Load lazily images (#62)

```
OPERATION = <Operation.LOAD_NPY: 3>
RESOLUTION = 100
Image:   0%|▍                                                                                            | 1/200 [00:00<00:00, 5084.00it/s]
Camera:   0%|                                                                                                        | 0/2 [00:00<?, ?it/s]
Traceback (most recent call last):
  File "/home/benjamin/robust_image_source_identification_on_modern_smartphones/datasets/raise/./benchmark_load_part_of_images.py", line 43, in <module>
    rawColorChannels = np.load(numpyFilePath, mmap_mode = 'r', allow_pickle = True)
                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/benjamin/venv/lib/python3.11/site-packages/numpy/lib/npyio.py", line 453, in load
    return format.open_memmap(file, mode=mmap_mode,
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/benjamin/venv/lib/python3.11/site-packages/numpy/lib/format.py", line 932, in open_memmap
    raise ValueError(msg)
ValueError: Array can't be memory-mapped: Python objects in dtype.
```

.gitignore

@@ -1,2 +1,3 @@
 *.pdf
 *.xopp
+__pycache__

algorithms/context_adaptive_interpolator/context_adaptive_interpolator.py

@@ -6,6 +6,7 @@ from wiener_filter import wienerFilter
 
 # Assume greyscale PIL image passed.
 # What about other color channels? See #11.
+# `THRESHOLD` seems to have been designed to assume 256 range based images.
 def contextAdaptiveInterpolator(I, IImage, showProgress = False):
     rImage = Image.new('L', (IImage.size[0] - 2, IImage.size[1] - 2))
     r = rImage.load()

algorithms/distance/rms_diff.py

@@ -14,5 +14,13 @@ def rmsDiffPil(im1, im2):
         map(lambda h, i: h*(i**2), h, range(256))
     ) / (float(im1.size[0]) * im1.size[1]))
 
-def rmsDiffNumpy(image0, image1):
+def normalizeImage(image):
+    image = image - image.min()
+    image = image / image.max()
+    return image
+
+def rmsDiffNumpy(image0, image1, normalize = False):
+    if normalize:
+        image0 = normalizeImage(image0)
+        image1 = normalizeImage(image1)
     return np.sqrt(np.mean(np.square(image0 - image1)))

algorithms/image_utils/image_utils.py

@@ -0,0 +1,41 @@
+from PIL import Image, ImageFont, ImageDraw
+import numpy as np
+from matplotlib import pyplot as plt
+import os
+
+def randomGaussianImage(scale, size):
+    return np.random.normal(loc = 0, scale = scale, size = size)
+
+# `cmap` source: https://matplotlib.org/3.8.0/api/_as_gen/matplotlib.pyplot.imshow.html
+def showImageWithMatplotlib(npArray, title = None, cmap = 'viridis'):
+    if title is not None:
+        plt.title(title)
+    plt.imshow(npArray, cmap = cmap)
+    plt.show()
+
+def toPilImage(npArray):
+    return Image.fromarray(npArray)
+
+def getPrnuShownAsSuch(size, gaussianNoise = 0):
+    # Supports `WIDTH` > `HEIGHT` and conversely.
+    WIDTH, HEIGHT = size
+    TEXT = 'PRNU'
+
+    imagePil = Image.new('L', size)
+    draw = ImageDraw.Draw(imagePil)
+    fontPath = os.path.expanduser('~/.local/share/fonts/impact.ttf')
+    for fontSize in range(1, HEIGHT + 1):
+        font = ImageFont.truetype(fontPath, fontSize)
+        if font.getlength(TEXT) > WIDTH:
+            break
+    # Center vertically, especially in the case `HEIGHT` > `WIDTH`.
+    draw.text((0, HEIGHT // 2 - fontSize // 2), TEXT, 255, font = font)
+    imageNpArray = np.array(imagePil)
+    gaussianNoiseNpArray = randomGaussianImage(gaussianNoise, size[::-1])
+    #prnuShownAsSuch = imageNpArray + gaussianNoiseNpArray
+    prnuShownAsSuch = imageNpArray
+    for y in range(HEIGHT):
+        for x in range(WIDTH):
+            if prnuShownAsSuch[y, x] != 0:
+                prnuShownAsSuch[y, x] += gaussianNoiseNpArray[y, x]
+    return prnuShownAsSuch

articles/Additive white Gaussian noise - Wikipedia/Additive white Gaussian noise - Wikipedia.pdf.txt

@@ -0,0 +1 @@
+https://en.wikipedia.org/w/index.php?title=Additive_white_Gaussian_noise&oldid=1181981955

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articles/Bayer filter - Wikipedia/Bayer filter - Wikipedia.pdf.txt

@@ -0,0 +1 @@
+https://en.wikipedia.org/w/index.php?title=Bayer_filter&oldid=1189637393

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articles/Bernoulli distribution - Wikipedia/Bernoulli distribution - Wikipedia.pdf.txt

@@ -0,0 +1 @@
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articles/Convolution - Wikipedia/Convolution - Wikipedia.pdf.txt

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articles/Covariance - Wikipedia/Covariance - Wikipedia.pdf.txt

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articles/Euclidean distance - Wikipedia/Euclidean distance - Wikipedia.pdf.txt

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articles/Focal length - Wikipedia/Focal length - Wikipedia.pdf.txt

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articles/Geometric mean - Wikipedia/Geometric mean - Wikipedia.pdf.txt

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articles/HOW TO READ SMARTPHONE CAMERA SPECS - Inquisitive Universe/HOW TO READ SMARTPHONE CAMERA SPECS - Inquisitive Universe.pdf.txt

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articles/Inch - Wikipedia/Inch - Wikipedia.pdf.txt

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articles/Inflection point - Wikipedia/Inflection point - Wikipedia.pdf.txt

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articles/IrfanView - Wikipedia/IrfanView - Wikipedia.pdf.txt

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articles/Law of large numbers - Wikipedia/Law of large numbers - Wikipedia.pdf.txt

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articles/Luminance - Wikipedia/Luminance - Wikipedia.pdf.txt

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articles/Mathématiques appliquées — Wikipédia/Mathématiques appliquées — Wikipédia.pdf.txt

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articles/Nikon Electronic File — Wikipédia/Nikon Electronic File — Wikipédia.pdf.txt

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articles/Non-local means - Wikipedia/Non-local means - Wikipedia.pdf.txt

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articles/Résumé de cours Variables aléatoires finies/Résumé de cours Variables aléatoires finies.pdf.txt

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articles/Self-supervised learning - Wikipedia/Self-supervised learning - Wikipedia.pdf.txt

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articles/Wu_ICIP_2012/Wu_ICIP_2012.xopp.xml

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articles/XnView - Wikipedia/XnView - Wikipedia.pdf.txt

