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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, SDSampler
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])
@pytest.mark.parametrize("no_half", [True, False])
def test_mat(strategy, no_half):
model = ModelManager(name="mat", device=device, no_half=no_half)
cfg = get_config(strategy)
for _ in range(10):
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",
)
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