Olivier Burri lacan

## Export_Images_Folder.ijm
/*
 * Complex Format EXPORT MACRO
 * By Olivier Burri @ EPFL - SV - PTECH - BIOP
 * Given a folder, extracts all series inside all multi-file files with given extension in new folders
 * Last edit: 13.02.2017
 */


 ////////////////////// SET PARAMETERS //////////////////////
 ////////////////////////////////////////////////////////////

## Replace detections as cells via erosion and add measurements.groovy
/**
 * Reduce Detection size to create new cell objects and add measurements
 * @author Olivier Burri
 * @date 20240212
 */

// By how much should the existing detections be reduced to create the new "nucleus"
def erosionDistancePx = -5

// Should the intensities be measured on a downsampled image?

## Parallel_HistrogramEqualize_All_Slices.groovy
#@ ImagePlus image
#@ Integer ( label = "Channel to correct (in case of multichannel images)", value = 1 ) ch_correct
#@ Integer ( label = "Reference Slice", value = 1 ) ref_slice
#@ Boolean ( label = "Use Manual Normalization Parameters Below", value = false ) is_normalize
#@ Integer ( Label = "Minimum" ) the_min
#@ Integer ( Label = "Maximum" ) the_max

// This variable will help us at the end to add the normalized channel back
def all_images = []

## resave-as-pyramid-with-fixed-bit-depth.groovy
// Resave image as a pyramidal ome.tiff at the desired downsample and bit depth
// @author Olivier Burri
// 16-bit magic trick inspired from
// https://forum.image.sc/t/software-recommendations-converting-a-32bit-ome-tiff-to-an-8bit-ome-tiff/43043
// But did not use scaling parameter

//  ****** PARAMETERS *********

// Output downsample (1 for full-resolution)
def outputDownsample = 1

## Example_RNG.groovy
// DISCLAIMER: The algorithm was taken from a ChatGPT prompt "What is the Groovy implementation of the 2D Relative Neighborhood Graph Algorithm"
// It is merey an alpha version of the code and has not been thorougly tested at all, nor has the code been optimized.
// The original prompt has been slightly modifided to make better use of ImageJ methods to display the graph


// Create a list of points for testing
def rand = new Random()
// Or to make it reproducible
//def rand = new Random( 1000 )

## Rename_VSI_files_from_csv.groovy
#@File originalDir (label="Directory with original VSI Files", style="directory")
#@File csvFile (label="CSV File with full VSI file path in columns 1 and new name in column 2")
#@String sep (label="CSV separator", value=";")

/*
 * Rename VSI images based on CSV reference file
 */

println sep
def csvImages = csvFile.readLines()*.split(sep)

## Fix_unmapped_image_entry_uris.groovy
/*
 * Update QuPath URIs
 * After the migration of our EPFL servers from svfasX to sv-nas1.rcp, some projects fail to open in a timely manner,
 * especially under Windows when  the full path to the server is used (instead of a networked drive).
 * The reason seems to be that QuPath is waiting for a response from the server, and the timeouts are long, so it takes a
 * really long time before the "Update URIs" window pops up
 *
 * This script aims to assist in fixing that by at least changing the host name (svfasX) to a valid host (sv-nas1.rcp)
 * that way, QuPath will immediately either find or fail to find the file, restoring normal behavior
 *

## Create_progress_monitor.groovy
// Example creating a dialog to run tasks in a script.
// We need to discern between headless and non-headless manually so far
// @author Olivier Burri
//

import qupath.lib.plugins.CommandLinePluginRunner
import qupath.lib.gui.PluginRunnerFX

// Define the runner, in this case we will not use it but it is quite powerful
def runner

## Export Annotations For Stardist.groovy
/*
// ABOUT
Exports Annotations for StarDist (Or other Deep Learning frameworks)

// INPUTS
You need rectangular annotations that have classes "Training" and "Validation"
After you have placed these annotations, lock them and start drawing the objects inside

