def motion(I1, I2):
"""Extract motion from two rainrate fields."""
# Convert the rainfall maps to unsigned byte, as required by the Optflow
# motion detection algorithms. Gaussian filter with std. dev. 3 is applied.
Iu = []
for i, I in enumerate([I1, I2]):
Iu.append(utils.rainfall_to_ubyte(I, R_min=0.05, R_max=15.0, filter_stddev=3.0))
# Compute the motion field by using the Python <-> C++ API and the Proesmans
# algorithm.
return extract_motion_proesmans(Iu[0], Iu[1], lam=25.0, num_iter=250, num_levels=6)
figure()
imshow(I2_, vmin=0.05, vmax=10)
cb = colorbar()
cb.set_label("rain rate (mm/h)")
xticks([])
yticks([])
savefig("input2.png", bbox_inches="tight")
# Convert the rainfall maps to unsigned byte, as required by the Optflow
# motion detection algorithms. Gaussian filter with std. dev. 3 is applied.
I1_ubyte = utils.rainfall_to_ubyte(I1, R_min=0.05, R_max=10.0, filter_stddev=3.0)
I2_ubyte = utils.rainfall_to_ubyte(I2, R_min=0.05, R_max=10.0, filter_stddev=3.0)
# Compute the motion field by using the Python <-> C++ API and the Proesmans
# algorithm.
V = extract_motion_proesmans(I1_ubyte, I2_ubyte, lam=25.0, num_iter=250,
num_levels=6)[0]
# Extrapolate the first input image five time steps.
for t in arange(1, 6):
I_extrap = semilagrangian(I1, V, t, 15, n_iter=3, inverse=True)
figure()
I = I_extrap.copy()
I[I < 0.05] = nan
imshow(I, vmin=0.05, vmax=10)
cb = colorbar()
cb.set_label("rain rate (mm/h)")
xticks([])
yticks([])
savefig("input_extrap_%d.png" % t, bbox_inches="tight")
close()
figure()
imshow(I2_, vmin=0.05, vmax=10)
cb = colorbar()
cb.set_label("rain rate (mm/h)")
xticks([])
yticks([])
savefig("input2.png", bbox_inches="tight")
# Convert the precipitation fields to unsigned byte, as required by the Optflow
# motion estimation algorithms. Gaussian filter with std. dev. 3 is applied.
I1_ubyte = utils.rainfall_to_ubyte(I1, R_min=0.05, R_max=10.0, filter_stddev=3.0)
I2_ubyte = utils.rainfall_to_ubyte(I2, R_min=0.05, R_max=10.0, filter_stddev=3.0)
# Compute the motion field by using the Python <-> C++ API and the Proesmans
# algorithm.
V = extract_motion_proesmans(I1_ubyte, I2_ubyte, lam=25.0, num_iter=250,
num_levels=6)[0]
# Extrapolate the first input image five time steps.
for t in arange(1, 6):
I_extrap = semilagrangian(I1, V, t, 15, n_iter=3, inverse=True)
figure()
I = I_extrap.copy()
I[I < 0.05] = nan
imshow(I, vmin=0.05, vmax=10)
cb = colorbar()
cb.set_label("rain rate (mm/h)")
xticks([])
yticks([])
savefig("input_extrap_%02d.png" % t, bbox_inches="tight")
close()
def motion(rr0ubyte, rr1ubyte, lam=25.0, num_iter=250, num_levels=6):
return extract_motion_proesmans(rr0ubyte,
rr1ubyte,
lam=lam,
num_iter=num_iter,
num_levels=num_levels)[0]
