Benjamin_Loison/Robust_image_source_identification_on_modern_smartphones
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Remove commented code

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Benjamin Loison
2024-05-13 15:21:30 +02:00
parent 31704c6e78
commit dbfc2756b2
1 changed files with 0 additions and 14 deletions
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14
datasets/raise/attribute_source_camera.py
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@@ -74,7 +74,6 @@ def getMultipleColorsImage(singleColorChannelImages):
return multipleColorsImage return multipleColorsImage
def getImagePrnuEstimateNpArray(singleColorChannelImages, multipleColorsImage, camera): def getImagePrnuEstimateNpArray(singleColorChannelImages, multipleColorsImage, camera):
#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 = {} singleColorChannelDenoisedImages = {}
for color in Color: for color in Color:
if DENOISER != Denoiser.MEAN: if DENOISER != Denoiser.MEAN:
@@ -85,15 +84,6 @@ def getImagePrnuEstimateNpArray(singleColorChannelImages, multipleColorsImage, c
singleColorChannelDenoisedImage = cameraColorMean singleColorChannelDenoisedImage = cameraColorMean
else: else:
cameraColorCurrentMean = cameraColorMean.mean 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 singleColorChannelDenoisedImage = cameraColorCurrentMean
singleColorChannelDenoisedImages[color] = singleColorChannelDenoisedImage singleColorChannelDenoisedImages[color] = singleColorChannelDenoisedImage
multipleColorsDenoisedImage = mergeSingleColorChannelImagesAccordingToBayerFilter(singleColorChannelDenoisedImages) multipleColorsDenoisedImage = mergeSingleColorChannelImagesAccordingToBayerFilter(singleColorChannelDenoisedImages)
@@ -149,10 +139,7 @@ for computeExtremes in tqdm(([True] if minColor is None or maxColor is None else
imagePrnuEstimateNpArray = getImagePrnuEstimateNpArray(singleColorChannelImages, multipleColorsImage, camera) imagePrnuEstimateNpArray = getImagePrnuEstimateNpArray(singleColorChannelImages, multipleColorsImage, camera)
cameraIterativeMean = camerasIterativeMean[camera] cameraIterativeMean = camerasIterativeMean[camera]
#print(f'{imagePrnuEstimateNpArray=}')
#exit(2)
cameraIterativeMean.add(imagePrnuEstimateNpArray) 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 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): if cameraIndex == numberOfCameras - 1 and (not PREDICT_ONLY_ON_WHOLE_TRAINING_SET or cameraTrainingImageIndex == numberOfTrainingImages - 1):
numberOfTrainingImagesAccuracy = 0 numberOfTrainingImagesAccuracy = 0
@@ -172,7 +159,6 @@ for computeExtremes in tqdm(([True] if minColor is None or maxColor is None else
multipleColorsImage = getMultipleColorsImage(singleColorChannelImages) multipleColorsImage = getMultipleColorsImage(singleColorChannelImages)
cameraTestingImageNoise = getImagePrnuEstimateNpArray(singleColorChannelImages, multipleColorsImage, camera) cameraTestingImageNoise = getImagePrnuEstimateNpArray(singleColorChannelImages, multipleColorsImage, camera)
#print(f'{cameraTestingImageNoise = }')
distance = rmsDiffNumpy(cameraTestingImageNoise, camerasIterativeMean[camera].mean) distance = rmsDiffNumpy(cameraTestingImageNoise, camerasIterativeMean[camera].mean)
print(f'{cameraTrainingImageIndex=} {cameraTestingImageIndex=} {camera=} {actualCamera=} {distance=}') print(f'{cameraTrainingImageIndex=} {cameraTestingImageIndex=} {camera=} {actualCamera=} {distance=}')
if minimalDistance is None or distance < minimalDistance: if minimalDistance is None or distance < minimalDistance: