Save import.py, replacing the following variables:
<private_key_path> with the full path to your private key.
<project_id> with the unique ID of the Project containing the data units you want to import labels for.
COCOimportfile.json with the full path of the COCO file containing the labels you want to import.
The COCO file MUST include the info field. If it is missing, add it as: "info": {},.
If necessary, modify the matching logic that maps COCO image IDs to Encord frame indices. This is particularly important in cases where filenames in the COCO file do not directly match those in the Encord Project or when multiple files have the same name.
The following import.py script is configured to import labels into single images with unique names and assumes that the category names in the COCO file match the names of your Ontology objects.
In practice, you must implement your own matching logic. An example where filenames in the COCO file do not directly match those in the Encord Project is provided below.
import jsonfrom pathlib import Pathfrom encord.utilities.coco.datastructure import FrameIndexfrom encord import EncordUserClientfrom encord.exceptions import OntologyError# Replace with the path to your SSH private keykeyfile = "<private_key_path>"# Authenticate with Encord using your SSH private keyuser_client = EncordUserClient.create_with_ssh_private_key(ssh_private_key_path=keyfile)# Replace with your Project IDproject = user_client.get_project("<project_id>")# Load the COCO annotations JSON file# Replace 'COCOimportfile.json' with the full path to your COCO filecoco_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_structurefor 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 seriesimage_id_to_frame_index = {}data_title_to_label_row = {lr.data_title: lr for lr in project.list_label_rows_v2()}for img in labels_dict['images']: if "video_title" in img.keys(): lr = data_title_to_label_row[img["video_title"]] frame_num = int(img["file_name"].split('/')[-1].split(".")[0]) else: lr = data_title_to_label_row[img['image_title']] frame_num = 0 # 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=frame_num)# Import the COCO labels into Encordproject.import_coco_labels( labels_dict, category_id_to_feature_hash, image_id_to_frame_index)