#@ ImagePlus Image 
#@ int (label="Delta (relative difference between threshold levels)", min=0, value=1) Delta 
#@ int (label="Minimum region area", min=0, value=500) MinArea 
#@ int (label="Maximum region area", min=0, value=2000) MaxArea 
#@ float (label="Maximum variation of stability for the region", min=0, value=0.5) MaxVar 
#@ String (label = "Identify bright/dark regions", choices={"Both (Default)", "Bright", "Dark"}, value = "Both (Default)") Method 
#@ Boolean (label = "Show bounding boxes as ROI", value=False) showBox
#@ Boolean (Label = "Show free shapes as ROI", value=true) showHull
'''
MSER : Maximally Stable Extremal Region Detector 
This detector performs successive thresholding with different thresholds : from bright to dark AND dark to bright by default (but one can optionnally choose only one direction too) 
The Bright to Dark option rather identify bright regions and vice-versa 
 
For each threshold it performs a connected component analysis 
Then it compare the evolution of a connected region over the level of thresholds : 
the more variation in the size of the region over the threshold levels levels, the less stable is the region. 
 
Finally the script retruns a set of stable regions that fits in the criterium of area and stability defined by the user 
 
It retruns this region as a cloud of point, as well as a bounding box for the region.  
In this macro, the bounding boxes is returned as a ROI stored into the roi manager 
 
A nice use can be to combine with the mask of the object using "AND" arithmetic so that we only retain the MSER region on the object ! 

TO DO : to prevent inclusion of regions within others, just paint all of them, the ones inside others are just overwritten/covered by the largest (like in the Knime worklow)
'''
from org.bytedeco.javacpp.opencv_features2d import MSER 
from org.bytedeco.javacpp.opencv_core       import RectVector, PointVectorVector
#from org.bytedeco.javacpp.opencv_imgproc	import convexHull, drawContours 
from ij 									import IJ, ImagePlus 
from ij.gui 								import Roi, PolygonRoi
from ij.plugin.frame						import RoiManager 
from ImageConverter							import ImProcToMat
from ijopencv.opencv 						import PointVectorPointRoiConverter
 
# Initialise ROI manager 
RM = RoiManager() 
rm = RM.getRoiManager() 
rm.runCommand("Show All") 
 
# Define defaut paramters for detector - according to doc those are used only for color images so lets use the default only 
min_diversity  = 1 
max_evolution  = 10 
area_threshold = 1.01 
min_margin     = 0.003 
edge_blur_size = 5  
 
# Initialise detector 
detector = MSER.create(Delta,MinArea,MaxArea,MaxVar,min_diversity,max_evolution,area_threshold,min_margin,edge_blur_size) 
 
# Set unilateral threshold if selected 
if Method != "Both (Default)": 
	print "Set to unidirectionnal thresholding" 
	detector.setPass2Only(True) 
 
# Get ImageProcessor from ImagePlus 
ImProc = Image.getProcessor().duplicate() 
 
# Detect stable regions 
if Method == "Dark": 
	# invert the image before doing the unidirectionnal detection 
	print "Invert image before unidirectionnal thresholding" 
	ImProc.invert() 
 
# Convert image to 8-bit (scaling) opencv matrix 
ImCV = ImProcToMat(ImProc, Bit=8) 
 
# Initialise result vectors 
Regions = PointVectorVector() # Regions contains n cloud of points (one/region). Each cloud of points is a PointVector, hence Regions is a Vector of PointVEctor 
BBox    = RectVector() 
 
# detect MSER regions 
detector.detectRegions(ImCV, Regions, BBox) 

# Initialise ROI converter
ROIconverter = PointVectorPointRoiConverter()

# Report the number of detected BBoxes (rather report it in a result table ?) 
N = BBox.size() 
message = "Found {} stable regions".format(N) 
print message 
IJ.log(message) 

# Turn the detection into ROI
for i in range(N): 

	if showHull:
		# HULL
		PointVec = Regions.get(i) 							# Recover the Point cloud as a PointVector
		PointRoi = ROIconverter.convert(PointVec, float)    # Convert to a IJ PointROI  
		Hull     = PointRoi.getConvexHull()  				# Do a convex Hull to prevent hollow shapes and reduce the number of points
		Hull_Roi = PolygonRoi(Hull, 2) 						# Convert the Polygon to a ROI (2 for type polygon)
		rm.addRoi(Hull_Roi)

	if showBox:
		# Bounding Boxes
		box = BBox.get(i) 
		BoxRoi = Roi(box.x(), box.y(), box.width(), box.height()) 
		rm.addRoi(BoxRoi)  

# Bring image back in focus 
IJ.selectWindow(Image.getTitle())