"""Micro-OD: A few-shot microscopy object detection benchmark."""

import json
import os

import datasets

_DESCRIPTION = """\
Micro-OD is a few-shot microscopy object detection benchmark spanning four
biological imaging domains: BBBC (malaria parasite detection), BCCD (blood
cell counting), LIVECell (RatC6 live-cell imaging), and NIH-3T3 (mouse
fibroblast imaging).

Two splits are provided:
  - example: 10 images per sub-dataset (40 total) — the few-shot support set.
  - test:    53 images per sub-dataset (212 total) — the evaluation query set.
"""

# Sorted alphabetically; used as ClassLabel names.
ALL_CLASSES = [
    "Gametocyte Cells",
    "Platelets",
    "Polygonal Cells",
    "Red Blood Cells",
    "Ring Cells",
    "Round Cells",
    "Schizont Cells",
    "Spindle Cells",
    "Trophozoite Cells",
    "White Blood Cells",
]

SUBDATASETS = ["BBBC", "BCCD", "LIVECell", "NIH-3T3"]


class MicroOD(datasets.GeneratorBasedBuilder):
    """Micro-OD dataset loader."""

    VERSION = datasets.Version("1.0.0")

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "image": datasets.Image(),
                    "image_id": datasets.Value("string"),
                    "subdataset": datasets.Value("string"),
                    "objects": datasets.Sequence(
                        feature={
                            # COCO format: [x_min, y_min, width, height]
                            "bbox": datasets.Sequence(
                                datasets.Value("float32"), length=4
                            ),
                            "label": datasets.ClassLabel(names=ALL_CLASSES),
                            "category": datasets.Value("string"),
                        }
                    ),
                }
            ),
        )

    def _split_generators(self, dl_manager):
        data_dir = os.path.dirname(os.path.abspath(__file__))
        return [
            datasets.SplitGenerator(
                name=datasets.splits.NamedSplit("example"),
                gen_kwargs={"split_path": os.path.join(data_dir, "example")},
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={"split_path": os.path.join(data_dir, "test")},
            ),
        ]

    def _generate_examples(self, split_path):
        idx = 0
        for subdataset in SUBDATASETS:
            annotation_path = os.path.join(
                split_path, subdataset, "annotation.jsonl"
            )
            image_base = os.path.join(split_path, subdataset)

            with open(annotation_path, encoding="utf-8") as f:
                for line in f:
                    line = line.strip()
                    if not line:
                        continue

                    entry = json.loads(line)
                    image_path = os.path.join(image_base, entry["image_path"])

                    bboxes, labels, categories = [], [], []
                    for category, boxes in entry["bbox"].items():
                        for box in boxes:
                            x_min, y_min = box[0]
                            x_max, y_max = box[1]
                            # Convert [[x_min,y_min],[x_max,y_max]] → COCO [x,y,w,h]
                            bboxes.append(
                                [
                                    float(x_min),
                                    float(y_min),
                                    float(x_max - x_min),
                                    float(y_max - y_min),
                                ]
                            )
                            labels.append(ALL_CLASSES.index(category))
                            categories.append(category)

                    yield idx, {
                        "image": image_path,
                        "image_id": f"{subdataset}/{entry['image_path']}",
                        "subdataset": subdataset,
                        "objects": {
                            "bbox": bboxes,
                            "label": labels,
                            "category": categories,
                        },
                    }
                    idx += 1