Project¶
- class Project[source]¶
Bases:
object
Project is a parent directory for dataset. Project object is immutable.
- Parameters
- Usage example
import supervisely as sly project_path = "/home/admin/work/supervisely/projects/lemons_annotated" project = sly.Project(project_path, sly.OpenMode.READ)
Methods
Makes a copy of the
Project
.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 project from Supervisely to the given directory.
Download project from Supervisely to the given directory in asynchronous mode.
Download project to the local directory in binary format.
get_classes_stats
get_item_paths
- rtype
Get train and val items information from project by given train and val counts.
Get train and val items information from project by given train and val datasets names.
Get train and val items information from project by given train and val tags names.
Get URL to datasets list in Supervisely.
Removes given classes from Project.
Removes classes from Project with the exception of some classes.
Remove items(images and annotations) without objects and tags from Project.
Remove items(images and annotations) without objects from Project.
Remove items(images and annotations) without tags from Project.
Saves given
meta
to project directory in json format.Convert Supervisely project to COCO format.
Makes a copy of the
Project
, converts annotations toRectangles
and updatesproject meta
.Convert Supervisely project to Pascal VOC format.
Makes a copy of the
Project
, converts annotations toBitmaps
and updatesproject meta
.Convert Supervisely project to YOLO format.
Uploads project to Supervisely from the given directory.
Uploads project to Supervisely from the given binary file and suitable only for projects downloaded in binary format.
validate
Attributes
Project datasets.
Path to the project directory.
Project meta.
Project name.
Project parent directory.
Total number of items in project.
Project type.
- class DatasetDict[source]¶
Bases:
supervisely.collection.key_indexed_collection.KeyIndexedCollection
Datasets
collection ofProject
.- item_type¶
alias of
supervisely.project.project.Dataset
- add(item)¶
Add given item to collection.
- Parameters
- item : KeyObject
ObjClassCollection
,TagMetaCollection
orTagCollection
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[KeyObject]
List of
ObjClassCollection
,TagMetaCollection
orTagCollection
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 : List[KeyObject], optional
List of
ObjClassCollection
,TagMetaCollection
orTagCollection
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
- key : List[KeyObject]
List of
ObjClassCollection
,TagMetaCollection
orTagCollection
objects.
- 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
orTagCollection
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
- 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
- key : List[KeyObject]
List of
ObjClassCollection
,TagMetaCollection
orTagCollection
objects.
- 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": [] # } # ]
- 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
- key : KeyIndexedCollection
KeyIndexedCollection object.
- 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
supervisely.project.project.Dataset
-
copy_data(dst_directory, dst_name=
None
, _validate_item=True
, _use_hardlink=False
)[source]¶ Makes a copy of the
Project
.- Parameters
- Returns
Project object.
- Return type
- Usage example
import supervisely as sly project = sly.Project("/home/admin/work/supervisely/projects/lemons_annotated", sly.OpenMode.READ) print(project.total_items) # Output: 6 new_project = project.copy_data("/home/admin/work/supervisely/projects/", "lemons_copy") print(new_project.total_items) # Output: 6
-
create_dataset(ds_name, ds_path=
None
)[source]¶ 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.
- ds_name :
- Returns
Dataset object
- Return type
- 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
-
static download(api, project_id, dest_dir, dataset_ids=
None
, log_progress=True
, batch_size=50
, cache=None
, progress_cb=None
, only_image_tags=False
, save_image_info=False
, save_images=True
, save_image_meta=False
, resume_download=False
)[source]¶ Download project from Supervisely to the given directory.
- Parameters
- api :
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.
- log_progress :
bool
Show uploading progress bar.
- batch_size :
int
, optional The number of images in the batch when they are loaded to a host.
- cache :
FileCache
, optional FileCache object.
- progress_cb : tqdm or callable, optional
Function for tracking download progress.
- only_image_tags :
bool
, optional Download project with only images tags (without objects tags).
- save_image_info :
bool
, optional Download images infos or not.
- save_images :
bool
, optional Download images or not.
- save_image_meta :
bool
, optional Download images metadata in JSON format or not.
- api :
- Returns
None
- Return type
NoneType
- Usage example
import supervisely as sly # Local destination Project folder save_directory = "/home/admin/work/supervisely/source/project" # Obtain server address and your api_token from environment variables # Edit those values if you run this notebook on your own PC address = os.environ['SERVER_ADDRESS'] token = os.environ['API_TOKEN'] # Initialize API object api = sly.Api(address, token) project_id = 8888 # Download Project sly.Project.download(api, project_id, save_directory) project_fs = sly.Project(save_directory, sly.OpenMode.READ)
-
async static download_async(api, project_id, dest_dir, dataset_ids=
None
, log_progress=True
, semaphore=None
, progress_cb=None
, only_image_tags=False
, save_image_info=False
, save_images=True
, save_image_meta=False
, images_ids=None
, resume_download=False
, **kwargs)[source]¶ Download project from Supervisely to the given directory in asynchronous mode.
- Parameters
- api :
Api
Supervisely API address and token.
- project_id :
int
Supervisely downloadable project ID.
- dest_dir :
str
Destination directory.
- dataset_ids :
list
[int
], optional Filter datasets by IDs.
- log_progress :
bool
Show uploading progress bar.
- semaphore :
asyncio.Semaphore
, optional Semaphore to limit the number of concurrent downloads of items.
- progress_cb : tqdm or callable, optional
Function for tracking download progress.
- only_image_tags :
bool
, optional Download project with only images tags (without objects tags).
- save_image_info :
bool
, optional Download images infos or not.
- save_images :
bool
, optional Download images or not.
- save_image_meta :
bool
, optional Download images metadata in JSON format or not.
- images_ids :
list
[int
], optional Filter images by IDs.
- resume_download :
bool
, optional Resume download enables to download only missing files avoiding erase of existing files.
- api :
- 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() project_id = 8888 save_directory = "/path/to/save/projects" loop = sly.utils.get_or_create_event_loop() coro = sly.Project.download_async(api, project_id, save_directory) if loop.is_running(): future = asyncio.run_coroutine_threadsafe(coroutine, loop) future.result() else: loop.run_until_complete(coroutine)
-
static download_bin(api, project_id, dest_dir=
None
, dataset_ids=None
, batch_size=100
, log_progress=True
, progress_cb=None
, return_bytesio=False
)[source]¶ Download project to the local directory in binary format. Faster than downloading project in the usual way. This type of project download is more suitable for creating local backups. It is also suitable for cases where you don’t need access to individual project files, such as images or annotations.
Binary file contains the following data: - ProjectInfo - ProjectMeta - List of DatasetInfo - List of ImageInfo - Dict of Figures - Dict of AlphaGeometries
- Parameters
- api :
Api
Supervisely API address and token.
- project_id :
int
Project ID to download.
- dest_dir :
str
, optional Destination path to local directory.
- dataset_ids :
list
[int
], optional Specified list of Dataset IDs which will be downloaded. If you want to download nested datasets, you should specify all nested IDs.
- batch_size :
int
, optional Size of a downloading batch.
- log_progress :
bool
, optional Show downloading logs in the output.
- progress_cb :
tqdm
orcallable
, optional Function for tracking download progress. Has a higher priority than log_progress.
- return_bytesio :
bool
, optional If True, returns BytesIO object instead of saving it to the disk.
- api :
- Returns
Path to the binary file or BytesIO object.
- Return type
str
orBytesIO
- Usage example
import supervisely as sly # Local destination Project folder save_directory = "/home/admin/work/supervisely/source/project" # Obtain server address and your api_token from environment variables # Edit those values if you run this notebook on your own PC address = os.environ['SERVER_ADDRESS'] token = os.environ['API_TOKEN'] # Initialize API object api = sly.Api(address, token) project_id = 8888 # Download Project in binary format project_bin_path = sly.Project.download_bin(api, project_id, save_directory)
- 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
- Raises
ValueError
if total_count != train_count + val_count- Returns
Tuple with lists of train items information and val items information
- Return type
- Usage example
import supervisely as sly train_count = 4 val_count = 2 train_items, val_items = sly.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
- Raises
KeyError
if dataset name not found in project- Returns
Tuple with lists of train items information and val items information
- Return type
- Usage example
import supervisely as sly train_datasets = ['ds1', 'ds2'] val_datasets = ['ds3', 'ds4'] train_items, val_items = sly.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
- Raises
ValueError
if untagged not in [“ignore”, “train”, “val”]- Returns
Tuple with lists of train items information and val items information
- Return type
- Usage example
import supervisely as sly train_tag_name = 'train' val_tag_name = 'val' train_items, val_items = sly.Project.get_train_val_splits_by_tag( project_path, train_tag_name, val_tag_name )
-
static remove_classes(project_dir, classes_to_remove=
None
, inplace=False
)[source]¶ Removes given classes from Project.
