ImageApi

class ImageApi[source]

Bases: supervisely.api.module_api.RemoveableBulkModuleApi

API for working with Image. ImageApi object is immutable.

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")

image_info = api.image.get_info_by_id(image_id) # api usage example

Methods

add_tag

Add tag with given ID to Image by ID.

add_tag_batch

Add tag with given ID to Images by IDs.

check_existing_hashes

Checks existing hashes for Images.

check_image_uploaded

Checks if Image has been uploaded.

copy

Copies Image with given ID to destination Dataset.

copy_batch

Copies Images with given IDs to Dataset.

copy_batch_optimized

Copies Images with given IDs to Dataset.

download

Downloads Image from Dataset to local path by ID.

download_bytes

Download Images with given IDs from Dataset in Binary format.

download_np

Download Image with given id in numpy format.

download_nps

Download Images with given IDs in numpy format.

download_nps_by_hashes

Download Images with given hashes in Supervisely server in numpy format.

download_nps_by_hashes_generator

rtype

Generator[Tuple[str, ndarray], None, None]

download_nps_generator

rtype

Generator[Tuple[int, ndarray], None, None]

download_path

Downloads Image from Dataset to local path by ID.

download_paths

Download Images with given ids and saves them for the given paths.

download_paths_by_hashes

Download Images with given hashes in Supervisely server and saves them for the given paths.

exists

Check if image with given name exists in dataset with given id.

get_filtered_list

List of filtered Images in the given Dataset.

get_free_name

Generates a free name for an entity with the given parent_id and name.

get_free_names

Returns list of free names for given dataset.

get_info_by_id

Get Image information by ID.

get_info_by_id_batch

Get Images information by ID.

get_info_by_name

Returns image info by image name from given dataset id.

get_list

List of Images in the given Dataset.

get_list_all_pages

Get list of all or limited quantity entities from the Supervisely server.

get_list_all_pages_generator

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_list_generator

Returns a generator that yields lists of images in the given Dataset or Project.

get_project_id

Gets Project ID by Image ID.

info_sequence

Get list of all ImageInfo field names.

info_tuple_name

Get string name of ImageInfo NamedTuple.

move

Moves Image with given ID to destination Dataset.

move_batch

Moves Images with given IDs to Dataset.

move_batch_optimized

Moves Images with given IDs to Dataset.

preview_url

Previews Image with the given resolution parameters.

raise_name_intersections_if_exist

Raises error if images with given names already exist in dataset.

remove

Remove image from supervisely by id.

remove_batch

Remove images from supervisely by ids.

storage_url

Get full Image URL link in Supervisely server.

update_meta

It is possible to add custom JSON data to every image for storing some additional information.

upload_dir

Uploads all images with supported extensions from given directory to Supervisely.

upload_dirs

Uploads all images with supported extensions from given directories to Supervisely.

upload_hash

Upload Image from given hash to Dataset.

upload_hashes

Upload images from given hashes to Dataset.

upload_id

Upload Image by ID to Dataset.

upload_ids

Upload Images by IDs to Dataset.

upload_link

Uploads Image from given link to Dataset.

upload_links

Uploads Images from given links to Dataset.

upload_medical_images

Upload medical 2D images (DICOM) to Supervisely and group them by specified or default tag.

upload_multispectral

Uploads multispectral image to Supervisely, if channels are provided, they will be uploaded as separate images.

upload_multiview_images

Uploads images to Supervisely and adds a tag to them.

upload_np

Upload given Image in numpy format with given name to Dataset.

upload_nps

Upload given Images in numpy format with given names to Dataset.

upload_path

Uploads Image with given name from given local path to Dataset.

upload_paths

Uploads Images with given names from given local path to Dataset.

url

Gets Image URL by ID.

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.ImageInfo

add_tag(image_id, tag_id, value=None)[source]

Add tag with given ID to Image by ID.

Parameters
image_id : int

Image ID in Supervisely.

tag_id : int

Tag ID in Supervisely.

value : int or str or None, optional

Tag value.

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()

image_id = 2389126
tag_id = 277083
api.image.add_tag(image_id, tag_id)
add_tag_batch(image_ids, tag_id, value=None, progress_cb=None, batch_size=100, tag_meta=None)[source]

Add tag with given ID to Images by IDs.

Parameters
image_ids : List[int]

List of Images IDs in Supervisely.

tag_id : int

Tag ID in Supervisely.

value : int or str or None, optional

Tag value.

progress_cb : tqdm or callable, optional

Function for tracking progress of adding tag.

batch_size : int, optional

Batch size

tag_meta : TagMeta, optional

Tag Meta. Needed for value validation, omit to skip validation

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()

image_ids = [2389126, 2389127]
tag_id = 277083
api.image.add_tag_batch(image_ids, tag_id)
check_existing_hashes(hashes, progress_cb=None)[source]

Checks existing hashes for Images.

Parameters
hashes : List[str]

List of hashes.

progress_cb : tqdm or callable, optional

Function for tracking progress of checking.

Returns

List of existing hashes

Return type

List[str]

Usage example

Checkout detailed example here (you must be logged into your Supervisely account)

# Helpful method when your uploading was interrupted
# You can check what images has been successfully uploaded by their hashes and what not
# And continue uploading the rest of the images from that point

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()

# Find project
project = api.project.get_info_by_id(WORKSPACE_ID, PROJECT_ID)

# Get paths of all images in a directory
images_paths = sly.fs.list_files('images_to_upload')

# Calculate hashes for all images paths
hash_to_image = {}
images_hashes = []

for idx, item in enumerate(images_paths):
    item_hash = sly.fs.get_file_hash(item)
    images_hashes.append(item_hash)
    hash_to_image[item_hash] = item

# Get hashes that are already on server
remote_hashes = api.image.check_existing_hashes(images_hashes)
already_uploaded_images = {hh: hash_to_image[hh] for hh in remote_hashes}
check_image_uploaded(hash)[source]

Checks if Image has been uploaded.