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+https://en.wikipedia.org/w/index.php?title=XnView&oldid=1214657498

articles/XnView - Wikipedia/XnView - Wikipedia.xopp.xml

@@ -0,0 +1,33 @@
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+<xournal creator="Xournal++ 1.1.3" fileversion="4">
+  <title>Xournal++ document - see https://github.com/xournalpp/xournalpp</title>
+  <page width="596.00000000" height="842.00000000">
+    <background type="pdf" domain="absolute" filename="/home/benjamin/Desktop/bens_folder/school/ens/asp/aria/internship/work/articles/XnView - Wikipedia/XnView - Wikipedia.pdf" pageno="1"/>
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articles/f-number - Wikipedia/f-number - Wikipedia.pdf.txt

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articles/f-number - Wikipedia/f-number - Wikipedia.xopp.xml

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+<xournal creator="Xournal++ 1.1.3" fileversion="4">
+  <title>Xournal++ document - see https://github.com/xournalpp/xournalpp</title>
+  <page width="596.00000000" height="842.00000000">
+    <background type="pdf" domain="absolute" filename="/home/benjamin/Desktop/bens_folder/school/ens/asp/aria/internship/work/articles/f-number - Wikipedia/f-number - Wikipedia.pdf" pageno="1"/>
+    <layer>
+      <stroke tool="highlighter" color="#ffff007f" width="2.83000000" fill="128">76.35571317 71.16192048 230.15500187 71.16192048</stroke>
+      <stroke tool="highlighter" color="#ffff007f" width="2.83000000" fill="128">242.75314654 71.60271191 437.87042392 71.60271191</stroke>
+      <stroke tool="highlighter" color="#ffff007f" width="2.83000000" fill="128">486.13858973 113.32688851 539.49158762 113.32688851</stroke>
+      <stroke tool="highlighter" color="#ffff007f" width="2.83000000" fill="128">463.31237503 118.35461697 504.63680411 118.35461697</stroke>
+      <stroke tool="highlighter" color="#ffff007f" width="2.83000000" fill="128">512.04319936 119.21877568 552.79744594 119.21877568</stroke>
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+      <text font="Sans" size="11.00000000" x="401.52364205" y="43.90977906" color="#00c0ffff" ts="0" fn="">unclear 1.4, 2, 2.8, 4 formula
+without even and odd definitions</text>
+      <stroke tool="highlighter" color="#ffff007f" width="2.83000000" fill="128">72.24754453 78.31414383 162.16584969 78.31414383</stroke>
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+      <text font="Sans" size="11.00000000" x="245.44277649" y="75.46508133" color="#00c0ffff" ts="0" fn="">?</text>
+      <text font="Sans" size="11.00000000" x="264.88189250" y="76.60925652" color="#00c0ffff" ts="0" fn="">?</text>
+      <text font="Sans" size="11.00000000" x="295.30311148" y="77.03564013" color="#00c0ffff" ts="0" fn="">?</text>
+      <stroke tool="highlighter" color="#ffff007f" width="2.83000000" fill="128">379.81561071 77.94301691 404.94074365 78.95911550</stroke>
+      <stroke tool="highlighter" color="#ffff007f" width="2.83000000" fill="128">437.83150704 78.08329790 452.45599422 78.08329790</stroke>
+      <stroke tool="highlighter" color="#ffff007f" width="2.83000000" fill="128">40.39369992 84.52163381 179.71648925 84.52163381</stroke>
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datasets/android/AndroidManifest.xml

@@ -0,0 +1,15 @@
+<?xml version="1.0" encoding="utf-8"?>
+<manifest xmlns:android="http://schemas.android.com/apk/res/android"
+          package="com.robust_image_source_identification_on_modern_smartphones.camera"
+          versionCode="1"
+          versionName="0.1">
+    <uses-sdk android:minSdkVersion="33"/>
+    <application android:label="Camera">
+        <activity android:name=".MainActivity" android:exported="true">
+            <intent-filter>
+                <action android:name="android.intent.action.MAIN"/>
+                <category android:name="android.intent.category.LAUNCHER"/>
+            </intent-filter>
+        </activity>
+    </application>
+</manifest>

datasets/android/compile.sh

@@ -0,0 +1,29 @@
+SDK_FOLDER=/usr/lib/android-sdk
+PLATFORM=$SDK_FOLDER/platforms/android-33
+BUILD_TOOLS=$SDK_FOLDER/build-tools/30.0.3
+
+SOURCE_FOLDER=com/robust_image_source_identification_on_modern_smartphones/camera
+APP_NAME=Camera
+
+mkdir -p build/gen build/apk
+
+"$BUILD_TOOLS/aapt" package -f -m -J build/gen/ -S res \
+      -M AndroidManifest.xml -I "$PLATFORM/android.jar"
+
+javac --release 11 -classpath "$PLATFORM/android.jar" -d build/obj \
+      build/gen/$SOURCE_FOLDER/R.java java/$SOURCE_FOLDER/MainActivity.java
+
+"$BUILD_TOOLS/d8" --release --lib "$PLATFORM/android.jar" \
+      --output build/apk/ build/obj/$SOURCE_FOLDER/*.class
+
+"$BUILD_TOOLS/aapt" package -f -M AndroidManifest.xml -S res/ \
+      -I "$PLATFORM/android.jar" \
+      -F build/$APP_NAME.unsigned.apk build/apk/
+
+"$BUILD_TOOLS/zipalign" -f -p 4 \
+      build/$APP_NAME.unsigned.apk build/$APP_NAME.aligned.apk
+
+"$BUILD_TOOLS/apksigner" sign --ks keystore.jks \
+      --ks-key-alias androidkey --ks-pass pass:android \
+      --key-pass pass:android --out build/$APP_NAME.apk \
+      build/$APP_NAME.aligned.apk

datasets/android/java/com/robust_image_source_identification_on_modern_smartphones/camera/MainActivity.java

@@ -0,0 +1,29 @@
+package com.robust_image_source_identification_on_modern_smartphones.camera;
+
+import android.app.Activity;
+import android.os.Bundle;
+import android.widget.TextView;
+import android.content.Context;
+import android.util.Log;
+import android.hardware.camera2.CameraManager;
+
+public class MainActivity extends Activity {
+    @Override
+    protected void onCreate(Bundle savedInstanceState) {
+        super.onCreate(savedInstanceState);
+        setContentView(R.layout.activity_main);
+
+        TextView text = (TextView)findViewById(R.id.my_text);
+        Context context = getApplicationContext();
+        log("Camera app initialized!");
+        try {
+            CameraManager cameraManager = (CameraManager)context.getSystemService(Context.CAMERA_SERVICE);
+            text.setText("Number of cameras: " + String.valueOf(cameraManager.getCameraIdList().length));
+        }
+        catch (Exception _e) {}
+    }
+
+    private void log(String toLog) {
+        Log.i("Robust image source identification on modern smartphones camera", toLog);
+    }
+}

datasets/android/res/layout/activity_main.xml

@@ -0,0 +1,14 @@
+<?xml version="1.0" encoding="utf-8"?>
+<LinearLayout
+    xmlns:android="http://schemas.android.com/apk/res/android"
+    android:layout_width="match_parent"
+    android:layout_height="match_parent"
+    android:gravity="center"
+    android:orientation="vertical">
+
+    <TextView
+        android:layout_width="wrap_content"
+        android:layout_height="wrap_content"
+        android:id="@+id/my_text"/>
+</LinearLayout>
+

datasets/fake/generate_dataset.py

@@ -1,3 +1,5 @@
+# Notes: https://gitea.lemnoslife.com/Benjamin_Loison/Robust_image_source_identification_on_modern_smartphones/issues/21
+
 import numpy as np
 from matplotlib import pyplot as plt
 from PIL import Image
@@ -10,6 +12,10 @@ from rms_diff import rmsDiffPil, rmsDiffNumpy
 sys.path.insert(0, '../../algorithms/context_adaptive_interpolator/')
 
 from context_adaptive_interpolator import contextAdaptiveInterpolator
+
+sys.path.insert(0, '../../algorithms/image_utils/')
+
+from image_utils import showImageWithMatplotlib, randomGaussianImage, toPilImage
 from tqdm import tqdm
 
