datasets/raise/attribute_source_camera.py

@@ -27,14 +27,15 @@ random.seed(0)
 for camera in IMAGES_CAMERAS_FOLDER:
     random.shuffle(imagesCamerasFileNames[camera])
 
-minimumNumberOfImagesCameras = 16#min([len(imagesCamerasFileNames[camera]) for camera in IMAGES_CAMERAS_FOLDER])
+minimumNumberOfImagesCameras = 4#min([len(imagesCamerasFileNames[camera]) for camera in IMAGES_CAMERAS_FOLDER])
 for camera in IMAGES_CAMERAS_FOLDER:
     imagesCamerasFileNames[camera] = imagesCamerasFileNames[camera][:minimumNumberOfImagesCameras]
-    print(camera, imagesCamerasFileNames[camera])
 
 numberOfCameras = len(IMAGES_CAMERAS_FOLDER)
 camerasIterativeMean = {camera: iterativeMean() for camera in IMAGES_CAMERAS_FOLDER}
 
+minColor = None
+maxColor = None
 # Assume that for each camera, its images have the same resolution.
 # The following consider a given color channel resolution, assuming they all have the same resolution.
 minimalColorChannelCameraResolution = None
@@ -45,17 +46,14 @@ for camera in IMAGES_CAMERAS_FOLDER:
     if minimalColorChannelCameraResolution is None or singleColorChannelImagesShape < minimalColorChannelCameraResolution:
         minimalColorChannelCameraResolution = singleColorChannelImagesShape
 
-minColor = None#13#None
-maxColor = None#7497#None
+minColor = 13#None
+maxColor = 7497#None
 
 accuracy = []
 numberOfTrainingImages = int(minimumNumberOfImagesCameras * TRAINING_PORTION)
 numberOfTestingImages = minimumNumberOfImagesCameras - int(minimumNumberOfImagesCameras * TRAINING_PORTION)
 cameraTestingImagesNoise = {}
 
-from utils import silentTqdm
-#tqdm = silentTqdm
-
 returnSingleColorChannelImage = lambda singleColorChannelImage, _minColor, _maxColor: singleColorChannelImage
 
 for computeExtremes in tqdm(([True] if minColor is None or maxColor is None else []) + [False], 'Compute extremes'):
@@ -68,7 +66,6 @@ for computeExtremes in tqdm(([True] if minColor is None or maxColor is None else
                 # Should make a function
                 imageFileName = imagesCamerasFileNames[camera][numberOfTrainingImages + cameraTestingImageIndex]
                 imageFilePath = f'{IMAGES_CAMERAS_FOLDER[camera]}/{imageFileName}'
-                print(f'{imageFilePath=}')
 
                 # Should make a function
                 singleColorChannelImages = {color: rescaleIfNeeded(getColorChannel(imageFilePath, color)[:minimalColorChannelCameraResolution[0],:minimalColorChannelCameraResolution[1]], minColor, maxColor) for color in Color}
@@ -77,7 +74,7 @@ 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]
+                cameraTestingImagesNoise[camera] = cameraTestingImagesNoise.get(camera, []) + [multipleColorsDenoisedImage]
     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]
@@ -97,7 +94,6 @@ for computeExtremes in tqdm(([True] if minColor is None or maxColor is None else
             cameraIterativeMean.add(imagePrnuEstimateNpArray)
             if cameraIndex == numberOfCameras - 1:
                 numberOfTrainingImagesAccuracy = 0
-                print(f'{numberOfTestingImages=} {numberOfCameras=}')
                 # Loop over each camera testing image folder.
                 for actualCamera in IMAGES_CAMERAS_FOLDER:
                     for cameraTestingImageIndex in tqdm(range(numberOfTestingImages), 'Camera testing image index'):
@@ -105,18 +101,17 @@ for computeExtremes in tqdm(([True] if minColor is None or maxColor is None else
                         minimalDistance = None
                         # 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)
+                            distance = rmsDiffNumpy(cameraTestingImagesNoise[camera][cameraTestingImageIndex], camerasIterativeMean[camera].mean)
                             print(f'{cameraTestingImageIndex=} {camera=} {actualCamera=} {distance=}')
                             if minimalDistance is None or distance < minimalDistance:
                                 minimalDistance = distance
                                 cameraPredicted = camera
-                        print(f'Predicted camera {cameraPredicted} {"good" if cameraPredicted == actualCamera else "bad"}')
                         if cameraPredicted == actualCamera:
                             numberOfTrainingImagesAccuracy += 1
                 accuracy += [numberOfTrainingImagesAccuracy / (numberOfTestingImages * numberOfCameras)]
 
 for camera in IMAGES_CAMERAS_FOLDER:
-    plt.imsave(f'{setting}_estimated_prnu_camera_{escapeFilePath(camera)}.png', (camerasIterativeMean[camera].mean))
+    plt.imsave(f'{setting}_estimated_prnu_subgroup_{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')