PointcloudEpisodeApi¶
- class PointcloudEpisodeApi[source]¶
Bases:
supervisely.api.pointcloud.pointcloud_api.PointcloudApi
API for working with
PointcloudEpisodes
.PointcloudEpisodeApi
object is immutable. Inherits fromPointcloudApi
.- Parameters
- api : Api
API connection to the server.
- Usage example
import os from dotenv import load_dotenv import supervisely as sly # Load secrets and create API object from .env file (recommended) # Learn more here: https://developer.supervisely.com/getting-started/basics-of-authentication if sly.is_development(): load_dotenv(os.path.expanduser("~/supervisely.env")) api = sly.Api.from_env() # Pass values into the API constructor (optional, not recommended) # api = sly.Api(server_address="https://app.supervise.ly", token="4r47N...xaTatb") pcd_epsodes_id = 19373295 pcd_epsodes_info = api.pointcloud_episode.get_info_by_id(pcd_epsodes_id) # api usage example
Methods
Attach images to point cloud.
Check if point clouds with given hashes are exist.
Download point cloud with given id on the given path.
Downloads Point cloud with given ID to local path.
Download Point clouds with given IDs and saves them to given local paths asynchronously.
Download a related context image from Supervisely to local directory by image id.
Downloads a related context image from Supervisely to local directory by image id.
Downloads a related context image from Supervisely to local directory by image id.
Checks if an entity with the given parent_id and name exists
Get a dictionary with frame_id and name of pointcloud by dataset id.
Generates a free name for an entity with the given parent_id and name.
Returns list of free names for given dataset.
Get point cloud information by ID in PointcloudInfo<PointcloudInfo> format.
Get information about an entity by its name from the Supervisely server.
Get list of information about all point cloud for a given dataset ID.
Get list of all or limited quantity entities from the Supervisely server.
This generator function retrieves a list of all or a limited quantity of entities from the Supervisely server, yielding batches of entities as they are retrieved
Get information about related context images.
Get max frame index for episode by dataset id.
Get list of all
PointcloudInfo
field names.Get string name of
PointcloudInfo
NamedTuple.Send message to the Annotation Tool and return info if tracking was stopped
Raises error if pointclouds with given names already exist in dataset.
Remove an entity with the specified ID from the Supervisely server.
Remove entities in batches from the Supervisely server.
Uploads all pointclouds with supported extensions from given directory to Supervisely.
Uploads all pointclouds with supported extensions from given directories to Supervisely.
Upload Pointcloud from given hash to Dataset.
Upload point clouds from given hashes to Dataset.
Upload point cloud with given path to Dataset.
Upload point clouds with given paths to Dataset.
Upload an image to the Supervisely.
Upload a batch of related images to the Supervisely.
Attributes
MAX_WAIT_ATTEMPTS
Maximum number of attempts that will be made to wait for a certain condition to be met.
WAIT_ATTEMPT_TIMEOUT_SEC
Number of seconds for intervals between attempts.
- InfoType¶
alias of
supervisely.api.module_api.PointCloudInfo
Attach images to point cloud.
- Parameters
- images_json : List[Dict]
List of dictionaries with dataset id, image name, hash and meta.
- camera_names : List[Dict]
List of camera informations.
- Returns
Response object
- Return type
Dict
- Usage example
import supervisely as sly os.environ['SERVER_ADDRESS'] = 'https://app.supervisely.com' os.environ['API_TOKEN'] = 'Your Supervisely API Token' api = sly.Api.from_env() img_paths = ["src/input/img/000001.png", "src/input/img/000002.png"] cam_paths = ["src/input/cam_info/000001.json", "src/input/cam_info/000002.json"] img_hashes = api.pointcloud.upload_related_images(img_paths) img_infos = [] for i, cam_info_file in enumerate(cam_paths): # reading cam_info with open(cam_info_file, "r") as f: cam_info = json.load(f) img_info = { "entityId": pcd_infos[i].id, "name": f"img_{i}.png", "hash": img_hashes[i], "meta": cam_info, } img_infos.append(img_info) api.pointcloud.add_related_images(img_infos)
- check_existing_hashes(hashes)¶
Check if point clouds with given hashes are exist.
- Parameters
- paths : List[str]
Point clouds hashes to check.
- Returns
List of point clouds hashes that are exist.