 IMAGE_SIZE = 64
@@ -18,30 +24,38 @@ NUMBER_OF_IMAGES_PER_PHONE = 10_000
 # Compared to images being 1.
 PRNU_FACTOR = 0.1
 
+IMAGE_SIZE_SHAPE = (IMAGE_SIZE, IMAGE_SIZE)
+
+np.random.seed(0)
+
 # Generate PRNUs and images of phones.
-# Is such `np.maximum` probabilistically correct with our theoretical method? See #19.
-def randomImage(scale):
-    return np.random.normal(loc = 0, scale = scale, size = (IMAGE_SIZE, IMAGE_SIZE))
+imagesWithoutPrnu = [[randomGaussianImage(scale = 1, size = IMAGE_SIZE_SHAPE) for _ in range(NUMBER_OF_IMAGES_PER_PHONE)] for phoneIndex in range(NUMBER_OF_PHONES)]
 
-imagesWithoutPrnu = [[randomImage(scale = 1) for _ in range(NUMBER_OF_IMAGES_PER_PHONE)] for phoneIndex in range(NUMBER_OF_PHONES)]
-
-prnus = [randomImage(scale = PRNU_FACTOR) for _ in range(NUMBER_OF_PHONES)]
+prnus = [randomGaussianImage(scale = PRNU_FACTOR, size = IMAGE_SIZE_SHAPE) for _ in range(NUMBER_OF_PHONES)]
 
 imagesWithPrnu = [[imageWithoutPrnu + prnus[phoneIndex] for imageWithoutPrnu in imagesWithoutPrnu[phoneIndex]] for phoneIndex in range(NUMBER_OF_PHONES)]
 
 allImages = np.max([np.max(imagesWithoutPrnu) + np.max(prnus) + np.max(imagesWithPrnu)])
 
-def toPilImage(npArray):
-    return Image.fromarray(npArray)
-
 def showImageWithPil(npArray):
     npArray -= npArray.min()
     npArray = (npArray / npArray.max()) * 255
     Image.fromarray(npArray).show()
 
-def showImageWithMatplotlib(npArray):
-    plt.imshow(npArray)
-    plt.show()
+plt.title('RMS between actual PRNU and the mean of the first $N$ images with PRNU (i.e. estimated PRNU)')
+plt.xlabel('$N$ first images with PRNU')
+plt.ylabel('RMS')
+plt.xscale('log')
+rmss = []
+mean = np.zeros(IMAGE_SIZE_SHAPE)
+for imageIndex in range(NUMBER_OF_IMAGES_PER_PHONE):
+    mean = (mean * imageIndex + imagesWithPrnu[0][imageIndex]) / (imageIndex + 1)
+    rms = rmsDiffNumpy(mean, prnus[0])
+    rmss += [rms]
+plt.plot(rmss)
+plt.show()
+
+##
 
 NUMBER_OF_ROWS = 5
 NUMBER_OF_COLUMNS = 3

datasets/noise_free_test_images/estimate_prnu.py

@@ -0,0 +1,100 @@
+# Notes: https://gitea.lemnoslife.com/Benjamin_Loison/Robust_image_source_identification_on_modern_smartphones/issues/25
+
+import os
+from PIL import Image
+import numpy as np
+import matplotlib.pyplot as plt
+import sys
+
+sys.path.insert(0, '../../algorithms/image_utils/')
+
+from image_utils import showImageWithMatplotlib, randomGaussianImage, toPilImage, getPrnuShownAsSuch
+
+sys.path.insert(0, '../../algorithms/context_adaptive_interpolator/')
+
+from context_adaptive_interpolator import contextAdaptiveInterpolator
+
+sys.path.insert(0, '../../algorithms/distance/')
+
+from rms_diff import rmsDiffNumpy
+
+from skimage.restoration import denoise_tv_chambolle
+
+datasetPath = 'no_noise_images'
+# Note that contrarily to `datasets/fake/`, here we do not have images being Gaussian with `scale` `1` but actual images with pixel values between 0 and 255.
+# In addition to the range difference, note that the distribution in the first set of images was a Gaussian and here is very different and specific.
+PRNU_FACTOR = 0.01
+NOISE_FACTOR = 0.1
+
+np.random.seed(0)
+
+SPLIT_N_X_N_S = [1, 2, 4]
+
+# len(SPLIT_N_X_N_S)
+fig, axes = plt.subplots(2, 4)
+fig.suptitle('PRNU estimation with different number of images having Gaussian noise and Gaussian noised PRNU')
+
+for splitNXNIndex, splitNXN in enumerate(SPLIT_N_X_N_S):
+    IMAGE_SIZE_SHAPE = [dimension // splitNXN for dimension in (704, 469)]
+
+    #prnuNpArray = 255 * randomGaussianImage(scale = PRNU_FACTOR, size = IMAGE_SIZE_SHAPE)
+    prnuNpArray = getPrnuShownAsSuch(IMAGE_SIZE_SHAPE, 255) * PRNU_FACTOR
+
+    def isIn256Range(x):
+        return 0 <= x and x <= 255
+
+    imagesPrnuEstimateNpArray = []
+    isFirstImage = True
+
+    for imageName in os.listdir(datasetPath):
+        if imageName.endswith('.png'):
+            imagePath = f'{datasetPath}/{imageName}'
+            imageWithoutPrnuPil = Image.open(imagePath).convert('F')
+            imageWithoutPrnuNpArray = np.array(imageWithoutPrnuPil)
+
+            m = IMAGE_SIZE_SHAPE[1]
+            n = IMAGE_SIZE_SHAPE[0]
+
+            imageWithoutPrnuNpArrayTiles = [imageWithoutPrnuNpArray[x : x + m, y : y + n] for x in range(0, imageWithoutPrnuNpArray.shape[0], m) for y in range(0, imageWithoutPrnuNpArray.shape[1], n)]
+            for imageWithoutPrnuNpArrayTile in imageWithoutPrnuNpArrayTiles:
+                #print(imageWithoutPrnuNpArrayTile.shape, tuple(IMAGE_SIZE_SHAPE[::-1]))
+                #if imageWithoutPrnuNpArrayTile.shape != tuple(IMAGE_SIZE_SHAPE[::-1]):
+                #    continue
+                imageNoise = randomGaussianImage(scale = 255 * NOISE_FACTOR, size = imageWithoutPrnuNpArrayTile.shape)
+                imageWithPrnuNpArray = imageWithoutPrnuNpArrayTile + prnuNpArray + imageNoise
+
+                if splitNXNIndex == 0 and isFirstImage:
+                    axis = axes[0]
+
+                    axis[0].set_title('First image without noise')
+                    axis[0].imshow(imageWithoutPrnuNpArrayTile)
+
+                    axis[1].set_title('Actual Gaussian noised PRNU')
+                    axis[1].imshow(prnuNpArray)
+
+                    axis[2].set_title('F. i. with G. n.')
+                    axis[2].imshow(imageWithoutPrnuNpArray + imageNoise)
+
+                    axis[3].set_title('F. i. with G. n. and PRNU')
+                    axis[3].imshow(imageWithoutPrnuNpArray + prnuNpArray + imageNoise)
+                isFirstImage = False
+
+                #assert all([isIn256Range(extreme) for extreme in [imageWithPrnuNpArray.max(), imageWithPrnuNpArray.min()]]), 'Adding the PRNU resulted in out of 256 bounds image'
+                imageWithPrnuPil = toPilImage(imageWithPrnuNpArray)
+                #imagePrnuEstimatePil = contextAdaptiveInterpolator(imageWithPrnuPil.load(), imageWithPrnuPil)
+                #imagePrnuEstimateNpArray = np.array(imagePrnuEstimatePil)
+                imagePrnuEstimateNpArray = imageWithPrnuNpArray - denoise_tv_chambolle(imageWithPrnuNpArray, weight=0.2, channel_axis=-1)
+
+                imagesPrnuEstimateNpArray += [imagePrnuEstimateNpArray]
+
+    cameraPrnuEstimateNpArray = np.array(imagesPrnuEstimateNpArray).mean(axis = 0)
+    rms = rmsDiffNumpy(cameraPrnuEstimateNpArray, prnuNpArray, True)
+    title = f'RMS with actual PRNU: {rmsDiffNumpy(cameraPrnuEstimateNpArray, prnuNpArray):.4f}\n(normalized RMS: {rmsDiffNumpy(cameraPrnuEstimateNpArray, prnuNpArray, True):.4f})'
+    axis = axes[1]
+    axis[splitNXNIndex].set_title(f'Number of images: {len(imagesPrnuEstimateNpArray)}\n{title}')
+    axis[splitNXNIndex].imshow(cameraPrnuEstimateNpArray)
+
+axes[1][3].axis('off')
+
+plt.tight_layout()
+plt.show()

