Olivier Burri lacan

## Export Contiguous Fields.groovy
// This script was created for Anna Hamacher, as per an internal discussion on the ImageSC forum
// that stemmed from https://forum.image.sc/t/viewing-slide-scans-from-operetta-perkin-elmer-cls-as-overview-montage-images/52942/13?u=oburri
// Code by Olivier Burri, EPFL - SV - PTECH - BIOP
// Edited by Anna Hamacher
// Last edition: July 2022

#@ File dir (label="Images Folder", style="directory")
#@Integer downsample (label="Downsample Factor", value=1)
#@String z_projection_method (label = "Projection Type", choices = {"No Projection", "Average Intensity", "Max Intensity", "Min Intensity", "Sum Slices", "Standard Deviation", "Median"} )
#@File outputDir (label="Output directory", style="directory")

## Demo_OMERO.ijm
print( "Your OMERO Server is Broken. Come back tomorrow" );

## Template matching using OpenCV.groovy
// Basic Template Matching using OpenCV with distance based NMS
// Author: Olivier Burri, EPFL - SV - PTECH - BIOP
// Inspiration:
//  - https://docs.opencv.org/master/d4/dc6/tutorial_py_template_matching.html
//  - https://towardsdatascience.com/non-maximum-suppression-nms-93ce178e177c
// Date: 2021-05-25

def downsample = 10
def templateClass = "Template"
def detectedClass = "Detected"

## useful_shortcuts.ijm
// Action Bar description file :useful_shortcuts
// Provided by Olivier Burri for PCB-02
// Date: 20210420
// Copy this file (Do not rename it) to your Fiji installation under plugins/ActionBar
run("Action Bar","/plugins/ActionBar/useful_shortcuts.ijm");
exit();

<line>
<button> 1 line 1
label=Close All

## CombineNISPositionXMLs.groovy
#@ File xmlFolder (label="Folder with XML Files to combine", style="directory")

// List all XML Files
def files = xmlFolder.list({d, f-> f ==~ /.*.xml/ } as FilenameFilter).collect{ new File( xmlFolder, it) }

// Make a result folder
resultfolder = new File( xmlFolder, "combined")
resultfolder.mkdirs()

// Get the reference from which we will append the others to

## concentric-circles.ijm
/**
 * Macro to create concentric circles and sectors based on relative distance to an outer edge for a "donut-like" shape
 *
 * Author: Olivier Burri, EPFL - SV - PTECH - BIOP
 *
 * For Kailie Batsche, from a request on the Image.sc forum:
 * https://forum.image.sc/t/cross-sectional-analysis-cut-into-8-equal-sectors-with-three-concentric-rings/50302?u=oburri
 *
 * Provided under GPL3
 * https://www.gnu.org/licenses/gpl-3.0.en.html

## MultiStackMontage All images.groovy
// Makes a montage with the selected settings from all open images
// By Olivier Burri, EPFL - SV - BIOP
// For Matjaz Panjan, Jožef Stefan Institute
// This needs the StackMontage plugin, which is available from the PTBIOP update site

def nRows = 2
def nCols = 1

// SCRIPT START

## 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 = []

## FWHM.ijm
/**
 * Calculate 1D Full Width at half Maximum of a single line or a series of lines added to the ROI Manager
 * Created by Olivier Burri and Romain Guiet, BioImaging & Optics Platform (BIOP)
 * Last Update: 2020.10.21
 *
 * Due to the simple nature of this code, no copyright applies
 *
 * Installation
 * ------------
 * From within Fiji Please use "Plugins > Macros > Install" and select this file

## FlatField_And_Stitch.groovy
/*
 * Flatfield correction followed by stitching
 *
 * It is often the case that tiled microscopy images contain artifacts due to uneven illumination.
 * This is due to the shape of the illumination, the quality of the optics and the size of the field of view, but
 * losses of up to 50% can be seen between the periphery and the center of a field of view. This loss usually follows
 * a parabolic function, but is not necessarily centered, causing artifacts when stitching tiled acquisitions
 *
 * While many methods exist to compensate for this, the simplest consists in acquiring a 'flat field' image by
 * taking a homogeneous sample (Chroma slides or even better, dye solutions [https://www.ncbi.nlm.nih.gov/pubmed/18173639]) and acquiring it for each channel of the dataset
	// This script was created for Anna Hamacher, as per an internal discussion on the ImageSC forum
	// that stemmed from https://forum.image.sc/t/viewing-slide-scans-from-operetta-perkin-elmer-cls-as-overview-montage-images/52942/13?u=oburri
	// Code by Olivier Burri, EPFL - SV - PTECH - BIOP
	// Edited by Anna Hamacher
	// Last edition: July 2022

