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mask_out.py
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mask_out.py
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from __future__ import print_function, division
from multiprocessing.spawn import import_main_path
import sys
import copy
import argparse
import numpy as np
import tifffile
import zarr
import skimage.transform
from aicsimageio import aics_image as AI
import pandas as pd
import numexpr as ne
from ome_types import from_tiff, to_xml
from os.path import abspath
from argparse import ArgumentParser as AP
import time
import dask
import dask.array as da
# This API is apparently changing in skimage 1.0 but it's not clear to
# me what the replacement will be, if any. We'll explicitly import
# this so it will break loudly if someone tries this with skimage 1.0.
try:
from skimage.util.dtype import _convert as dtype_convert
except ImportError:
from skimage.util.dtype import convert as dtype_convert
# arg parser
def get_args():
# Script description
description="""Subtracts background - Lunaphore platform"""
# Add parser
parser = AP(description=description, formatter_class=argparse.RawDescriptionHelpFormatter)
# Sections
inputs = parser.add_argument_group(title="Required Input", description="Path to required input file")
inputs.add_argument("-r", "--input", dest="root", action="store", required=True, help="File path to image file to be masked out.")
inputs.add_argument("--pixel-size", metavar="SIZE", dest = "pixel_size", type=float, default = None, action = "store",help="pixel size in microns; default is 1.0")
inputs.add_argument("--pyramid", dest="pyramid", required=False, default=True, help="Should output be pyramidal?")
inputs.add_argument("--tile-size", dest="tile_size", required=False, default=1024, help="Tile size for pyramid generation")
inputs.add_argument("--version", action="version", version="v0.5.0")
inputs.add_argument("--mask", dest="mask", action="store", required=True, help="File path to binary mask image file specifying regions to be excluded (1 to keep, 0 to exclude).")
outputs = parser.add_argument_group(title="Output", description="Path to output file")
outputs.add_argument("-o", "--output", dest="output", action="store", required=True, help="Path to output file")
arg = parser.parse_args()
# Standardize paths
arg.root = abspath(arg.root)
arg.mask = abspath(arg.mask)
arg.output = abspath(arg.output)
return arg
def preduce(coords, img_in, img_out):
print(img_in.dtype)
(iy1, ix1), (iy2, ix2) = coords
(oy1, ox1), (oy2, ox2) = np.array(coords) // 2
tile = skimage.img_as_float32(img_in[iy1:iy2, ix1:ix2])
tile = skimage.transform.downscale_local_mean(tile, (2, 2))
tile = dtype_convert(tile, 'uint16')
#tile = dtype_convert(tile, img_in.dtype)
img_out[oy1:oy2, ox1:ox2] = tile
def format_shape(shape):
return "%dx%d" % (shape[1], shape[0])
# NaN values return True for the statement below in this version of Python. Did not use math.isnan() since the values
# are strings if present
def isNaN(x):
return x != x
def subres_tiles(level, level_full_shapes, tile_shapes, outpath, scale):
print(f"\n processing level {level}")
assert level >= 1
num_channels, h, w = level_full_shapes[level]
tshape = tile_shapes[level] or (h, w)
tiff = tifffile.TiffFile(outpath)
zimg = zarr.open(tiff.aszarr(series=0, level=level-1, squeeze=False))
for c in range(num_channels):
sys.stdout.write(
f"\r processing channel {c + 1}/{num_channels}"
)
sys.stdout.flush()
th = tshape[0] * scale
tw = tshape[1] * scale
for y in range(0, zimg.shape[1], th):
for x in range(0, zimg.shape[2], tw):
a = zimg[c, y:y+th, x:x+tw, 0]
a = skimage.transform.downscale_local_mean(