datasets/noise_free_test_images/no_noise_images.zip.txt

@@ -0,0 +1 @@
+https://web.archive.org/web/20220121204219/https://mcolom.perso.math.cnrs.fr/download/no_noise_images/no_noise_images.zip

datasets/noise_free_test_images/webpage/noise_free_test_images.pdf.txt

@@ -0,0 +1 @@
+https://web.archive.org/web/20221230200626/https://mcolom.perso.math.cnrs.fr/pages/no_noise_images/

datasets/noise_free_test_images/webpage/noise_free_test_images.xopp.xml

@@ -0,0 +1,39 @@
+<?xml version="1.0" standalone="no"?>
+<xournal creator="Xournal++ 1.1.3" fileversion="4">
+  <title>Xournal++ document - see https://github.com/xournalpp/xournalpp</title>
+  <page width="596.00000000" height="842.00000000">
+    <background type="pdf" domain="absolute" filename="/home/benjamin/Desktop/bens_folder/school/ens/asp/aria/internship/work/datasets/noise_free_test_images/webpage/noise_free_test_images.pdf" pageno="1"/>
+    <layer>
+      <stroke tool="pen" ts="0" fn="" color="#00c0ffff" width="1.41000000" fill="255">44.16991935 149.86110719 276.63410082 149.86110719</stroke>
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+      <stroke tool="highlighter" color="#00c0ff7f" width="8.50000000" fill="128">42.99659414 184.92242482 112.65886477 184.92242482</stroke>
+      <stroke tool="highlighter" color="#00c0ff7f" width="8.50000000" fill="128">149.16792941 183.91166246 216.17115865 183.91166246</stroke>
+      <stroke tool="highlighter" color="#00c0ff7f" width="8.50000000" fill="128">294.03605816 184.15454202 354.65329212 184.15454202</stroke>
+      <stroke tool="highlighter" color="#00c0ff7f" width="8.50000000" fill="128">378.27254366 184.83142695 394.34985957 184.14821152</stroke>
+      <stroke tool="highlighter" color="#00c0ff7f" width="8.50000000" fill="128">85.58659829 199.62412937 411.27805159 199.62412937</stroke>
+      <stroke tool="highlighter" color="#ffff007f" width="8.50000000" fill="128">418.29380004 200.39743471 541.57682036 200.39743471</stroke>
+      <stroke tool="highlighter" color="#ffff007f" width="8.50000000" fill="128">44.73424234 213.56413029 69.64328426 213.56413029</stroke>
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+      <stroke tool="highlighter" color="#00c0ff7f" width="8.50000000" fill="128">370.62786512 257.40568303 547.67625847 257.40568303</stroke>
+      <stroke tool="highlighter" color="#00c0ff7f" width="8.50000000" fill="128">44.88887314 272.09091817 331.36022445 272.09091817</stroke>
+      <stroke tool="highlighter" color="#ffff007f" width="8.50000000" fill="128">350.89020532 268.80336793 450.39163443 268.80336793</stroke>
+      <stroke tool="highlighter" color="#ffff007f" width="8.50000000" fill="128">44.93306058 287.15898498 351.10131254 287.15898498</stroke>
+      <stroke tool="highlighter" color="#ffff007f" width="8.50000000" fill="128">45.27054464 307.01382219 263.17418553 307.01382219</stroke>
+      <stroke tool="highlighter" color="#ffff007f" width="8.50000000" fill="128">93.80213209 345.70257013 454.50227983 345.70257013</stroke>
+      <text font="Sans" size="11.00000000" x="45.82731792" y="353.90876455" color="#00c0ffff" ts="0" fn="">legends not matching actual webpage web-browser rendering</text>
+      <text font="Sans" size="11.00000000" x="48.63067627" y="32.63059998" color="#00c0ffff" ts="0" fn="">Almost identical content as `no_noise_images.zip/readme.txt`.</text>
+    </layer>
+  </page>
+  <page width="596.00000000" height="842.00000000">
+    <background type="pdf" pageno="2"/>
+    <layer/>
+  </page>
+  <page width="596.00000000" height="842.00000000">
+    <background type="pdf" pageno="3"/>
+    <layer/>
+  </page>
+</xournal>