- Return type
List[str]
- Usage example
import supervisely as sly os.environ['SERVER_ADDRESS'] = 'https://app.supervisely.com' os.environ['API_TOKEN'] = 'Your Supervisely API Token' api = sly.Api.from_env() pointcloud_id = 19618685 pcd_info = api.pointcloud.get_info_by_id(pointcloud_id) hash = api.pointcloud.check_existing_hashes([pcd_info.hash]) print(hash) # Output: # ['5w69Vv1i6JrqhU0Lw1UJAJFGPhgkIhs7O3f4QSwRfmE=']
- download_path(id, path)¶
Download point cloud with given id on the given path.
- Parameters
- Returns
None
- Return type
NoneType
- Usage example
import supervisely as sly os.environ['SERVER_ADDRESS'] = 'https://app.supervisely.com' os.environ['API_TOKEN'] = 'Your Supervisely API Token' api = sly.Api.from_env() storage_dir = sly.app.get_data_dir() pcd_id = 19373403 pcd_info = api.pointcloud.get_info_by_id(pcd_id) save_path = os.path.join(storage_dir, pcd_info.name) api.pointcloud.download_path(pcd_id, save_path) print(os.listdir(storage_dir)) # Output: ['000063.pcd']
-
async download_path_async(id, path, semaphore=
None
, range_start=None
, range_end=None
, headers=None
, chunk_size=1048576
, check_hash=True
, progress_cb=None
, progress_cb_type='number'
)¶ Downloads Point cloud with given ID to local path.
- Parameters
- id : int
Point cloud ID in Supervisely.
- path : str
Local save path for Point cloud.
- semaphore :
asyncio.Semaphore
, optional Semaphore for limiting the number of simultaneous downloads.
- range_start : int, optional
Start byte of range for partial download.
- range_end : int, optional
End byte of range for partial download.
- headers : dict, optional
Headers for request.
- chunk_size : int, optional
Size of chunk for partial download. Default is 1MB.
- check_hash : bool, optional
If True, checks hash of downloaded file. Check is not supported for partial downloads. When range is set, hash check is disabled.
- progress_cb : Optional[Union[tqdm, Callable]]
Function for tracking download progress.
- progress_cb_type : Literal["number", "size"], optional
Type of progress callback. Can be “number” or “size”. Default is “number”.
- Returns
None
- Return type
NoneType
- Usage example
import supervisely as sly import asyncio os.environ['SERVER_ADDRESS'] = 'https://app.supervisely.com' os.environ['API_TOKEN'] = 'Your Supervisely API Token' api = sly.Api.from_env() pcd_info = api.pointcloud.get_info_by_id(19373403) save_path = os.path.join("/path/to/save/", pcd_info.name) semaphore = asyncio.Semaphore(100) loop = asyncio.new_event_loop() asyncio.set_event_loop(loop) loop.run_until_complete( api.pointcloud.download_path_async(pcd_info.id, save_path, semaphore) )
-
async download_paths_async(ids, paths, semaphore=
None
, headers=None
, chunk_size=1048576
, check_hash=True
, progress_cb=None
, progress_cb_type='number'
)¶ Download Point clouds with given IDs and saves them to given local paths asynchronously.
- Parameters
- ids :
List[int]
List of Point cloud IDs in Supervisely.
- paths :
List[str]
Local save paths for Point clouds.
- semaphore :
asyncio.Semaphore
, optional Semaphore for limiting the number of simultaneous downloads.
- headers : dict, optional
Headers for request.
- show_progress : bool, optional
If True, shows progress bar.
- chunk_size : int, optional
Size of chunk for partial download. Default is 1MB.
- check_hash : bool, optional
If True, checks hash of downloaded file.
- progress_cb : Optional[Union[tqdm, Callable]]
Function for tracking download progress.
- progress_cb_type : Literal["number", "size"], optional
Type of progress callback. Can be “number” or “size”. Default is “number”.
- ids :
- Raises
ValueError
if len(ids) != len(paths)- Returns
None
- Return type
NoneType
- Usage example
import supervisely as sly import asyncio os.environ['SERVER_ADDRESS'] = 'https://app.supervisely.com' os.environ['API_TOKEN'] = 'Your Supervisely API Token' api = sly.Api.from_env() ids = [19373403, 19373404] paths = ["/path/to/save/000063.pcd", "/path/to/save/000064.pcd"] loop = asyncio.new_event_loop() asyncio.set_event_loop(loop) loop.run_until_complete(api.pointcloud.download_paths_async(ids, paths))
Download a related context image from Supervisely to local directory by image id.
