Example for stitching a fileseries with Ashlar

stitch_ashlar.py

from ashlar.fileseries import FileSeriesReader
from ashlar.reg import EdgeAligner, Mosaic, PyramidWriter
import numpy as np
import pandas as pd
import skimage.exposure, skimage.filters, skimage.io
import sys
import tifffile

input_tif_path = sys.argv[1]
pattern = "20231124_203726_S1_C902_P99_N99_F{series}.TIF"

print(f"Stitching TIF tiles: {input_tif_path}/{pattern}")

reader = FileSeriesReader(
    input_tif_path,
    pattern=pattern,
    width=2,
    height=2,
    overlap=0.01,
    pixel_size=0.120280945,
)

aligner = EdgeAligner(reader, channel=4, verbose=True)
aligner.run()

mosaic = Mosaic(aligner, aligner.mosaic_shape, channels=[4], verbose=True)

writer = PyramidWriter([mosaic], "output.ome.tif", verbose=True)
writer.run()
print()
print("Wrote output.ome.tif")


# Write out CSV with corrected position and shift distance for each FOV.
fields = [s for w, s in reader.metadata.all_series]
df = pd.DataFrame(
    np.hstack([aligner.positions, aligner.shifts]),
    columns=["Position_Y", "Position_X", "Shift_Y", "Shift_X"],
    index=pd.Series(fields, name="Field"),
).sort_index(
    axis="columns",
# Discard roundoff error in the low bits.
).round(5)
df.to_csv("fov_positions.csv")
print("Wrote fov_positions.csv")


# Take biggest subresolution without going over 3000 px on the longest side and
# write it out in JPEG format as a preview image.
out_tiff = tifffile.TiffFile("output.ome.tif")
preview_series = next(s for s in out_tiff.series[0].levels if max(s.shape) < 3000)
preview_img = preview_series.asarray()
vmin = np.exp(skimage.filters.threshold_otsu(np.log(preview_img[preview_img>0])))
vmax = np.percentile(preview_img, 99.9)
preview_img = skimage.exposure.rescale_intensity(preview_img, in_range=(vmin, vmax))
preview_img = skimage.util.img_as_ubyte(preview_img)
skimage.io.imsave("preview.jpg", preview_img)
print("Wrote preview.jpg")