datasets/rafael/240424/plot_dates.py

@@ -0,0 +1,65 @@
+from datetime import datetime
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+import matplotlib.dates as mdates
+
+from PIL import Image
+from PIL.ExifTags import TAGS
+
+import os
+
+path = 'photos'
+
+os.chdir(path)
+
+names = []
+dates = []
+
+for fileName in sorted(os.listdir()):
+    try:
+        image = Image.open(fileName)
+    except:
+        # Skip raw images.
+        continue
+    imageExif = image.getexif()
+    dateTimeKey = list(TAGS.keys())[list(TAGS.values()).index('DateTime')]
+    dateTime = imageExif[dateTimeKey]
+    names += [fileName.replace('DSC0', '').replace('.JPG', '')]
+    dates += [dateTime[:(-1 if fileName.endswith('.tif') else len(dateTime))]]
+
+# Convert date strings to datetime
+dates = [datetime.strptime(d, "%Y:%m:%d %H:%M:%S") for d in dates]
+
+# Choose some nice levels
+NUMBER_OF_LEVELS = 10
+actualLevels = range(-NUMBER_OF_LEVELS * 2 + 1, NUMBER_OF_LEVELS * 2 , 2)
+levels = np.tile(actualLevels,
+                 int(np.ceil(len(dates)/len(actualLevels))))[:len(dates)]
+
+# Create figure and plot a stem plot with the date
+fig, ax = plt.subplots(figsize=(8.8, 4), layout="constrained")
+#ax.set(title="Matplotlib release dates")
+
+ax.vlines(dates, 0, levels, color="tab:red")  # The vertical stems.
+ax.plot(dates, np.zeros_like(dates), "-o",
+        color="k", markerfacecolor="w")  # Baseline and markers on it.
+
+# annotate lines
+for d, l, r in zip(dates, levels, names):
+    ax.annotate(r, xy=(d, l),
+                xytext=(-3, np.sign(l)*3), textcoords="offset points",
+                horizontalalignment="right",
+                verticalalignment="bottom" if l > 0 else "top")
+
+# format x-axis with 4-month intervals
+ax.xaxis.set_major_formatter(mdates.DateFormatter("%Y:%m:%d %H:%M:%S"))
+plt.setp(ax.get_xticklabels(), rotation=30, ha="right")
+
+# remove y-axis and spines
+ax.yaxis.set_visible(False)
+ax.spines[["left", "top", "right"]].set_visible(False)
+
+ax.margins(y=0.1)
+plt.show()

datasets/raise/analyze_bayer_filter_mean_denoiser.py

@@ -0,0 +1,35 @@
+import numpy as np
+from utils import Color
+import os
+from tqdm import tqdm
+import rawpy
+import matplotlib.pyplot as plt
+
+os.chdir('flat-field/NEF')
+
+firstBayerFilterOccurrenceImages = []
+
+for fileName in tqdm(os.listdir()):
+    with rawpy.imread(fileName) as raw:
+        colorDesc = raw.color_desc.decode('ascii')
+        assert colorDesc == 'RGBG'
+        assert np.array_equal(raw.raw_pattern, np.array([[0, 1], [3, 2]], dtype = np.uint8))
+        # RG
+        # GB
+        firstBayerFilterOccurrenceImage = raw.raw_image_visible.copy()[:2, :2]
+        firstBayerFilterOccurrenceImages += [firstBayerFilterOccurrenceImage]
+
+firstBayerFilterOccurrenceImages = np.array(firstBayerFilterOccurrenceImages)
+print(rawImageVisible)
+
+NUMBER_OF_COLORS = 3
+HEX_COLOR = '#' + '%02x' * NUMBER_OF_COLORS
+COLOR_BASE = 256
+
+def getColor(colorIndex):
+    return HEX_COLOR % tuple((255 if colorIndex == colorIndexTmp else 0) for colorIndexTmp in range(NUMBER_OF_COLORS))
+
+for colorIndex, (colorY, colorX) in enumerate([(0, 0), (0, 1), (1, 1)]):
+    X = firstBayerFilterOccurrenceImages[:, colorY, colorX] - np.mean(firstBayerFilterOccurrenceImages, axis = 0)[colorY, colorX]
+    plt.hist(X, bins = len(set(X)), color = getColor(colorIndex), alpha = 0.3)
+plt.show()

