}

mapillary.interface.get_detections_with_map_feature_id(map_feature_id: str, fields: list = None)#

Extracting all detections made for a map feature key

  • Parameters

    • map_feature_id (int) – A map feature key as the argument
* **fields** (*list*) – The fields possible for the detection endpoint. Please see[https://www.mapillary.com/developer/api-documentation](https://www.mapillary.com/developer/api-documentation) for more information
  • Returns

    The GeoJSON in response

  • Return type

    GeoJSON

Usage:

>>> import mapillary as mly>>> mly.interface.set_access_token('MLY|XXX')>>> mly.interface.get_detections_with_map_feature_id(map_feature_id='1933525276802129')...     File "/home/saif/MLH/mapillary-python-sdk/mapillary/controller/rules/verify.py",...         line 227, in valid_id...             raise InvalidOptionError(... mly.models.exceptions.InvalidOptionError: InvalidOptionError: Given id value,...     "Id: 1933525276802129, image: False" while possible id options, [Id is image_id...     AND image is True, key is map_feature_id ANDimage is False]

mapillary.interface.get_image_close_to(latitude=- 122.1504711, longitude=37.485073, **kwargs)#

Function that takes a longitude, latitude as argument and outputs the near images. This makes an API call with the token set in set_access_token and returns a JSON object.

  • Parameters

    • longitude (float or double) – The longitude
* **latitude** (*float** or **double*) – The latitude

* **kwargs.fields** (*list*) – A list of options, either as [‘all’], or a list of fields.See [https://www.mapillary.com/developer/api-documentation/](https://www.mapillary.com/developer/api-documentation/), under ‘Fields’ for more insight.

* **kwargs.zoom** (*int*) – The zoom level of the tiles to obtain, defaults to 14

* **kwargs.radius** (*float** or **int** or **double*) – The radius of the images obtained from a center center

* **kwargs.image_type** (*str*) – The tile image_type to be obtained, either as ‘flat’, ‘pano’(panoramic), or ‘both’. See [https://www.mapillary.com/developer/api-documentation/](https://www.mapillary.com/developer/api-documentation/) under‘image_type Tiles’ for more information

* **kwargs.min_captured_at** (*str*) – The min date. Format from ‘YYYY’, to ‘YYYY-MM-DDTHH:MM:SS’

* **kwargs.max_captured_at** (*str*) – The max date. Format from ‘YYYY’, to ‘YYYY-MM-DDTHH:MM:SS’

* **kwargs.org_id** (*int*) – The organization id, ID of the organization this image (or sets ofimages) belong to. It can be absent. Thus, default is -1 (None)
  • Returns

    GeoJSON

  • Return type

    dict

Usage:

>>> import mapillary as mly>>> mly.interface.set_access_token('CLIENT_TOKEN_HERE')>>> mly.interface.get_image_close_to(longitude=31, latitude=30)... {'type': 'FeatureCollection', 'features': [{'type': 'Feature','geometry': {'type': 'Point', 'coordinates': [30.9912246465683,29.99794091267283]}, 'properties': {'captured_at': 1621008070596,'compass_angle': 322.56726074219, 'id': 499412381300321, 'is_pano':False, 'sequence_id': '94afmyyhq85xd9bi8p44ve'}} ...

mapillary.interface.get_image_looking_at(looker: dict, at: dict, **filters: dict)#

Function that takes two sets of latitude and longitude, where the 2nd set is the “looking at” location from 1st set’s perspective argument and outputs the near images. This makes an API call with the token set in set_access_token and returns a JSON object.

