def disp_deformed_grid_lines(self,level,color=None,lw=1):
# return
if self.hlines is None or self.vlines is None:
raise ValueError
hlines,vlines=self.hlines,self.vlines
# for lines,c in zip([hlines,vlines],['r','b']):
# pts_at_0=np.asarray([lines[:,0,:].flatten(),
# lines[:,1,:].flatten()]).T
# pts_at_0 = CpuGpuArray(pts_at_0.copy())
# pts_at_T=CpuGpuArray.zeros_like(pts_at_0)
# self.calc_T_fwd(pts_src=pts_at_0,
# pts_fwd=pts_at_T,
# level=level,verbose=0,int_quality=1)
# if self.nCols != self.nCols:
# raise NotImplementedError
# pts_at_T.gpu2cpu()
# lines_new_x=pts_at_T.cpu[:,0].reshape(lines[:,0,:].shape).copy()
# lines_new_y=pts_at_T.cpu[:,1].reshape(lines[:,0,:].shape).copy()
# for line_new_x,line_new_y in zip(lines_new_x,lines_new_y):
#
# plt.plot(line_new_x,line_new_y,c)
if color is None:
colors=['r','b']
else:
colors=[color,color]
s = hlines.shape
if s[2]<=1:
raise ValueError
p = 0
L = 50000
if L >=s[2]:
while p < np.ceil(s[2]):
hlines=self.hlines[:,:,p:p+L]
vlines=self.vlines[:,:,p:p+L]
p+=L
for lines,c in zip([hlines,vlines],colors):
pts_at_0=np.asarray([lines[:,0,:].flatten(),
lines[:,1,:].flatten()]).T
if pts_at_0.size==0:
break
pts_at_0 = CpuGpuArray(pts_at_0.copy())
pts_at_T=CpuGpuArray.zeros_like(pts_at_0)
self.calc_T_fwd(pts_src=pts_at_0,
pts_fwd=pts_at_T,
level=level,int_quality=1)
if self.nCols != self.nCols:
raise NotImplementedError
pts_at_T.gpu2cpu()
lines_new_x=pts_at_T.cpu[:,0].reshape(lines[:,0,:].shape).copy()
lines_new_y=pts_at_T.cpu[:,1].reshape(lines[:,0,:].shape).copy()
for line_new_x,line_new_y in zip(lines_new_x,lines_new_y):
plt.plot(line_new_x,line_new_y,c,lw=lw)
else:
raise NotImplementedError
def __init__(self,ms,level,data,
sigma_lm,
params_flow_int,
# src=None,dst=None,transformed=None
):
# ipshell('hi')
"""
Cost is level-dependent.
TODO: GPU in the LL part.
"""
src=data['src']
dst=data['dst']
transformed=data['transformed']
if not isinstance(src,CpuGpuArray):
raise ObsoleteError
if not isinstance(dst,CpuGpuArray):
raise ObsoleteError
if not isinstance(transformed,CpuGpuArray):
raise ObsoleteError
self.nCalls = 0
self.nCallbacks = 0
self.sigma_lm=sigma_lm
cpa_space=ms.L_cpa_space[level]
self.cpa_space = cpa_space
# 1/0
if src.shape[1] != cpa_space.dim_domain:
raise ValueError(src.shape,cpa_space.dim_domain)
#
# self.cpa_space = cpa_space
# self.cpa_cov_inv = msp.L_cpa_space_covs[level].cpa_cov_inv
self.mu = cpa_space.get_zeros_theta()
self.src = src
self.dst = dst
self.transformed = transformed
nPts = len(src)
self.nPts = nPts
self.err = CpuGpuArray.zeros_like(src)
self.ll = CpuGpuArray.zeros(nPts,dtype=src.dtype)
self.params_flow_int=params_flow_int
self._pat = PAT(pa_space=cpa_space,
Avees=cpa_space.get_zeros_PA())
def __init__(
self,
ms,
level,
data,
sigma_lm,
params_flow_int,
# src=None,dst=None,transformed=None
):
# ipshell('hi')
src = data['src']
dst = data['dst']
transformed = data['transformed']
if not isinstance(src, CpuGpuArray):
raise ObsoleteError
if not isinstance(dst, CpuGpuArray):
raise ObsoleteError
if not isinstance(transformed, CpuGpuArray):
raise ObsoleteError
self.nCalls = 0
self.nCallbacks = 0
self.sigma_lm = sigma_lm
cpa_space = ms.L_cpa_space[level]
self.cpa_space = cpa_space
if src.shape[1] != cpa_space.dim_domain:
raise ValueError(src.shape, cpa_space.dim_domain)
self.mu = cpa_space.get_zeros_theta()
self.src = src
self.dst = dst
self.transformed = transformed
nPts = len(src)
self.nPts = nPts
self.err = CpuGpuArray.zeros_like(src)
self.ll = CpuGpuArray.zeros(nPts, dtype=src.dtype)
if nPts <= 1:
raise ValueError
self.err_by_der = CpuGpuArray.zeros((nPts - 1, src.shape[1]),
dtype=src.dtype)
self.ll_by_der = CpuGpuArray.zeros(nPts - 1, dtype=src.dtype)
self.params_flow_int = params_flow_int
self._pat = PAT(pa_space=cpa_space, Avees=cpa_space.get_zeros_PA())
def __init__(self,ms,level,data,
sigma_lm,
params_flow_int,
# src=None,dst=None,transformed=None
):
# ipshell('hi')
src=data['src']
dst=data['dst']
transformed=data['transformed']
if not isinstance(src,CpuGpuArray):
raise ObsoleteError
if not isinstance(dst,CpuGpuArray):
raise ObsoleteError
if not isinstance(transformed,CpuGpuArray):
raise ObsoleteError
self.nCalls = 0
self.nCallbacks = 0
self.sigma_lm=sigma_lm
cpa_space=ms.L_cpa_space[level]
self.cpa_space = cpa_space
if src.shape[1] != cpa_space.dim_domain:
raise ValueError(src.shape,cpa_space.dim_domain)
self.mu = cpa_space.get_zeros_theta()
self.src = src
self.dst = dst
self.transformed = transformed
nPts = len(src)
self.nPts = nPts
self.err = CpuGpuArray.zeros_like(src)
self.ll = CpuGpuArray.zeros(nPts,dtype=src.dtype)
if nPts <= 1:
raise ValueError
self.err_by_der = CpuGpuArray.zeros((nPts-1,src.shape[1]),dtype=src.dtype)
self.ll_by_der = CpuGpuArray.zeros(nPts-1,dtype=src.dtype)
self.params_flow_int=params_flow_int
self._pat = PAT(pa_space=cpa_space,
Avees=cpa_space.get_zeros_PA())
def set_data(self, data):
if data.kind != 'landmarks':
raise NotImplementedError
self.kind = data.kind
src = data.src
dst = data.dst
self.landmarks_are_lin_ordered = data.landmarks_are_lin_ordered
if not isinstance(src, CpuGpuArray):
raise TypeError(type(src))
if not isinstance(dst, CpuGpuArray):
raise TypeError(type(dst))
if src.shape != dst.shape:
raise ValueError(src.shape, dst.shape)
self.src = src
self.dst = dst
self.transformed = CpuGpuArray.zeros_like(self.dst)
def set_data(self,data):
if data.kind!= 'landmarks':
raise NotImplementedError
self.kind=data.kind
src=data.src
dst=data.dst
self.landmarks_are_lin_ordered = data.landmarks_are_lin_ordered
if not isinstance(src,CpuGpuArray):
raise TypeError(type(src))
if not isinstance(dst,CpuGpuArray):
