Export Labels

The Encord SDK is designed to offer a versatile and targeted approach to accessing label information. This approach allows for a more customized handling of label information, catering to various needs and scenarios in data processing. The following table describes all values exported by the scripts on this page.

Key	Description
objectHash	The unique ID of the object instance. Two instances of the same object have different object hashes
Object name	The name of the object as defined in the Ontology. For example “Chicken”.
featureHash	The unique ID of the Ontology element. For example, the object “Chicken” in the Ontology. All instances have the same featureHash.
uid	The unique identifier for the object instance. Two instances of the same object have different uids.
Object color	The color used to label the object, as defined in the Ontology and seen in the Encord platform.
Ontology shape	The shape used to label the object, as defined in the Ontology. For example, polygon.
Classification name	The name of the Classification, as defined in the Ontology. For example “Day or night?”
Classification answer	The value of the Classification, as defined in the Ontology. For example “Day”
classificationHash	The unique identifier for the Classification instance.
Classification answer hash	The unique identifier for the Classification value
Attribute name	The name of the attribute, as defined in the Ontology. For example “Standing or sitting?”
Attribute answer	The name of the attribute value, as defined in the Ontology. For example “Sitting”
Attribute answer featureHash	The unique identifier for the attribute value.

Export Labels as JSON

The following script prints a JSON file containing all the labels in your Project.

Adding include_all_label_branches=True to list_label_rows_v2() includes all labels from all branches in Consensus Projects.

DEPRECATED @v186: For Data Groups, adding include_children=True to list_label_rows_v2() includes all labels from Data Groups.

We recommend using bundle in your scripts to improve performance. Keep bundle sizes under 1000 operations. Bundles of more than 1000 operations can degrade performance rather than improve performance.The optimal bundle size depends on your use case. For example, for videos with a large number of labels, start with 20 operations and adjust up or down based on performance.

Export labels to JSON

# Import dependencies
import json
import os

from encord import EncordUserClient

SSH_PATH = "/Users/chris-encord/ssh-private-key.txt" # Replace with file path to your SSH private key
PROJECT_ID = "00000000-0000-0000-0000-000000000000" # Replace with Project unique ID
BUNDLE_SIZE = 100  # Customize as needed
OUTPUT_JSON = "/Users/chris-encord/export-label-rows.json" # Replace with file path to save JSON output file

# Create user client using access key
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",
)

# Specify Project
project = user_client.get_project(PROJECT_ID)
assert project is not None, f"Project with ID {PROJECT_ID} could not be loaded"

# Get label rows for your Project
label_rows = project.list_label_rows_v2()
assert label_rows, f"No label rows found in project {PROJECT_ID}"

# Initialize label rows using bundles
with project.create_bundle(bundle_size=BUNDLE_SIZE) as bundle:
    for label_row in label_rows:
        label_row.initialise_labels(bundle=bundle)

# Collect all label row data
all_label_rows = [label_row.to_encord_dict() for label_row in label_rows]
assert all_label_rows, "No label row data collected for export"

# Save the collected label rows data to a JSON file
output_file = OUTPUT_JSON
assert output_file.endswith(".json"), "Output file must be a .json file"

with open(output_file, "w") as file:
    json.dump(all_label_rows, file, indent=4)

print(f"Label rows have been saved to {output_file}.")

Export Attributes

The following scripts get the attributes for all labels in a Project, as well as the frames that the attributes appear on. Single images only have a single frame.

We recommend using the script for exporting all attributes if the Project uses an Ontology with nested attributes.

# Import dependencies
import json

from encord import EncordUserClient
from encord.objects import ObjectInstance
from encord.objects.attributes import Attribute, TextAttribute

# User input
SSH_PATH = "/Users/chris-encord/ssh-private-key.txt"
PROJECT_ID = "00000000-0000-0000-0000-000000000000"
DATA_UNIT = "cherries-is"
OUTPUT_FILE_PATH = "/Users/chris-encord/text_attributes_output.json"
BUNDLE_SIZE = 100

# Create user client using access key
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",
)

# Specify Project
project = user_client.get_project(PROJECT_ID)
assert project is not None, f"Project with ID {PROJECT_ID} could not be loaded"

# Filter label rows for a specific data title
label_rows = project.list_label_rows_v2(data_title_eq=DATA_UNIT)
assert label_rows, f"No label rows found for data title '{DATA_UNIT}'"

# Initialize labels using bundle
with project.create_bundle(bundle_size=BUNDLE_SIZE) as bundle:
    for label_row in label_rows:
        label_row.initialise_labels(bundle=bundle)


# Function to extract text attributes and store in structured format
def extract_text_attributes(attribute: Attribute, object_instance: ObjectInstance, frame_number: int):
    if isinstance(attribute, TextAttribute):
        text_answer = object_instance.get_answer(attribute)
        return {
            "frame": frame_number + 1,
            "attribute_name": attribute.title,
            "attribute_hash": attribute.feature_node_hash,
            "text_answer": text_answer,
        }
    return None


# Collect results for saving
results = []

# Iterate through all object instances and collect text attribute data
for label_row in label_rows:
    object_instances = label_row.get_object_instances()
    assert object_instances, f"No object instances found in label row {label_row.uid}"

    for object_instance in object_instances:
        annotations = object_instance.get_annotations()
        assert annotations, f"No annotations found for object instance {object_instance.object_hash}"

        ontology_item = object_instance.ontology_item
        assert ontology_item and ontology_item.attributes, (
            f"Missing ontology item or attributes for object {object_instance.object_hash}"
        )

        for annotation in annotations:
            for attribute in ontology_item.attributes:
                attr_data = extract_text_attributes(attribute, object_instance, annotation.frame)
                if attr_data:
                    results.append(attr_data)
                    # Optional: also print to console
                    print(f"Frame {attr_data['frame']}:")
                    print(f"Text Attribute name: {attr_data['attribute_name']}")
                    print(f"Text Attribute hash: {attr_data['attribute_hash']}")
                    print(f"Text Attribute Answer: {attr_data['text_answer']}")

# Save results to JSON
assert OUTPUT_FILE_PATH.endswith(".json"), "Output file path must end with .json"

with open(OUTPUT_FILE_PATH, "w") as f:
    json.dump(results, f, indent=4)

print(f"\nText attribute data saved to: {OUTPUT_FILE_PATH}")

# Import dependencies
import json
import os

from encord import EncordUserClient
from encord.objects import ObjectInstance
from encord.objects.attributes import Attribute, RadioAttribute

# User input
SSH_PATH = "/Users/chris-encord/ssh-private-key.txt"
PROJECT_ID = "00000000-0000-0000-0000-000000000000"
DATA_UNIT = "cherries-010.jpg"
OUTPUT_FILE_PATH = "/Users/chris-encord/radio_attribute_output.json"
BUNDLE_SIZE = 100

# Create user client using access key
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 and label rows
project = user_client.get_project(PROJECT_ID)
assert project is not None, f"Project with ID {PROJECT_ID} could not be loaded"

label_rows = project.list_label_rows_v2(data_title_eq=DATA_UNIT)
assert label_rows, f"No label rows found for data unit: {DATA_UNIT}"

# Initialize label rows using bundle
with project.create_bundle(bundle_size=BUNDLE_SIZE) as bundle:
    for label_row in label_rows:
        label_row.initialise_labels(bundle=bundle)