- Parameters
- Returns
None
- Return type
NoneType
- Usage example
import supervisely as sly project = sly.Project(project_path, sly.OpenMode.READ) classes_to_remove = ['lemon'] project.remove_classes(project_path, classes_to_remove, inplace=True)
-
static remove_classes_except(project_dir, classes_to_keep=
None
, inplace=False
)[source]¶ Removes classes from Project with the exception of some classes.
- Parameters
- Returns
None
- Return type
NoneType
- Usage example
import supervisely as sly project = sly.Project(project_path, sly.OpenMode.READ) project.remove_classes_except(project_path, inplace=True)
-
static remove_items_without_both_objects_and_tags(project_dir, inplace=
False
)[source]¶ Remove items(images and annotations) without objects and tags from Project.
- Parameters
- Returns
None
- Return type
NoneType
- Usage example
import supervisely as sly sly.Project.remove_items_without_both_objects_and_tags(project_path, inplace=True)
-
static remove_items_without_objects(project_dir, inplace=
False
)[source]¶ Remove items(images and annotations) without objects from Project.
-
static remove_items_without_tags(project_dir, inplace=
False
)[source]¶ Remove items(images and annotations) without tags from Project.
- set_meta(new_meta)[source]¶
Saves given
meta
to project directory in json format.- Parameters
- new_meta :
ProjectMeta
ProjectMeta object.
- new_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
)[source]¶ 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).
- dest_dir :
- 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")
-
static to_detection_task(src_project_dir, dst_project_dir=
None
, inplace=False
, progress_cb=None
)[source]¶ Makes a copy of the
Project
, converts annotations toRectangles
and updatesproject meta
.- Parameters
- src_project_dir :
str
Path to source project directory.
- dst_project_dir :
str
, optional Path to destination project directory. Must be None If inplace=True.
- inplace :
bool
, optional Modifies source project If True. Must be False If dst_project_dir is specified.
- progress_cb : tqdm or callable, optional
Function for tracking download progress.
- src_project_dir :
- Returns
None
- Return type
NoneType
- Usage example
import supervisely as sly source_project = sly.Project("/home/admin/work/supervisely/projects/lemons_annotated", sly.OpenMode.READ) det_project_path = "/home/admin/work/supervisely/projects/lemons_detection" sly.Project.to_detection_task( src_project_dir=source_project.directory, dst_project_dir=det_project_path ) det_project = sly.Project(det_project_path, sly.OpenMode.READ)
-
to_pascal_voc(dest_dir=
None
, train_val_split_coef=0.8
, log_progress=True
, progress_cb=None
)[source]¶ 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).
- dest_dir :
- 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")
-
static to_segmentation_task(src_project_dir, dst_project_dir=
None
, inplace=False
, target_classes=None
, progress_cb=None
, segmentation_type='semantic'
, bg_name='__bg__'
, bg_color=None
)[source]¶ Makes a copy of the
Project
, converts annotations toBitmaps
and updatesproject meta
.You will able to get item’s segmentation masks location by
dataset.get_seg_path(item_name)
method.- Parameters
- src_project_dir :
str
Path to source project directory.
- dst_project_dir :
str
, optional Path to destination project directory. Must be None If inplace=True.
- inplace :
bool
, optional Modifies source project If True. Must be False If dst_project_dir is specified.
- target_classes :
list
[str
], optional Classes list to include to destination project. If segmentation_type=”semantic”, background class will be added automatically (by default “__bg__”).
- progress_cb : tqdm or callable, optional
Function for tracking download progress.
- segmentation_type :
str
One of: {“semantic”, “instance”}. If segmentation_type=”semantic”, background class will be added automatically (by default “__bg__”) and instances will be converted to non overlapping semantic segmentation mask.
