def _get_resized_image_stack(flist):
""" Load all images in a list and resize to OPTIONS['size']
When files are parsed, the variables are used in an index to provide
a method to reference a specific file name by its dimensions. This
function returns the variable index based on the input filename pattern.
Inputs:ed th
flist - Paths of list of images to load and resize
Outputs:
img_stack - A 3D stack of 2D images
X - width of image
Y - height of image
"""
#Initialize the output
br = BioReader(str(flist[0]))
X = br.num_x()
Y = br.num_y()
C = len(flist)
img_stack = np.zeros((OPTIONS['size'], OPTIONS['size'], C),
dtype=np.float32)
# Load every image as a z-slice
for ind, fname in zip(range(len(flist)), flist):
br = BioReader(str(fname))
I = np.squeeze(br.read_image())
img_stack[:, :, ind] = cv2.resize(
I, (OPTIONS['size'], OPTIONS['size']),
interpolation=cv2.INTER_LINEAR).astype(np.float32)
return img_stack, X, Y
{% endfor -%}
{% for inp,val in cookiecutter._inputs|dictsort -%}
{% for out,n in cookiecutter._outputs|dictsort -%}
{% if val.type=="collection" and cookiecutter.use_bfio -%}
# Loop through files in {{ inp }} image collection and process
for i,f in enumerate({{ inp }}_files):
# Load an image
br = BioReader(Path({{ inp }}).joinpath(f))
image = np.squeeze(br.read_image())
# initialize the output
out_image = np.zeros(image.shape,dtype=br._pix['type'])
""" Do some math and science - you should replace this """
logger.info('Processing image ({}/{}): {}'.format(i,len({{ inp }}_files),f))
out_image = awesome_math_and_science_function(image)
# Write the output
bw = BioWriter(Path({{ out }}).joinpath(f),metadata=br.read_metadata())
bw.write_image(np.reshape(out_image,(br.num_y(),br.num_x(),br.num_z(),1,1)))
{%- endif %}{% endfor %}{% endfor %}
finally:
{%- if cookiecutter.use_bfio %}
# Close the javabridge regardless of successful completion
logger.info('Closing the javabridge')
jutil.kill_vm()
{%- endif %}
# Exit the program
sys.exit()
read_workers = max([cpu_count()//3,1])
write_workers = max([cpu_count()-1,2])
loop_workers = max([3*cpu_count()//4,2])
# extract filenames from registration_string and similar_transformation_string
registration_set=registration_string.split()
similar_transformation_set=similar_transformation_string.split()
filename_len=len(template)
# seperate the filename of the moving image from the complete path
moving_image_name=registration_set[1][-1*filename_len:]
# read and downscale reference image
br_ref = BioReader(registration_set[0],max_workers=write_workers)
scale_factor=get_scale_factor(br_ref.num_y(),br_ref.num_x())
logger.info('Scale factor: {}'.format(scale_factor))
# intialize the scale factor and scale matrix(to be used to upscale the transformation matrices)
if method == 'Projective':
scale_matrix = np.array([[1,1,scale_factor],[1,1,scale_factor],[1/scale_factor,1/scale_factor,1]])
else:
scale_matrix = np.array([[1/scale_factor,1/scale_factor,1],[1/scale_factor,1/scale_factor,1]])
logger.info('Reading and downscaling reference image: {}'.format(Path(registration_set[0]).name))
reference_image_downscaled,max_val,min_val = get_scaled_down_images(br_ref,scale_factor,get_max=True)
br_ref.max_workers = read_workers
# read moving image
logger.info('Reading and downscaling moving image: {}'.format(Path(registration_set[1]).name))
br_mov = BioReader(registration_set[1],max_workers=write_workers)
# 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):
X = [x, min([x + chunk_size, br.num_x()])]
for y in range(0, br.num_y(), chunk_size):
Y = [y, min([y + chunk_size, br.num_y()])]
threads.append(
executor.submit(write_tile, br, bw, X, Y,
Z, C))
# Bioformats requires the first tile to be written
# before any other tile is written
if first_image or len(threads) == cpu_count():
threads[0].result()
del threads[0]
first_image = False
count += 1
if count % 5 == 0:
logger.info(
'{:.2f}% finished...'.format(