From 8d44e084e1bf5c3349857324a79315c4f4b581bb Mon Sep 17 00:00:00 2001
From: Jim-Encord <156204297+Jim-Encord@users.noreply.github.com>
Date: Wed, 8 Jan 2025 12:26:28 +0000
Subject: [PATCH] EC-3689 COCO Exporter (#602)

---
 encord/project.py                 |   39 ++
 encord/utilities/coco/exporter.py | 1014 +++++++++++++++++++++++++++++
 poetry.lock                       |  601 ++++++++++++++++-
 pyproject.toml                    |    5 +
 tests/objects/data/data_1.py      |    6 +-
 tests/test_coco_export.py         |  256 ++++++++
 6 files changed, 1918 insertions(+), 3 deletions(-)
 create mode 100644 encord/utilities/coco/exporter.py
 create mode 100644 tests/test_coco_export.py

diff --git a/encord/project.py b/encord/project.py
index ea4441d34..e8b13c324 100644
--- a/encord/project.py
+++ b/encord/project.py
@@ -1184,6 +1184,45 @@ def import_coco_labels(
             branch_name=branch_name,
         )
 
+    def export_coco_labels(
+        self,
+        label_hashes: Optional[List[str]] = None,
+        include_object_feature_hashes: Optional[Set[str]] = None,
+        include_classification_feature_hashes: Optional[Set[str]] = None,
+        branch_name: Optional[str] = None,
+    ) -> Dict[str, Any]:
+        """Export labels from the project to the COCO format.
+        This method requires the 'coco' extra to be installed. Install it using:
+        `pip install encord[coco]`.
+        Args:
+            label_hashes: List of label hashes to include. If not provided, all label rows will be included.
+            include_object_feature_hashes: If `None`, all objects will be included.
+                 Otherwise, only objects with the specified feature hashes will be included.
+            include_classification_feature_hashes: If `None`, all classifications will be included.
+                Otherwise, only classifications with the specified feature hashes will be included.
+            branch_name: Optionally specify a branch name. Defaults to the `main` branch.
+        Returns:
+            Dict[str, Any]: A dictionary in the COCO format containing the exported labels,
+                including annotations and metadata conforming to COCO standards.
+                The dictionary also includes additional fields specific to Encord,
+                providing supplementary information not defined in the COCO standard.
+        Raises:
+            ImportError: If the 'coco' extra dependencies are not installed.
+        """
+        from encord.utilities.coco.exporter import CocoExporter
+
+        label_rows = self.list_label_rows_v2(label_hashes=label_hashes, branch_name=branch_name)
+        with self.create_bundle() as bundle:
+            for row in label_rows:
+                row.initialise_labels(
+                    include_object_feature_hashes=include_object_feature_hashes,
+                    include_classification_feature_hashes=include_classification_feature_hashes,
+                    bundle=bundle,
+                )
+        labels = [row.to_encord_dict() for row in label_rows]
+        coco_labels = CocoExporter(labels, ontology=self.ontology_structure).export()
+        return coco_labels
+
     def get_collection(self, collection_uuid: Union[str, UUID]) -> ProjectCollection:
         return ProjectCollection._get_collection(
             project_client=self._client,
diff --git a/encord/utilities/coco/exporter.py b/encord/utilities/coco/exporter.py
new file mode 100644
index 000000000..4c15a0852
--- /dev/null
+++ b/encord/utilities/coco/exporter.py
@@ -0,0 +1,1014 @@
+try:
+    import pycocotools
+    import shapely
+except ImportError as e:
+    raise ImportError(
+        "The 'pycocotools' and 'shapely' packages are required for the COCO export. "
+        "Install them with: `pip install encord[coco]`"
+    ) from e
+
+import logging
+from collections import defaultdict
+from dataclasses import dataclass
+from itertools import chain
+from typing import Any, Dict, List, Optional, Set, Tuple, Union
+
+import numpy as np
+from pycocotools import mask as cocomask
+from pydantic import BaseModel
+from shapely.geometry import Polygon
+
+from encord.exceptions import EncordException
+from encord.objects.attributes import Attribute
+from encord.objects.common import PropertyType, Shape
+from encord.objects.ontology_object import Object
+from encord.objects.ontology_structure import OntologyStructure
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass(frozen=True)
+class DicomAnnotationData:
+    dicom_instance_uid: str
+    multiframe_frame_number: Optional[int]  # only present for multi-frame DICOMs
+    file_uri: str
+    width: int
+    height: int
+
+
+class CocoAnnotation(BaseModel):
+    area: float
+    bbox: Tuple[float, float, float, float]
+    category_id: int
+    id_: int
+    image_id: int
+    is_crowd: int
+    segmentation: Union[List[List[float]], Dict[str, Any]]
+    keypoints: Optional[List[float]] = None
+    num_keypoints: Optional[int] = None
+    track_id: Optional[int] = None
+    encord_track_uuid: Optional[str] = None
+    rotation: Optional[float] = None
+    classifications: Optional[Dict[str, Any]] = None
+    manual_annotation: Optional[bool] = None
+
+    def to_dict(self) -> Dict[str, Any]:
+        return {
+            "area": self.area,
+            "bbox": list(self.bbox),
+            "category_id": self.category_id,
+            "image_id": self.image_id,
+            "iscrowd": self.is_crowd,
+            "segmentation": self.segmentation,
+            "keypoints": self.keypoints,
+            "num_keypoints": self.num_keypoints,
+            "id": self.id_,
+            "attributes": {
+                k: v
+                for k, v in {
+                    "track_id": self.track_id,
+                    "encord_track_uuid": self.encord_track_uuid,
+                    "rotation": self.rotation,
+                    "classifications": self.classifications,
+                    "manual_annotation": self.manual_annotation,
+                }.items()
+                if v is not None
+            },
+        }
+
+
+@dataclass
+class Size:
+    width: int
+    height: int
+
+
+def get_polygon_from_dict(
+    polygon_dict: Dict[str, Any],
+    w: int,
+    h: int,
+) -> List[Tuple[float, float]]:
+    return [
+        (
+            polygon_dict[str(i)]["x"] * w,
+            polygon_dict[str(i)]["y"] * h,
+        )
+        for i in range(len(polygon_dict))
+    ]
+
+
+class CocoExporter:
+    """
+    This class is intentionally designed in a modular fashion to facilitate extensibility.
+    You are encouraged to subclass this exporter and modify any of the custom functions.
+    While return types are provided for convenience, they can also be subclassed to suit your specific requirements.
+    All functions that could be static are intentionally not made static, allowing you the option to access the `self`
+    object at any time.
+    """
+
+    def __init__(
+        self,
+        labels_list: List[Dict[str, Any]],
+        ontology: OntologyStructure,
+        include_videos: bool = True,
+    ) -> None:
+        self._labels_list = labels_list
+        self._ontology = ontology
+        self._coco_json: Dict[str, Any] = {}
+        self._current_annotation_id = 0
+        self._object_hash_to_track_id_map: Dict[str, int] = {}
+        self._coco_categories_id_to_ontology_object_map: Dict = {}  # TODO: do we need this?
