def add_czi(self, filename):
"""Add an additional czi file containing additional image(s).
Arguments:
filename (str): Path to the czi file to be added to the existing
MultiFinder object.
"""
new_czi = czi_io.load_multi_czi(filename)
stripped_fname = filename.split('/')[1]
if len(new_czi[0].shape) == 4: # if it's a single img czi, not multi
new_czi[0] = np.expand_dims(new_czi[0], axis=0)
if new_czi[0].shape[1:] != self.cell_im.shape[1:]: # if diff CZXY
raise ValueError('The new czi has a different shape than cell_im.')
if new_czi[1] != self.cell_channels:
raise ValueError('The new czi uses non-matching channels.')
self.filenames.append(stripped_fname)
self.f_to_s[stripped_fname] = range(
self.cell_im.shape[0], self.cell_im.shape[0] + new_czi.shape[0])
self.cell_im = np.concatenate(self.cell_im, new_czi)
def __init__(self,
filename,
bg_index=-1,
bg_filename='',
log_path=None,
oof_svm=None,
optim=False):
"""Create a MultiFinder object.
Arguments:
filename (str): The path to an image file to initialize the object.
bg_index (int, optional): If the initializing file is .czi format,
this indicates the index within the czi file array that
corresponds to the background image with no cells. Defaults to
-1, which means the background image isn't contained within
the initializing image file.
bg_filename (str, optional): The path to an image file
corresponding to the background image. This argument must be
provided if the background stage position is not contained
in the multi-image file provided by filename or an
exception will occur. It should not be provided if the
background image is contained within a multi-image file
provided by filename.
log_path (str, optional): The path to a log directory for saving
images with defects in blur elimination. Images from blur
elimination aren't saved unless this is provided.
oof_svm (str, optional): Path to a trained scikit-learn svm.SVC()
pickle to be used for eliminating out-of-focus planes of the
stack.
optim (bool, optional): Indicates whether or not this run is being
performed on a subset of the data for threshold optimization
purposes. Default is False (extract all images from the chosen
file); if True, will only pull out the first three images.
"""
self.filenames = [filename]
if bg_index == -1:
if bg_filename == '':
warn('No background image provided during initialization.')
self.bg_origin = 'separate' # separate czi or tiff file
self.bg_filename = bg_filename
self.log_path = log_path
if self.log_path is not None:
if not os.path.isdir(self.log_path):
try:
os.makedirs(self.log_path)
except FileExistsError:
pass
self.oof_svm = oof_svm
else:
self.bg_origin = 'slice' # slice of a multi-czi
if '.tif' in self.filenames[0]:
self.cell_im = io.imread(self.filenames[0])
# TODO: implement adding dimensions for multi-img/channels both
# here and a method to add them later
elif '.czi' in self.filenames[0]:
cell_czi = czi_io.load_multi_czi(self.filenames[0])
self.cell_im = cell_czi[0]
# if cell_im has shape C-Z-X-Y, not IMG-C-Z-X-Y, add axis for img
if len(self.cell_im.shape) == 4:
self.cell_im = np.expand_dims(self.cell_im, axis=0)
if optim:
print('Optimization mode: only using first three images.')
if self.cell_im.shape[0] > 2:
self.cell_im = self.cell_im[0:3, :, :, :, :]
# add filename:slice #s dict to indicate which imgs came from where
self.f_to_s = {self.filenames[0]: range(0, self.cell_im.shape[0])}
self.cell_channels = cell_czi[1]
if self.bg_filename != '':
if '.tif' in self.bg_filename:
self.bg_im = io.imread(self.bg_filename)
# TODO: implement adding dimensions for multi-img/channels both
# here and a method to add them later
elif '.czi' in self.bg_filename:
bg_czi = czi_io.load_single_czi(self.bg_filename)
self.bg_im = np.expand_dims(bg_czi[0], axis=0)
self.bg_channels = bg_czi[1]
elif bg_index != -1:
self.bg_im = self.cell_im[bg_index, :, :, :, :]
# remove parts of cell_im that correspond to bg
bg_mask = np.ones(shape=self.cell_im.shape, dtype=bool)
bg_mask[bg_index, :, :, :, :] = False
self.cell_im = self.cell_im[bg_mask]
self.bg_channels = self.cell_channels
if self.oof_svm is not None:
self.flagged_oof_ims = np.zeros(self.cell_im.shape[0])
self.flagged_z_ims = np.zeros(self.cell_im.shape[0])
im_vector = im_vector[order_vect]
slice_vector = slice_vector[order_vect]
# open czi ref_csv
ref_df = pd.read_csv(
'/n/denic_lab/Users/nweir/python_packages/csth-imaging/8-7_im_list.csv')
ind = 0
grad_arr = np.empty(shape=(50, 500))
print('beginning image processing....')
print('----------------------------------------------------------------------')
for i in np.unique(czi_vector):
print('Processing czi #' + str(i + 1) + ' of 15')
c_ims = im_vector[czi_vector == i]
c_slices = slice_vector[czi_vector == i]
c_czi = ref_df['files'].iloc[i]
print(' opening czi...')
(im_array, channels) = czi_io.load_multi_czi(c_czi)
im_array = im_array[:, channels.index(633), :, :, :] # get 633 sub-array
for j in range(0, c_ims.shape[0]):
print(' processing slice ' + str(j + 1) + ' of ' +
str(c_ims.shape[0]))
curr_slice = im_array[c_ims[j], c_slices[j], :, :].astype('uint16')
io.imsave(
'/n/denic_lab/Lab/csth-output/svm_test_633/' + str(ind) + '.tif',
curr_slice)
print(' generating gradient...')
grad_im = nd.gaussian_gradient_magnitude(curr_slice,
sigma=(0.25, 0.25))
grad_im = grad_im.astype('float32') / np.amax(grad_im.flatten())
grad_im = grad_im * 65535 # make 16 bit
print(' calculating histogram...')
hist, bin_edges = np.histogram(grad_im.flatten(),
args = parser.parse_args()
print(args)
czi_dir = args.czi_dir
if not czi_dir.endswith('/'):
czi_dir = czi_dir + '/'
output_dir = args.out_dir
os.chdir(czi_dir)
if not output_dir.endswith('/'):
output_dir = output_dir + '/'
if not os.path.exists(output_dir):
os.makedirs(output_dir)
czi_list = [f for f in os.listdir() if '.czi' in f]
for f in czi_list:
if 'Empty' in f:
continue
print('current file: ' + f)
czi_arr, channel_arr = czi_io.load_multi_czi(czi_dir + f)
print('czi array shape:')
print(czi_arr.shape)
if len(czi_arr.shape) == 4:
czi_arr = np.expand_dims(czi_arr, axis=0)
for im in range(0, czi_arr.shape[0]):
for c in range(0, czi_arr.shape[1]):
if channel_arr[c] == 561:
channel = '594'
else:
channel = str(channel_arr[c])
io.imsave(output_dir+f[:-4]+'_'+str(im)+'_'+channel+'.tif',
czi_arr[im, c, :, :, :])