Parameters
hash : str

Image hash in Supervisely.

Returns

True if Image with given hash exist, otherwise False

Return type

bool

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()

image_check_uploaded = api.image.check_image_uploaded("YZKQrZH5C0rBvGGA3p7hjWahz3/pV09u5m30Bz8GeYs=")
print(image_check_uploaded)
# Output: True
copy(dst_dataset_id, id, change_name_if_conflict=False, with_annotations=False)[source]

Copies Image with given ID to destination Dataset.

Parameters
dst_dataset_id : int

Destination Dataset ID in Supervisely.

id : int

Image ID in Supervisely.

change_name_if_conflict : bool, optional

If True adds suffix to the end of Image name when Dataset already contains an Image with identical name, If False and images with the identical names already exist in Dataset raises error.

with_annotations : bool, optional

If True Image will be copied to Dataset with annotations, otherwise only Images without annotations.

Returns

Information about Image. See info_sequence

Return type

ImageInfo

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_ds_id = 365184
img_id = 121236920

img_info = api.image.copy(dst_ds_id, img_id, with_annotations=True)
copy_batch(dst_dataset_id, ids, change_name_if_conflict=False, with_annotations=False, progress_cb=None)[source]

Copies Images with given IDs to Dataset.

Parameters
dst_dataset_id : int

Destination Dataset ID in Supervisely.

ids : List[int]

Images IDs in Supervisely.

change_name_if_conflict : bool, optional

If True adds suffix to the end of Image name when Dataset already contains an Image with identical name, If False and images with the identical names already exist in Dataset raises error.

with_annotations : bool, optional

If True Image will be copied to Dataset with annotations, otherwise only Images without annotations.

progress_cb : tqdm or callable, optional

Function for tracking the progress of copying.

Raises

TypeError if type of ids is not list

Raises

ValueError if images ids are from the destination Dataset

Returns

List with information about Images. See info_sequence

Return type

List[ImageInfo]

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()

ds_lemon_id = 1780

ds_lemon_img_infos = api.image.get_list(ds_lemon_id)

lemons_img_ids = [lemon_img_info.id for lemon_img_info in ds_lemon_img_infos]

ds_fruit_id = 2574
ds_fruit_img_infos = api.image.copy_batch(ds_fruit_id, lemons_img_ids, with_annotations=True)
copy_batch_optimized(src_dataset_id, src_image_infos, dst_dataset_id, with_annotations=True, progress_cb=None, dst_names=None, batch_size=500, skip_validation=False, save_source_date=True)[source]

Copies Images with given IDs to Dataset.

Parameters
src_dataset_id : int

Source Dataset ID in Supervisely.

src_image_infos : List [ ImageInfo ]

ImageInfo objects of images to copy.

dst_dataset_id : int

Destination Dataset ID in Supervisely.

with_annotations : bool, optional

If True Image will be copied to Dataset with annotations, otherwise only Images without annotations.

progress_cb : tqdm or callable, optional

Function for tracking the progress of copying.

dst_names : List [ ImageInfo ], optional

ImageInfo list with existing items in destination dataset.

batch_size : int, optional

Number of elements to copy for each request.

skip_validation : bool, optional

Flag for skipping additinal validations.

save_source_date : bool, optional

Save source annotation dates (creation and modification) or create a new date.

Raises

TypeError if type of src_image_infos is not list

Returns

List with information about Images. See info_sequence

Return type

List[ImageInfo]

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_ds_id = 2231
img_infos = api.image.get_list(src_ds_id)

dest_ds_id = 2574
dest_img_infos = api.image.copy_batch_optimized(src_ds_id, img_infos, dest_ds_id)
download(id, path)[source]

Downloads Image from Dataset to local path by ID.

Parameters
id : int

Image ID in Supervisely.

path : str

Local save path for Image.

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()

img_info = api.image.get_info_by_id(770918)
save_path = os.path.join("/home/admin/work/projects/lemons_annotated/ds1/test_imgs/", img_info.name)

api.image.download_path(770918, save_path)
download_bytes(dataset_id, ids, progress_cb=None)[source]

Download Images with given IDs from Dataset in Binary format.

Parameters
dataset_id : int

Dataset ID in Supervisely, where Images are located.

ids : List[int]

List of Image IDs in Supervisely.

progress_cb : tqdm or callable, optional

Function for tracking download progress.

Returns

List of Images in binary format

Return type

List[bytes]

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_bytes = api.image.download_bytes(dataset_id, [770918])
print(img_bytes)
# Output: [b'ÿØÿàJFIF\...]
download_np(id, keep_alpha=False)[source]

Download Image with given id in numpy format.

Parameters
id : int

Image ID in Supervisely.

keep_alpha : bool, optional

If True keeps alpha mask for image, otherwise don’t.

Returns

Image in RGB numpy matrix format

Return type

np.ndarray

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()

image_np = api.image.download_np(770918)
download_nps(dataset_id, ids, progress_cb=None, keep_alpha=False)[source]

Download Images with given IDs in numpy format.

Parameters
dataset_id : int

Dataset ID in Supervisely, where Images are located.

ids : List[int]

List of Images IDs in Supervisely.

progress_cb : tqdm or callable, optional

Function for tracking download progress.

keep_alpha : bool, optional

If True keeps alpha mask for Image, otherwise don’t.

Returns

List of Images in RGB numpy matrix format

Return type

List[np.ndarray]

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()

image_ids = [770918, 770919, 770920]
image_nps = api.image.download_nps(dataset_id, image_ids)
download_nps_by_hashes(hashes, keep_alpha=False, progress_cb=None)[source]

Download Images with given hashes in Supervisely server in numpy format.

Parameters
hashes : List[str]

List of images hashes in Supervisely.

progress_cb : tqdm or callable, optional

Function for tracking download progress.

Returns

List of images

Return type

class

List[np.ndarray]

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()

image_ids = [770918, 770919, 770920]
image_hashes = []

for img_id in image_ids:
    img_info = api.image.get_info_by_id(image_id)
    image_hashes.append(img_info.hash)

image_nps = api.image.download_nps_by_hashes(image_hashes)
download_path(id, path)[source]

Downloads Image from Dataset to local path by ID.

