def _more_init(self):
self.shape = tuple(self.shape)
self.coorv = ndgrid(*self.coor)
if not isinstance(self.coorv, (list, tuple)):
# 1D grid, wrap self.coorv as list:
self.coorv = [self.coorv]
self.npoints = 1
for i in range(len(self.shape)):
self.npoints *= self.shape[i]
self.tolerance = (max(self.max_coor) - min(self.min_coor)) * 1E-14
# nicknames: xcoor, ycoor, xcoorv, ycoorv, etc
for i in range(self.nsd):
self.__dict__[self.dirnames[i] + 'coor'] = self.coor[i]
self.__dict__[self.dirnames[i] + 'coorv'] = self.coorv[i]
if self.nsd == 3:
# make boundary coordinates for vectorization:
xdummy, \
self.ycoorv_xfixed_boundary, \
self.zcoorv_xfixed_boundary = ndgrid(0, self.ycoor, self.zcoor)
self.xcoorv_yfixed_boundary, \
ydummy, \
self.zcoorv_yfixed_boundary = ndgrid(self.xcoor, 0, self.zcoor)
self.xcoorv_yfixed_boundary, \
self.zcoorv_yfixed_boundary, \
zdummy = ndgrid(self.xcoor, self.ycoor, 0)
def CreateRow(xmin, xmax, N, kernel, theta):
dim = N.size
if dim == 1:
x = linspace(xmin[0], xmax[0], N[0])
R = DistanceVector(x, x[0], theta)
elif dim == 2:
x1 = linspace(xmin[0], xmax[0], N[0])
x2 = linspace(xmin[1], xmax[1], N[1])
xx, yy = ndgrid(x1, x2)
x = vstack((ravel(xx, order='F'), ravel(yy, order='F'))).transpose()
R = DistanceVector(x, x[0, :].transpose(), theta)
elif dim == 3:
x1 = linspace(xmin[0], xmax[0], N[0])
x2 = linspace(xmin[1], xmax[1], N[1])
x3 = linspace(xmin[2], xmax[2], N[2])
xx, yy, zz = ndgrid(x1, x2, x3)
x = vstack((ravel(xx, order='F'), ravel(yy, order='F'),
ravel(zz, order='F'))).transpose()
R = DistanceVector(x, x[0, :].transpose(), theta)
else:
print 'Wrong dimension'
row = kernel(R)
return row, x
def CreateRow(xmin,xmax,N,kernel,theta): dim = N.size if dim == 1: x = linspace(xmin[0],xmax[0],N[0]) R = DistanceVector(x,x[0],theta) elif dim == 2: x1 = linspace(xmin[0],xmax[0],N[0]) x2 = linspace(xmin[1],xmax[1],N[1]) xx, yy = ndgrid(x1,x2) x = vstack((ravel(xx, order = 'F'),ravel(yy, order = 'F'))).transpose() R = DistanceVector(x,x[0,:].transpose(),theta) elif dim == 3: x1 = linspace(xmin[0],xmax[0],N[0]) x2 = linspace(xmin[1],xmax[1],N[1]) x3 = linspace(xmin[2],xmax[2],N[2]) xx, yy, zz = ndgrid(x1,x2,x3) x = vstack((ravel(xx, order = 'F'),ravel(yy, order = 'F'),ravel(zz, order = 'F'))).transpose() R = DistanceVector(x,x[0,:].transpose(),theta) else: print 'Wrong dimension' row = kernel(R) return row, x
def _more_init(self):
self.shape = tuple(self.shape)
self.coorv = ndgrid(*self.coor)
if not isinstance(self.coorv, (list,tuple)):
# 1D grid, wrap self.coorv as list:
self.coorv = [self.coorv]
self.npoints = 1
for i in range(len(self.shape)):
self.npoints *= self.shape[i]
self.tolerance = (max(self.max_coor) - min(self.min_coor))*1E-14
# nicknames: xcoor, ycoor, xcoorv, ycoorv, etc
for i in range(self.nsd):
self.__dict__[self.dirnames[i]+'coor'] = self.coor[i]
self.__dict__[self.dirnames[i]+'coorv'] = self.coorv[i]
if self.nsd == 3:
# make boundary coordinates for vectorization:
xdummy, \
self.ycoorv_xfixed_boundary, \
self.zcoorv_xfixed_boundary = ndgrid(0, self.ycoor, self.zcoor)
self.xcoorv_yfixed_boundary, \
ydummy, \
self.zcoorv_yfixed_boundary = ndgrid(self.xcoor, 0, self.zcoor)
self.xcoorv_yfixed_boundary, \
self.zcoorv_yfixed_boundary, \
zdummy = ndgrid(self.xcoor, self.ycoor, 0)
def simplex_quad(n, N):
"""
n: number of dimensions (bins)
N: number of integration points per bin
"""
vert=eye(n+1, n)
if n==1:
q, w = rquad(N, 0)
else:
q, w=zip(*[rquad(N, n-(k+1)) for k in range(n)])
Q=ndgrid(*q); W=ndgrid(*w)
shaper=np.zeros([N, N, N*n])
for i in range(n):
shaper[:,:,i*N:(i+1)*N] = Q[i]
qu = reshape(shaper,[N**n,n], order='F')
shaper=np.zeros([N, N, N*n])
for i in range(n):
shaper[:,:,i*N:(i+1)*N] = W[i]
wu = reshape(shaper,[N**n,n], order='F')
m=eye(n+1)
m[1:n+1,0]=-1
c=np.dot(m, vert)
W=abs(det(c[1:n+1, :]))*prod(wu, axis=1)
qp=cumprod(qu, 1)
e=np.ones([N**n, 1])
np.append((1-qu[:, :n-1]), array(e), 1)
array([e, qp[:, :n-2], qp[:,n-1]])
X=np.dot(np.append(array(e),np.append((1-qu[:, :n-1]), array(e), 1)*array([array(e), qp[:, :n-2], qp[:,n-1]]).T,1), c)
return W, X
def flow(*args):
# xx,yy,zz,vv = flow()
# xx,yy,zz,vv = flow(n)
# xx,yy,zz,vv = flow(xx,yy,zz)
if len(args) == 0:
xx, yy, zz = ndgrid(linspace(0.1, 10, 50),
linspace(-3, 3, 25),
linspace(-3, 3, 25),
sparse=False)
elif len(args) == 1:
n = int(args[0])
xx, yy, zz = ndgrid(linspace(0.1, 10, 2 * n),
linspace(-3, 3, n),
linspace(-3, 3, n),
sparse=False)
elif len(args) == 3:
xx, yy, zz = args
else:
raise SyntaxError("Invalid number of arguments.")
