Import Labels/Annotations to Consensus Branches

# Import dependencies
import os
from encord import EncordUserClient, Project
from encord.objects import LabelRowV2, Object, OntologyStructure, ObjectInstance

from encord.objects.coordinates import BoundingBoxCoordinates, RotatableBoundingBoxCoordinates, PolygonCoordinates, PolylineCoordinates, PointCoordinate, BitmaskCoordinates


# Configuration
SSH_PATH = "file-path-to-your-ssh-key"
PROJECT_HASH = "unique-id-for-project"

# Specify a label_rows_v2 branch name for your predictions.
CONSENSUS_BRANCH_NAME = "encord-<name-of-your-consensus-branch>"

assert SSH_PATH is not None, "SSH path cannot be None"
assert PROJECT_HASH is not None, "Project hash cannot be None"

# Authenticate with Encord
user_client = EncordUserClient.create_with_ssh_private_key(
    ssh_private_key_path=SSH_PATH
)

# Access the project and prepare the branch for predictions
project = user_client.get_project(PROJECT_HASH)
consensus_branch_rows = project.list_label_rows_v2(branch_name=CONSENSUS_BRANCH_NAME)

if len(consensus_branch_rows) > 0:
  print("Branch is:", consensus_branch_rows[0].branch_name)

ontology_object = project.ontology_structure.objects[0]
bundle = project.create_bundle()
for row in consensus_branch_rows:
    row.initialise_labels(bundle=bundle)
bundle.execute()

for row in consensus_branch_rows:
    inst = ontology_object.create_instance()
    inst.set_for_frames(
        coordinates=BoundingBoxCoordinates(
            height=0.8,
            width=0.8,
            top_left_x=0.1,
            top_left_y=0.1,
        ),
        # Add the bounding box to the first frame
        frames=0,
        # There are multiple additional fields that can be set optionally:
        manual_annotation=False,
    )
    row.add_object_instance(inst)
    row.save(bundle=bundle)
bundle.execute()

import json
from pathlib import Path
from encord.utilities.coco.datastructure import FrameIndex
from encord import EncordUserClient
from encord.exceptions import OntologyError

# Authenticate client
SSH_PATH = "file-path-to-your-ssh-key"

# Specify a Project to import your predictions to. This Project must already exist in Encord.
PROJECT_HASH = "unique-id-for-project"

# Specify a label_rows_v2 branch name for your predictions.
CONSENSUS_BRANCH_NAME = "encord-<name-of-your-consensus-branch>"

# Authenticate with Encord using the path to your private key
user_client: EncordUserClient = EncordUserClient.create_with_ssh_private_key(
    ssh_private_key_path=SSH_PATH
)

# Replace with your project hash
project = user_client.get_project("PROJECT_HASH")

# Load the COCO annotations JSON file
# Replace 'COCOimportfile.json' with the full path to your COCO file
coco_file = Path("COCOimportfile.json")
labels_dict = json.loads(coco_file.read_text())

# Build a mapping from COCO category IDs to the feature hashes in your Encord Ontology. 
category_id_to_feature_hash = {}
ont_struct = project.ontology_structure
for coco_category in labels_dict["categories"]:
    try:
        ont_obj = ont_struct.get_child_by_title(coco_category["name"])
        category_id_to_feature_hash[coco_category["id"]] = ont_obj.feature_node_hash
    except OntologyError:
        print(f"Could not match {coco_category["name"]} in the Ontology. Import will crash if these are present.")

# Build a mapping from COCO image IDs to Encord frame indices
# This is only applicable for images, image groups, image sequences, and DICOM series
image_id_to_frame_index = {}
data_title_to_label_row = {lr.data_title: lr for lr in project.list_label_rows_v2("branch_name=CONSENSUS_BRANCH_NAME")}
for img in labels_dict["images"]:
    lr = data_title_to_label_row[img["file_name"]]

    # Creates a mapping between the COCO image IDs and the corresponding frame indices in Encord
    # In this example, the target frame is 0 because the files in the sample project are single images
    image_id_to_frame_index[img["id"]] = FrameIndex(lr.data_hash, frame=0)

# Import the COCO labels into Encord
project.import_coco_labels(
    labels_dict,
    category_id_to_feature_hash,
    image_id_to_frame_index
)

# Import dependencies
from encord import EncordUserClient
import json

SSH_PATH = "file-path-of-your-ssh-key"
PROJECT_HASH = "unique-id-for-your-project"

# Instantiate client. Replace \<private_key_path> with the path to the file containing your private key.
user_client = EncordUserClient.create_with_ssh_private_key(
    ssh_private_key_path=SSH_PATH
)

# Specify Project. Replace \<project_hash> with the hash of the Project you want to export labels for.
project = user_client.get_project(PROJECT_HASH)

# 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_all_label_branches=True) 

for label_row in label_rows:
    # Here we have the label row for the branch, but without labels themselves downloaded
    print(f"Title: {label_row.data_title}, branch: {label_row.branch_name}")

    # And now we download the label content itself (bounding boxes and stuff)
    label_row.initialise_labels()

    # Print essential label information for all objects
    for object_instance in label_row.get_object_instances():
        print (f"objectHash: {object_instance.object_hash}")
        print (f"Object name: {object_instance.object_name}")
        print (f"featureHash: {object_instance.feature_hash}")
        print (f"uid: {object_instance.ontology_item.uid}")
        print (f"Object color: {object_instance.ontology_item.color}")
        print (f"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 (attribute, object_instance)

    # Print all essential classification information
    for classification_instance in label_row.get_classification_instances():
        print (f"classificationHash: {classification_instance.classification_hash}")
        print (f"Classification name: {classification_instance.classification_name}")
        print (f"featureHash: {classification_instance.feature_hash}")
        print (f"Classification answer: {classification_instance.get_answer().value}")
        print (f"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(f"Classification appears on frame: {annotation.frame}")