Benjamin_Loison/Robust_image_source_identification_on_modern_smartphones
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Clean some debugging

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Benjamin Loison 2024-04-30 06:48:17 +02:00
parent 7a807b91d8
commit 53c3935bab
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1 changed files with 3 additions and 19 deletions
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22
datasets/raise/attribute_source_camera.py
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@ -29,7 +29,7 @@ for camera in IMAGES_CAMERAS_FOLDER:
minimumNumberOfImagesCameras = 16#min([len(imagesCamerasFileNames[camera]) for camera in IMAGES_CAMERAS_FOLDER])
for camera in IMAGES_CAMERAS_FOLDER:
imagesCamerasFileNames[camera] = imagesCamerasFileNames[camera][:minimumNumberOfImagesCameras]#[imagesCamerasFileNames[camera][0]] * minimumNumberOfImagesCameras#[:minimumNumberOfImagesCameras]
imagesCamerasFileNames[camera] = imagesCamerasFileNames[camera][:minimumNumberOfImagesCameras]
print(camera, imagesCamerasFileNames[camera])
numberOfCameras = len(IMAGES_CAMERAS_FOLDER)
@ -77,7 +77,7 @@ for computeExtremes in tqdm(([True] if minColor is None or maxColor is None else
multipleColorsDenoisedImage = mergeSingleColorChannelImagesAccordingToBayerFilter(singleColorChannelDenoisedImages)
imagePrnuEstimateNpArray = multipleColorsImage - multipleColorsDenoisedImage
cameraTestingImagesNoise[camera] = cameraTestingImagesNoise.get(camera, []) + [imagePrnuEstimateNpArray]#multipleColorsDenoisedImage]
cameraTestingImagesNoise[camera] = cameraTestingImagesNoise.get(camera, []) + [imagePrnuEstimateNpArray]
for cameraTrainingImageIndex in tqdm(range(minimumNumberOfImagesCameras if computeExtremes else numberOfTrainingImages), 'Camera training image index'):
for cameraIndex, camera in enumerate(tqdm(IMAGES_CAMERAS_FOLDER, 'Camera')):
imageFileName = imagesCamerasFileNames[camera][cameraTrainingImageIndex]
@ -93,13 +93,8 @@ for computeExtremes in tqdm(([True] if minColor is None or maxColor is None else
multipleColorsDenoisedImage = mergeSingleColorChannelImagesAccordingToBayerFilter(singleColorChannelDenoisedImages)
imagePrnuEstimateNpArray = multipleColorsImage - multipleColorsDenoisedImage
#print(camera)
#plt.imshow(multipleColorsImage)
#plt.show()
#exit(1)
cameraIterativeMean = camerasIterativeMean[camera]
cameraIterativeMean.add(imagePrnuEstimateNpArray)
plt.imsave(f'm_{escapeFilePath(camera)}.png', camerasIterativeMean[camera].mean)
if cameraIndex == numberOfCameras - 1:
numberOfTrainingImagesAccuracy = 0
print(f'{numberOfTestingImages=} {numberOfCameras=}')
@ -111,18 +106,7 @@ for computeExtremes in tqdm(([True] if minColor is None or maxColor is None else
# 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 = rmsDiffNumpy(cameraTestingImagesNoise[actualCamera][cameraTestingImageIndex], camerasIterativeMean[camera].mean)
'''
print(f'{camerasIterativeMean[camera].numberOfElementsInMean=}')
print(f'{cameraTestingImagesNoise[actualCamera][cameraTestingImageIndex].min()=}')
print(f'{cameraTestingImagesNoise[actualCamera][cameraTestingImageIndex].max()=}')
print(f'{camerasIterativeMean[camera].mean.min()=}')
print(f'{camerasIterativeMean[camera].mean.max()=}')
plt.imsave(f'a_{actualCamera}.png', cameraTestingImagesNoise[actualCamera][cameraTestingImageIndex])
plt.imsave(f'b_{camera}.png', camerasIterativeMean[camera].mean)
plt.show()
'''
print(f'{cameraTestingImageIndex=} {camera=} {actualCamera=} {distance=}')
#exit(1)
if minimalDistance is None or distance < minimalDistance:
minimalDistance = distance
cameraPredicted = camera
@ -132,7 +116,7 @@ for computeExtremes in tqdm(([True] if minColor is None or maxColor is None else
accuracy += [numberOfTrainingImagesAccuracy / (numberOfTestingImages * numberOfCameras)]
for camera in IMAGES_CAMERAS_FOLDER:
plt.imsave(f'{setting}_estimated_prnu_subgroup_{escapeFilePath(camera)}.png', (camerasIterativeMean[camera].mean))
plt.imsave(f'{setting}_estimated_prnu_camera_{escapeFilePath(camera)}.png', (camerasIterativeMean[camera].mean))
plt.title(f'Accuracy of camera source attribution thanks to a given number of images to estimate PRNUs with {DENOISER} denoiser')
plt.xlabel('Number of images to estimate PRNU')