  • Parameters

    • looker (dict) – The coordinate sets from where a certain point is being looked at

      Format:

      >>> {...     'lng': 'longitude',...     'lat': 'latitude'... }
* **at** (*dict*) – The coordinate sets to where a certain point is being looked at
Format:
```>>> {...     'lng': 'longitude',...     'lat': 'latitude'... }```


* **filters.min_captured_at** (*str*) – The minimum date to filter till

* **filters.max_captured_at** (*str*) – The maximum date to filter upto

* **filters.radius** (*float*) – The radius that the geometry points will lie in

* **filters.image_type** (*str*) – Either ‘pano’, ‘flat’ or ‘all’

* **filters.organization_id** (*str*) – The organization to retrieve the data for
  • Returns

    The GeoJSON response containing relevant features

  • Return type

    GeoJSON

Usage:

>>> import mapillary as mly>>> mly.interface.set_access_token('MLY|XXX')>>> data = mly.interface.get_image_looking_at(...        looker={...             'lng': 12.954940544167,...             'lat': 48.0537894275,...         },...         at={...             'lng': 12.955075073889,...             'lat': 48.053805939722,...         },...         radius = 5000,...     )>>> data... {'type': 'FeatureCollection', 'features': [{'type': 'Feature', 'geometry': {'type':... 'Point', 'coordinates': [12.954479455947876, 48.05091893670834]}, 'properties':... {'captured_at': 1612606959408, 'compass_angle': 21.201110839844, 'id': 1199705400459580,... 'is_pano': False, 'sequence_id': 'qrrqtke4a6vtygyc7w8rzc'}}, ... }

mapillary.interface.image_from_key(key: str, fields: list = None)#

Gets an image for the given key argument

  • Parameters

    • key (int) – The image unique key which will be used for image retrieval
* **fields** (*list*) – The fields to include. The field ‘geometry’ will always be includedso you do not need to specify it, or if you leave it off, it will still be returned.
Fields,

    1. altitude - float, original altitude from Exif

    2. atomic_scale - float, scale of the SfM reconstruction around the image

    3. camera_parameters - array of float, intrinsic camera parameters

    4. camera_type - enum, type of camera projection (perspective, fisheye, or
    spherical)

    5. captured_at - timestamp, capture time

    6. compass_angle - float, original compass angle of the image

    7. computed_altitude - float, altitude after running image processing

    8. computed_compass_angle - float, compass angle after running image processing

    9. computed_geometry - GeoJSON Point, location after running image processing

    10. computed_rotation - enum, corrected orientation of the image

    11. exif_orientation - enum, orientation of the camera as given by the exif tag(see: [https://sylvana.net/jpegcrop/exif_orientation.html](https://sylvana.net/jpegcrop/exif_orientation.html))

    12. geometry - GeoJSON Point geometry

    13. height - int, height of the original image uploaded

    14. thumb_256_url - string, URL to the 256px wide thumbnail

    15. thumb_1024_url - string, URL to the 1024px wide thumbnail

    16. thumb_2048_url - string, URL to the 2048px wide thumbnail

    17. merge_cc - int, id of the connected component of images that were alignedtogether

    18. mesh - { id: string, url: string } - URL to the mesh

    19. quality_score - float, how good the image is (experimental)

    20. sequence - string, ID of the sequence

    21. sfm_cluster - { id: string, url: string } - URL to the point cloud

    22. width - int, width of the original image uploaded
Refer to [https://www.mapillary.com/developer/api-documentation/#image](https://www.mapillary.com/developer/api-documentation/#image) for more details
  • Returns

    A GeoJSON string that represents the queried image

  • Return type

    str

Usage:

>>> import mapillary as mly>>> mly.interface.set_access_token('MLY|XXX')>>> mly.interface.image_from_key(...     key='VALID_IMAGE_KEY',...     fields=['captured_at', 'sfm_cluster', 'width']... )

mapillary.interface.image_thumbnail(image_id: str, resolution: int = 1024)#

Gets the thumbnails of images from the API

  • Parameters

    • image_id – Image key as the argument
* **resolution** – Option for the thumbnail size, with available resolutions:256, 1024, and 2048
  • Returns

    A URL for the thumbnail

  • Return type

    str

Usage:

>>> import mapillary as mly>>> mly.interface.set_access_token('MLY|XXX')>>> mly.interface.image_thumbnail(...     image_id='IMAGE_ID_HERE', resolution=1024... )

mapillary.interface.images_in_bbox(bbox: dict, **filters)#

Gets a complete list of images with custom filter within a BBox

  • Parameters

    • bbox (dict) – Bounding box coordinates

      Format:

      >>> {...     'west': 'BOUNDARY_FROM_WEST',...     'south': 'BOUNDARY_FROM_SOUTH',...     'east': 'BOUNDARY_FROM_EAST',...     'north': 'BOUNDARY_FROM_NORTH'... }
* **filters** (*dict*) – Different filters that may be applied to the output
Example filters:
```- max_captured_at- min_captured_at- image_type: pano, flat, or all- compass_angle- sequence_id- organization_id```
  • Returns