# Prepare data structure to collect all results
output_data = []


# Function to extract info from RadioAttribute (recursively if nested)
def extract_attributes(attribute: Attribute, object_instance: ObjectInstance, frame_number: int):
    frame_data = []

    if isinstance(attribute, RadioAttribute):
        attribute_answers = object_instance.get_answer(attribute)
        if not isinstance(attribute_answers, list):
            attribute_answers = [attribute_answers]

        for answer in attribute_answers:
            if answer is None:
                continue  # Skip empty answers

            answer_data = {
                "frame": frame_number + 1,
                "attribute_name": attribute.title,
                "attribute_hash": attribute.feature_node_hash,
                "answer_title": answer.title,
                "answer_hash": answer.feature_node_hash,
                "nested_attributes": [],
            }

            if hasattr(answer, "attributes") and answer.attributes:
                for nested_attribute in answer.attributes:
                    nested_data = extract_attributes(nested_attribute, object_instance, frame_number)
                    if nested_data:
                        answer_data["nested_attributes"].extend(nested_data)

            frame_data.append(answer_data)

    return frame_data


# Loop through label rows and extract attribute data
for label_row in label_rows:
    object_instances = label_row.get_object_instances()
    assert object_instances, f"No object instances found in label row {label_row.uid}"

    for object_instance in object_instances:
        annotations = object_instance.get_annotations()
        assert annotations, f"No annotations found for object instance {object_instance.object_hash}"

        ontology_item = object_instance.ontology_item
        assert ontology_item and ontology_item.attributes, (
            f"No ontology item or attributes found for object {object_instance.object_hash}"
        )

        for annotation in annotations:
            for attribute in ontology_item.attributes:
                attr_data = extract_attributes(attribute, object_instance, annotation.frame)
                if attr_data:
                    output_data.extend(attr_data)

# Save results to JSON
assert OUTPUT_FILE_PATH.endswith(".json"), "Output file path must end with .json"

with open(OUTPUT_FILE_PATH, "w") as f:
    json.dump(output_data, f, indent=4)

print(f"Radio attribute data saved to {OUTPUT_FILE_PATH}")

# Import dependencies
import json
import os

from encord import EncordUserClient
from encord.objects import ObjectInstance
from encord.objects.attributes import Attribute, ChecklistAttribute

# User input
SSH_PATH = "/Users/chris-encord/ssh-private-key.txt"
PROJECT_ID = "00000000-0000-0000-0000-000000000000"
DATA_UNIT = "cherries-010.jpg"
OUTPUT_FILE_PATH = "/Users/chris-encord/checklist_attributes_output.json"
BUNDLE_SIZE = 100

# Create user client using access key
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",
)

# Load the project and filter label rows
project = user_client.get_project(PROJECT_ID)
assert project is not None, f"Project with ID {PROJECT_ID} could not be loaded"

label_rows = project.list_label_rows_v2(data_title_eq=DATA_UNIT)
assert label_rows, f"No label rows found for data unit: {DATA_UNIT}"

# Initialize label rows using bundle
with project.create_bundle(bundle_size=BUNDLE_SIZE) as bundle:
    for label_row in label_rows:
        label_row.initialise_labels(bundle=bundle)


# Function to extract checklist attributes in structured format
def extract_attributes(attribute: Attribute, object_instance: ObjectInstance, frame_number: int):
    if isinstance(attribute, ChecklistAttribute):
        attribute_answers = object_instance.get_answer(attribute)
        if not isinstance(attribute_answers, list):
            attribute_answers = [attribute_answers]

        return {
            "frame": frame_number + 1,
            "attribute_name": attribute.title,
            "attribute_hash": attribute.feature_node_hash,
            "answers": [
                {
                    "title": answer.title,
                    "hash": answer.feature_node_hash,
                }
                for answer in attribute_answers
                if answer
            ],
        }
    return None


# Collect data for saving
results = []

# Iterate through label rows and extract checklist attribute data
for label_row in label_rows:
    object_instances = label_row.get_object_instances()
    assert object_instances, f"No object instances found in label row {label_row.uid}"

    for object_instance in object_instances:
        annotations = object_instance.get_annotations()
        assert annotations, f"No annotations found for object instance {object_instance.object_hash}"

        ontology_item = object_instance.ontology_item
        assert ontology_item and ontology_item.attributes, (
            f"No ontology item or attributes found for object {object_instance.object_hash}"
        )

        for annotation in annotations:
            for attribute in ontology_item.attributes:
                attr_data = extract_attributes(attribute, object_instance, annotation.frame)
                if attr_data:
                    results.append(attr_data)
                    print(f"Frame {attr_data['frame']}:")
                    print(f"Checklist Attribute name: {attr_data['attribute_name']}")
                    for answer in attr_data["answers"]:
                        print(f"Checklist answer: {answer['title']}")
                        print(f"Checklist answer hash: {answer['hash']}")

# Save to JSON
assert OUTPUT_FILE_PATH.endswith(".json"), "Output file path must end with .json"

with open(OUTPUT_FILE_PATH, "w") as f:
    json.dump(results, f, indent=4)

print(f"\nChecklist attribute data saved to: {OUTPUT_FILE_PATH}")

# Import dependencies
import json
from collections.abc import Iterable

from encord import EncordUserClient
from encord.objects import ObjectInstance
from encord.objects.attributes import Attribute, ChecklistAttribute, RadioAttribute, TextAttribute

# User input
SSH_PATH = "/Users/chris-encord/ssh-private-key.txt"
# SSH_PATH = get_ssh_key() # replace it with access key
PROJECT_ID = "00000000-0000-0000-0000-000000000000"
DATA_UNIT = "cherries-010.jpg"
OUTPUT_FILE_PATH = "/Users/chris-encord/all_attributes_output.json"
BUNDLE_SIZE = 100

# Create user client using access key
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",
)

# Load project and label rows
project = user_client.get_project(PROJECT_ID)
assert project is not None, f"Project with ID {PROJECT_ID} could not be loaded"

label_rows = project.list_label_rows_v2(data_title_eq=DATA_UNIT)
assert label_rows, f"No label rows found for data unit: {DATA_UNIT}"

# Initialize label rows using bundle
with project.create_bundle(bundle_size=BUNDLE_SIZE) as bundle:
    for label_row in label_rows:
        label_row.initialise_labels(bundle=bundle)

# Container for structured results
results = []


# Function to collect and print attributes
def extract_and_print_attributes(attribute: Attribute, object_instance: ObjectInstance, frame_number: int):
    attr_data = {
        "frame": frame_number + 1,
        "attribute_name": attribute.title,
        "attribute_hash": attribute.feature_node_hash,
        "attribute_type": None,
        "answers": [],
    }

    if isinstance(attribute, TextAttribute):
        attr_data["attribute_type"] = "TextAttribute"
        text_answer = object_instance.get_answer(attribute)
        attr_data["answers"].append({"text": text_answer or "No attribute answer"})

    elif isinstance(attribute, RadioAttribute):
        attr_data["attribute_type"] = "RadioAttribute"
        answer = object_instance.get_answer(attribute)
        if answer:
            answer_data = {"title": answer.title, "hash": answer.feature_node_hash, "nested_attributes": []}

            if hasattr(answer, "attributes") and answer.attributes:
                for nested_attribute in answer.attributes:
                    nested_result = extract_and_print_attributes(nested_attribute, object_instance, frame_number)
                    if nested_result:
                        answer_data["nested_attributes"].append(nested_result)

            attr_data["answers"].append(answer_data)
        else:
            attr_data["answers"].append({"note": "No attribute answer"})

    elif isinstance(attribute, ChecklistAttribute):
        attr_data["attribute_type"] = "ChecklistAttribute"
        answers = object_instance.get_answer(attribute)
        if answers:
            if not isinstance(answers, Iterable):
                answers = [answers]
            for answer in answers:
                attr_data["answers"].append({"title": answer.title, "hash": answer.feature_node_hash})
        else:
            attr_data["answers"].append({"note": "No attribute answer"})

    # Print the same info (optional)
    print(f"Frame {attr_data['frame']}: {attr_data['attribute_type']} name: {attr_data['attribute_name']}")
    for answer in attr_data["answers"]:
        if "title" in answer:
            print(f"Answer: {answer['title']}")
            print(f"Answer hash: {answer['hash']}")
        elif "text" in answer:
            print(f"Answer: {answer['text']}")
        elif "note" in answer:
            print(f"Note: {answer['note']}")
        if "nested_attributes" in answer:
            for nested in answer["nested_attributes"]:
                print(f"Nested attribute: {nested['attribute_name']} ({nested['attribute_hash']})")

    return attr_data


# Process all label rows
for label_row in label_rows:
    object_instances = label_row.get_object_instances()
    assert object_instances, f"No object instances found in label row {label_row.uid}"

    for object_instance in object_instances:
        annotations = object_instance.get_annotations()
        assert annotations, f"No annotations found for object instance {object_instance.object_hash}"

        assert object_instance.ontology_item and object_instance.ontology_item.attributes, (
            f"No attributes found for object {object_instance.object_hash}"
        )

        for annotation in annotations:
            for attribute in object_instance.ontology_item.attributes:
                extracted = extract_and_print_attributes(attribute, object_instance, annotation.frame)
                if extracted:
                    results.append(extracted)

# Save to JSON
assert OUTPUT_FILE_PATH.endswith(".json"), "Output file path must be a JSON file"

with open(OUTPUT_FILE_PATH, "w") as f:
    json.dump(results, f, indent=4)

print(f"\nAll attribute data saved to: {OUTPUT_FILE_PATH}")

Export Objects and Classifications

The following scripts do not support Ontologies with text classifications, or nested classifications. The scripts serve as a template for exporting object labels only.

The following script shows how to access and print essential label information for all object and classification instances, and all attributes in a Project. The example for videos, image groups, image sequences and DICOM outputs the frame numbers of each object, classification and attribute. Make sure you substitute:

The <private_key_path> with the full path to your private key.
The <project_id> with the ID of your Project.

# Import dependencies
from encord import EncordUserClient
from encord.objects.ontology_element import OntologyElement
from encord.objects.attributes import Attribute
from encord.objects import ObjectInstance

# Instantiate Encord client by substituting the path to your private key
user_client = EncordUserClient.create_with_ssh_private_key(
    ssh_private_key_path="<private_key_path>"
)

# Replace <project_id> with the hash of your Project
project = user_client.get_project("<project_id>")

# A function to extract and print essential information for all attributes
def print_attributes(attribute: Attribute, object_instance: ObjectInstance):
    print(f"Attribute name: {attribute.title}")
    print(f"Attribute hash: {attribute.feature_node_hash}")
    for answer_for_frames in object_instance.get_answer(attribute):
        print(f"Attribute answer \"{answer_for_frames.answer.title}\" is present on frames {answer_for_frames.ranges}")
        print ("Attribute answer hash " + answer_for_frames.answer.feature_node_hash)
        for attribute in answer_for_frames.answer.attributes:
            print(f"Attribute answer \"{answer_for_frames.answer.title}\" has nested attribute \"{attribute.title}\"")
            print_attributes(attribute, object_instance)

# Initialize label rows using bundles
label_rows = project.list_label_rows_v2()
with project.create_bundle() as bundle:
    for label_row in label_rows:
        label_row.initialise_labels(bundle=bundle)

for label_row in label_rows:
    # Print essential label information for all objects
    for object_instance in label_row.get_object_instances():
        print ("objectHash: " + object_instance.object_hash)
        print ("Object name: " + object_instance.object_name)
        print ("featureHash: " + object_instance.feature_hash)
        print ("uid: " + str(object_instance.ontology_item.uid))
        print ("Object color: " + object_instance.ontology_item.color)
        print ("Ontology shape: " + object_instance.ontology_item.shape)

        # Print the frame number and the location of the object on the frame
        for annotation in object_instance.get_annotations():
            print(f"Frame {annotation.frame} -> {annotation.coordinates}")

        # Print all attributes
        for attribute in object_instance.ontology_item.attributes:
            print_attributes(attribute, object_instance)

    # Print all essential classification information
    for classification_instance in label_row.get_classification_instances():
        print ("classificationHash: " + classification_instance.classification_hash)
        print ("Classification name: " + classification_instance.classification_name)
        print ("featureHash: " + classification_instance.feature_hash)
        print ("Classification answer: " + classification_instance.get_answer().value)
        print ("Classification answer hash: " + classification_instance.get_answer().feature_node_hash)

        # Print the frame number(s) that a classification appears on
        for annotation in classification_instance.get_annotations():
            print("Classification appears on frame " + str(annotation.frame))

from encord import EncordUserClient
from encord.objects.ontology_element import OntologyElement
from encord.objects.attributes import Attribute
from encord.objects import ObjectInstance

# Instantiate Encord client by substituting the path to your private key
user_client = EncordUserClient.create_with_ssh_private_key(
    ssh_private_key_path="<private_key_path>"
)

# Replace <project_id> with the ID of your Project
project = user_client.get_project("<project_id>")

# Print essential info for all attributes
def print_attributes(attribute: Attribute, object_instance: ObjectInstance):
    print(f"Attribute name: {attribute.title}")
    print(f"Attribute hash: {attribute.feature_node_hash}")
    for attribute_answer in object_instance.get_answer(attribute):
        print(f"Attribute answer: {attribute_answer.answer.title}")
        print ("Attribute answer hash " + attribute_answer.answer.feature_node_hash)
        for attribute in attribute_answer.answer.attributes:
            print(f"Attribute answer \"{attribute_answer.answer.title}\" has nested attribute \"{attribute.title}\"")
            print ("Attribute answer hash " + attribute_answer.answer.feature_node_hash)
            print_attributes(attribute, object_instance)

# Initialize label rows using bundles
label_rows = project.list_label_rows_v2()
with project.create_bundle() as bundle:
    for label_row in label_rows:
        label_row.initialise_labels(bundle=bundle)

for label_row in label_rows:
    # Print essential label information for all objects
    for object_instance in label_row.get_object_instances():
        print ("objectHash: " + object_instance.object_hash)
        print ("Object name: " + object_instance.object_name)
        print ("featureHash: " + object_instance.feature_hash)
        print ("uid: " + str(object_instance.ontology_item.uid))
        print ("Object color: " + object_instance.ontology_item.color)
        print ("Ontology shape: " + object_instance.ontology_item.shape)

        for annotation in object_instance.get_annotations():
            print(f"Frame {annotation.frame} -> {annotation.coordinates}")

        for attribute in object_instance.ontology_item.attributes:
            print_attributes(attribute, object_instance)

    for classification_instance in label_row.get_classification_instances():
        print ("classificationHash: " + classification_instance.classification_hash)
        print ("Classification name: " + classification_instance.classification_name)
        print ("featureHash: " + classification_instance.feature_hash)

        for annotation in classification_instance.get_annotations():
            print("Classification on frame " + str(annotation.frame))

objectHash: HI5408US
Object name: Chicken
featureHash: p6T5NeQ4
uid: 1
Object color: #D33115
Ontology Shape: bounding_box
Frame 101 -> BoundingBoxCoordinates(height=0.526, width=0.2514, top_left_x=0.5826, top_left_y=0.2431)
Frame 102 -> BoundingBoxCoordinates(height=0.5365, width=0.256, top_left_x=0.578, top_left_y=0.2413)
Frame 103 -> BoundingBoxCoordinates(height=0.5469, width=0.2615, top_left_x=0.5752, top_left_y=0.2378)
Frame 104 -> BoundingBoxCoordinates(height=0.5365, width=0.256, top_left_x=0.5798, top_left_y=0.2396)
Frame 105 -> BoundingBoxCoordinates(height=0.5365, width=0.256, top_left_x=0.5661, top_left_y=0.2396)
Frame 106 -> BoundingBoxCoordinates(height=0.5469, width=0.2615, top_left_x=0.5633, top_left_y=0.2361)
Frame 107 -> BoundingBoxCoordinates(height=0.5365, width=0.256, top_left_x=0.5532, top_left_y=0.2361)
Frame 108 -> BoundingBoxCoordinates(height=0.526, width=0.2514, top_left_x=0.556, top_left_y=0.2413)
Attribute name: Walking or Sitting?
featureHash: pHXXPbuc
Attribute answer "Walking" is present on frames [(101:104)]
Attribute answer featureHash: 6qeufh3l
Attribute answer "Sitting" is present on frames [(105:108)]
Attribute answer featureHash: hkInN4Sd
classificationHash: gffOkg9l
Classification name: Day or Night?
featureHash: FA6pAde3
Classification answer: Day
Classification answer hash: gasjasd
Classification appears on frame 352

Export Objects, Classifications, and Attributes by Frame

Videos with variable frame rates can result in misplaced labels.

