# upscale the rough homography matrix
logger.info("Inverting homography...")
if method=='Projective':
Rough_Homography_Upscaled=Rough_Homography_Downscaled*scale_matrix
homography_inverse=np.linalg.inv(Rough_Homography_Upscaled)
else:
Rough_Homography_Upscaled=Rough_Homography_Downscaled
homography_inverse=cv2.invertAffineTransform(Rough_Homography_Downscaled)
# Initialize the output file
bw = BioWriter(str(Path(outDir).joinpath(Path(registration_set[1]).name)),metadata=br_mov.read_metadata(),max_workers=write_workers)
bw.num_x(br_ref.num_x())
bw.num_y(br_ref.num_y())
bw.num_z(1)
bw.num_c(1)
bw.num_t(1)
# transformation variables
reg_shape = []
reg_tiles = []
reg_homography = []
# Loop through image tiles and start threads
logger.info("Starting threads...")
threads = []
first_tile = True
with ThreadPoolExecutor(max_workers=loop_workers) as executor:
for x in range(0,br_ref.num_x(),2048):
for y in range(0,br_ref.num_y(),2048):
if len(paths) == 0:
continue
# Initialize the output file
br = BioReader(paths[0]['file'])
file_name = filepattern.output_name(
filePattern, paths,
{c: paths[0][c]
for c in fp.variables if c != 'c'})
logger.info('Writing: {}'.format(file_name))
bw = BioWriter(str(Path(outDir).joinpath(file_name)),
metadata=br.read_metadata())
del br
# Modify the metadata to make sure channels are written correctly
bw.num_c(len(paths))
bw._metadata.image().Pixels.channel_count = bw.num_c()
# Process the data in tiles
threads = []
count = 0
total = bw.num_c() * bw.num_z() * \
(bw.num_x()//chunk_size + 1) * (bw.num_y()//chunk_size + 1)
with ThreadPoolExecutor(cpu_count()) as executor:
for c, file in enumerate(paths):
br = BioReader(file['file'])
C = [c]
first_image = True
for z in range(br.num_z()):
Z = [z, z + 1]
for x in range(0, br.num_x(), chunk_size):