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Commit 2ef8b8cc authored by Laurent Guerard's avatar Laurent Guerard
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Add compatibility with container files and formatting

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with 224 additions and 77 deletions
1_identify_fibers.py
+ 224
− 77
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......@@ -67,6 +67,7 @@ from ij.plugin import Duplicator, ImageCalculator, RoiEnlarger
from imcflibs import pathtools
from imcflibs.imagej import bioformats as bf
from imcflibs.imagej import misc
from loci.formats import ImageReader, MetadataTools
# ─── Functions ────────────────────────────────────────────────────────────────
......@@ -625,6 +626,62 @@ def setup_defined_ij(rm, rt):
IJ.log("\\Clear")
def get_series_info_from_ome_metadata(path_to_file, skip_labels=False):
"""Get the Bio-Formates series count from a file on disk.
Useful to access a specific image in a container format like .czi, .nd2, .lif...
Parameters
----------
path_to_file : str
The full path to the image file.
Returns
-------
int
The number of Bio-Formats series detected in the image file metadata.
"""
if not skip_labels:
reader = ImageReader()
reader.setFlattenedResolutions(False)
ome_meta = MetadataTools.createOMEXMLMetadata()
reader.setMetadataStore(ome_meta)
reader.setId(path_to_file)
series_count = reader.getSeriesCount()
reader.close()
return series_count, range(series_count)
else:
reader = ImageReader()
# reader.setFlattenedResolutions(True)
ome_meta = MetadataTools.createOMEXMLMetadata()
reader.setMetadataStore(ome_meta)
reader.setId(path_to_file)
series_count = reader.getSeriesCount()
series_ids = []
series_names = []
x = 0
y = 0
for i in range(series_count):
reader.setSeries(i)
if reader.getSizeX() > x and reader.getSizeY() > y:
name = ome_meta.getImageName(i)
if name not in ["label image", "macro image"]:
series_ids.append(i)
series_names.append(name)
x = reader.getSizeX()
y = reader.getSizeY()
print(series_names)
return len(series_ids), series_ids
# ─── Main Code ────────────────────────────────────────────────────────────────
if __name__ == "__main__":
......@@ -643,21 +700,69 @@ if __name__ == "__main__":
# open image using Bio-Formats
file_info = pathtools.parse_path(file)
misc.progressbar(index + 1, len(file_list), 1, "Opening : ")
raw = bf.import_image(file_info["full"])[0]
# get image info
raw_image_calibration = raw.getCalibration()
raw_image_title = fix_BF_czi_imagetitle(raw)
print("raw image title: ", str(raw_image_title))
# take care of paths and directories
output_dir = os.path.join(
out_dir_info["full"], str(raw_image_title), "1_identify_fibers"
_, series_index = get_series_info_from_ome_metadata(
file_info["full"], skip_labels=True
)
print("output_dir: ", str(output_dir))
print(series_index)
for list_index, serie_index in enumerate(series_index):
misc.progressbar(list_index + 1, len(series_index), 2, "Opening serie : ")
raw = bf.import_image(file_info["full"], series_number=serie_index)[0]
# get image info
raw_image_calibration = raw.getCalibration()
raw_image_title = fix_BF_czi_imagetitle(raw)
print("raw image title: ", str(raw_image_title))
# take care of paths and directories
output_dir = os.path.join(
out_dir_info["full"], str(raw_image_title), "1_identify_fibers"
)
print("output_dir: ", str(output_dir))
if not os.path.exists(str(output_dir)):
os.makedirs(str(output_dir))
# update the log for the user
misc.timed_log("Now working on " + str(raw_image_title))
if raw_image_calibration.scaled() is False:
IJ.log(
"Your image is not spatially calibrated! Size measurements are only possible in [px]."
)
# Only print settings once for the first file and first series
if index == 0 and serie_index == 0:
IJ.log(" -- settings used -- ")
IJ.log("area = " + str(minAr) + "-" + str(maxAr))
IJ.log("perimeter = " + str(minPer) + "-" + str(maxPer))
IJ.log("circularity = " + str(minCir) + "-" + str(maxCir))
IJ.log("ROI expansion [microns] = " + str(enlarge_radius))
IJ.log("Membrane channel = " + str(membrane_channel))
IJ.log("MHC positive fiber channel = " + str(fiber_channel))
# IJ.log("sub-tiling = " + str(tiling_factor))
IJ.log(" -- settings used -- ")
# image (pre)processing and segmentation (-> ROIs)# imp, firstC, lastC, firstZ,
# lastZ, firstT, lastT
membrane = Duplicator().run(
raw, membrane_channel, membrane_channel, 1, 1, 1, 1
)
if not os.path.exists(str(output_dir)):
os.makedirs(str(output_dir))
imp_bgd_corrected = do_background_correction(membrane)
IJ.run("Conversions...", "scale")
IJ.run(imp_bgd_corrected, "16-bit", "")
imp_result = run_tm(
imp_bgd_corrected,
1,
cellpose_dir.getPath(),
PretrainedModel.CYTO2,
30.0,
area_thresh=[minAr, maxAr],
circularity_thresh=[minCir, maxCir],
perimeter_thresh=[minPer, maxPer],
)
# update the log for the user
misc.timed_log("Now working on " + str(raw_image_title))