// OUTPUTS
----------

## HoughIncompleteCirclesSEM.ijm
// Hough Transform to find circles,
// Author: Olivier Burri, from a request of the Image.sc forum:
// https://forum.image.sc/t/find-circles-with-incomplete-boundaries/42357
// Due to the simple nature of this code, no copyright applies

#@ImagePlus image
#@Double hough_threshold (value=0.55)


// SOme housekeeping
	/*
	* Complex Format EXPORT MACRO
	* By Olivier Burri @ EPFL - SV - PTECH - BIOP
	* Given a folder, extracts all series inside all multi-file files with given extension in new folders
	* Last edit: 13.02.2017
	*/


	////////////////////// SET PARAMETERS //////////////////////
	////////////////////////////////////////////////////////////
	#@ ImagePlus image
	#@ Integer ( label = "Channel to correct (in case of multichannel images)", value = 1 ) ch_correct
	#@ Integer ( label = "Reference Slice", value = 1 ) ref_slice
	#@ Boolean ( label = "Use Manual Normalization Parameters Below", value = false ) is_normalize
	#@ Integer ( Label = "Minimum" ) the_min
	#@ Integer ( Label = "Maximum" ) the_max

	// This variable will help us at the end to add the normalized channel back
	def all_images = []
	// Resave image as a pyramidal ome.tiff at the desired downsample and bit depth
	// @author Olivier Burri
	// 16-bit magic trick inspired from
	// https://forum.image.sc/t/software-recommendations-converting-a-32bit-ome-tiff-to-an-8bit-ome-tiff/43043
	// But did not use scaling parameter

	// **** PARAMETERS *******

	// Output downsample (1 for full-resolution)
	def outputDownsample = 1
	// DISCLAIMER: The algorithm was taken from a ChatGPT prompt "What is the Groovy implementation of the 2D Relative Neighborhood Graph Algorithm"
	// It is merey an alpha version of the code and has not been thorougly tested at all, nor has the code been optimized.
	// The original prompt has been slightly modifided to make better use of ImageJ methods to display the graph


	// Create a list of points for testing
	def rand = new Random()
	// Or to make it reproducible
	//def rand = new Random( 1000 )
	#@File originalDir (label="Directory with original VSI Files", style="directory")
	#@File csvFile (label="CSV File with full VSI file path in columns 1 and new name in column 2")
	#@String sep (label="CSV separator", value=";")

	/*
	* Rename VSI images based on CSV reference file
	*/

	println sep
	def csvImages = csvFile.readLines()*.split(sep)
	/*
	* Update QuPath URIs
	* After the migration of our EPFL servers from svfasX to sv-nas1.rcp, some projects fail to open in a timely manner,
	* especially under Windows when the full path to the server is used (instead of a networked drive).
	* The reason seems to be that QuPath is waiting for a response from the server, and the timeouts are long, so it takes a
	* really long time before the "Update URIs" window pops up
	*
	* This script aims to assist in fixing that by at least changing the host name (svfasX) to a valid host (sv-nas1.rcp)
	* that way, QuPath will immediately either find or fail to find the file, restoring normal behavior
	*
	// Example creating a dialog to run tasks in a script.
	// We need to discern between headless and non-headless manually so far
	// @author Olivier Burri
	//

	import qupath.lib.plugins.CommandLinePluginRunner
	import qupath.lib.gui.PluginRunnerFX

	// Define the runner, in this case we will not use it but it is quite powerful
	def runner
	/*
	// ABOUT
	Exports Annotations for StarDist (Or other Deep Learning frameworks)

	// INPUTS
	You need rectangular annotations that have classes "Training" and "Validation"
	After you have placed these annotations, lock them and start drawing the objects inside

	// OUTPUTS
	----------
	// Hough Transform to find circles,
	// Author: Olivier Burri, from a request of the Image.sc forum:
	// https://forum.image.sc/t/find-circles-with-incomplete-boundaries/42357
	// Due to the simple nature of this code, no copyright applies

	#@ImagePlus image
	#@Double hough_threshold (value=0.55)


	// SOme housekeeping