Benjamin_Loison/Robust_image_source_identification_on_modern_smartphones
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#72: Verify pointer before actually implementing the correct second choice

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Benjamin Loison 2024-05-13 15:55:59 +02:00
parent dbfc2756b2
commit 50058d5d2e
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1 changed files with 9 additions and 1 deletions
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datasets/raise/attribute_source_camera.py
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@ -139,7 +139,15 @@ for computeExtremes in tqdm(([True] if minColor is None or maxColor is None else
imagePrnuEstimateNpArray = getImagePrnuEstimateNpArray(singleColorChannelImages, multipleColorsImage, camera)
cameraIterativeMean = camerasIterativeMean[camera]
cameraIterativeMean.add(imagePrnuEstimateNpArray)
if DENOISER != Denoiser.MEAN or PREDICT_ONLY_ON_WHOLE_TRAINING_SET:
cameraIterativeMean.add(imagePrnuEstimateNpArray)
else:
# Still use `cameraIterativeMean` to simplify the implementation.
# TODO: cameraIterativeMean.mean = mean([image_training_j_camera - cameraIterativeMean.mean for j in range(l)])
cameraIterativeMean.mean = 42
print(f'{cameraIterativeMean.mean = }')
print(f'{camerasIterativeMean[camera].mean = }')
exit(1)
# 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