a, (scale, scale)
)
if np.issubdtype(zimg.dtype, np.integer):
a = np.around(a)
a = a.astype('uint16')
yield a
# Define a function to apply the binary mask to a chunk of the image
def apply_mask(chunk, mask_chunk):
return chunk * mask_chunk
def main(args):
img_raw = AI.AICSImage(args.root)
mask_raw = AI.AICSImage(args.mask)
img = img_raw.get_image_dask_data("CYX")
mask = mask_raw.get_image_dask_data("CYX")
img = img.rechunk({0: img.shape[0], 1: args.tile_size, 2: args.tile_size})
mask = mask.rechunk({0: mask.shape[0], 1: args.tile_size, 2: args.tile_size})
# Stack the masked chunks back into a dask array
dask_masked_image = da.map_blocks(apply_mask, img, mask, dtype=img.dtype)
# Processing metadata - highly adapted to Lunaphore outputs
# check if metadata is present
try:
print(img_raw.metadata.images[0])
metadata = img_raw.metadata
except:
metadata = None
if args.pixel_size != None:
# If specified, the input pixel size is used
pixel_size = args.pixel_size
else:
try:
if img_raw.metadata.images[0].pixels.physical_size_x != None:
pixel_size = img_raw.metadata.images[0].pixels.physical_size_x
else:
pixel_size = 1.0
except:
# If no pixel size specified anywhere, use default 1.0
pixel_size = 1.0
print(args.pyramid)
print(int(args.tile_size)<=max(dask_masked_image[0].shape))
if (args.pyramid == True) and (int(args.tile_size)<=max(dask_masked_image[0].shape)):
# construct levels
tile_size = int(args.tile_size)
scale = 2
print()
dtype = dask_masked_image.dtype
base_shape = dask_masked_image[0].shape
num_channels = dask_masked_image.shape[0]
num_levels = (np.ceil(np.log2(max(base_shape) / tile_size)) + 1).astype(int)
factors = 2 ** np.arange(num_levels)
shapes = (np.ceil(np.array(base_shape) / factors[:,None])).astype(int)
print(base_shape)
print(np.ceil(np.log2(max(base_shape)/tile_size))+1)
print("Pyramid level sizes: ")
for i, shape in enumerate(shapes):
print(f" level {i+1}: {format_shape(shape)}", end="")
if i == 0:
print("(original size)", end="")
print()
print()
print(shapes)
level_full_shapes = []
for shape in shapes:
level_full_shapes.append((num_channels, shape[0], shape[1]))
level_shapes = shapes
tip_level = np.argmax(np.all(level_shapes < tile_size, axis=1))
tile_shapes = [
(tile_size, tile_size) if i <= tip_level else None
for i in range(len(level_shapes))
]
# write pyramid
with tifffile.TiffWriter(args.output, ome=True, bigtiff=True) as tiff:
tiff.write(
data = dask_masked_image,
shape = level_full_shapes[0],
subifds=int(num_levels-1),
dtype=dtype,
resolution=(10000 / pixel_size, 10000 / pixel_size, "centimeter"),
tile=tile_shapes[0]
)
for level, (shape, tile_shape) in enumerate(
zip(level_full_shapes[1:], tile_shapes[1:]), 1
):
tiff.write(
data = subres_tiles(level, level_full_shapes, tile_shapes, args.output, scale),
shape=shape,
subfiletype=1,
dtype=dtype,
tile=tile_shape
)
else:
# write image
with tifffile.TiffWriter(args.output, ome=True, bigtiff=True) as tiff:
tiff.write(
data = dask_masked_image.compute(),
shape = dask_masked_image.shape,
dtype=dask_masked_image.dtype,
resolution=(10000 / pixel_size, 10000 / pixel_size, "centimeter"),
)
try:
tifffile.tiffcomment(args.output, to_xml(metadata))
except:
pass
# note about metadata: the channels, planes etc were adjusted not to include the removed channels, however
# the channel ids have stayed the same as before removal. E.g if channels 1 and 2 are removed,
# the channel ids in the metadata will skip indices 1 and 2 (channel_id:0, channel_id:3, channel_id:4 ...)
print()
if __name__ == '__main__':
# Read in arguments
args = get_args()
# Run script
st = time.time()
main(args)
rt = time.time() - st
print(f"Script finished in {rt // 60:.0f}m {rt % 60:.0f}s")