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import abc
import json
import logging
import os
from collections import defaultdict
from dataclasses import asdict, dataclass
from typing import (
Any,
Dict,
Final,
Iterator,
List,
Literal,
Optional,
Sequence,
Tuple,
TypedDict,
Union,
get_args,
)
import datasets as ds
import numpy as np
from datasets.data_files import DataFilesDict
from PIL import Image
from PIL.Image import Image as PilImage
from pycocotools import mask as cocomask
from tqdm.auto import tqdm
logger = logging.getLogger(__name__)
JsonDict = Dict[str, Any]
ImageId = int
AnnotationId = int
LicenseId = int
CategoryId = int
Bbox = Tuple[float, float, float, float]
MscocoSplits = Literal["train", "val", "test"]
KEYPOINT_STATE: Final[List[str]] = ["unknown", "invisible", "visible"]
_CITATION = """
"""
_DESCRIPTION = """
"""
_HOMEPAGE = """
"""
_LICENSE = """
"""
_URLS = {
"2014": {
"images": {
"train": "http://images.cocodataset.org/zips/train2014.zip",
"validation": "http://images.cocodataset.org/zips/val2014.zip",
"test": "http://images.cocodataset.org/zips/test2014.zip",
},
"annotations": {
"train_validation": "http://images.cocodataset.org/annotations/annotations_trainval2014.zip",
"test_image_info": "http://images.cocodataset.org/annotations/image_info_test2014.zip",
},
},
"2015": {
"images": {
"test": "http://images.cocodataset.org/zips/test2015.zip",
},
"annotations": {
"test_image_info": "http://images.cocodataset.org/annotations/image_info_test2015.zip",
},
},
"2017": {
"images": {
"train": "http://images.cocodataset.org/zips/train2017.zip",
"validation": "http://images.cocodataset.org/zips/val2017.zip",
"test": "http://images.cocodataset.org/zips/test2017.zip",
"unlabeled": "http://images.cocodataset.org/zips/unlabeled2017.zip",
},
"annotations": {
"train_validation": "http://images.cocodataset.org/annotations/annotations_trainval2017.zip",
"stuff_train_validation": "http://images.cocodataset.org/annotations/stuff_annotations_trainval2017.zip",
"panoptic_train_validation": "http://images.cocodataset.org/annotations/panoptic_annotations_trainval2017.zip",
"test_image_info": "http://images.cocodataset.org/annotations/image_info_test2017.zip",
"unlabeled": "http://images.cocodataset.org/annotations/image_info_unlabeled2017.zip",
},
},
}
CATEGORIES: Final[List[str]] = [
"person",
"bicycle",
"car",
"motorcycle",
"airplane",
"bus",
"train",
"truck",
"boat",
"traffic light",
"fire hydrant",
"stop sign",
"parking meter",
"bench",
"bird",
"cat",
"dog",
"horse",
"sheep",
"cow",
"elephant",
"bear",
"zebra",
"giraffe",
"backpack",
"umbrella",
"handbag",
"tie",
"suitcase",
"frisbee",
"skis",
"snowboard",
"sports ball",
"kite",
"baseball bat",
"baseball glove",
"skateboard",
"surfboard",
"tennis racket",
"bottle",
"wine glass",
"cup",
"fork",
"knife",
"spoon",
"bowl",
"banana",
"apple",
"sandwich",
"orange",
"broccoli",
"carrot",
"hot dog",
"pizza",
"donut",
"cake",
"chair",
"couch",
"potted plant",
"bed",
"dining table",
"toilet",
"tv",
"laptop",
"mouse",
"remote",
"keyboard",
"cell phone",
"microwave",
"oven",
"toaster",
"sink",
"refrigerator",
"book",
"clock",
"vase",
"scissors",
"teddy bear",
"hair drier",
"toothbrush",
]
SUPER_CATEGORIES: Final[List[str]] = [
"person",
"vehicle",
"outdoor",
"animal",
"accessory",
"sports",
"kitchen",
"food",
"furniture",
"electronic",
"appliance",
"indoor",
]
@dataclass
class AnnotationInfo(object):
description: str
url: str
version: str
year: str
contributor: str
date_created: str
@classmethod
