train_utility/tests/test_splitter.py

from __future__ import annotations

from pathlib import Path

import pytest
import yaml

from yolo_webui.dataset_splitter import read_classes, split_dataset


def test_read_classes_custom_path(tmp_path: Path) -> None:
    custom_file = tmp_path / "custom_classes.txt"
    custom_file.write_text("classA\nclassB\n", encoding="utf-8")

    classes = read_classes(tmp_path, str(custom_file))
    assert classes == {0: "classA", 1: "classB"}


@pytest.mark.parametrize(
    ("names", "expected"),
    [
        (["cat", "dog"], {0: "cat", 1: "dog"}),
        ({0: "cat", 1: "dog"}, {0: "cat", 1: "dog"}),
    ],
)
def test_read_classes_custom_yaml(
    tmp_path: Path, names: object, expected: dict[int, str]
) -> None:
    custom_file = tmp_path / "custom_classes.yaml"
    custom_file.write_text(
        yaml.safe_dump({"names": names}, allow_unicode=True),
        encoding="utf-8",
    )

    assert read_classes(tmp_path, str(custom_file)) == expected


def test_missing_custom_classes_path_does_not_fall_back(tmp_path: Path) -> None:
    (tmp_path / "classes.txt").write_text("fallback\n", encoding="utf-8")

    with pytest.raises(ValueError, match="не существует"):
        read_classes(tmp_path, str(tmp_path / "typo.yaml"))


def test_read_classes_root_classes_txt(tmp_path: Path) -> None:
    classes_file = tmp_path / "classes.txt"
    classes_file.write_text("class0\nclass1\nclass2\n", encoding="utf-8")

    classes = read_classes(tmp_path, "")
    assert classes == {0: "class0", 1: "class1", 2: "class2"}


def test_read_classes_labels_classes_txt(tmp_path: Path) -> None:
    labels_dir = tmp_path / "labels"
    labels_dir.mkdir()
    classes_file = labels_dir / "classes.txt"
    classes_file.write_text("lbl0\nlbl1\n", encoding="utf-8")

    classes = read_classes(tmp_path, "")
    assert classes == {0: "lbl0", 1: "lbl1"}


def test_read_classes_from_yaml(tmp_path: Path) -> None:
    yaml_file = tmp_path / "dataset_config.yaml"
    yaml_data = {"names": ["yaml_cls0", "yaml_cls1"]}
    yaml_file.write_text(yaml.dump(yaml_data), encoding="utf-8")

    classes = read_classes(tmp_path, "")
    assert classes == {0: "yaml_cls0", 1: "yaml_cls1"}


def test_read_classes_fallback_scanning_labels(tmp_path: Path) -> None:
    labels_dir = tmp_path / "labels"
    labels_dir.mkdir()
    # Write some mock label files containing class IDs: 0, 2
    (labels_dir / "img1.txt").write_text("0 0.5 0.5 0.2 0.2\n", encoding="utf-8")
    (labels_dir / "img2.txt").write_text("2 0.4 0.4 0.1 0.1\n", encoding="utf-8")

    classes = read_classes(tmp_path, "")
    # Should generate class_0, class_1, class_2 since max_id is 2
    assert classes == {0: "class_0", 1: "class_1", 2: "class_2"}


def test_split_dataset_flow(tmp_path: Path) -> None:
    images_dir = tmp_path / "images"
    labels_dir = tmp_path / "labels"
    images_dir.mkdir()
    labels_dir.mkdir()

    # Create 4 image files
    for i in range(1, 5):
        (images_dir / f"img{i}.png").write_text("", encoding="utf-8")
        (labels_dir / f"img{i}.txt").write_text(f"0 0.5 0.5 0.1 0.1\n", encoding="utf-8")

    # Create classes.txt
    (tmp_path / "classes.txt").write_text("dummy_class\n", encoding="utf-8")

    # Split with 75% train ratio -> 3 train, 1 val
    train_count, val_count, yaml_path = split_dataset(
        dataset_dir=str(tmp_path),
        train_ratio=0.75,
        classes_path=""
    )

    assert train_count == 3
    assert val_count == 1
    assert Path(yaml_path).exists()

    # Verify dataset.yaml content
    with open(yaml_path, "r", encoding="utf-8") as f:
        data = yaml.safe_load(f)
        assert data["path"] == str(tmp_path)
        relative_output_dir = Path(yaml_path).parent.relative_to(tmp_path)
        assert data["train"] == (relative_output_dir / "train.txt").as_posix()
        assert data["val"] == (relative_output_dir / "val.txt").as_posix()
        assert data["names"] == {0: "dummy_class"}

