Add articles/ipol_style_guide/

Add articles/Hierarchical Data Format - Wikipedia/
Add debugging
2024-07-24 00:23:39 +02:00 · 2024-07-24 00:23:01 +02:00 · 2024-07-24 00:22:08 +02:00 · 2024-07-24 00:21:09 +02:00 · 2024-07-24 00:20:45 +02:00 · 2024-07-04 04:18:13 +02:00
19 changed files with 1196 additions and 55 deletions
--- a/algorithms/image_utils/image_utils.py
+++ b/algorithms/image_utils/image_utils.py
@@ -30,12 +30,12 @@ def getPrnuShownAsSuch(size, gaussianNoise = 0):
            break
    # Center vertically, especially in the case `HEIGHT` > `WIDTH`.
    draw.text((0, HEIGHT // 2 - fontSize // 2), TEXT, 255, font = font)
-    imageNpArray = np.array(imagePil)
+    imageNpArray = np.array(imagePil, dtype = np.float64)
    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
+                prnuShownAsSuch[y, x] = gaussianNoiseNpArray[y, x]
+    return prnuShownAsSuch
--- a/articles/Determining
+++ b/articles/Determining
@@ -151,7 +151,7 @@ pre-process PRNU estimation:
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@@ -324,6 +324,8 @@ it seems correct</text>
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+      <teximage text="\mathbf{X} \text{ and } \mathbf{Y} \text{ seem to be images not sets of images}\\ \text{so } \mathbf{\bar{X}} \text{ seems to be the average pixel value of } \mathbf{X} \text{ and not the average image}" left="45.48101807" top="625.96652830" right="321.24827059" bottom="651.70501709"/>
+      <text font="Sans" size="12.00000000" x="236.42767334" y="609.71469116" color="#00c0ffff" ts="0" fn="">looks symetric</text>
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@@ -502,7 +504,7 @@ interpolation step</text>
      <text font="Sans" size="12.00000000" x="474.48402594" y="649.18218944" color="#00c0ffff" ts="0" fn="">like?</text>
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      <text font="Sans" size="12.00000000" x="318.42626146" y="675.00273397" color="#00c0ffff" ts="0" fn="">related to DSNU row/column artifacts?</text>
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@@ -558,7 +560,6 @@ interpolation step</text>
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@@ -571,9 +572,7 @@ interpolation step</text>
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      <text font="Sans" size="12.00000000" x="135.44167763" y="306.34509378" color="#00c0ffff" ts="0" fn="">same for rows</text>
      <text font="Sans" size="12.00000000" x="104.01898849" y="279.02986889" color="#00c0ffff" ts="0" fn="">only b), no? and maybe not even completelly</text>
@@ -616,7 +615,6 @@ looks like</text>
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-      <teximage text="corr?" left="373.03048731" top="353.68157202" right="406.38148731" bottom="370.56257202"/>
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@@ -666,6 +664,14 @@ looks like</text>
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      <text font="Sans" size="12.00000000" x="56.83878603" y="373.35349702" color="#00c0ffff" ts="0" fn="">linear pattern no more used in the paper remaining?</text>
+      <teximage text="corr?\\ \text{The only other occurrence is in VI. D.}" left="327.64978027" top="349.43379211" right="447.58863276" bottom="370.27279020"/>
+      <text font="Sans" size="12.00000000" x="462.96081543" y="350.33961487" color="#00c0ffff" ts="0" fn="">I would say so</text>
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@@ -681,6 +687,12 @@ looks like</text>
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      <text font="Sans" size="12.00000000" x="138.43141666" y="24.27757578" color="#00c0ffff" ts="0" fn="">may not be a priority yet</text>
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+      <stroke tool="highlighter" color="#ffff007f" width="8.50000000" fill="128">396.40270996 718.35711670 532.32865787 718.35711670</stroke>
