def match0(self, af0kp, af0descriptors, af1kp, af1descriptors, wide = False):
"""
Given keypoints and descriptors for af0 and af1, returns
a list of pairs *(a,b)*, where
*a* is an index into this *af0*'s keypoints, and
*b* is an index into *af1*'s keypoints.
"""
if af0kp == [] or af1kp == []:
return []
if wide:
xs = self.wxs
ys = self.wys
else:
xs = self.xs
ys = self.ys
self.calls += 1
self.timer['Match'].start()
Xs = vop.array([k[0] for k in af1kp])
Ys = vop.array([k[1] for k in af1kp])
pairs = []
matcher = self.desc2matcher(af1descriptors)
for (i,(ki,di)) in enumerate(zip(af0kp, af0descriptors)):
predX = (abs(Xs - ki[0]) < xs)
predY = (abs(Ys - ki[1]) < ys)
hits = vop.where(predX & predY, 1, 0).tostring()
best = self.search(di, matcher, hits)
if best != None:
pairs.append((i, best[0], best[1]))
self.timer['Match'].stop()
return pairs
def match(self, af0, af1):
Xs = vop.array([k[0] for k in af1.kp])
Ys = vop.array([k[1] for k in af1.kp])
pairs = []
for (i, (ki, di)) in enumerate(zip(af0.kp, af0.descriptors)):
# hits = (Numeric.logical_and(Numeric.absolute(NXs - ki[0]) < 64, Numeric.absolute(NYs - ki[1]) < 32)).astype(Numeric.UnsignedInt8).tostring()
predX = (abs(Xs - ki[0]) < 64)
predY = (abs(Ys - ki[1]) < 32)
hits = vop.where(predX & predY, 1, 0).tostring()
best = VO.sad_search(di, af1.descriptors, hits)
if best != None:
pairs.append((i, best, 0))
return pairs
def match(self, af0, af1):
Xs = vop.array([k[0] for k in af1.kp])
Ys = vop.array([k[1] for k in af1.kp])
pairs = []
for (i, ki) in enumerate(af0.kp):
# hits = (Numeric.logical_and(Numeric.absolute(NXs - ki[0]) < 64, Numeric.absolute(NYs - ki[1]) < 32)).astype(Numeric.UnsignedInt8).tostring()
predX = (abs(Xs - ki[0]) < 64)
predY = (abs(Ys - ki[1]) < 32)
hits = vop.where(predX & predY, 1, 0).tostring()
for (j, c) in enumerate(hits):
if ord(c) != 0:
pairs.append((i, j))
return pairs
def f_uvd(f):
d_str = "".join([chr(min(x / 4, 255)) for x in f.disp_values])
d = vop.array(
Image.fromstring("L", (640, 480),
d_str).resize(chipsize, Image.NEAREST).tostring())
f_valid = d != 255.0
f_d = d / 4
u = vop.duplicate(vop.arange(chipsize[0]), chipsize[1]) * factor
v = vop.replicate(vop.arange(chipsize[1]), chipsize[0]) * factor
i = vop.array(
Image.fromstring("L", (640, 480), f.rawdata).resize(
chipsize, Image.NEAREST).tostring()) / 255.
return (f_valid, u, v, f_d, i)
def match(self, af0, af1):
Xs = vop.array([k[0] for k in af1.kp])
Ys = vop.array([k[1] for k in af1.kp])
pairs = []
for (i,(ki,di)) in enumerate(zip(af0.kp,af0.descriptors)):
# hits = (Numeric.logical_and(Numeric.absolute(NXs - ki[0]) < 64, Numeric.absolute(NYs - ki[1]) < 32)).astype(Numeric.UnsignedInt8).tostring()
predX = (abs(Xs - ki[0]) < 64)
predY = (abs(Ys - ki[1]) < 32)
hits = vop.where(predX & predY, 1, 0).tostring()
best = VO.sad_search(di, af1.descriptors, hits)
if best != None:
pairs.append((i, best, 0))
return pairs
def match(self, af0, af1):
Xs = vop.array([k[0] for k in af1.kp])
Ys = vop.array([k[1] for k in af1.kp])
pairs = []
for (i,ki) in enumerate(af0.kp):
# hits = (Numeric.logical_and(Numeric.absolute(NXs - ki[0]) < 64, Numeric.absolute(NYs - ki[1]) < 32)).astype(Numeric.UnsignedInt8).tostring()
predX = (abs(Xs - ki[0]) < 64)
predY = (abs(Ys - ki[1]) < 32)
hits = vop.where(predX & predY, 1, 0).tostring()
for (j,c) in enumerate(hits):
if ord(c) != 0:
pairs.append((i, j))
return pairs
def match(self, af0, af1):
if af0.kp == [] or af1.kp == []:
return []
Xs = vop.array([k[0] for k in af1.kp])
Ys = vop.array([k[1] for k in af1.kp])
pairs = []
for (i,(ki,di)) in enumerate(zip(af0.kp,af0.descriptors)):
predX = (abs(Xs - ki[0]) < 64)
predY = (abs(Ys - ki[1]) < 32)
hits = vop.where(predX & predY, 1, 0).tostring()
best = self.search(di, af1, hits)
if best != None:
pairs.append((i, best[0], best[1]))
return pairs
def setNumRansacIterations(self, i):
"""
Sets the number of RANSAC iterations.