Range of Consecutive Frames

The following scripts download and print the labels for a specified range of frames in videos.

To export labels for a single frame, make the <start_frame_number> the same as the <end_frame_number>. For example, to export labels on the 13th frame set <start_frame_number> = 13, and <end_frame_number> = 13.

# Import dependencies
import json

from encord import EncordUserClient
from encord.objects import ObjectInstance
from encord.objects.attributes import Attribute

# User input
SSH_PATH = "/Users/chris-encord/ssh-private-key.txt"
PROJECT_ID = "00000000-0000-0000-0000-000000000000"
DATA_UNIT = "cherries-is"
OUTPUT_FILE_PATH = "/Users/chris-encord/frame_range_output.json"
START_FRAME_NUMBER = 0
END_FRAME_NUMBER = 35
BUNDLE_SIZE = 100

# Create user client using access key
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",
)

# Load the project
project = user_client.get_project(PROJECT_ID)
assert project is not None, f"Project with ID {PROJECT_ID} could not be loaded"

# Get filtered label rows
label_rows = project.list_label_rows_v2(data_title_eq=DATA_UNIT)
assert label_rows, f"No label rows found for data unit: {DATA_UNIT}"

# Initialize labels using bundles
with project.create_bundle(bundle_size=BUNDLE_SIZE) as bundle:
    for label_row in label_rows:
        label_row.initialise_labels(bundle=bundle)


# Function to extract attributes in a structured format
def extract_attributes(attribute: Attribute, object_instance: ObjectInstance):
    try:
        answer = object_instance.get_answer(attribute)
    except Exception:
        answer = None
    return {
        "attribute_name": attribute.title,
        "attribute_hash": attribute.feature_node_hash,
        "attribute_answer": str(answer),
    }


# Result collection
results = []

# Loop over label rows
for label_row in label_rows:
    row_data = {
        "label_row_hash": label_row.label_hash,
        "data_title": label_row.data_title,
        "objects": [],
        "classifications": [],
    }

    # Process object annotations
    object_instances = label_row.get_object_instances()
    assert object_instances, f"No object instances found in label row {label_row.uid}"

    for object_instance in object_instances:
        annotations = object_instance.get_annotations()
        assert annotations, f"No annotations found for object instance {object_instance.object_hash}"

        assert object_instance.ontology_item and object_instance.ontology_item.attributes, (
            f"No attributes found for object {object_instance.object_hash}"
        )

        for annotation in annotations:
            if START_FRAME_NUMBER <= annotation.frame <= END_FRAME_NUMBER:
                obj_info = {
                    "frame": annotation.frame,
                    "object_hash": object_instance.object_hash,
                    "object_name": object_instance.object_name,
                    "feature_hash": object_instance.feature_hash,
                    "uid": str(object_instance.ontology_item.uid),
                    "color": object_instance.ontology_item.color,
                    "shape": object_instance.ontology_item.shape,
                    "label_location": str(annotation.coordinates),
                    "attributes": [],
                }

                print("Frame:", annotation.frame)
                print("objectHash:", object_instance.object_hash)
                print("Object name:", object_instance.object_name)
                print("featureHash:", object_instance.feature_hash)
                print("uid:", obj_info["uid"])
                print("Object color:", object_instance.ontology_item.color)
                print("Ontology shape:", object_instance.ontology_item.shape)
                print(f"Label location: {annotation.coordinates}")

                for attribute in object_instance.ontology_item.attributes:
                    attr_data = extract_attributes(attribute, object_instance)
                    obj_info["attributes"].append(attr_data)

                    print(f"Attribute name: {attr_data['attribute_name']}")
                    print(f"Attribute hash: {attr_data['attribute_hash']}")
                    print(f"Attribute answer: {attr_data['attribute_answer']}")

                row_data["objects"].append(obj_info)

    # Process classification annotations
    classification_instances = label_row.get_classification_instances()
    assert classification_instances is not None, f"No classification instances found in label row {label_row.uid}"

    for classification_instance in classification_instances:
        annotations = classification_instance.get_annotations()
        assert annotations, (
            f"No annotations found for classification instance {classification_instance.classification_hash}"
        )

        for annotation in annotations:
            if START_FRAME_NUMBER <= annotation.frame <= END_FRAME_NUMBER:
                try:
                    answer = classification_instance.get_answer()
                    answer_value = answer.value
                    answer_hash = answer.feature_node_hash
                except Exception:
                    answer_value = None
                    answer_hash = None

                classification_info = {
                    "frame": annotation.frame,
                    "classification_hash": classification_instance.classification_hash,
                    "classification_name": classification_instance.classification_name,
                    "feature_hash": classification_instance.feature_hash,
                    "value": answer_value,
                    "answer_hash": answer_hash,
                }

                print("Classification hash:", classification_info["classification_hash"])
                print("Classification name:", classification_info["classification_name"])
                print("Feature hash:", classification_info["feature_hash"])
                print("Classification value:", classification_info["value"])
                print("Classification answer hash:", classification_info["answer_hash"])

                row_data["classifications"].append(classification_info)

    results.append(row_data)

# Save to JSON
assert OUTPUT_FILE_PATH.endswith(".json"), "Output file path must be a JSON file"

with open(OUTPUT_FILE_PATH, "w") as f:
    json.dump(results, f, indent=4)

print(f"\nData saved to: {OUTPUT_FILE_PATH}")

# Import dependencies
import json
from collections.abc import Iterable

from encord import EncordUserClient
from encord.objects import ObjectInstance
from encord.objects.attributes import Attribute
from encord.objects.ontology_element import OntologyElement

# User input
SSH_PATH = "/Users/chris-encord/ssh-private-key.txt"
PROJECT_ID = "00000000-0000-0000-0000-000000000000"
OUTPUT_FILE_PATH = "/Users/chris-encord/frame_range_output.json"
START_FRAME_NUMBER = 0
END_FRAME_NUMBER = 2
BUNDLE_SIZE = 100

# Create user client using access key
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",
)

# Load project
project = user_client.get_project(PROJECT_ID)
assert project is not None, f"Project with ID {PROJECT_ID} could not be loaded"

# Retrieve all label rows
label_rows = project.list_label_rows_v2()
assert label_rows, "No label rows returned from the project"

# Initialize label rows using bundles
with project.create_bundle(bundle_size=BUNDLE_SIZE) as bundle:
    for label_row in label_rows:
        label_row.initialise_labels(bundle=bundle)


# Function to extract attribute info
def extract_attributes(attribute: Attribute, object_instance: ObjectInstance):
    try:
        answer = object_instance.get_answer(attribute)
    except Exception:
        answer = None
    return {
        "attribute_name": attribute.title,
        "attribute_hash": attribute.feature_node_hash,
        "attribute_answer": str(answer),
    }


# Store results here
results = []

# Iterate over label rows
for label_row in label_rows:
    row_data = {
        "label_row_hash": label_row.label_hash,
        "data_title": label_row.data_title,
        "objects": [],
        "classifications": [],
    }