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@@ -783,6 +795,15 @@ or it means same variance 3 in 2 axes?</text>
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      <text font="Sans" size="12.00000000" x="49.36018785" y="425.39423025" color="#00c0ffff" ts="0" fn="">not what I currently focus on</text>
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--- a/Wikipedia/Discrete
+++ b/Wikipedia/Discrete
@@ -0,0 +1 @@
+https://en.wikipedia.org/w/index.php?title=Discrete_cosine_transform&oldid=1225697709
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--- a/Wikipedia/Hierarchical
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+https://en.wikipedia.org/w/index.php?title=Hierarchical_Data_Format&oldid=1209615444
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+      <text font="Sans" size="12.00000000" x="452.09175872" y="378.60406499" color="#00c0ffff" ts="0" fn="">see (34)</text>
+      <text font="Sans" size="12.00000000" x="479.99473016" y="361.09989525" color="#00c0ffff" ts="0" fn="">see (26)</text>
+      <text font="Sans" size="12.00000000" x="308.01572735" y="389.19037438" color="#00c0ffff" ts="0" fn="">see (18)</text>
+      <stroke tool="pen" ts="0" fn="" color="#00c0ffff" width="1.41000000">347.42907632 371.46938447 343.90175346 384.63353262</stroke>
+      <text font="Sans" size="12.00000000" x="365.57879639" y="565.35534668" color="#00c0ffff" ts="0" fn="">according to Marina demo it is sPCE</text>
+      <text font="Sans" size="12.00000000" x="298.39611816" y="609.24667358" color="#00c0ffff" ts="0" fn="">what shape and size typically? 11 sized square
+chose Marina</text>
+      <text font="Sans" size="12.00000000" x="311.70404053" y="544.69454956" color="#00c0ffff" ts="0" fn="">why (0, 0) and not center? more generally
+how explain this formula?
+well with Marina np.roll there is such shift</text>
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@@ -1982,6 +2031,17 @@ have not achieved improvements as authors, right?</text>
      <text font="Sans" size="11.00000000" x="312.11756340" y="438.97829411" color="#00c0ffff" ts="0" fn="">do they provide the MATLAB code?
 No interesting occurrences of code, source (code), git,
 implementation, web</text>
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+      <text font="Sans" size="12.00000000" x="459.73004944" y="174.25554168" color="#00c0ffff" ts="0" fn="">how?</text>
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--- a/articles/ipol_style_guide/ipol_style_guide.pdf.txt
+++ b/articles/ipol_style_guide/ipol_style_guide.pdf.txt
@@ -0,0 +1 @@
+https://web.archive.org/web/20220911052242/https://tools.ipol.im/wiki/ref/manuscript_guidelines/ipol_style_guide.pdf
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+      <text font="Sans" size="12.00000000" x="303.69464111" y="223.37280273" color="#00c0ffff" ts="0" fn="">it seems that
+CCN(P, R) = sqrt(sPCE(P, R)) * sign(sPCE(P, R))</text>
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+      <text font="Sans" size="12.00000000" x="368.22591891" y="129.77836747" color="#00c0ffff" ts="0" fn="">?</text>
+      <text font="Sans" size="12.00000000" x="315.56872476" y="138.42515356" color="#00c0ffff" ts="0" fn="">as a shift of 1 pixel makes the thing incorrect
+cropping looks incorrect to me</text>
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+      <text font="Sans" size="12.00000000" x="439.83694488" y="186.13273046" color="#00c0ffff" ts="0" fn="">Pearson</text>
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+      <text font="Sans" size="12.00000000" x="317.81186368" y="307.05381362" color="#00c0ffff" ts="0" fn="">means quadratic complexity?</text>
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--- a/datasets/noise_free_test_images/estimate_prnu.py
+++ b/datasets/noise_free_test_images/estimate_prnu.py
@@ -24,7 +24,7 @@ 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
+NOISE_FACTOR = 0.2

