def test_identity(self):
im = Image.open("/u/konolige/vslam/data/indoor1/left-%04d.ppm" % 1000)
kp = [(x-16, y-16) for (x,y) in fast.fast(im.tostring(), im.size[0], im.size[1], 150, 40)]
cl1 = calonder.classifier()
cl1.train(im.tostring(), im.size[0], im.size[1], kp)
cl1.write('unittest.tree')
def testclassifier(kp, im, cl):
ma = calonder.BruteForceMatcher()
sigs = []
for (x,y) in kp:
patch = im.crop((x,y,x+32,y+32))
sig = cl.getSignature(patch.tostring(), patch.size[0], patch.size[1])
#print ["%3f" % x for x in sig.dump()]
sigs.append(sig)
ma.addSignature(sig)
for (i,(x,y)) in enumerate(kp):
patch = im.crop((x,y,x+32,y+32))
sig = cl.getSignature(patch.tostring(), patch.size[0], patch.size[1])
(index, distance) = ma.findMatch(sig)
self.assert_(i == index)
testclassifier(kp, im, cl1)
del cl1
# Now make another classifier, and read it from the file above
cl2 = calonder.classifier()
cl2.read('unittest.tree')
testclassifier(kp, im, cl2)
def decrypt(C, d, N):
"""Decrypt cipher with d block by block and convert them to ASCII."""
message = []
for c in C:
msg = f.fast(c, d, N)
message.append(str(unichr(msg)))
return message
def fermat(n):
for i in range(10):
# δημιουργία τυχαίου αριθμού βάσης
a = random.randint(1, n - 1)
gcd = fast.fast(a, n - 1, n) # a * 1 mod n
# εαν το gcd(a,n) ειναι 1 το ο n ειναι πρωτος
if gcd == 1:
return True
else:
#επιστρέφει false εαν ο αριθμός ειναι σύνθετος
return False
return True
def fermatTest(n, k):
"""
Fermat primality test
n: The number to test
k: Number of times to repeat the test
Returns: True if prime else False
"""
for i in range(k):
a = randint(2, n-2)
if fast(a,n-1,n) != 1:
return False
return True
def decrypt(C, ds, N):
"""
Decrypts array of integers C, using d and N of RSA
"""
f = open("message.txt", "w")
for d in ds:
f.write("==== D: " + str(d) + " ====\n")
try:
for c in C:
m = fast.fast(c, d, N)
f.write(str(chr(m)))
f.write("\n")
except:
f.write("FAILED")
f.write("\n")
def kp_d(frame):
fkp = fast.fast(frame.tostring(), frame.size[0], frame.size[1], 10, 15)
fkp = [(x, y, r) for (x, y, r) in fkp
if (16 <= x and 16 <= y and x <= frame.size[0] -
16 and y <= frame.size[1] - 16)]
fkp = fast.nonmax(fkp) #damn this is slow
kp = [(x, y) for (
x, y,
r) in sorted(fkp, key=lambda x: (x[2], x[0], x[1]), reverse=True)[:200]
]
print 'kp_d gave %d points' % len(kp)
descriptors = []
for (x, y) in kp:
patch = frame.crop((x - 16, y - 16, x + 16, y + 16))
sig = cl.getSignature(patch.tostring(), patch.size[0], patch.size[1])
descriptors.append(sig)
return descriptors
def kp_d(frame):
#sd = starfeature.star_detector(frame.size[0], frame.size[1], 5, 10.0, 10.0)
#kp = [ (x,y) for (x,y,s,r) in sd.detect(frame.tostring()) ]
fkp = fast.fast(frame.tostring(), frame.size[0], frame.size[1], 15, 9)
fkp = [(x, y, r) for (x, y, r) in fast.nonmax(fkp)
if (16 <= x and 16 <= y and x <= frame.size[0] -
16 and y <= frame.size[1] - 16)]
kp = [(x, y) for (x, y, r) in sorted(
fkp, key=lambda x: (x[2], x[0], x[1]), reverse=True)[:500]]
print 'kp_d gave %d points' % len(kp)
descriptors = []
for (x, y) in kp:
patch = frame.crop((x - 16, y - 16, x + 16, y + 16))
sig = cl.getSignature(patch.tostring(), patch.size[0],
patch.size[1])
descriptors.append(sig)
return descriptors
def get_features(frame, classifier):
# Get keypoints from FAST detector
fkp = fast.fast(frame.tostring(), frame.size[0], frame.size[1], 12, 9)
# Keep local maxima that are at 16 pixels away from image border
fkp = [(x, y, r) for (x, y, r) in fast.nonmax(fkp)
if (16 <= x and 16 <= y and x <= frame.size[0] -
16 and y <= frame.size[1] - 16)]
# Keep at most 500 strongest keypoints
kp = [(x, y) for (
x, y,
r) in sorted(fkp, key=lambda x: (x[2], x[0], x[1]), reverse=True)[:500]
]
# Compute Calonder descriptor for each keypoint
descriptors = []
for (x, y) in kp:
patch = frame.crop((x - 16, y - 16, x + 16, y + 16))
sig = classifier.getSignature(patch.tostring(), patch.size[0],
patch.size[1])
descriptors.append(sig)
return descriptors, kp
def get_features(self, frame, target_points):
assert len(frame.rawdata) == (frame.size[0] * frame.size[1])
return fast.fast(frame.rawdata, frame.size[0], frame.size[1],
int(self.thresh), 40)
def get_features(self, frame, target_points): assert len(frame.rawdata) == (frame.size[0] * frame.size[1]) return fast.fast(frame.rawdata, frame.size[0], frame.size[1], int(self.thresh), 40)
# Σειρά παίρνει ο έλεγχος για ποιοι πρώτοι αριθμοί αν τους πολλαπλασιασουμε βγάζουν γινομενο Ν
for i in range(len(prime_factors)):
for j in range(1, len(prime_factors)):
# Έλεγχος αν οι αριθμοί βγάζουν γινόμενο N
if (prime_factors[i] * prime_factors[j] == N):
p = prime_factors[i]
q = prime_factors[j]
# Βρίσκουμε το fn
fn = (p - 1) * (q - 1)
# τώρα βρισκουμε το κλειδι με το οποίο έγινε η κρυπτογράφηση
# θέλουμε έναν αριθμό x δηλαδη τέτοιον ώστε: e * x mod fn = 1
for x in range(fn):
if ((e * x) % fn == 1):
d = x
msg = ''
# το i ειναι το καθε block του c
# για κάθε μπλοκ του c i^d mod N με d private key
for i in C:
# print(i)--> Το εσωτερικό του C
# αποκρυπτογραφούμε block by block όπως λέει η εκφώνηση
# 3203 --> αντιστοιχεί στο πρτο γραμμα του μηνυματος κοκ..
k = fast.fast(i, d, N)
# chr() επιστρέφει το integer k στη unicode μορφή του
# κάθε φορά προσθέτουμε στο μήνυμα το επόμενο αποκρυπτογραφημένο γράμμα του C
msg += chr(k)
# print(msg) # για ελεγχο
print(f"To encrypted mhnyma einai: {msg}")
def test_identity(self):
im = Image.open("f0-left.png")
kp = [(x-16, y-16) for (x,y,r) in fast.fast(im.tostring(), im.size[0], im.size[1], 55, 0) if (x > 32) and (y > 32) and (x < (640-32)) and (y < (480-32))]
print "keypoints", len(kp)
dim = 176 # actual dimension will be min(176, |kp|)
cl1 = calonder.classifier()
if 0:
cl1.train(im.tostring(), im.size[0], im.size[1], kp, 50, 10, 100, dim, 0)
else:
# CMakefile.txt downloads current.rtc before this test runs
filename = 'current.rtc'
cl1.read(filename)
dim = cl1.dimension()
if 0:
for i in range(1000000):
print i
sigs = cl1.getSignatures(im.size, im.tostring(), kp)
#for (x,y) in kp:
# patch = im.crop((x,y,x+32,y+32))
# sig = cl1.getSignature(patch.tostring(), patch.size[0], patch.size[1])
return
def testclassifier(kp, im, cl):
ma = calonder.BruteForceMatcher(dim)
sigs = []
kp = kp[:]
for (x,y) in kp:
print x,y
patch = im.crop((x,y,x+32,y+32))
sig = cl.getSignature(patch.tostring(), patch.size[0], patch.size[1])
sigs.append(sig)
ma.addSignature(sig)
#print cl.getSignatures(im, kp)
for (i,(x,y)) in enumerate(kp):
patch = im.crop((x,y,x+32,y+32))
sig = cl.getSignature(patch.tostring(), patch.size[0], patch.size[1])
(index, distance) = ma.findMatch(sig)
print "i = %d, match = %u, distance = %.3f" % (i, index, distance)
self.assert_(i == index)
testclassifier(kp, im, cl1)
print "done"
print "Writing to unittest.tree... ",
cl1.write('unittest.tree')
print "done"
del cl1
# Now make another classifier, and read it from the file above
cl2 = calonder.classifier()
print "Reading classifier... ",
cl2.read('unittest.tree')
print "done"
testclassifier(kp, im, cl2)
f = open('sigs.pickle', 'w')
for (x,y) in kp[:3]:
patch = im.crop((x,y,x+32,y+32))
sig = cl2.getSignature(patch.tostring(), patch.size[0], patch.size[1])
pickle.dump(sig, f)
print "saved", sig
f.close()
f = open('sigs.pickle')
for i in range(3):
print "loaded", pickle.load(f)
def powerMod(a,g,N):
b = a % g
for i in range(g-1):
b = (a * b) % N
return b
print("=== (i) ===")
(g, u ,v) = gcd(126048, 5050)
print("GCD(126048, 5050) = " + str(g))
print(str(int(u)) + " " + str(int(v)))
print("")
print("=== (ii) ===")
for i in range(1002):
if (i*809)%1001 == 1:
print(i)
break
print("")
print("=== (iii) ===")
print("2^(100)mod101 = " + str(powerMod(2,100,101)))
print("")
print("=== (iv) ===")
print("2^(1234567)mod12345 = " + str(fast.fast(2,1234567,12345)))
print("130^(7654321)mod567 = " + str(fast.fast(130,7654321,567)))
print("")
def FAST(imdata, xsize, ysize, thresh, barrier=9):
kp = fast.fast(imdata, xsize, ysize, barrier, int(thresh))
return sorted(fast.nonmax(kp),
key=lambda x: (x[2], x[0], x[1]),
reverse=True)
def decrypt(c, d, n):
"""decrypting function"""
return fast(c, d, n)
def encrypt(m, e, n):
"""encrypting function"""
return fast(m, e, n)
