def point_shape_context(pt, cloud, hist_dim):
cloud = np.atleast_2d(cloud) - np.atleast_2d(pt)
r = math_utils.norms(cloud,1)
r_med = np.median(r)
if hist_dim == 2:
num_r_bins = 4
num_theta_bins = 9
r_bin_edges = r_med/2**(num_r_bins-1) * 2**np.arange(0, num_r_bins+1)
theta = np.arctan2(cloud[:,1], cloud[:,0])
theta_bin_edges = np.linspace(-np.pi, np.pi, num_theta_bins+1, endpoint = True)
hist,_,_ = np.histogram2d(r,theta, bins = (r_bin_edges, theta_bin_edges))
areas = np.pi * (r_bin_edges[1:]**2 - r_bin_edges[:-1]**2)/num_theta_bins
features = ((areas**(-1/2))[:,None] * hist).flatten()
features /= features.sum()
return features
else:
raise NotImplementedError
def unif_resample(x,n,tol=1,deg=3):
x = np.atleast_2d(x)
x = mu.remove_duplicate_rows(x)
dl = mu.norms(x[1:] - x[:-1],1)
l = np.cumsum(np.r_[0,dl])
(tck,_) = si.splprep(x.T,k=deg,s = tol**2*len(x),u=l)
newu = np.linspace(0,l[-1],n)
return np.array(si.splev(newu,tck)).T
def unif_resample(x,n,tol=0,deg=None):
if deg is None: deg = min(3, len(x) - 1)
x = np.atleast_2d(x)
x = math_utils.remove_duplicate_rows(x)
dl = math_utils.norms(x[1:] - x[:-1],1)
l = np.cumsum(np.r_[0,dl])
(tck,_) = si.splprep(x.T,k=deg,s = tol**2*len(x),u=l)
newu = np.linspace(0,l[-1],n)
return np.array(si.splev(newu,tck)).T
def unif_resample(x,n,weights,tol=.001,deg=3):
x = np.atleast_2d(x)
weights = np.atleast_2d(weights)
x = mu.remove_duplicate_rows(x)
x_scaled = x * weights
dl = mu.norms(x_scaled[1:] - x_scaled[:-1],1)
l = np.cumsum(np.r_[0,dl])
(tck,_) = si.splprep(x_scaled.T,k=deg,s = tol**2*len(x),u=l)
newu = np.linspace(0,l[-1],n)
out_scaled = np.array(si.splev(newu,tck)).T
out = out_scaled/weights
return out