Resolves #199 by checking for NaNs in Phi

scripts/spam-ldic

@@ -118,36 +118,43 @@ def correlateOneNode(nodeNumber):
         - deltaPhiNorm
     """
     PhiInit = PhiField[nodeNumber]
-    imagetteReturns = spam.DIC.getImagettes(im1, nodePositions[nodeNumber], args.HWS, PhiInit.copy(), im2, margin, im1mask=im1mask, minMaskCoverage=args.MASK_COVERAGE, greyThreshold=[args.GREY_LOW_THRESH, args.GREY_HIGH_THRESH], applyF='no', twoD=twoD)
-
-    if imagetteReturns['returnStatus'] == 1:
-        # compute displacement that will be taken by the getImagettes
-        initialDisplacement = numpy.round(PhiInit[0:3, 3]).astype(int)
-        PhiInit[0:3,-1] -= initialDisplacement
-
-        #print(imagetteReturns['imagette1'].shape)
-        #print(imagetteReturns['imagette2'].shape)
-        #print()
-
-        registerReturns = spam.DIC.register(imagetteReturns['imagette1'],
-                                            imagetteReturns['imagette2'],
-                                            im1mask=imagetteReturns['imagette1mask'],
-                                            PhiInit=PhiInit, # minus initial displacement above, which is in the search range and thus taken into account in imagette2
-                                            margin=1,  # see top of this file for compensation
-                                            maxIterations=args.MAX_ITERATIONS,
-                                            deltaPhiMin=args.MIN_DELTA_PHI,
-                                            updateGradient=args.UPDATE_GRADIENT,
-                                            interpolationOrder=args.INTERPOLATION_ORDER,
-                                            verbose=False,
-                                            imShowProgress=False)
-        goodPhi = registerReturns['Phi']
-        goodPhi[0:3,-1] += initialDisplacement
-        return nodeNumber, goodPhi, registerReturns['returnStatus'], registerReturns['error'], registerReturns['iterations'], registerReturns['deltaPhiNorm']
 
+    if numpy.isfinite(PhiInit).sum() == 16:
+        imagetteReturns = spam.DIC.getImagettes(im1, nodePositions[nodeNumber], args.HWS, PhiInit.copy(), im2, margin, im1mask=im1mask, minMaskCoverage=args.MASK_COVERAGE, greyThreshold=[args.GREY_LOW_THRESH, args.GREY_HIGH_THRESH], applyF='no', twoD=twoD)
+
+        if imagetteReturns['returnStatus'] == 1:
+            # compute displacement that will be taken by the getImagettes
+            initialDisplacement = numpy.round(PhiInit[0:3, 3]).astype(int)
+            PhiInit[0:3,-1] -= initialDisplacement
+
+            #print(imagetteReturns['imagette1'].shape)
+            #print(imagetteReturns['imagette2'].shape)
+            #print()
+
+            registerReturns = spam.DIC.register(imagetteReturns['imagette1'],
+                                                imagetteReturns['imagette2'],
+                                                im1mask=imagetteReturns['imagette1mask'],
+                                                PhiInit=PhiInit, # minus initial displacement above, which is in the search range and thus taken into account in imagette2
+                                                margin=1,  # see top of this file for compensation
+                                                maxIterations=args.MAX_ITERATIONS,
+                                                deltaPhiMin=args.MIN_DELTA_PHI,
+                                                updateGradient=args.UPDATE_GRADIENT,
+                                                interpolationOrder=args.INTERPOLATION_ORDER,
+                                                verbose=False,
+                                                imShowProgress=False)
+            goodPhi = registerReturns['Phi']
+            goodPhi[0:3,-1] += initialDisplacement
+            return nodeNumber, goodPhi, registerReturns['returnStatus'], registerReturns['error'], registerReturns['iterations'], registerReturns['deltaPhiNorm']
+
+        else:
+            badPhi = numpy.eye(4)
+            badPhi[0:3, 3] = numpy.nan
+            return nodeNumber, badPhi, imagetteReturns['returnStatus'], numpy.inf, 0, numpy.inf
     else:
+        ### Phi has nans or infs
         badPhi = numpy.eye(4)
         badPhi[0:3, 3] = numpy.nan
-        return nodeNumber, badPhi, imagetteReturns['returnStatus'], numpy.inf, 0, numpy.inf
+        return nodeNumber, badPhi, -7, numpy.inf, 0, numpy.inf
 
 
 # Loop over input images

tests/test_scripts.py

@@ -838,8 +838,8 @@ class testAll(unittest.TestCase):
         #######################################################
         ### 4. 2020-09-05 EA and OS: New "extreme" test with particles touching the boundaries
         #######################################################
-        im1 = spam.kalisphera.makeBlurryNoisySphere([100,100,100], [[50,50,50], [50,10,10]], [10, 10], 0.5, 0.05, background=0.0, foreground=1.0)
-        im2 = spam.kalisphera.makeBlurryNoisySphere([100,100,100], [[54,54,54], [54,14,14]], [10, 10], 0.5, 0.05, background=0.0, foreground=1.0)
+        im1 = spam.kalisphera.makeBlurryNoisySphere([100,100,100], [[50,50,50], [50,10,10]], [10, 10], 0.5, 0.01, background=0.0, foreground=1.0)
+        im2 = spam.kalisphera.makeBlurryNoisySphere([100,100,100], [[54,54,54], [54,14,14]], [10, 10], 0.5, 0.01, background=0.0, foreground=1.0)
         im1lab = scipy.ndimage.label(im1>0.5)[0]
 
         tifffile.imsave(testFolder + "extreme-im1.tif", im1)

tools/deformation/deformationFunction.py

@@ -186,7 +186,7 @@ def decomposeF(F, twoD=False):
 
     # Check for NaNs if any quit
     if numpy.isnan(F).sum() > 0:
-        print("deformationFunction.decomposeF(): Nan value in F. Exiting")
+        #print("deformationFunction.decomposeF(): Nan value in F. Exiting")
         return transformation
 
     # Check for inf if any quit