def __init__(
self,
nRows,
nCols,
vol_preserve,
base,
nLevels,
valid_outside=True,
tess='tri',
sigma_lm=1000 / 10, # worked pretty well (hands data)
# sigma_lm = 1000/100, # Didn't work so well
scale_spatial=1.0 * .1,
scale_value=100,
zero_v_across_bdry=[False] * 2,
wlp=1e-4,
scale_quiver=None):
self.base = base
self.nLevels = nLevels
self.sigma_lm = sigma_lm
self.wlp = wlp
self.tw = TransformWrapper(nRows=nRows,
nCols=nCols,
vol_preserve=vol_preserve,
nLevels=nLevels,
base=base,
scale_spatial=scale_spatial,
scale_value=scale_value,
tess=tess,
valid_outside=valid_outside,
zero_v_across_bdry=zero_v_across_bdry)
if scale_quiver is None:
raise ValueError
self.scale_quiver = scale_quiver
if not inside_spyder():
pylab.ion()
name = 'LFW_5_to_6'
data = get_data(name)
src = CpuGpuArray(data.src)
dst = CpuGpuArray(data.dst)
transformed = CpuGpuArray.zeros_like(src)
fname_results = os.path.splitext(data.fname)[0] + '_result.pkl'
FilesDirs.raise_if_dir_does_not_exist(os.path.dirname(fname_results))
print 'Loading', fname_results
results = Pkl.load(fname_results)
theta_est = results.theta
tw = TransformWrapper(**results.tw_args)
tw.create_grid_lines(step=0.1, factor=0.5)
scale_quiver = 1000 # The *smaller* this value is, the larger the plotted arrows will be.
level = -1 # pick the finest scale
cpa_space = tw.ms.L_cpa_space[level]
cpa_space.theta2Avees(theta_est)
cpa_space.update_pat()
tw.calc_T_fwd(src, transformed, level=level)
transformed.gpu2cpu()
tw.calc_v(level=level)
tw.v_dense.gpu2cpu()
plt.close('all')
disp(tw=tw,
def example(img=None,
tess='I',
eval_cell_idx=True,
eval_v=True,
show_downsampled_pts=True,
valid_outside=True,
base=[1, 1],
scale_spatial=.1,
scale_value=100,
permute_cell_idx_for_display=True,
nLevels=3,
vol_preserve=False,
zero_v_across_bdry=[0, 0],
use_lims_when_plotting=True):
show_downsampled_pts = bool(show_downsampled_pts)
eval_cell_idx = bool(eval_cell_idx)
eval_v = bool(eval_cell_idx)
valid_outside = bool(valid_outside)
permute_cell_idx_for_display = bool(permute_cell_idx_for_display)
vol_preserve = bool(vol_preserve)
if img is None:
img = Img(get_std_test_img())
else:
img = Img(img)
img = img[:, :, ::-1] # bgr2rgb
tw = TransformWrapper(
nRows=img.shape[0],
nCols=img.shape[1],
nLevels=nLevels,
base=base,
scale_spatial=scale_spatial, # controls the prior's smoothness
scale_value=scale_value, # controls the prior's variance
tess=tess,
vol_preserve=vol_preserve,
zero_v_across_bdry=zero_v_across_bdry,
valid_outside=valid_outside)
print tw
# You probably want to do that: padding image border with zeros
border_width = 1
img[:border_width] = 0
img[-border_width:] = 0
img[:, :border_width] = 0
img[:, -border_width:] = 0
# The tw.calc_T_fwd (or tw.calc_T_inv) is always done in gpu.
# After using it to compute new pts,
# you may want to use remap (to warp an image accordingly).
# If you will use tw.remap_fwd (or tw.remap_inv), which is done in gpu,
# then the image type can be either float32 or float64.
# But if you plan to use tw.tw.remap_fwd_opencv (or tw.remap_inv_opencv),
# which is done in cpu (hence slightly lower) but supports better
# interpolation methods, then the image type must be np.float32.
# img_original = CpuGpuArray(img.copy().astype(np.float32))
img_original = CpuGpuArray(img.copy().astype(np.float64))
img_wrapped_fwd = CpuGpuArray.zeros_like(img_original)
img_wrapped_bwd = CpuGpuArray.zeros_like(img_original)
seed = 0
np.random.seed(seed)
ms_Avees = tw.get_zeros_PA_all_levels()
ms_theta = tw.get_zeros_theta_all_levels()
for level in range(tw.ms.nLevels):
if level == 0:
tw.sample_gaussian(level,
ms_Avees[level],
ms_theta[level],
mu=None) # zero mean
else:
tw.sample_from_the_ms_prior_coarse2fine_one_level(ms_Avees,
ms_theta,
level_fine=level)
print('\nimg shape: {}\n'.format(img_original.shape))
# You don't have use these. You can use any 2d array
# that has two columns (regardless of the number of rows).
pts_src = tw.pts_src_dense
# Create buffers for the output
pts_fwd = CpuGpuArray.zeros_like(pts_src)
pts_inv = CpuGpuArray.zeros_like(pts_src)
for level in range(tw.ms.nLevels):
