Benjamin_Loison/Robust_image_source_identification_on_modern_smartphones
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Rename means to colorMeans

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To be clearer for future `attribute_source_camera.py` usage.
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Benjamin Loison 2024-05-03 02:13:53 +02:00
parent d8c7b8b0ef
commit 01d18a4943
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1 changed files with 3 additions and 3 deletions
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6
datasets/raise/extract_noise.py
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@ -75,7 +75,7 @@ def treatImage(imageFileName, computeExtremes = False, color = None):
if denoiser != 'mean':
imageDenoisedNpArray = denoise(imageNpArray, denoiser)
else:
imageDenoisedNpArray = means[color]
imageDenoisedNpArray = colorMeans[color]
imageNoiseNpArray = imageNpArray - imageDenoisedNpArray
estimatedPrnuIterativeMean.add(imageNoiseNpArray)
@ -91,14 +91,14 @@ if (minColor is None or maxColor is None) and denoiser != 'mean':
print(f'{maxColor=}')
if denoiser == 'mean':
means = {}
colorMeans = {}
for color in colors:
colorIterativeMean = iterativeMean()
for imageFileName in tqdm(imagesFileNames, f'Computing mean of {color} colored images'):
imageNpArray = getImageNpArray(imageFileName, False, color)
imageNpArray = gaussian_filter(imageNpArray, sigma = 5)
colorIterativeMean.add(imageNpArray)
means[color] = colorIterativeMean.mean
colorMeans[color] = colorIterativeMean.mean
fileName = f'mean_{imagesFolderPathFileName}_{color}'
# Then use `merge_single_color_channel_images_according_to_bayer_filter.py` to consider all color channels, instead of saving this single color channel as an image.
saveNpArray(fileName, colorIterativeMean.mean)