Remove no more relevant commented code for a test

Add and use imageDenoisedNpArray to ease using other denoisers
#33 : Switch to mono-thread
2024-04-06 00:06:13 +02:00 · 2024-04-06 00:03:50 +02:00 · 2024-04-06 00:00:24 +02:00 · 2024-04-05 23:57:37 +02:00 · 2024-04-05 19:01:31 +02:00 · 2024-04-05 14:12:21 +02:00
40 changed files with 990 additions and 13 deletions
--- a/.gitignore
+++ b/.gitignore
@@ -1,2 +1,3 @@
 *.pdf
 *.xopp
+__pycache__
--- a/algorithms/context_adaptive_interpolator/context_adaptive_interpolator.py
+++ b/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()
--- a/algorithms/distance/rms_diff.py
+++ b/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)))
--- a/algorithms/image_utils/image_utils.py
+++ b/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
--- a/Wikipedia/Darktable
+++ b/Wikipedia/Darktable
@@ -0,0 +1 @@
+https://en.wikipedia.org/w/index.php?title=Darktable&oldid=1204679832
--- a/Wikipedia/Darktable
+++ b/Wikipedia/Darktable
@@ -0,0 +1,24 @@
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--- a/Wikipedia/IrfanView
+++ b/Wikipedia/IrfanView
@@ -0,0 +1 @@
+https://en.wikipedia.org/w/index.php?title=IrfanView&oldid=1214028358
--- a/Wikipedia/IrfanView
+++ b/Wikipedia/IrfanView
@@ -0,0 +1,26 @@
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--- a/Wikipedia.pdf.txt
+++ b/Wikipedia.pdf.txt
@@ -0,0 +1 @@
+https://en.wikipedia.org/w/index.php?title=Law_of_large_numbers&oldid=1214684685
--- a/Wikipedia.xopp.xml
+++ b/Wikipedia.xopp.xml
@@ -0,0 +1,62 @@
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--- a/Wikipédia.pdf.txt
+++ b/Wikipédia.pdf.txt
@@ -0,0 +1 @@
+https://fr.wikipedia.org/w/index.php?title=Nikon_Electronic_File&oldid=209775164
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+++ b/Wikipédia.xopp.xml
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--- a/Wikipedia.pdf.txt
+++ b/Wikipedia.pdf.txt
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--- a/articles/Positional
+++ b/articles/Positional
@@ -0,0 +1 @@
+https://web.archive.org/web/20240405093114/https://bammey.com/research/polar/
--- a/articles/Positional
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--- a/Wikipedia.pdf.txt
+++ b/Wikipedia.pdf.txt
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+https://en.wikipedia.org/w/index.php?title=TIFF&oldid=1216190460
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--- a/articles/Wu_ICIP_2012/Wu_ICIP_2012.xopp.xml
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--- a/Wikipedia.pdf.txt
+++ b/Wikipedia.pdf.txt
@@ -0,0 +1 @@
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+++ b/Wikipedia.xopp.xml
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--- a/datasets/fake/generate_dataset.py
+++ b/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
--- a/datasets/noise_free_test_images/estimate_prnu.py
+++ b/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()
--- a/datasets/noise_free_test_images/no_noise_images.zip.txt
+++ b/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
--- a/datasets/noise_free_test_images/webpage/noise_free_test_images.pdf.txt
+++ b/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/
--- a/datasets/noise_free_test_images/webpage/noise_free_test_images.xopp.xml
