PointcloudProject¶

class PointcloudProject(directory, mode)[source]¶

Bases: VideoProject

A local Supervisely project for point cloud data.

Contains one or more PointcloudDataset datasets with point clouds and their annotations.

PointcloudProject is a parent directory for pointcloud datasets. PointcloudProject object is immutable.

Parameters:

directory : str¶: Path to pointcloud project directory.
mode¶: Determines working mode for the given project.

Usage Example:

import supervisely as sly

project_path = "/home/admin/work/supervisely/projects/ptc_project"
project = sly.PointcloudProject(project_path, sly.OpenMode.READ)

Methods

`add_blob_file`	Adds blob file to the project.
`build_snapshot`
`copy_data`	Makes a copy of the VideoProject.
`create_dataset`	Creates a subdirectory with given name and all intermediate subdirectories for items and annotations in project directory, and also adds created dataset to the collection of all datasets in the project.
`download`	Download pointcloud project from Supervisely to the given directory.
`download_async`
`download_bin`
`get_classes_stats`
`get_item_paths`	Get item paths for the project.
`get_train_val_splits_by_collections`	Not available for PointcloudProject class.
`get_train_val_splits_by_count`	Get train and val items information from project by given train and val counts.
`get_train_val_splits_by_dataset`	Get train and val items information from project by given train and val datasets names.
`get_train_val_splits_by_tag`	Get train and val items information from project by given train and val tags names.
`get_url`	Get URL to video datasets list in Supervisely.
`read_single`
`remove_classes`	Not available for VideoProject class.
`remove_classes_except`	Not available for VideoProject class.
`remove_items_without_both_objects_and_tags`	Not available for VideoProject class.
`remove_items_without_objects`	Not available for VideoProject class.
`remove_items_without_tags`	Not available for VideoProject class.
`restore_snapshot`
`set_key_id_map`	Save given KeyIdMap object to project dir in json format.
`set_meta`	Saves given project meta to project directory in json format.
`to_coco`	Convert Supervisely project to COCO format.
`to_detection_task`	Not available for VideoProject class.
`to_pascal_voc`	Convert Supervisely project to Pascal VOC format.
`to_segmentation_task`	Not available for VideoProject class.
`to_yolo`	Convert Supervisely project to YOLO format.
`upload`	Uploads pointcloud project to Supervisely from the given directory.
`upload_bin`
`validate`

Attributes

`blob_dir`	Directory for project blobs.
`blob_dir_name`
`blob_files`	List of blob files.
`datasets`	Project datasets.
`directory`	Path to the project directory.
`key_id_map`
`meta`	Project meta.
`name`	Project name.
`parent_dir`	Project parent directory.
`total_items`	Total number of items in project.
`type`	Project type.

class DatasetDict(items=None)[source]¶

Bases: KeyIndexedCollection

Collection of PointcloudDataset by name.

Base class for ObjClassCollection, TagMetaCollection and TagCollection instances. It is an analogue of python’s standard Dict. It allows to store objects inherited from KeyObject.

Parameters:

items : list, optional¶: List of ObjClassCollection, TagMetaCollection and TagCollection objects.

:raises DuplicateKeyError, when trying to add object with already existing key

Usage Example:

import supervisely as sly

item_cat = sly.TagMeta('cat', sly.TagValueType.NONE)
item_turtle = sly.TagMeta('turtle', sly.TagValueType.ANY_STRING)
collection = sly.collection.key_indexed_collection.KeyIndexedCollection([item_cat, item_turtle])
print(collection.to_json())
# Output: [
#     {
#         "name": "cat",
#         "value_type": "none",
#         "color": "#8A0F12",
#         "hotkey": "",
#         "applicable_type": "all",
#         "classes": []
#     },
#     {
#         "name": "turtle",
#         "value_type": "any_string",
#         "color": "#8A860F",
#         "hotkey": "",
#         "applicable_type": "all",
#         "classes": []
#     }
# ]

# Try to add item with a key that already exists in the collection
dublicate_item = sly.ObjClass('cat', sly.Rectangle)
new_collection = collection.add(dublicate_item)
# Output:
# DuplicateKeyError: "Key 'cat' already exists"

# Add item with a key that not exist in the collection
item_dog = sly.ObjClass('dog', sly.Rectangle)
new_collection = collection.add(item_dog)
print(new_collection.to_json())
# Output: [
#     {
#         "name": "cat",
#         "value_type": "none",
#         "color": "#668A0F",
#         "hotkey": "",
#         "applicable_type": "all",
#         "classes": []
#     },
#     {
#         "name": "turtle",
#         "value_type": "any_string",
#         "color": "#4D0F8A",
#         "hotkey": "",
#         "applicable_type": "all",
#         "classes": []
#     },
#     {
#         "title": "dog",
#         "shape": "rectangle",
#         "color": "#0F7F8A",
#         "geometry_config": {},
#         "hotkey": ""
#     }
# ]

item_type¶: alias of PointcloudDataset

add(item)¶

Add given item to collection.