    # Process object annotations
    object_instances = label_row.get_object_instances()

    for object_instance in object_instances:
        annotations = object_instance.get_annotations()
        assert annotations, f"No annotations found for object instance {object_instance.object_hash}"

        ontology_item = object_instance.ontology_item
        assert ontology_item and ontology_item.attributes, (
            f"Ontology item or attributes missing for object {object_instance.object_hash}"
        )

        for annotation in annotations:
            if START_FRAME_NUMBER <= annotation.frame <= END_FRAME_NUMBER:
                obj_info = {
                    "frame": annotation.frame,
                    "object_hash": object_instance.object_hash,
                    "object_name": object_instance.object_name,
                    "feature_hash": object_instance.feature_hash,
                    "uid": str(ontology_item.uid),
                    "color": ontology_item.color,
                    "shape": ontology_item.shape,
                    "label_location": str(annotation.coordinates),
                    "attributes": [],
                }

                print("Frame:", annotation.frame)
                print("objectHash:", object_instance.object_hash)
                print("Object name:", object_instance.object_name)
                print("featureHash:", object_instance.feature_hash)
                print("uid:", obj_info["uid"])
                print("Object color:", obj_info["color"])
                print("Ontology shape:", obj_info["shape"])
                print(f"Label location: {annotation.coordinates}")

                for attribute in ontology_item.attributes:
                    attr_data = extract_attributes(attribute, object_instance)
                    obj_info["attributes"].append(attr_data)

                    print(f"Attribute name: {attr_data['attribute_name']}")
                    print(f"Attribute hash: {attr_data['attribute_hash']}")
                    print(f"Attribute answer: {attr_data['attribute_answer']}")

                row_data["objects"].append(obj_info)

    # Process classification annotations
    classification_instances = label_row.get_classification_instances()
    assert classification_instances is not None, f"No classification instances found in label row {label_row.uid}"

    for classification_instance in classification_instances:
        annotations = classification_instance.get_annotations()
        assert annotations, (
            f"No annotations found for classification instance {classification_instance.classification_hash}"
        )

        for annotation in annotations:
            if START_FRAME_NUMBER <= annotation.frame <= END_FRAME_NUMBER:
                try:
                    answer = classification_instance.get_answer()
                    value = answer.value
                    answer_hash = answer.feature_node_hash
                except Exception:
                    value = None
                    answer_hash = None

                classification_info = {
                    "frame": annotation.frame,
                    "classification_hash": classification_instance.classification_hash,
                    "classification_name": classification_instance.classification_name,
                    "feature_hash": classification_instance.feature_hash,
                    "value": value,
                    "answer_hash": answer_hash,
                }

                print("Classification hash:", classification_info["classification_hash"])
                print("Classification name:", classification_info["classification_name"])
                print("Feature hash:", classification_info["feature_hash"])
                print("Classification value:", classification_info["value"])
                print("Classification answer hash:", classification_info["answer_hash"])

                row_data["classifications"].append(classification_info)

    results.append(row_data)

# Save everything to JSON
assert OUTPUT_FILE_PATH.endswith(".json"), "Output file path must end with .json"

with open(OUTPUT_FILE_PATH, "w") as f:
    json.dump(results, f, indent=4)

print(f"\nAnnotation data saved to: {OUTPUT_FILE_PATH}")

Frame: 0
objectHash: U88+HXj4
Object name: Person
featureHash: AaaPMAgE
uid: 1
Object color: #15d3bc
Ontology shape: bounding_box
Label location: BoundingBoxCoordinates(height=0.519561557605036, width=0.15380023056653513, top_left_x=0.43242033102766797, top_left_y=0.0028114478114478862)
Attribute name: Man or woman
Attribute hash: X0Xhz9ip
Attribute answer: NestableOption(feature_node_hash='MN57hMep', uid=[1, 1, 1], label='Man', value='Man', nested_options=[])
Frame: 44
objectHash: nZWuD66O
Object name: Person
featureHash: AaaPMAgE
uid: 1
Object color: #15d3bc
Ontology shape: bounding_box
Label location: BoundingBoxCoordinates(height=0.46671424388815697, width=0.1688282279314888, top_left_x=0.37747550230566534, top_left_y=0.07887095593617346)
Attribute name: Man or woman
Attribute hash: X0Xhz9ip
Attribute answer: None
Classification hash: biZZZov4
Classification name: Day or Night?
Feature hash: T+P+wwjw
Classification value: Day
Classification answer hash: t+QtqkFp

List of non-consecutive frames

The following scripts download and print the labels for specific frames in videos. Make sure you substitute:

The <private_key_path> with the full path to your private key.
The <project_hash> with the hash of your Project.
The <task_name> with the name of the file (in Encord) you want to export labels for (if using the task-specific script).
Replace the numbers in the list [10, 20, 30, 40] with the frames you want to export labels for.

# Import dependencies
from encord import EncordUserClient
from encord.objects.ontology_element import OntologyElement
from encord.objects.attributes import Attribute, Option
from encord.objects import ObjectInstance
from collections.abc import Iterable

# Instantiate Encord client
user_client = EncordUserClient.create_with_ssh_private_key(
    ssh_private_key_path="<private_key_path>"
)

# Replace with the hash of your Project
project = user_client.get_project("<project_hash>")

# Function to extract and print essential information for all attributes
def print_attributes(attribute: Attribute, object_instance: ObjectInstance):
    print(f"Attribute name: {attribute.title}")
    print(f"Attribute hash: {attribute.feature_node_hash}")
    print(f"Attribute answer: {object_instance.get_answer(attribute)}")

# List of specific frames to be exported
specific_frames = [10, 20, 30, 40]  # Replace with your specific frame numbers

# Initialize label rows using bundles
label_rows = project.list_label_rows_v2()
with project.create_bundle() as bundle:
    for label_row in label_rows:
        label_row.initialise_labels(bundle=bundle)

for label_row in label_rows:
    # Print essential label information for all objects
    for object_instance in label_row.get_object_instances():
        for annotation in object_instance.get_annotations():
            if annotation.frame in specific_frames:
                print("Frame: " + str(annotation.frame))
                print("objectHash: " + object_instance.object_hash)
                print("Object name: " + object_instance.object_name)
                print("featureHash: " + object_instance.feature_hash)
                print("uid: " + str(object_instance.ontology_item.uid))
                print("Object color: " + object_instance.ontology_item.color)
                print("Ontology shape: " + object_instance.ontology_item.shape)
                print(f"Label location: {annotation.coordinates}")

                # Print all attributes
                for attribute in object_instance.ontology_item.attributes:
                    print_attributes(attribute, object_instance)

    # Print essential classification information on specified frames
    for classification_instance in label_row.get_classification_instances():
        for annotation in classification_instance.get_annotations():
            if annotation.frame in specific_frames:
                print("Classification hash: " + classification_instance.classification_hash)
                print("Classification name: " + classification_instance.classification_name)
                print("Feature hash: " + classification_instance.feature_hash)
                print("Classification value: " + classification_instance.get_answer().value)
                print("Classification answer hash: " + classification_instance.get_answer().feature_node_hash)

# Import dependencies
from encord import EncordUserClient
from encord.objects.ontology_element import OntologyElement
from encord.objects.attributes import Attribute, Option
from encord.objects import ObjectInstance
from collections.abc import Iterable

# Instantiate Encord client
user_client = EncordUserClient.create_with_ssh_private_key(
    ssh_private_key_path="<private_key_path>"
)

# Replace with the hash of your Project
project = user_client.get_project("<project_hash>")

# Function to extract and print essential information for all attributes
def print_attributes(attribute: Attribute, object_instance: ObjectInstance):
    print(f"Attribute name: {attribute.title}")
    print(f"Attribute hash: {attribute.feature_node_hash}")
    print(f"Attribute answer: {object_instance.get_answer(attribute)}")