+        self._feature_hash_to_coco_category_id_map: Dict[str, int] = {}
+        self._data_hash_to_image_id_map: Dict[Tuple[str, int], int] = {}
+        """Map of (data_hash, frame_offset) to the image id"""
+
+        self._feature_hash_to_attribute_map: Optional[Dict[str, Attribute]] = None
+        self._id_and_object_hash_to_answers_map: Optional[Dict[Tuple[int, str], Dict]] = None
+        self._include_videos = include_videos
+
+    def export(self) -> Dict[str, Any]:
+        self._coco_json["info"] = self.get_info()
+        self._coco_json["categories"] = self.get_categories()
+        self._coco_json["images"] = self.get_images()
+        self._coco_json["annotations"] = [x.to_dict() for x in self.get_all_annotations()]
+
+        return self._coco_json
+
+    def get_info(self) -> Dict[str, Optional[str]]:
+        return {
+            "description": self.get_description(),
+            "contributor": None,  # TODO: these fields also need a response
+            "date_created": None,
+            # TODO: there is something in the labels, alternatively can start to return more from the SDK
+            "url": None,
+            "version": None,
+            "year": None,
+        }
+
+    def get_description(self) -> Optional[str]:
+        if len(self._labels_list) == 0:
+            res: Optional[str] = None
+        else:
+            res = str(self._labels_list[0]["data_title"])
+
+        return res
+
+    def get_categories(self) -> List[Dict[str, Any]]:
+        """This does not translate classifications as they are not part of the Coco spec."""
+        categories = []
+        for object_ in self._ontology.objects:
+            categories.append(self.get_category(object_))
+
+        return categories
+
+    def get_category(self, object_: Object) -> Dict[str, Any]:
+        super_category = self.get_super_category(object_)
+        ret: Dict[str, Any] = {
+            "supercategory": super_category,
+            "id": self.add_to_object_map_and_get_next_id(object_),
+            "name": self.get_category_name(object_),
+        }
+
+        if super_category == "point":
+            # TODO: we will have to do something similar for skeletons.
+            ret["keypoints"] = "keypoint"
+            ret["skeleton"] = []
+
+        return ret
+
+    def get_super_category(self, object_: Object) -> str:
+        return object_.shape.value
+
+    def add_to_object_map_and_get_next_id(self, object_: Object) -> int:
+        id_ = len(self._coco_categories_id_to_ontology_object_map) + 1
+        # Let the category id start at 1, not 0. Segmentation masks that use COCO annotations often create a bitmask
+        # with this category id. The bitmask must be above 0. See this link:
+        # https://cocodataset.org/#stuff-eval
+        self._coco_categories_id_to_ontology_object_map[id_] = object_
+        self._feature_hash_to_coco_category_id_map[object_.feature_node_hash] = id_
+
+        return id_
+
+    def get_category_name(self, object_: Object) -> str:
+        return object_.name
+
+    def get_images(self) -> List[Dict[str, Any]]:
+        """All the data is in the specific label_row"""
+        images = []
+
+        for labels in self._labels_list:
+            for data_unit in labels["data_units"].values():
+                data_type = data_unit["data_type"]
+                if "application/dicom" in data_type:
+                    images.extend(self.get_dicom(data_unit))
+                elif "video" not in data_type:
+                    images.append(self.get_image(data_unit))
+                else:
+                    images.extend(self.get_video_images(data_unit))
+
+        return images
+
+    def get_dicom(self, data_unit: Dict) -> List:
+        return [
+            self._dicom_label_to_coco_image(
+                int(key), data_unit["data_hash"], data_unit["width"], data_unit["height"], label
+            )
+            for key, label in data_unit["labels"].items()
+        ]
+
+    def get_image(self, data_unit: Dict[str, Any]) -> Dict[str, Any]:
+        # TODO: we probably want a map of this image id to image hash in our DB, including the image_group hash.
+        image_id = len(self._data_hash_to_image_id_map)
+        data_hash = data_unit["data_hash"]
+        self._data_hash_to_image_id_map[(data_hash, 0)] = image_id
+
+        return {
+            "coco_url": data_unit["data_link"],
+            "id": image_id,
+            "image_title": data_unit["data_title"],
+            "file_name": f'images/{data_unit["data_hash"]}.{data_unit["data_title"].split(".")[-1]}',
+            "height": data_unit["height"],
+            "width": data_unit["width"],
+        }
+
+    def get_video_images(self, data_unit: Dict[str, Any]) -> List[Dict[str, Any]]:
+        if not self._include_videos:
+            return []
+
+        video_title = data_unit["data_title"]
+        data_hash = data_unit["data_hash"]
+
+        images = []
+        coco_url = data_unit["data_link"]
+        height = data_unit["height"]
+        width = data_unit["width"]
+
+        for frame_num in data_unit["labels"].keys():
+            images.append(
+                self.get_video_image(
+                    data_hash,
+                    video_title,
+                    coco_url,
+                    height,
+                    width,
+                    int(frame_num),
+                )
+            )
+
+        return images
+
+    def _dicom_label_to_coco_image(
+        self, frame: int, data_hash: str, series_width: int, series_height: int, dicom_label: Dict
+    ) -> Dict[str, Any]:
+        image_id = len(self._data_hash_to_image_id_map)
+        # ideally this should be verify_arg, but currently we can't be sure that the metadata is on every frame
+        metadata = dicom_label.get("metadata")
+        self._data_hash_to_image_id_map[(data_hash, frame)] = image_id
+        if metadata:
+            file_name = f"dicom/{data_hash}/{metadata.dicom_instance_uid}"
+            if metadata.multiframe_frame_number is not None:
+                file_name += f"/{metadata.multiframe_frame_number}"
+
+            return {
+                "coco_url": metadata.file_uri,
+                "id": image_id,
+                "file_name": file_name,
+                "height": metadata.height,
+                "width": metadata.width,
+            }
+        else:
+            return {
+                "coco_url": "",
+                "id": image_id,
+                "file_name": f"dicom/{data_hash}/{frame}",
+                "height": series_height,
+                "width": series_width,
+            }
+
+    def get_video_image(
+        self,
+        data_hash: str,
+        video_title: str,
+        coco_url: str,
+        height: int,
+        width: int,
+        frame_num: int,
+    ) -> Dict[str, Any]:
+        image_id = len(self._data_hash_to_image_id_map)
+        self._data_hash_to_image_id_map[(data_hash, frame_num)] = image_id
+
+        return {
+            "coco_url": coco_url,
+            "id": image_id,
+            "video_title": video_title,
+            "file_name": f"videos/{data_hash}/{frame_num}.jpg",
+            "height": height,
+            "width": width,
+        }
+
+    def get_all_annotations(self) -> List[CocoAnnotation]:
+        annotations = []
+
+        # TODO: need to make sure at least one image
+        for labels in self._labels_list:
+            object_answers = labels["object_answers"]
+            object_actions = labels["object_actions"]
+
+            for data_unit in labels["data_units"].values():
+                data_hash = data_unit["data_hash"]
+
+                if "video" in data_unit["data_type"]:
+                    if not self._include_videos:
+                        continue
+                    for frame_num, frame_item in data_unit["labels"].items():
+                        image_id = self.get_image_id(data_hash, int(frame_num))
+                        objects = frame_item["objects"]
+                        annotations.extend(
+                            self.get_annotations(
+                                objects,
+                                image_id,
+                                object_answers,
+                                object_actions,
+                            )
+                        )
+
+                elif "application/dicom" in data_unit["data_type"]:
+                    # copy pasta:
+                    for frame_num, frame_item in data_unit["labels"].items():
+                        image_id = self.get_image_id(data_hash, int(frame_num))
+                        objects = frame_item["objects"]
+                        annotations.extend(
+                            self.get_annotations(
+                                objects,
+                                image_id,
+                                object_answers,
+                                object_actions,
+                            )
+                        )
+
+                else:
+                    image_id = self.get_image_id(data_hash)
+                    objects = data_unit["labels"].get("objects") or []
+                    annotations.extend(