Parameters
id : int

Image ID in Supervisely.

path : str

Local save path for Image.

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()

img_info = api.image.get_info_by_id(770918)
save_path = os.path.join("/home/admin/work/projects/lemons_annotated/ds1/test_imgs/", img_info.name)

api.image.download_path(770918, save_path)
download_paths(dataset_id, ids, paths, progress_cb=None)[source]

Download Images with given ids and saves them for the given paths.

Parameters
dataset_id : int

Dataset ID in Supervisely, where Images are located.

ids : List[int]

List of Image IDs in Supervisely.

paths : List[str]

Local save paths for Images.

progress_cb : tqdm or callable, optional

Function for tracking download progress.

Raises

ValueError if len(ids) != len(paths)

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()

local_save_dir = "/home/admin/work/projects/lemons_annotated/ds1/test_imgs"
save_paths = []
image_ids = [771755, 771756, 771757, 771758, 771759, 771760]
img_infos = api.image.get_info_by_id_batch(image_ids)

p = tqdm(desc="Images downloaded: ", total=len(img_infos))
for img_info in img_infos:
    save_paths.append(os.path.join(local_save_dir, img_info.name))

api.image.download_paths(2573, image_ids, save_paths, progress_cb=p)
# Progress:
# {"message": "progress", "event_type": "EventType.PROGRESS", "subtask": "Images downloaded: ", "current": 0, "total": 6, "timestamp": "2021-03-15T19:47:15.406Z", "level": "info"}
# {"message": "progress", "event_type": "EventType.PROGRESS", "subtask": "Images downloaded: ", "current": 1, "total": 6, "timestamp": "2021-03-15T19:47:16.366Z", "level": "info"}
# {"message": "progress", "event_type": "EventType.PROGRESS", "subtask": "Images downloaded: ", "current": 2, "total": 6, "timestamp": "2021-03-15T19:47:16.367Z", "level": "info"}
# {"message": "progress", "event_type": "EventType.PROGRESS", "subtask": "Images downloaded: ", "current": 3, "total": 6, "timestamp": "2021-03-15T19:47:16.367Z", "level": "info"}
# {"message": "progress", "event_type": "EventType.PROGRESS", "subtask": "Images downloaded: ", "current": 4, "total": 6, "timestamp": "2021-03-15T19:47:16.367Z", "level": "info"}
# {"message": "progress", "event_type": "EventType.PROGRESS", "subtask": "Images downloaded: ", "current": 5, "total": 6, "timestamp": "2021-03-15T19:47:16.368Z", "level": "info"}
# {"message": "progress", "event_type": "EventType.PROGRESS", "subtask": "Images downloaded: ", "current": 6, "total": 6, "timestamp": "2021-03-15T19:47:16.368Z", "level": "info"}
download_paths_by_hashes(hashes, paths, progress_cb=None)[source]

Download Images with given hashes in Supervisely server and saves them for the given paths.

Parameters
hashes : List[str]

List of images hashes in Supervisely.

paths : List[str]

List of paths to save images.

progress_cb : tqdm or callable, optional

Function for tracking download progress.

Raises

ValueError if len(hashes) != len(paths)

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()

dataset_id = 447130
dir_for_save = '/home/admin/Downloads/img'
hashes = []
paths = []
imgs_info = api.image.get_list(dataset_id)
for im_info in imgs_info:
    hashes.append(im_info.hash)
    # It is necessary to save images with the same names(extentions) as on the server
    paths.append(os.path.join(dir_for_save, im_info.name))
api.image.download_paths_by_hashes(hashes, paths)
exists(parent_id, name)[source]

Check if image with given name exists in dataset with given id.

Parameters
parent_id : int

Dataset ID in Supervisely.

name : str

Image name in Supervisely.

Returns

True if image exists, False otherwise.

Return type

bool

get_filtered_list(dataset_id=None, filters=None, sort='id', sort_order='asc', force_metadata_for_links=True, limit=None, return_first_response=False, project_id=None)[source]

List of filtered Images in the given Dataset. Differs in a more flexible filter format from the get_list() method.

Parameters
dataset_id : int

Dataset ID in which the Images are located.

filters : List[Dict], optional

List of params to sort output Images.

sort : str, optional

Field name to sort. One of {‘id’ (default), ‘name’, ‘description’, ‘labelsCount’, ‘createdAt’, ‘updatedAt’}.

sort_order : str, optional

Sort order. One of {‘asc’ (default), ‘desc’}

project_id : int

Project ID in which the Images are located.

Returns

Objects with image information from Supervisely.

Return type

List[ImageInfo]

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()

# Get list of Images with names containing subsequence '2008'
img_infos = api.image.get_filtered_list(dataset_id, filters=[{ 'type': 'images_filename', 'data': { 'value': '2008' } }])
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
parent_id : int

ID of the parent entity.

name : str

Name of the entity.

Returns

Returns free name.

Return type

str

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)[source]

Returns list of free names for given dataset.

Parameters
dataset_id : int

Dataset ID in Supervisely.

names : List[str]

List of names to check.

Returns

List of free names.

Return type

List[str]

get_info_by_id(id, force_metadata_for_links=True)[source]

Get Image information by ID.

Parameters
id : int

Image ID in Supervisely.

Returns

Object with image information from Supervisely.

Return type

ImageInfo

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()

# You can get Image ID by listing all images in the Dataset as shown in get_list
# Or you can open certain image in Supervisely Annotation Tool UI and get last digits of the URL
img_info = api.image.get_info_by_id(770918)
get_info_by_id_batch(ids, progress_cb=None, force_metadata_for_links=True)[source]

Get Images information by ID.

Parameters
ids : List[int]

Images IDs in Supervisely.

progress_cb : tqdm or callable, optional

Function for tracking the progress.

Returns

Objects with image information from Supervisely.