#######################################################################
# instead of the tw.sample_from_the_ms_prior() above,
# you may want to use one of the following.
# 1)
# tw.sample_gaussian(level,ms_Avees[level],ms_theta[level],mu=None)# zero mean
# 2)
# tw.sample_gaussian(level,ms_Avees[level],ms_theta[level],mu=some_user_specified_mu)
# The following should be used only for level>0 :
# 3)
# tw.sample_normal_in_one_level_using_the_coarser_as_mean(Avees_coarse=ms_Avees[level-1],
# Avees_fine=ms_Avees[level],
# theta_fine=ms_theta[level],
# level_fine=level)
#
#######################################################################
# You can also change the values this way:
# cpa_space = tw.ms.L_cpa_space[level]
# theta = cpa_space.get_zeros_theta()
# theta[:] = some values
# Avees = cpa_space.get_zeros_PA()
# cpa_space.theta2Avees(theta,Avees)
# cpa_space.update_pat(Avees)
# This step is important and must be done
# before are trying to "use" the new values of
# the (vectorized) A's.
tw.update_pat_from_Avees(ms_Avees[level], level)
if eval_v:
# Evaluating the velocity field.
# You don't have to do it in unless you want to visualize v.
# (when evaluting the treansformation, v will be internally
# evaluated anyway -- but its result won't be stored)
tw.calc_v(level=level)
# optional, if you want to time it
timer_gpu_T_fwd = GpuTimer()
# Simply calling
# tic = time.clock()
# and then
# tic = time.clock()
# won't work.
# In fact, most likely you will get that toc-tic is zero.
# You need to use the GpuTimer object. When you do that,
# one side effect is that suddenly the toc-tic from above will
# give you a more realistic result.
tic = time.clock()
timer_gpu_T_fwd.tic()
tw.calc_T_fwd(pts_src, pts_fwd, level=level)
timer_gpu_T_fwd.toc()
toc = time.clock()
print 'Time, in sec, for computing T_fwd:'
print timer_gpu_T_fwd.secs
print toc - tic # likely to be 0, unless you also used the GpuTimer.
# You can also time the inv of course. Results will be similar.
tw.calc_T_inv(pts_src, pts_inv, level=level)
if eval_cell_idx:
# cell_idx is computed here just for display.
cell_idx = CpuGpuArray.zeros(len(pts_src), dtype=np.int32)
tw.calc_cell_idx(pts_src,
cell_idx,
level,
permute_for_disp=permute_cell_idx_for_display)
# If may also want ro to time the remap.
# However, the remap is usually very fast (e.g, about 2 milisec).
# timer_gpu_remap_fwd = GpuTimer()
# tic = time.clock()
# timer_gpu_remap_fwd.tic()
# tw.remap_fwd(pts_inv=pts_inv,img=img_original,img_wrapped_fwd=img_wrapped_fwd)
tw.remap_fwd(pts_inv=pts_inv,
img=img_original,
img_wrapped_fwd=img_wrapped_fwd)
# timer_gpu_remap_fwd.toc()
# toc = time.clock()
# If the img type is np.float32, you may also use
# tw.remap_fwd_opencv instead of tw.remap_fw. The differences between
# the two methods are explained above
tw.remap_inv(pts_fwd=pts_fwd,
img=img_original,
img_wrapped_inv=img_wrapped_bwd)
# For display purposes, do gpu2cpu transfer
print("For display purposes, do gpu2cpu transfer")
if eval_cell_idx:
cell_idx.gpu2cpu()
if eval_v:
tw.v_dense.gpu2cpu()
pts_fwd.gpu2cpu()
pts_inv.gpu2cpu()
img_wrapped_fwd.gpu2cpu()
img_wrapped_bwd.gpu2cpu()
figsize = (12, 12)
plt.figure(figsize=figsize)
if eval_v:
plt.subplot(332)
tw.imshow_vx()
plt.title('vx')
plt.subplot(333)
tw.imshow_vy()
plt.title('vy')
if eval_cell_idx:
plt.subplot(331)
cell_idx_disp = cell_idx.cpu.reshape(img.shape[0], -1)
plt.imshow(cell_idx_disp)
plt.title('tess (type {})'.format(tess))
if show_downsampled_pts:
ds = 20
pts_src_grid = pts_src.cpu.reshape(tw.nRows, -1, 2)
pts_src_ds = pts_src_grid[::ds, ::ds].reshape(-1, 2)
pts_fwd_grid = pts_fwd.cpu.reshape(tw.nRows, -1, 2)
pts_fwd_ds = pts_fwd_grid[::ds, ::ds].reshape(-1, 2)
pts_inv_grid = pts_inv.cpu.reshape(tw.nRows, -1, 2)
pts_inv_ds = pts_inv_grid[::ds, ::ds].reshape(-1, 2)
use_lims = use_lims_when_plotting
# return tw
plt.subplot(334)
plt.plot(pts_src_ds[:, 0], pts_src_ds[:, 1], 'r.')
plt.title('pts ds')
tw.config_plt()
plt.subplot(335)
plt.plot(pts_fwd_ds[:, 0], pts_fwd_ds[:, 1], 'g.')
plt.title('fwd(pts)')
tw.config_plt(axis_on_or_off='on', use_lims=use_lims)
plt.subplot(336)
plt.plot(pts_inv_ds[:, 0], pts_inv_ds[:, 1], 'b.')
plt.title('inv(pts)')
tw.config_plt(axis_on_or_off='on', use_lims=use_lims)
plt.subplot(337)
plt.imshow(img_original.cpu.astype(np.uint8))
plt.title('img')
# plt.axis('off')
plt.subplot(338)
plt.imshow(img_wrapped_fwd.cpu.astype(np.uint8))
# plt.axis('off')
plt.title('fwd(img)')
plt.subplot(339)
plt.imshow(img_wrapped_bwd.cpu.astype(np.uint8))
# plt.axis('off')
plt.title('inv(img)')
return tw