    Output is a GeoJSON string that represents all the within a bbox after passing given filters

  • Return type

    str

Usage:

>>> import mapillary as mly>>> mly.interface.set_access_token('MLY|XXX')>>> mly.interface.images_in_bbox(...     bbox={...         'west': 'BOUNDARY_FROM_WEST',...         'south': 'BOUNDARY_FROM_SOUTH',...         'east': 'BOUNDARY_FROM_EAST',...         'north': 'BOUNDARY_FROM_NORTH'...     },...     max_captured_at='YYYY-MM-DD HH:MM:SS',...     min_captured_at='YYYY-MM-DD HH:MM:SS',...     image_type='pano',...     compass_angle=(0, 360),...     sequence_id='SEQUENCE_ID',...     organization_id='ORG_ID'... )

mapillary.interface.images_in_geojson(geojson: dict, **filters: dict)#

Extracts all images within a shape

  • Parameters

    • geojson (dict) – A geojson as the shape acting as the query extent
* **filters** (*dict** (**kwargs**)*) – Different filters that may be applied to the output, defaults to {}

* **filters.max_captured_at** (*str*) – The max date. Format from ‘YYYY’, to ‘YYYY-MM-DDTHH:MM:SS’

* **filters.min_captured_at** (*str*) – The min date. Format from ‘YYYY’, to ‘YYYY-MM-DDTHH:MM:SS’

* **filters.image_type** (*str*) – The tile image_type to be obtained, either as ‘flat’, ‘pano’(panoramic), or ‘all’. See [https://www.mapillary.com/developer/api-documentation/](https://www.mapillary.com/developer/api-documentation/) under‘image_type Tiles’ for more information

* **filters.compass_angle** (*int*) – The compass angle of the image

* **filters.sequence_id** (*str*) – ID of the sequence this image belongs to

* **filters.organization_id** (*str*) – ID of the organization this image belongs to. It can be absent
  • Returns

    A GeoJSON object

  • Return type

    mapillary.models.geojson.GeoJSON

Usage:

>>> import mapillary as mly>>> from mapillary.models.geojson import GeoJSON>>> import json>>> mly.interface.set_access_token('MLY|YYY')>>> data = mly.interface.images_in_geojson(json.load(open('my_geojson.geojson', mode='r')))>>> open('output_geojson.geojson', mode='w').write(data.encode())

mapillary.interface.images_in_shape(shape, **filters: dict)#

Extracts all images within a shape or polygon.

Format:

>>> {...    "type": "FeatureCollection",...     "features": [...        {...             "type": "Feature",...             "properties": {},...             "geometry": {...                 "type": "Polygon",...                 "coordinates": [...                     [...                         [...                             7.2564697265625,...                             43.69716905314008...                         ],...                         ......                     ]...                 ]...             }...         }...     ]... }
  • Parameters

    • shape (dict) – A shape that describes features, formatted as a geojson
* **filters** (*dict** (**kwargs**)*) – Different filters that may be applied to the output, defaults to {}