Return type

List[ImageInfo]

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_ids = [376728, 376729, 376730, 376731, 376732, 376733]
img_infos = image.get_info_by_id_batch(img_ids)
get_info_by_name(dataset_id, name, force_metadata_for_links=True)[source]

Returns image info by image name from given dataset id.

Parameters
dataset_id : int

Dataset ID in Supervisely, where Image is located.

name : str

Image name in Supervisely.

force_metadata_for_links : bool, optional

If True, returns full_storage_url and path_original fields in ImageInfo.

Returns

Object with image information from Supervisely.

Return type

ImageInfo

get_list(dataset_id=None, filters=None, sort='id', sort_order='asc', limit=None, force_metadata_for_links=True, return_first_response=False, project_id=None)[source]

List of Images in the given Dataset.

Parameters
dataset_id : int

Dataset ID in which the Images are located.

filters : List[Dict], optional

List of params to sort output Images.

sort : str, optional

Field name to sort. One of {‘id’ (default), ‘name’, ‘description’, ‘labelsCount’, ‘createdAt’, ‘updatedAt’}

sort_order : str, optional

Sort order. One of {‘asc’ (default), ‘desc’}

limit : int, optional

Max number of list elements. No limit if None (default).

force_metadata_for_links : bool, optional

If True, updates meta for images with remote storage links when listing.

return_first_response : bool, optional

If True, returns first response without waiting for all pages.

project_id : int

Project ID in which the Images are located.

Returns

Objects with image information from Supervisely.

Return type

List[ImageInfo]

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()

# Get list of Images with width = 1067
img_infos = api.image.get_list(dataset_id, filters=[{ 'field': 'width', 'operator': '=', 'value': '1067' }])
print(img_infos)
# Output: [ImageInfo(id=770915,
#                    name='IMG_3861.jpeg',
#                    link=None,
#                    hash='ZdpMD+ZMJx0R8BgsCzJcqM7qP4M8f1AEtoYc87xZmyQ=',
#                    mime='image/jpeg',
#                    ext='jpeg',
#                    size=148388,
#                    width=1067,
#                    height=800,
#                    labels_count=4,
#                    dataset_id=2532,
#                    created_at='2021-03-02T10:04:33.973Z',
#                    updated_at='2021-03-02T10:04:33.973Z',
#                    meta={},
#                    path_original='/h5un6l2bnaz1vj8a9qgms4-public/images/original/7/h/Vo/...jpg',
#                    full_storage_url='http://app.supervise.ly/h5un6l2bnaz1vj8a9qgms4-public/images/original/7/h/Vo/...jpg'),
#                    tags=[],
# ImageInfo(id=770916,
#           name='IMG_1836.jpeg',
#           link=None,
#           hash='YZKQrZH5C0rBvGGA3p7hjWahz3/pV09u5m30Bz8GeYs=',
#           mime='image/jpeg',
#           ext='jpeg',
#           size=140222,
#           width=1067,
#           height=800,
#           labels_count=3,
#           dataset_id=2532,
#           created_at='2021-03-02T10:04:33.973Z',
#           updated_at='2021-03-02T10:04:33.973Z',
#           meta={},
#           path_original='/h5un6l2bnaz1vj8a9qgms4-public/images/original/C/Y/Hq/...jpg',
#           full_storage_url='http://app.supervise.ly/h5un6l2bnaz1vj8a9qgms4-public/images/original/C/Y/Hq/...jpg'),
#           tags=[]
# ]
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_list_generator(dataset_id=None, filters=None, sort='id', sort_order='asc', limit=None, force_metadata_for_links=False, batch_size=None, project_id=None)[source]

Returns a generator that yields lists of images in the given Dataset or Project.

Parameters
dataset_id : int

Dataset ID in which the Images are located.

filters : List[Dict], optional

List of params to sort output Images.

sort : str, optional

Field name to sort. One of {‘id’ (default), ‘name’, ‘description’, ‘labelsCount’, ‘createdAt’, ‘updatedAt’}

sort_order : str, optional

Sort order. One of {‘asc’ (default), ‘desc’}

limit : int, optional

Max number of list elements. No limit if None (default).

force_metadata_for_links : bool, optional

If True, updates meta for images with remote storage links when listing.

batch_size : int, optional

Number of images to get in each request.

project_id : int

Project ID in which the Images are located.

Return type

Iterator[List[ImageInfo]]

Returns

Generator that yields lists of images in the given Dataset or Project.

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()

for images_batch in api.image.get_list_generator(dataset_id):
    print(images_batch)
get_project_id(image_id)[source]

Gets Project ID by Image ID.

Parameters
image_id : int

Image ID in Supervisely.

Returns

Project ID where Image is located.

Return type

int

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_id = 121236920
img_project_id = api.image.get_project_id(img_id)
print(img_project_id)
# Output: 53939
static info_sequence()[source]

Get list of all ImageInfo field names.

Returns

List of ImageInfo field names.`

Return type

List[str]

static info_tuple_name()[source]

Get string name of ImageInfo NamedTuple.

Returns

NamedTuple name.

Return type

str

move(dst_dataset_id, id, change_name_if_conflict=False, with_annotations=False)[source]

Moves Image with given ID to destination Dataset.

Parameters
dst_dataset_id : int

Destination Dataset ID in Supervisely.

id : int

Image ID in Supervisely.

change_name_if_conflict : bool, optional

If True adds suffix to the end of Image name when Dataset already contains an Image with identical name, If False and images with the identical names already exist in Dataset raises error.

with_annotations : bool, optional

If True Image will be copied to Dataset with annotations, otherwise only Images without annotations.

Returns

Information about Image. See info_sequence

Return type

ImageInfo

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_ds_id = 365484
img_id = 533336920

img_info = api.image.copy(dst_ds_id, img_id, with_annotations=True)
move_batch(dst_dataset_id, ids, change_name_if_conflict=False, with_annotations=False, progress_cb=None)[source]

Moves Images with given IDs to Dataset.