Parameters:

item¶: ObjClassCollection, TagMetaCollection or TagCollection object.

Returns:

New instance of KeyIndexedCollection

Return type:

KeyIndexedCollection

Usage Example:

import supervisely as sly

item_cat = sly.TagMeta('cat', sly.TagValueType.NONE)
item_turtle = sly.TagMeta('turtle', sly.TagValueType.ANY_STRING)
collection = sly.collection.key_indexed_collection.KeyIndexedCollection([item_cat, item_turtle])

# Remember that KeyIndexedCollection object is immutable, and we need to assign new instance of KeyIndexedCollection to a new variable
item_dog = sly.ObjClass('dog', sly.Rectangle)
new_collection = collection.add(item_dog)

add_items(items)¶

Add items from given list to collection.

Parameters:

items¶: List of ObjClassCollection, TagMetaCollection or TagCollection objects.

Returns:

New instance of KeyIndexedCollection

Return type:

KeyIndexedCollection

Usage Example:

import supervisely as sly

item_cat = sly.TagMeta('cat', sly.TagValueType.NONE)
item_turtle = sly.TagMeta('turtle', sly.TagValueType.ANY_STRING)
collection = sly.collection.key_indexed_collection.KeyIndexedCollection([item_cat, item_turtle])

# Remember that KeyIndexedCollection object is immutable, and we need to assign new instance of KeyIndexedCollection to a new variable
item_dog = sly.ObjClass('dog', sly.Rectangle)
item_mouse = sly.ObjClass('mouse', sly.Bitmap)
new_collection = collection.add_items([item_dog, item_mouse])

clone(items=None)¶

Makes a copy of KeyIndexedCollection with new fields, if fields are given, otherwise it will use fields of the original KeyIndexedCollection.

Parameters:

items=None¶: List of ObjClassCollection, TagMetaCollection or TagCollection objects.

Returns:

New instance of KeyIndexedCollection

Return type:

KeyIndexedCollection

Usage Example:

import supervisely as sly

item_cat = sly.TagMeta('cat', sly.TagValueType.NONE)
item_turtle = sly.TagMeta('turtle', sly.TagValueType.ANY_STRING)
collection = sly.collection.key_indexed_collection.KeyIndexedCollection([item_cat, item_turtle])

# Remember that KeyIndexedCollection object is immutable, and we need to assign new instance of KeyIndexedCollection to a new variable
new_collection = collection.clone()

difference(other)¶

Find difference between collection and given list of instances.

Parameters:

other¶: List of items to subtract from the collection.

Returns:

KeyIndexedCollection object

Return type:

KeyIndexedCollection

Usage Example:

import supervisely as sly

item_cat = sly.TagMeta('cat', sly.TagValueType.NONE)
item_turtle = sly.TagMeta('turtle', sly.TagValueType.ANY_STRING)
collection = sly.collection.key_indexed_collection.KeyIndexedCollection([item_cat, item_turtle])

item_dog = sly.TagMeta('dog', sly.TagValueType.NONE)
item_turtle = sly.TagMeta('turtle', sly.TagValueType.ANY_STRING)
items = [item_dog, item_turtle]

diff = collection.difference(items)
print(diff.to_json())
# Output: [
#     {
#         "name": "cat",
#         "value_type": "none",
#         "color": "#8A150F",
#         "hotkey": "",
#         "applicable_type": "all",
#         "classes": []
#     }
# ]

get(key, default=None)¶

Get item from collection with given key(name).

Parameters:

items : str: Name of KeyObject in collection.
default : optional¶: The value that is returned if there is no key in the collection.