    # Verify lists content
    output_dir = Path(yaml_path).parent
    train_list = (output_dir / "train.txt").read_text(encoding="utf-8").strip().split("\n")
    val_list = (output_dir / "val.txt").read_text(encoding="utf-8").strip().split("\n")

    assert len(train_list) == 3
    assert len(val_list) == 1

    # Verify paths are absolute
    assert Path(train_list[0]).is_absolute()
    assert Path(val_list[0]).is_absolute()


def test_split_dataset_rejects_single_image_without_writing_output(
    tmp_path: Path,
) -> None:
    images_dir = tmp_path / "images"
    labels_dir = tmp_path / "labels"
    images_dir.mkdir()
    labels_dir.mkdir()
    (images_dir / "only.jpg").write_bytes(b"")
    (labels_dir / "only.txt").write_text("0 0.5 0.5 1 1\n", encoding="utf-8")
    (tmp_path / "classes.txt").write_text("item\n", encoding="utf-8")

    with pytest.raises(ValueError, match="минимум 2"):
        split_dataset(str(tmp_path), 0.8, "")

    assert not (tmp_path / ".yolo-webui").exists()


def test_split_dataset_finds_nested_images_and_labels(tmp_path: Path) -> None:
    images_dir = tmp_path / "images" / "day"
    labels_dir = tmp_path / "labels" / "day"
    images_dir.mkdir(parents=True)
    labels_dir.mkdir(parents=True)
    for index in range(2):
        (images_dir / f"nested-{index}.jpg").write_bytes(b"")
        (labels_dir / f"nested-{index}.txt").write_text(
            "2 0.5 0.5 0.2 0.2\n",
            encoding="utf-8",
        )

    train_count, val_count, yaml_path = split_dataset(str(tmp_path), 0.8, "")

    assert (train_count, val_count) == (1, 1)
    data = yaml.safe_load(Path(yaml_path).read_text(encoding="utf-8"))
    assert data["names"] == {0: "class_0", 1: "class_1", 2: "class_2"}
    listed_images = "".join(
        (Path(yaml_path).parent / filename).read_text(encoding="utf-8")
        for filename in ("train.txt", "val.txt")
    )
    assert "images/day/nested-0.jpg" in listed_images
    assert "images/day/nested-1.jpg" in listed_images


def test_split_dataset_preserves_existing_split_files(tmp_path: Path) -> None:
    images_dir = tmp_path / "images"
    labels_dir = tmp_path / "labels"
    images_dir.mkdir()
    labels_dir.mkdir()
    for index in range(2):
        (images_dir / f"image-{index}.jpg").write_bytes(b"")
        (labels_dir / f"image-{index}.txt").write_text(
            "0 0.5 0.5 0.2 0.2\n",
            encoding="utf-8",
        )
    (tmp_path / "classes.txt").write_text("item\n", encoding="utf-8")
    user_split = tmp_path / "split"
    user_split.mkdir()
    (user_split / "train.txt").write_text("user data\n", encoding="utf-8")

    first_yaml = Path(split_dataset(str(tmp_path), 0.5, "")[2])
    second_yaml = Path(split_dataset(str(tmp_path), 0.5, "")[2])

    assert (user_split / "train.txt").read_text(encoding="utf-8") == "user data\n"
    assert first_yaml.parent != second_yaml.parent
    assert user_split not in first_yaml.parents


def test_split_dataset_preserves_custom_yaml_keys(tmp_path: Path) -> None:
    images_dir = tmp_path / "images"
    labels_dir = tmp_path / "labels"
    images_dir.mkdir()
    labels_dir.mkdir()
    for index in range(2):
        (images_dir / f"image-{index}.jpg").write_bytes(b"")
        (labels_dir / f"image-{index}.txt").write_text(
            "0 0.5 0.5 0.2 0.2\n",
            encoding="utf-8",
        )

    # Write a dataset YAML containing custom keys like kpt_shape
    dataset_yaml = tmp_path / "my_config.yaml"
    dataset_yaml.write_text(
        yaml.dump({
            "names": {0: "person"},
            "kpt_shape": [5, 3],
            "flip_idx": [0, 2, 1, 4, 3],
        }),
        encoding="utf-8",
    )

    # Run split specifying our YAML as the classes path
    _, _, out_yaml_path = split_dataset(str(tmp_path), 0.5, str(dataset_yaml))

    with open(out_yaml_path, "r", encoding="utf-8") as f:
        data = yaml.safe_load(f)
        assert data["kpt_shape"] == [5, 3]
        assert data["flip_idx"] == [0, 2, 1, 4, 3]
        assert data["names"] == {0: "person"}


def test_explicit_text_classes_override_root_yaml_names(tmp_path: Path) -> None:
    images_dir = tmp_path / "images"
    labels_dir = tmp_path / "labels"
    images_dir.mkdir()
    labels_dir.mkdir()
    for index in range(2):
        (images_dir / f"image-{index}.jpg").write_bytes(b"")
        (labels_dir / f"image-{index}.txt").write_text(
            "0 0.5 0.5 0.2 0.2\n",
            encoding="utf-8",
        )

    (tmp_path / "dataset.yaml").write_text(
        yaml.safe_dump({"names": {0: "old"}}),
        encoding="utf-8",
    )
    classes_path = tmp_path / "custom.txt"
    classes_path.write_text("new\n", encoding="utf-8")

    _, _, output_yaml = split_dataset(
        str(tmp_path),
        0.5,
        str(classes_path),
    )

    data = yaml.safe_load(Path(output_yaml).read_text(encoding="utf-8"))
    assert data["names"] == {0: "new"}