datasets/raise/attribute_source_camera.py

@@ -0,0 +1,204 @@
+#!/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, getColorMeans, getImageCrop, Denoiser
+import sys
+import os
+import random
+
+sys.path.insert(0, '../../algorithms/distance/')
+
+from rms_diff import rmsDiffNumpy
+
+# Configuration:
+
+DENOISER = Denoiser.MEAN
+IMAGES_CAMERAS_FOLDER = {
+    'RAISE': 'flat-field/nef',
+    'Rafael 23/04/24': 'rafael/230424',
+}
+TRAINING_PORTION = 0.5
+PREDICT_ONLY_ON_WHOLE_TRAINING_SET = False
+
+setting = ','.join([escapeFilePath(imageCameraFolder) for imageCameraFolder in IMAGES_CAMERAS_FOLDER]) + f'_{DENOISER}'
+
+imagesCamerasFileNames = {camera: [imageCameraFile for imageCameraFile in os.listdir(imageCameraFolder) if imageCameraFile.endswith('.NEF') or imageCameraFile.endswith('.ARW')] for camera, imageCameraFolder in IMAGES_CAMERAS_FOLDER.items()}
+# Fix randomness for reproducibility.
+random.seed(0)
+# Randomize order to not have a bias (chronological for instance) when split to make training and testing sets.
+for camera in IMAGES_CAMERAS_FOLDER:
+    random.shuffle(imagesCamerasFileNames[camera])
+
+# Limit number of images per camera with the one having the less images.
+minimumNumberOfImagesCameras = 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}
+
+# 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
+
+minColor = 0#None
+maxColor = 7952#None
+
+accuracy = []
+numberOfTrainingImages = int(minimumNumberOfImagesCameras * TRAINING_PORTION)
+numberOfTestingImages = minimumNumberOfImagesCameras - int(minimumNumberOfImagesCameras * TRAINING_PORTION)
+cameraTestingImagesNoise = {}
+
+def getImageFilePath(camera, cameraImageIndex):
+    imageFileName = imagesCamerasFileNames[camera][cameraImageIndex]
+    imageFilePath = f'{IMAGES_CAMERAS_FOLDER[camera]}/{imageFileName}'
+    return imageFilePath
+
+def getSingleColorChannelImages(camera, cameraImageIndex):
+    imageFilePath = getImageFilePath(camera, cameraImageIndex)
+    singleColorChannelImages = {color: rescaleIfNeeded(getImageCrop(getColorChannel(imageFilePath, color), minimalColorChannelCameraResolution), minColor, maxColor) for color in Color}
+    return singleColorChannelImages
+
+def getMultipleColorsImage(singleColorChannelImages):
+    multipleColorsImage = mergeSingleColorChannelImagesAccordingToBayerFilter(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
+    multipleColorsDenoisedImage = mergeSingleColorChannelImagesAccordingToBayerFilter(singleColorChannelDenoisedImages)
+    imagePrnuEstimateNpArray = multipleColorsImage - multipleColorsDenoisedImage
+    return imagePrnuEstimateNpArray
+
+imagesCamerasFilePaths = {camera: [f'{IMAGES_CAMERAS_FOLDER[camera]}/{imagesCamerasFileName}' for imagesCamerasFileName in imagesCamerasFileNames[camera]] for camera in imagesCamerasFileNames}
+# If the denoiser is `MEAN`:
+# 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}
+
+from utils import silentTqdm
+#tqdm = silentTqdm
+
+returnSingleColorChannelImage = lambda singleColorChannelImage, _minColor, _maxColor: singleColorChannelImage
+
+# Assume to have `{min,max}Color` hardcoded.
+# Can just load to memory `getSingleColorChannelImages`, see [Robust_image_source_identification_on_modern_smartphones/issues/62#issuecomment-1861](https://gitea.lemnoslife.com/Benjamin_Loison/Robust_image_source_identification_on_modern_smartphones/issues/62#issuecomment-1861).
+rescaleIfNeeded = rescaleRawImageForDenoiser
+cameraTrainingImages = {}
+for cameraTrainingImageIndex in tqdm(range(numberOfTrainingImages), 'Load to memory camera training image'):
+    for cameraIndex, camera in enumerate(IMAGES_CAMERAS_FOLDER):
+        singleColorChannelImages = getSingleColorChannelImages(camera, cameraTrainingImageIndex)
+        multipleColorsImage = getMultipleColorsImage(singleColorChannelImages)
+        cameraTrainingImages[camera] = cameraTrainingImages.get(camera, []) + [multipleColorsImage]
+
+singleColorChannelTestingImages = {camera: [] for camera in IMAGES_CAMERAS_FOLDER}
+for camera in IMAGES_CAMERAS_FOLDER:
+    for cameraTestingImageIndex in tqdm(range(numberOfTestingImages), 'Load to memory camera testing image'):
+        singleColorChannelImages = getSingleColorChannelImages(camera, numberOfTrainingImages + cameraTestingImageIndex)
+        singleColorChannelTestingImages[camera] += [singleColorChannelImages]
+
+# 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
+for computeExtremes in tqdm(([True] if minColor is None or maxColor is None else []) + [False], 'Compute extremes'):
+    rescaleIfNeeded = returnSingleColorChannelImage if computeExtremes else rescaleRawImageForDenoiser
+    # As the second loop firstly if the denoiser is not `MEAN` or if `MEAN` predicts only on the whole training set, then compute noises of testing images.
+    if not computeExtremes:
+        print(f'{minColor=} {maxColor=}')
+        if DENOISER != Denoiser.MEAN or PREDICT_ONLY_ON_WHOLE_TRAINING_SET:
+            print('Extracting noise of testing images')
+            for camera in tqdm(IMAGES_CAMERAS_FOLDER, 'Camera'):
+                for cameraTestingImageIndex in tqdm(range(numberOfTestingImages), 'Camera testing image index'):
+                    print(f'{camera=} {numberOfTrainingImages + cameraTestingImageIndex=}')
+
+                    singleColorChannelImages = singleColorChannelTestingImages[camera][cameraTestingImageIndex]#getSingleColorChannelImages(camera, numberOfTrainingImages + cameraTestingImageIndex)
+                    multipleColorsImage = getMultipleColorsImage(singleColorChannelImages)
+
+                    imagePrnuEstimateNpArray = getImagePrnuEstimateNpArray(singleColorChannelImages, multipleColorsImage, camera)
+
+                    cameraTestingImagesNoise[camera] = cameraTestingImagesNoise.get(camera, []) + [imagePrnuEstimateNpArray]
+    # As the first loop compute `{min,max}Color` based on testing images.
+    # As the second loop improve PRNU estimation by considering an additional testing image and if needed measure current learning step accuracy.
+    for cameraTrainingImageIndex in tqdm(range(minimumNumberOfImagesCameras if computeExtremes else numberOfTrainingImages), 'Camera training image index'):
+        for cameraIndex, camera in enumerate(tqdm(IMAGES_CAMERAS_FOLDER, 'Camera')):
+            singleColorChannelImages = getSingleColorChannelImages(camera, cameraTrainingImageIndex)
+            multipleColorsImage = getMultipleColorsImage(singleColorChannelImages)
+
+            if computeExtremes:
+                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)
+            # 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
+                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'):
+                        cameraPredicted = None
+                        minimalDistance = None
+                        #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 = singleColorChannelTestingImages[camera][cameraTestingImageIndex]#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=}')
+                            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)]
+
+# Save the estimated PRNU of each camera after having trained on the whole training set.
+for camera in IMAGES_CAMERAS_FOLDER:
+    plt.imsave(f'{setting}_estimated_prnu_camera_{escapeFilePath(camera)}.png', (camerasIterativeMean[camera].mean))
+
+# Plot and save the accuracy of camera source attribution thanks to a given number of images to estimate PRNUs with `DENOISER` denoiser.
+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')

datasets/raise/benchmark_load_part_of_images.py

@@ -0,0 +1,46 @@
+#!/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()
+
+    def __str__(self):
+        return self.name
+
+OPERATION = Operation.LOAD_NPY
+RESOLUTION = 100
+print(f'{OPERATION = }')
+if OPERATION == Operation.LOAD_NPY:
+    print(f'{RESOLUTION = }')
+
+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') or file.endswith('.ARW'):
+            #print(file)
+            imageFilePath = f'{imagesCameraFolder}/{file}'
+            numpyFilePath = f'{imageFilePath}.npy'
+            rawColorChannels = {}
+            if OPERATION in [Operation.LOAD_RAW, Operation.SAVE]:
+                for color in Color:
+                    rawColorChannel = getColorChannel(imageFilePath, color)
+                    rawColorChannels[color] = rawColorChannel
+            if OPERATION == Operation.SAVE:
+                np.save(numpyFilePath, rawColorChannels)
+            if OPERATION == Operation.LOAD_NPY:
+                rawColorChannels = np.load(numpyFilePath, mmap_mode = 'r', allow_pickle = True)
+                rawColorChannelsItem = rawColorChannels.item()
+                for color in Color:
+                    print(color, rawColorChannelsItem[color][:RESOLUTION].mean())