+++ b/datasets/noise_free_test_images/webpage/noise_free_test_images.xopp.xml
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+      <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>
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--- a/datasets/raise/extract_noise.py
+++ b/datasets/raise/extract_noise.py
@@ -0,0 +1,35 @@
+#!/usr/bin/python3
+
+from skimage.restoration import denoise_tv_chambolle
+from skimage import img_as_float
+import numpy as np
+from PIL import Image
+import os
+from tqdm import tqdm
+
+imagesFolderPath = 'flat-field'
+npArrayFilePath = 'mean.npy'
+
+mean = None
+numberOfImagesInMean = 0
+
+imagesFileNames = os.listdir(imagesFolderPath)
+
+def treatImage(imageFileName):
+    global mean, numberOfImagesInMean
+    imageFilePath = f'{imagesFolderPath}/{imageFileName}'
+    imagePil = Image.open(imageFilePath)
+    imageNpArray = img_as_float(np.array(imagePil))
+    imageDenoisedNpArray = denoise_tv_chambolle(imageNpArray, weight=0.2, channel_axis=-1)
+    imageNoiseNpArray = imageNpArray - imageDenoisedNpArray
+    if mean is None:
+        mean = imageNoiseNpArray
+    else:
+        mean = ((mean * numberOfImagesInMean) + imageNoiseNpArray) / (numberOfImagesInMean + 1)
+    numberOfImagesInMean += 1
+
+for imageFileName in tqdm(imagesFileNames):
+    treatImage(imageFileName)
+
+with open(npArrayFilePath, 'wb') as f:
+    np.save(f, mean)
--- a/datasets/raise/show_mean_noise.py
+++ b/datasets/raise/show_mean_noise.py
@@ -0,0 +1,7 @@
+import numpy as np
+import matplotlib.pyplot as plt
+
+npArray = np.load('mean.npy')
+npArrayMin = npArray.min()
+print(f'{npArrayMin=}')
+plt.imsave('np_array.png', npArray - npArrayMin)
--- a/datasets/raise/split_and_compare_prnus_of_subgroups.py
+++ b/datasets/raise/split_and_compare_prnus_of_subgroups.py
@@ -0,0 +1,53 @@
+from PIL import Image
+import numpy as np
+import matplotlib.pyplot as plt
+from tqdm import tqdm
+from skimage.restoration import denoise_tv_chambolle
+from skimage import img_as_float
+import sys
+
+sys.path.insert(0, '../../algorithms/image_utils/')
+
+from image_utils import showImageWithMatplotlib, toPilImage
+
+sys.path.insert(0, '../../algorithms/distance/')
+
+from rms_diff import rmsDiffNumpy
+
+NUMBER_OF_SUBGROUPS = 1
+
+IMAGES_FOLDER = 'flat-field/TIF'
+imagesFileNames = os.listdir(IMAGES_FOLDER)
+numberOfImagesPerSubgroup = len(imagesFileNames) // NUMBER_OF_SUBGROUPS
+
+# 1
+numberOfImagesThresholds = range(numberOfImagesPerSubgroup, numberOfImagesPerSubgroup + 1)
+
+# Assume random image order to not introduce a bias.
+subgroupsPrnuEstimatesNpArray = []
+for subgroupIndex in range(NUMBER_OF_SUBGROUPS):
+    imagesPrnuEstimateNpArray = []
+    for imageFileName in tqdm(imagesFileNames[numberOfImagesPerSubgroup * subgroupIndex : numberOfImagesPerSubgroup * (subgroupIndex + 1)]):
+        imagePath = f'{IMAGES_FOLDER}/{imageFileName}'
+        imagePil = Image.open(imagePath)
+        imageNpArray = img_as_float(np.array(imagePil))
+        imagePrnuEstimateNpArray = imageNpArray - denoise_tv_chambolle(imageNpArray, weight=0.2, channel_axis=-1)
+        imagesPrnuEstimateNpArray += [imagePrnuEstimateNpArray]
+
+    subgroupPrnuEstimateNpArray = []
+    # Not efficient mean computation.
