Benjamin_Loison/Robust_image_source_identification_on_modern_smartphones
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Add and use rescaleRawImageForDenoiser and updateExtremes (#59)

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Benjamin Loison
2024-04-30 02:04:17 +02:00
parent 671a692114
commit 2d67c2bca1
3 changed files with 44 additions and 25 deletions
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45
datasets/raise/split_and_compare_prnus_of_subgroups.py
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@@ -3,7 +3,7 @@
import numpy as np
import matplotlib.pyplot as plt
from tqdm import tqdm
from utils import denoise, iterativeMean, getColorChannel, escapeFilePath, Color, mergeSingleColorChannelImagesAccordingToBayerFilter
from utils import denoise, iterativeMean, getColorChannel, escapeFilePath, Color, mergeSingleColorChannelImagesAccordingToBayerFilter, rescaleRawImageForDenoiser, updateExtremes
import sys
import os
from random import shuffle
@@ -26,24 +26,35 @@ numberOfImagesPerSubgroup = len(imagesFileNames) // NUMBER_OF_SUBGROUPS
subgroupsIterativeMean = [iterativeMean() for _ in range(NUMBER_OF_SUBGROUPS)]
rmss = []
for subgroupImageIndex in tqdm(range(numberOfImagesPerSubgroup), 'Subgroup image index'):
for subgroupIndex in tqdm(range(NUMBER_OF_SUBGROUPS), 'Subgroup'):
imageIndex = (subgroupIndex * NUMBER_OF_SUBGROUPS) + subgroupImageIndex
imageFileName = imagesFileNames[imageIndex]
imageFilePath = f'{IMAGES_FOLDER}/{imageFileName}'
singleColorChannelImages = {color: getColorChannel(imageFilePath, color) for color in Color}
multipleColorsImage = mergeSingleColorChannelImagesAccordingToBayerFilter(singleColorChannelImages)
singleColorChannelDenoisedImages = {color: denoise(singleColorChannelImages[color], DENOISER) for color in Color}
multipleColorsDenoisedImage = mergeSingleColorChannelImagesAccordingToBayerFilter(singleColorChannelDenoisedImages)
minColor = None
maxColor = None
imagePrnuEstimateNpArray = multipleColorsImage - multipleColorsDenoisedImage
subgroupIterativeMean = subgroupsIterativeMean[subgroupIndex]
subgroupIterativeMean.add(imagePrnuEstimateNpArray)
if subgroupIndex == NUMBER_OF_SUBGROUPS - 1:
assert NUMBER_OF_SUBGROUPS == 2
rms = rmsDiffNumpy(subgroupIterativeMean.mean, subgroupsIterativeMean[1 - subgroupIndex].mean)
rmss += [rms]
for computeExtremes in tqdm(([True] if minColor is None or maxColor is None else []) + [False], 'Compute extremes'):
for subgroupImageIndex in tqdm(range(numberOfImagesPerSubgroup), 'Subgroup image index'):
for subgroupIndex in tqdm(range(NUMBER_OF_SUBGROUPS), 'Subgroup'):
imageIndex = (subgroupIndex * NUMBER_OF_SUBGROUPS) + subgroupImageIndex
imageFileName = imagesFileNames[imageIndex]
imageFilePath = f'{IMAGES_FOLDER}/{imageFileName}'
returnSingleColorChannelImage = lambda singleColorChannelImage, _minColor, _maxColor: singleColorChannelImage
rescaleIfNeeded = returnSingleColorChannelImage if computeExtremes else rescaleRawImageForDenoiser
singleColorChannelImages = {color: rescaleIfNeeded(getColorChannel(imageFilePath, color), minColor, maxColor) for color in Color}
multipleColorsImage = mergeSingleColorChannelImagesAccordingToBayerFilter(singleColorChannelImages)
if computeExtremes:
minColor, maxColor = updateExtremes(multipleColorsImage, minColor, maxColor)
continue
singleColorChannelDenoisedImages = {color: denoise(singleColorChannelImages[color], DENOISER) for color in Color}
multipleColorsDenoisedImage = mergeSingleColorChannelImagesAccordingToBayerFilter(singleColorChannelDenoisedImages)
imagePrnuEstimateNpArray = multipleColorsImage - multipleColorsDenoisedImage
subgroupIterativeMean = subgroupsIterativeMean[subgroupIndex]
subgroupIterativeMean.add(imagePrnuEstimateNpArray)
if subgroupIndex == NUMBER_OF_SUBGROUPS - 1:
assert NUMBER_OF_SUBGROUPS == 2
rms = rmsDiffNumpy(subgroupIterativeMean.mean, subgroupsIterativeMean[1 - subgroupIndex].mean)
rmss += [rms]
mostImagesSubgroupPrnuEstimatesNpArray = [subgroupIterativeMean.mean for subgroupIterativeMean in subgroupsIterativeMean]
minimum = np.min(mostImagesSubgroupPrnuEstimatesNpArray)