 np.random.seed(0)

@@ -34,7 +34,12 @@ SPLIT_N_X_N_S = [1, 2, 4]
 fig, axes = plt.subplots(2, 4)
 fig.suptitle('PRNU estimation with different number of images having Gaussian noise and Gaussian noised PRNU')

+def axisImShow(axis, im):
+    imShow = axis.imshow(im)
+    plt.colorbar(imShow, label = 'Intensity', ax = axis, orientation = 'horizontal')
+
 for splitNXNIndex, splitNXN in enumerate(SPLIT_N_X_N_S):
+    print(f'{splitNXN=}')
    IMAGE_SIZE_SHAPE = [dimension // splitNXN for dimension in (704, 469)]

    #prnuNpArray = 255 * randomGaussianImage(scale = PRNU_FACTOR, size = IMAGE_SIZE_SHAPE)
@@ -55,10 +60,13 @@ for splitNXNIndex, splitNXN in enumerate(SPLIT_N_X_N_S):
            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)]
+            imageWithoutPrnuNpArrayTiles = [imageWithoutPrnuNpArray[x * m : (x + 1) * m, y * n : (y + 1) * n] for x in range(splitNXN) for y in range(splitNXN)]
+            #print(f'{m=} {n=} {0 * m=} {0 * (m + 1)=}')
+            #print([imageWithoutPrnuNpArrayTile.shape for imageWithoutPrnuNpArrayTile in imageWithoutPrnuNpArrayTiles])
            for imageWithoutPrnuNpArrayTile in imageWithoutPrnuNpArrayTiles:
                #print(imageWithoutPrnuNpArrayTile.shape, tuple(IMAGE_SIZE_SHAPE[::-1]))
                #if imageWithoutPrnuNpArrayTile.shape != tuple(IMAGE_SIZE_SHAPE[::-1]):
+                #    exit(1)
                #    continue
                imageNoise = randomGaussianImage(scale = 255 * NOISE_FACTOR, size = imageWithoutPrnuNpArrayTile.shape)
                imageWithPrnuNpArray = imageWithoutPrnuNpArrayTile + prnuNpArray + imageNoise
@@ -67,16 +75,19 @@ for splitNXNIndex, splitNXN in enumerate(SPLIT_N_X_N_S):
                    axis = axes[0]

                    axis[0].set_title('First image without noise')
-                    axis[0].imshow(imageWithoutPrnuNpArrayTile)
+                    axisImShow(axis[0], imageWithoutPrnuNpArrayTile)

-                    axis[1].set_title('Actual Gaussian noised PRNU')
-                    axis[1].imshow(prnuNpArray)
+                    axis[1].set_title('First image Gaussian noise')
+                    axisImShow(axis[1], imageNoise)

-                    axis[2].set_title('F. i. with G. n.')
-                    axis[2].imshow(imageWithoutPrnuNpArray + imageNoise)
+                    axis[2].set_title('First image with Gaussian noise\nClipped between 0 and 255')
+                    axisImShow(axis[2], np.clip(imageWithoutPrnuNpArray + imageNoise, 0, 255))

-                    axis[3].set_title('F. i. with G. n. and PRNU')
-                    axis[3].imshow(imageWithoutPrnuNpArray + prnuNpArray + imageNoise)
+                    axis[3].set_title('Actual Gaussian noised PRNU')
+                    axisImShow(axis[3], prnuNpArray)
+
+                    axes[1][0].set_title('First image with Gaussian noise and PRNU\nClipped between 0 and 255')
+                    axisImShow(axes[1][0], np.clip(imageWithoutPrnuNpArray + prnuNpArray + imageNoise, 0, 255))
                isFirstImage = False

                #assert all([isIn256Range(extreme) for extreme in [imageWithPrnuNpArray.max(), imageWithPrnuNpArray.min()]]), 'Adding the PRNU resulted in out of 256 bounds image'
@@ -91,10 +102,8 @@ for splitNXNIndex, splitNXN in enumerate(SPLIT_N_X_N_S):
    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')
+    axis[splitNXNIndex + 1].set_title(f'PRNU estimation\nwith {len(imagesPrnuEstimateNpArray)} images')#\n{title}
+    axisImShow(axis[splitNXNIndex + 1], cameraPrnuEstimateNpArray)

 plt.tight_layout()
 plt.show()
--- a/datasets/raise/benchmark_load_part_of_images.py
+++ b/datasets/raise/benchmark_load_part_of_images.py
@@ -19,7 +19,7 @@ class Operation(Enum):
    def __str__(self):
        return self.name.lower()

-OPERATION = Operation.LOAD_NPY
+OPERATION = Operation.SAVE
 RESOLUTION = 100
 print(f'{OPERATION = }')
 if OPERATION == Operation.LOAD_NPY:
--- a/datasets/raise/extract_4_color_channel_images_from_raw.py
+++ b/datasets/raise/extract_4_color_channel_images_from_raw.py
@@ -15,7 +15,8 @@ def getImageFilePath(imageIndex):

 def getImageColorChannel(imageIndex, color):
    imageFilePath = getImageFilePath(imageIndex)
-    return getColorChannel(imageFilePath, color)
+    colorChannel = getColorChannel(imageFilePath, color)
+    return colorChannel

 def crop(image, interestingPosition, yRange, xRange):
    return image[interestingPosition[0] - yRange: interestingPosition[0] + yRange, interestingPosition[1] - xRange: interestingPosition[1] + xRange]
--- a/datasets/raise/fft/analyze_fft_ellipses.py
+++ b/datasets/raise/fft/analyze_fft_ellipses.py
@@ -31,11 +31,13 @@ for axis in Axis:
    axisProfile = PROFILES_OF_AXES[axis]
    colorsOfAxes[axis] = plt.plot(axisProfile, label = axis)[0].get_color()