Parameters
dst_dataset_id : int

Destination Dataset ID in Supervisely.

ids : List[int]

Images IDs in Supervisely.

change_name_if_conflict : bool, optional

If True adds suffix to the end of Image name when Dataset already contains an Image with identical name, If False and images with the identical names already exist in Dataset raises error.

with_annotations : bool, optional

If True Image will be copied to Dataset with annotations, otherwise only Images without annotations.

progress_cb : tqdm or callable, optional

Function for tracking the progress of moving.

Raises

TypeError if type of ids is not list

Raises

ValueError if images ids are from the destination Dataset

Returns

List with information about Images. See info_sequence

Return type

List[ImageInfo]

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()

ds_lemon_id = 1780
ds_kiwi_id = 1233

ds_lemon_img_infos = api.image.get_list(ds_lemon_id)
ds_kiwi_img_infos = api.image.get_list(ds_kiwi_id)

fruit_img_ids = []
for lemon_img_info, kiwi_img_info in zip(ds_lemon_img_infos, ds_kiwi_img_infos):
    fruit_img_ids.append(lemon_img_info.id)
    fruit_img_ids.append(kiwi_img_info.id)

ds_fruit_id = 2574
ds_fruit_img_infos = api.image.move_batch(ds_fruit_id, fruit_img_ids, with_annotations=True)
move_batch_optimized(src_dataset_id, src_image_infos, dst_dataset_id, with_annotations=True, progress_cb=None, dst_names=None, batch_size=500, skip_validation=False, save_source_date=True)[source]

Moves Images with given IDs to Dataset.

Parameters
src_dataset_id : int

Source Dataset ID in Supervisely.

src_image_infos : List [ ImageInfo ]

ImageInfo objects of images to move.

dst_dataset_id : int

Destination Dataset ID in Supervisely.

with_annotations : bool, optional

If True Image will be copied to Dataset with annotations, otherwise only Images without annotations.

progress_cb : tqdm or callable, optional

Function for tracking the progress of moving.

dst_names : List [ ImageInfo ], optional

ImageInfo list with existing items in destination dataset.

batch_size : int, optional

Number of elements to copy for each request.

skip_validation : bool, optional

Flag for skipping additinal validations.

save_source_date : bool, optional

Save source annotation dates (creation and modification) or create a new date.

Raises

TypeError if type of src_image_infos is not list

Returns

List with information about Images. See info_sequence

Return type

List[ImageInfo]

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_ds_id = 2231
img_infos = api.image.get_list(src_ds_id)

dest_ds_id = 2574
dest_img_infos = api.image.move_batch_optimized(src_ds_id, img_infos, dest_ds_id)
preview_url(url, width=None, height=None, quality=70, ext='jpeg', method='auto')[source]

Previews Image with the given resolution parameters. Learn more about resize parameters here.

Parameters
url : str

Full Image storage URL.

width : int

Preview Image width.

height : int

Preview Image height.

quality : int

Preview Image quality.

ext : str, optional

Preview Image extension, available values: “jpeg”, “png”.

method : str, optional

Preview Image resize method, available values: “fit”, “fill”, “fill-down”, “force”, “auto”.

Returns

New URL with resized Image

Return type

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()

image_id = 376729
img_info = api.image.get_info_by_id(image_id)
img_preview_url = api.image.preview_url(img_info.full_storage_url, width=512, height=256)
raise_name_intersections_if_exist(dataset_id, names, message=None)[source]

Raises error if images with given names already exist in dataset. Default error message: “Images with the following names already exist in dataset [ID={dataset_id}]: {name_intersections}. Please, rename images and try again or set change_name_if_conflict=True to rename automatically on upload.”

Parameters
dataset_id : int

Dataset ID in Supervisely.

names : List[str]

List of names to check.

message : str, optional

Error message.

Returns

None

Return type

None

remove(image_id)[source]

Remove image from supervisely by id. All image IDs must belong to the same dataset. Therefore, it is necessary to sort IDs before calling this method.

Parameters
image_id : int

Images ID in Supervisely.

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()

image_id = 2389126
api.image.remove(image_id)
remove_batch(ids, progress_cb=None, batch_size=50)[source]

Remove images from supervisely by ids.

Parameters
ids : List[int]

List of Images IDs in Supervisely.

progress_cb : tqdm or callable, optional

Function for tracking progress of removing.

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()

image_ids = [2389126, 2389127]
api.image.remove_batch(image_ids)
storage_url(path_original)[source]

Get full Image URL link in Supervisely server.

Parameters
path_original : str

Original Image path in Supervisely server.

Returns

Full Image URL link in Supervisely server

Return type

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()

image_id = 376729
img_info = api.image.get_info_by_id(image_id)
img_storage_url = api.image.storage_url(img_info.path_original)
update_meta(id, meta)[source]

It is possible to add custom JSON data to every image for storing some additional information. Updates Image metadata by ID. Metadata is visible in Labeling Tool. Supervisely also have 2 apps: import metadata and export metadata

Parameters
id : int

Image ID in Supervisely.

meta : dict

Image metadata.

Raises

TypeError if meta type is not dict

Returns

Image information in dict format with new meta

Return type

dict

Usage example
import os
import json
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()

image_info = api.image.get_info_by_id(id=3212008)
print(image_info.meta)
# Output: {}

new_meta = {'Camera Make': 'Canon', 'Color Space': 'sRGB', 'Focal Length': '16 mm'}
new_image_info = api.image.update_meta(id=3212008, meta=new_meta)

image_info = api.image.get_info_by_id(id=3212008)
print(json.dumps(obj=image_info.meta, indent=4))
# Output: {
#     "Camera Make": "Canon",
#     "Color Space": "sRGB",
#     "Focal Length": "16 mm"
# }
upload_dir(dataset_id, dir_path, recursive=True, change_name_if_conflict=True, progress_cb=None)[source]

Uploads all images with supported extensions from given directory to Supervisely. Optionally, uploads images from subdirectories of given directory.

