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Flush print output buffer and handle different RGB channel axes
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@msorelli
msorelli committed Oct 1, 2024
1 parent 1f041bf commit 3fdde0f
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Showing 7 changed files with 169 additions and 118 deletions.
6 changes: 3 additions & 3 deletions foa3d/__main__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7,16 +7,16 @@
def foa3d(cli_args):

# load 3D microscopy image or 4D array of fiber orientation vectors
img, ts_msk, is_tiled, is_fovec, save_dir, tmp_dir, img_name = load_microscopy_image(cli_args)
img, ts_msk, ch_ax, is_fovec, save_dir, tmp_dir, img_name = load_microscopy_image(cli_args)

# get resources configuration
ram, jobs = get_resource_config(cli_args)

# conduct parallel 3D Frangi-based fiber orientation analysis on batches of basic image slices
if not is_fovec:
fbr_vec_img, iso_fbr_img, px_sz, img_name \
= parallel_frangi_on_slices(img, cli_args, save_dir[0], tmp_dir, img_name,
ts_msk=ts_msk, ram=ram, jobs=jobs, is_tiled=is_tiled)
= parallel_frangi_on_slices(img, ch_ax, cli_args, save_dir[0], tmp_dir, img_name,
ts_msk=ts_msk, ram=ram, jobs=jobs)

# skip Frangi filter stage if orientation vectors were directly provided as input
else:
Expand Down
71 changes: 37 additions & 34 deletions foa3d/input.py
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Original file line number Diff line number Diff line change
Expand Up @@ -15,9 +15,9 @@

from foa3d.output import create_save_dirs
from foa3d.preprocessing import config_anisotropy_correction
from foa3d.printing import (color_text, print_image_shape, print_import_time,
print_native_res)
from foa3d.utils import (create_background_mask, create_memory_map,
from foa3d.printing import (color_text, print_flushed, print_image_shape,
print_import_time, print_native_res)
from foa3d.utils import (create_background_mask, create_memory_map, detect_ch_axis,
get_item_bytes, get_output_prefix)


Expand Down Expand Up @@ -59,8 +59,7 @@ def get_cli_parser():
' - .npy (fiber orientation vectors)\n'
'* image axes order:\n'
' - grayscale image: (Z, Y, X)\n'
' - RGB image: (Z, Y, X, C)\n'
' - RGB tiled image: (Z, C, Y, X)\n'
' - RGB image: (Z, Y, X, C) or (Z, C, Y, X)\n'
' - NumPy vector image: (Z, Y, X, C)\n'
' - HDF5 vector image: (Z, Y, X, C)\n'
' - TIFF vector image: (Z, C, Y, X)\n')
Expand Down Expand Up @@ -109,7 +108,7 @@ def get_cli_parser():
return cli_args


def get_image_info(img, px_sz, msk_bc, fb_ch, ch_ax=None, is_tiled=False):
def get_image_info(img, px_sz, msk_bc, fb_ch, ch_ax):
"""
Get information on the input 3D microscopy image.
Expand All @@ -129,10 +128,7 @@ def get_image_info(img, px_sz, msk_bc, fb_ch, ch_ax=None, is_tiled=False):
myelinated fibers channel
ch_ax: int
channel axis
is_tiled: bool
True for tiled reconstructions aligned using ZetaStitcher
RGB image channel axis (either 1 or 3, or None for grayscale images)
Returns
-------
Expand All @@ -155,16 +151,12 @@ def get_image_info(img, px_sz, msk_bc, fb_ch, ch_ax=None, is_tiled=False):

# adapt channel axis
img_shp = np.asarray(img.shape)
ndim = len(img_shp)
if ndim == 4:
ch_ax = 1 if is_tiled else -1
elif ndim == 3:
if ch_ax is None:
fb_ch = None
msk_bc = False

# get info on 3D microscopy image
if ch_ax is not None:
else:
img_shp = np.delete(img_shp, ch_ax)

img_shp_um = np.multiply(img_shp, px_sz)
img_item_sz = get_item_bytes(img)

Expand Down Expand Up @@ -394,11 +386,11 @@ def load_microscopy_image(cli_args):
img: numpy.ndarray or NumPy memory-map object
3D microscopy image or array of fiber orientation vectors
tissue_msk: numpy.ndarray (dtype=bool)
ts_msk: numpy.ndarray (dtype=bool)
tissue reconstruction binary mask
is_tiled: bool
True for tiled microscopy reconstructions aligned using ZetaStitcher
ch_ax: int
RGB image channel axis (either 1 or 3, or None for grayscale images)
is_fovec: bool
True when pre-estimated fiber orientation vectors
Expand All @@ -424,23 +416,23 @@ def load_microscopy_image(cli_args):
tic = perf_counter()
if is_fovec:
img = load_orient(img_path, img_name, img_fmt, is_mmap=is_mmap, tmp_dir=tmp_dir)
tissue_msk = None
ts_msk = None