+'''
 for axis in Axis:
    for interestingValueOfAxisIndex, interestingValueOfAxis in enumerate(INTERESTING_VALUES_OF_AXES[axis]):
        linestyle = ':' if interestingValueOfAxisIndex % 2 == 0 else '--'
        label = f'{axis} local {"minimum" if interestingValueOfAxisIndex == 0 else "maximum"}' if interestingValueOfAxisIndex <= 1 else None
        plt.axvline(interestingValueOfAxis, color = colorsOfAxes[axis], alpha = 0.3, linestyle = linestyle, label = label)
+'''

 def flattenDictValues(dict_):
    return itertools.chain(*dict_.values())
@@ -43,6 +45,7 @@ def flattenDictValues(dict_):
 # TODO: How to avoid hardcoding the default color being `black`?
 MATPLOTLIB_DEFAULT_COLOR = 'black'

+'''
 allInterestingValuesOfAxes = list(set(flattenDictValues(INTERESTING_VALUES_OF_AXES)))
 ticks = list(plt.xticks()[0]) + allInterestingValuesOfAxes
 plt.xticks(ticks)
@@ -62,6 +65,7 @@ allInterestingIntensitiesOfAxes = list(flattenDictValues(INTERESTING_PROFILES_OF
 allInterestingIntensitiesOfAxesMin = min(allInterestingIntensitiesOfAxes)
 allInterestingIntensitiesOfAxesMax = max(allInterestingIntensitiesOfAxes)
 plt.ylim(allInterestingIntensitiesOfAxesMin, allInterestingIntensitiesOfAxesMax)
+'''

 plt.legend()
 plt.show()
--- a/datasets/raise/fft/generate_vignetting.py
+++ b/datasets/raise/fft/generate_vignetting.py
@@ -29,12 +29,12 @@ for y in tqdm(range(SHAPE[0])):
 def checkOriginalPoint(point):
    print(f'distance = {getDistance(*point)} intensity = {image[tuple(point)]}')

-for point in [CENTER, [0, 0], [CENTER[0], int(CENTER[1] + getDistance(0, 0) / 2)]]:
-    checkOriginalPoint(point)
+#for point in [CENTER, [0, 0], [CENTER[0], int(CENTER[1] + getDistance(0, 0) / 2)]]:
+#    checkOriginalPoint(point)

 pad = (SHAPE[1] - SHAPE[0]) // 2
 cropImage = image[:, pad:-pad]

 #plt.imshow(image, cmap = 'grey')
-plt.imsave('vignetting.png', image * 2.55, cmap = 'grey', vmin = 0, vmax = 255)
+plt.imsave('vignetting_odd.png', image * 2.55, cmap = 'grey', vmin = 0, vmax = 255)
 plt.show()
--- a/datasets/raise/fft/verify_dots.py
+++ b/datasets/raise/fft/verify_dots.py
@@ -6,12 +6,12 @@ import sys

 sys.path.insert(0, '../')

-from utils import isARawImage, Color, getColorChannel, mergeSingleColorChannelImagesAccordingToBayerFilter, iterativeMean
+from utils import isARawImage, Color, getColorChannel, mergeSingleColorChannelImagesAccordingToBayerFilter, iterativeMean, getPearsonCorrelationDistance

 FOLDERS = {
    'Rafael 23/04/24': '../rafael/230424',
    'RAISE flat-field': '../flat-field/nef',
-    'RAISE not flat-field': '../not_flat-field/nef',
+    'RAISE non-flat-field': '../non-flat-field/nef',
 }

 def getImageMergedColorChannels(filePath):
@@ -34,7 +34,15 @@ def getEstimatedPrnu(folder):
        estimatedPrnu.add(imageMergedColorChannels - meanImages.mean)
    return estimatedPrnu.mean

-estimatedPrnu = getEstimatedPrnu(FOLDERS['Rafael 23/04/24'])
+estimatedPrnus = {folderName: getEstimatedPrnu(folder) for folderName, folder in FOLDERS.items()}