+                        self.get_annotations(
+                            objects,
+                            image_id,
+                            object_answers,
+                            object_actions,
+                        )
+                    )
+
+        return annotations
+
+    def get_annotations(
+        self,
+        objects: List[Dict],
+        image_id: int,
+        object_answers: Dict,
+        object_actions: Dict,
+    ) -> List[CocoAnnotation]:
+        annotations = []
+
+        for object_ in objects:
+            shape = object_["shape"]
+
+            for image_data in self._coco_json["images"]:
+                if image_data["id"] == image_id:
+                    size = Size(width=image_data["width"], height=image_data["height"])
+
+            if shape == Shape.BOUNDING_BOX.value:
+                annotations.append(
+                    self.get_bounding_box(
+                        object_,
+                        image_id,
+                        size,
+                        object_answers,
+                        object_actions,
+                    )
+                )
+            elif shape == Shape.ROTATABLE_BOUNDING_BOX.value:
+                annotations.append(
+                    self.get_rotatable_bounding_box(
+                        object_,
+                        image_id,
+                        size,
+                        object_answers,
+                        object_actions,
+                    )
+                )
+            elif shape == Shape.POLYGON.value:
+                annotations.append(
+                    self.get_polygon(
+                        object_,
+                        image_id,
+                        size,
+                        object_answers,
+                        object_actions,
+                    )
+                )
+            elif shape == Shape.POLYLINE.value:
+                annotations.append(
+                    self.get_polyline(
+                        object_,
+                        image_id,
+                        size,
+                        object_answers,
+                        object_actions,
+                    )
+                )
+            elif shape == Shape.BITMASK.value:
+                annotations.append(
+                    self.get_bitmask(
+                        object_,
+                        image_id,
+                        size,
+                        object_answers,
+                        object_actions,
+                    )
+                )
+            elif shape == Shape.POINT.value:
+                annotations.append(
+                    self.get_point(
+                        object_,
+                        image_id,
+                        size,
+                        object_answers,
+                        object_actions,
+                    )
+                )
+            elif shape == Shape.SKELETON.value:
+                annotations.append(
+                    self.get_skeleton(
+                        object_,
+                        image_id,
+                        size,
+                        object_answers,
+                        object_actions,
+                    )
+                )
+
+        return annotations
+
+    def get_bounding_box(
+        self,
+        object_: Dict,
+        image_id: int,
+        size: Size,
+        object_answers: Dict,
+        object_actions: Dict,
+    ) -> CocoAnnotation:
+        x, y = (
+            object_["boundingBox"]["x"] * size.width,
+            object_["boundingBox"]["y"] * size.height,
+        )
+        w, h = (
+            object_["boundingBox"]["w"] * size.width,
+            object_["boundingBox"]["h"] * size.height,
+        )
+        area = w * h
+        segmentation = [[x, y, x + w, y, x + w, y + h, x, y + h]]
+        bbox = (x, y, w, h)
+        category_id = self.get_category_id(object_)
+        id_, is_crowd, track_id, encord_track_uuid, manual_annotation = self.get_coco_annotation_default_fields(object_)
+
+        classifications = self.get_flat_classifications(object_, image_id, object_answers, object_actions)
+
+        return CocoAnnotation(
+            area=area,
+            bbox=bbox,
+            category_id=category_id,
+            id_=id_,
+            image_id=image_id,
+            is_crowd=is_crowd,
+            segmentation=segmentation,
+            keypoints=None,
+            num_keypoints=None,
+            track_id=track_id,
+            encord_track_uuid=encord_track_uuid,
+            rotation=None,
+            classifications=classifications,
+            manual_annotation=manual_annotation,
+        )
+
+    def get_rotatable_bounding_box(
+        self,
+        object_: Dict,
+        image_id: int,
+        size: Size,
+        object_answers: Dict,
+        object_actions: Dict,
+    ) -> CocoAnnotation:
+        x, y = (
+            object_["rotatableBoundingBox"]["x"] * size.width,
+            object_["rotatableBoundingBox"]["y"] * size.height,
+        )
+        w, h = (
+            object_["rotatableBoundingBox"]["w"] * size.width,
+            object_["rotatableBoundingBox"]["h"] * size.height,
+        )
+
+        area = w * h
+        segmentation = [[x, y, x + w, y, x + w, y + h, x, y + h]]
+        bbox = (x, y, w, h)
+        category_id = self.get_category_id(object_)
+        id_, is_crowd, track_id, encord_track_uuid, manual_annotation = self.get_coco_annotation_default_fields(object_)
+
+        rotation = object_["rotatableBoundingBox"]["theta"]
+
+        classifications = self.get_flat_classifications(object_, image_id, object_answers, object_actions)
+
+        return CocoAnnotation(
+            area=area,
+            bbox=bbox,
+            category_id=category_id,
+            id_=id_,
+            image_id=image_id,
+            is_crowd=is_crowd,
+            segmentation=segmentation,
+            keypoints=None,
+            num_keypoints=None,
+            track_id=track_id,
+            encord_track_uuid=encord_track_uuid,
+            rotation=rotation,
+            classifications=classifications,
+            manual_annotation=manual_annotation,
+        )
+
+    def get_polygon(
+        self,
+        object_: Dict,
+        image_id: int,
+        size: Size,
+        object_answers: Dict,
+        object_actions: Dict,
+    ) -> CocoAnnotation:
+        polygon = get_polygon_from_dict(object_["polygon"], size.width, size.height)
+        segmentation = [list(chain(*polygon))]
+        _polygon = Polygon(polygon)
+        area: float = _polygon.area
+        x, y, x_max, y_max = _polygon.bounds
+        w, h = x_max - x, y_max - y
+
+        bbox = (x, y, w, h)
+        category_id = self.get_category_id(object_)
+        id_, is_crowd, track_id, encord_track_uuid, manual_annotation = self.get_coco_annotation_default_fields(object_)
+
+        classifications = self.get_flat_classifications(
+            object_,
+            image_id,
+            object_answers,
+            object_actions,
+        )
+
+        return CocoAnnotation(
+            area=area,
+            bbox=bbox,
+            category_id=category_id,
+            id_=id_,
+            image_id=image_id,
+            is_crowd=is_crowd,
+            segmentation=segmentation,
+            keypoints=None,
+            num_keypoints=None,
+            track_id=track_id,
+            encord_track_uuid=encord_track_uuid,
+            rotation=None,
+            classifications=classifications,
+            manual_annotation=manual_annotation,
+        )
+
+    def get_polyline(
+        self,
+        object_: Dict,
+        image_id: int,
+        size: Size,
+        object_answers: Dict,
+        object_actions: Dict,
+    ) -> CocoAnnotation:
+        """Polylines are technically not supported in COCO, but here we use a trick to allow a representation."""
+        polygon = get_polygon_from_dict(object_["polyline"], size.width, size.height)
+        polyline_coordinate = self.join_polyline_from_polygon(list(chain(*polygon)))
+        segmentation = [polyline_coordinate]
+        area = 0
+        bbox = self.get_bbox_for_polyline(polygon)
+        category_id = self.get_category_id(object_)
+        id_, is_crowd, track_id, encord_track_uuid, manual_annotation = self.get_coco_annotation_default_fields(object_)
+
+        classifications = self.get_flat_classifications(object_, image_id, object_answers, object_actions)
+
+        return CocoAnnotation(
+            area=area,
+            bbox=bbox,
+            category_id=category_id,
+            id_=id_,
+            image_id=image_id,
+            is_crowd=is_crowd,
+            segmentation=segmentation,
+            keypoints=None,
+            num_keypoints=None,
+            track_id=track_id,
+            encord_track_uuid=encord_track_uuid,
+            rotation=None,
+            classifications=classifications,
+            manual_annotation=manual_annotation,
+        )
+
+    def get_bbox_for_polyline(
+        self,
+        polygon: List[Tuple[float, float]],
+    ) -> Tuple[float, float, float, float]:
+        if len(polygon) == 2:
+            # We have the edge case of a single edge polygon.