# List of specific frames
specific_frames = [10, 20, 30, 40]  # Replace with your specific frame numbers

# Initialize label rows using bundles
label_rows = project.list_label_rows_v2()
with project.create_bundle() as bundle:
    for label_row in label_rows:
        label_row.initialise_labels(bundle=bundle)

for label_row in label_rows:
    # Print essential label information for all objects
    for object_instance in label_row.get_object_instances():
        for annotation in object_instance.get_annotations():
            if annotation.frame in specific_frames:
                print("Frame: " + str(annotation.frame))
                print("objectHash: " + object_instance.object_hash)
                print("Object name: " + object_instance.object_name)
                print("featureHash: " + object_instance.feature_hash)
                print("uid: " + str(object_instance.ontology_item.uid))
                print("Object color: " + object_instance.ontology_item.color)
                print("Ontology shape: " + object_instance.ontology_item.shape)
                print(f"Label location: {annotation.coordinates}")

                # Print all attributes
                for attribute in object_instance.ontology_item.attributes:
                    print_attributes(attribute, object_instance)

    # Print essential classification information on specified frames
    for classification_instance in label_row.get_classification_instances():
        for annotation in classification_instance.get_annotations():
            if annotation.frame in specific_frames:
                print("Classification hash: " + classification_instance.classification_hash)
                print("Classification name: " + classification_instance.classification_name)
                print("Feature hash: " + classification_instance.feature_hash)
                print("Classification value: " + classification_instance.get_answer().value)
                print("Classification answer hash: " + classification_instance.get_answer().feature_node_hash)

Frame: 10
objectHash: U88+HXj4
Object name: Person
featureHash: AaaPMAgE
uid: 1
Object color: #15d3bc
Ontology shape: bounding_box
Label location: BoundingBoxCoordinates(height=0.519561557605036, width=0.15380023056653513, top_left_x=0.43242033102766797, top_left_y=0.0028114478114478862)
Attribute name: Man or woman
Attribute hash: X0Xhz9ip
Attribute answer: NestableOption(feature_node_hash='MN57hMep', uid=[1, 1, 1], label='Man', value='Man', nested_options=[])
Frame: 20
objectHash: nZWuD66O
Object name: Person
featureHash: AaaPMAgE
uid: 1
Object color: #15d3bc
Ontology shape: bounding_box
Label location: BoundingBoxCoordinates(height=0.46671424388815697, width=0.1688282279314888, top_left_x=0.37747550230566534, top_left_y=0.07887095593617346)
Attribute name: Man or woman
Attribute hash: X0Xhz9ip
Attribute answer: None
Classification hash: biZZZov4
Classification name: Day or Night?
Feature hash: T+P+wwjw
Classification value: Day
Classification answer hash: t+QtqkFp

Saving Frames with Labels

ffmpeg can be used to save all frames with labels as an image, to be used in machine learning applications. The script below shows how this is done when exporting a list of non-consecutive frames from a specific video.

ffmpeg must be installed for this script to work.

Make sure you substitute:

The <output_folder_path> with the full path to the output folder you want the output image files to be saved.
The <private_key_path> with the full path to your private key.
The <project_id> with the ID of your Project.
The <task_name> with the name of the file in Encord you want to export labels for.
The <path_to_your_video_file> with the full path to the video file you are exporting labels for.
The def get_video_path function to return your actual video path corresponding to the label row.
Replace the numbers in the list [10, 20, 30, 40] with the frames you want to export labels for.

# Import dependencies
from encord import EncordUserClient
from encord.objects.ontology_element import OntologyElement
from encord.objects.attributes import Attribute, Option
from encord.objects import ObjectInstance
from collections.abc import Iterable
import subprocess
import os

# Specify the output folder path. Replace with your desired folder path
output_folder = "<output_folder_path>"

# Create the output folder if it doesn't exist
if not os.path.exists(output_folder):
    os.makedirs(output_folder)

def extract_frame_to_image(video_file, frame_number, output_file):
    """
    Uses FFmpeg to extract a specific frame from a video and saves it as an image.
    """
    ffmpeg_command = [
        'ffmpeg', '-i', video_file, '-vf', f'select=eq(n\,{frame_number})',
        '-vframes', '1', output_file
    ]
    subprocess.run(ffmpeg_command, check=True)

# Instantiate Encord client
user_client = EncordUserClient.create_with_ssh_private_key(
    ssh_private_key_path="<private_key_path>"
)

# Replace with the ID of your Project
project = user_client.get_project("<project_id>")

# Function to extract and print essential information for all attributes
def print_attributes(attribute: Attribute, object_instance: ObjectInstance):
    print(f"Attribute name: {attribute.title}")
    print(f"Attribute hash: {attribute.feature_node_hash}")
    print(f"Attribute answer: {object_instance.get_answer(attribute)}")

# List of specific frames to be exported. Replace with your specific frame numbers
specific_frames = [10, 20, 30, 40]

# Path to the video file you are exporting labels for
def get_video_path(label_row)
    return "<path_to_your_video_file>"

# Initialize label rows using bundles
label_rows = project.list_label_rows_v2()
with project.create_bundle() as bundle:
    for label_row in label_rows:
        label_row.initialise_labels(bundle=bundle)

for label_row in label_rows:
    # Print essential label information for all objects
    video_path = get_video_path(label_row)
    for object_instance in label_row.get_object_instances():
        for annotation in object_instance.get_annotations():
            if annotation.frame in specific_frames:
                print("Frame: " + str(annotation.frame))
                print("objectHash: " + object_instance.object_hash)
                print("Object name: " + object_instance.object_name)
                print("featureHash: " + object_instance.feature_hash)
                print("uid: " + str(object_instance.ontology_item.uid))
                print("Object color: " + object_instance.ontology_item.color)
                print("Ontology shape: " + object_instance.ontology_item.shape)
                print(f"Label location: {annotation.coordinates}")

                # Define the output path for the image
                output_image_path = os.path.join(output_folder, f"frame_{annotation.frame}.png")

                # Extract and save the specific frame as an image
                extract_frame_to_image(video_path, annotation.frame, output_image_path)

                # Print all attributes
                for attribute in object_instance.ontology_item.attributes:
                    print_attributes(attribute, object_instance)

    # Print essential classification information on specified frames
    for classification_instance in label_row.get_classification_instances():
        for annotation in classification_instance.get_annotations():
            if annotation.frame in specific_frames:
                print("Classification hash: " + classification_instance.classification_hash)
                print("Classification name: " + classification_instance.classification_name)
                print("Feature hash: " + classification_instance.feature_hash)
                print("Classification value: " + classification_instance.get_answer().value)
                print("Classification answer hash: " + classification_instance.get_answer().feature_node_hash)

Label Editor Coordinates

All label locations are exported as normalized coordinates ranging from 0 to 1. This means that the corners of the frame or image correspond to the coordinates (1,1), (1,0), (0,0), (0,1) regardless of frame dimensions. To get the pixel values of any normalized coordinates, multiply them by the width or height of the label (given in pixels).

“x” and “h” coordinates of a label should be multiplied by the pixel width.
“y” and “w” coordinates of a label should be multiplied by the pixel height.