+    for numberOfImagesIndex, numberOfImages in enumerate(numberOfImagesThresholds):
+        subgroupPrnuEstimateNpArray += [np.array(imagesPrnuEstimateNpArray[:numberOfImages]).mean(axis = 0)]
+        plt.imsave('prnu.png', subgroupPrnuEstimateNpArray[-1])
+    subgroupsPrnuEstimatesNpArray += [subgroupPrnuEstimateNpArray]
+
+rmss = []
+for numberOfImagesIndex, numberOfImages in enumerate(numberOfImagesThresholds):
+    rms = rmsDiffNumpy(subgroupsPrnuEstimatesNpArray[0][numberOfImagesIndex], subgroupsPrnuEstimatesNpArray[1][numberOfImagesIndex])
+    rmss += [rms]
+
+plt.title('RMS between both subgroups estimated PRNUs for a given number of images among them')
+plt.xlabel('Number of images of each subgroup')
+plt.ylabel('RMS between both subgroups estimated PRNUs')
+plt.plot(rmss)
+plt.show()
--- a/datasets/raise/website/RAISE
+++ b/datasets/raise/website/RAISE
@@ -0,0 +1 @@
+https://web.archive.org/web/20221206120232/http://loki.disi.unitn.it/RAISE/download.html
--- a/datasets/raise/website/RAISE
+++ b/datasets/raise/website/RAISE
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--- a/datasets/raise/website/RAISE
+++ b/datasets/raise/website/RAISE
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--- a/datasets/raise/website/RAISE
+++ b/datasets/raise/website/RAISE
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--- a/datasets/raise/website/RAISE
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Author	SHA1	Message	Date
Benjamin Loison	8748e648b5	Remove no more relevant commented code for a test	2024-04-06 00:06:13 +02:00
Benjamin Loison	15c41c0463	Add and use `imageDenoisedNpArray` to ease using other denoisers	2024-04-06 00:03:50 +02:00
Benjamin Loison	b18080e9f5	#33 : Switch to mono-thread	2024-04-06 00:00:24 +02:00
Benjamin Loison	a83e8bb1e0	Replace tabs with spaces in `datasets/raise/extract_noise.py`	2024-04-05 23:57:37 +02:00
Benjamin Loison	ba452096d5	#24 : WebArchive `articles/Positional Learning Quentin Bammey` As there is no WebArchive error anymore.	2024-04-05 19:01:31 +02:00
Benjamin Loison	d271b502fa	Add `articles/Pixel binning - Wikipedia/`	2024-04-05 14:12:21 +02:00
Benjamin Loison	658aa34600	Add `articles/Self-supervised learning - Wikipedia`	2024-04-05 11:37:33 +02:00
Benjamin Loison	4d267bf540	Add `articles/Positional Learning Quentin Bammey/`	2024-04-05 11:32:20 +02:00
Benjamin Loison	606821d45e	Add `show_mean_noise.py`	2024-04-03 22:18:18 +02:00
Benjamin Loison	b354f59ac5	Make parallel production ready	2024-04-03 19:15:21 +02:00
Benjamin Loison	0f394faa61	Multicores prototype	2024-04-03 18:20:31 +02:00
Benjamin Loison	faed1e33e8	#33 : Add `datasets/raise/extract_noise.py`	2024-04-03 18:20:08 +02:00
Benjamin Loison	4dd52aae90	Use `plt.imsave` instead of `toPilImage`	2024-04-03 16:57:49 +02:00
Benjamin Loison	d3af12ce3e	Try to have clear figure for thesis audition	2024-04-03 15:20:24 +02:00
Benjamin Loison	aafb7ebc92	Plot an exhaustive curve to make sure that results are not just by chance	2024-04-02 23:58:41 +02:00
Benjamin Loison	631ed6de34	#31 : Establish expected wanted results about finding an identical PRNU for both subgroups	2024-04-02 21:05:15 +02:00
Benjamin Loison	f09665f856	Make first rendering with both subgroups	2024-04-02 19:58:59 +02:00