+            first_point = polygon[0]
+            second_point = polygon[1]
+            x = float(min(first_point[0], second_point[0]))
+            y = float(min(first_point[1], second_point[1]))
+            w = float(abs(first_point[0] - second_point[0]))
+            h = float(abs(first_point[1] - second_point[1]))
+            return x, y, w, h
+        else:
+            shapely_polygon = Polygon(polygon)
+            x, y, x_max, y_max = shapely_polygon.bounds  # type: ignore[attr-defined]
+            w, h = x_max - x, y_max - y
+
+            return x, y, w, h
+
+    @staticmethod
+    def join_polyline_from_polygon(
+        polygon: List[float],
+    ) -> List[float]:
+        """
+        Essentially a trick to represent a polyline in coco. We pretend for this to be a polygon and join every
+        coordinate from the end back to the beginning, so it will essentially be an area-less polygon.
+        This function technically changes the input polygon in place.
+        """
+        if len(polygon) % 2 != 0:
+            raise RuntimeError("The polygon has an unaccepted shape.")
+
+        idx = len(polygon) - 2
+        while idx >= 0:
+            y_coordinate = polygon[idx]
+            x_coordinate = polygon[idx + 1]
+            polygon.append(y_coordinate)
+            polygon.append(x_coordinate)
+            idx -= 2
+
+        return polygon
+
+    def get_bitmask(
+        self,
+        object_: Dict,
+        image_id: int,
+        size: Size,
+        object_answers: Dict,
+        object_actions: Dict,
+    ) -> CocoAnnotation:
+        bitmask = object_["bitmask"]
+        # Note: It's essential to transpose the input and output coordinates when using pycocotools' functions,
+        # in order to address the distinction between treating masks as C-contiguous (row-major order, Encord's
+        # implementation) versus pycocotools' expectation of Fortran-contiguous (column-major order, COCO's API
+        # implementation) data.
+
+        # Obtain the COCO compatible RLE string (convert from row-major to column-major order)
+        transposed_segmentation = dict(counts=bitmask["rleString"], size=[bitmask["width"], bitmask["height"]])
+        mask = np.asfortranarray(cocomask.decode(transposed_segmentation).T)
+        segmentation = cocomask.encode(mask)
+        bbox = tuple(cocomask.toBbox(segmentation))
+        area = float(cocomask.area(segmentation))
+        # Convert RLE string from bytes (which is not a JSON serializable type) to a string format
+        segmentation["counts"] = segmentation["counts"].decode("ascii")
+
+        category_id = self.get_category_id(object_)
+        id_, _, track_id, encord_track_uuid, manual_annotation = self.get_coco_annotation_default_fields(object_)
+        is_crowd = 1
+
+        classifications: Optional[Dict] = self.get_flat_classifications(
+            object_, image_id, object_answers, object_actions
+        )
+
+        return CocoAnnotation(
+            area=area,
+            bbox=bbox,
+            category_id=category_id,
+            id_=id_,
+            image_id=image_id,
+            is_crowd=is_crowd,
+            segmentation=segmentation,
+            keypoints=None,
+            num_keypoints=None,
+            track_id=track_id,
+            encord_track_uuid=encord_track_uuid,
+            rotation=None,
+            classifications=classifications,
+            manual_annotation=manual_annotation,
+        )
+
+    def get_point(
+        self,
+        object_: Dict,
+        image_id: int,
+        size: Size,
+        object_answers: Dict,
+        object_actions: Dict,
+    ) -> CocoAnnotation:
+        x, y = (
+            object_["point"]["0"]["x"] * size.width,
+            object_["point"]["0"]["y"] * size.height,
+        )
+        w, h = 0, 0
+        area = 0
+        segmentation = [[x, y]]
+        keypoints = [x, y, 2]
+        num_keypoints = 1
+
+        bbox = (x, y, w, h)
+        category_id = self.get_category_id(object_)
+        id_, is_crowd, track_id, encord_track_uuid, manual_annotation = self.get_coco_annotation_default_fields(object_)
+
+        classifications: Optional[Dict] = self.get_flat_classifications(
+            object_, image_id, object_answers, object_actions
+        )
+
+        return CocoAnnotation(
+            area=area,
+            bbox=bbox,
+            category_id=category_id,
+            id_=id_,
+            image_id=image_id,
+            is_crowd=is_crowd,
+            segmentation=segmentation,
+            keypoints=keypoints,
+            num_keypoints=num_keypoints,
+            track_id=track_id,
+            encord_track_uuid=encord_track_uuid,
+            rotation=None,
+            classifications=classifications,
+            manual_annotation=manual_annotation,
+        )
+
+    def get_skeleton(
+        self,
+        object_: Dict,
+        image_id: int,
+        size: Size,
+        object_answers: Dict,
+        object_actions: Dict,
+    ) -> CocoAnnotation:
+        area = 0
+        segmentation: List = []
+        keypoints = []
+
+        for point in object_["skeleton"].values():
+            keypoints += [
+                point["x"] * size.width,
+                point["y"] * size.height,
+                2,
+            ]
+
+        num_keypoints = len(keypoints) // 3
+        xs, ys = (
+            keypoints[::3],
+            keypoints[1::3],
+        )
+        x, y, x_max, y_max = min(xs), min(ys), max(xs), max(ys)
+        w, h = x_max - x, y_max - y
+
+        bbox = (x, y, w, h)
+        category_id = self.get_category_id(object_)
+        id_, is_crowd, track_id, encord_track_uuid, manual_annotation = self.get_coco_annotation_default_fields(object_)
+
+        classifications: Optional[Dict] = self.get_flat_classifications(
+            object_, image_id, object_answers, object_actions
+        )
+
+        return CocoAnnotation(
+            area=area,
+            bbox=bbox,
+            category_id=category_id,
+            id_=id_,
+            image_id=image_id,
+            is_crowd=is_crowd,
+            segmentation=segmentation,
+            keypoints=keypoints,
+            num_keypoints=num_keypoints,
+            track_id=track_id,
+            encord_track_uuid=encord_track_uuid,
+            rotation=None,
+            classifications=classifications,
+            manual_annotation=manual_annotation,
+        )
+
+    def get_flat_classifications(
+        self, object_: Dict, image_id: int, object_answers: Dict, object_actions: Dict
+    ) -> Dict[str, Any]:
+        object_hash = object_["objectHash"]
+        feature_hash = object_["featureHash"]
+
+        feature_hash_to_attribute_map: Dict[str, Attribute] = self.get_feature_hash_to_flat_object_attribute_map()
+        id_and_object_hash_to_answers_map = self.get_id_and_object_hash_to_answers_map(object_actions)
+
+        classifications = self.get_flat_static_classifications(
+            object_hash, feature_hash, object_answers, feature_hash_to_attribute_map
+        )
+        dynamic_classifications = self.get_flat_dynamic_classifications(
