def getValueForDoc(_self, doc):
curDocID = values.docID()
if doc < curDocID:
raise ValueError("doc=" + doc + " is before curDocID=" + curDocID)
if doc > curDocID:
curDocID = values.advance(doc)
if curDocID == doc:
return Float.intBitsToFloat(int(values.longValue()))
else:
return 0.0
def getValueForDoc(_self, doc):
curDocID = values.docID()
if doc < curDocID:
raise ValueError("doc=" + doc + " is before curDocID=" +
curDocID)
if doc > curDocID:
curDocID = values.advance(doc)
if curDocID == doc:
return Float.intBitsToFloat(values.longValue())
else:
return 0.0
def FindMinMax(CorrMapCV, Unique=True, MinMax="Max", Score_Threshold=0.5, Tolerance=0):
'''
Detect Minima(s) or Maxima(s) in the correlation map
The function uses for that the MinMaxLoc from opencv for unique detection
or the MaximumFinder from ImageJ for multi-detection (in this case, for min detection the correlation map is inverted)
- Unique : True if we look for one global min/max, False if we want local ones
- MinMax : "Min" if we look for Minima, "Max" if we look for maxima
- Score_Threshold : in range [0;1] (correlation maps are normalised)
- Tolerance : Parameters for flood-fill. Not used here so should be set to 0
Returns List of Min/Max : [(X, Y, Coefficient), ... ]
'''
#### Detect min/maxima of correlation map ####
Extrema = []
## if 1 hit expected
if Unique: # Look for THE global min/max
minVal = DoublePointer(1)
maxVal = DoublePointer(1)
minLoc = Point()
maxLoc = Point()
minMaxLoc(CorrMapCV, minVal, maxVal, minLoc, maxLoc, Mat())
if MinMax=="Min": # For method based on difference we look at the min value
X = minLoc.x()
Y = minLoc.y()
Coeff = minVal.get()
elif MinMax=="Max":
X = maxLoc.x()
Y = maxLoc.y()
Coeff = maxVal.get()
# Append to the output list
Extrema.append( (X, Y, Coeff) )
## if more hit expected
else: # expect more than 1 template/image. Do a multi minima/maxima detection
## OLD CODE LEFT BUT DONT DO IT, NOT GOOD PREACTICE !! Normalise the correlation map to 0-1 (easier to apply the threshold)
## Rather use normalised method only for the computation of the correlation map (by default normalised method are 0-1 bound)
# The normalised method are already normalised to 0-1 but it is more work to take it into account in this function
# WARNING : this normalisation normalise regarding to the maxima of the given correlation map
# Not good because it means the thresholding is define relative to a correlation map : a score of 1 might not correspond to a correlation of 1
# But only means that the pixel was the maximum of this particular correlation map
#CorrMapNormCV = Mat()
#normalize(CorrMapCV, CorrMapNormCV, 0, 1, NORM_MINMAX, CV_32FC1, None)
# Visualisation for debugging
#CorrMapNorm = MatToImProc(CorrMapNormCV)
#CorrMapNorm = ImagePlus("Normalised", CorrMapNorm)
#CorrMapNorm.show()
### Maxima/Minima detection on the correlation map
## Difference-based method : we look for min value. For that we detect maxima on an inverted correlation map
if MinMax=="Min":
# Invert the correlation map : min becomes maxima
One = Scalar(1.0)
CorrMapInvCV = subtract(One, CorrMapCV).asMat()
#CorrMapInv = MatToImProc(CorrMapInvCV)
#CorrMapInv = ImagePlus("Inverted", CorrMapInv)
#CorrMapInv.show()
# Apply a "TO ZERO" threshold on the correlation map : to compensate the fact that maxima finder does not have a threshold argument
# TO ZERO : below the threshold set to 0, above left untouched
# NB : 1-score_threshold since we have inverted the image : we want to look for minima of value <x so we have to look for maxima of value>1-x in the inverted image
CorrMapThreshCV = Mat()
threshold(CorrMapInvCV, CorrMapThreshCV, 1-Score_Threshold, 0, CV_THRESH_TOZERO)
CorrMapThresh = MatToImProc(CorrMapThreshCV) # Keep this conversion, not only for visualisation
#CorrMapThreshImp = ImagePlus("Thresholded", CorrMapThresh)
#CorrMapThreshImp.show()
## Correlation-based method : we look for maxima
elif MinMax=="Max":
# Apply a "TO ZERO" threshold on the correlation map : to compensate the fact that maxima finder does not have a threshold argument
# TO ZERO : below the threshold set to 0, above left untouched
# NB : 1-score_threshold since we have inverted the image : we want to look for minima of value <x so we have to look for maxima of value>1-x in the inverted image
CorrMapThreshCV = Mat()
threshold(CorrMapCV, CorrMapThreshCV, Score_Threshold, 0, CV_THRESH_TOZERO)
CorrMapThresh = MatToImProc(CorrMapThreshCV) # Keep this conversion, not only for visualisation
#CorrMapThreshImp = ImagePlus("Thresholded", CorrMapThresh)
#CorrMapThreshImp.show()
## For both cases (Multi-Min/Max-detection) detect maxima on the thresholded map
# Detect local maxima
excludeOnEdge = False # otherwise miss quite a lot of them
Polygon = MaximumFinder().getMaxima(CorrMapThresh, Tolerance, excludeOnEdge)
# Maxima as list of points
#roi = PointRoi(Polygon)
# Generate Hit from max coordinates
#print Polygon.npoints," maxima detected in this score map"
if Polygon.npoints!=0: # Check that there are some points indeed. Otherwise Polygon.xpoints and ypoints are anyway initialised with [0,0,0,0] even if Npoints=0 !
for i in range(Polygon.npoints): # dont directly loop on xpoints and ypoint since initialised with [0,0,0,0] ie less than 4 points we get some 0,0 coordinates
# Get point coordinates
X, Y = Polygon.xpoints[i], Polygon.ypoints[i]
# Get Coeff
Coeff = CorrMapThresh.getPixel(X, Y)
Coeff = Float.intBitsToFloat(Coeff) # require java.lang.Float
# Revert the score again if we were detecting minima initially (since found as maxima of a reverted correlation map)
if MinMax=="Min":
Coeff = 1-Coeff
# Wrap into corresponding hit
Extrema.append( (X, Y, Coeff) )
return Extrema