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import typing as t
from abc import ABC
from pathlib import Path
import h5py
import napari
import numpy as np
from agora.io.cells import Cells
from agora.io.metadata import parse_metadata
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from aliby.io.image import dispatch_image
from aliby.tile.tiler import Tiler
def colormap(channel):
"""Find default colormap."""
if "GFP" in channel:
colormap = "green"
elif "Cherry" in channel or "RFP" in channel:
colormap = "red"
else:
colormap = "gray"
return colormap
class BaseImageViewer(ABC):
"""Base class with routines common to all ImageViewers."""
def __init__(self, h5file_path):
"""Initialise from a Path to a h5 file."""
self.h5file_path = h5file_path
print(f"Viewing {str(h5file_path)}")
self.full = {}
def get_tiles(self, trap_id, tps, cell_only=True):
"""Get dict of tiles with channel indices as keys."""
tiles_dict = {}
channels = self.tiler.channels
channel_indices = [channels.index(ch) for ch in channels]
for ch_index, ch in zip(channel_indices, channels):
tile_dict_for_ch = self.get_all_tiles(tps, ch_index)
tiles = [x[trap_id] for x in tile_dict_for_ch.values()]
if ch == "Brightfield":
tiles_dict[ch] = tiles
else:
masks = [
self.cells.at_time(tp, kind="mask").get(trap_id, [])
for tp in tps
]
# some masks may be empty
default_mask = [np.ones(self.cells.tile_size).astype(bool)]
nmasks = [m if m else default_mask for m in masks]
# combine all masks for each time point
stacked_masks = [
np.stack([mask for mask in masks_tp]).max(axis=0)
for masks_tp in nmasks
]
# make tiles with fluorescence only in mask pixels
new_tiles = []
for tile, stacked_mask in zip(tiles, stacked_masks):
tile[~stacked_mask] = 0
new_tiles.append(tile)
tiles_dict[ch] = new_tiles
return tiles_dict
def get_outlines(self, trap_id, tps):
"""Get uniquely labelled outlines for each cell time point."""
# get outlines for each time point
outlines = [
self.cells.at_time(tp, kind="edgemask").get(trap_id, [])
for tp in tps
]
# get cell labels for each time point
cell_labels = [
self.cells.labels_at_time(tp).get(trap_id, []) for tp in tps
]
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# generate one image with all cell outlines uniquely labelled per tile
labelled_outlines = [
(
np.stack(
[
(outline * label)
for outline, label in zip(outlines_tp, labels_tp)
]
).max(axis=0)
if len(labels_tp)
else np.zeros(self.cells.tile_size).astype(bool)
)
for outlines_tp, labels_tp in zip(outlines, cell_labels)
]
return labelled_outlines
def get_all_tiles(
self,
tps,
channel_index,
z=0,
):
"""
Get dict with time points as keys and all available tiles as values.
We assume there is only a single channel.
"""
z = z or self.tiler.ref_z
ch_tps = [(channel_index, tp) for tp in tps]
for ch, tp in ch_tps:
if (ch, tp) not in self.full:
self.full[(ch, tp)] = self.tiler.get_tiles_timepoint(
tp, channels=[ch], z=[z]
)[:, 0, 0, z, ...]
tile_dict = {tp: self.full[(ch, tp)] for ch, tp in ch_tps}
return tile_dict
def get_data_for_viewing(self, trap_id, tps):
"""Get images and outlines as multidimensional arrays for Napari."""
