> ## Documentation Index > Fetch the complete documentation index at: https://docs.encord.com/llms.txt > Use this file to discover all available pages before exploring further. # Data Groups & Label Spaces Working with Data Group labels requires the use of Label Spaces. Each Data Group's Label Row contains multiple Label Spaces, with each Space corresponding to a specific Data Group item. [Multi-layer Data Groups](/sdk-documentation/index-sdk/sdk-data-groups#multi-layer-data-groups) also contain a root Label Space that contains labels pertaining to the Data Group as a whole. ## Get Label Spaces The following script gets all Label Spaces in Label Row and prints their IDs. ```python Get All Label Space IDs theme={"dark"} from encord import EncordUserClient, Project # User input SSH_PATH = "" # Replace with the file path to your SSH private key PROJECT_ID = "" # Replace with the unique Project ID DATA_TITLE = "" # Replace with the title of the data unit # --- Connect to Encord --- user_client: EncordUserClient = EncordUserClient.create_with_ssh_private_key( ssh_private_key_path=SSH_PATH, # For US platform users use "https://api.us.encord.com" domain="https://api.encord.com", ) # Get Project project: Project = user_client.get_project(PROJECT_ID) # Get all Label Rows for the data unit rows = project.list_label_rows_v2(data_title_eq=DATA_TITLE) # Assume we want the first Label Row lr = rows[0] lr.initialise_labels() # Get all Label Spaces for the Label Row label_spaces = lr.get_spaces() # Print IDs for space in label_spaces: print( f"spaceId: {space.space_id}, " f"file_name: {space.metadata.file_name}, " f"layout_key: {space.metadata.layout_key}" ) ``` ## Get & Update Labels Use the following scripts to get all annotations in a Label Space. The script shows how annotations can be updated by changing the `last_edited_by` and annotation coordinates for each annotation in the Label Space. Ensure that you replace: * All variables in the `User input` section at the top of the script. * The names of objects in your Ontology. * The type and position of the label(s). Label Spaces can be specified using: * A storage item's unique ID: `label_space = lr.get_space(id="7E3KERd9arYTiPicaijP6c1LfI73", type_="image")` * Its [Data Group](/sdk-documentation/index-sdk/sdk-data-groups) layout key: `label_space = lr.get_space(layout_key="2", type_="image")`. Using `layout_key`s lets you reuse the same code across multiple label rows and Data Groups (for example, "left-video" and "right-video"), without needing to look up the underlying storage item ID each time. ```python Image theme={"dark"} from encord import EncordUserClient, Project from encord.objects.coordinates import BoundingBoxCoordinates # User input SSH_PATH = "" # Replace with the file path to your SSH private key PROJECT_ID = "" # Replace with the unique Project ID DATA_TITLE = "" # Replace with the title of the data unit LABEL_SPACE = "" # Replace with the unique ID of the label space # --- Connect to Encord --- user_client: EncordUserClient = EncordUserClient.create_with_ssh_private_key( ssh_private_key_path=SSH_PATH, # For US platform users use "https://api.us.encord.com" domain="https://api.encord.com", ) project: Project = user_client.get_project(PROJECT_ID) # Get all Label Rows for the data unit rows = project.list_label_rows_v2(data_title_eq=DATA_TITLE) # Assume we want the first Label Row lr = rows[0] lr.initialise_labels() # Replace ID with the unique ID of the Label Space label_space = lr.get_space(id=LABEL_SPACE, type_="image") # Get object instances object_instances = label_space.get_object_instances() # Get annotations for annotation in label_space.get_annotations(type_="object"): print(annotation.coordinates) print(annotation.last_edited_by) print(annotation.last_edited_at) print(annotation.confidence) print(annotation.object_hash) print(annotation.space) # Update the annotations annotation.last_edited_by = "user@encord.com" annotation.coordinates = BoundingBoxCoordinates( top_left_x=0.6, top_left_y=0.4, width=0.3, height=0.1, ) # Save Label Row lr.save() ``` ```python Image Sequence theme={"dark"} from encord import EncordUserClient, Project from encord.objects.coordinates import BoundingBoxCoordinates # User input SSH_PATH = "" # Replace with the file path to your SSH private key PROJECT_ID = "" # Replace with the unique Project ID DATA_TITLE = "" # Replace with the title of the data unit LABEL_SPACE = "" # Replace with the unique ID of the label space # --- Connect to Encord --- user_client: EncordUserClient = EncordUserClient.create_with_ssh_private_key( ssh_private_key_path=SSH_PATH, # For US platform users use "https://api.us.encord.com" domain="https://api.encord.com", ) project: Project = user_client.get_project(PROJECT_ID) # Get all Label Rows for the data unit rows = project.list_label_rows_v2(data_title_eq=DATA_TITLE) # Assume we want the first Label Row lr = rows[0] lr.initialise_labels() # Replace ID with the unique ID of the Label Space label_space = lr.get_space(id=LABEL_SPACE, type_="image_sequence") # Get object instances object_instances = label_space.get_object_instances() # Get annotations for annotation in label_space.get_annotations(type_="object"): print(annotation.coordinates) print(annotation.last_edited_by) print(annotation.last_edited_at) print(annotation.confidence) print(annotation.frame) print(annotation.object_hash) print(annotation.space) # Update the annotations annotation.last_edited_by = "user@encord.com" annotation.coordinates = BoundingBoxCoordinates( top_left_x=0.6, top_left_y=0.4, width=0.3, height=0.1, ) # Save Label Row lr.save() ``` ```python Video theme={"dark"} from encord import EncordUserClient, Project from encord.objects.coordinates import BoundingBoxCoordinates # User input SSH_PATH = "" # Replace with the file path to your SSH private key PROJECT_ID = "" # Replace with the unique Project ID DATA_TITLE = "" # Replace with the title of the data unit LABEL_SPACE = "" # Replace with the unique ID of the label space # --- Connect to Encord --- user_client: EncordUserClient = EncordUserClient.create_with_ssh_private_key( ssh_private_key_path=SSH_PATH, # For US platform users use "https://api.us.encord.com" domain="https://api.encord.com", ) project: Project = user_client.get_project(PROJECT_ID) # Get all Label Rows for the data unit rows = project.list_label_rows_v2(data_title_eq=DATA_TITLE) # Assume we want the first Label Row lr = rows[0] lr.initialise_labels() # Replace ID with the unique ID of the Label Space label_space = lr.get_space(id=LABEL_SPACE, type_="video") # Get object instances object_instances = label_space.get_object_instances() # Get annotations for annotation in label_space.get_annotations(type_="object"): print(annotation.coordinates) print(annotation.last_edited_by) print(annotation.last_edited_at) print(annotation.confidence) print(annotation.frame) print(annotation.object_hash) print(annotation.space) # Update the annotations annotation.last_edited_by = "user@encord.com" annotation.coordinates = BoundingBoxCoordinates( top_left_x=0.6, top_left_y=0.4, width=0.3, height=0.1, ) # Save Label Row lr.save() ``` ```python Audio theme={"dark"} from encord import EncordUserClient, Project from encord.objects import Range # User input SSH_PATH = "" # Replace with the file path to your SSH private key PROJECT_ID = "" # Replace with the unique Project ID DATA_TITLE = "" # Replace with the title of the data unit LABEL_SPACE = "" # Replace with the unique ID of the label space # --- Connect to Encord --- user_client: EncordUserClient = EncordUserClient.create_with_ssh_private_key( ssh_private_key_path=SSH_PATH, # For US platform users use "https://api.us.encord.com" domain="https://api.encord.com", ) project: Project = user_client.get_project(PROJECT_ID) # Get all Label Rows for the data unit rows = project.list_label_rows_v2(data_title_eq=DATA_TITLE) # Assume we want the first Label Row lr = rows[0] lr.initialise_labels() # Replace ID with the unique ID of the Label Space label_space = lr.get_space(id=LABEL_SPACE, type_="audio") # Get object instances object_instances = label_space.get_object_instances() # Get annotations for annotation in label_space.get_annotations(type_="object"): print(annotation.ranges) print(annotation.last_edited_by) print(annotation.last_edited_at) print(annotation.object_hash) print(annotation.space) # Update the annotations annotation.last_edited_by = "user@encord.com" annotation.ranges = Range(start=0, end=100) # Save Label Row lr.save() ``` ```python Text theme={"dark"} from encord import EncordUserClient, Project from encord.objects import Range # User input SSH_PATH = "" # Replace with the file path to your SSH private key PROJECT_ID = "" # Replace with the unique Project ID DATA_TITLE = "" # Replace with the title of the data unit LABEL_SPACE = "" # Replace with the unique ID of the label space # --- Connect to Encord --- user_client: EncordUserClient = EncordUserClient.create_with_ssh_private_key( ssh_private_key_path=SSH_PATH, # For US platform users use "https://api.us.encord.com" domain="https://api.encord.com", ) project: Project = user_client.get_project(PROJECT_ID) # Get all Label Rows for the data unit rows = project.list_label_rows_v2(data_title_eq=DATA_TITLE) # Assume we want the first Label Row lr = rows[0] lr.initialise_labels() # Replace ID with the unique ID of the Label Space label_space = lr.get_space(id=LABEL_SPACE, type_="text") # Get object instances object_instances = label_space.get_object_instances() # Get annotations for annotation in label_space.get_annotations(type_="object"): print(annotation.last_edited_by) print(annotation.last_edited_at) print(annotation.object_hash) print(annotation.space) # Update the annotations annotation.last_edited_by = "user@encord.com" annotation.ranges = Range(start=0, end=100) # Save Label Row lr.save() ``` ```python HTML theme={"dark"} from encord import EncordUserClient, Project from encord.objects import HtmlRange, HtmlNode # User input SSH_PATH = "" # Replace with the file path to your SSH private key PROJECT_ID = "" # Replace with the unique Project ID DATA_TITLE = "" # Replace with the title of the data unit LABEL_SPACE = "" # Replace with the unique ID of the label space # --- Connect to Encord --- user_client: EncordUserClient = EncordUserClient.create_with_ssh_private_key( ssh_private_key_path=SSH_PATH, # For US platform users use "https://api.us.encord.com" domain="https://api.encord.com", ) project: Project = user_client.get_project(PROJECT_ID) # Get all Label Rows for the data unit rows = project.list_label_rows_v2(data_title_eq=DATA_TITLE) # Assume we want the first Label Row lr = rows[0] lr.initialise_labels() # Replace ID with the unique ID of the Label Space label_space = lr.get_space(id=LABEL_SPACE, type_="html") # Get object instances object_instances = label_space.get_object_instances() # Get annotations for annotation in label_space.get_annotations(type_="object"): print(annotation.last_edited_by) print(annotation.last_edited_at) print(annotation.object_hash) print(annotation.space) # Update the annotations annotation.last_edited_by = "user@encord.com" annotation.range = HtmlRange(start=HtmlNode(xpath="start", offset=0), end=HtmlNode(xpath="end", offset=1)) # Save Label Row lr.save() ``` ```python PDF theme={"dark"} from encord import EncordUserClient, Project from encord.objects.coordinates import BoundingBoxCoordinates # User input SSH_PATH = "" # Replace with the file path to your SSH private key PROJECT_ID = "" # Replace with the unique Project ID DATA_TITLE = "" # Replace with the title of the data unit LABEL_SPACE = "" # Replace with the unique ID of the label space # --- Connect to Encord --- user_client: EncordUserClient = EncordUserClient.create_with_ssh_private_key( ssh_private_key_path=SSH_PATH, # For US platform users use "https://api.us.encord.com" domain="https://api.encord.com", ) project: Project = user_client.get_project(PROJECT_ID) # Get all Label Rows for the data unit rows = project.list_label_rows_v2(data_title_eq=DATA_TITLE) # Assume we want the first Label Row lr = rows[0] lr.initialise_labels() # Replace ID with the unique ID of the Label Space label_space = lr.get_space(id=LABEL_SPACE, type_="pdf") # Get object instances object_instances = label_space.get_object_instances() # Get annotations for annotation in label_space.get_annotations(type_="object"): print(annotation.coordinates) print(annotation.last_edited_by) print(annotation.last_edited_at) print(annotation.confidence) print(annotation.object_hash) print(annotation.space) # Update the annotations annotation.last_edited_by = "user@encord.com" annotation.coordinates = BoundingBoxCoordinates( top_left_x=0.6, top_left_y=0.4, width=0.3, height=0.1, ) # Save Label Row lr.save() ``` ```python DICOM / NIfTI theme={"dark"} from encord import EncordUserClient, Project from encord.objects.coordinates import BoundingBoxCoordinates # User input SSH_PATH = "" # Replace with the file path to your SSH private key PROJECT_ID = "" # Replace with the unique Project ID DATA_TITLE = "" # Replace with the title of the data unit LABEL_SPACE = "" # Replace with the unique ID of the label space # --- Connect to Encord --- user_client: EncordUserClient = EncordUserClient.create_with_ssh_private_key( ssh_private_key_path=SSH_PATH, # For US platform users use "https://api.us.encord.com" domain="https://api.encord.com", ) project: Project = user_client.get_project(PROJECT_ID) # Get all Label Rows for the data unit rows = project.list_label_rows_v2(data_title_eq=DATA_TITLE) # Assume we want the first Label Row lr = rows[0] lr.initialise_labels() # Replace ID with the unique ID of the Label Space label_space = lr.get_space(id=LABEL_SPACE, type_="medical") # Get object instances object_instances = label_space.get_object_instances() # Get annotations for annotation in label_space.get_annotations(type_="object"): print(annotation.coordinates) print(annotation.last_edited_by) print(annotation.last_edited_at) print(annotation.confidence) print(annotation.frame) print(annotation.object_hash) print(annotation.space) # Update the annotations annotation.last_edited_by = "user@encord.com" annotation.coordinates = BoundingBoxCoordinates( top_left_x=0.6, top_left_y=0.4, width=0.3, height=0.1, ) # Save Label Row lr.save() ``` ## Add Labels The following scripts add a single object with an attribute, and a classification to two different Label Spaces. Attributes are set on the object instance itself, so the same attribute values apply across all Label Spaces the instance is added to. Ensure that you replace: * All variables in the `User input` section at the top of the script. * The names of objects in your Ontology. * The type and position of the label(s). Use the `on_overlap="replace"` parameter in the `put_classification_instance` and `put_object_instance` methods if you want existing labels to be replaced by the new object. ```python Image theme={"dark"} from encord import EncordUserClient, Project from encord.objects import Object, Classification, ClassificationInstance, ObjectInstance, LabelRowV2 from encord.objects.coordinates import BoundingBoxCoordinates from encord.objects.options import Option # User input SSH_PATH = "" # Replace with the file path to your SSH private key PROJECT_ID = "" # Replace with the unique Project ID DATA_TITLE = "" # Replace with the title of the data unit LABEL_SPACE = "" # Replace with the unique ID of the label space # --- Connect to Encord --- user_client: EncordUserClient = EncordUserClient.create_with_ssh_private_key( ssh_private_key_path=SSH_PATH, # For US platform users use "https://api.us.encord.com" domain="https://api.encord.com", ) # Get Project project: Project = user_client.get_project(PROJECT_ID) # Get all Label Rows for the data unit rows = project.list_label_rows_v2(data_title_eq=DATA_TITLE) # Assume we want the first Label Row lr = rows[0] lr.initialise_labels() # Find bounding box object "Cherry" in the Ontology. ontology_structure = project.ontology_structure box_ontology_object: Object = ontology_structure.get_child_by_title(title="Cherry", type_=Object) # Find classification "Day or Night" in the Ontology classification: Classification = ontology_structure.get_child_by_title(title="Day or Night", type_=Classification) # Find classification answer "Day" in the Ontology classification_answer = classification.get_child_by_title( title="Day", type_=Option ) # Get Label Spaces label_space_1 = lr.get_space(layout_key="Fruit_1", type_="image") label_space_2 = lr.get_space(layout_key="Fruit_2", type_="image") # Create bounding box instance bb_inst: ObjectInstance = box_ontology_object.create_instance() # Find the text attribute "Ripeness description" in the Ontology and set the answer attribute = box_ontology_object.get_child_by_title("Ripeness description") bb_inst.set_answer(attribute=attribute, answer="Fully ripe, deep red color with no visible blemishes") # Create classification instance classification_inst: ClassificationInstance = classification.create_instance() classification_inst.set_answer( answer=classification_answer ) # Add the bounding box instance to both Label Spaces label_space_1.put_object_instance( object_instance=bb_inst, on_overlap="replace", coordinates=BoundingBoxCoordinates( top_left_x=0.6, top_left_y=0.4, width=0.3, height=0.2 ), frames=[0, 1, 2] ) label_space_2.put_object_instance( object_instance=bb_inst, on_overlap="replace", coordinates=BoundingBoxCoordinates( top_left_x=0.6, top_left_y=0.4, width=0.3, height=0.2 ), frames=[0, 1, 2] ) # Add the classification instance to both Label Spaces label_space_1.put_classification_instance( classification_instance=classification_inst, frames=[0, 1, 2] ) label_space_2.put_classification_instance( classification_instance=classification_inst, frames=[0, 1, 2] ) lr.save() print(f"Saved label row for {lr.data_title}") ``` ```python Image Sequence theme={"dark"} from encord import EncordUserClient, Project from encord.objects import Object, Classification, ClassificationInstance, ObjectInstance, LabelRowV2 from encord.objects.coordinates import BoundingBoxCoordinates from encord.objects.options import Option # User input SSH_PATH = "" # Replace with the file path to your SSH private key PROJECT_ID = "" # Replace with the unique Project ID DATA_TITLE = "" # Replace with the title of the data unit LABEL_SPACE = "" # Replace with the unique ID of the label space # --- Connect to Encord --- user_client: EncordUserClient = EncordUserClient.create_with_ssh_private_key( ssh_private_key_path=SSH_PATH, # For US platform users use "https://api.us.encord.com" domain="https://api.encord.com", ) # Get Project project: Project = user_client.get_project(PROJECT_ID) # Get all Label Rows for the data unit rows = project.list_label_rows_v2(data_title_eq=DATA_TITLE) # Assume we want the first Label Row lr = rows[0] lr.initialise_labels() # Find bounding box object "Person" in the Ontology. ontology_structure = project.ontology_structure box_ontology_object: Object = ontology_structure.get_child_by_title(title="Person", type_=Object) # Find classification "Day or Night" in the Ontology classification: Classification = ontology_structure.get_child_by_title(title="Day or Night", type_=Classification) # Find classification answer "Day" in the Ontology classification_answer = classification.get_child_by_title( title="Day", type_=Option ) # Get Label Spaces label_space_1 = lr.get_space(layout_key="cctv_1", type_="image_sequence") label_space_2 = lr.get_space(layout_key="cctv_2", type_="image_sequence") # Create bounding box instance bb_inst: ObjectInstance = box_ontology_object.create_instance() # Find the text attribute "Visibility description" in the Ontology and set the answer attribute = box_ontology_object.get_child_by_title("Visibility description") bb_inst.set_answer(attribute=attribute, answer="Face fully visible. Body partially occluded.") # Create classification instance classification_inst: ClassificationInstance = classification.create_instance() classification_inst.set_answer( answer=classification_answer ) # Add the bounding box instance to both Label Spaces label_space_1.put_object_instance( object_instance=bb_inst, on_overlap="replace", coordinates=BoundingBoxCoordinates( top_left_x=0.6, top_left_y=0.4, width=0.3, height=0.2 ), frames=[0, 1, 2] ) label_space_2.put_object_instance( object_instance=bb_inst, on_overlap="replace", coordinates=BoundingBoxCoordinates( top_left_x=0.6, top_left_y=0.4, width=0.3, height=0.2 ), frames=[0, 1, 2] ) # Add the classification instance to both Label Spaces label_space_1.put_classification_instance( classification_instance=classification_inst, frames=[0, 1, 2] ) label_space_2.put_classification_instance( classification_instance=classification_inst, frames=[0, 1, 2] ) # Save Label Row lr.save() print(f"Saved label row for {lr.data_title}") ``` ```python Video theme={"dark"} from encord import EncordUserClient, Project from encord.objects import Object, Classification, ClassificationInstance, ObjectInstance, LabelRowV2 from encord.objects.coordinates import BoundingBoxCoordinates from encord.objects.options import Option # User input SSH_PATH = "" # Replace with the file path to your SSH private key PROJECT_ID = "" # Replace with the unique Project ID DATA_TITLE = "" # Replace with the title of the data unit LABEL_SPACE = "" # Replace with the unique ID of the label space # --- Connect to Encord --- user_client: EncordUserClient = EncordUserClient.create_with_ssh_private_key( ssh_private_key_path=SSH_PATH, # For US platform users use "https://api.us.encord.com" domain="https://api.encord.com", ) # Get Project project: Project = user_client.get_project(PROJECT_ID) # Get all Label Rows for the data unit rows = project.list_label_rows_v2(data_title_eq=DATA_TITLE) # Assume we want the first Label Row lr = rows[0] lr.initialise_labels() # Find bounding box object "Person" in the Ontology. ontology_structure = project.ontology_structure box_ontology_object: Object = ontology_structure.get_child_by_title(title="Person", type_=Object) # Find classification "Day or Night" in the Ontology classification: Classification = ontology_structure.get_child_by_title(title="Day or Night", type_=Classification) # Find classification answer "Day" in the Ontology classification_answer = classification.get_child_by_title( title="Day", type_=Option ) # Get Label Spaces label_space_1 = lr.get_space(layout_key="cctv_1", type_="video") label_space_2 = lr.get_space(layout_key="cctv_2", type_="video") # Create bounding box instance bb_inst: ObjectInstance = box_ontology_object.create_instance() # Find the text attribute "Visibility description" in the Ontology object and set the answer attribute = box_ontology_object.get_child_by_title("Visibility description") bb_inst.set_answer(attribute=attribute, answer="Face fully visible. Body partially occluded.") # Create classification instance classification_inst: ClassificationInstance = classification.create_instance() classification_inst.set_answer( answer=classification_answer ) # Add the bounding box instance to both Label Spaces label_space_1.put_object_instance( object_instance=bb_inst, on_overlap="replace", coordinates=BoundingBoxCoordinates( top_left_x=0.6, top_left_y=0.4, width=0.3, height=0.2 ), frames=[0, 1, 2] ) label_space_2.put_object_instance( object_instance=bb_inst, on_overlap="replace", coordinates=BoundingBoxCoordinates( top_left_x=0.6, top_left_y=0.4, width=0.3, height=0.2 ), frames=[0, 1, 2] ) # Add the classification instance to both Label Spaces label_space_1.put_classification_instance( classification_instance=classification_inst, frames=[0, 1, 2] ) label_space_2.put_classification_instance( classification_instance=classification_inst, frames=[0, 1, 2] ) # Save Label Row lr.save() print(f"Saved label row for {lr.data_title}") ``` ```python Audio theme={"dark"} from encord import EncordUserClient, Project from encord.objects import Object, Classification, ClassificationInstance, ObjectInstance, LabelRowV2, Range from encord.objects.options import Option # User input SSH_PATH = "" # Replace with the file path to your SSH private key PROJECT_ID = "" # Replace with the unique Project ID DATA_TITLE = "" # Replace with the title of the data unit LABEL_SPACE = "" # Replace with the unique ID of the label space # --- Connect to Encord --- user_client: EncordUserClient = EncordUserClient.create_with_ssh_private_key( ssh_private_key_path=SSH_PATH, # For US platform users use "https://api.us.encord.com" domain="https://api.encord.com", ) # Get Project project: Project = user_client.get_project(PROJECT_ID) # Get all Label Rows for the data unit rows = project.list_label_rows_v2(data_title_eq=DATA_TITLE) # Assume we want the first Label Row lr = rows[0] lr.initialise_labels() # Find audio region object "Rick James" in the Ontology. ontology_structure = project.ontology_structure ontology_object: Object = ontology_structure.get_child_by_title(title="Rick James", type_=Object) # Find classification "Contains Lyrics?" in the Ontology classification: Classification = ontology_structure.get_child_by_title(title="Contains Lyrics?", type_=Classification) # Find classification answer "Yes" in the Ontology classification_answer = classification.get_child_by_title( title="Yes", type_=Option ) # Get Label Spaces label_space = lr.get_space(layout_key="music_1", type_="audio") label_space = lr.get_space(layout_key="music_2", type_="audio") # Create audio region instance audio_inst: ObjectInstance = ontology_object.create_instance() # Find the text attribute "Description" in the Ontology object and set the answer attribute = ontology_object.get_child_by_title("Description") bb_inst.set_answer(attribute=attribute, answer="Very Funky") # Create classification instance classification_inst: ClassificationInstance = classification.create_instance() # Set classification answer classification_inst.set_answer( answer=classification_answer ) # Add the audio region instance to both Label Spaces label_space_1.put_object_instance( object_instance=audio_inst, on_overlap="replace", ranges=Range(start=100, end=2000) ) label_space_2.put_object_instance( object_instance=audio_inst, on_overlap="replace", ranges=Range(start=100, end=2000) ) # Add the classification instance to both Label Spaces label_space_1.put_classification_instance( classification_instance=classification_inst ) label_space_2.put_classification_instance( classification_instance=classification_inst ) # Save Label Row lr.save() print(f"Saved label row for {lr.data_title}") ``` ```python Text theme={"dark"} from encord import EncordUserClient, Project from encord.objects import Object, Classification, ClassificationInstance, ObjectInstance, LabelRowV2, Range from encord.objects.options import Option # User input SSH_PATH = "" # Replace with the file path to your SSH private key PROJECT_ID = "" # Replace with the unique Project ID DATA_TITLE = "" # Replace with the title of the data unit LABEL_SPACE = "" # Replace with the unique ID of the label space # --- Connect to Encord --- user_client: EncordUserClient = EncordUserClient.create_with_ssh_private_key( ssh_private_key_path=SSH_PATH, # For US platform users use "https://api.us.encord.com" domain="https://api.encord.com", ) # Get Project project: Project = user_client.get_project(PROJECT_ID) # Get all Label Rows for the data unit rows = project.list_label_rows_v2(data_title_eq=DATA_TITLE) # Assume we want the first Label Row lr = rows[0] lr.initialise_labels() # Find text region object "Quote" in the Ontology. ontology_structure = project.ontology_structure ontology_object: Object = ontology_structure.get_child_by_title(title="Quote", type_=Object) # Find classification "Easy to Read?" in the Ontology classification: Classification = ontology_structure.get_child_by_title(title="Easy to Read?", type_=Classification) # Find classification answer "Yes" in the Ontology classification_answer = classification.get_child_by_title( title="Yes", type_=Option ) # Get Label Spaces label_space_1 = lr.get_space(layout_key="text_1", type_="text") label_space_2 = lr.get_space(layout_key="text_2", type_="text") # Create text region instance text_inst: ObjectInstance = ontology_object.create_instance() # Find the text attribute "Author" in the Ontology object and set the answer attribute = ontology_object.get_child_by_title("Author") bb_inst.set_answer(attribute=attribute, answer="Bertrand Russel") # Create classification instance classification_inst: ClassificationInstance = classification.create_instance() # Set classification answer classification_inst.set_answer( answer=classification_answer ) # Add the text region instance to both Label Spaces label_space_1.put_object_instance( object_instance=text_inst, on_overlap="replace", ranges=Range(start=100, end=2000) ) label_space_2.put_object_instance( object_instance=text_inst, on_overlap="replace", ranges=Range(start=100, end=2000) ) # Add the classification instance to both Label Spaces label_space_1.put_classification_instance( classification_instance=classification_inst ) label_space_2.put_classification_instance( classification_instance=classification_inst ) # Save Label Row lr.save() print(f"Saved label row for {lr.data_title}") ``` ```python HTML theme={"dark"} from encord import EncordUserClient, Project from encord.objects import Object, Classification, ClassificationInstance, ObjectInstance, LabelRowV2, HtmlRange, HtmlNode from encord.objects.options import Option # User input SSH_PATH = "" # Replace with the file path to your SSH private key PROJECT_ID = "" # Replace with the unique Project ID DATA_TITLE = "" # Replace with the title of the data unit LABEL_SPACE = "" # Replace with the unique ID of the label space # --- Connect to Encord --- user_client: EncordUserClient = EncordUserClient.create_with_ssh_private_key( ssh_private_key_path=SSH_PATH, # For US platform users use "https://api.us.encord.com" domain="https://api.encord.com", ) # Get Project project: Project = user_client.get_project(PROJECT_ID) # Get all Label Rows for the data unit rows = project.list_label_rows_v2(data_title_eq=DATA_TITLE) # Assume we want the first Label Row lr = rows[0] lr.initialise_labels() # Find text region object "Quote" in the Ontology. ontology_structure = project.ontology_structure ontology_object: Object = ontology_structure.get_child_by_title(title="Quote", type_=Object) # Find classification "Easy to Read?" in the Ontology classification: Classification = ontology_structure.get_child_by_title(title="Easy to Read?", type_=Classification) # Find classification answer "Yes" in the Ontology classification_answer = classification.get_child_by_title( title="Yes", type_=Option ) # Get Label Spaces label_space_1 = lr.get_space(layout_key="website_1", type_="html") label_space_2 = lr.get_space(layout_key="website_2", type_="html") # Create text region instance text_inst: ObjectInstance = ontology_object.create_instance() # Find the text attribute "Author" in the Ontology object and set the answer attribute = ontology_object.get_child_by_title("Author") bb_inst.set_answer(attribute=attribute, answer="Bertrand Russel") # Create classification instance classification_inst: ClassificationInstance = classification.create_instance() # Set classification answer classification_inst.set_answer( answer=classification_answer ) # Add the text region instance to both Label Spaces label_space_1.put_object_instance( object_instance=text_inst, on_overlap="replace", ranges= HtmlRange(start=HtmlNode(xpath="start", offset=0), end=HtmlNode(xpath="end", offset=1)) ) label_space_2.put_object_instance( object_instance=text_inst, on_overlap="replace", ranges= HtmlRange(start=HtmlNode(xpath="start", offset=0), end=HtmlNode(xpath="end", offset=1)) ) # Add the classification instance to both Label Spaces label_space_1.put_classification_instance( classification_instance=classification_inst ) label_space_2.put_classification_instance( classification_instance=classification_inst ) # Save Label Row lr.save() print(f"Saved label row for {lr.data_title}") ``` ```python PDF theme={"dark"} from encord import EncordUserClient, Project from encord.objects import Object, Classification, ClassificationInstance, ObjectInstance, LabelRowV2 from encord.objects.coordinates import BoundingBoxCoordinates from encord.objects.options import Option # User input SSH_PATH = "" # Replace with the file path to your SSH private key PROJECT_ID = "" # Replace with the unique Project ID DATA_TITLE = "" # Replace with the title of the data unit LABEL_SPACE = "" # Replace with the unique ID of the label space # --- Connect to Encord --- user_client: EncordUserClient = EncordUserClient.create_with_ssh_private_key( ssh_private_key_path=SSH_PATH, # For US platform users use "https://api.us.encord.com" domain="https://api.encord.com", ) # Get Project project: Project = user_client.get_project(PROJECT_ID) # Get all Label Rows for the data unit rows = project.list_label_rows_v2(data_title_eq=DATA_TITLE) # Assume we want the first Label Row lr = rows[0] lr.initialise_labels() # Find bounding box object "Region of Interest" in the Ontology. ontology_structure = project.ontology_structure box_ontology_object: Object = ontology_structure.get_child_by_title(title="Region of Interest", type_=Object) # Find classification "Readable?" in the Ontology classification: Classification = ontology_structure.get_child_by_title(title="Readable?", type_=Classification) # Find classification answer "Yes" in the Ontology classification_answer = classification.get_child_by_title( title="Yes", type_=Option ) # Get Label Spaces label_space_1 = lr.get_space(layout_key="doc_1", type_="pdf") label_space_2 = lr.get_space(layout_key="doc_2", type_="pdf") # Create bounding box instance bb_inst: ObjectInstance = box_ontology_object.create_instance() # Find the text attribute "Language" in the Ontology object and set the answer attribute = box_ontology_object.get_child_by_title("Language") bb_inst.set_answer(attribute=attribute, answer="Russian") # Create classification instance classification_inst: ClassificationInstance = classification.create_instance() # Set classification answer classification_inst.set_answer( answer=classification_answer ) # Add the bounding box instance to both Label Spaces. Frame refers to page number. label_space_1.put_object_instance( object_instance=bb_inst, on_overlap="replace", coordinates=BoundingBoxCoordinates( top_left_x=0.6, top_left_y=0.4, width=0.3, height=0.2 ), frames=[0] ) label_space_2.put_object_instance( object_instance=bb_inst, on_overlap="replace", coordinates=BoundingBoxCoordinates( top_left_x=0.6, top_left_y=0.4, width=0.3, height=0.2 ), frames=[0] ) # Add the classification instance to both Label Spaces label_space_1.put_classification_instance( classification_instance=classification_inst, frames=[0] ) label_space_2.put_classification_instance( classification_instance=classification_inst, frames=[0] ) # Save Label Row lr.save() print(f"Saved label row for {lr.data_title}") ``` ```python DICOM / NIfTI theme={"dark"} from encord import EncordUserClient, Project from encord.objects import Object, Classification, ClassificationInstance, ObjectInstance, LabelRowV2 from encord.objects.coordinates import BoundingBoxCoordinates from encord.objects.options import Option # User input SSH_PATH = "" # Replace with the file path to your SSH private key PROJECT_ID = "" # Replace with the unique Project ID DATA_TITLE = "" # Replace with the title of the data unit LABEL_SPACE = "" # Replace with the unique ID of the label space # --- Connect to Encord --- user_client: EncordUserClient = EncordUserClient.create_with_ssh_private_key( ssh_private_key_path=SSH_PATH, # For US platform users use "https://api.us.encord.com" domain="https://api.encord.com", ) # Get Project project: Project = user_client.get_project(PROJECT_ID) # Get all Label Rows for the data unit rows = project.list_label_rows_v2(data_title_eq=DATA_TITLE) # Assume we want the first Label Row lr = rows[0] lr.initialise_labels() # Find bounding box object "Region of Interest" in the Ontology. ontology_structure = project.ontology_structure box_ontology_object: Object = ontology_structure.get_child_by_title(title="Region of Interest", type_=Object) # Find classification "Valid Scan?" in the Ontology classification: Classification = ontology_structure.get_child_by_title(title="Valid Scan?", type_=Classification) # Find classification answer "Yes" in the Ontology classification_answer = classification.get_child_by_title( title="Yes", type_=Option ) # Get Label Spaces label_space_1 = lr.get_space(layout_key="scan_1", type_="medical") label_space_2 = lr.get_space(layout_key="scan_2", type_="medical") # Create bounding box instance bb_inst: ObjectInstance = box_ontology_object.create_instance() # Find the text attribute "Abnormality description" in the Ontology object and set the answer attribute = box_ontology_object.get_child_by_title("Abnormality description") bb_inst.set_answer(attribute=attribute, answer="Nodule detected, approximately 3mm in diameter") # Create classification instance classification_inst: ClassificationInstance = classification.create_instance() # Set classification answer classification_inst.set_answer( answer=classification_answer ) # Add the bounding box instance to both Label Spaces label_space_1.put_object_instance( object_instance=bb_inst, on_overlap="replace", coordinates=BoundingBoxCoordinates( top_left_x=0.6, top_left_y=0.4, width=0.3, height=0.2 ), frames=[0] ) label_space_2.put_object_instance( object_instance=bb_inst, on_overlap="replace", coordinates=BoundingBoxCoordinates( top_left_x=0.6, top_left_y=0.4, width=0.3, height=0.2 ), frames=[0] ) # Add the classification instance to both Label Spaces label_space_1.put_classification_instance( classification_instance=classification_inst, frames=[0] ) label_space_2.put_classification_instance( classification_instance=classification_inst, frames=[0] ) # Save Label Row lr.save() print(f"Saved label row for {lr.data_title}") ``` ```python Multi Layer Data Group theme={"dark"} from encord import EncordUserClient, Project from encord.objects import Object, Classification, ClassificationInstance, ObjectInstance from encord.objects.coordinates import BoundingBoxCoordinates from encord.objects.options import Option # User input SSH_PATH = "" # Replace with the file path to your SSH private key PROJECT_ID = "" # Replace with the unique Project ID DATA_TITLE = "" # Replace with the title of the data unit # --- Connect to Encord --- user_client: EncordUserClient = EncordUserClient.create_with_ssh_private_key( ssh_private_key_path=SSH_PATH, # For US platform users use "https://api.us.encord.com" domain="https://api.encord.com", ) # Get Project project: Project = user_client.get_project(PROJECT_ID) # Get all Label Rows for the data unit rows = project.list_label_rows_v2(data_title_eq=DATA_TITLE) # Assume we want the first Label Row lr = rows[0] lr.initialise_labels() # --- Ontology setup --- ontology_structure = project.ontology_structure # Find bounding box object "Cherry" in the Ontology box_ontology_object: Object = ontology_structure.get_child_by_title(title="Cherry", type_=Object) # Find classification "Day or Night" in the Ontology classification: Classification = ontology_structure.get_child_by_title(title="Day or Night", type_=Classification) # Find classification answer "Day" in the Ontology classification_answer = classification.get_child_by_title(title="Day", type_=Option) # --- Get Label Spaces for each image layer in the data group --- # Use layout_key to target individual image layers within the data group root_space = lr.get_space(id="root", type_="image") # Root space: applies to the data group as a whole label_space_1 = lr.get_space(layout_key="Fruit_1", type_="image") # Layer 1 label_space_2 = lr.get_space(layout_key="Fruit_2", type_="image") # Layer 2 label_space_3 = lr.get_space(layout_key="Fruit_3", type_="image") # Layer 3 # --- Create instances --- # Create bounding box instance bb_inst: ObjectInstance = box_ontology_object.create_instance() # Set a text attribute answer on the bounding box attribute = box_ontology_object.get_child_by_title("Ripeness description") bb_inst.set_answer(attribute=attribute, answer="Fully ripe, deep red color with no visible blemishes") # Create classification instance and set answer classification_inst: ClassificationInstance = classification.create_instance() classification_inst.set_answer(answer=classification_answer) # --- Add a root-level label that