-plt.imshow(estimatedPrnu)
+# TODO: largest common crop
+
+print(f'{corr(estimatedPrnus["Rafael 23/04/24"], estimatedPrnus["Rafael 23/04/24"])=}')
+print(f'{corr(estimatedPrnus["Rafael 23/04/24"], estimatedPrnus["RAISE flat-field"])=}')
+print(f'{corr(estimatedPrnus["RAISE flat-field"], estimatedPrnus["Rafael 23/04/24"])=}')
+print(f'{corr(estimatedPrnus["Rafael 23/04/24"], estimatedPrnus["RAISE non-flat-field"])=}')
+print(f'{corr(estimatedPrnus["RAISE flat-field"], estimatedPrnus["RAISE non-flat-field"])=}')
+
+plt.imshow(estimatedPrnus['RAISE flat-field'])
 plt.show()
--- a/datasets/raise/utils.py
+++ b/datasets/raise/utils.py
@@ -10,13 +10,13 @@ import builtins as __builtin__
 from scipy.ndimage import gaussian_filter
 import os
 from skimage.restoration import estimate_sigma
-from scipy import fftpack
+from scipy import fftpack, stats
 import imageio

 class Color(Enum):
    RED = auto()
    GREEN_RIGHT = auto()
-    GREEN_BOTTOM = auto()
+    GREEN_LEFT = auto()
    BLUE = auto()

    def __str__(self):
@@ -44,7 +44,7 @@ class Distance(Enum):
        return self.name.replace('_', '').title()

 def getPearsonCorrelationDistance(firstImage, secondImage):
-    pearsonCorrelation = scipy.stats.pearsonr(firstImage.flatten(), secondImage.flatten()).statistic
+    pearsonCorrelation = stats.pearsonr(firstImage.flatten(), secondImage.flatten()).statistic
    if pearsonCorrelation < 0:
        print('Negative Pearson correlation, investigate further!')
        exit(1)
@@ -117,29 +117,44 @@ RAW_IMAGE_FILE_EXTENSIONS = [
    'nef',
 ]

+SUPPORTED_RAW_PATTERNS = [
+    # RG
+    # GB
+    [[0, 1],
+     [3, 2]],
+    # GB
+    # RG
+    [[3, 2],
+     [0, 1]],
+]
+
 def getRawColorChannel(raw, color):
    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
+    assert any(np.array_equal(raw.raw_pattern, np.array(supportedRawPattern, dtype = np.uint8)) for supportedRawPattern in SUPPORTED_RAW_PATTERNS)
    rawImageVisible = raw.raw_image_visible.copy()

-    redRawImageVisible = rawImageVisible[::2, ::2]
-    greenRightRawImageVisible = rawImageVisible[::2, 1::2]
+    topLeftRawImageVisible = rawImageVisible[::2, ::2]
+    topRightRawImageVisible = rawImageVisible[::2, 1::2]

-    greenBottomRawImageVisible = rawImageVisible[1::2, ::2]
-    blueRawImageVisible = rawImageVisible[1::2, 1::2]
+    bottomLeftRawImageVisible = rawImageVisible[1::2, ::2]
+    bottomRightRawImageVisible = rawImageVisible[1::2, 1::2]

-    match color:
-        case Color.RED:
-            imageNpArray = redRawImageVisible
-        case Color.GREEN_RIGHT:
-            imageNpArray = greenRightRawImageVisible
-        case Color.GREEN_BOTTOM:
-            imageNpArray = greenBottomRawImageVisible
-        case Color.BLUE:
-            imageNpArray = blueRawImageVisible
+    if np.array_equal(raw.raw_pattern, np.array(SUPPORTED_RAW_PATTERNS[0], dtype = np.uint8)):
+        imageNpArrays = {
+            Color.RED: topLeftRawImageVisible,
+            Color.GREEN_RIGHT: topRightRawImageVisible,
+            Color.GREEN_LEFT: bottomLeftRawImageVisible,
+            Color.BLUE: bottomRightRawImageVisible,
+        }
+    else: # elif np.array_equal(raw.raw_pattern, np.array(SUPPORTED_RAW_PATTERNS[1], dtype = np.uint8)):
+        imageNpArrays = {
+            Color.GREEN_LEFT: topLeftRawImageVisible,
+            Color.BLUE: topRightRawImageVisible,
+            Color.RED: bottomLeftRawImageVisible,
+            Color.GREEN_RIGHT: bottomRightRawImageVisible,
+        }
+    imageNpArray = imageNpArrays[color]
    return imageNpArray

 def isARawImage(imageFilePath):
@@ -161,7 +176,6 @@ def getColorChannel(imageFilePath, color):
 def escapeFilePath(filePath):
    return filePath.replace('/', '_')

