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import glob |
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import numpy |
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import PIL |
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import PIL.Image |
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import skimage |
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import skimage.metrics |
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import torch |
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import run |
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run.args_strModel = 'l1' |
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if __name__ == '__main__': |
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fltPsnr = [] |
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fltSsim = [] |
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for strTruth in sorted(glob.glob('./middlebury/*/frame10i11.png')): |
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tenOne = torch.FloatTensor(numpy.ascontiguousarray(numpy.array(PIL.Image.open(strTruth.replace('frame10i11', 'frame10')))[:, :, ::-1].transpose(2, 0, 1).astype(numpy.float32) * (1.0 / 255.0))) |
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tenTwo = torch.FloatTensor(numpy.ascontiguousarray(numpy.array(PIL.Image.open(strTruth.replace('frame10i11', 'frame11')))[:, :, ::-1].transpose(2, 0, 1).astype(numpy.float32) * (1.0 / 255.0))) |
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npyEstimate = (run.estimate(tenOne, tenTwo, [0.5])[0].clip(0.0, 1.0).numpy().transpose(1, 2, 0) * 255.0).round().astype(numpy.uint8) |
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fltPsnr.append(skimage.metrics.peak_signal_noise_ratio(image_true=numpy.array(PIL.Image.open(strTruth))[:, :, ::-1], image_test=npyEstimate, data_range=255)) |
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fltSsim.append(skimage.metrics.structural_similarity(im1=numpy.array(PIL.Image.open(strTruth))[:, :, ::-1], im2=npyEstimate, data_range=255, channel_axis=2)) |
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print('computed average psnr', numpy.mean(fltPsnr)) |
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print('computed average ssim', numpy.mean(fltSsim)) |
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