for i in DarkNames
]
print 'Correcting images with darks'
CorrectedImages = [i - k for i, k in zip(Radiography, Dark)]
print 'Crop zoomed region'
ZoomedImages = [i[445:775, 510:615] for i in CorrectedImages]
print 'Get line profiles in zoomed region'
CoordinatesLeadPhantom = [(30, 310), (35, 10)]
# The lineprofiler gives back a tuple with coordinates and line profile based
# on these cordinates. To plot the profile, we can access it with
# LineProfile[i][1].
LineProfileLeadPhantom = [
lineprofiler.lineprofile(i, CoordinatesLeadPhantom) for i in ZoomedImages
]
print 'Get min, mean, max and STD for each set of images (original, corrected' \
' and zoomed).'
MinImage = [numpy.min(i) for i in Radiography]
MeanImage = [numpy.mean(i) for i in Radiography]
MaxImage = [numpy.max(i) for i in Radiography]
STDImage = [numpy.std(i) for i in Radiography]
MinCorrected = [numpy.min(i) for i in CorrectedImages]
MeanCorrected = [numpy.mean(i) for i in CorrectedImages]
MaxCorrected = [numpy.max(i) for i in CorrectedImages]
STDCorrected = [numpy.std(i) for i in CorrectedImages]
plt.subplot(243)
my_display_image(CorrectedData[counter])
plt.title("Image - Dark")
plt.subplot(244)
my_display_histogram(CorrectedData[counter])
# Select two line profiles on corrected image.
# The two profiles are along the first two lines of the resolution phantom
Coordinates = [((566, 350), (543, 776)), ((726, 350), (703, 776))]
MyColors = ["#D1B9D4", "#D1D171", "#84DEBD"]
for ProfileCounter, CurrentCoordinates in enumerate(Coordinates):
SelectedPoints, LineProfile = lineprofiler.lineprofile(
CorrectedData[counter], CurrentCoordinates, showimage=False
)
# Draw selection on corrected image
plt.figure(counter + 1, figsize=(16, 9))
plt.subplot(243)
my_display_image(CorrectedData[counter])
plt.plot(
(SelectedPoints[0][0], SelectedPoints[1][0]),
(SelectedPoints[0][1], SelectedPoints[1][1]),
color=MyColors[ProfileCounter],
marker="o",
)
plt.plot(SelectedPoints[0][0], SelectedPoints[0][1], color="yellow", marker="o")
plt.plot(SelectedPoints[1][0], SelectedPoints[1][1], color="black", marker="o")
plt.title("Image - Dark")
plt.xlabel('Voltage [kV]')
plt.ylabel('Current [uA]')
plt.xlim([20, 70])
plt.ylim([0, 75])
# Show the current image
plt.subplot(422)
plt.cla()
plt.title('Contrast stretched Image %s/%s: %s\nWith line profiles '
'and red region used for mean and STD' % (
ImageCounter + 1, len(ImageList),
os.path.basename(ImageName)))
Img = read_raw(ImageName)
plt.imshow(contrast_stretch(Img))
# Show where line profiles have been calculated
for CoordinateCounter, CurrentCoordinates in enumerate(Coordinates):
SelectedPoints, LineProfile = lineprofiler.lineprofile(
Img, CurrentCoordinates, showimage=False)
plt.plot((SelectedPoints[0][0], SelectedPoints[1][0]),
(SelectedPoints[0][1], SelectedPoints[1][1]),
color=UserColors[CoordinateCounter], marker='o')
plt.plot(SelectedPoints[0][0], SelectedPoints[0][1],
color='yellow', marker='o', alpha=0.618)
plt.plot(SelectedPoints[1][0], SelectedPoints[1][1],
color='black', marker='o', alpha=0.618)
plt.axis('off')
# Crop region for Standard deviation
CropSize = 900
CropStart = [50, 700]
Crop = Img[CropStart[0]:CropStart[0] + CropSize,
CropStart[1]:CropStart[1] + CropSize]
# Draw crop region in original image
currentAxis = plt.gca()
plt.title('')
plt.subplot(243)
my_display_image(CorrectedData[counter])
plt.title('Image - Dark')
plt.subplot(244)
my_display_histogram(CorrectedData[counter])
# Select two line profiles on corrected image.
# The two profiles are along the first two lines of the resolution phantom
Coordinates = [((566, 350), (543, 776)), ((726, 350), (703, 776))]
MyColors = ["#D1B9D4", "#D1D171", "#84DEBD"]
for ProfileCounter, CurrentCoordinates in enumerate(Coordinates):
SelectedPoints, LineProfile = lineprofiler.lineprofile(
CorrectedData[counter], CurrentCoordinates, showimage=False)
# Draw selection on corrected image
plt.figure(counter + 1, figsize=(16, 9))
plt.subplot(243)
my_display_image(CorrectedData[counter])
plt.plot((SelectedPoints[0][0], SelectedPoints[1][0]),
(SelectedPoints[0][1], SelectedPoints[1][1]),
color=MyColors[ProfileCounter],
marker='o')
plt.plot(SelectedPoints[0][0],
SelectedPoints[0][1],
color='yellow',
marker='o')
plt.plot(SelectedPoints[1][0],
SelectedPoints[1][1],
for i in RadiographyNames]
Dark = [numpy.fromfile(i, dtype=numpy.int16).reshape(CameraHeight, CameraWidth)
for i in DarkNames]
print 'Correcting images with darks'
CorrectedImages = [i - k for i, k in zip(Radiography, Dark)]
print 'Crop zoomed region'
ZoomedImages = [i[445:775, 510:615] for i in CorrectedImages]
print 'Get line profiles in zoomed region'
CoordinatesLeadPhantom = [(30, 310), (35, 10)]
# The lineprofiler gives back a tuple with coordinates and line profile based
# on these cordinates. To plot the profile, we can access it with
# LineProfile[i][1].
LineProfileLeadPhantom = [lineprofiler.lineprofile(i, CoordinatesLeadPhantom)
for i in ZoomedImages]
print 'Get min, mean, max and STD for each set of images (original, corrected' \
' and zoomed).'
MinImage = [numpy.min(i) for i in Radiography]
MeanImage = [numpy.mean(i) for i in Radiography]
MaxImage = [numpy.max(i) for i in Radiography]
STDImage = [numpy.std(i) for i in Radiography]
MinCorrected = [numpy.min(i) for i in CorrectedImages]
MeanCorrected = [numpy.mean(i) for i in CorrectedImages]
MaxCorrected = [numpy.max(i) for i in CorrectedImages]
STDCorrected = [numpy.std(i) for i in CorrectedImages]