	#@ File dir (label="Images Folder", style="directory")
	#@Integer downsample (label="Downsample Factor", value=1)
	#@String z_projection_method (label = "Projection Type", choices = {"No Projection", "Average Intensity", "Max Intensity", "Min Intensity", "Sum Slices", "Standard Deviation", "Median"} )
	#@File outputDir (label="Output directory", style="directory")
	// Basic Template Matching using OpenCV with distance based NMS
	// Author: Olivier Burri, EPFL - SV - PTECH - BIOP
	// Inspiration:
	// - https://docs.opencv.org/master/d4/dc6/tutorial_py_template_matching.html
	// - https://towardsdatascience.com/non-maximum-suppression-nms-93ce178e177c
	// Date: 2021-05-25

	def downsample = 10
	def templateClass = "Template"
	def detectedClass = "Detected"
	// Action Bar description file :useful_shortcuts
	// Provided by Olivier Burri for PCB-02
	// Date: 20210420
	// Copy this file (Do not rename it) to your Fiji installation under plugins/ActionBar
	run("Action Bar","/plugins/ActionBar/useful_shortcuts.ijm");
	exit();

	<line>
	<button> 1 line 1
	label=Close All
	#@ File xmlFolder (label="Folder with XML Files to combine", style="directory")

	// List all XML Files
	def files = xmlFolder.list({d, f-> f ==~ /.*.xml/ } as FilenameFilter).collect{ new File( xmlFolder, it) }

	// Make a result folder
	resultfolder = new File( xmlFolder, "combined")
	resultfolder.mkdirs()

	// Get the reference from which we will append the others to
	/**
	* Macro to create concentric circles and sectors based on relative distance to an outer edge for a "donut-like" shape
	*
	* Author: Olivier Burri, EPFL - SV - PTECH - BIOP
	*
	* For Kailie Batsche, from a request on the Image.sc forum:
	* https://forum.image.sc/t/cross-sectional-analysis-cut-into-8-equal-sectors-with-three-concentric-rings/50302?u=oburri
	*
	* Provided under GPL3
	* https://www.gnu.org/licenses/gpl-3.0.en.html
	// Makes a montage with the selected settings from all open images
	// By Olivier Burri, EPFL - SV - BIOP
	// For Matjaz Panjan, Jožef Stefan Institute
	// This needs the StackMontage plugin, which is available from the PTBIOP update site

	def nRows = 2
	def nCols = 1

	// SCRIPT START
	#@ 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 = []
	/**
	* Calculate 1D Full Width at half Maximum of a single line or a series of lines added to the ROI Manager
	* Created by Olivier Burri and Romain Guiet, BioImaging & Optics Platform (BIOP)
	* Last Update: 2020.10.21
	*
	* Due to the simple nature of this code, no copyright applies
	*
	* Installation
	* ------------
	* From within Fiji Please use "Plugins > Macros > Install" and select this file
	/*
	* Flatfield correction followed by stitching
	*
	* It is often the case that tiled microscopy images contain artifacts due to uneven illumination.
	* This is due to the shape of the illumination, the quality of the optics and the size of the field of view, but
	* losses of up to 50% can be seen between the periphery and the center of a field of view. This loss usually follows
	* a parabolic function, but is not necessarily centered, causing artifacts when stitching tiled acquisitions
	*
	* While many methods exist to compensate for this, the simplest consists in acquiring a 'flat field' image by
	* taking a homogeneous sample (Chroma slides or even better, dye solutions [https://www.ncbi.nlm.nih.gov/pubmed/18173639]) and acquiring it for each channel of the dataset