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import json
import os
import datasets
import dotenv
from pycocotools.coco import COCO
logger = datasets.logging.get_logger(__name__)
_BASE_IMAGE_METADATA_FEATURES = {
"image_id": datasets.Value("int32"),
"width": datasets.Value("int32"),
"height": datasets.Value("int32"),
"file_name": datasets.Value("string"),
"coco_url": datasets.Value("string"),
"task_type": datasets.Value("string"),
}
_BASE_REGION_FEATURES = {
# NOTE: one of them is 900100184613, which is out of the range of int32
"region_id": datasets.Value("int64"),
"image_id": datasets.Value("int32"),
"phrases": [datasets.Value("string")],
"x": datasets.Value("int32"),
"y": datasets.Value("int32"),
"width": datasets.Value("int32"),
"height": datasets.Value("int32"),
}
_BASE_MASK_FEATURES = {
"size": [datasets.Value("int32")],
"counts": datasets.Value("string"),
}
_BASE_MASK_REGION_FEATURES = {
"region_id": datasets.Value("int64"),
"image_id": datasets.Value("int32"),
"phrases": [datasets.Value("string")],
"x": datasets.Value("int32"),
"y": datasets.Value("int32"),
"width": datasets.Value("int32"),
"height": datasets.Value("int32"),
"mask": _BASE_MASK_FEATURES,
# "area": datasets.Value("int32"),
# "phrase_conf": datasets.Value("float32"),
}
_ANNOTATION_FEATURES = {
"region_descriptions": {"regions": [_BASE_REGION_FEATURES]},
"mask_region_descriptions": {"regions": [_BASE_MASK_REGION_FEATURES]},
}
class V3DetBuilderConfig(datasets.BuilderConfig):
def __init__(
self,
name,
splits,
with_image: bool = True,
with_mask: bool = True,
v3det_base_dir: str = None,
v3det_base_annotations_dir: str = None,
task_type: str = "recognition",
**kwargs,
):
super().__init__(name, **kwargs)
self.splits = splits
self.with_image = with_image
self.with_mask = with_mask
self.v3det_base_dir = v3det_base_dir
self.v3det_base_annotations_dir = v3det_base_annotations_dir
self.task_type = task_type
@property
def features(self):
annoation_type = "mask_region_descriptions" if self.with_mask else "region_descriptions"
logger.info(f"Using annotation type: {annoation_type} due to with_mask={self.with_mask}")
return datasets.Features(
{
**({"image": datasets.Image()} if self.with_image else {}),
**_BASE_IMAGE_METADATA_FEATURES,
**_ANNOTATION_FEATURES[annoation_type],
}
)
# Name of the dataset usually match the script name with CamelCase instead of snake_case
class V3DetDataset(datasets.GeneratorBasedBuilder):
"""An example dataset script to work with the local (downloaded) V3Det dataset"""
VERSION = datasets.Version("0.0.0")
BUILDER_CONFIG_CLASS = V3DetBuilderConfig
BUILDER_CONFIGS = [
# NOTE: we do not need test as it lacks visual promptsc
# V3DetBuilderConfig(name="2017", splits=["train", "valid", "test"]),
V3DetBuilderConfig(name="v1", splits=["train", "valid"]),
]
DEFAULT_CONFIG_NAME = "v1"
config: V3DetBuilderConfig
def _info(self):
return datasets.DatasetInfo(features=self.config.features)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
# This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
# If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
if self.config.with_mask is True:
raise ValueError("This v3det does not support `with_mask=True`.")
# data_dir = self.config.data_dir
v3det_base_dir = self.config.v3det_base_dir
v3det_base_annotations_dir = self.config.v3det_base_annotations_dir
if v3det_base_dir is None:
raise ValueError(
"This script is supposed to work with local (downloaded) objects356 dataset. The argument `v3det_base_dir` in `load_dataset()` is required."
)
if v3det_base_annotations_dir is None:
raise ValueError(
"This script is supposed to work with local (downloaded) objects356 dataset. The argument `v3det_base_annotations_dir` in `load_dataset()` is required."
)
# NOTE: the config is from https://?.blob.core.windows.net/onemodel/?/V3Det/, which is provided by XK Huang.
_DL_URLS = {
"train": v3det_base_dir,
"val": v3det_base_dir,
"annotations_train": os.path.join(v3det_base_annotations_dir, "v3det_2023_v1_train.json"),
"annotations_val": os.path.join(v3det_base_annotations_dir, "v3det_2023_v1_val.json"),
}
archive_path = _DL_URLS
splits = []
for split in self.config.splits:
if split == "train":
dataset = datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"json_path": archive_path["annotations_train"],
"image_dir": archive_path["train"],
"split": "train",
},
)
elif split in ["val", "valid", "validation", "dev"]:
dataset = datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"json_path": archive_path["annotations_val"],
"image_dir": archive_path["val"],
"split": "val",
},
)
else:
continue
splits.append(dataset)
return splits
# NOTE: There are 6 urls are expired. (09/06/23)
MISSING_IMAGES = {
"images/n11708857/9_80_2421297739_7c2a68f404_c.jpg",
"images/n11794139/10_47_5109432138_4201649ce4_c.jpg",
"images/a00012462/11_256_9645439415_6f50f1599f_c.jpg",
"images/Q494417/34_1125_10108827626_78b07f281b_c.jpg",
"images/Q3216816/10_1372_8165953126_129e378cc2_c.jpg",
"images/a00006662/34_145_18056000502_e12645da0a_c.jpg",
}
def _generate_examples(
# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
self,
json_path,
image_dir,
split,
):
"""Yields examples as (key, example) tuples."""
coco = COCO(json_path)
img_ids = coco.getImgIds()
for idx, img_id in enumerate(img_ids):
img = coco.imgs[img_id]
image_metadata = {
"coco_url": "",
"file_name": img["file_name"],
"height": img["height"],
"width": img["width"],
"image_id": img["id"],
}
# NOTE: the config is from https://?.blob.core.windows.net/?/data_raw/V3Det/, which is provided by XK Huang.
image_dict = {"image": os.path.join(image_dir, img["file_name"])} if self.config.with_image else {}
if img["file_name"] in self.MISSING_IMAGES:
logger.warning(f"Image {img['file_name']} in V3Det is missing. Skipping.")
continue
if img_id not in coco.imgToAnns:
continue
annotation = []
for ann in coco.imgToAnns[img_id]:
x, y, width, height = ann["bbox"]
x, y, width, height = int(x), int(y), int(width), int(height)
annotation_dict = {
# NOTE: one of them is 900100184613, which is out of the range of int32
"region_id": ann["id"],
"image_id": ann["image_id"],
"x": x,
"y": y,
"width": width,
"height": height,
}
phrases = []
category_id = ann["category_id"]
category = coco.cats[category_id]
phrases.append(category["name"])
# TODO: add supercategory
# phrases.append(category["supercategory"])
annotation_dict["phrases"] = phrases
if self.config.with_mask:
mask_dict = coco.annToRLE(ann)
mask_dict = {
"size": mask_dict["size"],
"counts": mask_dict["counts"].decode("utf-8"), # NOTE: otherwise, it leads to core dump error.
}
annotation_dict["mask"] = mask_dict
annotation.append(annotation_dict)
annotation = {"regions": annotation}
yield idx, {**image_dict, **image_metadata, **annotation, "task_type": self.config.task_type}
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