- Parameters
- Returns
List of dictionaries with informations about related images
- Return type
List
- Usage example
import supervisely as sly os.environ['SERVER_ADDRESS'] = 'https://app.supervisely.com' os.environ['API_TOKEN'] = 'Your Supervisely API Token' api = sly.Api.from_env() save_path = "src/output/img_0.png" img_info = api.pointcloud.get_list_related_images(pcd_info.id)[0] api.pointcloud.download_related_image(img_info["id"], save_path) print(f"Context image has been successfully downloaded to '{save_path}'")
# Output: # Context image has been successfully downloaded to ‘src/output/img_0.png’
Downloads a related context image from Supervisely to local directory by image id.
- Parameters
- id : int
Point cloud ID in Supervisely.
- path : str
Local save path for Point cloud.
- semaphore :
asyncio.Semaphore
, optional Semaphore for limiting the number of simultaneous downloads.
- headers : dict, optional
Headers for request.
- chunk_size : int, optional
Size of chunk for partial download. Default is 1MB.
- check_hash : bool, optional
If True, checks hash of downloaded file.
- progress_cb : Optional[Union[tqdm, Callable]]
Function for tracking download progress.
- progress_cb_type : Literal["number", "size"], optional
Type of progress callback. Can be “number” or “size”. Default is “number”.
- Returns
None
- Return type
NoneType
- Usage example
import supervisely as sly import asyncio os.environ['SERVER_ADDRESS'] = 'https://app.supervisely.com' os.environ['API_TOKEN'] = 'Your Supervisely API Token' api = sly.Api.from_env() img_info = api.pointcloud.get_list_related_images(19373403)[0] save_path = os.path.join("/path/to/save/", img_info.name) semaphore = asyncio.Semaphore(100) loop = asyncio.new_event_loop() asyncio.set_event_loop(loop) loop.run_until_complete( api.pointcloud.download_related_image_async(19373403, save_path, semaphore) )
Downloads a related context image from Supervisely to local directory by image id.
- Parameters
- ids : int
Related context imgage IDs in Supervisely.
- paths : str
Local save paths for Point clouds.
- semaphore :
asyncio.Semaphore
, optional Semaphore for limiting the number of simultaneous downloads.
- headers : dict, optional
Headers for request.
- check_hash : bool, optional
If True, checks hash of downloaded file.
- progress_cb : Optional[Union[tqdm, Callable]]
Function for tracking download progress.
- progress_cb_type : Literal["number", "size"], optional
Type of progress callback. Can be “number” or “size”. Default is “number”.
- Returns
None
- Return type
NoneType
- Usage example
import supervisely as sly import asyncio os.environ['SERVER_ADDRESS'] = 'https://app.supervisely.com' os.environ['API_TOKEN'] = 'Your Supervisely API Token' api = sly.Api.from_env() img_infos = api.pointcloud.get_list_related_images(19373403) ids = [img_info.id for img_info in img_infos] save_paths = [os.path.join("/path/to/save/", img_info.name) for img_info in img_infos] semaphore = asyncio.Semaphore(100) loop = asyncio.new_event_loop() asyncio.set_event_loop(loop) loop.run_until_complete( api.pointcloud.download_related_images_async(ids, save_paths, semaphore) )
- exists(parent_id, name)¶
Checks if an entity with the given parent_id and name exists
- Parameters
- Returns
Returns True if entity exists, and False if not
- Return type
- Usage example
import supervisely as sly # You can connect to API directly address = 'https://app.supervise.ly/' token = 'Your Supervisely API Token' api = sly.Api(address, token) # Or you can use API from environment os.environ['SERVER_ADDRESS'] = 'https://app.supervisely.com' os.environ['API_TOKEN'] = 'Your Supervisely API Token' api = sly.Api.from_env() name = "IMG_0315.jpeg" dataset_id = 55832 exists = api.image.exists(dataset_id, name) print(exists) # True
- get_frame_name_map(dataset_id)[source]¶
Get a dictionary with frame_id and name of pointcloud by dataset id.
- Parameters
- Returns
Dict with frame_id and name of pointcloud.