+            object_hash,
+            feature_hash,
+            image_id,
+            id_and_object_hash_to_answers_map,
+        )
+        classifications.update(dynamic_classifications)
+
+        return classifications
+
+    def get_feature_hash_to_flat_object_attribute_map(self) -> Dict[str, Attribute]:
+        if self._feature_hash_to_attribute_map is not None:
+            return self._feature_hash_to_attribute_map
+
+        ret: Dict[str, Attribute] = {}
+
+        for object_ in self._ontology.objects:
+            for attribute in object_.attributes:
+                ret[attribute.feature_node_hash] = attribute
+
+        self._feature_hash_to_attribute_map = ret
+
+        return ret
+
+    def get_flat_static_classifications(
+        self,
+        object_hash: str,
+        object_feature_hash: str,
+        object_answers: Dict[str, Any],
+        feature_hash_to_attribute_map: Dict[str, Attribute],
+    ) -> Dict[str, Any]:
+        ret = {}
+        classifications = object_answers[object_hash]["classifications"]
+        for classification in classifications:
+            feature_hash = classification["featureHash"]
+            if feature_hash not in feature_hash_to_attribute_map:
+                # This will be a deeply nested attribute
+                continue
+
+            attribute = feature_hash_to_attribute_map[feature_hash]
+            answers = classification["answers"]
+
+            if attribute.get_property_type() == PropertyType.TEXT:
+                ret.update(self.get_text_answer(attribute, answers))
+            elif attribute.get_property_type() == PropertyType.RADIO:
+                ret.update(self.get_radio_answer(attribute, answers))
+            elif attribute.get_property_type() == PropertyType.CHECKLIST:
+                ret.update(self.get_checklist_answer(attribute, answers))
+
+        self.add_unselected_attributes(object_feature_hash, ret, match_dynamic_attributes=False)
+
+        return ret
+
+    def get_id_and_object_hash_to_answers_map(
+        self,
+        object_actions: Dict[str, Any],
+    ) -> Dict[Tuple[int, str], Dict]:
+        if self._id_and_object_hash_to_answers_map is not None:
+            return self._id_and_object_hash_to_answers_map
+
+        ret: Dict[Tuple[int, str], Dict[str, Any]] = defaultdict(Dict)
+        feature_hash_to_attribute_map = self.get_feature_hash_to_flat_object_attribute_map()
+        for object_hash, payload in object_actions.items():
+            for action in payload["actions"]:
+                feature_hash = action["featureHash"]
+                if feature_hash not in feature_hash_to_attribute_map:
+                    # This will be a deeply nested attribute
+                    continue
+
+                attribute = feature_hash_to_attribute_map[feature_hash]
+                answers = action["answers"]
+                answers_dict: Dict[str, Any] = {}
+
+                if attribute.get_property_type() == PropertyType.TEXT:
+                    answers_dict.update(self.get_text_answer(attribute, answers))
+                elif attribute.get_property_type() == PropertyType.RADIO:
+                    answers_dict.update(self.get_radio_answer(attribute, answers))
+                elif attribute.get_property_type() == PropertyType.CHECKLIST:
+                    answers_dict.update(self.get_checklist_answer(attribute, answers))
+
+                for sub_range in action["range"]:
+                    for i in range(sub_range[0], sub_range[1] + 1):
+                        ret[(i, object_hash)].update(answers_dict)
+
+        self._id_and_object_hash_to_answers_map = ret
+
+        return ret
+
+    def get_flat_dynamic_classifications(
+        self,
+        object_hash: str,
+        feature_hash: str,
+        image_id: int,
+        id_and_object_hash_to_answers_map: Dict[Tuple[int, str], Dict[str, Any]],
+    ) -> Dict[str, Any]:
+        ret = {}
+        id_and_object_hash = (image_id, object_hash)
+
+        if id_and_object_hash in id_and_object_hash_to_answers_map:
+            ret = id_and_object_hash_to_answers_map[(image_id, object_hash)]
+
+        self.add_unselected_attributes(feature_hash, ret, match_dynamic_attributes=True)
+
+        return ret
+
+    def add_unselected_attributes(
+        self, feature_hash: str, attributes_dict: Dict[str, Optional[bool]], match_dynamic_attributes: bool
+    ) -> None:
+        """
+        Attributes which have never been selected will not show up in the actions map. They will need to be
+        added separately. NOTE: this assumes uniqueness of features. Quite an edge case but if it ever comes
+        up it needs to be solved somewhere here.
+        """
+        # TODO: This could be improved
+        all_attributes = self.get_attributes_for_feature_hash(feature_hash)
+        for attribute in all_attributes:
+            is_matching_attribute = attribute.dynamic == match_dynamic_attributes
+            if is_matching_attribute:
+                if attribute.get_property_type() == PropertyType.CHECKLIST:
+                    for option in attribute.options:  # type: ignore[union-attr]
+                        if option.label not in attributes_dict:
+                            # We need to add the default of False.
+                            attributes_dict[option.label] = False
+                else:
+                    if attribute.name not in attributes_dict:
+                        attributes_dict[attribute.name] = None
+
+    def get_attributes_for_feature_hash(self, feature_hash: str) -> List[Attribute]:
+        ret = []
+
+        for object_ in self._ontology.objects:
+            if object_.feature_node_hash == feature_hash:
+                for attribute in object_.attributes:
+                    ret.append(attribute)
+                break
+
+        return ret
+
+    def get_radio_answer(self, attribute: Attribute, answers: List[Dict[str, str]]) -> Dict[str, str]:
+        answer = answers[0]  # radios only have one answer by definition
+        return {attribute.name: answer["name"]}
+
+    def get_checklist_answer(self, attribute: Attribute, answers: List[Dict[str, Any]]) -> Dict[str, bool]:
+        ret: Dict[str, bool] = {}
+        found_checklist_answers: Set[str] = set()
+
+        for answer in answers:
+            found_checklist_answers.add(answer["name"])
+
+        for option in attribute.options:  # type: ignore[union-attr]
+            label = option.label
+            ret[label] = label in found_checklist_answers
+
+        return ret
+
+    def get_text_answer(self, attribute: Attribute, answers: str) -> Dict[str, Any]:
+        return {attribute.name: answers}
+
+    def get_category_id(self, object_: Dict[str, Any]) -> int:
+        feature_hash = object_["featureHash"]
+        try:
+            return self._feature_hash_to_coco_category_id_map[feature_hash]
+        except KeyError:
+            raise EncordException(
+                f"The feature_hash `{feature_hash}` was not found in the provided ontology. Please "
+                f"ensure that the ontology matches the labels provided."