Returns:

ObjClassCollection, TagMetaCollection or TagCollection object

Return type:

KeyObject

Usage Example:

import supervisely as sly

item_cat = sly.TagMeta('cat', sly.TagValueType.NONE)
item_turtle = sly.TagMeta('turtle', sly.TagValueType.ANY_STRING)
collection = sly.collection.key_indexed_collection.KeyIndexedCollection([item_cat, item_turtle])

item_cat = collection.get('cat')
print(item_cat)
# Output:
# Name:  cat                      Value type:none          Possible values:None       Hotkey                  Applicable toall        Applicable classes[]

item_not_exist = collection.get('no_item', {1: 2})
print(item_not_exist)
# Output:
# {1: 2}

has_key(key)¶

Check if given key(item name exist in collection).

Parameters:

key : str¶: The key to look for in the collection.

Returns:

Is the key in the collection or not

Return type:

bool

Usage Example:

import supervisely as sly

item_cat = sly.TagMeta('cat', sly.TagValueType.NONE)
item_turtle = sly.TagMeta('turtle', sly.TagValueType.ANY_STRING)
collection = sly.collection.key_indexed_collection.KeyIndexedCollection([item_cat, item_turtle])

collection.has_key('cat') # True
collection.has_key('hamster') # False

intersection(other)¶

Find intersection of given list of instances with collection items.

Parameters:

other¶: List of items to intersect with the collection.

Raises:

ValueError – if find items with same keys(item names)

Returns:

KeyIndexedCollection object

Return type:

KeyIndexedCollection

Usage Example:

import supervisely as sly

item_cat = sly.TagMeta('cat', sly.TagValueType.NONE)
item_turtle = sly.TagMeta('turtle', sly.TagValueType.ANY_STRING)
collection = sly.collection.key_indexed_collection.KeyIndexedCollection([item_cat, item_turtle])

item_dog = sly.TagMeta('dog', sly.TagValueType.NONE)
item_turtle = sly.TagMeta('turtle', sly.TagValueType.ANY_STRING)
items = [item_dog, item_turtle]

intersection = collection.intersection(items)
print(intersection.to_json())
# Output: [
#     {
#         "name": "turtle",
#         "value_type": "any_string",
#         "color": "#760F8A",
#         "hotkey": "",
#         "applicable_type": "all",
#         "classes": []
#     }
# ]

items()¶

Get list of all items in collection.

Returns:

List of ObjClassCollection, TagMetaCollection or TagCollection objects

Return type:

List[KeyObject]

Usage Example:

import supervisely as sly

item_cat = sly.TagMeta('cat', sly.TagValueType.NONE)
item_turtle = sly.TagMeta('turtle', sly.TagValueType.ANY_STRING)
collection = sly.collection.key_indexed_collection.KeyIndexedCollection([item_cat, item_turtle])
items = collection.items()
print(items)
# Output:
# [<supervisely.annotation.tag_meta.TagMeta object at 0x7fd08eae4340>,
#  <supervisely.annotation.tag_meta.TagMeta object at 0x7fd08eae4370>]

keys()¶

Get list of all keys(item names) in collection.

Returns:

List of collection keys

Return type:

List[str]

Usage Example:

import supervisely as sly

item_cat = sly.TagMeta('cat', sly.TagValueType.NONE)
item_turtle = sly.TagMeta('turtle', sly.TagValueType.ANY_STRING)
collection = sly.collection.key_indexed_collection.KeyIndexedCollection([item_cat, item_turtle])
keys = collection.keys() # ['cat', 'turtle']

merge(other)¶

Merge collection and other KeyIndexedCollection object.

Parameters:

other¶: Other collection to merge with.

Raises:

ValueError – if item name from given list is in collection but items in both are different

Returns:

KeyIndexedCollection object

Return type:

KeyIndexedCollection

Usage Example:

import supervisely as sly

item_cat = sly.TagMeta('cat', sly.TagValueType.NONE)
item_turtle = sly.TagMeta('turtle', sly.TagValueType.ANY_STRING)
collection = sly.collection.key_indexed_collection.KeyIndexedCollection([item_cat, item_turtle])

item_dog = sly.TagMeta('dog', sly.TagValueType.NONE)
item_turtle = sly.TagMeta('turtle', sly.TagValueType.ANY_STRING)
other_collection = sly.collection.key_indexed_collection.KeyIndexedCollection([item_dog, item_turtle])

merge = collection.merge(other_collection)
print(merge.to_json())
# Output: [
#     {
#         "name": "dog",
#         "value_type": "none",
#         "color": "#8A6C0F",
#         "hotkey": "",
#         "applicable_type": "all",
#         "classes": []
#     },
#     {
#         "name": "cat",
#         "value_type": "none",
#         "color": "#0F4A8A",
#         "hotkey": "",
#         "applicable_type": "all",
#         "classes": []
#     },
#     {
#         "name": "turtle",
#         "value_type": "any_string",
#         "color": "#4F0F8A",
#         "hotkey": "",
#         "applicable_type": "all",
#         "classes": []
#     }
# ]

remove_items(keys)¶

Remove items from collection by given list of keys. Creates a new instance of KeyIndexedCollection.