- Return type
Dict
- Usage example
import supervisely as sly os.environ['SERVER_ADDRESS'] = 'https://app.supervisely.com' os.environ['API_TOKEN'] = 'Your Supervisely API Token' api = sly.Api.from_env() dataset_id = 62664 frame_to_name_map = api.pointcloud_episode.get_frame_name_map(dataset_id) print(frame_to_name_map) # Output: # {0: '001', 1: '002'}
- get_free_name(parent_id, name)¶
Generates a free name for an entity with the given parent_id and name. Adds an increasing suffix to original name until a unique name is found.
- Parameters
- Returns
Returns free name.
- Return type
- Usage example
import supervisely as sly # You can connect to API directly address = 'https://app.supervise.ly/' token = 'Your Supervisely API Token' api = sly.Api(address, token) # Or you can use API from environment os.environ['SERVER_ADDRESS'] = 'https://app.supervisely.com' os.environ['API_TOKEN'] = 'Your Supervisely API Token' api = sly.Api.from_env() name = "IMG_0315.jpeg" dataset_id = 55832 free_name = api.image.get_free_name(dataset_id, name) print(free_name) # IMG_0315_001.jpeg
- get_free_names(dataset_id, names)¶
Returns list of free names for given dataset.
- get_info_by_id(id)¶
Get point cloud information by ID in PointcloudInfo<PointcloudInfo> format.
- Parameters
- Returns
Information about point cloud. See
info_sequence
- Return type
PointcloudInfo
- Usage example
import supervisely as sly os.environ['SERVER_ADDRESS'] = 'https://app.supervisely.com' os.environ['API_TOKEN'] = 'Your Supervisely API Token' api = sly.Api.from_env() pcd_id = 19373403 pcd_info = api.pointcloud.get_info_by_id(pcd_id) print(pcd_info) # Output: # PointcloudInfo( # id=19373403, # frame=None, # description='', # name='000063.pcd', # team_id=435, # workspace_id=687, # project_id=17231, # dataset_id=55875, # link=None, # hash='7EcJCyhq15V4NnZ8oiPrKQckmXXypO4saqFN7kgH08Y=', # path_original='/h5unms4-public/point_clouds/Z/h/bc/roHZP5nP2.pcd', # cloud_mime='image/pcd', # figures_count=4, # objects_count=4, # tags=[], # meta={}, # created_at='2023-02-07T19:36:44.897Z', # updated_at='2023-02-07T19:36:44.897Z' # )
-
get_info_by_name(parent_id, name, fields=
[]
)¶ Get information about an entity by its name from the Supervisely server.
- Parameters
- Usage example
import supervisely as sly # You can connect to API directly address = 'https://app.supervise.ly/' token = 'Your Supervisely API Token' api = sly.Api(address, token) # Or you can use API from environment os.environ['SERVER_ADDRESS'] = 'https://app.supervisely.com' os.environ['API_TOKEN'] = 'Your Supervisely API Token' api = sly.Api.from_env() dataset_id = 55832 name = "IMG_0315.jpeg" info = api.image.get_info_by_name(dataset_id, name) print(info) # Output: ImageInfo(id=19369643, name='IMG_0315.jpeg', ...)
-
get_list(dataset_id, filters=
None
)¶ Get list of information about all point cloud for a given dataset ID.
- Parameters
- dataset_id : int
Dataset
ID in Supervisely.- filters : List[Dict[str, str]], optional
List of parameters to sort output Pointclouds. See: https://dev.supervise.ly/api-docs/#tag/Point-Clouds/paths/~1point-clouds.list/get
- Returns
List of the point clouds objects from the dataset with given id.
- Return type
List[PointcloudInfo]
- Usage example
import supervisely as sly os.environ['SERVER_ADDRESS'] = 'https://app.supervisely.com' os.environ['API_TOKEN'] = 'Your Supervisely API Token' api = sly.Api.from_env() dataset_id = 62664 pcd_infos = api.pointcloud_episode.get_list(dataset_id) print(pcd_infos) # Output: [PointcloudInfo(...), PointcloudInfo(...)] id_list = [19618654, 19618657, 19618660] filtered_pointcloud_infos = api.pointcloud.get_list(dataset_id, filters=[{'field': 'id', 'operator': 'in', 'value': id_list}]) print(filtered_pointcloud_infos) # Output: # [PointcloudInfo(id=19618654, ...), PointcloudInfo(id=19618657, ...), PointcloudInfo(id=19618660, ...)]
-
get_list_all_pages(method, data, progress_cb=
None
, convert_json_info_cb=None
, limit=None
, return_first_response=False
)¶ Get list of all or limited quantity entities from the Supervisely server.