Parameters
dataset_id : int

Dataset ID in Supervisely.

dir_path : str

Path to directory with images.

recursive : bool, optional

If True uploads images from subdirectories of given directory recursively, otherwise only images from given directory.

change_name_if_conflict : bool, optional

If True adds suffix to the end of Image name when Dataset already contains an Image with identical name, If False and images 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 images infos

Return type

List[ImageInfo]

upload_dirs(dataset_id, dir_paths, recursive=True, change_name_if_conflict=True, progress_cb=None)[source]

Uploads all images with supported extensions from given directories to Supervisely. Optionally, uploads images from subdirectories of given directories.

Parameters
dataset_id : int

Dataset ID in Supervisely.

dir_paths : List[str]

List of paths to directories with images.

recursive : bool, optional

If True uploads images from subdirectories of given directories recursively, otherwise only images from given directories.

change_name_if_conflict : bool, optional

If True adds suffix to the end of Image name when Dataset already contains an Image with identical name, If False and images 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 images infos

Return type

List[ImageInfo]

upload_hash(dataset_id, name, hash, meta=None)[source]

Upload Image from given hash to Dataset.

Parameters
dataset_id : int

Dataset ID in Supervisely.

name : str

Image name with extension.

hash : str

Image hash.

meta : dict, optional

Image metadata.

Returns

Information about Image. See info_sequence

Return type

ImageInfo

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 = 452984
im_info = api.image.get_info_by_id(193940090)
hash = im_info.hash
# It is necessary to upload image with the same name(extention) as in src dataset
name = im_info.name
meta = {1: 'meta_example'}
new_in_info = api.image.upload_hash(dst_dataset_id, name, hash, meta)
print(json.dumps(new_in_info, indent=4))
# Output: [
#     196793586,
#     "IMG_0748.jpeg",
#     null,
#     "NEjmnmdd7DOzaFAKK/nCIl5CtcwZeMkhW3CHe875p9g=",
#     "image/jpeg",
#     "jpeg",
#     66885,
#     600,
#     500,
#     0,
#     452984,
#     "2021-03-16T09:09:45.587Z",
#     "2021-03-16T09:09:45.587Z",
#     {
#         "1": "meta_example"
#     },
#     "/h5un6l2bnaz1vj8a9qgms4-public/images/original/P/a/kn/W2mzMQg435d6wG0.jpg",
#     "https://app.supervise.ly/h5un6l2bnaz1vj8a9qgms4-public/images/original/P/a/kn/W2mzMQg435hiHJAPgMU.jpg"
# ]
upload_hashes(dataset_id, names, hashes, progress_cb=None, metas=None, batch_size=50, skip_validation=False)[source]

Upload images from given hashes to Dataset.

Parameters
dataset_id : int

Dataset ID in Supervisely.

names : List[str]

Images names with extension.

hashes : List[str]

Images hashes.

progress_cb : tqdm or callable, optional

Function for tracking the progress of uploading.

metas : List[dict], optional

Images metadata.

batch_size : int, optional

Number of images to upload in one batch.

skip_validation : bool, optional

Skips validation for images, can result in invalid images being uploaded.

Returns

List with information about Images. See info_sequence

Return type

List[ImageInfo]

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 = 447130
hashes = []
names = []
metas = []
imgs_info = api.image.get_list(src_dataset_id)
# Create lists of hashes, images names and meta information for each image
for im_info in imgs_info:
    hashes.append(im_info.hash)
    # It is necessary to upload images with the same names(extentions) as in src dataset
    names.append(im_info.name)
    metas.append({im_info.name: im_info.size})

dst_dataset_id = 452984
progress = sly.Progress("Images upload: ", len(hashes))
new_imgs_info = api.image.upload_hashes(dst_dataset_id, names, hashes, progress.iters_done_report, metas)
# Output:
# {"message": "progress", "event_type": "EventType.PROGRESS", "subtask": "Images downloaded: ", "current": 0, "total": 10, "timestamp": "2021-03-16T11:59:07.444Z", "level": "info"}
# {"message": "progress", "event_type": "EventType.PROGRESS", "subtask": "Images downloaded: ", "current": 10, "total": 10, "timestamp": "2021-03-16T11:59:07.644Z", "level": "info"}
upload_id(dataset_id, name, id, meta=None)[source]

Upload Image by ID to Dataset.

Parameters
dataset_id : int

Destination Dataset ID in Supervisely.

name : str

Image name with extension.

id : int

Source image ID in Supervisely.

meta : dict, optional

Image metadata.

Returns

Information about Image. See info_sequence

Return type

ImageInfo

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 = 452984
im_info = api.image.get_info_by_id(193940090)
id = im_info.id
# It is necessary to upload image with the same name(extention) as in src dataset
name = im_info.name
meta = {1: 'meta_example'}
new_in_info = api.image.upload_id(dst_dataset_id, name, id, meta)
print(json.dumps(new_in_info, indent=4))
# Output: [
#     196793605,
#     "IMG_0748.jpeg",
#     null,
#     "NEjmnmdd7DOzaFAKK/nCIl5CtcwZeMkhW3CHe875p9g=",
#     "image/jpeg",
#     "jpeg",
#     66885,
#     600,
#     500,
#     0,
#     452984,
#     "2021-03-16T09:27:12.620Z",
#     "2021-03-16T09:27:12.620Z",
#     {
#         "1": "meta_example"
#     },
#     "/h5un6l2bnaz1vj8a9qgms4-public/images/original/P/a/kn/W2mzMQg435d6wG0AJGJTOsL1FqMUNOPqu4VdzFAN36LqtGwBIE4AmLOQ1BAxuIyB0bHJAPgMU.jpg",
#     "https://app.supervise.ly/h5un6l2bnaz1vj8a9qgms4-public/images/original/P/a/kn/iEaDEkejnfnb1Tz56ka0hiHJAPgMU.jpg"
# ]
upload_ids(dataset_id, names, ids, progress_cb=None, metas=None, batch_size=50, force_metadata_for_links=True, infos=None, skip_validation=False)[source]

Upload Images by IDs to Dataset.