- bg_name :
str
, optional Default background class name, used for semantic segmentation.
- bg_color :
list
, optional. Default is [0, 0, 0] Default background class color, used for semantic segmentation.
- src_project_dir :
- Returns
None
- Return type
NoneType
- Usage example
import supervisely as sly source_project = sly.Project("/home/admin/work/supervisely/projects/lemons_annotated", sly.OpenMode.READ) seg_project_path = "/home/admin/work/supervisely/projects/lemons_segmentation" sly.Project.to_segmentation_task( src_project_dir=source_project.directory, dst_project_dir=seg_project_path ) seg_project = sly.Project(seg_project_path, sly.OpenMode.READ)
-
to_yolo(dest_dir=
None
, task_type='detection'
, log_progress=True
, progress_cb=None
)[source]¶ Convert Supervisely project to YOLO format.
- Parameters
- 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")
-
static upload(dir, api, workspace_id, project_name=
None
, log_progress=True
, progress_cb=None
)[source]¶ Uploads project to Supervisely from the given directory.
If you have a metadata.json files in the project directory for images, you will be able to upload images with added custom sort parameter. To do this, use context manager
api.image.add_custom_sort()
with the desired key name from the metadata.json file which will be used for sorting. More about project struture: https://developer.supervisely.com/getting-started/supervisely-annotation-format/project-structure#project-structure-example Refer to the example section for usage details.- Parameters
- dir :
str
Path to project directory.
- api :
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.
- dir :
- Returns
Project ID and name. It is recommended to check that returned project name coincides with provided project name.
- Return type
- Usage example
import supervisely as sly # Local folder with Project project_directory = "/home/admin/work/supervisely/source/project" # Obtain server address and your api_token from environment variables # Edit those values if you run this notebook on your own PC address = os.environ['SERVER_ADDRESS'] token = os.environ['API_TOKEN'] # Initialize API object api = sly.Api(address, token) # Upload Project project_id, project_name = sly.Project.upload( project_directory, api, workspace_id=45, project_name="My Project" ) # Upload project with added custom sort order # This context manager processes every image and adds a custom sort order # if `meta` is present in the image info file or image meta file. # Otherwise, it will be uploaded without a custom sort order. with api.image.add_custom_sort(key="key_name"): project_id, project_name = sly.Project.upload( project_directory, api, workspace_id=45, project_name="My Project" )
-
static upload_bin(api, file, workspace_id, project_name=
None
, with_custom_data=True
, log_progress=True
, progress_cb=None
, skip_missed=False
)[source]¶ Uploads project to Supervisely from the given binary file and suitable only for projects downloaded in binary format. This method is a counterpart to
download_bin()
. Faster than uploading project in the usual way.- Parameters
- api :
Api
Supervisely API address and token.
- file :
str
orBytesIO
Path to the binary file or BytesIO object.
- 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.
- with_custom_data :
bool
, optional If True, custom data from source project will be added to a new project.
- log_progress :
bool
, optional Show uploading progress bar.
- progress_cb : tqdm or callable, optional
Function for tracking upload progress for datasets. Has a higher priority than log_progress.
- skip_missed :
bool
, optional Skip missed images.
- api :
- Returns
ProjectInfo object.
- Return type
ProjectInfo
- Usage example
import supervisely as sly # Local folder with Project project_path = "/home/admin/work/supervisely/source/project/222_ProjectName" # Obtain server address and your api_token from environment variables # Edit those values if you run this notebook on your own PC address = os.environ['SERVER_ADDRESS'] token = os.environ['API_TOKEN'] # Initialize API object api = sly.Api(address, token) # Upload Project project_info = sly.Project.upload_bin( api, project_path, workspace_id=45, project_name="My Project" )
- property datasets¶
Project datasets.
- Returns
Datasets
- Return type
- 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¶
Path to the project directory.
- Returns
Path to the project directory
- Return type
- 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¶
Project meta.
- Returns
ProjectMeta object
- Return type
- 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¶
Project name.
- Returns
Project name.
- Return type
- 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¶
Project parent directory.
- Returns
Path to project parent directory
- Return type
- 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¶
Total number of items in project.
- Returns
Total number of items in project
- Return type
- 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