def testNormalizedCorrelationHorzShift2D_SameArray_AllAlgorithms(self):
nrow = 251
ncol = 250
maxLag = 3
nloop = 4
x1 = rand(ncol, nrow)
x2 = deepcopy(x1)
t1a = timeit.time.clock()
for loop in xrange(0, nloop):
arrayA = normalizedCorrelationHorzShift2D(x1, x2, maxLag, Algorithm.BRUTE_FORCE)
t2a = timeit.time.clock()
t1b = timeit.time.clock()
for loop in xrange(0, nloop):
arrayB = normalizedCorrelationHorzShift2D(x1, x2, maxLag, Algorithm.INNER_1D)
t2b = timeit.time.clock()
t1c = timeit.time.clock()
for loop in xrange(0, nloop):
arrayC = normalizedCorrelationHorzShift2D(x1, x2, maxLag, Algorithm.INNER_2D)
t2c = timeit.time.clock()
timeA = (t2a - t1a)
timeB = (t2b - t1b)
timeC = (t2c - t1c)
print("\ntestSame_NormalizedCorrelationHorzShift2D")
print(" Algorithm.BRUTE_FORCE -- time; %.6f sec -- ratio; %12.6f" % (timeA, (timeA / timeA)))
print(" Algorithm.INNER_1D -- time; %.6f sec -- ratio; %12.6f" % (timeB, (timeA / timeB)))
print(" Algorithm.INNER_2D -- time; %.6f sec -- ratio; %12.6f" % (timeC, (timeA / timeC)))
print
for shift in range(-maxLag, maxLag+1):
print("shift,A,B,C; %+3d %+12.6f %+12.6f %+12.6f" % (shift, arrayA[shift + maxLag], arrayB[shift + maxLag], arrayC[shift + maxLag]))
assert arrayA[maxLag] == 1
assert arrayB[maxLag] == 1
assert arrayC[maxLag] == 1
arrayA = list(arrayA)
arrayB = list(arrayB)
arrayC = list(arrayC)
maxIndexA = arrayA.index(max(arrayA)) - maxLag
maxIndexB = arrayB.index(max(arrayB)) - maxLag
maxIndexC = arrayC.index(max(arrayC)) - maxLag
print
print("Index of maximum A,B,C", maxIndexA, maxIndexB, maxIndexC)
print("Correlation at maximum A,B,C",
arrayA[maxIndexA + maxLag], arrayB[maxIndexB + maxLag], arrayC[maxIndexC + maxLag])
def testNormalizedCorrelationHorzShift2D_ShiftedArray(self):
nrow = 400
ncol = 300
maxLag = 5
for shift in range(-maxLag, (maxLag + 1)):
x1 = numpy.zeros((ncol, nrow))
x2 = numpy.zeros((ncol, nrow))
for krow in range(0, nrow):
for kcol in range(0, ncol):
x2[kcol,krow] = krow
if (krow + shift) >= 0 and (krow + shift) < nrow:
x1[kcol,krow + shift] = krow
t1 = timeit.time.clock()
array = normalizedCorrelationHorzShift2D(x1, x2, maxLag, Algorithm.INNER_2D)
t2 = timeit.time.clock()
print("\ntestShift_NormalizedCorrelationHorzShift2D -- time; %.6f sec" % (t2 - t1))
# for kshift in range(-maxLag, maxLag+1):
# print("kshift,corr; %+3d %+12.6f" % (kshift, array[kshift + maxLag]))
array = list(array)
maxIndex = array.index(max(array)) - maxLag
print("Index of maximum ", maxIndex)
print("shift", shift)
assert maxIndex == shift
# im0_small = im0_small[200:500,0:800]
# im1_small = im1_small[200:500,0:800]
# find the means of the images
mean0 = np.mean(im0_small)
mean1 = np.mean(im1_small)
print("mean1,mean2; %+12.4f %+12.4f" % (mean0, mean1))
# subtract the means from the small images
im0_small -= mean0
im1_small -= mean1
maxLag = 40
# estimate the horizontal shift for the 2D images
array = normalizedCorrelationHorzShift2D(im0_small, im1_small, maxLag)
for shift in range(-maxLag, maxLag + 1):
print("shift,corr; %+3d %+12.6f" % (shift, array[shift + maxLag]))
array = list(array)
maxIndex = array.index(max(array)) - maxLag
print("Index of maximum ", maxIndex)
# exit()
# plot them
image = [array]
# plt.subplot(1,3,1)
# plt.imshow(image, cmap = "binary", interpolation='nearest')
plt.subplot(1, 2, 1)
plt.imshow(im0_small, cmap="binary", interpolation="nearest")
# im0_small = im0_small[200:500,0:800]
# im1_small = im1_small[200:500,0:800]
#find the means of the images
mean0 = np.mean(im0_small)
mean1 = np.mean(im1_small)
print("mean1,mean2; %+12.4f %+12.4f" % (mean0, mean1))
#subtract the means from the small images
im0_small -= mean0
im1_small -= mean1
maxLag = 40
# estimate the horizontal shift for the 2D images
array = normalizedCorrelationHorzShift2D(im0_small, im1_small, maxLag)
for shift in range(-maxLag, maxLag + 1):
print("shift,corr; %+3d %+12.6f" % (shift, array[shift + maxLag]))
array = list(array)
maxIndex = array.index(max(array)) - maxLag
print("Index of maximum ", maxIndex)
# exit()
#plot them
image = [array]
# plt.subplot(1,3,1)
# plt.imshow(image, cmap = "binary", interpolation='nearest')
plt.subplot(1, 2, 1)
plt.imshow(im0_small, cmap="binary", interpolation='nearest')