Parameters:

keys : List[str]¶: List of keys(item names) in collection.

Returns:

New instance of KeyIndexedCollection

Return type:

KeyIndexedCollection

to_json()¶

Convert the KeyIndexedCollection to a json serializable list.

Returns:

List of json serializable dicts

Return type:

List[dict]

Usage Example:

import supervisely as sly

item_cat = sly.TagMeta('cat', sly.TagValueType.NONE)
item_turtle = sly.TagMeta('turtle', sly.TagValueType.ANY_STRING)
collection = sly.collection.key_indexed_collection.KeyIndexedCollection([item_cat, item_turtle])
collection_json = collection.to_json()
# Output: [
#     {
#         "name": "cat",
#         "value_type": "none",
#         "color": "#8A0F12",
#         "hotkey": "",
#         "applicable_type": "all",
#         "classes": []
#     },
#     {
#         "name": "turtle",
#         "value_type": "any_string",
#         "color": "#8A860F",
#         "hotkey": "",
#         "applicable_type": "all",
#         "classes": []
#     }
# ]

dataset_class¶: alias of PointcloudDataset

static download(api, project_id, dest_dir, dataset_ids=None, download_pointclouds=True, download_related_images=True, download_pointclouds_info=False, batch_size=10, log_progress=True, progress_cb=None, **kwargs)[source]¶

Download pointcloud project from Supervisely to the given directory.

Parameters:

api¶: Supervisely API address and token.
project_id : int¶: Supervisely downloadable project ID.
dest_dir : str¶: Destination directory.
dataset_ids : List[int], optional¶: Dataset IDs.
download_pointclouds : bool, optional¶: Download pointcloud data files or not.
download_related_images : bool, optional¶: Download related images or not.
download_pointclouds_info : bool, optional¶: Download pointcloud info .json files or not.
batch_size : int, optional¶: The number of images in the batch when they are loaded to a host.
log_progress : bool¶: Show uploading progress bar.
progress_cb : tqdm or callable, optional¶: Function for tracking download progress.

Returns:

None

Return type:

None

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()

# Download Project
project_id = 8888
save_directory = "/home/admin/work/supervisely/source/ptc_project"
sly.PointcloudProject.download(api, project_id, save_directory)
project_fs = sly.PointcloudProject(save_directory, sly.OpenMode.READ)

static get_train_val_splits_by_collections(project_dir, train_collections, val_collections, project_id, api)[source]¶: Not available for PointcloudProject class. :raises NotImplementedError: in all cases.

static get_train_val_splits_by_count(project_dir, train_count, val_count)[source]¶

Get train and val items information from project by given train and val counts.

Parameters:

project_dir : str¶: Path to project directory.
train_count : int¶: Number of train items.
val_count : int¶: Number of val items.

Raises:

ValueError – if total_count != train_count + val_count

Returns:

Tuple with lists of train items information and val items information

Return type:

Tuple[List[PointcloudItemInfo], List[PointcloudItemInfo]]

Usage Example:

from supervisely.project.pointcloud_project import PointcloudProject

project_path = "/home/admin/work/supervisely/projects/pointcloud_project"
project = PointcloudProject(project_path, sly.OpenMode.READ)
train_count = 4
val_count = 2
train_items, val_items = project.get_train_val_splits_by_count(project_path, train_count, val_count)

static get_train_val_splits_by_dataset(project_dir, train_datasets, val_datasets)[source]¶

Get train and val items information from project by given train and val datasets names.

Parameters:

project_dir : str¶: Path to project directory.
train_datasets : List[str]¶: List of train datasets names.
val_datasets : List[str]¶: List of val datasets names.

