datasets/raise/attribute_source_camera.py

@@ -7,6 +7,7 @@ from utils import denoise, iterativeMean, getColorChannel, escapeFilePath, Color
 import sys
 import os
 import random
+import scipy
 
 sys.path.insert(0, '../../algorithms/distance/')
 
@@ -43,14 +44,13 @@ camerasIterativeMean = {camera: iterativeMean() for camera in IMAGES_CAMERAS_FOL
 # Compute the minimal color channel camera resolution.
 # 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 = (100, 100)#None
-if minimalColorChannelCameraResolution is None:
-    for camera in IMAGES_CAMERAS_FOLDER:
-        imageFileName = imagesCamerasFileNames[camera][0]
-        imageFilePath = f'{IMAGES_CAMERAS_FOLDER[camera]}/{imageFileName}'
-        singleColorChannelImagesShape = getColorChannel(imageFilePath, Color.RED).shape
-        if minimalColorChannelCameraResolution is None or singleColorChannelImagesShape < minimalColorChannelCameraResolution:
-            minimalColorChannelCameraResolution = singleColorChannelImagesShape
+minimalColorChannelCameraResolution = None
+for camera in IMAGES_CAMERAS_FOLDER:
+    imageFileName = imagesCamerasFileNames[camera][0]
+    imageFilePath = f'{IMAGES_CAMERAS_FOLDER[camera]}/{imageFileName}'
+    singleColorChannelImagesShape = getColorChannel(imageFilePath, Color.RED).shape
+    if minimalColorChannelCameraResolution is None or singleColorChannelImagesShape < minimalColorChannelCameraResolution:
+        minimalColorChannelCameraResolution = singleColorChannelImagesShape
 
 minColor = 0#None
 maxColor = 7952#None
@@ -75,18 +75,7 @@ def getMultipleColorsImage(singleColorChannelImages):
     return multipleColorsImage
 
 def getImagePrnuEstimateNpArray(singleColorChannelImages, multipleColorsImage, camera):
-    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
-                singleColorChannelDenoisedImage = cameraColorCurrentMean
-        singleColorChannelDenoisedImages[color] = singleColorChannelDenoisedImage
+    singleColorChannelDenoisedImages = {color: denoise(singleColorChannelImages[color], DENOISER) if DENOISER != Denoiser.MEAN else cameraColorMeans[camera][color] for color in Color}
     multipleColorsDenoisedImage = mergeSingleColorChannelImagesAccordingToBayerFilter(singleColorChannelDenoisedImages)
     imagePrnuEstimateNpArray = multipleColorsImage - multipleColorsDenoisedImage
     return imagePrnuEstimateNpArray
@@ -96,24 +85,13 @@ 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 {color: iterativeMean() for color in Color}) for camera in imagesCamerasFilePaths}
+    cameraColorMeans = {camera: (getColorMeans(imagesCamerasFilePaths[camera][:numberOfTrainingImages], Color, minimalColorChannelCameraResolution) if PREDICT_ONLY_ON_WHOLE_TRAINING_SET else iterativeMean()) for camera in imagesCamerasFilePaths}
 
 from utils import silentTqdm
 #tqdm = 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
@@ -145,17 +123,10 @@ 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]
-            if DENOISER != Denoiser.MEAN or PREDICT_ONLY_ON_WHOLE_TRAINING_SET:
-                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 cameraTrainingImages[camera][:cameraTrainingImageIndex + 1]], axis = 0)
+            cameraIterativeMean.add(imagePrnuEstimateNpArray)
             # 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
@@ -168,15 +139,8 @@ 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:
-                            if DENOISER != Denoiser.MEAN:
-                                cameraTestingImageNoise = cameraTestingImagesNoise[actualCamera][cameraTestingImageIndex]
-                            else:
-                                singleColorChannelImages = getSingleColorChannelImages(camera, numberOfTrainingImages + cameraTestingImageIndex)
-                                multipleColorsImage = getMultipleColorsImage(singleColorChannelImages)
-                                cameraTestingImageNoise = getImagePrnuEstimateNpArray(singleColorChannelImages, multipleColorsImage, camera)
-
-                            distance = rmsDiffNumpy(cameraTestingImageNoise, camerasIterativeMean[camera].mean)
-                            print(f'{cameraTrainingImageIndex=} {cameraTestingImageIndex=} {camera=} {actualCamera=} {distance=}')
+                            distance = abs(scipy.stats.pearsonr(cameraTestingImagesNoise[actualCamera][cameraTestingImageIndex].flatten(), camerasIterativeMean[camera].mean.flatten()).statistic - 1)
+                            print(f'{cameraTestingImageIndex=} {camera=} {actualCamera=} {distance=}')
                             if minimalDistance is None or distance < minimalDistance:
                                 minimalDistance = distance
                                 cameraPredicted = camera

datasets/raise/benchmark_load_part_of_images.py

@@ -1,41 +0,0 @@
-#!/usr/bin/env python
-
-from utils import getColorChannel, Color
-import os
-from tqdm import tqdm
-import numpy as np
-from enum import Enum, auto
-
-IMAGES_CAMERAS_FOLDER = {
-    'RAISE': 'flat-field/nef',
-    'Rafael 23/04/24': 'rafael/230424',
-}
-
-class Operation(Enum):
-    LOAD_RAW = auto()
-    SAVE = auto()
-    LOAD_NPY = auto()
-
-OPERATION = Operation.LOAD_RAW
-RESOLUTION = 100
-
-for camera in tqdm(IMAGES_CAMERAS_FOLDER, 'Camera'):
-    imagesCameraFolder = IMAGES_CAMERAS_FOLDER[camera]
-    for file in tqdm(os.listdir(imagesCameraFolder), 'Image'):
-        if file.endswith('.NEF'):
-            #print(file)
-            imageFilePath = f'{imagesCameraFolder}/{file}'
-            numpyFilePath = f'{imageFilePath}.npy'
-            for color in Color:
-                if OPERATION in [Operation.LOAD_RAW, Operation.SAVE]:
-                    rawColorChannel = getColorChannel(imageFilePath, color)
-                if OPERATION == Operation.SAVE:
-                    np.save(numpyFilePath, {color: rawColorChannel})
-                if OPERATION == Operation.LOAD_NPY:
-                    rawColorChannel = np.load(numpyFilePath, allow_pickle = True)
-                    #print(type(rawColorChannel))
-                    #print(rawColorChannel)
-                    #print(rawColorChannel.shape)
-                    print(rawColorChannel.item()[color].mean())
-                break
-    break