raise TypeError(type(dst))
if src.shape != dst.shape:
raise ValueError(src.shape,dst.shape)
self.src = src
self.dst = dst
self.transformed = CpuGpuArray.zeros_like(self.dst)
def set_data(self,x,y,range_start,range_end):
"""
For now, assumes dst was evaluated on evenly-space points
"""
if x.shape != y.shape:
raise ValueError(x.shape,y.shape)
if x.dtype != np.float64:
raise TypeError(x.dtype)
if y.dtype != np.float64:
raise TypeError(y.dtype)
nPts = len(x)
self.x=x
self.y=y
self.y_scale = range_end-range_start
self.y_offset = range_start
dst = (y-self.y_offset)/self.y_scale
if dst.ndim == 1:
dst = dst.reshape(nPts,1).copy()
if not isinstance(dst,CpuGpuArray):
dst = CpuGpuArray(dst)
self.dst=dst
# cpa_space = self.tw.ms.L_cpa_space[0]
domain_start,domain_end = self.domain_start,self.domain_end
# self.interval = np.linspace(domain_start,domain_end,nPts)
# line = (x - domain_start) / ( domain_end - domain_start)
line = self.manipulate_predictors(x)
if line.ndim == 1:
line = line.reshape(nPts,1).copy()
self.src=CpuGpuArray(line)
self.transformed = CpuGpuArray.zeros_like(self.src)
def set_data(self, x, y, range_start, range_end):
"""
For now, assumes dst was evaluated on evenly-space points
"""
if x.shape != y.shape:
raise ValueError(x.shape, y.shape)
if x.dtype != np.float64:
raise TypeError(x.dtype)
if y.dtype != np.float64:
raise TypeError(y.dtype)
nPts = len(x)
self.x = x
self.y = y
self.y_scale = range_end - range_start
self.y_offset = range_start
dst = (y - self.y_offset) / self.y_scale
if dst.ndim == 1:
dst = dst.reshape(nPts, 1).copy()
if not isinstance(dst, CpuGpuArray):
dst = CpuGpuArray(dst)
self.dst = dst
# cpa_space = self.tw.ms.L_cpa_space[0]
domain_start, domain_end = self.domain_start, self.domain_end
# self.interval = np.linspace(domain_start,domain_end,nPts)
# line = (x - domain_start) / ( domain_end - domain_start)
line = self.manipulate_predictors(x)
if line.ndim == 1:
line = line.reshape(nPts, 1).copy()
self.src = CpuGpuArray(line)
self.transformed = CpuGpuArray.zeros_like(self.src)
def __init__(
self,
ms,
level,
data,
sigma_signal,
params_flow_int,
interp_type_for_ll,
# src=None,dst=None,transformed=None
):
# ipshell('hi')
if interp_type_for_ll not in self.supported_interp_types:
msg = """
interp_type_for_ll must be in
['gpu_linear',
cv2.INTER_LINEAR,
cv2.INTER_CUBIC,
cv2.INTER_LANCZOS4]
"""
raise ValueError(msg, interp_type_for_ll)
self.interp_type_for_ll = interp_type_for_ll
src = data['src']
transformed = data['transformed']
signal = data['signal']
for obj in [src, transformed]:
if not isinstance(obj, CpuGpuArray):
raise TypeError
for obj in [signal.src, signal.dst, signal.transformed]:
if not isinstance(obj, CpuGpuArray):
raise TypeError
self.nCalls = 0
self.nCallbacks = 0
self.sigma_signal = sigma_signal
cpa_space = ms.L_cpa_space[level]
self.cpa_space = cpa_space
if src.shape[1] != cpa_space.dim_domain:
raise ValueError(src.shape, cpa_space.dim_domain)
# self.mu = cpa_simple_mean(cpa_space)
self.my = cpa_space.get_zeros_theta()
self.src = src
self.transformed = transformed
self.signal = signal
# self.dim_signal = signal.src.shape[1]
if signal.src.ndim == 2:
self.dim_signal = 2
else:
raise NotImplementedError
if self.dim_signal != 2:
raise NotImplementedError(signal.src.shape)
if self.signal.src.shape != self.signal.dst.shape:
raise ValueError
if self.signal.src.shape != self.signal.transformed.shape:
raise ValueError
nPts = len(src)
self.nPts = nPts
# self.err = CpuGpuArray.zeros_like(src)
# self.signal.err = CpuGpuArray.zeros_like(src)
self.signal.err = CpuGpuArray.zeros_like(self.signal.src)
self.ll = CpuGpuArray.zeros(nPts, dtype=src.dtype)
if nPts <= 1:
raise ValueError
self.params_flow_int = params_flow_int
self._pat = PAT(pa_space=cpa_space, Avees=cpa_space.get_zeros_PA())
if 0:
ds0,ds1,ds2=10,10,10
# pts_grid=cpa_space.x_dense_grid[:,::ds0,::ds1,50:51]
pts_grid=cpa_space.x_dense_grid[:,::ds0,::ds1,::ds2]
pts=np.vstack([pts_grid[0].ravel(),
pts_grid[1].ravel(),
pts_grid[2].ravel()]).T.copy()
pts = CpuGpuArray(pts)
print pts_grid.shape
print pts.shape
pts_transformed = CpuGpuArray.zeros_like(pts)
mu = cpa_space.get_zeros_theta()
np.random.seed(0)
# theta *= 4
#
cpa_space.theta2Avees(theta=theta)
cpa_space.update_pat()
def example(tess='I',
base=[2, 2, 2],
nLevels=1,
zero_v_across_bdry=[True] * 3,
vol_preserve=False,
nRows=100,
nCols=100,
nSlices=100,
use_mayavi=False,
eval_v=False,
eval_cell_idx=False):
tw = TransformWrapper(nRows=nRows,
nCols=nCols,
nSlices=nSlices,
nLevels=nLevels,
base=base,
zero_v_across_bdry=zero_v_across_bdry,
tess=tess,
valid_outside=False,
only_local=False,
vol_preserve=vol_preserve)
print_iterable(tw.ms.L_cpa_space)
print tw
# create some fake 3D image.
img = np.zeros((nCols, nRows, nSlices), dtype=np.float64)
# img[:]=np.random.random_integers(0,255,img.shape)
# Fill the image with the x coordinates as fake values
img[:] = tw.pts_src_dense.cpu[:, 0].reshape(img.shape)
img0 = CpuGpuArray(img.copy().astype(np.float64))
img_wrapped_fwd = CpuGpuArray.zeros_like(img0)
img_wrapped_inv = CpuGpuArray.zeros_like(img0)
seed = 0
np.random.seed(seed)
ms_Avees = tw.get_zeros_PA_all_levels()
ms_theta = tw.get_zeros_theta_all_levels()
if tess == 'II':
for level in range(tw.ms.nLevels):
cpa_space = tw.ms.L_cpa_space[level]
Avees = ms_Avees[level]
# 1/0
if level == 0:
tw.sample_gaussian(level,
ms_Avees[level],
ms_theta[level],
mu=None) # zero mean
# ms_theta[level].fill(0)
# ms_theta[level][-4]=10
cpa_space.theta2Avees(theta=ms_theta[level], Avees=Avees)
else:
tw.sample_from_the_ms_prior_coarse2fine_one_level(
ms_Avees, ms_theta, level_fine=level)
else:
