lama_cleaner/tests/test_model.py

from pathlib import Path

import cv2
import pytest
import torch

from lama_cleaner.model_manager import ModelManager
from lama_cleaner.schema import Config, HDStrategy, LDMSampler

current_dir = Path(__file__).parent.absolute().resolve()
save_dir = current_dir / 'result'
save_dir.mkdir(exist_ok=True, parents=True)
device = 'cuda' if torch.cuda.is_available() else 'cpu'
device = torch.device(device)


def get_data(fx: float = 1, fy: float = 1.0, img_p=current_dir / "image.png", mask_p=current_dir / "mask.png"):
    img = cv2.imread(str(img_p))
    img = cv2.cvtColor(img, cv2.COLOR_BGRA2RGB)
    mask = cv2.imread(str(mask_p), cv2.IMREAD_GRAYSCALE)
    img = cv2.resize(img, None, fx=fx, fy=fy, interpolation=cv2.INTER_AREA)
    mask = cv2.resize(mask, None, fx=fx, fy=fy, interpolation=cv2.INTER_NEAREST)
    return img, mask


def get_config(strategy, **kwargs):
    data = dict(
        ldm_steps=1,
        ldm_sampler=LDMSampler.plms,
        hd_strategy=strategy,
        hd_strategy_crop_margin=32,
        hd_strategy_crop_trigger_size=200,
        hd_strategy_resize_limit=200,
    )
    data.update(**kwargs)
    return Config(**data)


def assert_equal(model, config, gt_name,
                 fx: float = 1, fy: float = 1,
                 img_p=current_dir / "image.png",
                 mask_p=current_dir / "mask.png"):
    img, mask = get_data(fx=fx, fy=fy, img_p=img_p, mask_p=mask_p)
    print(f"Input image shape: {img.shape}")
    res = model(img, mask, config)
    cv2.imwrite(
        str(save_dir / gt_name),
        res,
        [int(cv2.IMWRITE_JPEG_QUALITY), 100, int(cv2.IMWRITE_PNG_COMPRESSION), 0],
    )

    """
    Note that JPEG is lossy compression, so even if it is the highest quality 100, 
    when the saved images is reloaded, a difference occurs with the original pixel value. 
    If you want to save the original images as it is, save it as PNG or BMP.
    """
    # gt = cv2.imread(str(current_dir / gt_name), cv2.IMREAD_UNCHANGED)
    # assert np.array_equal(res, gt)


@pytest.mark.parametrize(
    "strategy", [HDStrategy.ORIGINAL, HDStrategy.RESIZE, HDStrategy.CROP]
)
def test_lama(strategy):
    model = ModelManager(name="lama", device=device)
    assert_equal(
        model,
        get_config(strategy),
        f"lama_{strategy[0].upper() + strategy[1:]}_result.png",
    )

    fx = 1.3
    assert_equal(
        model,
        get_config(strategy),
        f"lama_{strategy[0].upper() + strategy[1:]}_fx_{fx}_result.png",
        fx=1.3,
    )


@pytest.mark.parametrize(
    "strategy", [HDStrategy.ORIGINAL, HDStrategy.RESIZE, HDStrategy.CROP]
)
@pytest.mark.parametrize("ldm_sampler", [LDMSampler.ddim, LDMSampler.plms])
def test_ldm(strategy, ldm_sampler):
    model = ModelManager(name="ldm", device=device)
    cfg = get_config(strategy, ldm_sampler=ldm_sampler)
    assert_equal(
        model, cfg, f"ldm_{strategy[0].upper() + strategy[1:]}_{ldm_sampler}_result.png"
    )

    fx = 1.3
    assert_equal(
        model,
        cfg,
        f"ldm_{strategy[0].upper() + strategy[1:]}_{ldm_sampler}_fx_{fx}_result.png",
        fx=fx,
    )


@pytest.mark.parametrize(
    "strategy", [HDStrategy.ORIGINAL, HDStrategy.RESIZE, HDStrategy.CROP]
)
@pytest.mark.parametrize("zits_wireframe", [False, True])
def test_zits(strategy, zits_wireframe):
    model = ModelManager(name="zits", device=device)
    cfg = get_config(strategy, zits_wireframe=zits_wireframe)
    # os.environ['ZITS_DEBUG_LINE_PATH'] = str(current_dir / 'zits_debug_line.jpg')
    # os.environ['ZITS_DEBUG_EDGE_PATH'] = str(current_dir / 'zits_debug_edge.jpg')
    assert_equal(
        model,
        cfg,
        f"zits_{strategy[0].upper() + strategy[1:]}_wireframe_{zits_wireframe}_result.png",
    )

    fx = 1.3
    assert_equal(
        model,
        cfg,
        f"zits_{strategy.capitalize()}_wireframe_{zits_wireframe}_fx_{fx}_result.png",
        fx=fx,
    )


@pytest.mark.parametrize(
    "strategy", [HDStrategy.ORIGINAL]
)
def test_mat(strategy):
    model = ModelManager(name="mat", device=device)
    cfg = get_config(strategy)

    assert_equal(
        model,
        cfg,
        f"mat_{strategy.capitalize()}_result.png",
    )


@pytest.mark.parametrize(
    "strategy", [HDStrategy.ORIGINAL]
)
def test_fcf(strategy):
    model = ModelManager(name="fcf", device=device)
    cfg = get_config(strategy)

    assert_equal(
        model,
        cfg,
        f"fcf_{strategy.capitalize()}_result.png",
        fx=2,
        fy=2
    )

    assert_equal(
        model,
        cfg,
        f"fcf_{strategy.capitalize()}_result.png",
        fx=3.8,
        fy=2
    )


@pytest.mark.parametrize(
    "strategy", [HDStrategy.ORIGINAL, HDStrategy.RESIZE, HDStrategy.CROP]
)
@pytest.mark.parametrize("cv2_flag", ['INPAINT_NS', 'INPAINT_TELEA'])
@pytest.mark.parametrize("cv2_radius", [3, 15])
def test_cv2(strategy, cv2_flag, cv2_radius):
    model = ModelManager(
        name="cv2",
        device=torch.device(device),
    )
    cfg = get_config(strategy, cv2_flag=cv2_flag, cv2_radius=cv2_radius)
    assert_equal(
        model,
        cfg,
        f"sd_{strategy.capitalize()}_{cv2_flag}_{cv2_radius}.png",
        img_p=current_dir / "overture-creations-5sI6fQgYIuo.png",
        mask_p=current_dir / "overture-creations-5sI6fQgYIuo_mask.png",
    )


@pytest.mark.parametrize("strategy", [HDStrategy.ORIGINAL, HDStrategy.RESIZE, HDStrategy.CROP])
def test_manga(strategy):
    model = ModelManager(
        name="manga",
        device=torch.device(device),
    )
    cfg = get_config(strategy)
    assert_equal(
        model,
        cfg,
        f"sd_{strategy.capitalize()}.png",
        img_p=current_dir / "overture-creations-5sI6fQgYIuo.png",
        mask_p=current_dir / "overture-creations-5sI6fQgYIuo_mask.png",
    )