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| import numpy as np | |
| from basicsr.archs.rrdbnet_arch import RRDBNet | |
| from realesrgan.utils import RealESRGANer | |
| def test_realesrganer(): | |
| # initialize with default model | |
| restorer = RealESRGANer( | |
| scale=4, | |
| model_path='experiments/pretrained_models/RealESRGAN_x4plus.pth', | |
| model=None, | |
| tile=10, | |
| tile_pad=10, | |
| pre_pad=2, | |
| half=False) | |
| assert isinstance(restorer.model, RRDBNet) | |
| assert restorer.half is False | |
| # initialize with user-defined model | |
| model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4) | |
| restorer = RealESRGANer( | |
| scale=4, | |
| model_path='experiments/pretrained_models/RealESRGAN_x4plus_anime_6B.pth', | |
| model=model, | |
| tile=10, | |
| tile_pad=10, | |
| pre_pad=2, | |
| half=True) | |
| # test attribute | |
| assert isinstance(restorer.model, RRDBNet) | |
| assert restorer.half is True | |
| # ------------------ test pre_process ---------------- # | |
| img = np.random.random((12, 12, 3)).astype(np.float32) | |
| restorer.pre_process(img) | |
| assert restorer.img.shape == (1, 3, 14, 14) | |
| # with modcrop | |
| restorer.scale = 1 | |
| restorer.pre_process(img) | |
| assert restorer.img.shape == (1, 3, 16, 16) | |
| # ------------------ test process ---------------- # | |
| restorer.process() | |
| assert restorer.output.shape == (1, 3, 64, 64) | |
| # ------------------ test post_process ---------------- # | |
| restorer.mod_scale = 4 | |
| output = restorer.post_process() | |
| assert output.shape == (1, 3, 60, 60) | |
| # ------------------ test tile_process ---------------- # | |
| restorer.scale = 4 | |
| img = np.random.random((12, 12, 3)).astype(np.float32) | |
| restorer.pre_process(img) | |
| restorer.tile_process() | |
| assert restorer.output.shape == (1, 3, 64, 64) | |
| # ------------------ test enhance ---------------- # | |
| img = np.random.random((12, 12, 3)).astype(np.float32) | |
| result = restorer.enhance(img, outscale=2) | |
| assert result[0].shape == (24, 24, 3) | |
| assert result[1] == 'RGB' | |
| # ------------------ test enhance with 16-bit image---------------- # | |
| img = np.random.random((4, 4, 3)).astype(np.uint16) + 512 | |
| result = restorer.enhance(img, outscale=2) | |
| assert result[0].shape == (8, 8, 3) | |
| assert result[1] == 'RGB' | |
| # ------------------ test enhance with gray image---------------- # | |
| img = np.random.random((4, 4)).astype(np.float32) | |
| result = restorer.enhance(img, outscale=2) | |
| assert result[0].shape == (8, 8) | |
| assert result[1] == 'L' | |
| # ------------------ test enhance with RGBA---------------- # | |
| img = np.random.random((4, 4, 4)).astype(np.float32) | |
| result = restorer.enhance(img, outscale=2) | |
| assert result[0].shape == (8, 8, 4) | |
| assert result[1] == 'RGBA' | |
| # ------------------ test enhance with RGBA, alpha_upsampler---------------- # | |
| restorer.tile_size = 0 | |
| img = np.random.random((4, 4, 4)).astype(np.float32) | |
| result = restorer.enhance(img, outscale=2, alpha_upsampler=None) | |
| assert result[0].shape == (8, 8, 4) | |
| assert result[1] == 'RGBA' | |