+            ) from None
+
+    def get_coco_annotation_default_fields(
+        self, object_: Dict[str, Any]
+    ) -> Tuple[
+        int,
+        int,
+        Optional[int],
+        Optional[str],
+        Optional[bool],
+    ]:
+        id_ = self.next_annotation_id()
+        is_crowd = 0
+
+        track_id = self.get_and_set_track_id(object_hash=object_["objectHash"])
+        encord_track_uuid = object_["objectHash"]
+        manual_annotation = object_["manualAnnotation"]
+
+        return id_, is_crowd, track_id, encord_track_uuid, manual_annotation
+
+    def next_annotation_id(self) -> int:
+        next_ = self._current_annotation_id
+        self._current_annotation_id += 1
+
+        return next_
+
+    def get_and_set_track_id(self, object_hash: str) -> int:
+        if object_hash in self._object_hash_to_track_id_map:
+            return self._object_hash_to_track_id_map[object_hash]
+        else:
+            next_track_id = len(self._object_hash_to_track_id_map)
+            self._object_hash_to_track_id_map[object_hash] = next_track_id
+            return next_track_id
+
+    def get_image_id(self, data_hash: str, frame_num: int = 0) -> int:
+        return self._data_hash_to_image_id_map[(data_hash, frame_num)]
diff --git a/poetry.lock b/poetry.lock
index bb1f0d590..b26f64cb2 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -222,6 +222,80 @@ files = [
     {file = "colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44"},
 ]
 
+[[package]]
+name = "contourpy"
+version = "1.1.1"
+description = "Python library for calculating contours of 2D quadrilateral grids"
+optional = true
+python-versions = ">=3.8"
+files = [
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+    {version = ">=1.26.0rc1,<2.0", markers = "python_version >= \"3.12\""},
+]
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+[package.extras]
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+mypy = ["contourpy[bokeh,docs]", "docutils-stubs", "mypy (==1.4.1)", "types-Pillow"]
+test = ["Pillow", "contourpy[test-no-images]", "matplotlib"]
+test-no-images = ["pytest", "pytest-cov", "wurlitzer"]
+
 [[package]]
 name = "cryptography"
 version = "42.0.8"
@@ -276,6 +350,21 @@ ssh = ["bcrypt (>=3.1.5)"]
 test = ["certifi", "pretend", "pytest (>=6.2.0)", "pytest-benchmark", "pytest-cov", "pytest-xdist"]
 test-randomorder = ["pytest-randomly"]
 
+[[package]]
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+version = "0.12.1"
+description = "Composable style cycles"
+optional = true
+python-versions = ">=3.8"
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+tests = ["pytest", "pytest-cov", "pytest-xdist"]
+
 [[package]]
 name = "deepdiff"
 version = "6.3.0"
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+interpolatable = ["munkres", "pycairo", "scipy"]
+lxml = ["lxml (>=4.0)"]
+pathops = ["skia-pathops (>=0.5.0)"]
+plot = ["matplotlib"]
+repacker = ["uharfbuzz (>=0.23.0)"]
+symfont = ["sympy"]
+type1 = ["xattr"]
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+woff = ["brotli (>=1.0.1)", "brotlicffi (>=0.8.0)", "zopfli (>=0.1.4)"]
+
 [[package]]
 name = "identify"
 version = "2.5.24"
@@ -359,6 +521,28 @@ files = [
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 ]
 
+[[package]]
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+version = "6.4.5"
+description = "Read resources from Python packages"
+optional = true
+python-versions = ">=3.8"
+files = [
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+
+[package.extras]
+check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)"]
+cover = ["pytest-cov"]
+doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+enabler = ["pytest-enabler (>=2.2)"]
+test = ["jaraco.test (>=5.4)", "pytest (>=6,!=8.1.*)", "zipp (>=3.17)"]
+type = ["pytest-mypy"]
+
 [[package]]
 name = "iniconfig"
 version = "2.0.0"
@@ -370,6 +554,197 @@ files = [
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 ]
 
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 version = "1.1.374"
@@ -956,6 +1475,64 @@ docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "pygments-g
 testing = ["build[virtualenv]", "filelock (>=3.4.0)", "flake8-2020", "ini2toml[lite] (>=0.9)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "pip (>=19.1)", "pip-run (>=8.8)", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-mypy (>=0.9.1)", "pytest-perf", "pytest-ruff", "pytest-timeout", "pytest-xdist", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
 testing-integration = ["build[virtualenv]", "filelock (>=3.4.0)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "pytest", "pytest-enabler", "pytest-xdist", "tomli", "virtualenv (>=13.0.0)", "wheel"]
 
+[[package]]
+name = "shapely"
+version = "2.0.6"
+description = "Manipulation and analysis of geometric objects"
+optional = true
+python-versions = ">=3.7"
+files = [
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+    {file = "shapely-2.0.6-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:e1c84c3f53144febf6af909d6b581bc05e8785d57e27f35ebaa5c1ab9baba13b"},
+    {file = "shapely-2.0.6-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2ad2fae12dca8d2b727fa12b007e46fbc522148a584f5d6546c539f3464dccde"},
+    {file = "shapely-2.0.6-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b3304883bd82d44be1b27a9d17f1167fda8c7f5a02a897958d86c59ec69b705e"},
+    {file = "shapely-2.0.6-cp310-cp310-win32.whl", hash = "sha256:3ec3a0eab496b5e04633a39fa3d5eb5454628228201fb24903d38174ee34565e"},
+    {file = "shapely-2.0.6-cp310-cp310-win_amd64.whl", hash = "sha256:28f87cdf5308a514763a5c38de295544cb27429cfa655d50ed8431a4796090c4"},
+    {file = "shapely-2.0.6-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:5aeb0f51a9db176da9a30cb2f4329b6fbd1e26d359012bb0ac3d3c7781667a9e"},
+    {file = "shapely-2.0.6-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:9a7a78b0d51257a367ee115f4d41ca4d46edbd0dd280f697a8092dd3989867b2"},
+    {file = "shapely-2.0.6-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f32c23d2f43d54029f986479f7c1f6e09c6b3a19353a3833c2ffb226fb63a855"},
+    {file = "shapely-2.0.6-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b3dc9fb0eb56498912025f5eb352b5126f04801ed0e8bdbd867d21bdbfd7cbd0"},
+    {file = "shapely-2.0.6-cp311-cp311-win32.whl", hash = "sha256:d93b7e0e71c9f095e09454bf18dad5ea716fb6ced5df3cb044564a00723f339d"},
+    {file = "shapely-2.0.6-cp311-cp311-win_amd64.whl", hash = "sha256:c02eb6bf4cfb9fe6568502e85bb2647921ee49171bcd2d4116c7b3109724ef9b"},
+    {file = "shapely-2.0.6-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:cec9193519940e9d1b86a3b4f5af9eb6910197d24af02f247afbfb47bcb3fab0"},
+    {file = "shapely-2.0.6-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:83b94a44ab04a90e88be69e7ddcc6f332da7c0a0ebb1156e1c4f568bbec983c3"},
+    {file = "shapely-2.0.6-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:537c4b2716d22c92036d00b34aac9d3775e3691f80c7aa517c2c290351f42cd8"},
+    {file = "shapely-2.0.6-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:98fea108334be345c283ce74bf064fa00cfdd718048a8af7343c59eb40f59726"},
+    {file = "shapely-2.0.6-cp312-cp312-win32.whl", hash = "sha256:42fd4cd4834747e4990227e4cbafb02242c0cffe9ce7ef9971f53ac52d80d55f"},
+    {file = "shapely-2.0.6-cp312-cp312-win_amd64.whl", hash = "sha256:665990c84aece05efb68a21b3523a6b2057e84a1afbef426ad287f0796ef8a48"},
+    {file = "shapely-2.0.6-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:42805ef90783ce689a4dde2b6b2f261e2c52609226a0438d882e3ced40bb3013"},
+    {file = "shapely-2.0.6-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:6d2cb146191a47bd0cee8ff5f90b47547b82b6345c0d02dd8b25b88b68af62d7"},
+    {file = "shapely-2.0.6-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e3fdef0a1794a8fe70dc1f514440aa34426cc0ae98d9a1027fb299d45741c381"},
