def run(self):
tiff_ring_reader = TiffRingReader()
nr_filters = len(sc.other["RECORDED_WAVELENGTHS"])
# analyze all the first image files
image_files = get_image_files_from_folder(self.flatfield_folder)
image_files = filter(lambda image_name: "F0" in image_name,
image_files)
# helper function to take maximum of two images
def maximum_of_two_images(image_1, image_name_2):
image_2 = tiff_ring_reader.read(
os.path.join(self.flatfield_folder, image_name_2),
nr_filters)[0].get_image()
return np.maximum(image_1, image_2)
# now reduce to maximum of all the single images
flat_maximum = reduce(lambda x, y: maximum_of_two_images(x, y),
image_files, 0)
msi = Msi(image=flat_maximum)
msi.set_wavelengths(sc.other["RECORDED_WAVELENGTHS"])
# write flatfield as nrrd
writer = NrrdWriter(msi)
writer.write(self.output().path)
def getFakeMsi():
# build a fake multispectral image with 5 dimensions.
image = np.concatenate((np.ones((5, 5, 1)), np.ones(
(5, 5, 1)) * 2, np.ones((5, 5, 1)) * 3, np.ones(
(5, 5, 1)) * 4, np.ones((5, 5, 1)) * 5),
axis=-1)
msi = Msi(image)
msi.set_wavelengths(np.array([5, 4, 3, 2, 1]))
return msi
def getFakeMsi():
# build a fake multispectral image with 5 dimensions.
image = np.concatenate((np.ones((5, 5, 1)),
np.ones((5, 5, 1)) * 2,
np.ones((5, 5, 1)) * 3,
np.ones((5, 5, 1)) * 4,
np.ones((5, 5, 1)) * 5),
axis=-1)
msi = Msi(image)
msi.set_wavelengths(np.array([5, 4, 3, 2, 1]))
return msi
def run(self):
tiff_ring_reader = TiffRingReader()
nr_filters = len(sc.other["RECORDED_WAVELENGTHS"])
# analyze all the first image files
image_files = get_image_files_from_folder(self.dark_folder,
suffix="F0.tiff")
# returns the mean dark image vector of all inputted dark image
# overly complicated TODO SW: make this simple code readable.
dark_means = map(
lambda image_name: msimani.calculate_mean_spectrum(
tiff_ring_reader.read(
os.path.join(self.dark_folder, image_name), nr_filters)[0]
), image_files)
dark_means_sum = reduce(lambda x, y: x + y.get_image(), dark_means, 0)
final_dark_mean = dark_means_sum / len(dark_means)
msi = Msi(image=final_dark_mean)
msi.set_wavelengths(sc.other["RECORDED_WAVELENGTHS"])
# write flatfield as nrrd
writer = NrrdWriter(msi)
writer.write(self.output().path)
def run(self):
tiff_ring_reader = TiffRingReader()
nr_filters = len(sc.other["RECORDED_WAVELENGTHS"])
# analyze all the first image files
image_files = get_image_files_from_folder(self.dark_folder,
suffix="F0.tiff")
# returns the mean dark image vector of all inputted dark image
# overly complicated TODO SW: make this simple code readable.
dark_means = map(lambda image_name:
msimani.calculate_mean_spectrum(
tiff_ring_reader.read(os.path.join(self.dark_folder, image_name),
nr_filters)[0]),
image_files)
dark_means_sum = reduce(lambda x, y: x+y.get_image(), dark_means, 0)
final_dark_mean = dark_means_sum / len(dark_means)
msi = Msi(image=final_dark_mean)
msi.set_wavelengths(sc.other["RECORDED_WAVELENGTHS"])
# write flatfield as nrrd
writer = NrrdWriter(msi)
writer.write(self.output().path)
def run(self):
tiff_ring_reader = TiffRingReader()
nr_filters = len(sc.other["RECORDED_WAVELENGTHS"])
# analyze all the first image files
image_files = get_image_files_from_folder(self.flatfield_folder)
image_files = filter(lambda image_name: "F0" in image_name, image_files)
# helper function to take maximum of two images
def maximum_of_two_images(image_1, image_name_2):
image_2 = tiff_ring_reader.read(os.path.join(self.flatfield_folder,
image_name_2),
nr_filters)[0].get_image()
return np.maximum(image_1, image_2)
# now reduce to maximum of all the single images
flat_maximum = reduce(lambda x, y: maximum_of_two_images(x, y),
image_files, 0)
msi = Msi(image=flat_maximum)
msi.set_wavelengths(sc.other["RECORDED_WAVELENGTHS"])
# write flatfield as nrrd
writer = NrrdWriter(msi)
writer.write(self.output().path)