Raises:

KeyError – if dataset name not found in project

Returns:

Tuple with lists of train items information and val items information

Return type:

Tuple[List[PointcloudItemInfo], List[PointcloudItemInfo]]

Usage Example:

import supervisely as sly

project_path = "/home/admin/work/supervisely/projects/pointcloud_project"
project = sly.PointcloudProject(project_path, sly.OpenMode.READ)
train_datasets = ['ds1', 'ds2']
val_datasets = ['ds3', 'ds4']
train_items, val_items = project.get_train_val_splits_by_dataset(project_path, train_datasets, val_datasets)

static get_train_val_splits_by_tag(project_dir, train_tag_name, val_tag_name, untagged='ignore')[source]¶

Get train and val items information from project by given train and val tags names.

Parameters:

project_dir : str¶: Path to project directory.
train_tag_name : str¶: Train tag name.
val_tag_name : str¶: Val tag name.
untagged : str, optional¶: Actions in case of absence of train_tag_name and val_tag_name in project.

Raises:

ValueError – if untagged not in [“ignore”, “train”, “val”]

Returns:

Tuple with lists of train items information and val items information

Return type:

Tuple[List[PointcloudItemInfo], List[PointcloudItemInfo]]

Usage Example:

import supervisely as sly

project_path = "/home/admin/work/supervisely/projects/pointcloud_project"
project = sly.PointcloudProject(project_path, sly.OpenMode.READ)
train_tag_name = 'train'
val_tag_name = 'val'
train_items, val_items = project.get_train_val_splits_by_tag(project_path, train_tag_name, val_tag_name)

static get_url(id)¶

Get URL to video datasets list in Supervisely.

Parameters:

id : int¶: VideoProject ID in Supervisely.

Returns:

URL to datasets list.

Return type:

str

Usage Example:

from supervisely import VideoProject

project_id = 10093
datasets_link = VideoProject.get_url(project_id)

print(datasets_link)
# Output: "/projects/10093/datasets"

static remove_classes(project_dir, classes_to_remove=None, inplace=False)¶: Not available for VideoProject class. :raises NotImplementedError: in all cases.

static remove_classes_except(project_dir, classes_to_keep=None, inplace=False)¶: Not available for VideoProject class. :raises NotImplementedError: in all cases.

static remove_items_without_both_objects_and_tags(project_dir, inplace=False)¶: Not available for VideoProject class. :raises NotImplementedError: in all cases.

static remove_items_without_objects(project_dir, inplace=False)¶: Not available for VideoProject class. :raises NotImplementedError: in all cases.

static remove_items_without_tags(project_dir, inplace=False)¶: Not available for VideoProject class. :raises NotImplementedError: in all cases.

static to_detection_task(src_project_dir, dst_project_dir=None, inplace=False, progress_cb=None)¶: Not available for VideoProject class. :raises NotImplementedError: in all cases.

static to_segmentation_task(src_project_dir, dst_project_dir=None, inplace=False, target_classes=None, progress_cb=None, segmentation_type='semantic')¶: Not available for VideoProject class. :raises NotImplementedError: in all cases.

static upload(directory, api, workspace_id, project_name=None, log_progress=True, progress_cb=None)[source]¶

Uploads pointcloud project to Supervisely from the given directory.

Parameters:

directory : str¶: Path to project directory.
api¶: Supervisely API address and token.
workspace_id : int¶: Workspace ID, where project will be uploaded.
project_name : str, optional¶: Name of the project in Supervisely. Can be changed if project with the same name is already exists.
log_progress : bool¶: Show uploading progress bar.
progress_cb : tqdm or callable, optional¶: Function for tracking download progress.

Returns:

Project ID and name. It is recommended to check that returned project name coincides with provided project name.

Return type:

int, str

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()

# Upload Pointcloud Project
project_directory = "/home/admin/work/supervisely/source/ptc_project"
project_id, project_name = sly.PointcloudProject.upload(
    project_directory,
    api,
    workspace_id=45,
    project_name="My Pointcloud Project"
)

add_blob_file(file_name)¶

Adds blob file to the project.

Parameters:

file_name : str¶: File name.

Returns:

None

Return type:

NoneType

Usage Example:

import supervisely as sly

project = sly.Project("/home/admin/work/supervisely/projects/lemons_annotated", sly.OpenMode.READ)
project.add_blob_file("blob_file.tar")

copy_data(dst_directory, dst_name=None, _validate_item=True, _use_hardlink=False)¶

Makes a copy of the VideoProject.

