Benjamin_Loison/Robust_image_source_identification_on_modern_smartphones
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Add mean DENOISER support

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Benjamin Loison 2024-05-03 02:47:43 +02:00
parent 9d33abcea3
commit fd6a9868fe
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1 changed files with 11 additions and 7 deletions
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18
datasets/raise/attribute_source_camera.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, rescaleRawImageForDenoiser, updateExtremes, saveNpArray
from utils import denoise, iterativeMean, getColorChannel, escapeFilePath, Color, mergeSingleColorChannelImagesAccordingToBayerFilter, rescaleRawImageForDenoiser, updateExtremes, saveNpArray, getColorMeans
import sys
import os
import random
@ -12,7 +12,7 @@ sys.path.insert(0, '../../algorithms/distance/')
from rms_diff import rmsDiffNumpy
DENOISER = 'wavelet'
DENOISER = 'mean'
IMAGES_CAMERAS_FOLDER = {
'RAISE': 'flat-field/nef',
'Rafael 23/04/24': 'rafael/230424',
@ -27,7 +27,7 @@ random.seed(0)
for camera in IMAGES_CAMERAS_FOLDER:
random.shuffle(imagesCamerasFileNames[camera])
minimumNumberOfImagesCameras = 16#min([len(imagesCamerasFileNames[camera]) for camera in IMAGES_CAMERAS_FOLDER])
minimumNumberOfImagesCameras = min([len(imagesCamerasFileNames[camera]) for camera in IMAGES_CAMERAS_FOLDER])
for camera in IMAGES_CAMERAS_FOLDER:
imagesCamerasFileNames[camera] = imagesCamerasFileNames[camera][:minimumNumberOfImagesCameras]
print(camera, imagesCamerasFileNames[camera])
@ -67,12 +67,16 @@ def getMultipleColorsImage(singleColorChannelImages):
multipleColorsImage = mergeSingleColorChannelImagesAccordingToBayerFilter(singleColorChannelImages)
return multipleColorsImage
def getImagePrnuEstimateNpArray(singleColorChannelImages, multipleColorsImage):
singleColorChannelDenoisedImages = {color: denoise(singleColorChannelImages[color], DENOISER) for color in Color}
def getImagePrnuEstimateNpArray(singleColorChannelImages, multipleColorsImage, camera):
singleColorChannelDenoisedImages = {color: denoise(singleColorChannelImages[color], DENOISER) if DENOISER != 'mean' else cameraColorMeans[camera][color] for color in Color}
multipleColorsDenoisedImage = mergeSingleColorChannelImagesAccordingToBayerFilter(singleColorChannelDenoisedImages)
imagePrnuEstimateNpArray = multipleColorsImage - multipleColorsDenoisedImage
return imagePrnuEstimateNpArray
imagesCamerasFilePaths = {camera: [f'{IMAGES_CAMERAS_FOLDER[camera]}/{imagesCamerasFileName}' for imagesCamerasFileName in imagesCamerasFileNames[camera]] for camera in imagesCamerasFileNames}
#print(imagesCamerasFilePaths)
cameraColorMeans = {camera: getColorMeans(imagesCamerasFilePaths[camera], Color, DENOISER) for camera in imagesCamerasFilePaths}
from utils import silentTqdm
#tqdm = silentTqdm
@ -90,7 +94,7 @@ for computeExtremes in tqdm(([True] if minColor is None or maxColor is None else
singleColorChannelImages = getSingleColorChannelImages(camera, numberOfTrainingImages + cameraTestingImageIndex)
multipleColorsImage = getMultipleColorsImage(singleColorChannelImages)
imagePrnuEstimateNpArray = getImagePrnuEstimatedNpArray(singleColorChannelImages, multipleColorsImage)
imagePrnuEstimateNpArray = getImagePrnuEstimateNpArray(singleColorChannelImages, multipleColorsImage, camera)
cameraTestingImagesNoise[camera] = cameraTestingImagesNoise.get(camera, []) + [imagePrnuEstimateNpArray]
for cameraTrainingImageIndex in tqdm(range(minimumNumberOfImagesCameras if computeExtremes else numberOfTrainingImages), 'Camera training image index'):
@ -102,7 +106,7 @@ for computeExtremes in tqdm(([True] if minColor is None or maxColor is None else
minColor, maxColor = updateExtremes(multipleColorsImage, minColor, maxColor)
continue
imagePrnuEstimateNpArray = getImagePrnuEstimatedNpArray(singleColorChannelImages, multipleColorsImage)
imagePrnuEstimateNpArray = getImagePrnuEstimateNpArray(singleColorChannelImages, multipleColorsImage, camera)
cameraIterativeMean = camerasIterativeMean[camera]
cameraIterativeMean.add(imagePrnuEstimateNpArray)