# get outlines and tiles
outlines = self.get_outlines(trap_id, tps)
tiles_dict = self.get_tiles(trap_id, tps)
channels = list(tiles_dict.keys())
# put time series into one array with dimensions TCZYX
ydim, xdim = tiles_dict[list(tiles_dict.keys())[0]][0].shape
ts_images = np.zeros(
(tps.size, len(tiles_dict), 1, ydim, xdim)
).astype(int)
ts_labels = np.zeros((tps.size, 1, ydim, xdim)).astype(int)
# make array of time series of tiles
for ch_index, channel in enumerate(tiles_dict):
for tp_index in range(tps.size):
ts_images[tp_index, ch_index, 0, ...] = tiles_dict[channel][
tp_index
]
# make array of time series of outlines with no channels dimension
for tp_index in range(tps.size):
ts_labels[tp_index, 0, ...] = outlines[tp_index]
return ts_images, ts_labels, channels
def view(self, trap_id, tps=10):
"""
Use Napari to view all channels and outlines for a particular trap.
Fluorescence channels will not be immediately visible.
Parameters
----------
trap_id: int
The trap to be viewed.
tps: int or array of ints
Either the last time point to be viewed or a rage of time points
to view.
If None, all time points will be viewed, but gathering the images
will be slow.
"""
if tps is None:
tps = np.arange(self.cells.ntimepoints)
elif type(tps) is int:
tps = np.arange(tps)
ts_images, ts_labels, channels = self.get_data_for_viewing(
trap_id, tps
)
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# launch napari
viewer = napari.Viewer()
viewer.add_image(
ts_images[:, channels.index("Brightfield"), ...],
name="Brightfield",
)
viewer.add_labels(ts_labels, name="outlines")
# fluorescence channels are not initially visible
for i, channel in enumerate(channels):
if channel != "Brightfield":
viewer.add_image(
ts_images[:, i, ...],
name=channel,
colormap=colormap(channel),
visible=False,
opacity=0.5,
)
class LocalImageViewer(BaseImageViewer):
"""
View images from local files.
File are either zarr or organised in directories.
"""
def __init__(self, h5file: str, image_file: str):
"""Initialise using a h5file and a zarr file of images."""
h5file_path = Path(h5file)
image_file_path = Path(image_file)
if h5file_path.exists() and image_file_path.exists():
super().__init__(h5file_path)
with dispatch_image(image_file_path)(image_file_path) as image:
self.tiler = Tiler.from_h5(image, h5file_path)
self.cells = Cells.from_source(h5file_path)
traps_with_labels = [
i for i, labels in enumerate(self.cells.labels) if labels
]
print(f"Traps with labels {traps_with_labels}.")
print(f"Maximum number of time points {self.cells.ntimepoints}.")
else:
if not h5file_path.exists():
print(f" Trouble loading {h5file}.")
if not image_file_path.exists():
print(f" Trouble loading {image_file}.")
class RemoteImageViewer(BaseImageViewer):
"""Fetching remote images with tiling and outline display."""
credentials = ("host", "username", "password")
def __init__(self, h5file: str, server_info: t.Dict[str, str]):
"""Initialise using a h5file and importing aliby.io.omero."""
from aliby.io.omero import UnsafeImage as OImage
h5file_path = Path(h5file)
super().__init__(h5file_path)
server_info = server_info or {
k: self.attrs["parameters"]["general"][k] for k in self.credentials
}
logfiles_meta = parse_metadata(h5file_path.parent)
image_id = logfiles_meta.get("image_id")
if image_id is None:
with h5py.File(h5file_path, "r") as f:
image_id = f.attrs.get("image_id")
if image_id is None:
raise ("No valid image_id found in metadata.")
image = OImage(image_id, **server_info)
self.tiler = Tiler.from_h5(image, h5file_path)
self.cells = Cells.from_source(h5file_path)
def get_files(
aliby_input: str,
aliby_output: str,
omero_name: str,
position: str,
):
"""Find the h5 file and corresponding zarr file for one position."""
h5files = [str(f) for f in (Path(aliby_output) / omero_name).glob("*.h5")]
h5file = [f for f in h5files if position in f][0]
image_file_name = h5file.split("/")[-1].split(".")[0] + ".zarr"
image_file = str(Path(aliby_input) / omero_name / image_file_name)
return [h5file, image_file]