Parameters:

dst_directory : str¶: Path to video project parent directory.
dst_name : str, optional¶: Video Project name.
_validate_item : bool, optional¶: Checks input files format.
_use_hardlink : bool, optional¶: If True creates a hardlink pointing to src named dst, otherwise don’t.

Returns:

New instance of VideoProject object.

Return type:

VideoProject

Usage Example:

import supervisely as sly

project = sly.VideoProject("/home/admin/work/supervisely/projects/videos_example", sly.OpenMode.READ)
print(project.total_items)
# Output: 6

new_project = project.copy_data("/home/admin/work/supervisely/projects/", "videos_example_copy")
print(new_project.total_items)
# Output: 6

create_dataset(ds_name, ds_path=None)¶

Creates a subdirectory with given name and all intermediate subdirectories for items and annotations in project directory, and also adds created dataset to the collection of all datasets in the project.

Parameters:

ds_name : str¶: Dataset name.

Returns:

Dataset.

Return type:

Dataset

Usage Example:

import supervisely as sly

project = sly.Project("/home/admin/work/supervisely/projects/lemons_annotated", sly.OpenMode.READ)

for dataset in project.datasets:
    print(dataset.name)

# Output: ds1
#         ds2

project.create_dataset("ds3")
for dataset in project.datasets:
    print(dataset.name)

# Output: ds1
#         ds2
#         ds3

get_item_paths(item_name)¶: Get item paths for the project. Not Implemented for Project class.

set_key_id_map(new_map)¶: Save given KeyIdMap object to project dir in json format. :param new_map: KeyIdMap object. :type new_map: KeyIdMap

set_meta(new_meta)¶

Saves given project meta to project directory in json format.

Parameters:

new_meta¶: Project meta.

Returns:

None

Return type:

NoneType

Usage Example:

import supervisely as sly

proj_lemons = sly.Project("/home/admin/work/supervisely/projects/lemons_annotated", sly.OpenMode.READ)
proj_kiwi = sly.Project("/home/admin/work/supervisely/projects/kiwi_annotated", sly.OpenMode.READ)

proj_lemons.set_meta(proj_kiwi.meta)

print(project.proj_lemons)
# Output:
# +-------+--------+----------------+--------+
# |  Name | Shape  |     Color      | Hotkey |
# +-------+--------+----------------+--------+
# |  kiwi | Bitmap |  [255, 0, 0]   |        |
# +-------+--------+----------------+--------+

to_coco(dest_dir=None, copy_images=False, with_captions=False, log_progress=True, progress_cb=None)¶

Convert Supervisely project to COCO format.

Parameters:

dest_dir : str, optional¶: Destination directory.
copy_images : bool¶: Copy images to the destination directory.
with_captions : bool¶: Return captions for images.
log_progress : bool¶: Show uploading progress bar.
progress_cb : callable, optional¶: Function for tracking conversion progress (for all items in the project).

Returns:

None

Return type:

NoneType

Usage Example:

import supervisely as sly

# Local folder with Project
project_directory = "/home/admin/work/supervisely/source/project"

# Convert Project to COCO format
sly.Project(project_directory).to_coco(log_progress=True)

# or

from supervisely.convert import to_coco
to_coco(project_directory, dest_dir="./coco_project")

to_pascal_voc(dest_dir=None, train_val_split_coef=0.8, log_progress=True, progress_cb=None)¶

Convert Supervisely project to Pascal VOC format.

Parameters:

dest_dir : str, optional¶: Destination directory.
train_val_split_coef : float, optional¶: Coefficient for splitting images into train and validation sets.
log_progress : bool¶: Show uploading progress bar.
progress_cb : callable, optional¶: Function for tracking conversion progress (for all items in the project).

Returns:

None

Return type:

NoneType

Usage Example:

import supervisely as sly

# Local folder with Project
project_directory = "/home/admin/work/supervisely/source/project"

# Convert Project to YOLO format
sly.Project(project_directory).to_pascal_voc(log_progress=True)

# or

from supervisely.convert import to_pascal_voc
to_pascal_voc(project_directory, dest_dir="./pascal_voc_project")

to_yolo(dest_dir=None, task_type='detect', log_progress=True, progress_cb=None, val_datasets=None)¶

Convert Supervisely project to YOLO format.

