Since a recipe is linked with an ontology, it allows for making changes with labels and attributes. When the recipe is set as the default one for a dataset, the same applies for the dataset entity - it can be used for making changes with the labels and attributes which are ultimately linked to it through the recipe and its ontology.

9. Working With Recipes

# Get recipe from a list
recipe =[0]
# Get recipe by ID - ID can be retrieved from the page URL when opening the recipe in the platform
recipe ='your-recipe-id')
# Delete recipe - applies only for deleted datasets'your-recipe-id').delete()

10. Cloning Recipes

When you want to create a new recipe that’s only slightly different from an existing recipe, it can be easier to start by cloning the original recipe and then making changes on its clone.shallow: If True, link to existing ontology,If false clone all ontologies that are links to the recipe as well.

dataset = project.datasets.get(dataset_name="myDataSet")
recipe ="recipe_id")
recipe2 = recipe.clone(shallow=False)

11. View Dataset Labels

# as objects
labels = dataset.labels
# as instance map
labels = dataset.instance_map

12. Add Labels by Dataset

Working with dataset labels can be done one-by-one or as a list.The Dataset entity documentation details all label options - read here.

# Add multiple labels
dataset.add_labels(label_list=['person', 'animal', 'object'])
# Add single label with specific color and attributes
dataset.add_label(label_name='person', color=(34, 6, 231))
# Add single label with a thumbnail/icon
dataset.add_label(label_name='person', icon_path='/home/project/images/icon.jpg')

13. Add Labels Using Label Object

# Create Labels list using Label object
labels = [
    dl.Label(tag='Donkey', color=(255, 100, 0)),
    dl.Label(tag='Mammoth', color=(34, 56, 7)),
    dl.Label(tag='Bird', color=(100, 14, 150))
# Add Labels to Dataset
# or you can also create a recipe from the label list
recipe ='My-Recipe-name', labels=labels)

14. Add a Label and Sub-Labels

label = dl.Label(tag='Fish',
                 color=(34, 6, 231),
                                    color=(34, 6, 231)),
                                    color=(34, 6, 231))]
# or you can also create a recipe from the label list
recipe ='My-Recipe-name', labels=labels)

15. Add Hierarchy Labels with Nested

Different options for hierarchy label creation.

# Option A
# add father label
labels = dataset.add_label(label_name="animal", color=(123, 134, 64))
# add child label
labels = dataset.add_label(label_name="animal.Dog", color=(45, 34, 164))
# add grandchild label
labels = dataset.add_label(label_name="animal.Dog.poodle")
# Option B: only if you dont have attributes
# parent and grandparent (animal and dog) will be generated automatically
labels = dataset.add_label(label_name="animal.Dog.poodle")
# Option C: with the Big Dict
nested_labels = [
    {'label_name': 'animal.Dog',
     'color': '#220605',
     'children': [{'label_name': 'poodle',
                   'color': '#298345'},
                  {'label_name': 'labrador',
                   'color': '#298651'}]},
    {'label_name': '',
     'color': '#287605',
     'children': [{'label_name': 'Persian',
                   'color': '#298345'},
                  {'label_name': 'Balinese',
                   'color': '#298651'}]}
# Add Labels to the dataset:
labels = dataset.add_labels(label_list=nested_labels)

16. Delete Labels by Dataset

dataset.delete_labels(label_names=['Cat', 'Dog'])

17. Update Label Features

# update existing label , if not exist fails
dataset.update_label(label_name='Cat', color="#000080")
# update label, if not exist add it
dataset.update_label(label_name='Cat', color="#fcba03", upsert=True)