Benjamin_Loison/Robust_image_source_identification_on_modern_smartphones
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Load training images to memory to ease implementation and make execution

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This commit is contained in:
Benjamin Loison
2024-05-13 16:26:58 +02:00
parent 54178c1101
commit 86093a40c0
1 changed files with 12 additions and 5 deletions
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17
datasets/raise/attribute_source_camera.py
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@@ -102,6 +102,17 @@ from utils import silentTqdm
returnSingleColorChannelImage = lambda singleColorChannelImage, _minColor, _maxColor: singleColorChannelImage
# Assume to have `{min,max}Color` hardcoded.
print('Load training images to memory')
rescaleIfNeeded = rescaleRawImageForDenoiser
cameraTrainingImages = {}
for cameraTrainingImageIndex in tqdm(range(numberOfTrainingImages), 'Camera training image index'):
for cameraIndex, camera in enumerate(tqdm(IMAGES_CAMERAS_FOLDER, 'Camera')):
singleColorChannelImages = getSingleColorChannelImages(camera, cameraTrainingImageIndex)
multipleColorsImage = getMultipleColorsImage(singleColorChannelImages)
cameraTrainingImages[camera] = cameraTrainingImages.get(camera, []) + [multipleColorsImage]
print('Training images loaded to memory')
# 2 loops:
# - the first one is about computing `{min,max}Color`
# - the second one is about estimating better and better the PRNU of each camera, as consider more and more training images and measuring the resulting attribution of cameras
@@ -143,11 +154,7 @@ for computeExtremes in tqdm(([True] if minColor is None or maxColor is None else
cameraIterativeMean.add(imagePrnuEstimateNpArray)
else:
# Still use `cameraIterativeMean` to simplify the implementation.
cameraIterativeMean.mean = np.mean([cameraTrainingImage - mergeSingleColorChannelImagesAccordingToBayerFilter({color: cameraColorMeans[camera][color].mean for color in Color}) for cameraTrainingImage in [multipleColorsImage]], axis = 0)#[:cameraTrainingImageIndex + 1]])
print(f'{cameraIterativeMean.mean = }')
print(f'{camerasIterativeMean[camera].mean = }')
if cameraIterativeMean.mean[0, 0] != 0:
exit(1)
cameraIterativeMean.mean = np.mean([cameraTrainingImage - mergeSingleColorChannelImagesAccordingToBayerFilter({color: cameraColorMeans[camera][color].mean for color in Color}) for cameraTrainingImage in cameraTrainingImages[camera][:cameraTrainingImageIndex + 1]], axis = 0)
# 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