Parameters:

dest_dir : str, optional¶: Destination directory.
task_type : str, optional¶: Task type for YOLO format. Possible values: ‘detection’, ‘segmentation’, ‘pose’.
log_progress : bool¶: Show uploading progress bar.
progress_cb : callable, optional¶: Function for tracking conversion progress (for all items in the project).
val_datasets : List[str], optional¶: List of dataset names for validation. Full dataset names are required (e.g., ‘ds0/nested_ds1/ds3’). If specified, datasets from the list will be marked as val, others as train. If not specified, the function will determine the validation datasets automatically.

Returns:

None

Return type:

NoneType

Usage Example:

import supervisely as sly

# Local folder with Project
project_directory = "/home/admin/work/supervisely/source/project"

# Convert Project to YOLO format
sly.Project(project_directory).to_yolo(log_progress=True)

# or

from supervisely.convert import to_yolo
to_yolo(project_directory, dest_dir="./yolo_project")

property blob_dir : str¶

Directory for project blobs. Blobs are .tar files with images. Used for fast data transfer.

Returns:

Path to project blob directory

Return type:

str

Usage Example:

import supervisely as sly

project = sly.Project("/home/admin/work/supervisely/projects/lemons_annotated", sly.OpenMode.READ)
print(project.blob_dir)
# Output: '/home/admin/work/supervisely/projects/lemons_annotated/blob'

property blob_files : list[str]¶

List of blob files.

Returns:

List of blob files

Return type:

list

Usage Example:

import supervisely as sly

project = sly.Project("/home/admin/work/supervisely/projects/lemons_annotated", sly.OpenMode.READ)
print(project.blob_files)
# Output: []

property datasets : supervisely.project.project.Project.DatasetDict¶

Project datasets.

Returns:

Datasets

Return type:

DatasetDict

Usage Example:

import supervisely as sly

project = sly.Project("/home/admin/work/supervisely/projects/lemons_annotated", sly.OpenMode.READ)
for dataset in project.datasets:
    print(dataset.name)

# Output: ds1
#         ds2

property directory : str¶

Path to the project directory.

Returns:

Path to the project directory

Return type:

str

Usage Example:

import supervisely as sly

project = sly.Project("/home/admin/work/supervisely/projects/lemons_annotated", sly.OpenMode.READ)
print(project.directory)
# Output: '/home/admin/work/supervisely/projects/lemons_annotated'

property meta : supervisely.project.project_meta.ProjectMeta¶

Project meta.

Returns:

Project meta.

Return type:

ProjectMeta

Usage Example:

import supervisely as sly

project = sly.Project("/home/admin/work/supervisely/projects/lemons_annotated", sly.OpenMode.READ)
print(project.meta)
# Output:
# +-------+--------+----------------+--------+
# |  Name | Shape  |     Color      | Hotkey |
# +-------+--------+----------------+--------+
# |  kiwi | Bitmap |  [255, 0, 0]   |        |
# | lemon | Bitmap | [81, 198, 170] |        |
# +-------+--------+----------------+--------+
# Tags
# +------+------------+-----------------+--------+---------------+--------------------+
# | Name | Value type | Possible values | Hotkey | Applicable to | Applicable classes |
# +------+------------+-----------------+--------+---------------+--------------------+

property name : str¶

Project name.

Returns:

Project name.

Return type:

str

Usage Example:

import supervisely as sly

project = sly.Project("/home/admin/work/supervisely/projects/lemons_annotated", sly.OpenMode.READ)
print(project.name)
# Output: 'lemons_annotated'

property parent_dir : str¶

Project parent directory.

Returns:

Path to project parent directory

Return type:

str

Usage Example:

import supervisely as sly

project = sly.Project("/home/admin/work/supervisely/projects/lemons_annotated", sly.OpenMode.READ)
print(project.parent_dir)
# Output: '/home/admin/work/supervisely/projects'

property total_items : int¶

Total number of items in project.

Returns:

Total number of items in project

Return type:

int

Usage Example:

import supervisely as sly

project = sly.Project("/home/admin/work/supervisely/projects/lemons_annotated", sly.OpenMode.READ)
print(project.total_items)
# Output: 12

property type : str¶

Project type.

Returns:

Project type.

Return type:

str

Usage Example:

import supervisely as sly

project = sly.PointcloudProject("/home/admin/work/supervisely/projects/pointclouds", sly.OpenMode.READ)
print(project.type)
# Output: 'point_clouds'