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 __init__(self, *args):
self.cl = calonder.classifier()
#self.cl.setThreshold(0.0)
search = [ '/u/prdata/calonder_trees/current.rtc', '/u/jamesb/current.rtc', None ]
for filename in search:
if not filename:
print "Failed to find current.rtc in", search
assert 0
if os.access(filename, os.R_OK):
self.cl.read(filename)
break
DescriptorScheme.__init__(self, *args)
def match_runs(reference_files, query_files):
print "loading vocabulary tree..."
vt = place_recognition.load("/u/mihelich/images/holidays/holidays.tree")
cl = calonder.classifier()
cl.read('/u/prdata/calonder_trees/current.rtc')
print "adding database images..."
for i in reference_files[2]:
# Add image to vocabulary tree
name = reference_files[0] % i
img = Image.open(name)
desc, kp = get_features(img, cl)
vt.add(img, desc)
print '%s has %d feature points' % (name, len(kp))
# Save coordinates and descriptors
file = open(reference_files[1] % (i, "key"), "w")
for (pt, d) in zip(kp, desc):
# Format: x y [d...]
file.write("%i %i %s\n" % (pt[0], pt[1], str(list(d))))
print "getting top-k views for query images..."
N = len(reference_files[2])
k = 32
M = []
for i in query_files[2]:
# Get indexes of best N matching views from reference run
name = query_files[0] % i
img = Image.open(name)
desc, kp = get_features(img, cl)
print '%s has %d feature points' % (name, len(kp))
scores = vt.topN(img, desc, N)
M.append(scores)
top_n = sorted(enumerate(scores), key=operator.itemgetter(1))[:k]
# Save filenames of matching views
file = open(query_files[1] % (i, "match"), "w")
for (index, score) in top_n:
file.write("%s %f\n" %
(reference_files[0] % reference_files[2][index], score))
return M
def __init__(self):
self.cl = calonder.classifier()
#self.cl.setThreshold(0.0)
self.cl.read('/u/prdata/calonder_trees/land50.trees')
self.ma = calonder.BruteForceMatcher()
def __init__(self):
self.cl = calonder.classifier()
#self.cl.setThreshold(0.0)
self.cl.read('/u/prdata/calonder_trees/land50.trees')
self.ma = calonder.BruteForceMatcher()
print 'opening images...'
outlet_train = [
Image.open(outlet_files % i).convert('L') for i in range(416, 427)
]
no_out_train = [
Image.open(no_out_files % i).convert('L') for i in range(416, 427)
]
#outlet_test = [ Image.open(outlet_files % i).convert('L') for i in range(372, 416) + range(427, 471) ]
#no_out_test = [ Image.open(no_out_files % i).convert('L') for i in range(372, 416) + range(427, 471) ]
print 'loading trees...'
vt = place_recognition.load('/u/mihelich/images/holidays/holidays_b5d4.tree')
#vt = place_recognition.load('/u/mihelich/images/holidays/holidays.tree')
#vt = place_recognition.vocabularytree()
#vt.build(outlet_train, 5, 4, False)
cl = calonder.classifier()
cl.read('/u/prdata/calonder_trees/current.rtc')
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)
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)
