def RANSAC(listOfCorrespondences, Niter=1000, epsilon=4, acceptableProbFailure=1e-9):
"""H_best: the best estimation of homorgraphy (3-by-3 matrix)"""
"""inliers: A list of booleans that describe whether the element in listOfCorrespondences
an inlier or not"""
""" 6.815 can bypass acceptableProbFailure"""
numInliers = 0
inliers = [False] * len(listOfCorrespondences)
H_best = np.zeros((3, 3))
for n in xrange(Niter):
correspondences = random.sample(listOfCorrespondences, 4)
listOfPairs = A7PairsToA6Pairs(correspondences)
H = a6.computeHomography(listOfPairs)
currentInliers = []
for c in listOfCorrespondences:
estimate = H.dot(A7PointToA6Point(c.pt1))
estimate[0], estimate[1] = (estimate[0] / estimate[2], estimate[1] / estimate[2])
actual = A7PointToA6Point(c.pt2)
error = math.sqrt(sum((estimate - actual) ** 2))
currentInliers.append(bool(error < epsilon))
currentNumInliers = sum(bool(x) for x in currentInliers)
if currentNumInliers > numInliers:
numInliers = currentNumInliers
inliers = currentInliers
H_best = H.copy()
if (1 - (float(numInliers) / len(listOfCorrespondences)) ** 4) ** n < acceptableProbFailure:
break
return (H_best, inliers)
def RANSAC(listOfCorrespondences, Niter=1000, epsilon=4, acceptableProbFailure=1e-9):
"""H_best: the best estimation of homorgraphy (3-by-3 matrix)
inliers: list of booleans that describe whether the element in listOfCorrespondences
an inlier or not"""
number_of_correspondences = len(listOfCorrespondences)
H_best = None
inliers = []
number_of_inliers = 0
for i in range(Niter):
feature_pairs = random.sample(listOfCorrespondences, 4)
homography = a6.computeHomography(A7PairsToA6Pairs(feature_pairs))
loop_inliers = []
for correspondence in listOfCorrespondences:
transformed_point = np.dot(homography, A7PointToA6Point(correspondence.pt1))
transformed_point[0] /= transformed_point[2]
transformed_point[1] /= transformed_point[2]
target_point = A7PointToA6Point(correspondence.pt2)
loop_inliers.append(math.sqrt(np.sum((transformed_point - target_point) ** 2)) < epsilon)
loop_inliers_count = loop_inliers.count(True)
if loop_inliers_count > number_of_inliers:
number_of_inliers = loop_inliers_count
inliers = loop_inliers
H_best = homography
if ((1 - (loop_inliers_count / number_of_correspondences) ** 4) ** i) < acceptableProbFailure:
break
return (H_best, inliers)
def RANSAC(listOfCorrespondences,
Niter=1000,
epsilon=4,
acceptableProbFailure=1e-9):
'''H_best: the best estimation of homorgraphy (3-by-3 matrix)'''
'''inliers: A list of booleans that describe whether the element in listOfCorrespondences
an inlier or not'''
''' 6.815 can bypass acceptableProbFailure'''
numInliers = 0
inliers = [False] * len(listOfCorrespondences)
H_best = np.zeros((3, 3))
for n in xrange(Niter):
correspondences = random.sample(listOfCorrespondences, 4)
listOfPairs = A7PairsToA6Pairs(correspondences)
H = a6.computeHomography(listOfPairs)
currentInliers = []
for c in listOfCorrespondences:
estimate = H.dot(A7PointToA6Point(c.pt1))
estimate[0], estimate[1] = (estimate[0] / estimate[2],
estimate[1] / estimate[2])
actual = A7PointToA6Point(c.pt2)
error = math.sqrt(sum((estimate - actual)**2))
currentInliers.append(bool(error < epsilon))
currentNumInliers = sum(bool(x) for x in currentInliers)
if currentNumInliers > numInliers:
numInliers = currentNumInliers
inliers = currentInliers
H_best = H.copy()
if (1 - (float(numInliers) / len(listOfCorrespondences))**
4)**n < acceptableProbFailure:
break
return (H_best, inliers)
def RANSAC(listOfCorrespondences, Niter=1000, epsilon=4, acceptableProbFailure=1e-9):
'''H_best: the best estimation of homorgraphy (3-by-3 matrix)'''
'''inliers: A list of booleans that describe whether the element in listOfCorrespondences
an inlier or not'''
''' 6.815 can bypass acceptableProbFailure'''
cLen = len(listOfCorrespondences)
maxInliers = 0
H_best = np.zeros((3,3))
for i in xrange(Niter):
samples = rnd.sample( listOfCorrespondences, 4 )
H = a6.computeHomography( A7PairsToA6Pairs(samples) )
areInliers = map( mapH, [H]*cLen, listOfCorrespondences, [epsilon]*cLen )
numInliers = areInliers.count(True)
if(maxInliers < numInliers):
H_best = H
maxInliers = numInliers