* **filters.max_captured_at** (*str*) – The max date. Format from ‘YYYY’, to ‘YYYY-MM-DDTHH:MM:SS’

* **filters.min_captured_at** (*str*) – The min date. Format from ‘YYYY’, to ‘YYYY-MM-DDTHH:MM:SS’

* **filters.image_type** (*str*) – The tile image_type to be obtained, either as ‘flat’, ‘pano’(panoramic), or ‘all’. See [https://www.mapillary.com/developer/api-documentation/](https://www.mapillary.com/developer/api-documentation/) under‘image_type Tiles’ for more information

* **filters.compass_angle** (*int*) – The compass angle of the image

* **filters.sequence_id** (*str*) – ID of the sequence this image belongs to

* **filters.organization_id** (*str*) – ID of the organization this image belongs to. It can be absent
  • Returns

    A GeoJSON object

  • Return type

    mapillary.models.geojson.GeoJSON

Usage:

>>> import mapillary as mly>>> import json>>> mly.interface.set_access_token('MLY|XXX')>>> data = mly.interface.images_in_shape(json.load(open('polygon.geojson', mode='r')))>>> open('output_geojson.geojson', mode='w').write(data.encode())

mapillary.interface.map_feature_points_in_bbox(bbox: dict, filter_values: list = None, **filters: dict)#

Extracts map feature points within a bounding box (bbox)

  • Parameters

    • bbox (dict) – bbox coordinates as the argument

      Example:

