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19 lines
670 B
Python
19 lines
670 B
Python
import numpy as np
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import piq # type: ignore
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import torch
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from PIL import Image
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def compare_images(img_1: Image.Image, img_2: Image.Image) -> tuple[int, float]:
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x1, x2 = (
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torch.tensor(np.array(x).astype(np.float32)).permute(2, 0, 1).unsqueeze(0) / 255.0 for x in (img_1, img_2)
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)
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return (piq.psnr(x1, x2), piq.ssim(x1, x2).item()) # type: ignore
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def ensure_similar_images(img_1: Image.Image, img_2: Image.Image, min_psnr: int = 45, min_ssim: float = 0.99):
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psnr, ssim = compare_images(img_1, img_2)
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assert (psnr >= min_psnr) and (
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ssim >= min_ssim
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), f"PSNR {psnr} / SSIM {ssim}, expected at least {min_psnr} / {min_ssim}"
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