Benjamin_Loison/Robust_image_source_identification_on_modern_smartphones
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base: Benjamin_Loison:2bfedf54ad34b070e44203d6c29b63193dca4f6f
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Benjamin_Loison:prnu_written_as_such
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compare: Benjamin_Loison:0c7108b3ec5f0f69b56e6e91c9a2fcbc5728fa12
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Benjamin_Loison:master
Benjamin_Loison:prnu_written_as_such

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2bfedf54ad ... 0c7108b3ec

2 changed files with 1 additions and 6 deletions
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datasets/raise/extract_4_color_channel_images_from_raw.py
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@ -15,8 +15,7 @@ def getImageFilePath(imageIndex):
def getImageColorChannel(imageIndex, color):
imageFilePath = getImageFilePath(imageIndex)
colorChannel = getColorChannel(imageFilePath, color)
return colorChannel
return getColorChannel(imageFilePath, color)
def crop(image, interestingPosition, yRange, xRange):
return image[interestingPosition[0] - yRange: interestingPosition[0] + yRange, interestingPosition[1] - xRange: interestingPosition[1] + xRange]

4
datasets/raise/fft/analyze_fft_ellipses.py
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@ -31,13 +31,11 @@ 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())
@ -45,7 +43,6 @@ 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)
@ -65,7 +62,6 @@ allInterestingIntensitiesOfAxes = list(flattenDictValues(INTERESTING_PROFILES_OF
allInterestingIntensitiesOfAxesMin = min(allInterestingIntensitiesOfAxes)
allInterestingIntensitiesOfAxesMax = max(allInterestingIntensitiesOfAxes)
plt.ylim(allInterestingIntensitiesOfAxesMin, allInterestingIntensitiesOfAxesMax)
'''
plt.legend()
plt.show()