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import cv2
from facexlib.utils.face_restoration_helper import FaceRestoreHelper
from gfpgan.archs.gfpganv1_arch import GFPGANv1
from gfpgan.archs.gfpganv1_clean_arch import GFPGANv1Clean
from gfpgan.utils import GFPGANer
def test_gfpganer():
# initialize with the clean model
restorer = GFPGANer(
model_path='experiments/pretrained_models/GFPGANCleanv1-NoCE-C2.pth',
upscale=2,
arch='clean',
channel_multiplier=2,
bg_upsampler=None)
# test attribute
assert isinstance(restorer.gfpgan, GFPGANv1Clean)
assert isinstance(restorer.face_helper, FaceRestoreHelper)
# initialize with the original model
restorer = GFPGANer(
model_path='experiments/pretrained_models/GFPGANv1.pth',
upscale=2,
arch='original',
channel_multiplier=1,
bg_upsampler=None)
# test attribute
assert isinstance(restorer.gfpgan, GFPGANv1)
assert isinstance(restorer.face_helper, FaceRestoreHelper)
# ------------------ test enhance ---------------- #
img = cv2.imread('tests/data/gt/00000000.png', cv2.IMREAD_COLOR)
result = restorer.enhance(img, has_aligned=False, paste_back=True)
assert result[0][0].shape == (512, 512, 3)
assert result[1][0].shape == (512, 512, 3)
assert result[2].shape == (1024, 1024, 3)
# with has_aligned=True
result = restorer.enhance(img, has_aligned=True, paste_back=False)
assert result[0][0].shape == (512, 512, 3)
assert result[1][0].shape == (512, 512, 3)
assert result[2] is None
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