      >>> _ = {...     'west': 'BOUNDARY_FROM_WEST',...     'south': 'BOUNDARY_FROM_SOUTH',...     'east': 'BOUNDARY_FROM_EAST',...     'north': 'BOUNDARY_FROM_NORTH'... }
* **filter_values** (*list*) – a list of filter values supported by the API
Example:
```>>> _ = ['object--support--utility-pole', 'object--street-light']```


* **filters** (*dict*) – kwarg filters to be applied on the resulted GeoJSON
Chronological filters,

    * *existed_at*: checks if a feature existed after a certain date depending on the time
    it was first seen at.

    * *existed_before*: filters out the features that existed after a given date
  • Returns

    GeoJSON Object

  • Return type

    dict

Usage:

>>> import mapillary as mly>>> mly.interface.set_access_token('MLY|XXX')>>> mly.interface.map_feature_points_in_bbox(...     bbox={...         'west': 'BOUNDARY_FROM_WEST',...         'south': 'BOUNDARY_FROM_SOUTH',...         'east': 'BOUNDARY_FROM_EAST',...         'north': 'BOUNDARY_FROM_NORTH'...     },...     filter_values=['object--support--utility-pole', 'object--street-light'],...     existed_at='YYYY-MM-DD HH:MM:SS',...     existed_before='YYYY-MM-DD HH:MM:SS'... )

mapillary.interface.map_features_in_geojson(geojson: dict, **filters: dict)#

Extracts all map features within a geojson’s boundaries

  • Parameters

    • geojson (dict) – A geojson as the shape acting as the query extent
* **filters** (*dict** (**kwargs**)*) – Different filters that may be applied to the output, defaults to {}

* **filters.zoom** (*int*) – The zoom level of the tiles to obtain, defaults to 14

* **filters.max_captured_at** (*str*) – The max date. Format from ‘YYYY’, to ‘YYYY-MM-DDTHH:MM:SS’

* **filters.min_captured_at** (*str*) – The min date. Format from ‘YYYY’, to ‘YYYY-MM-DDTHH:MM:SS’

* **filters.image_type** (*str*) – The tile image_type to be obtained, either as ‘flat’, ‘pano’(panoramic), or ‘all’. See [https://www.mapillary.com/developer/api-documentation/](https://www.mapillary.com/developer/api-documentation/) under‘image_type Tiles’ for more information

* **filters.compass_angle** (*int*) – The compass angle of the image

* **filters.sequence_id** (*str*) – ID of the sequence this image belongs to

* **filters.organization_id** (*str*) – ID of the organization this image belongs to. It can be absent
  • Returns

    A GeoJSON object

  • Return type

    mapillary.models.geojson.GeoJSON

Usage:

>>> import mapillary as mly>>> import json>>> mly.interface.set_access_token('MLY|YYY')>>> data = mly.interface.map_features_in_geojson(...     json.load(...         open('my_geojson.geojson', mode='r')...     )... )>>> open('output_geojson.geojson', mode='w').write(data.encode())

mapillary.interface.map_features_in_shape(shape: dict, **filters: dict)#

Extracts all map features within a shape/polygon

Format:

>>> _ = {...     "type": "FeatureCollection",...     "features": [...         {...             "type": "Feature",...             "properties": {},...             "geometry": {...                 "type": "Polygon",...                 "coordinates": [...                     [...                         [...                             7.2564697265625,...                             43.69716905314008...                         ],...                         ......                     ]...                 ]...             }...         }...     ]... }
  • Parameters

    • shape (dict) – A shape that describes features, formatted as a geojson
* **filters** (*dict** (**kwargs**)*) – Different filters that may be applied to the output, defaults to {}

* **filters.zoom** (*int*) – The zoom level of the tiles to obtain, defaults to 14

* **filters.max_captured_at** (*str*) – The max date. Format from ‘YYYY’, to ‘YYYY-MM-DDTHH:MM:SS’

* **filters.min_captured_at** (*str*) – The min date. Format from ‘YYYY’, to ‘YYYY-MM-DDTHH:MM:SS’

* **filters.image_type** (*str*) – The tile image_type to be obtained, either as ‘flat’, ‘pano’(panoramic), or ‘all’. See [https://www.mapillary.com/developer/api-documentation/](https://www.mapillary.com/developer/api-documentation/) under‘image_type Tiles’ for more information

* **filters.compass_angle** (*int*) – The compass angle of the image

* **filters.sequence_id** (*str*) – ID of the sequence this image belongs to

* **filters.organization_id** (*str*) – ID of the organization this image belongs to. It can be absent
  • Returns

    A GeoJSON object

  • Return type

    mapillary.models.geojson.GeoJSON

Usage:

>>> import mapillary as mly>>> import json>>> mly.interface.set_access_token('MLY|XXX')>>> data = mly.interface.map_features_in_shape(json.load(open('polygon.geojson', mode='r')))>>> open('output_geojson.geojson', mode='w').write(data.encode())

mapillary.interface.save_locally(geojson_data: str, file_path: str = '/home/saif/MLH/mapillary-python-sdk/src/mapillary', file_name: str = None, extension: str = 'geojson')#

This function saves the geojson data locally as a file with the given file name, path, and format.

  • Parameters

    • geojson_data (str) – The GeoJSON data to be stored
* **file_path** (*str*) – The path to save the data to. Defaults to the current directory path

* **file_name** (*str*) – The name of the file to be saved. Defaults to ‘geojson’

* **extension** (*str*) – The format to save the data as. Defaults to ‘geojson’

Note:

Allowed file format values at the moment are,    - geojson    - CSV

TODO: More file format will be supported further in developemtn TODO: Suggestions and help needed at mapillary/mapillary-python-sdk!

  • Returns

    None

  • Return type

    None

Usage:

>>> import mapillary as mly>>> mly.interface.set_access_token('MLY|XXX')>>> mly.interface.save_locally(...     geojson_data=geojson_data,...     file_path=os.path.dirname(os.path.realpath(__file__)),...     file_name='test_geojson',...     extension='geojson'... )>>> mly.interface.save_locally(...     geojson_data=geojson_data,...     file_path=os.path.dirname(os.path.realpath(__file__)),...     file_name='local_geometries',...     extension='csv'... )

mapillary.interface.sequences_in_bbox(bbox: dict, **filters)#

Gets a complete list of all sequences of images that satisfy given filters within a BBox.

  • Parameters

    • bbox (dict) – Bounding box coordinates

      Example:

      >>> _ = {...     'west': 'BOUNDARY_FROM_WEST',...     'south': 'BOUNDARY_FROM_SOUTH',...     'east': 'BOUNDARY_FROM_EAST',...     'north': 'BOUNDARY_FROM_NORTH'... }
* **filters** (*dict*) – Different filters that may be applied to the output
Example filters:
```- max_captured_at- min_captured_at- image_type: pano, flat, or all- org_id```
  • Returns