# import raw 3D microscopy image
else:
img, tissue_msk = load_raw(img_path, img_name, img_fmt,
is_tiled=is_tiled, is_mmap=is_mmap, tmp_dir=tmp_dir, msk_mip=msk_mip, fb_ch=fb_ch)
img, ts_msk, ch_ax = load_raw(img_path, img_name, img_fmt,
is_tiled=is_tiled, is_mmap=is_mmap, tmp_dir=tmp_dir, msk_mip=msk_mip, fb_ch=fb_ch)

# print import time
print_import_time(tic)

# print volume image shape
print_image_shape(cli_args, img, is_tiled) if not is_fovec else print()
print_image_shape(cli_args, img, ch_ax) if not is_fovec else print_flushed()

# create saving directory
save_dir = create_save_dirs(img_path, img_name, cli_args, is_fovec=is_fovec)

return img, tissue_msk, is_tiled, is_fovec, save_dir, tmp_dir, img_name
return img, ts_msk, ch_ax, is_fovec, save_dir, tmp_dir, img_name


def load_orient(img_path, img_name, img_fmt, is_mmap=False, tmp_dir=None):
Expand Down Expand Up @@ -473,7 +465,7 @@ def load_orient(img_path, img_name, img_fmt, is_mmap=False, tmp_dir=None):
"""

# print heading
print(color_text(0, 191, 255, "\nFiber Orientation Data Import\n"))
print_flushed(color_text(0, 191, 255, "\nFiber Orientation Data Import\n"))

# load fiber orientations
if img_fmt == 'npy':
Expand All @@ -485,15 +477,17 @@ def load_orient(img_path, img_name, img_fmt, is_mmap=False, tmp_dir=None):
img = img_file.get(img_file.keys()[0])
elif img_fmt == 'tif' or img_fmt == 'tiff':
img = tiff.imread(img_path)
img = np.moveaxis(img, 1, -1)
ch_ax = detect_ch_axis(img)
if ch_ax == 1:
img = np.moveaxis(img, 1, -1)
if is_mmap:
img = create_memory_map(img.shape, dtype=img.dtype, name=img_name, tmp_dir=tmp_dir, arr=img, mmap_mode='r')

# check array shape
if img.ndim != 4:
raise ValueError('Invalid 3D fiber orientation dataset (ndim != 4)!')
else:
print("Loading {} orientation dataset...\n".format(img_name))
print_flushed("Loading {} orientation dataset...\n".format(img_name))

return img

Expand Down Expand Up @@ -537,19 +531,22 @@ def load_raw(img_path, img_name, img_fmt, is_tiled=False, is_mmap=False, tmp_dir
ts_msk: numpy.ndarray (dtype=bool)
tissue reconstruction binary mask
ch_ax: int
RGB image channel axis (either 1 or 3)
"""

# print heading
print(color_text(0, 191, 255, "\nMicroscopy Image Import\n"))
print_flushed(color_text(0, 191, 255, "\nMicroscopy Image Import\n"))

# load microscopy tiled reconstruction (aligned using ZetaStitcher)
if is_tiled:
print("Loading {} tiled reconstruction...\n".format(img_name))
print_flushed("Loading {} tiled reconstruction...\n".format(img_name))
img = VirtualFusedVolume(img_path)

# load microscopy z-stack
else:
print("Loading {} z-stack...\n".format(img_name))
print_flushed("Loading {} z-stack...\n".format(img_name))
img_fmt = img_fmt.lower()
if img_fmt == 'npy':
img = np.load(img_path)
Expand All @@ -561,14 +558,20 @@ def load_raw(img_path, img_name, img_fmt, is_tiled=False, is_mmap=False, tmp_dir
# create image memory map
if is_mmap:
img = create_memory_map(img.shape, dtype=img.dtype, name=img_name, tmp_dir=tmp_dir, arr=img, mmap_mode='r')

# detect channel axis (RGB images)
ch_ax = detect_ch_axis(img)

# generate tissue reconstruction mask
if msk_mip:
dims = len(img.shape)

# grayscale image
if dims == 3:
img_fbr = img
elif dims == 4:
img_fbr = img[:, fb_ch, :, :] if is_tiled else img[..., fb_ch]
# RGB image
elif dims == 4:
img_fbr = img[:, fb_ch, :, :] if ch_ax == 1 else img[..., fb_ch]

# compute MIP (naive for loop to minimize the required RAM)
ts_mip = np.zeros(img_fbr.shape[1:], dtype=img_fbr.dtype)
Expand All @@ -580,4 +583,4 @@ def load_raw(img_path, img_name, img_fmt, is_tiled=False, is_mmap=False, tmp_dir
else:
ts_msk = None

return img, ts_msk
return img, ts_msk, ch_ax
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