From 53c3935bab1cc81028f06b3608b88a5919f44944 Mon Sep 17 00:00:00 2001 From: Benjamin Loison <12752145+Benjamin-Loison@users.noreply.github.com> Date: Tue, 30 Apr 2024 06:48:17 +0200 Subject: [PATCH] Clean some debugging --- datasets/raise/attribute_source_camera.py | 22 +++------------------- 1 file changed, 3 insertions(+), 19 deletions(-) diff --git a/datasets/raise/attribute_source_camera.py b/datasets/raise/attribute_source_camera.py index 3e5aeaf..79a4c85 100755 --- a/datasets/raise/attribute_source_camera.py +++ b/datasets/raise/attribute_source_camera.py @@ -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')