applies to the whole data group --- # Adding to root_space applies the classification to the entire data group. # No need to add it to individual layer spaces. root_space.put_classification_instance( classification_instance=classification_inst, frames=[0] ) # --- Add bounding box labels to each individual image layer --- for space in [label_space_1, label_space_2, label_space_3]: space.put_object_instance( object_instance=bb_inst, on_overlap="replace", coordinates=BoundingBoxCoordinates( top_left_x=0.6, top_left_y=0.4, width=0.3, height=0.2 ), frames=[0] ) # Save the label row lr.save() print(f"Saved label row for {lr.data_title}") ``` ## Remove Labels The following scripts remove an object from a Label Space. Ensure that you replace: * All variables in the `User input` section at the top of the script. * The names of objects in your Ontology. * The type and position of the label(s). ```python Image theme={"dark"} from encord import EncordUserClient, Project from encord.objects import Object, Classification, ClassificationInstance, ObjectInstance, LabelRowV2 from encord.objects.options import Option # User input SSH_PATH = "" # Replace with the file path to your SSH private key PROJECT_ID = "" # Replace with the unique Project ID DATA_TITLE = "" # Replace with the title of the data unit LABEL_SPACE = "" # Replace with the unique ID of the label space OBJECT_INSTANCE = "" # Replace with the object hash of the object you want to remove CLASSIFICATION_INSTANCE = "" # Replace with the classification hash of the classification you want to remove # --- Connect to Encord --- user_client: EncordUserClient = EncordUserClient.create_with_ssh_private_key( ssh_private_key_path=SSH_PATH, # For US platform users use "https://api.us.encord.com" domain="https://api.encord.com", ) # Get Project project: Project = user_client.get_project(PROJECT_ID) # Get all Label Rows for the data unit rows = project.list_label_rows_v2(data_title_eq=DATA_TITLE) # Assume we want the first Label Row lr = rows[0] lr.initialise_labels() # Get Label Space label_space = lr.get_space(layout_key="Fruit", type_="image") # Print first object hash in each label space label_spaces = lr.get_spaces() for space in label_spaces: print(space.get_object_instances()[0].object_hash) # Remove object label_space.remove_object_instance( object_hash=OBJECT_INSTANCE ) # Remove classification label_space.remove_classification_instance( classification_hash=CLASSIFICATION_INSTANCE ) # Save label row lr.save() print(f"Saved label row for {lr.data_title}") ``` ```python Image Sequence theme={"dark"} from encord import EncordUserClient, Project from encord.objects import Object, Classification, ClassificationInstance, ObjectInstance, LabelRowV2 from encord.objects.options import Option # User input SSH_PATH = "" # Replace with the file path to your SSH private key PROJECT_ID = "" # Replace with the unique Project ID DATA_TITLE = "" # Replace with the title of the data unit LABEL_SPACE = "" # Replace with the unique ID of the label space OBJECT_INSTANCE = "" # Replace with the object hash of the object you want to remove CLASSIFICATION_INSTANCE = "" # Replace with the classification hash of the classification you want to remove # --- Connect to Encord --- user_client: EncordUserClient = EncordUserClient.create_with_ssh_private_key( ssh_private_key_path=SSH_PATH, # For US platform users use "https://api.us.encord.com" domain="https://api.encord.com", ) # Get Project project: Project = user_client.get_project(PROJECT_ID) # Get all Label Rows for the data unit rows = project.list_label_rows_v2(data_title_eq=DATA_TITLE) # Assume we want the first Label Row lr = rows[0] lr.initialise_labels() # Get Label Space label_space = lr.get_space(layout_key="cctv", type_="image_sequence") # Print first object hash in each label space label_spaces = lr.get_spaces() for space in label_spaces: print(space.get_object_instances()[0].object_hash) # Remove object label_space.remove_object_instance( object_hash=OBJECT_INSTANCE ) # Remove classification label_space.remove_classification_instance( classification_hash=CLASSIFICATION_INSTANCE ) # Save label row lr.save() print(f"Saved label row for {lr.data_title}") ``` ```python Video theme={"dark"} from encord import EncordUserClient, Project from encord.objects import Object, Classification, ClassificationInstance, ObjectInstance, LabelRowV2 from encord.objects.options import Option # User input SSH_PATH = "" # Replace with the file path to your SSH private key PROJECT_ID = "" # Replace with the unique Project ID DATA_TITLE = "" # Replace with the title of the data unit LABEL_SPACE = "" # Replace with the unique ID of the label space OBJECT_INSTANCE = "" # Replace with the object hash of the object you want to remove CLASSIFICATION_INSTANCE = "" # Replace with the classification hash of the classification you want to remove # --- Connect to Encord --- user_client: EncordUserClient = EncordUserClient.create_with_ssh_private_key( ssh_private_key_path=SSH_PATH, # For US platform users use "https://api.us.encord.com" domain="https://api.encord.com", ) # Get Project project: Project = user_client.get_project(PROJECT_ID) # Get all Label Rows for the data unit rows = project.list_label_rows_v2(data_title_eq=DATA_TITLE) # Assume we want the first Label Row lr = rows[0] lr.initialise_labels() # Get Label Space label_space = lr.get_space(layout_key="cctv", type_="video") # Print first object hash in each label space label_spaces = lr.get_spaces() for space in label_spaces: print(space.get_object_instances()[0].object_hash) # Remove object label_space.remove_object_instance( object_hash=OBJECT_INSTANCE ) # Remove classification label_space.remove_classification_instance( classification_hash=CLASSIFICATION_INSTANCE ) # Save label row lr.save() print(f"Saved label row for {lr.data_title}") ``` ```python Audio theme={"dark"} from encord import EncordUserClient, Project from encord.objects import Object, Classification, ClassificationInstance, ObjectInstance, LabelRowV2 from encord.objects.options import Option # User input SSH_PATH = "" # Replace with the file path to your SSH private key PROJECT_ID = "" # Replace with the unique Project ID DATA_TITLE = "" # Replace with the title of the data unit LABEL_SPACE = "" # Replace with the unique ID of the label space OBJECT_INSTANCE = "" # Replace with the object hash of the object you want to remove CLASSIFICATION_INSTANCE = "" # Replace with the classification hash of the classification you want to remove # --- Connect to Encord --- user_client: EncordUserClient = EncordUserClient.create_with_ssh_private_key( ssh_private_key_path=SSH_PATH, # For US platform users use "https://api.us.encord.com" domain="https://api.encord.com", ) # Get Project project: Project = user_client.get_project(PROJECT_ID) # Get all Label Rows for the data unit rows = project.list_label_rows_v2(data_title_eq=DATA_TITLE) # Assume we want the first Label Row lr = rows[0] lr.initialise_labels() # Get Label Space label_space = lr.get_space(layout_key="mp3", type_="audio") # Print first object hash in each label space label_spaces = lr.get_spaces() for space in label_spaces: print(space.get_object_instances()[0].object_hash) # Remove object label_space.remove_object_instance( object_hash=OBJECT_INSTANCE ) # Remove classification label_space.remove_classification_instance( classification_hash=CLASSIFICATION_INSTANCE ) # Save label row lr.save() print(f"Saved label row for {lr.data_title}") ``` ```python Text theme={"dark"} from encord import EncordUserClient, Project from encord.objects import Object, Classification, ClassificationInstance, ObjectInstance, LabelRowV2 from encord.objects.options import Option # User input SSH_PATH = "" # Replace with the file path to your SSH private key PROJECT_ID = "" # Replace with the unique Project ID DATA_TITLE = "" # Replace with the title of the data unit LABEL_SPACE = "" # Replace with the unique ID of the label space OBJECT_INSTANCE = "" # Replace with the object hash of the object you want to remove CLASSIFICATION_INSTANCE = "" # Replace with the classification hash of the