# For tess='I' in 3D, I have yet to implement the coarse-to-fine sampling.
for level in range(tw.ms.nLevels):
cpa_space = tw.ms.L_cpa_space[level]
velTess = cpa_space.zeros_velTess()
ms_Avees[level].fill(0)
Avees = ms_Avees[level]
tw.sample_gaussian_velTess(level, Avees, velTess, mu=None)
print 'img shape:', img0.shape
# You don't have use these. You can use any 2d array
# that has 3 columns (regardless of the number of rows).
pts_src = tw.pts_src_dense
pts_src = CpuGpuArray(pts_src.cpu[::1].copy())
# Create a buffer for the output
pts_fwd = CpuGpuArray.zeros_like(pts_src)
pts_inv = CpuGpuArray.zeros_like(pts_src)
for level in range(tw.ms.nLevels):
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)
print 'level', level
print
print 'number of points:', len(pts_src)
print 'number of cells:', tw.ms.L_cpa_space[level].nC
# 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)
tw.remap_fwd(pts_inv, img0, img_wrapped_fwd)
tw.remap_inv(pts_fwd, img0, img_wrapped_inv)
# 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_inv.gpu2cpu()
if use_mayavi:
ds = 1 # downsampling factor
i = 17
pts_src_grid = pts_src.cpu.reshape(tw.nRows, tw.nCols, -1, 3)
pts_src_ds = pts_src_grid[::ds, ::ds, i].reshape(-1, 3)
pts_fwd_grid = pts_fwd.cpu.reshape(tw.nRows, tw.nCols, -1, 3)
pts_fwd_ds = pts_fwd_grid[::ds, ::ds, i].reshape(-1, 3)
pts_inv_grid = pts_inv.cpu.reshape(tw.nRows, tw.nCols, -1, 3)
pts_inv_ds = pts_inv_grid[::ds, ::ds, i].reshape(-1, 3)
from of.my_mayavi import *
mayavi_mlab_close_all()
mayavi_mlab_figure_bgwhite('src')
x, y, z = pts_src_ds.T
mayavi_mlab_plot3d(x, y, z)
mayavi_mlab_figure_bgwhite('fwd')
x, y, z = pts_fwd_ds.T
mayavi_mlab_plot3d(x, y, z)
figsize = (12, 12)
plt.figure(figsize=figsize)
i = 17 # some slice
plt.subplot(131)
plt.imshow(img0.cpu[:, :, i].astype(np.uint8), interpolation="Nearest")
plt.title('slice from img')
plt.subplot(132)
plt.imshow(img_wrapped_fwd.cpu[:, :, i].astype(np.uint8),
interpolation="Nearest")
plt.axis('off')
plt.title('slice from fwd(img)')
plt.subplot(133)
plt.imshow(img_wrapped_inv.cpu[:, :, i].astype(np.uint8),
interpolation="Nearest")
plt.axis('off')
plt.title('slice from inv(img)')
if 0: # debug
cpa_space = tw.ms.L_cpa_space[level]
if eval_v:
vx = tw.v_dense.cpu[:, 0].reshape(
cpa_space.x_dense_grid_img.shape[1:])
vy = tw.v_dense.cpu[:, 1].reshape(
cpa_space.x_dense_grid_img.shape[1:])
vz = tw.v_dense.cpu[:, 2].reshape(
cpa_space.x_dense_grid_img.shape[1:])
plt.figure()
plt.imshow(vz[:, :, 17], interpolation="Nearest")
plt.colorbar()
plt.title('vz in some slice')
return tw
def __init__(self,nRows,nCols,vol_preserve=False,
nLevels=1,
base=[2,2],
scale_spatial=1.0 * .1,
scale_value=100,
zero_v_across_bdry=[False]*2, # For now, don't change that.
tess = None,
valid_outside=True,
only_local=False,
cont_constraints_are_separable=False):
"""
Input params:
nRows: number of rows in the image
nCols: number of cols in the image
vol_preserve: boolean flag (area-preserving or not)
nLevels: number of levels in the multiscale representation
base = (# of cells in X direction, # of cells in Y direction)
Determines the resolution of the tesselation as the base
level of the multiscale representation.
scale_spatial: paramter for the Gaussian prior. Higher val <=> more smoothness
scale_value: paramter for the Gaussian prior. Higher val <=> larger covariance
"""
super(type(self),self).__init__(
vol_preserve=vol_preserve,
nLevels=nLevels,
base=base,
scale_spatial=scale_spatial,
scale_value=scale_value,
zero_v_across_bdry=zero_v_across_bdry,
tess=tess,
valid_outside=valid_outside,
only_local=only_local,
cont_constraints_are_separable=cont_constraints_are_separable)
self.nRows = self.args.nRows = nRows
self.nCols = self.args.nCols = nCols
print self.args
# print
if tess=='tri':
tess='I'
if tess=='rect':
tess=='II'
if tess not in ['I','II']:
raise ValueError(tess,"tess must be in ['I','II']")
if only_local and tess !='I':
raise NotImplementedError
self.nRows=nRows
self.nCols=nCols
XMINS=[0,0]
XMAXS=[nCols,nRows] # Note: This inclusive; e.g., if your image is
# 512x512, XMAXS=[512,512], not [511,511]
# XMINS=[-nCols/2,-nRows/2]
# XMAXS=[ nCols/2, nRows/2]
warp_around=[False,False] # For now, don't change that.
# zero_v_across_bdry=[False,False] # For now, don't change that.
Nx = XMAXS[0]-XMINS[0]
Ny = XMAXS[1]-XMINS[1]
self.config_plt = ConfigPlt(Nx=Nx,Ny=Ny)
Ngrids=[Nx,Ny]
ms=Multiscale(XMINS,XMAXS,zero_v_across_bdry,
vol_preserve,
warp_around=warp_around,
nLevels=nLevels,base=base,
tess=tess,
Ngrids=Ngrids,
valid_outside=valid_outside,
only_local=only_local,
cont_constraints_are_separable=cont_constraints_are_separable)
self.ms=ms
if only_local == False:
self.msp=MultiscaleCoarse2FinePrior(ms,scale_spatial=scale_spatial,
scale_value=scale_value,
left_blk_std_dev=1.0/100,right_vec_scale=1)
else:
self.msp = None
self.pts_src_dense = CpuGpuArray(ms.L_cpa_space[0].x_dense_img.copy())
self.v_dense = CpuGpuArray.zeros_like(self.pts_src_dense)
self.transformed_dense = CpuGpuArray.zeros_like(self.pts_src_dense)
self.params_flow_int = get_params_flow_int()
self.params_flow_int.nStepsODEsolver = 10 # Usually this is enough.
self.params_flow_int_coarse = copy.deepcopy(self.params_flow_int)
self.params_flow_int_coarse.nTimeSteps /= 10
self.params_flow_int_coarse.dt *= 10
self.params_flow_int_fine = copy.deepcopy(self.params_flow_int)
self.params_flow_int_fine.nTimeSteps *= 10
self.params_flow_int_fine.dt /= 10
self.ms_pats = ms.pats
def set_dense(self):
self.src_dense = self.tw.pts_src_dense
self.transformed_dense = CpuGpuArray.zeros_like(self.src_dense)
# x_select = x_select.copy()
# x_select = x[::100/2].copy()
x_select = CpuGpuArray(x_dense.cpu[::100/2].copy())
# x_select[:]=np.linspace(.1,1.0,len(x_select))
# x_select[3:-1] +=.1
# x_select[6:-1] -=.05
# pat = PAT(pa_space=cpa_space,Avees=sample_Avees_all_levels[level])
cpa_space.update_pat(Avees=sample_Avees_all_levels[level])
#
v_dense = CpuGpuArray.zeros_like(x_dense)
cpa_space.calc_v(pts=x_dense,out=v_dense)
src=x_dense
print '#pts =',len(src)
transformed = CpuGpuArray.zeros_like(src)
tic=time.clock()
cpa_space.calc_T(pts = src, mysign=1,out=transformed,**params_flow_int)
toc = time.clock()
print "time (src)",toc-tic
def set_data(self,x,signal_src,signal_dst,isbinary):
"""
For now, assumes dst was evaluated on evenly-space points
// Is the comment above still current?
"""
self.isbinary=isbinary
nPts = len(x)
if x.ndim !=2 or x.shape[1]!=2:
raise ValueError(x.shape)
if signal_src.shape != signal_dst.shape:
raise ValueError(gnal_src.shape , signal_dst.shape)
if signal_src.ndim !=2:
# Want a single channel
raise ValueError(signal_src.shape)
# signal_src = signal_src.reshape(nPts,1).astype(np.float64)
# signal_dst = signal_dst.reshape(nPts,1).astype(np.float64)
signal_src = signal_src.astype(np.float64)
signal_dst = signal_dst.astype(np.float64)
# if nPts != signal_src.shape[0]:
# raise ValueError( nPts , signal_src.shape)
# if x.shape[0] != signal_dst.shape[0]:
# raise ValueError( nPts , signal_dst.shape)
if nPts != signal_src.size:
raise ValueError( nPts , signal_src.shape)
if x.shape[0] != signal_dst.size:
raise ValueError( nPts , signal_dst.shape)
if x.dtype != np.float64:
raise TypeError(x.dtype)
if signal_src.dtype != np.float64:
raise TypeError(signal_src.dtype)
if signal_dst.dtype != np.float64:
raise TypeError(signal_dst.dtype)
if signal_src.ndim == 1:
raise ValueError(signal_src.ndim)
if signal_dst.ndim == 1:
raise ValueError(signal_dst.ndim)
if not isinstance(signal_src,CpuGpuArray):
signal_src = CpuGpuArray(signal_src)
if not isinstance(signal_dst,CpuGpuArray):
signal_dst = CpuGpuArray(signal_dst)
self.signal = Bunch()
self.signal.src=signal_src
self.signal.dst=signal_dst
self.src = x
self.transformed = CpuGpuArray.zeros_like(self.src)
self.signal.transformed=CpuGpuArray.zeros_like(signal_src)
def example(tess='I',base=[2,2,2],nLevels=1,
zero_v_across_bdry=[True]*3,
vol_preserve=False,
nRows=100, nCols=100,nSlices=100,
use_mayavi=False,
eval_v=False,
eval_cell_idx=False):
tw = TransformWrapper(nRows=nRows,
nCols=nCols,
nSlices=nSlices,
nLevels=nLevels,
base=base,
zero_v_across_bdry=zero_v_across_bdry,
tess=tess,
valid_outside=False,
only_local=False,
vol_preserve=vol_preserve)
print_iterable(tw.ms.L_cpa_space)
print tw
# create some fake 3D image.
img = np.zeros((nCols,nRows,nSlices),dtype=np.float64)
# img[:]=np.random.random_integers(0,255,img.shape)
# Fill the image with the x coordinates as fake values
img[:]=tw.pts_src_dense.cpu[:,0].reshape(img.shape)
img0 = CpuGpuArray(img.copy().astype(np.float64))
img_wrapped_fwd= CpuGpuArray.zeros_like(img0)
img_wrapped_inv= CpuGpuArray.zeros_like(img0)
seed=0
np.random.seed(seed)
ms_Avees=tw.get_zeros_PA_all_levels()
ms_theta=tw.get_zeros_theta_all_levels()
if tess == 'II' :
for level in range(tw.ms.nLevels):
cpa_space = tw.ms.L_cpa_space[level]
Avees = ms_Avees[level]
# 1/0
if level==0:
tw.sample_gaussian(level,ms_Avees[level],ms_theta[level],mu=None)# zero mean
# ms_theta[level].fill(0)
# ms_theta[level][-4]=10
cpa_space.theta2Avees(theta=ms_theta[level],Avees=Avees)
else:
tw.sample_from_the_ms_prior_coarse2fine_one_level(ms_Avees,ms_theta,
level_fine=level)
else:
# For tess='I' in 3D, I have yet to implement the coarse-to-fine sampling.
for level in range(tw.ms.nLevels):
cpa_space = tw.ms.L_cpa_space[level]
velTess = cpa_space.zeros_velTess()
ms_Avees[level].fill(0)
Avees = ms_Avees[level]
tw.sample_gaussian_velTess(level,Avees,velTess,mu=None)
print 'img shape:',img0.shape
# You don't have use these. You can use any 2d array
# that has 3 columns (regardless of the number of rows).
pts_src = tw.pts_src_dense
pts_src=CpuGpuArray(pts_src.cpu[::1].copy())
# Create a buffer for the output
pts_fwd = CpuGpuArray.zeros_like(pts_src)
pts_inv = CpuGpuArray.zeros_like(pts_src)
for level in range(tw.ms.nLevels):
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)
print 'level',level
print
print 'number of points:',len(pts_src)
print 'number of cells:',tw.ms.L_cpa_space[level].nC
# 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)
tw.remap_fwd(pts_inv,img0,img_wrapped_fwd)
tw.remap_inv(pts_fwd,img0,img_wrapped_inv)
# 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_inv.gpu2cpu()
if use_mayavi:
ds=1 # downsampling factor
i= 17
pts_src_grid = pts_src.cpu.reshape(tw.nRows,tw.nCols,-1,3)
pts_src_ds=pts_src_grid[::ds,::ds,i].reshape(-1,3)
pts_fwd_grid = pts_fwd.cpu.reshape(tw.nRows,tw.nCols,-1,3)
pts_fwd_ds=pts_fwd_grid[::ds,::ds,i].reshape(-1,3)
pts_inv_grid = pts_inv.cpu.reshape(tw.nRows,tw.nCols,-1,3)
pts_inv_ds=pts_inv_grid[::ds,::ds,i].reshape(-1,3)
from of.my_mayavi import *
mayavi_mlab_close_all()
mayavi_mlab_figure_bgwhite('src')
x,y,z=pts_src_ds.T
mayavi_mlab_plot3d(x,y,z)
mayavi_mlab_figure_bgwhite('fwd')
x,y,z=pts_fwd_ds.T
mayavi_mlab_plot3d(x,y,z)
figsize = (12,12)
plt.figure(figsize=figsize)
i= 17 # some slice
plt.subplot(131)
plt.imshow(img0.cpu[:,:,i].astype(np.uint8),interpolation="Nearest")
plt.title('slice from img')
plt.subplot(132)
plt.imshow(img_wrapped_fwd.cpu[:,:,i].astype(np.uint8),interpolation="Nearest")
plt.axis('off')
plt.title('slice from fwd(img)')
plt.subplot(133)
plt.imshow(img_wrapped_inv.cpu[:,:,i].astype(np.uint8),interpolation="Nearest")
plt.axis('off')
plt.title('slice from inv(img)')
if 0: # debug
cpa_space=tw.ms.L_cpa_space[level]
if eval_v:
vx=tw.v_dense.cpu[:,0].reshape(cpa_space.x_dense_grid_img.shape[1:])
vy=tw.v_dense.cpu[:,1].reshape(cpa_space.x_dense_grid_img.shape[1:])
vz=tw.v_dense.cpu[:,2].reshape(cpa_space.x_dense_grid_img.shape[1:])
plt.figure()
plt.imshow(vz[:,:,17],interpolation="Nearest");plt.colorbar()
plt.title('vz in some slice')
return tw
velTess*=-1
tw.update_pat_from_velTess(velTess,level=0)
plt.subplot(222)
plt.plot(x,pts_recovered_cf,'g')
plt.grid('on')
from of.gpu import CpuGpuArray
pts_fwd = CpuGpuArray.zeros_like(tw.x_dense)
tic=time.clock()
tw.calc_T_fwd(tw.x_dense,pts_fwd,level=0,int_quality=1)
pts_fwd.gpu2cpu()
toc=time.clock()
print 'time',toc-tic
# 1/0
pts_recovered = CpuGpuArray.zeros_like(tw.x_dense)
tw.calc_T_inv(pts_fwd,pts_recovered,level=0)
pts_recovered.gpu2cpu()
plt.plot(tw.x_dense.cpu,pts_fwd.cpu)
plt.plot(tw.x_dense.cpu,pts_recovered.cpu)
from pycuda import autoinit
from of.gpu import CpuGpuArray
import numpy as np
msg = """
The code below is for landmarks,
not signals"""
raise NotImplementedError(msg)
yy, xx = np.mgrid[-2:2:1, -2:2:1]
x = np.vstack([xx.ravel(), yy.ravel()]).T
del xx, yy
x = CpuGpuArray(x.copy().astype(np.float))
print x
y = np.random.standard_normal(x.shape)
y = CpuGpuArray(y)
err = CpuGpuArray.zeros_like(y)
nPts = len(err)
ll = CpuGpuArray.zeros(nPts)
calc_signal_err_per_sample(x.gpu, y.gpu, err.gpu)
sigma = 1.0
calc_ll_per_sample(ll.gpu, err.gpu, sigma)
err.gpu2cpu()
ll.gpu2cpu()
print np.allclose(
ll.cpu, -0.5 * (err.cpu[:, 0]**2 + err.cpu[:, 1]**2) / (sigma**2))
print 'level: ',level
print cpa_space
# cpa_calcs=CpaCalcs(Nx=Nx,Ny=Ny,use_GPU_if_possible=True)
# As = cpa_space.Avees2As(sample_Avees_all_levels[level])
# As[:,0,:]=0
# sample_Avees_all_levels[level] = cpa_space.As2Avees(As)
# sample_cpa_all_levels[level] = cpa_space.project(sample_Avees_all_levels[level])
# ipshell('hi')
# pat= PAT(pa_space=cpa_space,Avees=sample_Avees_all_levels[level])
cpa_space.update_pat(Avees=sample_Avees_all_levels[level])
pts = CpuGpuArray(cpa_space.x_dense_img)
v_dense = CpuGpuArray.zeros_like(pts)
cpa_space.calc_v(pts=pts,out=v_dense )
v_dense.gpu2cpu() # for display
plt.figure(level);
of.plt.maximize_figure()
scale=[.4,0.25][cpa_space.vol_preserve]
scale = 1 * Nx * 30
scale = np.sqrt((v_dense.cpu**2).sum(axis=1)).mean() / 10
for h in [233,236][:1]:
def example(base=[5],
scale_spatial=100,
nLevels=2,
zero_v_across_bdry=[1],
use_local_basis=True):
nPtsDense = 10000
tw = TransformWrapper(nCols=100,
nLevels=nLevels,
base=base,
scale_spatial=scale_spatial,
nPtsDense=nPtsDense,
zero_v_across_bdry=zero_v_across_bdry)
print_iterable(tw.ms.L_cpa_space)
seed = 0
np.random.seed(seed)
for level in range(tw.ms.nLevels):
cpa_space = tw.ms.L_cpa_space[level]
Avees = cpa_space.Avees
velTess = cpa_space.zeros_velTess()
if use_local_basis:
if 0:
tw.sample_gaussian_velTess(level, Avees, velTess, mu=None)
Avees *= 0.001
velTess *= 0.001
else:
if not zero_v_across_bdry[0]:
velTess[:] = 10 * np.random.standard_normal(velTess.shape)
cpa_space.velTess2Avees(velTess=velTess, Avees=Avees)
else:
velTess[1:-1] = 10 * np.random.standard_normal(
velTess[1:-1].shape)
cpa_space.velTess2Avees(velTess=velTess, Avees=Avees)
cpa_space.velTess2Avees(velTess=velTess, Avees=Avees)
else:
theta = cpa_space.get_zeros_theta()
tw.sample_gaussian(level, Avees, theta, mu=None)
# theta/=10
cpa_space.theta2Avees(theta=theta, Avees=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(Avees, level)
pts_src = tw.x_dense
tw.calc_v(level=level, pts=pts_src, v=tw.v_dense)
tw.v_dense.gpu2cpu()
pts_fwd = CpuGpuArray.zeros_like(
pts_src) # Create a buffer for the output
tw.calc_T_fwd(pts_src, pts_fwd, level=level)
pts_fwd.gpu2cpu()
pts_inv = CpuGpuArray.zeros_like(
pts_src) # Create a buffer for the output
tw.calc_T_inv(pts_src, pts_inv, level=level)
pts_inv.gpu2cpu()
plt.figure(level)
plt.clf()
interval = pts_src.cpu # interval doesn't have to be pts_src.cpu
Visualize.simple(tw.x_dense,
tw.v_dense,
interval,
pts_src,
transformed_fwd=pts_fwd,
transformed_inv=pts_inv,
cpa_space=cpa_space)
return tw
use_local=inference_params.use_local)
theta_est = theta.copy()
fname_results = os.path.splitext(data.fname)[0] + '_result.pkl'
FilesDirs.raise_if_dir_does_not_exist(os.path.dirname(fname_results))
tosave = {'tw_args': inference_record.tw_args, 'theta': inference_record.theta}
tosave = Bunch(**tosave)
Pkl.dump(fname_results, tosave, override=True)
if 1:
from disp import disp
tw.create_grid_lines(step=0.1, factor=0.5)
src = data.src
dst = data.dst
transformed = CpuGpuArray.zeros_like(src)
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()
disp(tw=tw,
theta=theta_est,
src=src,
dst=dst,
if 0:
ds0,ds1,ds2=10,10,10
# pts_grid=cpa_space.x_dense_grid[:,::ds0,::ds1,50:51]
pts_grid=cpa_space.x_dense_grid[:,::ds0,::ds1,::ds2]
pts=np.vstack([pts_grid[0].ravel(),
pts_grid[1].ravel(),
pts_grid[2].ravel()]).T.copy()
pts = CpuGpuArray(pts)
pts_fwd = CpuGpuArray.zeros_like(pts)
mu = cpa_space.get_zeros_theta()
# np.random.seed(0)
theta = np.random.multivariate_normal(mean=mu,cov=cpa_covs.cpa_cov)
theta *=100
##
# theta.fill(0)
# theta[8]=1
# theta[10]=1
cpa_space.theta2Avees(theta=theta)
def example(base=[5],
scale_spatial=100,
nLevels=2,
zero_v_across_bdry=[1],
use_local_basis=True):
nPtsDense = 10000
tw = TransformWrapper(nCols=100,
nLevels=nLevels,
base=base,
scale_spatial=scale_spatial,
nPtsDense=nPtsDense,
zero_v_across_bdry=zero_v_across_bdry)
print_iterable(tw.ms.L_cpa_space)
seed=0
np.random.seed(seed)
for level in range(tw.ms.nLevels):
cpa_space = tw.ms.L_cpa_space[level]
Avees = cpa_space.Avees
velTess = cpa_space.zeros_velTess()
if use_local_basis:
if 0:
tw.sample_gaussian_velTess(level,Avees,velTess,mu=None)
Avees*=0.001
velTess*=0.001
else:
if not zero_v_across_bdry[0]:
velTess[:]=10*np.random.standard_normal(velTess.shape)
cpa_space.velTess2Avees(velTess=velTess,Avees=Avees)
else:
velTess[1:-1]=10*np.random.standard_normal(velTess[1:-1].shape)
cpa_space.velTess2Avees(velTess=velTess,Avees=Avees)
cpa_space.velTess2Avees(velTess=velTess,Avees=Avees)
else:
theta= cpa_space.get_zeros_theta()
tw.sample_gaussian(level,Avees,theta,mu=None)
# theta/=10
cpa_space.theta2Avees(theta=theta,Avees=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(Avees,level)
pts_src = tw.x_dense
tw.calc_v(level=level,pts=pts_src,v=tw.v_dense)
tw.v_dense.gpu2cpu()
pts_fwd = CpuGpuArray.zeros_like(pts_src) # Create a buffer for the output
tw.calc_T_fwd(pts_src,pts_fwd,level=level)
pts_fwd.gpu2cpu()
pts_inv = CpuGpuArray.zeros_like(pts_src) # Create a buffer for the output
tw.calc_T_inv(pts_src,pts_inv,level=level)
pts_inv.gpu2cpu()
plt.figure(level)
plt.clf()
interval = pts_src.cpu # interval doesn't have to be pts_src.cpu
Visualize.simple(tw.x_dense,tw.v_dense,interval,pts_src,
transformed_fwd=pts_fwd,transformed_inv=pts_inv,
cpa_space=cpa_space)
return tw
velTess *= -1
tw.update_pat_from_velTess(velTess, level=0)
closed_form_int.calc_phi_multiple_pts(x=pts_fwd_cf,
velTess=velTess,
pts_fwd=pts_recovered_cf,
t=t)
velTess *= -1
tw.update_pat_from_velTess(velTess, level=0)
plt.subplot(222)
plt.plot(x, pts_recovered_cf, 'g')
plt.grid('on')
from of.gpu import CpuGpuArray
pts_fwd = CpuGpuArray.zeros_like(tw.x_dense)
tic = time.clock()
tw.calc_T_fwd(tw.x_dense, pts_fwd, level=0, int_quality=1)
pts_fwd.gpu2cpu()
toc = time.clock()
print 'time', toc - tic
# 1/0
pts_recovered = CpuGpuArray.zeros_like(tw.x_dense)
tw.calc_T_inv(pts_fwd, pts_recovered, level=0)
pts_recovered.gpu2cpu()
plt.plot(tw.x_dense.cpu, pts_fwd.cpu)
plt.plot(tw.x_dense.cpu, pts_recovered.cpu)
plt.legend([r'$T(x)$', r'$(T^{-1}\circ T)(x)$', r'$T_{\mathrm{alg}}(x)$'],
def set_dense(self):
self.src_dense = self.tw.pts_src_dense
self.transformed_dense = CpuGpuArray.zeros_like(self.src_dense)
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
def __init__(self,nCols,vol_preserve=False,
nLevels=1,
base=[5],
# scale_spatial=1.0 * .1,
# scale_value=100,
scale_spatial=1.0 * 10,
scale_value=2,
zero_v_across_bdry=[True] # For now, don't change that.
,nPtsDense=None,
only_local=False
):
"""
"""
super(type(self),self).__init__(
vol_preserve=vol_preserve,
nLevels=nLevels,
base=base,
scale_spatial=scale_spatial,
scale_value=scale_value,
zero_v_across_bdry=zero_v_across_bdry,
tess=None,
valid_outside=None,
only_local=only_local)
self.nCols = self.args.nCols = nCols
self.args.nPtsDense=nPtsDense
self.nCols=nCols
XMINS=[0]
XMAXS=[nCols] # Note: This is inclusive
warp_around=[False] # For now, don't change that.
# zero_v_across_bdry=[True] # For now, don't change that.
Nx = XMAXS[0]-XMINS[0]
Ngrids=[Nx]
ms=Multiscale(XMINS,XMAXS,zero_v_across_bdry,
vol_preserve,
warp_around=warp_around,
nLevels=nLevels,base=base ,
Ngrids=Ngrids)
self.ms=ms
self.msp=MultiscaleCoarse2FinePrior(ms,scale_spatial=scale_spatial,scale_value=scale_value,
# left_blk_std_dev=1.0/100,
# right_vec_scale=1
left_blk_std_dev=1.0,
right_vec_scale=1.0
)
# self.pts_src_dense = ms.L_cpa_space[0].get_x_dense(1000)
# self.x_dense= ms.L_cpa_space[0].get_x_dense(1000)
# self.v_dense = CpuGpuArray.zeros_like(self.x_dense)
self.nPtsDense=nPtsDense
if nPtsDense is None:
raise ObsoleteError
else:
self.x_dense= ms.L_cpa_space[0].get_x_dense(nPtsDense)
self.v_dense = CpuGpuArray.zeros_like(self.x_dense)
self.transformed_dense = CpuGpuArray.zeros_like(self.x_dense)
self.params_flow_int = get_params_flow_int()
self.params_flow_int.nStepsODEsolver = 10 # Usually this is enough.
#
self.params_flow_int_coarse = copy.deepcopy(self.params_flow_int)
self.params_flow_int_coarse.nTimeSteps /= 10
self.params_flow_int_coarse.dt *= 10
self.params_flow_int_fine = copy.deepcopy(self.params_flow_int)
self.params_flow_int_fine.nTimeSteps *= 10
self.params_flow_int_fine.dt /= 10
self.ms_pats = ms.pats
def __init__(self,ms,level,data,
sigma_signal,
params_flow_int,
interp_type_for_ll,
# src=None,dst=None,transformed=None
):
# ipshell('hi')
if interp_type_for_ll not in self.supported_interp_types:
msg = """
interp_type_for_ll must be in
['gpu_linear',
cv2.INTER_LINEAR,
cv2.INTER_CUBIC,
cv2.INTER_LANCZOS4]
"""
raise ValueError(msg,interp_type_for_ll)
self.interp_type_for_ll=interp_type_for_ll
src=data['src']
transformed=data['transformed']
signal=data['signal']
for obj in [src,transformed]:
if not isinstance(obj,CpuGpuArray):
raise TypeError
for obj in [signal.src,signal.dst,signal.transformed]:
if not isinstance(obj,CpuGpuArray):
raise TypeError
self.nCalls = 0
self.nCallbacks = 0
self.sigma_signal=sigma_signal
cpa_space=ms.L_cpa_space[level]
self.cpa_space = cpa_space
if src.shape[1] != cpa_space.dim_domain:
raise ValueError(src.shape,cpa_space.dim_domain)
# self.mu = cpa_simple_mean(cpa_space)
self.my = cpa_space.get_zeros_theta()
self.src = src
self.transformed = transformed
self.signal = signal
# self.dim_signal = signal.src.shape[1]
if signal.src.ndim==2:
self.dim_signal = 2
else:
raise NotImplementedError
if self.dim_signal != 2:
raise NotImplementedError(signal.src.shape)
if self.signal.src.shape != self.signal.dst.shape:
raise ValueError
if self.signal.src.shape != self.signal.transformed.shape:
raise ValueError
nPts = len(src)
self.nPts = nPts
# self.err = CpuGpuArray.zeros_like(src)
# self.signal.err = CpuGpuArray.zeros_like(src)
self.signal.err = CpuGpuArray.zeros_like(self.signal.src)
self.ll = CpuGpuArray.zeros(nPts,dtype=src.dtype)
if nPts <= 1:
raise ValueError
self.params_flow_int=params_flow_int
self._pat = PAT(pa_space=cpa_space,
Avees=cpa_space.get_zeros_PA())
def disp(self,sampler,interp_type_during_visualization):
level=sampler.level
theta=sampler.theta_current
tw=self.tw
# interval=self.interval
# interval_dense=self.interval_dense
markersize = 5
fontsize=30
cpa_space = tw.ms.L_cpa_space[level]
plt.subplot(231)
sampler.plot_ll()
plt.title('ll',fontsize=fontsize)
sampler.plot_wlp()
sampler.plot_wlp_plus_ll()
if sampler.lp_func:
plt.legend(['ll','wlp','ll+wlp'])
plt.subplot(232)
sampler.plot_ar()
plt.title('accept ratio',fontsize=fontsize)
# print theta
cpa_space.theta2As(theta=theta)
tw.update_pat_from_Avees(level=level)
tw.calc_v(level=level)
tw.v_dense.gpu2cpu()
src = self.src
# dst = self.dst
transformed = self.transformed
# src_dense=self.src_dense
# transformed_dense=self.transformed_dense
# tw.calc_T(src_dense, transformed_dense, mysign=1, level=level,
#
# transformed_dense.gpu2cpu()
tw.calc_T_inv(src, transformed, level=level,
int_quality=+1)
transformed.gpu2cpu()
if interp_type_during_visualization=='gpu_linear':
my_dtype = np.float64
else:
my_dtype = np.float32 # For opencv
img_src = self.signal.src.cpu.reshape(tw.nRows,tw.nCols)
img_src = CpuGpuArray(img_src.astype(my_dtype))
img_wrapped = CpuGpuArray.zeros_like(img_src)
img_dst = self.signal.dst.cpu.reshape(tw.nRows,tw.nCols)
img_dst = CpuGpuArray(img_dst)
if interp_type_during_visualization=='gpu_linear':
tw.remap_fwd(transformed,img_src,img_wrapped)
else:
tw.remap_fwd_opencv(transformed,img_src,img_wrapped,interp_type_during_visualization)
img_wrapped.gpu2cpu()
plt.subplot(233)
plt.imshow(img_src.cpu,interpolation="None")
plt.gray()
cpa_space.plot_cells('r')
tw.config_plt(axis_on_or_off='on')
plt.title(r'$I_{\mathrm{src}}$')
plt.subplot(234)
plt.imshow(img_wrapped.cpu,interpolation="None")
plt.gray()
# cpa_space.plot_cells('w')
tw.config_plt(axis_on_or_off='on')
plt.title(r'$I_{\mathrm{src}}\circ T^{\theta}$')
plt.subplot(235)
plt.imshow(img_dst.cpu,interpolation="None")
plt.gray()
plt.title(r'$I_{\mathrm{dst}}$')
# cpa_space.plot_cells('w')
tw.config_plt(axis_on_or_off='on')
plt.subplot(2,6,11)
self.tw.imshow_vx()
pylab.jet()
tw.config_plt(axis_on_or_off='on')
plt.title(r'$v_x$')
plt.subplot(2,6,12)
self.tw.imshow_vy()
pylab.jet()
tw.config_plt(axis_on_or_off='on')
plt.title(r'$v_y$')
# pts_grid[1].ravel(),
# pts_grid[2].ravel()]).T.copy()
pts = cpa_space.x_dense
if 0:
ds0, ds1, ds2 = 10, 10, 10
# pts_grid=cpa_space.x_dense_grid[:,::ds0,::ds1,50:51]
pts_grid = cpa_space.x_dense_grid[:, ::ds0, ::ds1, ::ds2]
pts = np.vstack(
[pts_grid[0].ravel(), pts_grid[1].ravel(),
pts_grid[2].ravel()]).T.copy()
pts = CpuGpuArray(pts)
pts_fwd = CpuGpuArray.zeros_like(pts)
mu = cpa_space.get_zeros_theta()
# np.random.seed(0)
theta = np.random.multivariate_normal(mean=mu, cov=cpa_covs.cpa_cov)
theta *= 100
##
# theta.fill(0)
# theta[8]=1
# theta[10]=1
cpa_space.theta2Avees(theta=theta)
cpa_space.update_pat()
# 1/0
# continue
print 'level: ', level
print cpa_space
# cpa_calcs=CpaCalcs(Nx=Nx,Ny=Ny,use_GPU_if_possible=True)
# As = cpa_space.Avees2As(sample_Avees_all_levels[level])
# As[:,0,:]=0
# sample_Avees_all_levels[level] = cpa_space.As2Avees(As)
# sample_cpa_all_levels[level] = cpa_space.project(sample_Avees_all_levels[level])
# ipshell('hi')
# pat= PAT(pa_space=cpa_space,Avees=sample_Avees_all_levels[level])
cpa_space.update_pat(Avees=sample_Avees_all_levels[level])
pts = CpuGpuArray(cpa_space.x_dense_img)
v_dense = CpuGpuArray.zeros_like(pts)
cpa_space.calc_v(pts=pts, out=v_dense)
v_dense.gpu2cpu() # for display
plt.figure(level)
of.plt.maximize_figure()
scale = [.4, 0.25][cpa_space.vol_preserve]
scale = 1 * Nx * 30
scale = np.sqrt((v_dense.cpu**2).sum(axis=1)).mean() / 10
for h in [233, 236][:1]:
plt.subplot(h)
cpa_space.quiver(cpa_space.x_dense_grid_img,
v_dense,
def __init__(
self,
nRows,
nCols,
vol_preserve=False,
nLevels=1,
base=[2, 2],
scale_spatial=1.0 * .1,
scale_value=100,
zero_v_across_bdry=[False] * 2, # For now, don't change that.
