def init_paths(self, data_name, path, N_distances, first_distance=1):
"""Generate paths images & flatfields"""
#set data_names
data_names, ff_names = init_names(data_name,
N_distances,
first_distance=first_distance)
#find images
imlist = var.im_folder(path)
#set proper paths
images = np.zeros(N_distances, 'object')
flats = np.zeros(N_distances, 'object')
for i in np.arange(len(images)):
#sort image paths
images[i] = [
path + im for im in imlist
if (im.startswith(data_names[i])) and not (im.startswith('.'))
]
flats[i] = [
path + im for im in imlist if im.startswith(ff_names[i])
]
self.images = images
self.flats = flats
def init_paths(self, data_name, path, **kwargs):
"""Generate paths images & flatfields"""
#set variables
param = Processor().init_parameters(**kwargs)
if 'N_distances' in self.__dict__:
N_distances = self.N_distances
else:
N_distances = param['N_distances']
#set data_names
data_names, ff_names = Processor().init_names(data_name, N_distances)
#find images
imlist = var.im_folder(path)
#set proper paths
images = np.zeros(N_distances, 'object')
flats = np.zeros(N_distances, 'object')
for i in np.arange(len(images)):
#sort image paths
images[i] = [
path + im for im in imlist
if (im.startswith(data_names[i])) and not (im.startswith('.'))
]
flats[i] = [
path + im for im in imlist if im.startswith(ff_names[i])
]
self.images = images
self.flats = flats
def init_paths(self, data_name, path, distance_indexes):
"""Generate paths images & flatfields"""
#set data_names
data_names, ff_names = init_names_custom(
data_name=data_name, distance_indexes=distance_indexes)
#find images
imlist = var.im_folder(path)
# create a filter for unnecessary images
tail = '_00000.tiff'
if len(data_names[0]) != len(data_names[-1]):
print("""
WARNING! Different distances in your dataset
have different naming lengths.
File names can't be arranged.
Try to reduce the number of distances (<10) or modify the script.
""")
sys.exit()
else:
data_lencheck = len(data_names[0] + tail)
ff_lencheck = len(ff_names[0] + tail)
#set proper paths
N_distances = len(distance_indexes)
images = np.zeros(N_distances, 'object')
flats = np.zeros(N_distances, 'object')
for i in np.arange(len(images)):
#sort image paths
images[i] = [
path + im for im in imlist if (im.startswith(data_names[i]))
and not (im.startswith('.')) and (len(im) == data_lencheck)
]
flats[i] = [
path + im for im in imlist
if im.startswith(ff_names[i]) and (len(im) == ff_lencheck)
]
self.images = images
self.flats = flats
#set the ROI of image first, the logic corresponds to FIJI (to be read (x,y,x1,y1 at the image - inverse to numpy!)
ROI = (0, 0, 2048, 2048)
folder = '/Users/au704469/Documents/Postdoc/Results/X-ray_tomography/Brain_organoid_P14_DESY_Nov2020/Data_test/144mm/'
image_name = 'try0_full_144mm_1_00001.tiff'
#let's read the image and crop it
image = tifffile.imread(folder + image_name)
image = image[ROI[1]:ROI[3], ROI[0]:ROI[2]]
# now let's read all flatfield files from the folder
# in our case flatfield files start with the prefix 'ff_'
#create the list all images in the folder
imlist = var.im_folder(folder)
#read ff-files
flatfield = np.asarray([
tifffile.imread(folder + im) for im in imlist
if im.startswith('ff_' + image_name)
])
flatfield = flatfield[:, ROI[1]:ROI[3], ROI[0]:ROI[2]]
# please transpose the ff-array for the further ff-correction
flatfield = np.transpose(flatfield, (1, 2, 0))
# let's divide our image by the flatfield, using SSIM metrix
# images should be set as special classes:
image_class = SSIM.SSIM_const(image)