datasets/raise/extract_noise.py

@@ -0,0 +1,95 @@
+#!/usr/bin/env python
+
+import numpy as np
+import os
+from tqdm import tqdm
+import csv
+from utils import Color, denoise, iterativeMean, escapeFilePath, saveNpArray, getColorMeans, getImageNpArray, Denoiser
+import matplotlib.pyplot as plt
+
+IMAGES_FOLDER_PATH = 'rafael/230424'
+imagesFolderPathFileName = escapeFilePath(IMAGES_FOLDER_PATH)
+DENOISER = Denoiser.WAVELET
+
+RAISE_NOT_FLAT_FIELDS = False
+# `[Color.RED, Color.GREEN_RIGHT, ...]` or `Color` or `[None]` for not raw images.
+COLORS = [None]
+
+imagesFileNames = os.listdir(IMAGES_FOLDER_PATH + ('/png' if RAISE_NOT_FLAT_FIELDS else ''))
+
+if RAISE_NOT_FLAT_FIELDS:
+    files = {}
+
+    with open('RAISE_all.csv') as csvfile:
+        reader = csv.DictReader(csvfile)
+        for row in tqdm(list(reader), 'CSV parsing'):
+            file = row['File'] + '.png'
+            files[file] = row
+
+    imagesFileNames = [imageFileName for imageFileName in tqdm(imagesFileNames, 'Filtering images') if files[imageFileName]['Device'] == 'Nikon D7000' and Image.open(f'{IMAGES_FOLDER_PATH}/png/{imageFileName}').size == (4946, 3278)]
+
+# Among:
+# - `None`
+# - `'sky'`
+# - `'wall'`
+type_ = None
+if type_ is not None:
+    ranges = {
+        'sky': range(2_699, 2_807),
+        'wall': range(2_807, 2_912),
+    }
+    imagesFileNames = [f'DSC0{imageIndex}.ARW' for imageIndex in ranges[type_]]
+    imagesFolderPathFileName += f'_{type_}'
+
+minColor = None
+maxColor = None
+
+def getImageFilePath(imageFileName):
+    if RAISE_NOT_FLAT_FIELDS:
+        imageFileName = imageFileName.replace('.png', '.NEF')
+        imageFilePath = f'{IMAGES_FOLDER_PATH}/nef/{imageFileName}'
+    else:
+        imageFilePath = f'{IMAGES_FOLDER_PATH}/{imageFileName}'
+    return imageFilePath
+
+# `color` is the actual color to estimate PRNU with.
+def treatImage(imageFileName, computeExtremes = False, color = None):
+    global estimatedPrnuIterativeMean
+    imageFilePath = getImageFilePath(imageFileName)
+    imageNpArray = getImageNpArray(imageFilePath, computeExtremes, color, DENOISER)
+    if imageNpArray is None:
+        return
+    if DENOISER != Denoiser.MEAN:
+        imageDenoisedNpArray = denoise(imageNpArray, DENOISER)
+    else:
+        imageDenoisedNpArray = colorMeans[color]
+    imageNoiseNpArray = imageNpArray - imageDenoisedNpArray
+    estimatedPrnuIterativeMean.add(imageNoiseNpArray)
+
+if (minColor is None or maxColor is None) and DENOISER != Denoiser.MEAN:
+    # Assuming same intensity scale across color channels.
+    for imageFileName in tqdm(imagesFileNames, 'Computing extremes of images'):
+        for color in COLORS:
+            treatImage(imageFileName, computeExtremes = True, color = color)
+
+    # To skip this step next time.
+    # Maybe thanks to `rawpy.RawPy` fields, possibly stating device maximal value, can avoid doing so to some extent.
+    print(f'{minColor=}')
+    print(f'{maxColor=}')
+
+if DENOISER == Denoiser.MEAN:
+    colorMeans = getColorMeans(imagesFileNames, COLORS)
+    for color in Color:
+        colorMeans[color] = colorMeans[color]
+        fileName = f'mean_{imagesFolderPathFileName}_{color}'
+        # Then use `merge_single_color_channel_images_according_to_bayer_filter.py` to consider all color channels, instead of saving this single color channel as an image.
+        saveNpArray(fileName, colorMeans[color])
+
+for color in COLORS:
+    estimatedPrnuIterativeMean = iterativeMean()
+
+    for imageFileName in tqdm(imagesFileNames, f'Denoising images for color {color}'):
+        treatImage(imageFileName, color = color)
+
+    npArrayFilePath = f'mean_{imagesFolderPathFileName}_{DENOISER}_{color}'
+    saveNpArray(npArrayFilePath, estimatedPrnuIterativeMean.mean)

datasets/raise/flat-field_pointer.py

@@ -0,0 +1,92 @@
+import matplotlib.pyplot as plt
+import numpy as np
+from matplotlib.backend_bases import MouseButton
+import matplotlib.image as mpimg
+import os
+from tqdm import tqdm
+
+# `Zoom to rectangle` shortcut is `o`.
+
+os.chdir('flat-field/TIF')
+
+fileNames = sorted(os.listdir())[41:]
+fileNameIndex = 0
+xys = []
+progressBar = tqdm(total = len(fileNames))
+
+def displayImage():
+    global fileNameIndex
+    if len(fileNames) > fileNameIndex:
+        fileName = fileNames[fileNameIndex]
+        image = mpimg.imread(fileName)
+
+        fig, ax = plt.subplots()
+
+        ax.imshow(image)
+        fileNameIndex += 1
+        plt.connect('button_press_event', onClick)
+
+        mng = plt.get_current_fig_manager()
+        mng.full_screen_toggle()
+
+        fig.canvas.toolbar.zoom()
+        fig.show()
+
+def onClick(event):
+    global xys
+    button = event.button
+    if button is MouseButton.RIGHT:
+        xy = [event.xdata, event.ydata]
+        xys += [xy]
+    if button is MouseButton.MIDDLE or button is MouseButton.RIGHT:
+        if button is MouseButton.MIDDLE:
+            print(f'Skipped {fileName}')
+        plt.close()
+        displayImage()
+    progressBar.update(1)
+
+displayImage()
+
+##
+
+xys = [[3061.83568617998, 842.2814890347822], [3048.9553647053262, 891.7951806771933], [3109.6923734795832, 878.8472318972372], [3095.1992301129835, 859.646383417094], [3044.950680354029, 830.531447782855], [3034.039239345914, 775.7097977790371], [3034.0562409262707, 753.1780473232427], [2991.7499740162116, 742.1417475753151], [3021.5541233968024, 737.7119154247914], [3022.412107608028, 703.0028101114289], [3094.4565209481857, 680.7339885543926], [3101.840188177543, 692.6886985125186], [3150.09819940581, 686.3913997808281], [3148.4955568040136, 694.3747788064451], [3165.156239230552, 703.9346483213995], [3158.7235775979357, 710.9522942666542], [3156.5302112965014, 697.5993396871294], [3106.8119050930513, 698.4561327048264], [3151.4728367372877, 694.3380683877142], [3137.283014559081, 662.9543422436452], [3008.1987322850605, 692.0180565561739], [3036.647953172549, 705.1188029786949], [2988.9166660643627, 684.0851408200217], [3001.5070402052334, 684.1944057536488], [2967.282534077184, 680.6072888791263], [2983.5140619626286, 693.469904886339], [2979.972210442175, 702.9033995969894], [2974.7535915953795, 709.6086279669086], [2972.55952140686, 700.4689248964266], [2967.1769510144627, 703.4861098128929], [2968.4714535085923, 708.5924315970815], [2978.725084963369, 725.0064286729727], [2992.7472737250696, 718.9682686788141], [2999.106406229902, 713.0463041988097], [2981.5746364633296, 692.5739107725338], [3014.22885383495, 675.1638989177214], [3004.351989366563, 635.6738446427469], [2964.9109157636794, 580.2416938434527], [2976.672441933737, 599.3922555819147], [2972.9623072548534, 578.6218471929612], [2694.5865522760287, 972.5526873692775], [2924.1211630176217, 1115.9816839025543], [2910.2861978522596, 1095.5172128924614], [2917.4530137143342, 1067.9680470058072], [2919.3469031337613, 1068.44539073963], [2959.4757277956496, 1105.6227950624348], [2936.370033263817, 1157.9142039933472], [2965.3568614486203, 1145.3186319170795], [2951.1991473505136, 1070.8989245366433], [2946.108094501549, 1045.891549058405], [2947.19213475732, 1074.4303801863052], [2943.690837961984, 1071.4536958497956], [3021.083385372544, 1116.881810271317], [3051.7205580454297, 1103.0992679894152], [3107.0873956690143, 972.8919609441568], [2861.932349690137, 1131.4847600231471], [2789.583044951935, 964.6512841164608]]
+
+x, y = [[xy[dimensionIndex] for xy in xys] for dimensionIndex in range(2)]
+
+plt.title('Center wall marker locations')
+
+plt.scatter(x, y, c = range(len(x)))
+for xyIndex, xy in enumerate(xys):
+    plt.text(xy[0], xy[1] + 10, str(xyIndex), horizontalalignment = 'center')
+
+plt.show()
+
+##
+
+greatestDistances = []
+
+#for xy in xys:
+for xy, otherXy in zip(xys, xys[1:]):
+    greatestDistance = None
+    #for otherXy in xys:
+    if xy != otherXy:
+        distance = sum([(xy[dimensionIndex] - otherXy[dimensionIndex]) ** 2 for dimensionIndex in range(2)]) ** 0.5
+        if greatestDistance is None or distance > greatestDistance:
+            greatestDistance = distance
+    greatestDistances += [greatestDistance]
+
+fig, ax = plt.subplots()
+plt.title('Distance between a wall marker location and the next one')
+ax.boxplot(greatestDistances)
+
+ys = []
+
+for percentile in [25, 50, 75]:
+    y = np.percentile(greatestDistances, percentile)
+    ys += [y]
+    ax.axhline(y = y)
+
+ax.set_yticks(list(ax.get_yticks())[1:-1] + ys)
+
+ax.set_xticks([], [])
+fig.show()