Benjamin Loison	ea00f42c58	Add `datasets/raise/split_and_compare_prnus_of_subgroups.py`	2024-04-02 18:45:40 +02:00
Benjamin Loison	90a806a7e2	Move webpage articles to `datasets/raise/website/`	2024-04-02 18:31:27 +02:00
Benjamin Loison	c69221a336	Update annotations	2024-04-02 11:54:54 +02:00
Benjamin Loison	4df8a4f16e	Add `articles/RAISE - Dataset Download/`	2024-04-02 11:51:54 +02:00
Benjamin Loison	dfb384a8be	Update annotations	2024-04-02 11:49:39 +02:00
Benjamin Loison	491e7c906b	Update annotations	2024-04-02 11:47:51 +02:00
Benjamin Loison	1c7fc33e06	Add `articles/Darktable - Wikipedia/`	2024-04-02 11:47:45 +02:00
Benjamin Loison	57298e2e2e	Update annotations	2024-04-02 11:43:57 +02:00
Benjamin Loison	adc224334e	Add `articles/XnView - Wikipedia/`	2024-04-02 11:43:13 +02:00
Benjamin Loison	34d922901f	Add `articles/IrfanView - Wikipedia/`	2024-04-02 11:42:29 +02:00
Benjamin Loison	bd18868bc7	Add `articles/Nikon Electronic File — Wikipédia/`	2024-04-02 11:34:38 +02:00
Benjamin Loison	fe7d2f0cb0	Add `articles/Raster graphics - Wikipedia/`	2024-04-02 11:33:39 +02:00
Benjamin Loison	3351c909af	Update annotations	2024-04-02 11:32:35 +02:00
Benjamin Loison	2f33ecf02a	Add `articles/TIFF - Wikipedia/`	2024-04-02 11:30:42 +02:00
Benjamin Loison	f5e99fe4d3	Update annotations	2024-04-02 11:24:22 +02:00
Benjamin Loison	e0b41b3682	Add `articles/RAISE - Guide/`	2024-04-02 11:16:05 +02:00
Benjamin Loison	5165c5a638	Update annotations	2024-04-02 11:12:01 +02:00
Benjamin Loison	7f2ab40b70	Add `articles/RAISE - The Raw Images Dataset/`	2024-04-02 11:03:54 +02:00
Benjamin Loison	0d6a956db8	Shorten figure titles	2024-04-02 11:03:47 +02:00
Benjamin Loison	f0db61efe2	Make single figure to compare multiple `splitNXN` values	2024-03-29 13:47:27 +01:00
Benjamin Loison	81852d5dc6	Make Gaussian noise in `getPrnuShownAsSuch` only on text	2024-03-29 13:22:36 +01:00
Benjamin Loison	ec7c5c6688	Add `gaussianNoise` to `getPrnuShownAsSuch` but applies on the whole PRNU image	2024-03-29 13:16:50 +01:00
Benjamin Loison	2d96bdc225	Not correct generalized `SPLIT_N_X_N` due to `imageWithoutPrnuNpArrayTile`	2024-03-29 13:11:24 +01:00
Benjamin Loison	96bbd50a3b	Generalize with a partial image `SPLIT_N_X_N`	2024-03-29 12:57:56 +01:00
Benjamin Loison	4bf9ef8206	Add `splitNxN` compatible with `1` and `4`	2024-03-29 12:44:42 +01:00
Benjamin Loison	60eceb575e	Simplify grayscale rendering	2024-03-29 12:16:13 +01:00
Benjamin Loison	b375acbb3a	Use `v{min,max}` for enforcing matplotlib colormap If it proceeds linearly to covers the complete value range of the supplied data then doing so is unnecessary and even if it is another not very different scale transformation then it is still fine for my goal.	2024-03-29 12:11:41 +01:00
Benjamin Loison	5fa61f7ff8	Make PRNU compatible with 4x4 split	2024-03-29 11:23:48 +01:00
Benjamin Loison	8351e46437	Comparison of an image without and with Gaussian noise and PRNU	2024-03-29 01:55:46 +01:00
Benjamin Loison	2f66e82f22	Show both RMS and RMS normalized	2024-03-29 01:47:01 +01:00
Benjamin Loison	3ccec5bbd0	Add RMS computation	2024-03-29 01:35:48 +01:00
Benjamin Loison	f297060f42	Split images in 4x4 to increase PRNU estimation accuracy	2024-03-29 01:21:26 +01:00
Benjamin Loison	9b57d3441c	Render PRNU estimate taking into account all images	2024-03-29 01:10:31 +01:00