Export Labels from Specific Workflow Stages

from encord import EncordUserClient

# User input

SSH_PATH = "/Users/chris-encord/ssh-private-key.txt" # Replace with file path to your SSH private key
PROJECT_ID = "00000000-0000-0000-0000-000000000000" # Replace with the unique Project ID
WORKFLOW_STAGE = "Annotation" # Replace with the name of the Workflow stage

# Authentic
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",
)

# Load project
project = user_client.get_project(PROJECT_ID)

# Fetch label rows in Annotation stage
label_rows = project.list_label_rows_v2(
    workflow_graph_node_title_eq=WORKFLOW_STAGE
)

# Initialise labels
with project.create_bundle() as bundle:
    for label_row in label_rows:
        label_row.initialise_labels(bundle=bundle)

# Export rows
for label_row in label_rows:
    print(label_row.data_title)
    print(label_row.to_encord_dict())

from encord import EncordUserClient

SSH_PATH = "/Users/chris-encord/ssh-private-key.txt" # Replace with file path to your SSH private key
PROJECT_ID = "00000000-0000-0000-0000-000000000000" # Replace with the unique Project ID
WORKFLOW_STAGE = "Review%" # Replace with the name of the Workflow stage


# Authenticate
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",
)

# Load project
project = user_client.get_project(PROJECT_ID)

# Fetch rows matching Review*
label_rows = project.list_label_rows_v2(
    workflow_graph_node_title_like=WORKFLOW_STAGE
)

# Initialise labels
with project.create_bundle() as bundle:
    for label_row in label_rows:
        label_row.initialise_labels(bundle=bundle)

# Export rows
for label_row in label_rows:
    stage = label_row.workflow_graph_node.title
    print(f"{label_row.data_title} is in stage: {stage}")
    print(label_row.to_encord_dict())

from encord import EncordUserClient

SSH_PATH = "/Users/chris-encord/ssh-private-key.txt" # Replace with file path to your SSH private key
PROJECT_ID = "00000000-0000-0000-0000-000000000000" # Replace with the unique Project ID
WORKFLOW_STAGE = "Review%" # Replace with the name of the Workflow stage


# Authenticate
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",
)

# Load project
project = user_client.get_project(PROJECT_ID)

# Fetch all label rows
label_rows = project.list_label_rows_v2()

# Initialise labels
with project.create_bundle() as bundle:
    for label_row in label_rows:
        label_row.initialise_labels(bundle=bundle)

# Process depending on stage
for label_row in label_rows:
    stage = label_row.workflow_graph_node.title if label_row.workflow_graph_node else None

    print(f"{label_row.data_title} is in stage: {stage}")

    if stage == "Annotation":
        print("Processing annotation row")
        print(label_row.to_encord_dict())

    elif stage == "Review":
        print("Processing review row")
        print(label_row.to_encord_dict())

Export all Consensus labels

Consensus Projects introduce the concept of BRANCHES within the task workflow. A Consensus annotation task consists of a MAIN branch and one sub-branch for each Annotator. Each time an annotator saves or submits a task, the annotator creates their own branch on a task. The MAIN branch remains empty of labels until a reviewer specifies that a task is in consensus. When consensus is reached, the labels that are the best representative set move to the MAIN branch. To export all labels (labels generated for every branch on every data unit) from your Consensus Project, use include_all_branches=True. Make sure you:

Substitute the <private_key_path> with the full path to your private key.
Substitute the <project_id> with the ID of your Project.
If you only want to export the MAIN branch, remove include_all_label_branches=True.

Adding include_all_label_branches=True to list_label_rows_v2() includes all labels from all branches in Consensus Projects.

DEPRECATED @v186: For Data Groups, adding include_children=True to list_label_rows_v2() includes all labels from Data Groups.

import json

from encord import EncordUserClient

# User input
SSH_PATH = "/Users/chris-encord/ssh-private-key.txt"
PROJECT_ID = "00000000-0000-0000-0000-000000000000"
OUTPUT_FILE_PATH = "/Users/chris-encord/frame_range_output.json"
BUNDLE_SIZE = 100

# Create user client using access key
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",
)

# Specify Project. Replace <project_id> with the ID of the Project you want to export labels for.
project = user_client.get_project(PROJECT_ID)

# Downloads a local copy of all the labels
# Without the include_all_label_branches flag only the MAIN branch labels export
label_rows = project.list_label_rows_v2(include_children=True, include_all_label_branches=True)

output_data = []  # This will hold the output data to be saved as JSON

# Initialize label rows using bundles
with project.create_bundle(bundle_size=BUNDLE_SIZE) as bundle:
    for label_row in label_rows:
        label_row.initialise_labels(bundle=bundle)

# Collecting data for JSON output
for label_row in label_rows:
    label_row_data = {
        "title": label_row.data_title,
        "branch": label_row.branch_name,
        "objects": [],
        "classifications": [],
    }

    # Collect object instances
    for object_instance in label_row.get_object_instances():
        object_data = {
            "object_hash": object_instance.object_hash,
            "object_name": object_instance.object_name,
            "feature_hash": object_instance.feature_hash,
            "ontology_item": {
                "uid": object_instance.ontology_item.uid,
                "color": object_instance.ontology_item.color,
                "shape": object_instance.ontology_item.shape,
                "attributes": [
                    {"name": attribute.name, "value": attribute.value}
                    for attribute in object_instance.ontology_item.attributes
                ],
            },
            "annotations": [
                {"frame": annotation.frame, "coordinates": annotation.coordinates}
                for annotation in object_instance.get_annotations()
            ],
        }
        label_row_data["objects"].append(object_data)

    # Collect classification instances
    for classification_instance in label_row.get_classification_instances():
        classification_data = {
            "classification_hash": classification_instance.classification_hash,
            "classification_name": classification_instance.classification_name,
            "feature_hash": classification_instance.feature_hash,
            "classification_answer": {
                "value": classification_instance.get_answer().value,
                "hash": classification_instance.get_answer().feature_node_hash,
            },
            "annotations": [{"frame": annotation.frame} for annotation in classification_instance.get_annotations()],
        }
        label_row_data["classifications"].append(classification_data)

    # Add label row data to the output list
    output_data.append(label_row_data)

# Saving to JSON file
output_file_path = OUTPUT_FILE_PATH  # Replace with the desired file path
with open(output_file_path, "w") as json_file:
    json.dump(output_data, json_file, indent=4)

print(f"Output saved to {output_file_path}")

Export all Data Group labels

Adding include_all_label_branches=True to list_label_rows_v2() includes all labels from all branches in Consensus Projects.

DEPRECATED @v186: Adding include_children=True to list_label_rows_v2() includes all labels from Data Groups.

# Import dependencies
import json
import os

from encord import EncordUserClient

SSH_PATH = "/Users/chris-encord/ssh-private-key.txt" # Replace with file path to your SSH private key
PROJECT_ID = "00000000-0000-0000-0000-000000000000" # Replace with Project unique ID
BUNDLE_SIZE = 100  # Customize as needed
OUTPUT_JSON = "/Users/chris-encord/export-label-rows.json" # Replace with file path to save JSON output file

# Create user client using access key
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",
)

# Specify Project
project = user_client.get_project(PROJECT_ID)
assert project is not None, f"Project with ID {PROJECT_ID} could not be loaded"

# Get label rows for your Project
label_rows = project.list_label_rows_v2()
assert label_rows, f"No label rows found in project {PROJECT_ID}"

# Initialize label rows using bundles
with project.create_bundle(bundle_size=BUNDLE_SIZE) as bundle:
    for label_row in label_rows:
        label_row.initialise_labels(bundle=bundle)

# Collect all label row data
all_label_rows = [label_row.to_encord_dict() for label_row in label_rows]
assert all_label_rows, "No label row data collected for export"

# Save the collected label rows data to a JSON file
output_file = OUTPUT_JSON
assert output_file.endswith(".json"), "Output file must be a .json file"

with open(output_file, "w") as file:
    json.dump(all_label_rows, file, indent=4)

print(f"Label rows have been saved to {output_file}.")

# Import dependencies
import json
import os

from encord import EncordUserClient

SSH_PATH = "/Users/chris-encord/ssh-private-key.txt" # Replace with file path to your SSH private key
PROJECT_ID = "00000000-0000-0000-0000-000000000000" # Replace with Project unique ID
BUNDLE_SIZE = 100  # Customize as needed
OUTPUT_JSON = "/Users/chris-encord/export-label-rows.json" # Replace with file path to save JSON output file

# Create user client using access key
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",
)

# Specify Project
project = user_client.get_project(PROJECT_ID)
assert project is not None, f"Project with ID {PROJECT_ID} could not be loaded"

# Get label rows for your Project
label_rows = project.list_label_rows_v2(include_children=True)
assert label_rows, f"No label rows found in project {PROJECT_ID}"

# Initialize label rows using bundles
with project.create_bundle(bundle_size=BUNDLE_SIZE) as bundle:
    for label_row in label_rows:
        label_row.initialise_labels(bundle=bundle)

# Collect all label row data
all_label_rows = [label_row.to_encord_dict() for label_row in label_rows]
assert all_label_rows, "No label row data collected for export"

# Save the collected label rows data to a JSON file
output_file = OUTPUT_JSON
assert output_file.endswith(".json"), "Output file must be a .json file"

with open(output_file, "w") as file:
    json.dump(all_label_rows, file, indent=4)

print(f"Label rows have been saved to {output_file}.")

