Add PRNU estimation computation
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@ -8,21 +8,23 @@ sys.path.insert(0, '../')
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from utils import isARawImage, Color, getColorChannel, mergeSingleColorChannelImagesAccordingToBayerFilter, iterativeMean
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#images = []
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folder = '../rafael/230424'
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meanImages = iterativeMean()
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filePaths = [f'{folder}/{file}' for file in os.listdir(folder) if isARawImage(file)]
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for file in tqdm(os.listdir(folder)):
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if isARawImage(file):
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#print(file)
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filePath = f'{folder}/{file}'
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imageColorChannels = {color: getColorChannel(filePath, color) for color in Color}
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imageMergedColorChannels = mergeSingleColorChannelImagesAccordingToBayerFilter(imageColorChannels)
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#images += [imageMergedColorChannels]
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meanImages.add(imageMergedColorChannels)
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for filePath in tqdm(filePaths):
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imageColorChannels = {color: getColorChannel(filePath, color) for color in Color}
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imageMergedColorChannels = mergeSingleColorChannelImagesAccordingToBayerFilter(imageColorChannels)
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meanImages.add(imageMergedColorChannels)
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#print(np.mean(images, axis = 0).shape)
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print(meanImages.shape)
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#print(meanImages.mean.shape)
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plt.imshow()
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# Is not there anything clever than this to do? See [Robust_image_source_identification_on_modern_smartphones/issues/72](https://gitea.lemnoslife.com/Benjamin_Loison/Robust_image_source_identification_on_modern_smartphones/issues/72).
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estimatedPrnu = iterativeMean()
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for filePath in tqdm(filePaths):
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imageColorChannels = {color: getColorChannel(filePath, color) for color in Color}
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imageMergedColorChannels = mergeSingleColorChannelImagesAccordingToBayerFilter(imageColorChannels)
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estimatedPrnu.add(imageMergedColorChannels - meanImages.mean)
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plt.imshow(estimatedPrnu.mean)
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plt.show()
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