classification you want to remove # --- Connect to Encord --- user_client: EncordUserClient = EncordUserClient.create_with_ssh_private_key( ssh_private_key_path=SSH_PATH, # For US platform users use "https://api.us.encord.com" domain="https://api.encord.com", ) # Get Project project: Project = user_client.get_project(PROJECT_ID) # Get all Label Rows for the data unit rows = project.list_label_rows_v2(data_title_eq=DATA_TITLE) # Assume we want the first Label Row lr = rows[0] lr.initialise_labels() # Get Label Space label_space = lr.get_space(layout_key="text", type_="text") # Print first object hash in each label space label_spaces = lr.get_spaces() for space in label_spaces: print(space.get_object_instances()[0].object_hash) # Remove object label_space.remove_object_instance( object_hash=OBJECT_INSTANCE ) # Remove classification label_space.remove_classification_instance( classification_hash=CLASSIFICATION_INSTANCE ) # Save label row lr.save() print(f"Saved label row for {lr.data_title}") ``` ```python HTML theme={"dark"} from encord import EncordUserClient, Project from encord.objects import Object, Classification, ClassificationInstance, ObjectInstance, LabelRowV2 from encord.objects.options import Option # User input SSH_PATH = "" # Replace with the file path to your SSH private key PROJECT_ID = "" # Replace with the unique Project ID DATA_TITLE = "" # Replace with the title of the data unit LABEL_SPACE = "" # Replace with the unique ID of the label space OBJECT_INSTANCE = "" # Replace with the object hash of the object you want to remove CLASSIFICATION_INSTANCE = "" # Replace with the classification hash of the classification you want to remove # --- Connect to Encord --- user_client: EncordUserClient = EncordUserClient.create_with_ssh_private_key( ssh_private_key_path=SSH_PATH, # For US platform users use "https://api.us.encord.com" domain="https://api.encord.com", ) # Get Project project: Project = user_client.get_project(PROJECT_ID) # Get all Label Rows for the data unit rows = project.list_label_rows_v2(data_title_eq=DATA_TITLE) # Assume we want the first Label Row lr = rows[0] lr.initialise_labels() # Get Label Space label_space = lr.get_space(layout_key="website", type_="html") # Print first object hash in each label space label_spaces = lr.get_spaces() for space in label_spaces: print(space.get_object_instances()[0].object_hash) # Remove object label_space.remove_object_instance( object_hash=OBJECT_INSTANCE ) # Remove classification label_space.remove_classification_instance( classification_hash=CLASSIFICATION_INSTANCE ) # Save label row lr.save() print(f"Saved label row for {lr.data_title}") ``` ```python DICOM / NIfTI theme={"dark"} from encord import EncordUserClient, Project from encord.objects import Object, Classification, ClassificationInstance, ObjectInstance, LabelRowV2 from encord.objects.options import Option # User input SSH_PATH = "" # Replace with the file path to your SSH private key PROJECT_ID = "" # Replace with the unique Project ID DATA_TITLE = "" # Replace with the title of the data unit LABEL_SPACE = "" # Replace with the unique ID of the label space OBJECT_INSTANCE = "" # Replace with the object hash of the object you want to remove CLASSIFICATION_INSTANCE = "" # Replace with the classification hash of the classification you want to remove # --- Connect to Encord --- user_client: EncordUserClient = EncordUserClient.create_with_ssh_private_key( ssh_private_key_path=SSH_PATH, # For US platform users use "https://api.us.encord.com" domain="https://api.encord.com", ) # Get Project project: Project = user_client.get_project(PROJECT_ID) # Get all Label Rows for the data unit rows = project.list_label_rows_v2(data_title_eq=DATA_TITLE) # Assume we want the first Label Row lr = rows[0] lr.initialise_labels() # Get Label Space label_space = lr.get_space(layout_key="scan", type_="medical") # Print first object hash in each label space label_spaces = lr.get_spaces() for space in label_spaces: print(space.get_object_instances()[0].object_hash) # Remove object label_space.remove_object_instance( object_hash=OBJECT_INSTANCE ) # Remove classification label_space.remove_classification_instance( classification_hash=CLASSIFICATION_INSTANCE ) # Save label row lr.save() print(f"Saved label row for {lr.data_title}") ``` ```python Multi-Layered Labels theme={"dark"} from encord import EncordUserClient, Project from encord.objects import Object, Classification, ClassificationInstance, ObjectInstance, LabelRowV2 from encord.objects.coordinates import BoundingBoxCoordinates from encord.objects.options import Option # User input SSH_PATH = "" # Replace with the file path to your SSH private key PROJECT_ID = "" # Replace with the unique Project ID DATA_TITLE = "" # Replace with the title of the data unit # --- Connect to Encord --- user_client: EncordUserClient = EncordUserClient.create_with_ssh_private_key( ssh_private_key_path=SSH_PATH, # For US platform users use "https://api.us.encord.com" domain="https://api.encord.com", ) # Get Project project: Project = user_client.get_project(PROJECT_ID) # Get all Label Rows for the data unit rows = project.list_label_rows_v2(data_title_eq=DATA_TITLE) # Assume we want the first Label Row lr = rows[0] lr.initialise_labels() # Find bounding box object "Cherry" in the Ontology ontology_structure = project.ontology_structure box_ontology_object: Object = ontology_structure.get_child_by_title(title="Cherry", type_=Object) # Find classification "Day or Night" in the Ontology classification: Classification = ontology_structure.get_child_by_title(title="Day or Night", type_=Classification) # Find classification answer "Day" in the Ontology classification_answer = classification.get_child_by_title( title="Day", type_=Option ) # Get the root Label Space. The root space pertains to the Data Group as a whole, including multi-layered labels root_space = lr.get_space(id="root", type_="image") # Create bounding box instance bb_inst: ObjectInstance = box_ontology_object.create_instance() # Find the text attribute "Ripeness description" in the Ontology and set the answer attribute = box_ontology_object.get_child_by_title("Ripeness description") bb_inst.set_answer(attribute=attribute, answer="Fully ripe, deep red color with no visible blemishes") # Create classification instance classification_inst: ClassificationInstance = classification.create_instance() classification_inst.set_answer( answer=classification_answer ) # Add the bounding box instance to the root Label Space root_space.put_object_instance( object_instance=bb_inst, on_overlap="replace", coordinates=BoundingBoxCoordinates( top_left_x=0.6, top_left_y=0.4, width=0.3, height=0.2 ), frames=[0] ) # Add the classification instance to the root Label Space root_space.put_classification_instance( classification_instance=classification_inst, frames=[0] ) lr.save() print(f"Saved label row for {lr.data_title}") ``` ## Put It All Together The following is an example demonstrating how to use Label Spaces. We have Data Groups with five data units in the following layout: ```pgsql Data units theme={"dark"} +-------------------------------------------+ | text file | +------------------+------------------------+ | video 1 | video 2 | +------------------+------------------------+ | video 3 | video 4 | +------------------+------------------------+ ``` ```pgsql layout_key theme={"dark"} +-------------------------------------------+ | instructions | +------------------+------------------------+ | top-left | top-right | +------------------+------------------------+ | bottom-left | bottom-left | +------------------+------------------------+ ``` ### Create Example Data Groups The following script creates the Data Groups and specifies the layout. ```python Create Data Group expandable theme={"dark"} from uuid import UUID from encord.constants.enums import DataType from encord.objects.metadata import DataGroupMetadata from encord.orm.storage import DataGroupCustom, StorageItemType from encord.user_client import EncordUserClient # --- Configuration --- SSH_PATH = "/Users/chris-encord/ssh-private-key.txt" # Replace with the file path to your access key FOLDER_ID = "00000000-0000-0000-0000-000000000000" # Replace with the Folder ID DATASET_ID = "00000000-0000-0000-0000-000000000000" # Replace with the Dataset ID PROJECT_ID = "00000000-0000-0000-0000-000000000000" # Replace with the Project ID # --- Connect to Encord --- user_client: EncordUserClient = EncordUserClient.create_with_ssh_private_key( ssh_private_key_path=SSH_PATH, # For US platform users use "https://api.us.encord.com" domain="https://api.encord.com", ) folder = user_client.get_storage_folder(FOLDER_ID) # --- Reusable layout and settings --- layout = { "direction": "column", "first": {"type": "data_unit", "key": "instructions"}, "second": { "direction": "column", "first": { "direction": "row", "first": {"type": "data_unit", "key": "top-left"}, "second": {"type": "data_unit", "key": "top-right"}, "splitPercentage": 50, }, "second": { "direction": "row", "first": {"type": "data_unit", "key": "bottom-left"}, "second": {"type": "data_unit", "key": "bottom-right"}, "splitPercentage": 50, }, "splitPercentage": 50, }, "splitPercentage": 20, } settings = {"tile_settings": {"instructions": {"is_read_only": True}}} # --- Group definitions (name + UUIDs) --- groups = [ { "name": "group-001", "uuids": { "instructions": UUID("00000000-0000-0000-0000-000000000000"), # Replace with File ID of clustered_event_log_01.txt "top-left": UUID("11111111-1111-1111-1111-111111111111"), # Replace with File ID of 00001_normalized.mp4 "top-right": UUID("22222222-2222-2222-2222-222222222222"), # Replace with File ID of 00002_normalized.mp4 "bottom-left": UUID("33333333-3333-3333-3333-333333333333"), # Replace with File ID of 00009.mp4 "bottom-right": UUID("44444444-4444-4444-4444-444444444444"), # Replace with File ID of 00011_normalized.mp4 }, }, { "name": "group-002", "uuids": { "instructions": UUID("55555555-5555-5555-5555-555555555555"), # Replace with File ID of clustered_event_log_02.txt "top-left": UUID("66666666-6666-6666-6666-666666666666"), # Replace with File ID of 00012.mp4 "top-right": UUID("77777777-7777-7777-7777-777777777777"), # Replace with File ID of 00020.mp4 "bottom-left": UUID("88888888-8888-8888-8888-888888888888"), # Replace with File ID of 00030.mp4 "bottom-right": UUID("99999999-9999-9999-9999-999999999999"), # Replace with File ID of 00033.mp4 }, }, { "name": "group-003", "uuids": { "instructions": UUID("12312312-3123-1231-2312-312312312312"), # Replace with File ID of clustered_event_log_03.txt "top-left": UUID("23232323-2323-2323-2323-232323232323"), # Replace with File ID of 00034.mp4 "top-right": UUID("31313131-3131-3131-3131-313131313131"), # Replace with File ID of 00035_normalized.mp4 "bottom-left": UUID("45645645-6456-4564-5645-645645645645"), # Replace with File ID of 00038_normalized.mp4 "bottom-right": UUID("56565656-6565-5656-6565-656565656565 "), # Replace with File ID of 00045.mp4 }, }, # More groups... ] # Create the data groups for g in groups: group = folder.create_data_group( DataGroupCustom( name=g["name"], layout=layout, layout_contents=g["uuids"], settings=settings, ) ) print(f"✅ Created group '{g['name']}' with UUID {group}") # Add all the data groups in a folder to a Dataset group_items = folder.list_items(item_types=[StorageItemType.GROUP]) d = user_client.get_dataset(DATASET_ID) d.link_items([item.uuid for item in group_items]) # Add the Dataset with the Data Groups to a Project p = user_client.get_project(PROJECT_ID) rows = p.list_label_rows_v2(include_children=True) # Label Rows of Data Groups use DataGroupMetadata for the layout to Annotate and Review for row in rows: if row.data_type == DataType.GROUP: row.initialise_labels() assert isinstance(row.metadata, DataGroupMetadata) print(row.metadata.children) ``` ### Pre-label Data Groups We want to pre-label the classifications for all videos with the **layout\_key** `top-left` in the Data Groups with **Yes**. This way annotators only need to update the classification for `top-left` videos where there the model predictions and summaries are incorrect. Our example Project uses a Dataset that contains our Data Groups. The Ontology looks like this: **Classifications** * `Prediction correct?` * `YES!` (Radio button) * `No` (Radio button) * `What's wrong?` (Text) * `Summary correct?` * `YES!` (Radio button) * `No` (Radio button) * `What's wrong?` (Text) ![Data Groups Ontology](https://storage.googleapis.com/docs-media.encord.com/E2E%20Artifacts/Screenshots/e2e-data-groups-ontology.png) ```python Import Classification values on top_left videos expandable theme={"dark"} from __future__ import annotations from encord import EncordUserClient, Project from encord.objects import Classification, ClassificationInstance, LabelRowV2 from encord.objects.options import Option # User input: Edit these values as you require SSH_PATH = "/Users/chris-encord/ssh-private-key.txt" PROJECT_HASH = "00000000-0000-0000-0000-000000000000" # Only apply to THIS label space LABEL_SPACE = "top-left" CLASSIFICATIONS = [ "Prediction correct?", "Summary correct?", ] ANSWER_TITLE = "YES!" # Frames to apply the classification to FRAMES = [0] # change as needed # Connect to Encord user_client: EncordUserClient = EncordUserClient.create_with_ssh_private_key( ssh_private_key_path=SSH_PATH, # For US users use "https://api.us.encord.com" domain="https://api.encord.com", ) # Get project for which predictions are to be added. project: Project = user_client.get_project(PROJECT_HASH) ontology = project.ontology_structure # Fetch ALL label rows all_rows: list[LabelRowV2] = project.list_label_rows_v2(include_children=True) # Identify Data Group data_group_parents = [row for row in all_rows if row.group_hash is None] print(f"Found {len(data_group_parents)} data groups") # Process each Data Group for parent in data_group_parents: parent.initialise_labels() # Helpful: print what spaces actually exist on this group label row spaces = parent.get_spaces() print(f"Data group '{parent.data_title}' → {len(spaces)} spaces") print(" Available layout keys:", [s.metadata.layout_key for s in spaces]) # Get the target label space from the PARENT row try: label_space = parent.get_space(layout_key=LABEL_SPACE, type_="video") except Exception as e: print(f" ⚠ Could not find space '{LABEL_SPACE}' on group '{parent.data_title}': {e}") continue for classification_title in CLASSIFICATIONS: classification: Classification = ontology.get_child_by_title( title=classification_title, type_=Classification, ) yes_option: Option = classification.get_child_by_title( title=ANSWER_TITLE, type_=Option, ) classification_inst: ClassificationInstance = classification.create_instance() classification_inst.set_answer(yes_option) label_space.put_classification_instance( classification_instance=classification_inst ) parent.save() print(f" ✔ Labeled group row: {parent.data_title} (space: {LABEL_SPACE})") ``` ## Accessing Storage Item Metadata With Label Spaces Label spaces provide a direct link to their storage items using the `space_id` property. It maps to the `uuid` of the corresponding `StorageItem`. `space.space_id` is equivalent to the `uuid` of the storage item. This holds for all label spaces, not just image Data Groups. This means you can use `get_spaces()` on a label row to traverse from a label space to its storage item and access all metadata such as image dimensions. The following example demonstrates this using a Data Group of plain images, fetching the height and width of each image through its label space: ```python Get image dimensions from storage item theme={"dark"} from encord import EncordUserClient SSH_PATH = "" PROJECT_ID = "" # Instantiate Encord client user_client = EncordUserClient.create_with_ssh_private_key( ssh_private_key_path=SSH_PATH ) project = user_client.get_project(PROJECT_ID) label_rows = project.list_label_rows_v2() lr = label_rows[0] # Replace with your target label row lr.initialise_labels() # Fetch all storage items item_ids = [space.space_id for space in lr.get_spaces()] items = user_client.get_storage_items(item_ids) for item in items: print(f"Height is {item.height}") print(f"Width is {item.width}") ```