Export Bitmasks

The following code example illustrates how to download / export and save Bitmask labels:

import cv2
import numpy as np
from encord import EncordUserClient

# Instantiate Encord client by substituting the path to your private key
user_client = EncordUserClient.create_with_ssh_private_key(
    ssh_private_key_path="<private_key_path>"
)

# Specify a project using project hash
project = user_client.get_project("<project_hash>")

# Obtain labels for the media of interest
label_row = project.list_label_rows_v2()[0]
label_row.initialise_labels()

# Find annotation
# In this example, it is just a first object on a first frame
object_with_bitmask_annotation = label_row.get_frame_views()[
    0
].get_object_instances()[0]

# Get a bitmask annotation
# In this example, it is a first annotation on the object
bitmask_annotation = object_with_bitmask_annotation.get_annotations()[0]

# Convert bitmask to a numpy array
# Obtained array is a binary mask, so to work with it as an image,
# it is necessary to convert it to a different datatype and scale
bitmask = bitmask_annotation.coordinates.to_numpy_array().astype(np.uint8)

bitmask[bitmask == 1] = 255

# And now we can save the mask as a grayscale image
cv2.imwrite("./mask_as_an_image.png", bitmask)

Decode Bitmask Labels

Bitmask labels exported from Encord as JSON or COCO are encoded as an rleString. This rleString can be converted to an image or a binary array.

Use the COCO API to decode Bitmasks when using the COCO export format.

Create Bitmask Image

# Import dependencies
import json
from pathlib import Path
import numpy as np
from encord.objects.coordinates import BitmaskCoordinates
from PIL import Image

# Read the JSON file.
# Replace labels.json with the path to the JSON file containing your Bitmask
labels = json.loads(Path("labels.json").read_text())
output_folder = Path("output")
output_folder.mkdir(parents=True, exist_ok=True)

# Finds the rleString and saves it to a .png image
found_any = False
for label_row in labels:
    for du in label_row["data_units"].values():
        slice_nums = list(du["labels"].keys())
        for slice_num in slice_nums:
            for obj in du["labels"][slice_num]["objects"]:
                bmc = BitmaskCoordinates.from_dict(obj)
                nparr = np.array(bmc)
                img = Image.fromarray(nparr)
                # img.show()
                # Replace example.png with the name of the output file
                img.save(output_folder / f"mask_{int(slice_num):05d}_{obj['objectHash']}.png")
print("Decoding complete.")

from pycocotools.coco import COCO
import numpy as np
from pycocotools import mask as maskUtils
from PIL import Image
import json


# Read the COCO file. Replace 'labels.json' with the path to your COCO format annotations file
coco_annotations_path = 'labels.json'
coco = COCO(coco_annotations_path)

# Get all annotation IDs (you might want to filter or select specific ones)
annIds = coco.getAnnIds()
anns = coco.loadAnns(annIds)

# Decode each rleString and save as a .png image
for i, ann in enumerate(anns):
    if 'segmentation' in ann and isinstance(ann['segmentation'], dict):

        rle = ann['segmentation']
        
        mask = maskUtils.decode(rle)

        img = Image.fromarray(mask * 255).convert('L')  # Convert to grayscale

# Replace example.png with the name of the output file
        img.save("example.png")

print("Decoding complete.")

Create Binary Array

The rleStringcan be converted into an array of 1’s and 0’s using the script below.

from encord.objects.coordinates import BitmaskCoordinates

# Create a numpy array
np_mask = BitmaskCoordinates.from_dict(obj).to_numpy_array() 
print(np_mask.astype(int))

Bulk Export Labels

Use the bundle function to significantly improve export performance.

We strongly recommend NOT bundling more than 1000 operations at once, because bundling more than 1000 operations can reduce performance instead of improving performance.

In the following code, ensure you replace:

<private_key_path> with the full path to your private key.
<project-id> with the ID of the Project you want to export labels for.
#Perform label row operation before in this loop with the label row operations you want to perform.
Optionally, change the value of BUNDLE_SIZE to suit your needs. We strongly recommend NOT bundling more than 1000 operations at once, because bundling more than 1000 operations can reduce performance instead of improving performance.

# Import dependencies
from pathlib import Path

from encord import EncordUserClient, Project
from encord.objects import (
    Object,
    ObjectInstance,
    OntologyStructure,
)
from encord.objects.coordinates import BoundingBoxCoordinates

SSH_PATH = "<private-key-path>"
PROJECT_ID = "<project-id>"

# Authenticate
user_client: EncordUserClient = EncordUserClient.create_with_ssh_private_key(ssh_private_key_path=SSH_PATH)

# Gets Project to export labels
project: Project = user_client.get_project(PROJECT_ID)

label_rows = project.list_label_rows_v2()
BUNDLE_SIZE = 100

# Initialize label rows using bundles
with project.create_bundle(bundle_size=BUNDLE_SIZE) as bundle:
    for label_row in label_rows:
        label_row.initialise_labels(bundle=bundle)

for label_row in label_rows:

    # Write any label row operation here. For example import, or export label rows.


# Saving changes to label rows using bundles
with project.create_bundle(bundle_size=BUNDLE_SIZE) as bundle:
    for label_row in label_rows:
        label_row.save(bundle=bundle)

COCO Format Export

Use the following script to bulk export labels in the COCO format. Ensure that you replace:

<private-key-path> with the full path to your private key for authentication.
<project-id> with the ID of the Project for which you want to export labels.

COCO Export

from encord import EncordUserClient
import json


SSH_PATH = "/Users/chris-encord/ssh-private-key.txt" # Replace with the access key
PROJECT_ID = "00000000-0000-0000-0000-000000000000" # Replace with the unique ID for the Project

# Create user client using access key
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 = user_client.get_project(PROJECT_ID)
coco_labels = project.export_coco_labels()

# Save the coco_labels dictionary to a .json file
with open("coco_labels.json", "w") as json_file:
    json.dump(coco_labels, json_file)

Get Started

General

Data

Ontologies

Projects

Labels

DICOM

Export Labels as JSON

Export Attributes

Export Objects and Classifications

Export Objects, Classifications, and Attributes by Frame

Range of Consecutive Frames

List of non-consecutive frames

Saving Frames with Labels

Label Editor Coordinates

Export Labels from Specific Workflow Stages

Export all Consensus labels

Export all Data Group labels

Export Bitmasks

Decode Bitmask Labels

Bulk Export Labels

COCO Format Export

​Export Labels as JSON

​Export Attributes

​Export Objects and Classifications

​Export Objects, Classifications, and Attributes by Frame

​Range of Consecutive Frames

​List of non-consecutive frames

​Saving Frames with Labels

​Label Editor Coordinates

​Export Labels from Specific Workflow Stages

​Export all Consensus labels

​Export all Data Group labels

​Export Bitmasks

​Decode Bitmask Labels

​Bulk Export Labels

​COCO Format Export

Export Labels as JSON

Export Attributes

Export Objects and Classifications

Export Objects, Classifications, and Attributes by Frame

Range of Consecutive Frames

List of non-consecutive frames

Saving Frames with Labels

Label Editor Coordinates

Export Labels from Specific Workflow Stages

Export all Consensus labels

Export all Data Group labels

Export Bitmasks

Decode Bitmask Labels

Bulk Export Labels

COCO Format Export