def from_dict(cls, json_dict: JsonDict) -> "AnnotationInfo":
return cls(**json_dict)
@dataclass
class LicenseData(object):
url: str
license_id: LicenseId
name: str
@classmethod
def from_dict(cls, json_dict: JsonDict) -> "LicenseData":
return cls(
license_id=json_dict["id"],
url=json_dict["url"],
name=json_dict["name"],
)
@dataclass
class ImageData(object):
image_id: ImageId
license_id: LicenseId
file_name: str
coco_url: str
height: int
width: int
date_captured: str
flickr_url: str
@classmethod
def from_dict(cls, json_dict: JsonDict) -> "ImageData":
return cls(
image_id=json_dict["id"],
license_id=json_dict["license"],
file_name=json_dict["file_name"],
coco_url=json_dict["coco_url"],
height=json_dict["height"],
width=json_dict["width"],
date_captured=json_dict["date_captured"],
flickr_url=json_dict["flickr_url"],
)
@property
def shape(self) -> Tuple[int, int]:
return (self.height, self.width)
@dataclass
class CategoryData(object):
category_id: int
name: str
supercategory: str
@classmethod
def from_dict(cls, json_dict: JsonDict) -> "CategoryData":
return cls(
category_id=json_dict["id"],
name=json_dict["name"],
supercategory=json_dict["supercategory"],
)
@dataclass
class AnnotationData(object):
annotation_id: AnnotationId
image_id: ImageId
@dataclass
class CaptionsAnnotationData(AnnotationData):
caption: str
@classmethod
def from_dict(cls, json_dict: JsonDict) -> "CaptionsAnnotationData":
return cls(
annotation_id=json_dict["id"],
image_id=json_dict["image_id"],
caption=json_dict["caption"],
)
class UncompressedRLE(TypedDict):
counts: List[int]
size: Tuple[int, int]
class CompressedRLE(TypedDict):
counts: bytes
size: Tuple[int, int]
@dataclass
class InstancesAnnotationData(AnnotationData):
segmentation: Union[np.ndarray, CompressedRLE]
area: float
iscrowd: bool
bbox: Tuple[float, float, float, float]
category_id: int
@classmethod
def compress_rle(
cls,
segmentation: Union[List[List[float]], UncompressedRLE],
iscrowd: bool,
height: int,
width: int,
) -> CompressedRLE:
if iscrowd:
rle = cocomask.frPyObjects(segmentation, h=height, w=width)
else:
rles = cocomask.frPyObjects(segmentation, h=height, w=width)
rle = cocomask.merge(rles)
return rle # type: ignore
@classmethod
def rle_segmentation_to_binary_mask(
cls, segmentation, iscrowd: bool, height: int, width: int
) -> np.ndarray:
rle = cls.compress_rle(
segmentation=segmentation, iscrowd=iscrowd, height=height, width=width
)
return cocomask.decode(rle) # type: ignore
@classmethod
def rle_segmentation_to_mask(
cls,
segmentation: Union[List[List[float]], UncompressedRLE],
iscrowd: bool,
height: int,
width: int,
) -> np.ndarray:
binary_mask = cls.rle_segmentation_to_binary_mask(
segmentation=segmentation, iscrowd=iscrowd, height=height, width=width
)
return binary_mask * 255
@classmethod
def from_dict(
cls,
json_dict: JsonDict,
images: Dict[ImageId, ImageData],
decode_rle: bool,
) -> "InstancesAnnotationData":
segmentation = json_dict["segmentation"]
image_id = json_dict["image_id"]
image_data = images[image_id]
iscrowd = bool(json_dict["iscrowd"])
segmentation_mask = (
cls.rle_segmentation_to_mask(
segmentation=segmentation,
iscrowd=iscrowd,
height=image_data.height,
width=image_data.width,
)
if decode_rle
else cls.compress_rle(
segmentation=segmentation,
iscrowd=iscrowd,
height=image_data.height,
width=image_data.width,
)
)
return cls(
#
# for AnnotationData
#
annotation_id=json_dict["id"],
image_id=image_id,
#
# for InstancesAnnotationData
#
segmentation=segmentation_mask,
area=json_dict["area"],
iscrowd=iscrowd,
