Add SearchFirst macro for weka classifier

scripts/SearchFirst_Trainable_Classifier.ijm

@@ -0,0 +1,112 @@
+// SPDX-FileCopyrightText: 2024 Friedrich Miescher Institute for Biomedical Research (FMI), Basel (Switzerland)
+//
+// SPDX-License-Identifier: MIT
+
+path = getArgument();
+classifierPath = "/path/to/classifier_v1.model";
+mag_firstpass = 4;
+mag_secondpass = 20;
+overlap_ratio = 0.05;
+
+// === don't edit below this line
+mag_ratio = mag_firstpass / mag_secondpass;
+
+// list all files in path
+folderName = File.getName(path);
+fileList = getFileList(path);
+
+// setup segmentation folder
+parent = File.getParent(path);
+seg_folder = parent + File.separator + folderName + "_segmentation";
+File.makeDirectory(seg_folder);
+
+setBatchMode(false);
+// loop over image data files (start with folder name)
+for (i = 0; i < fileList.length; i++) {
+	if (startsWith(fileList[i], folderName) && endsWith(fileList[i], ".tif")) {
+		process(path, fileList[i], seg_folder);
+	}
+}
+run("Quit");
+
+function process(folder, tifFile, seg_folder) {
+	// process a single tif file, write a csv file with coordinates
+	name = File.getNameWithoutExtension(folder + File.separator + tifFile);
+	print("Processing: " + name);
+	open(folder + File.separator + tifFile);
+	run("Subtract Background...", "rolling=50 light sliding");
+	run("Bin...", "x=4 y=4 bin=Average");
+	run("Trainable Weka Segmentation");
+	wait(300);
+	call("trainableSegmentation.Weka_Segmentation.loadClassifier", classifierPath);
+	//call("trainableSegmentation.Weka_Segmentation.getResult");
+	call("trainableSegmentation.Weka_Segmentation.getProbability");
+	selectImage("Probability maps");
+	run("Duplicate...", "use");
+	selectImage("class 1");
+	saveAs("Tiff", seg_folder + File.separator + name + "_prob.tif");
+	run("Gaussian Blur...", "sigma=5 slice");
+	setOption("BlackBackground", true);
+	setThreshold(0.4, 1.0);
+	run("Convert to Mask", "background=Dark black");
+	// Remove small particles
+	maskId = getImageID();
+	// save image for quality control
+	saveAs("PNG", seg_folder + File.separator + name + "_mask.png");
+
+	// resize image by overlap ratio and magnification ratio
+	n_tiles_x = Math.ceil((1 + overlap_ratio) / mag_ratio); // 6
+	n_tiles_y = Math.ceil((1 + overlap_ratio) / mag_ratio);
+	print("n_tiles: " + n_tiles_x);
+
+	w = getWidth(); // 500
+	h = getHeight();
+	tile_w = (w * (1 - overlap_ratio) * mag_ratio); // 80
+	tile_h = (h * (1 - overlap_ratio) * mag_ratio);
+	print("tile_w: " + tile_w);
+
+	offset_x = w/2;
+	offset_y = h/2;
+	margin_x = w * mag_ratio * overlap_ratio; // 20
+	margin_y = h * mag_ratio * overlap_ratio;
+	print("margin_x: " + margin_x);
+
+	crop_w = w - margin_x; // 500 => 480
+	crop_h = h - margin_y;
+	print("crop_w: " + crop_w);
+	run("Canvas Size...", "width=&crop_w height=&crop_h position=Center");
+
+	target_w = n_tiles_x * tile_w; // 
+	target_h = n_tiles_y * tile_h;
+	print("target_w: " + target_w);
+	run("Canvas Size...", "width=&target_w height=&target_h position=Top-Left zero");
+	run("Duplicate...", "title=mask"); // DEBUG
+	run("32-bit");
+	run("Size...", "width=&n_tiles_x height=&n_tiles_y depth=1 constrain average interpolation=None");
+
+	// write csv
+	height = getHeight();
+	width = getWidth();
+	rowIndex = 0;
+	Table.create("Coordinates " + tifFile);
+	for (x = 0; x < width; x++) {
+		for (y = 0; y < height; y++) {
+			if (getPixel(x, y) > 1.0) {
+				Table.set("X", rowIndex, 4.0 * (x * tile_w + (0.5 * tile_w) + (margin_x/2)));
+				Table.set("Y", rowIndex, 4.0 * (y * tile_h + (0.5 * tile_h) + (margin_y/2)));
+				rowIndex++;
+			}
+		}
+	}
+	Table.showRowNumbers(true);
+	Table.saveColumnHeader(false);
+	Table.update();
+	Table.save(folder + File.separator + name + ".csv");
+	close(Table.title);
+	close("*");
+}
+
+
+// (optional) read all Z slices for given file
+
+//