......@@ -700,84 +805,126 @@ if __name__ == "__main__":
os.path.join(output_dir, raw_image_title + "_all_fibers_binary"),
)
command.run(Labels2CompositeRois, True, "rm", rm, "imp", imp_result).get()
IJ.saveAs(
imp_result,
"Tiff",
os.path.join(
output_dir,
raw_image_title + "_" + str(serie_index) + "_all_fibers_binary",
),
)
enlarge_all_rois(enlarge_radius, rm, raw_image_calibration.pixelWidth)
renumber_rois(rm)
save_all_rois(
rm, os.path.join(output_dir, raw_image_title + "_all_fiber_rois.zip")
)
command.run(Labels2CompositeRois, True, "rm", rm, "imp", imp_result).get()
# check for positive fibers
if fiber_channel > 0:
if min_fiber_intensity == 0:
min_fiber_intensity = get_threshold_from_method(
raw, fiber_channel, "Mean"
)[0]
IJ.log("automatic intensity threshold detection: True")
IJ.log("fiber intensity threshold: " + str(min_fiber_intensity))
change_all_roi_color(rm, "blue")
positive_fibers = select_positive_fibers(
raw, fiber_channel, rm, min_fiber_intensity
)
change_subset_roi_color(rm, positive_fibers, "magenta")
save_selected_rois(
enlarge_all_rois(enlarge_radius, rm, raw_image_calibration.pixelWidth)
renumber_rois(rm)
save_all_rois(
rm,
positive_fibers,
os.path.join(
output_dir, raw_image_title + "_mhc_positive_fiber_rois.zip"
output_dir,
raw_image_title + "_" + str(serie_index) + "_all_fiber_rois.zip",
),
)
# measure size & shape, save
IJ.run(
"Set Measurements...",
"area perimeter shape feret's redirect=None decimal=4",
)
IJ.run("Clear Results", "")
measure_in_all_rois(raw, membrane_channel, rm)
rt = ResultsTable.getResultsTable("Results")
# check for positive fibers
if fiber_channel > 0:
if min_fiber_intensity == 0:
min_fiber_intensity = get_threshold_from_method(
raw, fiber_channel, "Mean"
)[0]
IJ.log("automatic intensity threshold detection: True")
IJ.log("fiber intensity threshold: " + str(min_fiber_intensity))
change_all_roi_color(rm, "blue")
positive_fibers = select_positive_fibers(
raw, fiber_channel, rm, min_fiber_intensity
)
change_subset_roi_color(rm, positive_fibers, "magenta")
save_selected_rois(
rm,
positive_fibers,
os.path.join(
output_dir,
raw_image_title
+ "_"
+ str(serie_index)
+ "_mhc_positive_fiber_rois.zip",
),
)
# measure size & shape, save
IJ.run(
"Set Measurements...",
"area perimeter shape feret's redirect=None decimal=4",
)
IJ.run("Clear Results", "")
measure_in_all_rois(raw, membrane_channel, rm)
# print(rt.size())
rt = ResultsTable.getResultsTable("Results")
if fiber_channel > 0:
# print(rt.size())
preset_results_column(rt, "MHC Positive Fibers (magenta)", "NO")
# print(rt.size())
add_results(rt, "MHC Positive Fibers (magenta)", positive_fibers, "YES")
# print(rt.size())
rt.save(os.path.join(output_dir, raw_image_title + "_all_fibers_results.csv"))
# print("saved the all_fibers_results.csv")
# dress up the original image, save a overlay-png, present original to the user
rm.show()
raw.show()
show_all_rois_on_image(rm, raw)
raw.setDisplayMode(IJ.COMPOSITE)
enhance_contrast(raw)
IJ.run(
"From ROI Manager", ""
) # ROIs -> overlays so they show up in the saved png
qc_duplicate = raw.duplicate()
IJ.saveAs(
qc_duplicate, "PNG", output_dir + "/" + raw_image_title + "_all_fibers"
)
qc_duplicate.close()
wm.toFront(raw.getWindow())
IJ.run("Remove Overlay", "")
raw.setDisplayMode(IJ.GRAYSCALE)
show_all_rois_on_image(rm, raw)
IJ.selectWindow("Log")
IJ.saveAs("Text", str(output_dir + "/" + raw_image_title + "_all_fibers_Log"))
membrane.close()
imp_bgd_corrected.close()
imp_result.close()
if close_raw == True:
raw.close()
if fiber_channel > 0:
# print(rt.size())
preset_results_column(rt, "MHC Positive Fibers (magenta)", "NO")
# print(rt.size())
add_results(rt, "MHC Positive Fibers (magenta)", positive_fibers, "YES")
# print(rt.size())
rt.save(
os.path.join(
output_dir,
raw_image_title
+ "_"
+ str(serie_index)
+ "_all_fibers_results.csv",
)
)
# print("saved the all_fibers_results.csv")
# dress up the original image, save a overlay-png, present original to the user
rm.show()
raw.show()
show_all_rois_on_image(rm, raw)
raw.setDisplayMode(IJ.COMPOSITE)
enhance_contrast(raw)
IJ.run(
"From ROI Manager", ""
) # ROIs -> overlays so they show up in the saved png
qc_duplicate = raw.duplicate()
IJ.saveAs(
qc_duplicate,
"PNG",
output_dir
+ "/"
+ raw_image_title
+ "_"
+ str(serie_index)
+ "_all_fibers",
)
qc_duplicate.close()
wm.toFront(raw.getWindow())
IJ.run("Remove Overlay", "")
raw.setDisplayMode(IJ.GRAYSCALE)
show_all_rois_on_image(rm, raw)
IJ.selectWindow("Log")
IJ.saveAs(
"Text",
str(
output_dir
+ "/"
+ raw_image_title
+ "_"
+ str(serie_index)
+ "_all_fibers_Log"
),
)
membrane.close()
imp_bgd_corrected.close()
imp_result.close()
if close_raw == True:
raw.close()
total_execution_time_min = (time.time() - execution_start_time) / 60.0
IJ.log("total time in minutes: " + str(total_execution_time_min))
......
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