+    {file = "shapely-2.0.6-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:2c665a0301c645615a107ff7f52adafa2153beab51daf34587170d85e8ba6805"},
+    {file = "shapely-2.0.6-cp313-cp313-win32.whl", hash = "sha256:0334bd51828f68cd54b87d80b3e7cee93f249d82ae55a0faf3ea21c9be7b323a"},
+    {file = "shapely-2.0.6-cp313-cp313-win_amd64.whl", hash = "sha256:d37d070da9e0e0f0a530a621e17c0b8c3c9d04105655132a87cfff8bd77cc4c2"},
+    {file = "shapely-2.0.6-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:fa7468e4f5b92049c0f36d63c3e309f85f2775752e076378e36c6387245c5462"},
+    {file = "shapely-2.0.6-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ed5867e598a9e8ac3291da6cc9baa62ca25706eea186117034e8ec0ea4355653"},
+    {file = "shapely-2.0.6-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:81d9dfe155f371f78c8d895a7b7f323bb241fb148d848a2bf2244f79213123fe"},
+    {file = "shapely-2.0.6-cp37-cp37m-win32.whl", hash = "sha256:fbb7bf02a7542dba55129062570211cfb0defa05386409b3e306c39612e7fbcc"},
+    {file = "shapely-2.0.6-cp37-cp37m-win_amd64.whl", hash = "sha256:837d395fac58aa01aa544495b97940995211e3e25f9aaf87bc3ba5b3a8cd1ac7"},
+    {file = "shapely-2.0.6-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:c6d88ade96bf02f6bfd667ddd3626913098e243e419a0325ebef2bbd481d1eb6"},
+    {file = "shapely-2.0.6-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:8b3b818c4407eaa0b4cb376fd2305e20ff6df757bf1356651589eadc14aab41b"},
+    {file = "shapely-2.0.6-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1bbc783529a21f2bd50c79cef90761f72d41c45622b3e57acf78d984c50a5d13"},
+    {file = "shapely-2.0.6-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:2423f6c0903ebe5df6d32e0066b3d94029aab18425ad4b07bf98c3972a6e25a1"},
+    {file = "shapely-2.0.6-cp38-cp38-win32.whl", hash = "sha256:2de00c3bfa80d6750832bde1d9487e302a6dd21d90cb2f210515cefdb616e5f5"},
+    {file = "shapely-2.0.6-cp38-cp38-win_amd64.whl", hash = "sha256:3a82d58a1134d5e975f19268710e53bddd9c473743356c90d97ce04b73e101ee"},
+    {file = "shapely-2.0.6-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:392f66f458a0a2c706254f473290418236e52aa4c9b476a072539d63a2460595"},
+    {file = "shapely-2.0.6-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:eba5bae271d523c938274c61658ebc34de6c4b33fdf43ef7e938b5776388c1be"},
+    {file = "shapely-2.0.6-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7060566bc4888b0c8ed14b5d57df8a0ead5c28f9b69fb6bed4476df31c51b0af"},
+    {file = "shapely-2.0.6-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b02154b3e9d076a29a8513dffcb80f047a5ea63c897c0cd3d3679f29363cf7e5"},
+    {file = "shapely-2.0.6-cp39-cp39-win32.whl", hash = "sha256:44246d30124a4f1a638a7d5419149959532b99dfa25b54393512e6acc9c211ac"},
+    {file = "shapely-2.0.6-cp39-cp39-win_amd64.whl", hash = "sha256:2b542d7f1dbb89192d3512c52b679c822ba916f93479fa5d4fc2fe4fa0b3c9e8"},
+    {file = "shapely-2.0.6.tar.gz", hash = "sha256:997f6159b1484059ec239cacaa53467fd8b5564dabe186cd84ac2944663b0bf6"},
+]
+
+[package.dependencies]
+numpy = ">=1.14,<3"
+
+[package.extras]
+docs = ["matplotlib", "numpydoc (==1.1.*)", "sphinx", "sphinx-book-theme", "sphinx-remove-toctrees"]
+test = ["pytest", "pytest-cov"]
+
 [[package]]
 name = "six"
 version = "1.16.0"
@@ -1073,7 +1650,29 @@ platformdirs = ">=3.5.1,<4"
 docs = ["furo (>=2023.5.20)", "proselint (>=0.13)", "sphinx (>=7.0.1)", "sphinx-argparse (>=0.4)", "sphinxcontrib-towncrier (>=0.2.1a0)", "towncrier (>=23.6)"]
 test = ["covdefaults (>=2.3)", "coverage (>=7.2.7)", "coverage-enable-subprocess (>=1)", "flaky (>=3.7)", "packaging (>=23.1)", "pytest (>=7.3.1)", "pytest-env (>=0.8.1)", "pytest-freezer (>=0.4.6)", "pytest-mock (>=3.10)", "pytest-randomly (>=3.12)", "pytest-timeout (>=2.1)", "setuptools (>=67.8)", "time-machine (>=2.9)"]
 
+[[package]]
+name = "zipp"
+version = "3.20.2"
+description = "Backport of pathlib-compatible object wrapper for zip files"
+optional = true
+python-versions = ">=3.8"
+files = [
+    {file = "zipp-3.20.2-py3-none-any.whl", hash = "sha256:a817ac80d6cf4b23bf7f2828b7cabf326f15a001bea8b1f9b49631780ba28350"},
+    {file = "zipp-3.20.2.tar.gz", hash = "sha256:bc9eb26f4506fda01b81bcde0ca78103b6e62f991b381fec825435c836edbc29"},
+]
+
+[package.extras]
+check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)"]
+cover = ["pytest-cov"]
+doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+enabler = ["pytest-enabler (>=2.2)"]
+test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"]
+type = ["pytest-mypy"]
+
+[extras]
+coco = ["pycocotools", "shapely"]
+
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.8"
-content-hash = "6faf04b9ddb7ff497a295e0d7a92670f4f19924dbf997a48e94229ee7057414f"
+content-hash = "9f298290a098bbe5076054e29c2590350a0bd5db2d71e1693906d330957de648"
diff --git a/pyproject.toml b/pyproject.toml
index adf96be6b..6065f9f9c 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -27,6 +27,11 @@ cryptography = ">=3.4.8"
 tqdm = "^4.32.1"
 pydantic = ">=1.10.14"
 orjson = ">=2"
+pycocotools = {version = "^2.0.7", optional = true}
+shapely = {version = "^2.0.4", optional = true}
+
+[tool.poetry.extras]
+coco = ["pycocotools", "shapely"]
 
 [tool.poetry.group.dev.dependencies]
 pytest = "^7.4.1"
diff --git a/tests/objects/data/data_1.py b/tests/objects/data/data_1.py
index 9c47e364d..8cc6db6bd 100644
--- a/tests/objects/data/data_1.py
+++ b/tests/objects/data/data_1.py
@@ -1,4 +1,6 @@
-labels = {
+from typing import Any, Dict
+
+labels: Dict[str, Any] = {
     "label_hash": "1b4398aa-1d4d-4873-8470-2ac9fa373ac0",
     "branch_name": "main",
     "created_at": "2023-02-09 14:12:03",
@@ -167,7 +169,7 @@
     "label_status": "LABELLED",
 }
 
-ontology = {
+ontology: Dict[str, Any] = {
     "objects": [
         {"id": "1", "name": "Epiglottis ", "color": "#D33115", "shape": "bounding_box", "featureNodeHash": "249c9370"},
         {"id": "2", "name": "Larynx", "color": "#E27300", "shape": "bounding_box", "featureNodeHash": "e3c87d43"},
diff --git a/tests/test_coco_export.py b/tests/test_coco_export.py
new file mode 100644
index 000000000..f2bd9a98c
--- /dev/null
+++ b/tests/test_coco_export.py
@@ -0,0 +1,256 @@
+from typing import Any, Dict
+from unittest.mock import patch
+
+import pytest
+
+from encord.objects.ontology_structure import OntologyStructure
+from tests.objects.data.data_1 import labels as BASE_LABEL_DICT
+from tests.objects.data.data_1 import ontology as BASE_ONTOLOGY_DICT
+
+ontology_structure = OntologyStructure.from_dict(BASE_ONTOLOGY_DICT)
+EXPECTED_COCO_RESULT: Dict[str, Any] = {
+    "info": {
+        "description": "failing_video_new.mp4",
+        "contributor": None,
+        "date_created": None,
+        "url": None,
+        "version": None,
+        "year": None,
+    },
+    "categories": [
+        {"supercategory": "bounding_box", "id": 1, "name": "Epiglottis "},
+        {"supercategory": "bounding_box", "id": 2, "name": "Larynx"},
+        {"supercategory": "bounding_box", "id": 3, "name": "Oesophagus "},
+        {"supercategory": "polyline", "id": 4, "name": "Z-line "},
+        {"supercategory": "bounding_box", "id": 5, "name": "Stomach"},
+        {"supercategory": "bounding_box", "id": 6, "name": "Antrum "},
+        {"supercategory": "polygon", "id": 7, "name": "Incisura "},
+        {"supercategory": "bounding_box", "id": 8, "name": "D1"},
+        {"supercategory": "bounding_box", "id": 9, "name": "D2"},
+        {"supercategory": "bounding_box", "id": 10, "name": "LES"},
+        {"supercategory": "bounding_box", "id": 11, "name": "Pyloric opening "},
+        {"supercategory": "bounding_box", "id": 12, "name": "Fundus "},
+        {"supercategory": "point", "id": 13, "name": "point", "keypoints": "keypoint", "skeleton": []},
+    ],
+    "images": [
+        {