Parameters
dataset_id : int

Destination Dataset ID in Supervisely.

names : List[str]

Source images names with extension.

ids : List[int]

Images IDs.

progress_cb : tqdm or callable, optional

Function for tracking the progress of uploading.

metas : List[dict], optional

Images metadata.

batch_size : int, optional

Number of images to upload in one batch.

force_metadata_for_links : bool, optional

Calculate metadata for links. If False, metadata will be empty.

infos : List[ImageInfo], optional

List of ImageInfo objects. If None, will be requested from server.

skip_validation : bool, optional

Skips validation for images, can result in invalid images being uploaded.

Returns

List with information about Images. See info_sequence

Return type

List[ImageInfo]

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 = 447130

ids = []
names = []
metas = []
imgs_info = api.image.get_list(src_dataset_id)
# Create lists of ids, images names and meta information for each image
for im_info in imgs_info:
    ids.append(im_info.id)
    # It is necessary to upload images with the same names(extentions) as in src dataset
    names.append(im_info.name)
    metas.append({im_info.name: im_info.size})

dst_dataset_id = 452984
progress = sly.Progress("Images upload: ", len(ids))
new_imgs_info = api.image.upload_ids(dst_dataset_id, names, ids, progress.iters_done_report, metas)
# Output:
# {"message": "progress", "event_type": "EventType.PROGRESS", "subtask": "Images downloaded: ", "current": 0, "total": 10, "timestamp": "2021-03-16T12:31:36.550Z", "level": "info"}
# {"message": "progress", "event_type": "EventType.PROGRESS", "subtask": "Images downloaded: ", "current": 10, "total": 10, "timestamp": "2021-03-16T12:31:37.119Z", "level": "info"}

Uploads Image from given link to Dataset.

Parameters
dataset_id : int

Dataset ID in Supervisely.

name : str

Image name with extension.

link : str

Link to Image.

meta : dict, optional

Image metadata.

force_metadata_for_links : bool, optional

Calculate metadata for link. If False, metadata will be empty.

Returns

Information about Image. See info_sequence

Return type

ImageInfo

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_name = 'Avatar.jpg'
img_link = 'https://m.media-amazon.com/images/M/MV5BMTYwOTEwNjAzMl5BMl5BanBnXkFtZTcwODc5MTUwMw@@._V1_.jpg'

img_info = api.image.upload_link(dataset_id, img_name, img_link)

Uploads Images from given links to Dataset.

Parameters
dataset_id : int

Dataset ID in Supervisely.

names : List[str]

Images names with extension.

links : List[str]

Links to Images.

progress_cb : tqdm or callable, optional

Function for tracking the progress of uploading.

metas : List[dict], optional

Images metadata.

force_metadata_for_links : bool, optional

Calculate metadata for links. If False, metadata will be empty.

skip_validation : bool, optional

Skips validation for images, can result in invalid images being uploaded.

Returns

List with information about Images. See info_sequence

Return type

List[ImageInfo]

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_names = ['Avatar.jpg', 'Harry Potter.jpg', 'Avengers.jpg']
img_links = ['https://m.media-amazon.com/images/M/MV5BMTYwOTEwNjAzMl5BMl5BanBnXkFtZTcwODc5MTUwMw@@._V1_.jpg',
             'https://m.media-amazon.com/images/M/MV5BNDYxNjQyMjAtNTdiOS00NGYwLWFmNTAtNThmYjU5ZGI2YTI1XkEyXkFqcGdeQXVyMTMxODk2OTU@._V1_.jpg',
             'https://m.media-amazon.com/images/M/MV5BNjQ3NWNlNmQtMTE5ZS00MDdmLTlkZjUtZTBlM2UxMGFiMTU3XkEyXkFqcGdeQXVyNjUwNzk3NDc@._V1_.jpg']

img_infos = api.image.upload_links(dataset_id, img_names, img_links)
upload_medical_images(dataset_id, paths, group_tag_name=None, metas=None, progress_cb=None)[source]

Upload medical 2D images (DICOM) to Supervisely and group them by specified or default tag.

Parameters
dataset_id : int

Dataset ID in Supervisely.

paths : List[str]

List of paths to images.

group_tag_name : str, optional

Group name. All images will be assigned by tag with this group name. If group_tag_name is None, the images will be grouped by one of the default tags.

metas : List[Dict], optional

List of dictionaries which adds a customizable meta for every image provided in paths parameter.

progress_cb : tqdm or callable, optional

Function for tracking upload progress.

Returns

List of uploaded images infos.

Return type

List[ImageInfo]

Raises
  • Exception – If tag does not exist in project or tag is not of type ANY_STRING

  • Exception – If length of metas is not equal to the length of paths.

Usage example
import os
from dotenv import load_dotenv
from tqdm import tqdm

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()

dataset_id = 123456
paths = ['path/to/medical_01.dcm', 'path/to/medical_02.dcm']
metas = [{'meta':'01'}, {'meta':'02'}]
group_tag_name = 'StudyInstanceUID'

pbar = tqdm(desc="Uploading images", total=len(paths))
image_infos = api.image.upload_medical_images(dataset_id, paths, group_tag_name, metas)
upload_multispectral(dataset_id, image_name, channels=None, rgb_images=None, progress_cb=None)[source]

Uploads multispectral image to Supervisely, if channels are provided, they will be uploaded as separate images. If rgb_images are provided, they will be uploaded without splitting into channels as RGB images.

