From 2ef8b8ccca30969df2239c72a57e409a691e7435 Mon Sep 17 00:00:00 2001
From: Laurent Guerard <laurent.guerard@unibas.ch>
Date: Wed, 12 Feb 2025 13:53:12 +0100
Subject: [PATCH] Add compatibility with container files and formatting
---
1_identify_fibers.py | 301 ++++++++++++++++++++++++++++++++-----------
1 file changed, 224 insertions(+), 77 deletions(-)
diff --git a/1_identify_fibers.py b/1_identify_fibers.py
index 186b492..ffb0f5f 100755
--- a/1_identify_fibers.py
+++ b/1_identify_fibers.py
@@ -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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