- Parameters
- method : str
Request method name
- data : dict
Dictionary with request body info
- progress_cb : Progress, optional
Function for tracking download progress.
- convert_json_info_cb : Callable, optional
Function for convert json info
- limit : int, optional
Number of entity to retrieve
- return_first_response : bool, optional
Specify if return first response
-
get_list_all_pages_generator(method, data, progress_cb=
None
, convert_json_info_cb=None
, limit=None
, return_first_response=False
)¶ This generator function retrieves a list of all or a limited quantity of entities from the Supervisely server, yielding batches of entities as they are retrieved
- Parameters
- method : str
Request method name
- data : dict
Dictionary with request body info
- progress_cb : Progress, optional
Function for tracking download progress.
- convert_json_info_cb : Callable, optional
Function for convert json info
- limit : int, optional
Number of entity to retrieve
- return_first_response : bool, optional
Specify if return first response
Get information about related context images.
- Parameters
- id : int
Point cloud ID in Supervisely.
- Returns
List of dictionaries with informations about related images
- Return type
List
- Usage example
import supervisely as sly os.environ['SERVER_ADDRESS'] = 'https://app.supervisely.com' os.environ['API_TOKEN'] = 'Your Supervisely API Token' api = sly.Api.from_env() pcd_id = 19373403 img_infos = api.pointcloud.get_list_related_images(pcd_id) img_info = img_infos[0] print(img_info) # Output: # { # 'pathOriginal': '/h5un6qgms4-public/images/original/S/j/hJ/PwMg.png', # 'id': 473302, # 'entityId': 19373403, # 'createdAt': '2023-01-09T08:50:33.225Z', # 'updatedAt': '2023-01-09T08:50:33.225Z', # 'meta': { # 'deviceId': 'cam_2'}, # 'fileMeta': {'mime': 'image/png', # 'size': 893783, # 'width': 1224, # 'height': 370 # }, # 'hash': 'vxA+emfDNUkFP9P6oitMB5Q0rMlnskmV2jvcf47OjGU=', # 'link': None, # 'preview': '/previews/q/ext:jpeg/resize:fill:50:0:0/q:50/plain/h5ad-public/images/original/S/j/hJ/PwMg.png', # 'fullStorageUrl': 'https://dev.supervise.ly/hs4-public/images/original/S/j/hJ/PwMg.png', # 'name': 'img00' # }
- get_max_frame_idx(dataset_id)[source]¶
Get max frame index for episode by dataset id. This method is useful for uploading pointclouds to the episode in parts.
- Parameters
- Returns
Max frame index.
- Return type
- Usage example
import supervisely as sly os.environ['SERVER_ADDRESS'] = 'https://app.supervisely.com' os.environ['API_TOKEN'] = 'Your Supervisely API Token' api = sly.Api.from_env() dataset_id = 62664 max_frame = api.pointcloud_episode.get_max_frame(dataset_id) print(max_frame) # Output: # 1
- static info_sequence()¶
Get list of all
PointcloudInfo
field names.- Returns
List of
PointcloudInfo
field names.`- Return type
- static info_tuple_name()¶
Get string name of
PointcloudInfo
NamedTuple.- Returns
NamedTuple name.
- Return type
- Usage example
import supervisely as sly os.environ['SERVER_ADDRESS'] = 'https://app.supervisely.com' os.environ['API_TOKEN'] = 'Your Supervisely API Token' api = sly.Api.from_env() tuple_name = api.pointcloud.info_tuple_name() print(tuple_name) # PointCloudInfo
- notify_progress(track_id, dataset_id, pcd_ids, current, total)[source]¶
Send message to the Annotation Tool and return info if tracking was stopped
-
raise_name_intersections_if_exist(dataset_id, names, message=
None
)¶ Raises error if pointclouds with given names already exist in dataset. Default error message: “Pointclouds with the following names already exist in dataset [ID={dataset_id}]: {name_intersections}. Please, rename pointclouds and try again or set change_name_if_conflict=True to rename automatically on upload.”
- remove(id)¶
Remove an entity with the specified ID from the Supervisely server.
- Parameters
- id : int
Entity ID in Supervisely.
