Benjamin_Loison/Robust_image_source_identification_on_modern_smartphones
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#72: Implement, possibly incorrectly, first choice

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Benjamin Loison 2024-05-13 15:20:17 +02:00
parent 5e6702e007
commit 31704c6e78
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1 changed files with 39 additions and 5 deletions
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datasets/raise/attribute_source_camera.py
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@ -7,7 +7,6 @@ from utils import denoise, iterativeMean, getColorChannel, escapeFilePath, Color
import sys
import os
import random
import scipy
sys.path.insert(0, '../../algorithms/distance/')
@ -75,7 +74,28 @@ def getMultipleColorsImage(singleColorChannelImages):
return multipleColorsImage
def getImagePrnuEstimateNpArray(singleColorChannelImages, multipleColorsImage, camera):
singleColorChannelDenoisedImages = {color: denoise(singleColorChannelImages[color], DENOISER) if DENOISER != Denoiser.MEAN else cameraColorMeans[camera][color] for color in Color}
#singleColorChannelDenoisedImages = {color: denoise(singleColorChannelImages[color], DENOISER) if DENOISER != Denoiser.MEAN else (cameraColorMeans[camera][color] if PREDICT_ONLY_ON_WHOLE_TRAINING_SET else (cameraColorMeans[camera][color].mean if )) for color in Color}
singleColorChannelDenoisedImages = {}
for color in Color:
if DENOISER != Denoiser.MEAN:
singleColorChannelDenoisedImage = denoise(singleColorChannelImages[color], DENOISER)
else:
cameraColorMean = cameraColorMeans[camera][color]
if PREDICT_ONLY_ON_WHOLE_TRAINING_SET:
singleColorChannelDenoisedImage = cameraColorMean
else:
cameraColorCurrentMean = cameraColorMean.mean
if cameraColorCurrentMean is None:
print('`cameraColorCurrentMean` is `None`!')
exit(2)
'''
if cameraColorCurrentMean is None:
singleColorChannelDenoisedImage = singleColorChannelImages[color]
else:
singleColorChannelDenoisedImage = cameraColorCurrentMean
'''
singleColorChannelDenoisedImage = cameraColorCurrentMean
singleColorChannelDenoisedImages[color] = singleColorChannelDenoisedImage
multipleColorsDenoisedImage = mergeSingleColorChannelImagesAccordingToBayerFilter(singleColorChannelDenoisedImages)
imagePrnuEstimateNpArray = multipleColorsImage - multipleColorsDenoisedImage
return imagePrnuEstimateNpArray
@ -85,7 +105,7 @@ imagesCamerasFilePaths = {camera: [f'{IMAGES_CAMERAS_FOLDER[camera]}/{imagesCame
# If `PREDICT_ONLY_ON_WHOLE_TRAINING_SET`, then compute the means of camera images to empower the `MEAN` denoiser.
# Otherwise initialize these means of camera images to `iterativeMean`.
if DENOISER == Denoiser.MEAN:
cameraColorMeans = {camera: (getColorMeans(imagesCamerasFilePaths[camera][:numberOfTrainingImages], Color, minimalColorChannelCameraResolution) if PREDICT_ONLY_ON_WHOLE_TRAINING_SET else iterativeMean()) for camera in imagesCamerasFilePaths}
cameraColorMeans = {camera: (getColorMeans(imagesCamerasFilePaths[camera][:numberOfTrainingImages], Color, minimalColorChannelCameraResolution) if PREDICT_ONLY_ON_WHOLE_TRAINING_SET else {color: iterativeMean() for color in Color}) for camera in imagesCamerasFilePaths}
from utils import silentTqdm
#tqdm = silentTqdm
@ -123,10 +143,16 @@ for computeExtremes in tqdm(([True] if minColor is None or maxColor is None else
minColor, maxColor = updateExtremes(multipleColorsImage, minColor, maxColor)
continue
if DENOISER == Denoiser.MEAN:
for color in Color:
cameraColorMeans[camera][color].add(singleColorChannelImages[color])
imagePrnuEstimateNpArray = getImagePrnuEstimateNpArray(singleColorChannelImages, multipleColorsImage, camera)
cameraIterativeMean = camerasIterativeMean[camera]
#print(f'{imagePrnuEstimateNpArray=}')
#exit(2)
cameraIterativeMean.add(imagePrnuEstimateNpArray)
#continue
# If we are considering the last camera and (not `PREDICT_ONLY_ON_WHOLE_TRAINING_SET` or we are considering the last training image), then we proceeded an additional image for all cameras and we can predict the accuracy at this learning step.
if cameraIndex == numberOfCameras - 1 and (not PREDICT_ONLY_ON_WHOLE_TRAINING_SET or cameraTrainingImageIndex == numberOfTrainingImages - 1):
numberOfTrainingImagesAccuracy = 0
@ -139,8 +165,16 @@ for computeExtremes in tqdm(([True] if minColor is None or maxColor is None else
#plt.imsave(f'{escapeFilePath(actualCamera)}_{cameraTestingImageIndex}.png', cameraTestingImagesNoise[actualCamera][cameraTestingImageIndex])
# Loop over each camera to compute closeness between the considered testing image noise and the estimated PRNUs of the various cameras.
for camera in IMAGES_CAMERAS_FOLDER:
distance = abs(scipy.stats.pearsonr(cameraTestingImagesNoise[actualCamera][cameraTestingImageIndex].flatten(), camerasIterativeMean[camera].mean.flatten()).statistic - 1)
print(f'{cameraTestingImageIndex=} {camera=} {actualCamera=} {distance=}')
if DENOISER != Denoiser.MEAN:
cameraTestingImageNoise = cameraTestingImagesNoise[actualCamera][cameraTestingImageIndex]
else:
singleColorChannelImages = getSingleColorChannelImages(camera, numberOfTrainingImages + cameraTestingImageIndex)
multipleColorsImage = getMultipleColorsImage(singleColorChannelImages)
cameraTestingImageNoise = getImagePrnuEstimateNpArray(singleColorChannelImages, multipleColorsImage, camera)
#print(f'{cameraTestingImageNoise = }')
distance = rmsDiffNumpy(cameraTestingImageNoise, camerasIterativeMean[camera].mean)
print(f'{cameraTrainingImageIndex=} {cameraTestingImageIndex=} {camera=} {actualCamera=} {distance=}')
if minimalDistance is None or distance < minimalDistance:
minimalDistance = distance
cameraPredicted = camera