Benjamin Loison	d59a251b1f	Revert to some extent previous commit	2024-03-29 01:07:48 +01:00
Benjamin Loison	4382b3d649	Add `PRNU_FACTOR` `0.1` and `0.01` view on a single figure	2024-03-29 01:05:28 +01:00
Benjamin Loison	dfe2540c02	Apply Context-Adaptive Interpolator	2024-03-29 00:06:13 +01:00
Benjamin Loison	9a3cfd7ba1	Add PRNU showing such	2024-03-28 22:24:16 +01:00
Benjamin Loison	ba5a1b742b	#24 : Make executions reproducible	2024-03-28 22:20:40 +01:00
Benjamin Loison	a99e942d3a	Add `datasets/noise_free_test_images/estimate_prnu.py`	2024-03-28 17:06:58 +01:00
Benjamin Loison	0953fb7475	Add issue reference to `datasets/fake/generate_dataset.py`	2024-03-28 17:06:36 +01:00
Benjamin Loison	82e7026264	Add `algorithms/image_utils/image_utils.py` and move there `randomGaussianImage` and `showImageWithMatplotlib`	2024-03-28 16:32:38 +01:00
Benjamin Loison	70ccb094d5	Rename `randomImage` to `randomGaussianImage`	2024-03-28 16:18:32 +01:00
Benjamin Loison	64eaeddf98	Update annotations	2024-03-28 15:26:57 +01:00
Benjamin Loison	18d34a3101	Add `datasets/noise_free_test_images/no_noise_images.zip.txt`	2024-03-28 15:25:21 +01:00
Benjamin Loison	cbc778c4dc	Move `datasets/noise_free_test_images/noise_free_test_images.{pdf.txt,xopp.xml}` to `datasets/noise_free_test_images/webpage/`	2024-03-28 15:22:28 +01:00
Benjamin Loison	5af7afaf75	Add `datasets/noise_free_test_images/`	2024-03-28 15:03:57 +01:00
Benjamin Loison	77d6c038fe	Update annotations	2024-03-28 14:50:45 +01:00
Benjamin Loison	69368a9057	Add `articles/Law of large numbers - Wikipedia/`	2024-03-28 14:50:41 +01:00
Benjamin Loison	abfc9772e8	Update annotations	2024-03-28 14:47:13 +01:00
Benjamin Loison	d716574973	Update annotations	2024-03-26 18:23:30 +01:00
Benjamin Loison	2093f2e613	Update annotations	2024-03-26 18:21:12 +01:00
Benjamin Loison	9028d4d12c	Add `__pycache__` to `.gitignore` Note that I do not precisely know if Pyzo or Python script shell execution generate `__pycache__`.	2024-03-26 18:21:01 +01:00
Benjamin Loison	811b355989	Add `articles/Shot noise - Wikipedia/`	2024-03-26 18:20:28 +01:00
Benjamin Loison	cfe718da7d	Make `x`-axis logarithmic	2024-03-26 02:32:03 +01:00
Benjamin Loison	2bc13c5949	#21 : Make a RMS curve depending on the number of images considered for the mean	2024-03-26 01:59:22 +01:00
Benjamin Loison	c2862eaf43	#21 : Incorrect mean, as it is a RMS mean, not a mean of images	2024-03-26 01:55:08 +01:00
				`@@ -0,0 +1 @@`
				`https://en.wikipedia.org/w/index.php?title=Darktable&oldid=1204679832`
				`@@ -0,0 +1 @@`
				`https://fr.wikipedia.org/w/index.php?title=Nikon_Electronic_File&oldid=209775164`
				`@@ -0,0 +1 @@`
				`https://web.archive.org/web/20240405093114/https://bammey.com/research/polar/`
				`@@ -0,0 +1 @@`
				`https://web.archive.org/web/20220121204219/https://mcolom.perso.math.cnrs.fr/download/no_noise_images/no_noise_images.zip`
				`@@ -0,0 +1 @@`
				`https://web.archive.org/web/20221230200626/https://mcolom.perso.math.cnrs.fr/pages/no_noise_images/`
				`@@ -0,0 +1 @@`
				`https://web.archive.org/web/20221206120232/http://loki.disi.unitn.it/RAISE/download.html`
				`@@ -0,0 +1 @@`
				`https://web.archive.org/web/20200525105705/http://loki.disi.unitn.it/RAISE/guide.html`
				`@@ -0,0 +1 @@`
				`https://web.archive.org/web/20200716082639/http://loki.disi.unitn.it/RAISE/index.php`