"""
self.ransac_iterations = i
# random.random is relatively slow, so run it here and keep the
# values in r0,r1,r2.
self.r0 = vop.array(
[self.rnd.random() for i in range(self.ransac_iterations)])
self.r1 = vop.array(
[self.rnd.random() for i in range(self.ransac_iterations)])
self.r2 = vop.array(
[self.rnd.random() for i in range(self.ransac_iterations)])
def scavenger(self, diff_pose, frame):
af0 = self.keyframe
af1 = frame
Xs = vop.array([k[0] for k in af1.kp])
Ys = vop.array([k[1] for k in af1.kp])
pairs = []
fwd_pose = ~diff_pose
for (i,(ki,di)) in enumerate(zip(af0.kp,af0.descriptors)):
(x,y,d) = self.cam.cam2pix(*fwd_pose.xform(*self.cam.pix2cam(*ki)))
predX = (abs(Xs - x) < 4)
predY = (abs(Ys - y) < 4)
hits = vop.where(predX & predY, 1, 0).tostring()
best = self.descriptor_scheme.search(di, af1, hits)
if best != None:
pairs.append((i, best[0], best[1]))
self.pairs = [(i0,i1) for (i0,i1,d) in pairs]
solution = self.solve(af0.kp, af1.kp, self.pairs)
return solution
def scavenger(self, diff_pose, af0, af1):
Xs = vop.array([k[0] for k in af1.features()])
Ys = vop.array([k[1] for k in af1.features()])
pairs = []
fwd_pose = ~diff_pose
ds = af1.descriptor_scheme
matcher = ds.desc2matcher(af1.descriptors())
for (i, (ki, di)) in enumerate(zip(af0.features(), af0.descriptors())):
(x, y,
d) = self.cam.cam2pix(*fwd_pose.xform(*self.cam.pix2cam(*ki)))
predX = (abs(Xs - x) < 4)
predY = (abs(Ys - y) < 4)
hits = vop.where(predX & predY, 1, 0).tostring()
best = ds.search(di, matcher, hits)
if best != None:
pairs.append((i, best[0], best[1]))
self.pairs = [(i0, i1) for (i0, i1, d) in pairs]
if False:
import pylab
f0, f1 = af0, af1
for (a, b) in self.pairs:
pylab.plot([f0.features()[a][0],
f1.features()[b][0]],
[f0.features()[a][1],
f1.features()[b][1]])
pylab.imshow(numpy.fromstring(af0.lf.tostring(),
numpy.uint8).reshape(480, 640),
cmap=pylab.cm.gray)
pylab.scatter([x for (x, y, d) in f0.features()],
[y for (x, y, d) in f0.features()],
label='%d kp' % f0.id,
c='red')
pylab.scatter([x for (x, y, d) in f1.features()],
[y for (x, y, d) in f1.features()],
label='%d kp' % f1.id,
c='green')
pylab.legend()
pylab.show()
solution = self.solve(af0.features(), af1.features(), self.pairs)
return solution
def vop3(L):
""" Turn a list of triplets into a triplet of vop arrays """
x0 = vop.array([x for (x, _, _) in L])
y0 = vop.array([y for (_, y, _) in L])
z0 = vop.array([z for (_, _, z) in L])
return (x0, y0, z0)