Parameters
dataset_id : int

dataset ID to upload images to

image_name : str

name of the image with extension.

channels : List[np.ndarray], optional

list of numpy arrays with image channels

rgb_images : List[str], optional

list of paths to RGB images which will be uploaded as is

progress_cb : tqdm or callable, optional

function for tracking upload progress

Returns

list of uploaded images infos

Return type

List[ImageInfo]

Usage example
import os
import supervisely as sly

os.environ['SERVER_ADDRESS'] = 'https://app.supervisely.com'
os.environ['API_TOKEN'] = 'Your Supervisely API Token'

# Load secrets and create API object from .env file (recommended)
# Learn more here: https://developer.supervisely.com/getting-started/basics-of-authentication
load_dotenv(os.path.expanduser("~/supervisely.env"))

api = sly.Api.from_env()

image_name = "demo1.png"
image = cv2.imread(f"demo_data/{image_name}")

# Extract channels as 2d numpy arrays: channels = [a, b, c]
channels = [image[:, :, i] for i in range(image.shape[2])]

image_infos = api.image.upload_multispectral(api, dataset.id, image_name, channels)
upload_multiview_images(dataset_id, group_name, paths, metas=None, progress_cb=None)[source]

Uploads images to Supervisely and adds a tag to them.

Parameters
dataset_id : int

Dataset ID in Supervisely.

tag_name : str

Tag name in Supervisely. If tag does not exist in project, create it first. Tag must be of type ANY_STRING.

group_name : str

Group name. All images will be assigned by tag with this group name.

paths : List[str]

List of paths to images.

metas : Optional[List[Dict]]

List of dictionaries which adds a customizable meta for every image provided in paths parameter.

progress_cb : Optional[Union[tqdm, Callable]]

Function for tracking upload progress.

Returns

List of uploaded images infos

Return type

List[ImageInfo]

Raises

Exception – if tag does not exist in project or tag is not of type ANY_STRING

Usage example
import os
from dotenv import load_dotenv

import supervisely as sly

os.environ['SERVER_ADDRESS'] = 'https://app.supervisely.com'
os.environ['API_TOKEN'] = 'Your Supervisely API Token'

# Load secrets and create API object from .env file (recommended)
# Learn more here: https://developer.supervisely.com/getting-started/basics-of-authentication
load_dotenv(os.path.expanduser("~/supervisely.env"))

api = sly.Api.from_env()

dataset_id = 123456
paths = ['path/to/audi_01.png', 'path/to/audi_02.png']
group_name = 'audi'

image_infos = api.image.upload_multiview_images(dataset_id, group_name, paths)
upload_np(dataset_id, name, img, meta=None)[source]

Upload given Image in numpy format with given name to Dataset.

Parameters
dataset_id : int

Dataset ID in Supervisely.

name : str

Image name with extension.

img : np.ndarray

image in RGB format(numpy matrix)

meta : dict, optional

Image metadata.

Returns

Information about Image. See info_sequence

Return type

ImageInfo

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_np = sly.image.read("/home/admin/Downloads/7777.jpeg")
img_info = api.image.upload_np(dataset_id, name="7777.jpeg", img=img_np)
upload_nps(dataset_id, names, imgs, progress_cb=None, metas=None)[source]

Upload given Images in numpy format with given names to Dataset.

Parameters
dataset_id : int

Dataset ID in Supervisely.

names : List[str]

Images names with extension.

imgs : List[np.ndarray]

Images in RGB numpy matrix format

progress_cb : tqdm or callable, optional

Function for tracking the progress of uploading.

metas : List[dict], optional

Images metadata.

Returns

List with information about Images. See info_sequence

Return type

List[ImageInfo]

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_np_1 = sly.image.read("/home/admin/Downloads/7777.jpeg")
img_np_2 = sly.image.read("/home/admin/Downloads/8888.jpeg")
img_np_3 = sly.image.read("/home/admin/Downloads/9999.jpeg")

img_names = ["7777.jpeg", "8888.jpeg", "9999.jpeg"]
img_nps = [img_np_1, img_np_2, img_np_3]

img_infos = api.image.upload_nps(dataset_id, names=img_names, imgs=img_nps)
upload_path(dataset_id, name, path, meta=None)[source]

Uploads Image with given name from given local path to Dataset.

Parameters
dataset_id : int

Dataset ID in Supervisely.

name : str

Image name with extension.

path : str

Local Image path.

meta : dict, optional

Image metadata.

Returns

Information about Image. See info_sequence

Return type

ImageInfo

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_info = api.image.upload_path(dataset_id, name="7777.jpeg", path="/home/admin/Downloads/7777.jpeg")
upload_paths(dataset_id, names, paths, progress_cb=None, metas=None)[source]

Uploads Images with given names from given local path to Dataset.

Parameters
dataset_id : int

Dataset ID in Supervisely.

names : List[str]

List of Images names with extension.

paths : List[str]

List of local Images pathes.

progress_cb : tqdm or callable, optional

Function for tracking the progress of uploading.

metas : List[dict], optional

Images metadata.

Raises

ValueError if len(names) != len(paths)

Returns

List with information about Images. See info_sequence

Return type

List[ImageInfo]

Usage example
os.environ['SERVER_ADDRESS'] = 'https://app.supervisely.com'
os.environ['API_TOKEN'] = 'Your Supervisely API Token'
api = sly.Api.from_env()

img_names = ["7777.jpeg", "8888.jpeg", "9999.jpeg"]
image_paths = ["/home/admin/Downloads/img/770918.jpeg", "/home/admin/Downloads/img/770919.jpeg", "/home/admin/Downloads/img/770920.jpeg"]

img_infos = api.image.upload_path(dataset_id, names=img_names, paths=img_paths)
url(team_id, workspace_id, project_id, dataset_id, image_id)[source]

Gets Image URL by ID.

Parameters
team_id : int

Team ID in Supervisely.

workspace_id : int

Workspace ID in Supervisely.

project_id : int

Project ID in Supervisely.

dataset_id : int

Dataset ID in Supervisely.

image_id : int

Image ID in Supervisely.

Returns

Image URL

Return type

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()

team_id = 16087
workspace_id = 23821
project_id = 53939
dataset_id = 254737
image_id = 121236920

img_url = api.image.url(team_id, workspace_id, project_id, dataset_id, image_id)
print(url)
# Output: https://app.supervise.ly/app/images/16087/23821/53939/254737#image-121236920