+            "coco_url": "cord-videos-dev/lFW59RQ9jcT4vHZeG14m8QWJKug1/cd63592b-a9e4-47d7-8563-aaed75c5afdc",
+            "id": 0,
+            "video_title": "failing_video_new.mp4",
+            "file_name": "videos/cd63592b-a9e4-47d7-8563-aaed75c5afdc/0.jpg",
+            "height": 480,
+            "width": 640,
+        },
+        {
+            "coco_url": "cord-videos-dev/lFW59RQ9jcT4vHZeG14m8QWJKug1/cd63592b-a9e4-47d7-8563-aaed75c5afdc",
+            "id": 1,
+            "video_title": "failing_video_new.mp4",
+            "file_name": "videos/cd63592b-a9e4-47d7-8563-aaed75c5afdc/1.jpg",
+            "height": 480,
+            "width": 640,
+        },
+        {
+            "coco_url": "cord-videos-dev/lFW59RQ9jcT4vHZeG14m8QWJKug1/cd63592b-a9e4-47d7-8563-aaed75c5afdc",
+            "id": 2,
+            "video_title": "failing_video_new.mp4",
+            "file_name": "videos/cd63592b-a9e4-47d7-8563-aaed75c5afdc/2.jpg",
+            "height": 480,
+            "width": 640,
+        },
+        {
+            "coco_url": "cord-videos-dev/lFW59RQ9jcT4vHZeG14m8QWJKug1/cd63592b-a9e4-47d7-8563-aaed75c5afdc",
+            "id": 3,
+            "video_title": "failing_video_new.mp4",
+            "file_name": "videos/cd63592b-a9e4-47d7-8563-aaed75c5afdc/3.jpg",
+            "height": 480,
+            "width": 640,
+        },
+    ],
+    "annotations": [
+        {
+            "area": 2601.9041279999997,
+            "bbox": [189.696, 120.768, 54.976, 47.327999999999996],
+            "category_id": 1,
+            "image_id": 0,
+            "iscrowd": 0,
+            "segmentation": [[189.696, 120.768, 244.672, 120.768, 244.672, 168.096, 189.696, 168.096]],
+            "keypoints": None,
+            "num_keypoints": None,
+            "id": 0,
+            "attributes": {
+                "track_id": 0,
+                "encord_track_uuid": "428fba2b",
+                "classifications": {},
+                "manual_annotation": True,
+            },
+        },
+        {
+            "area": 0.0,
+            "bbox": [271.552, 165.984, 0.0, 0.0],
+            "category_id": 13,
+            "image_id": 0,
+            "iscrowd": 0,
+            "segmentation": [[271.552, 165.984]],
+            "keypoints": [271.552, 165.984, 2.0],
+            "num_keypoints": 1,
+            "id": 1,
+            "attributes": {
+                "track_id": 1,
+                "encord_track_uuid": "82724bb2",
+                "classifications": {},
+                "manual_annotation": True,
+            },
+        },
+        {
+            "area": 999.9974399999999,
+            "bbox": [195.32800000000003, 238.512, 40.064, 24.959999999999997],
+            "category_id": 1,
+            "image_id": 0,
+            "iscrowd": 0,
+            "segmentation": [
+                [
+                    195.32800000000003,
+                    238.512,
+                    235.39200000000002,
+                    238.512,
+                    235.39200000000002,
+                    263.472,
+                    195.32800000000003,
+                    263.472,
+                ]
+            ],
+            "keypoints": None,
+            "num_keypoints": None,
+            "id": 2,
+            "attributes": {
+                "track_id": 2,
+                "encord_track_uuid": "u8BIOLmY",
+                "classifications": {},
+                "manual_annotation": True,
+            },
+        },
+        {
+            "area": 2768.498688,
+            "bbox": [340.032, 283.872, 63.104, 43.872],
+            "category_id": 1,
+            "image_id": 0,
+            "iscrowd": 0,
+            "segmentation": [
+                [340.032, 283.872, 403.13599999999997, 283.872, 403.13599999999997, 327.744, 340.032, 327.744]
+            ],
+            "keypoints": None,
+            "num_keypoints": None,
+            "id": 3,
+            "attributes": {
+                "track_id": 3,
+                "encord_track_uuid": "/ghwDXVK",
+                "classifications": {},
+                "manual_annotation": True,
+            },
+        },
+        {
+            "area": 2538.319872,
+            "bbox": [169.024, 116.016, 54.016000000000005, 46.992],
+            "category_id": 1,
+            "image_id": 1,
+            "iscrowd": 0,
+            "segmentation": [
+                [169.024, 116.016, 223.04000000000002, 116.016, 223.04000000000002, 163.008, 169.024, 163.008]
+            ],
+            "keypoints": None,
+            "num_keypoints": None,
+            "id": 4,
+            "attributes": {
+                "track_id": 0,
+                "encord_track_uuid": "428fba2b",
+                "classifications": {},
+                "manual_annotation": False,
+            },
+        },
+        {
+            "area": 999.9974399999999,
+            "bbox": [195.32800000000003, 238.512, 40.064, 24.959999999999997],
+            "category_id": 1,
+            "image_id": 1,
+            "iscrowd": 0,
+            "segmentation": [
+                [
+                    195.32800000000003,
+                    238.512,
+                    235.39200000000002,
+                    238.512,
+                    235.39200000000002,
+                    263.472,
+                    195.32800000000003,
+                    263.472,
+                ]
+            ],
+            "keypoints": None,
+            "num_keypoints": None,
+            "id": 5,
+            "attributes": {
+                "track_id": 2,
+                "encord_track_uuid": "u8BIOLmY",
+                "classifications": {},
+                "manual_annotation": True,
+            },
+        },
+        {
+            "area": 2538.319872,
+            "bbox": [169.024, 114.0, 54.016000000000005, 46.992],
+            "category_id": 1,
+            "image_id": 2,
+            "iscrowd": 0,
+            "segmentation": [
+                [169.024, 114.0, 223.04000000000002, 114.0, 223.04000000000002, 160.992, 169.024, 160.992]
+            ],
+            "keypoints": None,
+            "num_keypoints": None,
+            "id": 6,
+            "attributes": {
+                "track_id": 0,
+                "encord_track_uuid": "428fba2b",
+                "classifications": {},
+                "manual_annotation": False,
+            },
+        },
+        {
+            "area": 2538.319872,
+            "bbox": [171.968, 114.0, 54.016000000000005, 46.992],
+            "category_id": 1,
+            "image_id": 3,
+            "iscrowd": 0,
+            "segmentation": [
+                [171.968, 114.0, 225.98399999999998, 114.0, 225.98399999999998, 160.992, 171.968, 160.992]
+            ],
+            "keypoints": None,
+            "num_keypoints": None,
+            "id": 7,
+            "attributes": {
+                "track_id": 0,
+                "encord_track_uuid": "428fba2b",
+                "classifications": {},
+                "manual_annotation": False,
+            },
+        },
+    ],
+}
+
+
+def test_coco_exporter_without_coco_extra():
+    # Simulate the absence of `pycocotools` and `shapely` packages
+    with patch.dict("sys.modules", {"pycocotools": None, "shapely": None}):
+        with pytest.raises(ImportError, match="The 'pycocotools' and 'shapely' packages are required"):
+            from encord.utilities.coco.exporter import CocoExporter
+
+            output = CocoExporter([BASE_LABEL_DICT], ontology_structure).export()
+            assert output == EXPECTED_COCO_RESULT
+
+
+def test_coco_exporter_with_coco_extra():
+    try:
+        from encord.utilities.coco.exporter import CocoExporter
+    except ImportError:
+        return
+    output = CocoExporter([BASE_LABEL_DICT], ontology_structure).export()
+    assert output == EXPECTED_COCO_RESULT