From 31704c6e78c04af21f4cc1ac92c7227aadf60a02 Mon Sep 17 00:00:00 2001
From: Benjamin Loison <12752145+Benjamin-Loison@users.noreply.github.com>
Date: Mon, 13 May 2024 15:20:17 +0200
Subject: [PATCH] #72: Implement, possibly incorrectly, first choice

---
 datasets/raise/attribute_source_camera.py | 44 ++++++++++++++++++++---
 1 file changed, 39 insertions(+), 5 deletions(-)

diff --git a/datasets/raise/attribute_source_camera.py b/datasets/raise/attribute_source_camera.py
index 24f6b63..6ba705e 100755
--- a/datasets/raise/attribute_source_camera.py
+++ b/datasets/raise/attribute_source_camera.py
@@ -7,7 +7,6 @@ from utils import denoise, iterativeMean, getColorChannel, escapeFilePath, Color
 import sys
 import os
 import random
-import scipy
 
 sys.path.insert(0, '../../algorithms/distance/')
 
@@ -75,7 +74,28 @@ def getMultipleColorsImage(singleColorChannelImages):
     return multipleColorsImage
 
 def getImagePrnuEstimateNpArray(singleColorChannelImages, multipleColorsImage, camera):
-    singleColorChannelDenoisedImages = {color: denoise(singleColorChannelImages[color], DENOISER) if DENOISER != Denoiser.MEAN else cameraColorMeans[camera][color] for color in Color}
+    #singleColorChannelDenoisedImages = {color: denoise(singleColorChannelImages[color], DENOISER) if DENOISER != Denoiser.MEAN else (cameraColorMeans[camera][color] if PREDICT_ONLY_ON_WHOLE_TRAINING_SET else (cameraColorMeans[camera][color].mean if )) for color in Color}
+    singleColorChannelDenoisedImages = {}
+    for color in Color:
+        if DENOISER != Denoiser.MEAN:
+            singleColorChannelDenoisedImage = denoise(singleColorChannelImages[color], DENOISER)
+        else:
+            cameraColorMean = cameraColorMeans[camera][color]
+            if PREDICT_ONLY_ON_WHOLE_TRAINING_SET:
+                singleColorChannelDenoisedImage = cameraColorMean
+            else:
+                cameraColorCurrentMean = cameraColorMean.mean
+                if cameraColorCurrentMean is None:
+                    print('`cameraColorCurrentMean` is `None`!')
+                    exit(2)
+                '''
+                if cameraColorCurrentMean is None:
+                    singleColorChannelDenoisedImage = singleColorChannelImages[color]
+                else:
+                    singleColorChannelDenoisedImage = cameraColorCurrentMean
+                '''
+                singleColorChannelDenoisedImage = cameraColorCurrentMean
+        singleColorChannelDenoisedImages[color] = singleColorChannelDenoisedImage
     multipleColorsDenoisedImage = mergeSingleColorChannelImagesAccordingToBayerFilter(singleColorChannelDenoisedImages)
     imagePrnuEstimateNpArray = multipleColorsImage - multipleColorsDenoisedImage
     return imagePrnuEstimateNpArray
@@ -85,7 +105,7 @@ imagesCamerasFilePaths = {camera: [f'{IMAGES_CAMERAS_FOLDER[camera]}/{imagesCame
 # If `PREDICT_ONLY_ON_WHOLE_TRAINING_SET`, then compute the means of camera images to empower the `MEAN` denoiser.
 # Otherwise initialize these means of camera images to `iterativeMean`.
 if DENOISER == Denoiser.MEAN:
-    cameraColorMeans = {camera: (getColorMeans(imagesCamerasFilePaths[camera][:numberOfTrainingImages], Color, minimalColorChannelCameraResolution) if PREDICT_ONLY_ON_WHOLE_TRAINING_SET else iterativeMean()) for camera in imagesCamerasFilePaths}
+    cameraColorMeans = {camera: (getColorMeans(imagesCamerasFilePaths[camera][:numberOfTrainingImages], Color, minimalColorChannelCameraResolution) if PREDICT_ONLY_ON_WHOLE_TRAINING_SET else {color: iterativeMean() for color in Color}) for camera in imagesCamerasFilePaths}
 
 from utils import silentTqdm
 #tqdm = silentTqdm
@@ -123,10 +143,16 @@ for computeExtremes in tqdm(([True] if minColor is None or maxColor is None else
                 minColor, maxColor = updateExtremes(multipleColorsImage, minColor, maxColor)
                 continue
 
+            if DENOISER == Denoiser.MEAN:
+                for color in Color:
+                    cameraColorMeans[camera][color].add(singleColorChannelImages[color])
             imagePrnuEstimateNpArray = getImagePrnuEstimateNpArray(singleColorChannelImages, multipleColorsImage, camera)
 
             cameraIterativeMean = camerasIterativeMean[camera]
+            #print(f'{imagePrnuEstimateNpArray=}')
+            #exit(2)
             cameraIterativeMean.add(imagePrnuEstimateNpArray)
+            #continue
             # 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
@@ -139,8 +165,16 @@ for computeExtremes in tqdm(([True] if minColor is None or maxColor is None else
                         #plt.imsave(f'{escapeFilePath(actualCamera)}_{cameraTestingImageIndex}.png', cameraTestingImagesNoise[actualCamera][cameraTestingImageIndex])
                         # 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 = abs(scipy.stats.pearsonr(cameraTestingImagesNoise[actualCamera][cameraTestingImageIndex].flatten(), camerasIterativeMean[camera].mean.flatten()).statistic - 1)
-                            print(f'{cameraTestingImageIndex=} {camera=} {actualCamera=} {distance=}')
+                            if DENOISER != Denoiser.MEAN:
+                                cameraTestingImageNoise = cameraTestingImagesNoise[actualCamera][cameraTestingImageIndex]
+                            else:
+                                singleColorChannelImages = getSingleColorChannelImages(camera, numberOfTrainingImages + cameraTestingImageIndex)
+                                multipleColorsImage = getMultipleColorsImage(singleColorChannelImages)
+                                cameraTestingImageNoise = getImagePrnuEstimateNpArray(singleColorChannelImages, multipleColorsImage, camera)
+
+                            #print(f'{cameraTestingImageNoise = }')
+                            distance = rmsDiffNumpy(cameraTestingImageNoise, camerasIterativeMean[camera].mean)
+                            print(f'{cameraTrainingImageIndex=} {cameraTestingImageIndex=} {camera=} {actualCamera=} {distance=}')
                             if minimalDistance is None or distance < minimalDistance:
                                 minimalDistance = distance
                                 cameraPredicted = camera