bbox=json_dict["bbox"],
category_id=json_dict["category_id"],
)
@dataclass
class PersonKeypoint(object):
x: int
y: int
v: int
state: str
@dataclass
class PersonKeypointsAnnotationData(InstancesAnnotationData):
num_keypoints: int
keypoints: List[PersonKeypoint]
@classmethod
def v_keypoint_to_state(cls, keypoint_v: int) -> str:
return KEYPOINT_STATE[keypoint_v]
@classmethod
def get_person_keypoints(
cls, flatten_keypoints: List[int], num_keypoints: int
) -> List[PersonKeypoint]:
keypoints_x = flatten_keypoints[0::3]
keypoints_y = flatten_keypoints[1::3]
keypoints_v = flatten_keypoints[2::3]
assert len(keypoints_x) == len(keypoints_y) == len(keypoints_v)
keypoints = [
PersonKeypoint(x=x, y=y, v=v, state=cls.v_keypoint_to_state(v))
for x, y, v in zip(keypoints_x, keypoints_y, keypoints_v)
]
assert len([kp for kp in keypoints if kp.state != "unknown"]) == num_keypoints
return keypoints
@classmethod
def from_dict(
cls,
json_dict: JsonDict,
images: Dict[ImageId, ImageData],
decode_rle: bool,
) -> "PersonKeypointsAnnotationData":
segmentation = json_dict["segmentation"]
image_id = json_dict["image_id"]
image_data = images[image_id]
iscrowd = bool(json_dict["iscrowd"])
segmentation_mask = (
cls.rle_segmentation_to_mask(
segmentation=segmentation,
iscrowd=iscrowd,
height=image_data.height,
width=image_data.width,
)
if decode_rle
else cls.compress_rle(
segmentation=segmentation,
iscrowd=iscrowd,
height=image_data.height,
width=image_data.width,
)
)
flatten_keypoints = json_dict["keypoints"]
num_keypoints = json_dict["num_keypoints"]
keypoints = cls.get_person_keypoints(flatten_keypoints, num_keypoints)
return cls(
#
# for AnnotationData
#
annotation_id=json_dict["id"],
image_id=image_id,
#
# for InstancesAnnotationData
#
segmentation=segmentation_mask,
area=json_dict["area"],
iscrowd=iscrowd,
bbox=json_dict["bbox"],
category_id=json_dict["category_id"],
#
# PersonKeypointsAnnotationData
#
num_keypoints=num_keypoints,
keypoints=keypoints,
)
class LicenseDict(TypedDict):
license_id: LicenseId
name: str
url: str
class BaseExample(TypedDict):
image_id: ImageId
image: PilImage
file_name: str
coco_url: str
height: int
width: int
date_captured: str
flickr_url: str
license_id: LicenseId
license: LicenseDict
class CaptionAnnotationDict(TypedDict):
annotation_id: AnnotationId
caption: str
class CaptionExample(BaseExample):
annotations: List[CaptionAnnotationDict]
class CategoryDict(TypedDict):
category_id: CategoryId
name: str
supercategory: str
class InstanceAnnotationDict(TypedDict):
annotation_id: AnnotationId
area: float
bbox: Bbox
image_id: ImageId
category_id: CategoryId
category: CategoryDict
iscrowd: bool
segmentation: np.ndarray
class InstanceExample(BaseExample):
annotations: List[InstanceAnnotationDict]
class KeypointDict(TypedDict):
x: int
y: int
v: int
state: str
class PersonKeypointAnnotationDict(InstanceAnnotationDict):
num_keypoints: int
keypoints: List[KeypointDict]
class PersonKeypointExample(BaseExample):
annotations: List[PersonKeypointAnnotationDict]
class MsCocoProcessor(object, metaclass=abc.ABCMeta):
def load_image(self, image_path: str) -> PilImage:
return Image.open(image_path)
def load_annotation_json(self, ann_file_path: str) -> JsonDict:
logger.info(f"Load annotation json from {ann_file_path}")
with open(ann_file_path, "r") as rf:
ann_json = json.load(rf)
return ann_json
def load_licenses_data(
self, license_dicts: List[JsonDict]
) -> Dict[LicenseId, LicenseData]:
licenses = {}
for license_dict in license_dicts:
license_data = LicenseData.from_dict(license_dict)
licenses[license_data.license_id] = license_data
return licenses
def load_images_data(
self,
image_dicts: List[JsonDict],
tqdm_desc: str = "Load images",
) -> Dict[ImageId, ImageData]:
images = {}
for image_dict in tqdm(image_dicts, desc=tqdm_desc):
image_data = ImageData.from_dict(image_dict)
images[image_data.image_id] = image_data
return images
def load_categories_data(
self,
category_dicts: List[JsonDict],
tqdm_desc: str = "Load categories",
) -> Dict[CategoryId, CategoryData]:
categories = {}
for category_dict in tqdm(category_dicts, desc=tqdm_desc):
category_data = CategoryData.from_dict(category_dict)
categories[category_data.category_id] = category_data
return categories
def get_features_base_dict(self):
return {
"image_id": ds.Value("int64"),
"image": ds.Image(),
"file_name": ds.Value("string"),
"coco_url": ds.Value("string"),
"height": ds.Value("int32"),
"width": ds.Value("int32"),
"date_captured": ds.Value("string"),
"flickr_url": ds.Value("string"),
"license_id": ds.Value("int32"),
"license": {
"url": ds.Value("string"),
"license_id": ds.Value("int8"),
"name": ds.Value("string"),
},
}
@abc.abstractmethod
def get_features(self, *args, **kwargs) -> ds.Features:
raise NotImplementedError
@abc.abstractmethod
def load_data(
self, ann_dicts: List[JsonDict], tqdm_desc: Optional[str] = None, **kwargs
):
raise NotImplementedError
@abc.abstractmethod
def generate_examples(
self,
image_dir: str,
images: Dict[ImageId, ImageData],
annotations: Dict[ImageId, List[CaptionsAnnotationData]],
licenses: Dict[LicenseId, LicenseData],
**kwargs,
):
raise NotImplementedError
class CaptionsProcessor(MsCocoProcessor):
def get_features(self, *args, **kwargs) -> ds.Features:
features_dict = self.get_features_base_dict()
annotations = ds.Sequence(
{
"annotation_id": ds.Value("int64"),
"image_id": ds.Value("int64"),
"caption": ds.Value("string"),
}
)
features_dict.update({"annotations": annotations})
return ds.Features(features_dict)
def load_data(
self,
ann_dicts: List[JsonDict],
tqdm_desc: str = "Load captions data",
**kwargs,
) -> Dict[ImageId, List[CaptionsAnnotationData]]:
annotations = defaultdict(list)
for ann_dict in tqdm(ann_dicts, desc=tqdm_desc):
ann_data = CaptionsAnnotationData.from_dict(ann_dict)
annotations[ann_data.image_id].append(ann_data)
return annotations
def generate_examples(
self,
image_dir: str,
images: Dict[ImageId, ImageData],
annotations: Dict[ImageId, List[CaptionsAnnotationData]],
licenses: Dict[LicenseId, LicenseData],
**kwargs,
) -> Iterator[Tuple[int, CaptionExample]]:
for idx, image_id in enumerate(images.keys()):
image_data = images[image_id]
image_anns = annotations[image_id]
assert len(image_anns) > 0
image = self.load_image(
image_path=os.path.join(image_dir, image_data.file_name),
)
example = asdict(image_data)
example["image"] = image
example["license"] = asdict(licenses[image_data.license_id])
example["annotations"] = []
for ann in image_anns:
example["annotations"].append(asdict(ann))
yield idx, example # type: ignore
class InstancesProcessor(MsCocoProcessor):
def get_features_instance_dict(self, decode_rle: bool):
segmentation_feature = (
ds.Image()
if decode_rle
else {
"counts": ds.Sequence(ds.Value("int64")),
"size": ds.Sequence(ds.Value("int32")),
}
)
return {
"annotation_id": ds.Value("int64"),
"image_id": ds.Value("int64"),
"segmentation": segmentation_feature,
"area": ds.Value("float32"),
"iscrowd": ds.Value("bool"),
"bbox": ds.Sequence(ds.Value("float32"), length=4),
"category_id": ds.Value("int32"),
"category": {
"category_id": ds.Value("int32"),
"name": ds.ClassLabel(
num_classes=len(CATEGORIES),
names=CATEGORIES,
),
"supercategory": ds.ClassLabel(
num_classes=len(SUPER_CATEGORIES),
names=SUPER_CATEGORIES,
),
},
}
def get_features(self, decode_rle: bool) -> ds.Features:
features_dict = self.get_features_base_dict()
annotations = ds.Sequence(
self.get_features_instance_dict(decode_rle=decode_rle)
)
features_dict.update({"annotations": annotations})
return ds.Features(features_dict)
def load_data( # type: ignore[override]
self,
ann_dicts: List[JsonDict],
images: Dict[ImageId, ImageData],
decode_rle: bool,
tqdm_desc: str = "Load instances data",
) -> Dict[ImageId, List[InstancesAnnotationData]]:
annotations = defaultdict(list)
ann_dicts = sorted(ann_dicts, key=lambda d: d["image_id"])
for ann_dict in tqdm(ann_dicts, desc=tqdm_desc):
ann_data = InstancesAnnotationData.from_dict(
ann_dict, images=images, decode_rle=decode_rle
)
annotations[ann_data.image_id].append(ann_data)
return annotations
def generate_examples( # type: ignore[override]
self,
image_dir: str,
images: Dict[ImageId, ImageData],
annotations: Dict[ImageId, List[InstancesAnnotationData]],
licenses: Dict[LicenseId, LicenseData],
categories: Dict[CategoryId, CategoryData],
) -> Iterator[Tuple[int, InstanceExample]]:
for idx, image_id in enumerate(images.keys()):
image_data = images[image_id]
image_anns = annotations[image_id]
if len(image_anns) < 1:
logger.warning(f"No annotation found for image id: {image_id}.")
continue
image = self.load_image(
image_path=os.path.join(image_dir, image_data.file_name),
)
example = asdict(image_data)
example["image"] = image
example["license"] = asdict(licenses[image_data.license_id])
example["annotations"] = []
for ann in image_anns:
ann_dict = asdict(ann)
category = categories[ann.category_id]
ann_dict["category"] = asdict(category)
example["annotations"].append(ann_dict)
yield idx, example # type: ignore
class PersonKeypointsProcessor(InstancesProcessor):
def get_features(self, decode_rle: bool) -> ds.Features:
features_dict = self.get_features_base_dict()
features_instance_dict = self.get_features_instance_dict(decode_rle=decode_rle)
features_instance_dict.update(
{
"keypoints": ds.Sequence(
{
"state": ds.Value("string"),
"x": ds.Value("int32"),
"y": ds.Value("int32"),
"v": ds.Value("int32"),
}
),
"num_keypoints": ds.Value("int32"),
}
)
annotations = ds.Sequence(features_instance_dict)
features_dict.update({"annotations": annotations})
return ds.Features(features_dict)
def load_data( # type: ignore[override]
self,
ann_dicts: List[JsonDict],
images: Dict[ImageId, ImageData],
decode_rle: bool,
tqdm_desc: str = "Load person keypoints data",
) -> Dict[ImageId, List[PersonKeypointsAnnotationData]]:
annotations = defaultdict(list)
ann_dicts = sorted(ann_dicts, key=lambda d: d["image_id"])
for ann_dict in tqdm(ann_dicts, desc=tqdm_desc):
ann_data = PersonKeypointsAnnotationData.from_dict(
ann_dict, images=images, decode_rle=decode_rle
)
annotations[ann_data.image_id].append(ann_data)
return annotations
def generate_examples( # type: ignore[override]
self,
image_dir: str,
images: Dict[ImageId, ImageData],
annotations: Dict[ImageId, List[PersonKeypointsAnnotationData]],
licenses: Dict[LicenseId, LicenseData],
categories: Dict[CategoryId, CategoryData],
) -> Iterator[Tuple[int, PersonKeypointExample]]:
for idx, image_id in enumerate(images.keys()):
image_data = images[image_id]
image_anns = annotations[image_id]
if len(image_anns) < 1:
# If there are no persons in the image,
# no keypoint annotations will be assigned.
continue
image = self.load_image(
image_path=os.path.join(image_dir, image_data.file_name),
)
example = asdict(image_data)
example["image"] = image
example["license"] = asdict(licenses[image_data.license_id])
example["annotations"] = []
for ann in image_anns:
ann_dict = asdict(ann)
category = categories[ann.category_id]
ann_dict["category"] = asdict(category)
example["annotations"].append(ann_dict)
yield idx, example # type: ignore
class MsCocoConfig(ds.BuilderConfig):
YEARS: Tuple[int, ...] = (
2014,
2017,
)
TASKS: Tuple[str, ...] = (
"captions",
"instances",
"person_keypoints",
)
def __init__(
self,
year: int,
coco_task: Union[str, Sequence[str]],
version: Optional[Union[ds.Version, str]],
decode_rle: bool = False,
data_dir: Optional[str] = None,
data_files: Optional[DataFilesDict] = None,
description: Optional[str] = None,
) -> None:
super().__init__(
name=self.config_name(year=year, task=coco_task),
version=version,
data_dir=data_dir,
data_files=data_files,
description=description,
)
self._check_year(year)
self._check_task(coco_task)
self._year = year
self._task = coco_task
self.processor = self.get_processor()
self.decode_rle = decode_rle
def _check_year(self, year: int) -> None:
assert year in self.YEARS, year
def _check_task(self, task: Union[str, Sequence[str]]) -> None:
if isinstance(task, str):
assert task in self.TASKS, task
elif isinstance(task, list) or isinstance(task, tuple):
for t in task:
assert t, task
else:
raise ValueError(f"Invalid task: {task}")
@property
def year(self) -> int:
return self._year
@property
def task(self) -> str:
if isinstance(self._task, str):
return self._task
elif isinstance(self._task, list) or isinstance(self._task, tuple):
return "-".join(sorted(self._task))
else:
raise ValueError(f"Invalid task: {self._task}")
def get_processor(self) -> MsCocoProcessor:
if self.task == "captions":
return CaptionsProcessor()
elif self.task == "instances":
return InstancesProcessor()
elif self.task == "person_keypoints":
return PersonKeypointsProcessor()
else:
raise ValueError(f"Invalid task: {self.task}")
@classmethod
def config_name(cls, year: int, task: Union[str, Sequence[str]]) -> str:
if isinstance(task, str):
return f"{year}-{task}"
elif isinstance(task, list) or isinstance(task, tuple):
task = "-".join(task)
return f"{year}-{task}"
else:
raise ValueError(f"Invalid task: {task}")
def dataset_configs(year: int, version: ds.Version) -> List[MsCocoConfig]:
return [
MsCocoConfig(
year=year,
coco_task="captions",
version=version,
),
MsCocoConfig(
year=year,
coco_task="instances",
version=version,
),
MsCocoConfig(
year=year,
coco_task="person_keypoints",
version=version,
),
# MsCocoConfig(
# year=year,
# coco_task=("captions", "instances"),
# version=version,
# ),
# MsCocoConfig(
# year=year,
# coco_task=("captions", "person_keypoints"),
# version=version,
# ),
]
def configs_2014(version: ds.Version) -> List[MsCocoConfig]:
return dataset_configs(year=2014, version=version)
def configs_2017(version: ds.Version) -> List[MsCocoConfig]:
return dataset_configs(year=2017, version=version)
class MsCocoDataset(ds.GeneratorBasedBuilder):
VERSION = ds.Version("1.0.0")
BUILDER_CONFIG_CLASS = MsCocoConfig
BUILDER_CONFIGS = configs_2014(version=VERSION) + configs_2017(version=VERSION)
@property
def year(self) -> int:
config: MsCocoConfig = self.config # type: ignore
return config.year
@property
def task(self) -> str:
config: MsCocoConfig = self.config # type: ignore
return config.task
def _info(self) -> ds.DatasetInfo:
processor: MsCocoProcessor = self.config.processor
features = processor.get_features(decode_rle=self.config.decode_rle)
return ds.DatasetInfo(
description=_DESCRIPTION,
citation=_CITATION,
homepage=_HOMEPAGE,
license=_LICENSE,
features=features,
)
def _split_generators(self, dl_manager: ds.DownloadManager):
file_paths = dl_manager.download_and_extract(_URLS[f"{self.year}"])
imgs = file_paths["images"] # type: ignore
anns = file_paths["annotations"] # type: ignore
return [
ds.SplitGenerator(
name=ds.Split.TRAIN, # type: ignore
gen_kwargs={
"base_image_dir": imgs["train"],
"base_annotation_dir": anns["train_validation"],
"split": "train",
},
),
ds.SplitGenerator(
name=ds.Split.VALIDATION, # type: ignore
gen_kwargs={
"base_image_dir": imgs["validation"],
"base_annotation_dir": anns["train_validation"],
"split": "val",
},
),
# ds.SplitGenerator(
# name=ds.Split.TEST, # type: ignore
# gen_kwargs={
# "base_image_dir": imgs["test"],
# "test_image_info_path": anns["test_image_info"],
# "split": "test",
# },
# ),
]
def _generate_train_val_examples(
self, split: str, base_image_dir: str, base_annotation_dir: str
):
image_dir = os.path.join(base_image_dir, f"{split}{self.year}")
ann_dir = os.path.join(base_annotation_dir, "annotations")
ann_file_path = os.path.join(ann_dir, f"{self.task}_{split}{self.year}.json")
processor: MsCocoProcessor = self.config.processor
ann_json = processor.load_annotation_json(ann_file_path=ann_file_path)
# info = AnnotationInfo.from_dict(ann_json["info"])
licenses = processor.load_licenses_data(license_dicts=ann_json["licenses"])
images = processor.load_images_data(image_dicts=ann_json["images"])
category_dicts = ann_json.get("categories")
categories = (
processor.load_categories_data(category_dicts=category_dicts)
if category_dicts is not None
else None
)
config: MsCocoConfig = self.config
yield from processor.generate_examples(
annotations=processor.load_data(
ann_dicts=ann_json["annotations"],
images=images,
decode_rle=config.decode_rle,
),
categories=categories,
image_dir=image_dir,
images=images,
licenses=licenses,
)
def _generate_test_examples(self, test_image_info_path: str):
raise NotImplementedError
def _generate_examples(
self,
split: MscocoSplits,
base_image_dir: Optional[str] = None,
base_annotation_dir: Optional[str] = None,
test_image_info_path: Optional[str] = None,
):
if split == "test" and test_image_info_path is not None:
yield from self._generate_test_examples(
test_image_info_path=test_image_info_path
)
elif (
split in get_args(MscocoSplits)
and base_image_dir is not None
and base_annotation_dir is not None
):
yield from self._generate_train_val_examples(
split=split,
base_image_dir=base_image_dir,
base_annotation_dir=base_annotation_dir,
)
else:
raise ValueError(
f"Invalid arguments: split = {split}, "
f"base_image_dir = {base_image_dir}, "
f"base_annotation_dir = {base_annotation_dir}, "
f"test_image_info_path = {test_image_info_path}",
)
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