ai-training/tests/test_augmentation_nonfunc.py

import random
import shutil
import sys
import types
from pathlib import Path
from types import SimpleNamespace

import cv2
import numpy as np
import pytest

from tests.conftest import apply_constants_patch

if "matplotlib" not in sys.modules:
    _mpl = types.ModuleType("matplotlib")
    _plt = types.ModuleType("matplotlib.pyplot")
    _mpl.pyplot = _plt
    sys.modules["matplotlib"] = _mpl
    sys.modules["matplotlib.pyplot"] = _plt


def _patch_augmentation_paths(monkeypatch, base: Path):
    import augmentation as aug
    import constants as c

    apply_constants_patch(monkeypatch, base)
    monkeypatch.setattr(aug, "data_images_dir", c.data_images_dir)
    monkeypatch.setattr(aug, "data_labels_dir", c.data_labels_dir)
    monkeypatch.setattr(aug, "processed_images_dir", c.processed_images_dir)
    monkeypatch.setattr(aug, "processed_labels_dir", c.processed_labels_dir)
    monkeypatch.setattr(aug, "processed_dir", c.processed_dir)


def _augment_annotation_with_total(monkeypatch):
    import augmentation as aug

    orig = aug.Augmentator.augment_annotation

    def wrapped(self, image_file):
        self.total_to_process = self.total_images_to_process
        return orig(self, image_file)

    monkeypatch.setattr(aug.Augmentator, "augment_annotation", wrapped)


def _seed():
    random.seed(42)
    np.random.seed(42)


@pytest.mark.resilience
def test_rt_aug_01_corrupted_image_skipped(
    tmp_path, monkeypatch, fixture_images_dir, fixture_labels_dir
):
    _patch_augmentation_paths(monkeypatch, tmp_path)
    _augment_annotation_with_total(monkeypatch)
    _seed()
    import constants as c
    from augmentation import Augmentator

    img_dir = Path(c.data_images_dir)
    lbl_dir = Path(c.data_labels_dir)
    img_dir.mkdir(parents=True, exist_ok=True)
    lbl_dir.mkdir(parents=True, exist_ok=True)
    stem = sorted(fixture_images_dir.glob("*.jpg"))[0].stem
    shutil.copy2(fixture_images_dir / f"{stem}.jpg", img_dir / f"{stem}.jpg")
    shutil.copy2(fixture_labels_dir / f"{stem}.txt", lbl_dir / f"{stem}.txt")
    raw = (fixture_images_dir / f"{stem}.jpg").read_bytes()[:200]
    (img_dir / "corrupted_trunc.jpg").write_bytes(raw)
    Augmentator().augment_annotations()
    proc_img = Path(c.processed_images_dir)
    assert len(list(proc_img.glob("*.jpg"))) == 8


@pytest.mark.resilience
def test_rt_aug_02_missing_label_no_crash(tmp_path, monkeypatch, fixture_images_dir):
    _patch_augmentation_paths(monkeypatch, tmp_path)
    _augment_annotation_with_total(monkeypatch)
    import constants as c
    from augmentation import Augmentator

    img_dir = Path(c.data_images_dir)
    lbl_dir = Path(c.data_labels_dir)
    img_dir.mkdir(parents=True, exist_ok=True)
    lbl_dir.mkdir(parents=True, exist_ok=True)
    stem = "no_label_here"
    shutil.copy2(sorted(fixture_images_dir.glob("*.jpg"))[0], img_dir / f"{stem}.jpg")
    aug = Augmentator()
    aug.total_images_to_process = 1
    aug.augment_annotation(SimpleNamespace(name=f"{stem}.jpg"))
    assert len(list(Path(c.processed_images_dir).glob("*.jpg"))) == 0


@pytest.mark.resilience
def test_rt_aug_03_narrow_bbox_fewer_or_eight_variants(
    tmp_path, monkeypatch, fixture_images_dir
):
    _patch_augmentation_paths(monkeypatch, tmp_path)
    _seed()
    from augmentation import Augmentator
    from dto.imageLabel import ImageLabel

    stem = "narrow_bbox"
    proc_img = Path(tmp_path) / "azaion" / "data-processed" / "images" / f"{stem}.jpg"
    proc_lbl = Path(tmp_path) / "azaion" / "data-processed" / "labels" / f"{stem}.txt"
    proc_img.parent.mkdir(parents=True, exist_ok=True)
    proc_lbl.parent.mkdir(parents=True, exist_ok=True)
    src_img = sorted(fixture_images_dir.glob("*.jpg"))[0]
    img = cv2.imdecode(np.fromfile(str(src_img), dtype=np.uint8), cv2.IMREAD_COLOR)
    aug = Augmentator()
    labels = [[0.5, 0.5, 0.0005, 0.0005, 0]]
    img_ann = ImageLabel(
        image_path=str(proc_img),
        image=img,
        labels_path=str(proc_lbl),
        labels=labels,
    )
    out = aug.augment_inner(img_ann)
    assert 1 <= len(out) <= 8


@pytest.mark.resource_limit
def test_rl_aug_01_augment_inner_exactly_eight_outputs(
    tmp_path, monkeypatch, fixture_images_dir, fixture_labels_dir
):
    _patch_augmentation_paths(monkeypatch, tmp_path)
    _seed()
    from augmentation import Augmentator
    from dto.imageLabel import ImageLabel

    stem = sorted(fixture_images_dir.glob("*.jpg"))[0].stem
    img_path = fixture_images_dir / f"{stem}.jpg"
    lbl_path = fixture_labels_dir / f"{stem}.txt"
    img = cv2.imdecode(np.fromfile(str(img_path), dtype=np.uint8), cv2.IMREAD_COLOR)
    aug = Augmentator()
    labels = aug.read_labels(lbl_path)
    proc_img = Path(tmp_path) / "azaion" / "data-processed" / "images" / f"{stem}.jpg"
    proc_lbl = Path(tmp_path) / "azaion" / "data-processed" / "labels" / f"{stem}.txt"
    proc_img.parent.mkdir(parents=True, exist_ok=True)
    proc_lbl.parent.mkdir(parents=True, exist_ok=True)
    img_ann = ImageLabel(
        image_path=str(proc_img),
        image=img,
        labels_path=str(proc_lbl),
        labels=labels,
    )
    out = aug.augment_inner(img_ann)
    assert len(out) == 8