datasets/raise/generate_histogram.py

@@ -0,0 +1,41 @@
+import os
+from PIL import Image
+import matplotlib.pyplot as plt
+import numpy as np
+from tqdm import tqdm
+
+os.chdir('flat-field/TIF')
+
+NUMBER_OF_COLORS = 3
+HEX_COLOR = '#' + '%02x' * NUMBER_OF_COLORS
+COLOR_BASE = 256
+
+def getColor(colorIntensity, colorIndex):
+    return HEX_COLOR % tuple((colorIntensity if colorIndex == colorIndexTmp else 0) for colorIndexTmp in range(NUMBER_OF_COLORS))
+
+def getHistogram(fileName):
+    image = Image.open(fileName)
+    #image = image.crop((1, 1, 2, 2))
+    #print(image.size)
+
+    histogram = image.histogram()
+
+    colors = [histogram[COLOR_BASE * colorIndex:COLOR_BASE * (colorIndex + 1)] for colorIndex in range(NUMBER_OF_COLORS)]
+    return colors
+
+def plotHistogram(colors):
+    for colorIndex, color in enumerate(colors):
+        for colorIntensity in range(COLOR_BASE):
+            plt.bar(colorIntensity, color[colorIntensity], color = getColor(colorIntensity, colorIndex), alpha = 0.3)
+
+fileNameColors = []
+
+for fileName in tqdm(os.listdir()):
+    colors = getHistogram(fileName)
+    fileNameColors += [colors]
+
+meanFileNameColors = np.mean(fileNameColors, axis = 0)
+
+plotHistogram(meanFileNameColors)
+
+plt.show()

datasets/raise/merge_single_color_channel_images_according_to_bayer_filter.py

@@ -0,0 +1,17 @@
+#!/usr/bin/env python
+
+from utils import Color, mergeSingleColorChannelImagesAccordingToBayerFilter
+import matplotlib.pyplot as plt
+import numpy as np
+
+PREFIX = 'mean_rafael_arw_sky_mean_'
+
+def getImageByColor(color):
+    filePath = PREFIX + f'{color}.npy'
+    image = np.load(filePath)
+    return image
+
+singleColorChannelImages = {color: getImageByColor(color) for color in Color}
+multipleColorsImage = mergeSingleColorChannelImagesAccordingToBayerFilter(singleColorChannelImages)
+
+plt.imsave(PREFIX + 'multiple_colors.png', multipleColorsImage)

datasets/raise/merge_with_same_color_scale_single_color_channel_images_according_to_bayer_filter.py

@@ -0,0 +1,39 @@
+#!/usr/bin/env python
+
+from utils import Denoiser, Color, mergeSingleColorChannelImagesAccordingToBayerFilter
+import matplotlib.pyplot as plt
+import numpy as np
+
+PREFIX = 'means/mean_rafael_230424_'
+
+def getImageByColor(color, denoiser):
+    filePath = PREFIX + f'{denoiser}_{color}.npy'
+    image = np.load(filePath)
+    return image
+
+multipleColorsImageDenoisers = {}
+
+DENOISERS = [
+    Denoiser.BILATERAL,
+    Denoiser.WAVELET,
+    Denoiser.MEAN,
+]
+
+for denoiser in DENOISERS:
+    singleColorChannelImages = {color: getImageByColor(color, denoiser) for color in Color}
+    multipleColorsImage = mergeSingleColorChannelImagesAccordingToBayerFilter(singleColorChannelImages)
+    multipleColorsImageDenoisers[denoiser] = multipleColorsImage
+
+plt.boxplot(list(map(np.ravel, multipleColorsImageDenoisers.values())), labels = DENOISERS)
+plt.grid(True)
+plt.show()
+
+def getExtreme(extreme):
+    vExtreme = extreme(list(multipleColorsImageDenoisers.values()))
+    return vExtreme
+
+vMin, vMax = [getExtreme(extreme) for extreme in [np.min, np.max]]
+
+for denoiser in DENOISERS:
+    multipleColorsImage = multipleColorsImageDenoisers[denoiser]
+    plt.imsave(PREFIX + f'{denoiser}_multiple_colors.png', multipleColorsImage)

datasets/raise/show_mean_noise.py

@@ -0,0 +1,10 @@
+import numpy as np
+import matplotlib.pyplot as plt
+
+fileName = 'mean_flat-field_nef_wavelet_blue'
+npArray = np.load(f'{fileName}.npy')
+# For other than raw images:
+#npArray = (npArray - npArray.min()) / (npArray.max() - npArray.min())
+plt.imsave(f'{fileName}.png', npArray)
+#plt.imshow(npArray)
+#plt.show()