    Output is a GeoJSON string that contains all the filtered sequences within a bbox. Sequences would NOT be cut at BBox boundary, would select all sequences which are partially or entirely in BBox

  • Return type

    str

Usage:

>>> import mapillary as mly>>> mly.interface.set_access_token('MLY|XXX')>>> mly.interface.sequences_in_bbox(...     bbox={...         'west': 'BOUNDARY_FROM_WEST',...         'south': 'BOUNDARY_FROM_SOUTH',...         'east': 'BOUNDARY_FROM_EAST',...         'north': 'BOUNDARY_FROM_NORTH'...     },...     max_captured_at='YYYY-MM-DD HH:MM:SS',...     min_captured_at='YYYY-MM-DD HH:MM:SS',...     image_type='pano',...     org_id='ORG_ID'... )

mapillary.interface.set_access_token(token: str)#

A function allowing the user to set an access token for the session, which they can create at https://www.mapillary.com/dashboard/developers. Takes token as an argument and sets a global variable used by other functions making API requests. For more information what the details of authentication, please check out the blog post at Mapillary. https://blog.mapillary.com/update/2021/06/23/getting-started-with-the-new-mapillary-api-v4.html

  • Parameters

    token (str) – The token itself that would be set and accessed globally. Must be obtained

  • Returns

    None

  • Return type

    None

Usage:

>>> import mapillary as mly>>> mly.interface.set_access_token('CLIENT_TOKEN_HERE')

mapillary.interface.traffic_signs_in_bbox(bbox: dict, filter_values: list = None, **filters: dict)#

Extracts traffic signs within a bounding box (bbox)

  • Parameters

    • bbox (dict) – bbox coordinates as the argument

      Example:

      >>> {...     'west': 'BOUNDARY_FROM_WEST',...     'south': 'BOUNDARY_FROM_SOUTH',...     'east': 'BOUNDARY_FROM_EAST',...     'north': 'BOUNDARY_FROM_NORTH'... }
* **filter_values** (*list*) – a list of filter values supported by the API,
Example:
```>>> ['regulatory--advisory-maximum-speed-limit--g1', 'regulatory--atvs-permitted--g1']```


* **filters** (*dict*) – kwarg filters to be applied on the resulted GeoJSON
Chronological filters,

    * *existed_at*: checks if a feature existed after a certain date depending on the time
    it was first seen at.

    * *existed_before*: filters out the features that existed after a given date
  • Returns

    GeoJSON Object

  • Return type

    dict

Usage:

>>> import mapillary as mly>>> mly.interface.set_access_token('MLY|XXX')>>> mly.interface.traffic_signs_in_bbox(...    bbox={...         'west': 'BOUNDARY_FROM_WEST',...         'south': 'BOUNDARY_FROM_SOUTH',...         'east': 'BOUNDARY_FROM_EAST',...         'north': 'BOUNDARY_FROM_NORTH'...    },...    filter_values=[...        'regulatory--advisory-maximum-speed-limit--g1',...        'regulatory--atvs-permitted--g1'...    ],...    existed_at='YYYY-MM-DD HH:MM:SS',...    existed_before='YYYY-MM-DD HH:MM:SS'... )

Module contents#

mapillary.init#

This module imports the necessary parts of the SDK

  • Copyright: (c) 2021 Facebook
  • License: MIT LICENSE