# convert to spherical coordinates:
theta = arctan2(zz, yy)
phi = arctan2(xx, sqrt(yy**2 + zz**2))
r = sqrt(xx**2 + yy**2 + zz**2)
rv = 2 / r * (3 / (2 - cos(phi))**2 - 1)
phiv = -2 * sin(phi) / (2 - cos(phi)) / r
thetav = zeros(shape(r))
# convert back to cartesian coordinates:
xv = rv * cos(phiv) * cos(thetav)
yv = rv * cos(phiv) * sin(thetav)
zv = rv * sin(phiv)
vv = log(sqrt(xv**2 + yv**2 + zv**2))
return xx, yy, zz, vv
def flow(*args):
# xx,yy,zz,vv = flow()
# xx,yy,zz,vv = flow(n)
# xx,yy,zz,vv = flow(xx,yy,zz)
if len(args) == 0:
xx, yy, zz = ndgrid(linspace(0.1, 10, 50),
linspace(-3, 3, 25),
linspace(-3, 3, 25),
sparse=False)
elif len(args) == 1:
n = int(args[0])
xx, yy, zz = ndgrid(linspace(0.1, 10, 2*n),
linspace(-3, 3, n),
linspace(-3, 3, n),
sparse=False)
elif len(args) == 3:
xx, yy, zz = args
else:
raise SyntaxError("Invalid number of arguments.")
# convert to spherical coordinates:
theta = arctan2(zz, yy)
phi = arctan2(xx, sqrt(yy**2 + zz**2))
r = sqrt(xx**2 + yy**2 + zz**2)
rv = 2/r*(3/(2-cos(phi))**2 - 1)
phiv = -2*sin(phi)/(2-cos(phi))/r
thetav = zeros(shape(r))
# convert back to cartesian coordinates:
xv = rv*cos(phiv)*cos(thetav)
yv = rv*cos(phiv)*sin(thetav)
zv = rv*sin(phiv)
vv = log(sqrt(xv**2 + yv**2 + zv**2))
return xx, yy, zz, vv
def peaks(*args):
# z = peaks()
# z = peaks(n)
# z = peaks(x,y)
n = 49
nargs = len(args)
if nargs in (0, 1):
if nargs == 1:
n = int(args[0])
x, y = ndgrid(linspace(-3, 3, n), linspace(-3, 3, n))
elif nargs == 2:
x, y = args
else:
raise SyntaxError("Invalid number of arguments.")
return 3*(1-x)**2*exp(-x**2-(y+1)**2) \
- 10*(x/5-x**3-y**5)*exp(-x**2-y**2) - 1/3*exp(-(x+1)**2-y**2)
def peaks(*args):
# z = peaks()
# z = peaks(n)
# z = peaks(x,y)
n = 49
nargs = len(args)
if nargs in (0,1):
if nargs == 1:
n = int(args[0])
x, y = ndgrid(linspace(-3,3,n),linspace(-3,3,n))
elif nargs == 2:
x, y = args
else:
raise SyntaxError("Invalid number of arguments.")
return 3*(1-x)**2*exp(-x**2-(y+1)**2) \
- 10*(x/5-x**3-y**5)*exp(-x**2-y**2) - 1/3*exp(-(x+1)**2-y**2)
# Initial target size.
target_sz = np.array([initstate[3], initstate[2]])
# Parameters according to the paper.
padding = 1 # Extra area.
rho = 0.075 # Learning parameter rho.
sz = target_sz * (1 + padding) # Context region size.
# Parameters of scale update - scale ratio, lambda, average frames.
scale, lambada, num = 1, 0.55, 15
# Pre-computed confidence map.
alapha = 2.25
rs, cs = sn.ndgrid(np.array(range(sz[0])) + 1 - sz[0]/2.,
np.array(range(sz[1])) + 1 - sz[1]/2.)
dist = rs**2. + cs**2.
conf = np.exp(-0.5 * np.sqrt(dist) / alapha)
# normalization
conf = conf / conf.sum(axis=0).sum()
# frequency
conff = np.fft.fft2(conf)
# store pre-computed weight window
hamming_window = np.outer(np.hamming(sz[0]),
np.hanning(sz[1]).T)