-
 def getNewIndex(index, offset):
    newIndex = (index - offset) * 2 + offset
    return newIndex
@@ -179,7 +193,7 @@ def mergeSingleColorChannelImagesAccordingToBayerFilter(singleColorChannelImages
    '''
    multipleColorsImage[::2,::2] = singleColorChannelImages[Color.RED]
    multipleColorsImage[1::2,::2] = singleColorChannelImages[Color.GREEN_RIGHT]
-    multipleColorsImage[::2,1::2] = singleColorChannelImages[Color.GREEN_BOTTOM]
+    multipleColorsImage[::2,1::2] = singleColorChannelImages[Color.GREEN_LEFT]
    multipleColorsImage[1::2,1::2] = singleColorChannelImages[Color.BLUE]
    return multipleColorsImage
Author	SHA1	Message	Date
Benjamin Loison	57fc05ed2c	Add `articles/ipol_style_guide/`	2024-07-24 00:23:39 +02:00
Benjamin Loison	66c1a50d3b	Add `articles/Hierarchical Data Format - Wikipedia/`	2024-07-24 00:23:01 +02:00
Benjamin Loison	19f8e010e0	Add debugging	2024-07-24 00:22:08 +02:00
Benjamin Loison	7386290e87	Update annotations	2024-07-24 00:21:09 +02:00
Benjamin Loison	025f8d0bee	Remove debugging	2024-07-24 00:20:45 +02:00
Benjamin Loison	3a4100b779	Use `np.clip`	2024-07-04 04:18:13 +02:00
Benjamin Loison	98426db9dd	Using matplotlib clipping	2024-07-04 04:11:12 +02:00
Benjamin Loison	d24c883a92	Restore clipping `6f961a46cf`	2024-07-04 04:08:36 +02:00
Benjamin Loison	5bc0337dc8	Forcing color map	2024-07-04 04:08:10 +02:00
Benjamin Loison	9d20940cb1	cmap try	2024-07-04 04:03:54 +02:00
Benjamin Loison	6f961a46cf	Add clipping	2024-07-04 03:58:30 +02:00
Benjamin Loison	a17ec28cc3	Add color scales	2024-07-04 03:36:36 +02:00
Benjamin Loison	dd7a6892e5	Add `articles/Discrete cosine transform - Wikipedia/`	2024-06-28 13:55:24 +02:00
Benjamin Loison	ad5bb767c2	Add `articles/JPEG - Wikipedia/`	2024-06-28 12:57:21 +02:00
Benjamin Loison	0fca7c84a0	Support Nikon D90 `.nef`s	2024-06-17 13:48:21 +00:00
Benjamin Loison	715b5296c9	Compare PRNUs	2024-06-11 17:54:29 +02:00
Benjamin Loison	c2cf139261	Correct `pearsonr` `import`	2024-06-11 17:53:34 +02:00
Benjamin Loison	aa5c210539	Comment `checkOriginalPoint`	2024-06-11 17:52:50 +02:00
Benjamin Loison	2bfedf54ad	Remove `INTERESTING_VALUES_OF_AXES` usage	2024-06-11 17:51:57 +02:00
Benjamin Loison	a9861a761f	Use an intermediary variable in `getImageColorChannel`	2024-06-11 17:35:26 +02:00
Benjamin Loison	0c7108b3ec	Make `benchmark_load_part_of_images.py` production ready	2024-06-11 16:41:43 +02:00
Benjamin Loison	959fb43dd0	Update annotations of `Determining Image Origin and Integrity Using Sensor Noise`	2024-06-11 16:41:23 +02:00
Benjamin Loison	0d22c36e96	Update annotations of `On the Sensor Pattern Noise Estimation in Image Forensics: A Systematic Empirical Evaluation`	2024-06-11 16:41:02 +02:00
				`@@ -0,0 +1 @@`
				`https://en.wikipedia.org/w/index.php?title=Discrete_cosine_transform&oldid=1225697709`
				`@@ -0,0 +1 @@`
				`https://web.archive.org/web/20220911052242/https://tools.ipol.im/wiki/ref/manuscript_guidelines/ipol_style_guide.pdf`