def __init__(
self,
models,
angs=30,
wmax=10,
verbose=False,
):
model, self.target, self.match = mkMatcher(angs, wmax)
self.mod = [model(m) for m in readModels(models)]
for k, m in enumerate(self.mod):
m.id = k
try:
locs = np.loadtxt(models + '/locations.txt').reshape(-1, 7)
if verbose:
print(locs)
T = []
for ax, ay, az, dx, dy, dz, s in locs:
t = rotation(vec(1, 0, 0), np.radians(ax), homog=True)
t = rotation(vec(0, 1, 0), np.radians(ay), homog=True) @ t
t = rotation(vec(0, 0, 1), np.radians(az), homog=True) @ t
t = scale(vec(s, s, s)) @ t
t = desp(vec(dx, dy, dz)) @ t
T.append(t)
except:
T = [np.eye(4) for _ in self.mod]
self.p3d = [
move(t, (m.original * [[1, -1]])) for t, m in zip(T, self.mod)
]
if verbose:
print(self.p3d)
def mymoments(c):
cf = c.astype(float)
cr = np.roll(cf, -1, 0)
x1 = cf[:, 0]
y1 = cf[:, 1]
x2 = cr[:, 0]
y2 = cr[:, 1]
dx = x2 - x1
dy = y2 - y1
x12 = x1**2
x22 = x2**2
y12 = y1**2
y22 = y2**2
q1 = 2 * y1 + y2
q2 = y1 + 2 * y2
s0 = x1 * y2 - x2 * y1
sx = np.sum(2 * x1 * x2 * dy - x22 * q1 + x12 * q2) / 12
sy = np.sum(-2 * y1 * y2 * dx + y22 * (2 * x1 + x2) - y12 *
(2 * x2 + x1)) / 12
sx2 = np.sum((x12 * x2 + x1 * x22) * dy + (x1**3 - x2**3) * (y1 + y2)) / 12
sy2 = np.sum(-(y12 * y2 + y1 * y22) * dx - (y1**3 - y2**3) *
(x1 + x2)) / 12
sxy = np.sum(s0 * (x1 * q1 + x2 * q2)) / 24
s = np.sum(s0) / 2
mx = sx / s
my = sy / s
cx = sx2 / s - mx**2
cy = sy2 / s - my**2
cxy = sxy / s - mx * my
(l1, l2, ad) = eig22(cx, cy, cxy)
return (vec(mx, my), (np.sqrt(l1), np.sqrt(l2), ad))
def mymoments(c):
m = cv.moments(c.astype(np.float32)) # int32, float32, but not float64!
s = m['m00']
mx = m['m10'] / s
my = m['m01'] / s
cx = m['mu20'] / s
cy = m['mu02'] / s
cxy = m['mu11'] / s
(l1, l2, ad) = eig22(cx, cy, cxy)
return (vec(mx, my), (np.sqrt(l1), np.sqrt(l2), ad))
def __init__(self, x, lab='?', full=False, eps_w=10, **args):
self.orig = x
(A, self.s1, self.s2, ad), H, W = whitenedMoments(x, th=eps_w)
self.H = H
self.W = W
self.SBar, self.S, self.K = KSsignature(W)
self.Ts = [
np.dot(t, H)
for t in canonicTransforms((self.SBar, self.K), **args)
]
self.can = [warpW(x, T, sz=(100, 100), sigma=3) for T in self.Ts]
self.dtc = [dt(c) for c in self.can]
self.auxh1 = [
np.hstack([self.can[k], self.dtc[k]]) for k in range(len(self.can))
]
self.auxh2 = [
np.hstack([self.dtc[k], self.can[k]]) for k in range(len(self.can))
]
pl = min(x.shape)
self.pad = np.pad(x, pl, 'constant')
if full:
self.dto = dt(self.pad) / self.s1
self.dtco = [
warpW(self.dto,
T,
sz=(100, 100),
sigma=3,
offset=pl,
borderMode=cv.BORDER_REPLICATE) for T in self.Ts
]
self.auxh3 = [
np.hstack([self.can[k], self.dtco[k]])
for k in range(len(self.can))
]
self.auxh4 = [
np.hstack([self.dtco[k], self.can[k]])
for k in range(len(self.can))
]
conts = extractContours(255 - 255 * self.pad.astype(np.uint8),
minarea=0,
minredon=0,
reduprec=0.5)
assert len(conts) > 0, (len(conts), lab)
self.wcont = sorted(conts, key=len)[-1]
self.invar = invar(spectralFeat(self.wcont))
r, s, t, g = orientability((self.SBar, self.S, self.K))
self.skf = vec(self.S.max(), s, (self.K.min() + self.K.max()) / 2, r,
t / r, g / r)
self.lab = lab
def autoscale(cont):
(x1, y1), (x2, y2) = cont.min(0), cont.max(0)
s = max(x2 - x1, y2 - y1)
c = vec(x1 + x2, y1 + y2) / 2
h = np.dot(scale(1 / vec(s, s)), desp(-c))
return htrans(h, cont)
def whitener(cont):
(c, (s1, s2, a)) = mymoments(cont)
return np.dot(np.dot(rot3(a), scale(1 / vec(s1, s2))),
np.dot(rot3(-a), desp(-c)))