78 lines
2.4 KiB
Python
78 lines
2.4 KiB
Python
from pathlib import Path
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import cv2
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import pytest
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import torch
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from iopaint.schema import LDMSampler, HDStrategy, InpaintRequest, SDSampler
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import numpy as np
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current_dir = Path(__file__).parent.absolute().resolve()
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save_dir = current_dir / "result"
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save_dir.mkdir(exist_ok=True, parents=True)
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def check_device(device: str) -> int:
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if device == "cuda" and not torch.cuda.is_available():
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pytest.skip("CUDA is not available, skip test on cuda")
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if device == "mps" and not torch.backends.mps.is_available():
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pytest.skip("mps is not available, skip test on mps")
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steps = 2 if device == "cpu" else 20
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return steps
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def assert_equal(
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model,
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config: InpaintRequest,
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gt_name,
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fx: float = 1,
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fy: float = 1,
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img_p=current_dir / "image.png",
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mask_p=current_dir / "mask.png",
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):
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img, mask = get_data(fx=fx, fy=fy, img_p=img_p, mask_p=mask_p)
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print(f"Input image shape: {img.shape}")
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res = model(img, mask, config)
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ok = cv2.imwrite(
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str(save_dir / gt_name),
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res,
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[int(cv2.IMWRITE_JPEG_QUALITY), 100, int(cv2.IMWRITE_PNG_COMPRESSION), 0],
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)
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assert ok, save_dir / gt_name
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"""
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Note that JPEG is lossy compression, so even if it is the highest quality 100,
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when the saved images is reloaded, a difference occurs with the original pixel value.
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If you want to save the original images as it is, save it as PNG or BMP.
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"""
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# gt = cv2.imread(str(current_dir / gt_name), cv2.IMREAD_UNCHANGED)
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# assert np.array_equal(res, gt)
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def get_data(
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fx: float = 1,
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fy: float = 1.0,
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img_p=current_dir / "image.png",
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mask_p=current_dir / "mask.png",
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):
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img = cv2.imread(str(img_p))
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img = cv2.cvtColor(img, cv2.COLOR_BGRA2RGB)
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mask = cv2.imread(str(mask_p), cv2.IMREAD_GRAYSCALE)
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img = cv2.resize(img, None, fx=fx, fy=fy, interpolation=cv2.INTER_AREA)
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mask = cv2.resize(mask, None, fx=fx, fy=fy, interpolation=cv2.INTER_NEAREST)
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return img, mask
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def get_config(**kwargs):
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data = dict(
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sd_sampler=kwargs.get("sd_sampler", SDSampler.uni_pc),
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ldm_steps=1,
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ldm_sampler=LDMSampler.plms,
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hd_strategy=kwargs.get("strategy", HDStrategy.ORIGINAL),
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hd_strategy_crop_margin=32,
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hd_strategy_crop_trigger_size=200,
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hd_strategy_resize_limit=200,
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)
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data.update(**kwargs)
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return InpaintRequest(image="", mask="", **data)
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