- Usage example
import supervisely as sly # You can connect to API directly address = 'https://app.supervise.ly/' token = 'Your Supervisely API Token' api = sly.Api(address, token) # Or you can use API from environment os.environ['SERVER_ADDRESS'] = 'https://app.supervisely.com' os.environ['API_TOKEN'] = 'Your Supervisely API Token' api = sly.Api.from_env() image_id = 19369643 api.image.remove(image_id)
-
remove_batch(ids, progress_cb=
None
, batch_size=50
)¶ Remove entities in batches from the Supervisely server. All entity IDs must belong to the same nesting (for example team, or workspace, or project, or dataset). Therefore, it is necessary to sort IDs before calling this method.
- Parameters
- ids : List[int]
IDs of entities in Supervisely.
- progress_cb : Callable
Function for control remove progress.
- Usage example
import supervisely as sly # You can connect to API directly address = 'https://app.supervise.ly/' token = 'Your Supervisely API Token' api = sly.Api(address, token) # Or you can use API from environment os.environ['SERVER_ADDRESS'] = 'https://app.supervisely.com' os.environ['API_TOKEN'] = 'Your Supervisely API Token' api = sly.Api.from_env() image_ids = [19369645, 19369646, 19369647] api.image.remove_batch(image_ids)
-
upload_dir(dataset_id, dir_path, recursive=
True
, change_name_if_conflict=True
, progress_cb=None
)¶ Uploads all pointclouds with supported extensions from given directory to Supervisely. Optionally, uploads pointclouds from subdirectories of given directory.
- Parameters
- dataset_id : int
Dataset ID in Supervisely.
- dir_path : str
Path to directory with pointclouds.
- recursive : bool, optional
If True, will upload pointclouds from subdirectories of given directory recursively. Otherwise, will upload pointclouds only from given directory.
- change_name_if_conflict : bool, optional
If True adds suffix to the end of Pointcloud name when Dataset already contains an Pointcloud with identical name, If False and pointclouds with the identical names already exist in Dataset raises error.
- progress_cb : Optional[Union[tqdm, Callable]]
Function for tracking upload progress.
- Returns
List of uploaded pointclouds infos
- Return type
List[PointcloudInfo]
-
upload_dirs(dataset_id, dir_paths, recursive=
True
, change_name_if_conflict=True
, progress_cb=None
)¶ Uploads all pointclouds with supported extensions from given directories to Supervisely. Optionally, uploads pointclouds from subdirectories of given directories.
- Parameters
- dataset_id : int
Dataset ID in Supervisely.
- dir_paths : List[str]
List of paths to directories with pointclouds.
- recursive : bool, optional
If True, will upload pointclouds from subdirectories of given directories recursively. Otherwise, will upload pointclouds only from given directories.
- change_name_if_conflict : bool, optional
If True adds suffix to the end of Pointclouds name when Dataset already contains an Pointclouds with identical name, If False and pointclouds with the identical names already exist in Dataset raises error.
- progress_cb : Optional[Union[tqdm, Callable]]
Function for tracking upload progress.
- Returns
List of uploaded pointclouds infos
- Return type
List[Pointclouds]
-
upload_hash(dataset_id, name, hash, meta=
None
)¶ Upload Pointcloud from given hash to Dataset.
- Parameters
- Returns
Information about point cloud. See
info_sequence
- Return type
PointcloudInfo
- Usage example
import supervisely as sly os.environ['SERVER_ADDRESS'] = 'https://app.supervisely.com' os.environ['API_TOKEN'] = 'Your Supervisely API Token' api = sly.Api.from_env() dst_dataset_id = 62693 src_pointcloud_id = 19618685 pcd_info = api.pointcloud.get_info_by_id(src_pointcloud_id) hash, name, meta = pcd_info.hash, pcd_info.name, pcd_info.meta new_pcd_info = api.pointcloud.upload_hash(dst_dataset_id.id, name, hash, meta) print(new_pcd_info) # Output: # PointcloudInfo( # id=19619507, # frame=None, # description='', # name='0000000031.pcd', # team_id=None, # workspace_id=None, # project_id=None, # dataset_id=62694, # link=None, # hash='5w69Vv1i6JrqhU0Lw1UJAJFGPVWUzDG7O3f4QSwRfmE=', # path_original='/j8a9qgms4-public/point_clouds/I/3/6U/L7YBY.pcd', # cloud_mime='image/pcd', # figures_count=None, # objects_count=None, # tags=None, # meta={'frame': 31}, # created_at='2023-04-05T10:59:44.656Z', # updated_at='2023-04-05T10:59:44.656Z' # )
-
upload_hashes(dataset_id, names, hashes, metas=
None
, progress_cb=None
)¶ Upload point clouds from given hashes to Dataset.