x = float(maxInliers)/cLen
probFailure = pow( (1-pow(x,4)), i+1 )
if (probFailure<acceptableProbFailure):
break
inliers = map( mapH, [H_best]*cLen, listOfCorrespondences, [epsilon]*cLen )
return (H_best, inliers)
def testComputeNHomographies():
pointList1 = [np.array([209, 218, 1]), np.array([425, 300, 1]), np.array([209, 337, 1]), np.array([396, 336, 1])]
pointList2 = [np.array([232, 4, 1]), np.array([465, 62, 1]), np.array([247, 125, 1]), np.array([433, 102, 1])]
listOfPairs = zip(pointList1, pointList2)
h1 = a6.computeHomography(listOfPairs)
h2 = a6.computeNHomographies([listOfPairs], 1)
print h1, h2
def testComputeTransformedBBox():
im1=io.imread('stata/stata-1.png')
im2=io.imread('stata/stata-2.png')
pointList1=[np.array([209, 218, 1]), np.array([425, 300, 1]), np.array([209, 337, 1]), np.array([396, 336, 1])]
pointList2=[np.array([232, 4, 1]), np.array([465, 62, 1]), np.array([247, 125, 1]), np.array([433, 102, 1])]
listOfPairsS=zip(pointList1, pointList2)
HS=a6.computeHomography(listOfPairsS)
shape = np.shape(im2)
print a6.computeTransformedBBox(shape, HS)
def testComputeAndApplyHomographyStata():
im1=io.imread('stata/stata-1.png')
im2=io.imread('stata/stata-2.png')
pointList1=[np.array([209, 218, 1]), np.array([425, 300, 1]), np.array([209, 337, 1]), np.array([396, 336, 1])]
pointList2=[np.array([232, 4, 1]), np.array([465, 62, 1]), np.array([247, 125, 1]), np.array([433, 102, 1])]
listOfPairsS=zip(pointList1, pointList2)
HS=a6.computeHomography(listOfPairsS)
#multiply by 0.2 to better show the transition
out=im2*0.5
a6.applyHomography(im1, out, HS, True)
io.imwrite(out, "stata_computeAndApplyHomography.png")
def testComputeAndApplyHomographyFun():
im1=io.imread('fun/room1.png')
im2=io.imread('fun/room2.png')
pointList1=[np.array([327, 258, 1], dtype=np.float64), np.array([75, 437, 1], dtype=np.float64), np.array([224, 364, 1], dtype=np.float64), np.array([423, 449, 1], dtype=np.float64)]
pointList2=[np.array([294, 50, 1], dtype=np.float64), np.array([50, 227, 1], dtype=np.float64), np.array([190, 161, 1], dtype=np.float64), np.array([366, 240, 1], dtype=np.float64)]
listOfPairsS=zip(pointList1, pointList2)
HS=a6.computeHomography(listOfPairsS)
#multiply by 0.2 to better show the transition
out=im2*0.5
a6.applyHomography(im1, out, HS, True)
io.imwrite(out, "fun.png")
def testComputeAndApplyHomographyPoster():
green = io.getImage("green.png")
poster = io.getImage("poster.png")
h, w = poster.shape[0] - 1, poster.shape[1] - 1
pointListPoster = [np.array([0, 0, 1]), np.array([0, w, 1]), np.array([h, w, 1]), np.array([h, 0, 1])]
pointListT = [np.array([170, 95, 1]), np.array([171, 238, 1]), np.array([233, 235, 1]), np.array([239, 94, 1])]
listOfPairs = zip(pointListPoster, pointListT)
H = a6.computeHomography(listOfPairs)
# print H
a6.applyHomography(poster, green, H, True)
io.imwrite(green, "HWDueAt9pm_computeHomography.png")
def makeStreetSign():
sign = io.imread("highway.png")
people = io.imread("coverphoto.png")
h, w = people.shape[0] - 1, people.shape[1] - 1
peoplecorners = [np.array([0, 0, 1]), np.array([0, w, 1]), np.array([h, w, 1]), np.array([h, 0, 1])]
pointList1 = [
np.array([105, 94, 1], dtype=np.float64),
np.array([110, 200, 1], dtype=np.float64),
np.array([162, 200, 1], dtype=np.float64),
np.array([159, 92, 1], dtype=np.float64),
]
listOfPairs = zip(peoplecorners, pointList1)
H = a6.computeHomography(listOfPairs)
a6.applyHomography(people, sign, H, True)
io.imwrite(sign, "Fun.png")
def testComputeAndApplyHomographyPrudential():
pru = io.getImage("pruSkyline.png")
poster = io.getImage("andrewTag.png")
h, w = poster.shape[0]-1, poster.shape[1]-1
pointListPoster=[np.array([0, 0, 1]), np.array([0, w, 1]), np.array([h, w, 1]), np.array([h, 0, 1])]
#pointListPru=[np.array([78, 120, 1]), np.array([99, 160, 1]), np.array([150, 159, 1]), np.array([132, 118, 1])]
pointListPru=[np.array([78, 120, 1], dtype=np.float64), np.array([99, 160, 1], dtype=np.float64), np.array([207, 156, 1], dtype=np.float64), np.array([194, 115, 1], dtype=np.float64)]
listOfPairs=zip(pointListPoster, pointListPru)
H = a6.computeHomography(listOfPairs)
#print H
a6.applyHomography(poster, pru, H, True)
io.imwrite(pru, "Fun.png")
