Add attribute_source_camera.py

It is a modified copy of `split_and_compare_prnus_of_subgroups.py`.

datasets/raise/attribute_source_camera.py

@@ -0,0 +1,83 @@
+#!/usr/bin/env python
+
+import numpy as np
+import matplotlib.pyplot as plt
+from tqdm import tqdm
+from utils import denoise, iterativeMean, getColorChannel, escapeFilePath, Color, mergeSingleColorChannelImagesAccordingToBayerFilter, rescaleRawImageForDenoiser, updateExtremes, saveNpArray
+import sys
+import os
+import random
+
+sys.path.insert(0, '../../algorithms/distance/')
+
+from rms_diff import rmsDiffNumpy
+
+DENOISER = 'wavelet'
+IMAGES_CAMERAS_FOLDER = {
+    'RAISE': 'flat-field/nef',
+    'Rafael 23/04/24': 'rafael/230424',
+}
+TRAINING_PORTION = 0.5
+
+setting = ','.join([escapeFilePath(imageCameraFolder) for imageCameraFolder in IMAGES_CAMERAS_FOLDER]) + f'_{DENOISER}'
+
+imagesCamerasFileNames = {camera: os.listdir(imageCameraFolder) for camera, imageCameraFolder in IMAGES_CAMERAS_FOLDER.items()}
+random.seed(0)
+# To not have a bias (chronological for instance) when split to make training and testing sets.
+for camera in IMAGES_CAMERAS_FOLDER:
+    #print(imagesCamerasFileNames[camera][:3])
+    random.shuffle(imagesCamerasFileNames[camera])
+    #print(imagesCamerasFileNames[camera][:3])
+    #exit(1)
+
+minimumNumberOfImagesCameras = 10#min([len(imagesCamerasFileNames[camera]) for camera in IMAGES_CAMERAS_FOLDER])
+#print(minimumNumberOfImagesCameras)
+#exit(1)
+for camera in IMAGES_CAMERAS_FOLDER:
+    IMAGES_CAMERAS_FOLDER[camera] = IMAGES_CAMERAS_FOLDER[camera][:minimumNumberOfImagesCameras]
+
+numberOfCameras = len(IMAGES_CAMERAS_FOLDER)
+camerasIterativeMean = [iterativeMean() for _ in range(numberOfCameras)]
+
+minColor = None
+maxColor = None
+
+accuracy = []
+
+returnSingleColorChannelImage = lambda singleColorChannelImage, _minColor, _maxColor: singleColorChannelImage
+
+for computeExtremes in tqdm(([True] if minColor is None or maxColor is None else []) + [False], 'Compute extremes'):
+    rescaleIfNeeded = returnSingleColorChannelImage if computeExtremes else rescaleRawImageForDenoiser
+    for cameraImageIndex in tqdm(range(minimumNumberOfImagesCameras * TRAINING_PORTION), 'Camera image index'):
+        for cameraIndex, camera in enumerate(tqdm(IMAGES_CAMERAS_FOLDER, 'Camera')):
+            imageFileName = imagesCamerasFileNames[camera][cameraImageIndex]
+            imageFilePath = f'{IMAGES_CAMERAS_FOLDER[camera]}/{imageFileName}'
+            singleColorChannelImages = {color: rescaleIfNeeded(getColorChannel(imageFilePath, color), minColor, maxColor) for color in Color}
+            multipleColorsImage = mergeSingleColorChannelImagesAccordingToBayerFilter(singleColorChannelImages)
+
+            if computeExtremes:
+                minColor, maxColor = updateExtremes(multipleColorsImage, minColor, maxColor)
+                continue
+
+            singleColorChannelDenoisedImages = {color: denoise(singleColorChannelImages[color], DENOISER) for color in Color}
+            multipleColorsDenoisedImage = mergeSingleColorChannelImagesAccordingToBayerFilter(singleColorChannelDenoisedImages)
+
+            imagePrnuEstimateNpArray = multipleColorsImage - multipleColorsDenoisedImage
+            cameraIterativeMean = subgroupsIterativeMean[cameraIndex]
+            cameraIterativeMean.add(imagePrnuEstimateNpArray)
+            if cameraIndex == numberOfCameras - 1:
+                rms = rmsDiffNumpy(subgroupIterativeMean.mean, subgroupsIterativeMean[1 - cameraIndex].mean)
+                rmss += [rms]
+    if computeExtremes:
+        print(f'{minColor=} {maxColor=}')
+
+for camera in range(IMAGES_CAMERAS_FOLDER):
+    plt.imsave(f'{setting}_estimated_prnu_subgroup_{escapeFilePath(camera)}.png', (subgroupsIterativeMean[cameraIndex].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')
+plt.ylabel('Accuracy of camera source attribution')
+plt.plot(accuracy)
+internalTitle = f'{setting}_accuracy_of_camera_source_attribution'
+saveNpArray(internalTitle, accuracy)
+plt.savefig(f'{internalTitle}.svg')