- Parameters
- Returns
List of informations about Pointclouds. See
info_sequence
- Return type
List[
PointcloudInfo
]- Usage example
import supervisely as sly os.environ['SERVER_ADDRESS'] = 'https://app.supervisely.com' os.environ['API_TOKEN'] = 'Your Supervisely API Token' api = sly.Api.from_env() src_dataset_id = 62664 dst_dataset_id = 62690 src_pcd_infos = api.pointcloud.get_list(src_dataset_id) names = [pcd.name for pcd in src_pcd_infos[:4]] hashes = [pcd.hash for pcd in src_pcd_infos[:4]] metas = [pcd.meta for pcd in src_pcd_infos[:4]] dst_pcd_infos = api.pointcloud.get_list(dst_dataset_id) print(f"{len(dst_pcd_infos)} pointcloud before upload.") # Output: # 0 pointcloud before upload. new_pcd_infos = api.pointcloud.upload_hashes(dst_dataset_id, names, hashes, metas) print(f"{len(new_pcd_infos)} pointcloud after upload.") # Output: # 4 pointcloud after upload.
-
upload_path(dataset_id, name, path, meta=
None
)¶ Upload point cloud with given path to Dataset.
- Parameters
- Returns
Information about point cloud
- Return type
- Usage example
import supervisely as sly os.environ['SERVER_ADDRESS'] = 'https://app.supervisely.com' os.environ['API_TOKEN'] = 'Your Supervisely API Token' api = sly.Api.from_env() pcd_file = "src/input/pcd/000000.pcd" pcd_info = api.pointcloud.upload_path(dataset.id, name="pcd_0.pcd", path=pcd_file) print(f'Point cloud "{pcd_info.name}" uploaded to Supervisely with ID:{pcd_info.id}') # Output: # Point cloud "pcd_0.pcd" uploaded to Supervisely with ID:19618685
-
upload_paths(dataset_id, names, paths, progress_cb=
None
, metas=None
)¶ Upload point clouds with given paths to Dataset.
- Parameters
- Returns
List of informations about point clouds
- Return type
List[PointcloudInfo]
- Usage example
import supervisely as sly os.environ['SERVER_ADDRESS'] = 'https://app.supervisely.com' os.environ['API_TOKEN'] = 'Your Supervisely API Token' api = sly.Api.from_env() paths = ["src/input/pcd/000001.pcd", "src/input/pcd/000002.pcd"] pcd_infos = api.pointcloud.upload_paths(dataset.id, names=["pcd_1.pcd", "pcd_2.pcd"], paths=paths) print(f'Point clouds uploaded to Supervisely with IDs: {[pcd_info.id for pcd_info in pcd_infos]}') # Output: # Point clouds uploaded to Supervisely with IDs: [19618685, 19618686]
Upload an image to the Supervisely. It generates us a hash for image.
- Parameters
- path : str
Image path.
- Returns
Hash for image. See
info_sequence
- Return type
- Usage example
import supervisely as sly os.environ['SERVER_ADDRESS'] = 'https://app.supervisely.com' os.environ['API_TOKEN'] = 'Your Supervisely API Token' api = sly.Api.from_env() img_file = src/input/img/000000.png" img_hash = api.pointcloud.upload_related_image(img_file) print(img_hash) # Output: # +R6dFy8nMEq6k82vHLxuakpqVBmyTTPj5hXdPfjAv/c=
Upload a batch of related images to the Supervisely. It generates us a hashes for images.
- Parameters
- paths : List[str]
Images pathes.
- Returns
List of hashes for images. See
info_sequence
- Return type
List[str]
- Usage example
import supervisely as sly os.environ['SERVER_ADDRESS'] = 'https://app.supervisely.com' os.environ['API_TOKEN'] = 'Your Supervisely API Token' api = sly.Api.from_env() img_paths = ["src/input/img/000001.png", "src/input/img/000002.png"] img_hashes = api.pointcloud.upload_related_images(img_paths) # Output: # [+R6dFy8nMEq6k82vHLxuakpqVBmyTTPjdfGdPfjAv/c=, +hfjbufnbkLhJb32vHLxuakpqVBmyTTPj5hXdPfhhj1c]