tess=None,
valid_outside=True,
only_local=False,
cont_constraints_are_separable=False):
"""
Input params:
nRows: number of rows in the image
nCols: number of cols in the image
vol_preserve: boolean flag (area-preserving or not)
nLevels: number of levels in the multiscale representation
base = (# of cells in X direction, # of cells in Y direction)
Determines the resolution of the tesselation as the base
level of the multiscale representation.
scale_spatial: paramter for the Gaussian prior. Higher val <=> more smoothness
scale_value: paramter for the Gaussian prior. Higher val <=> larger covariance
"""
super(type(self), self).__init__(
vol_preserve=vol_preserve,
nLevels=nLevels,
base=base,
scale_spatial=scale_spatial,
scale_value=scale_value,
zero_v_across_bdry=zero_v_across_bdry,
tess=tess,
valid_outside=valid_outside,
only_local=only_local,
cont_constraints_are_separable=cont_constraints_are_separable)
self.nRows = self.args.nRows = nRows
self.nCols = self.args.nCols = nCols
print self.args
# print
if tess == 'tri':
tess = 'I'
if tess == 'rect':
tess == 'II'
if tess not in ['I', 'II']:
raise ValueError(tess, "tess must be in ['I','II']")
if only_local and tess != 'I':
raise NotImplementedError
self.nRows = nRows
self.nCols = nCols
XMINS = [0, 0]
XMAXS = [nCols, nRows] # Note: This inclusive; e.g., if your image is
# 512x512, XMAXS=[512,512], not [511,511]
# XMINS=[-nCols/2,-nRows/2]
# XMAXS=[ nCols/2, nRows/2]
warp_around = [False, False] # For now, don't change that.
# zero_v_across_bdry=[False,False] # For now, don't change that.
Nx = XMAXS[0] - XMINS[0]
Ny = XMAXS[1] - XMINS[1]
self.config_plt = ConfigPlt(Nx=Nx, Ny=Ny)
Ngrids = [Nx, Ny]
ms = Multiscale(
XMINS,
XMAXS,
zero_v_across_bdry,
vol_preserve,
warp_around=warp_around,
nLevels=nLevels,
base=base,
tess=tess,
Ngrids=Ngrids,
valid_outside=valid_outside,
only_local=only_local,
cont_constraints_are_separable=cont_constraints_are_separable)
self.ms = ms
if only_local == False:
self.msp = MultiscaleCoarse2FinePrior(ms,
scale_spatial=scale_spatial,
scale_value=scale_value,
left_blk_std_dev=1.0 / 100,
right_vec_scale=1)
else:
self.msp = None
self.pts_src_dense = CpuGpuArray(ms.L_cpa_space[0].x_dense_img.copy())
self.v_dense = CpuGpuArray.zeros_like(self.pts_src_dense)
self.transformed_dense = CpuGpuArray.zeros_like(self.pts_src_dense)
self.params_flow_int = get_params_flow_int()
self.params_flow_int.nStepsODEsolver = 10 # Usually this is enough.
self.params_flow_int_coarse = copy.deepcopy(self.params_flow_int)
self.params_flow_int_coarse.nTimeSteps /= 10
self.params_flow_int_coarse.dt *= 10
self.params_flow_int_fine = copy.deepcopy(self.params_flow_int)
self.params_flow_int_fine.nTimeSteps *= 10
self.params_flow_int_fine.dt /= 10
self.ms_pats = ms.pats
msg="""
The code below is for landmarks,
not signals"""
raise NotImplementedError(msg)
yy,xx = np.mgrid[-2:2:1,-2:2:1]
x = np.vstack([xx.ravel(),yy.ravel()]).T
del xx,yy
x = CpuGpuArray(x.copy().astype(np.float))
print x
y = np.random.standard_normal(x.shape)
y = CpuGpuArray(y)
err = CpuGpuArray.zeros_like(y)
nPts = len(err)
ll = CpuGpuArray.zeros(nPts)
calc_signal_err_per_sample(x.gpu,y.gpu,err.gpu)
sigma=1.0
calc_ll_per_sample(ll.gpu,err.gpu,sigma)
err.gpu2cpu()
ll.gpu2cpu()
print np.allclose( ll.cpu,
-0.5*(err.cpu[:,0]**2+err.cpu[:,1]**2)/(sigma**2))
def disp_deformed_grid_lines(self, level, color=None, lw=1):
# return
if self.hlines is None or self.vlines is None:
raise ValueError
hlines, vlines = self.hlines, self.vlines
# for lines,c in zip([hlines,vlines],['r','b']):
# pts_at_0=np.asarray([lines[:,0,:].flatten(),
# lines[:,1,:].flatten()]).T
# pts_at_0 = CpuGpuArray(pts_at_0.copy())
# pts_at_T=CpuGpuArray.zeros_like(pts_at_0)
# self.calc_T_fwd(pts_src=pts_at_0,
# pts_fwd=pts_at_T,
# level=level,verbose=0,int_quality=1)
# if self.nCols != self.nCols:
# raise NotImplementedError
# pts_at_T.gpu2cpu()
# lines_new_x=pts_at_T.cpu[:,0].reshape(lines[:,0,:].shape).copy()
# lines_new_y=pts_at_T.cpu[:,1].reshape(lines[:,0,:].shape).copy()
# for line_new_x,line_new_y in zip(lines_new_x,lines_new_y):
#
# plt.plot(line_new_x,line_new_y,c)
if color is None:
colors = ['r', 'b']
else:
colors = [color, color]
s = hlines.shape
if s[2] <= 1:
raise ValueError
p = 0
L = 50000
if L >= s[2]:
while p < np.ceil(s[2]):
hlines = self.hlines[:, :, p:p + L]
vlines = self.vlines[:, :, p:p + L]
p += L
for lines, c in zip([hlines, vlines], colors):
pts_at_0 = np.asarray(
[lines[:, 0, :].flatten(), lines[:, 1, :].flatten()]).T
if pts_at_0.size == 0:
break
pts_at_0 = CpuGpuArray(pts_at_0.copy())
pts_at_T = CpuGpuArray.zeros_like(pts_at_0)
self.calc_T_fwd(pts_src=pts_at_0,
pts_fwd=pts_at_T,
level=level,
int_quality=1)
if self.nCols != self.nCols:
raise NotImplementedError
pts_at_T.gpu2cpu()
lines_new_x = pts_at_T.cpu[:, 0].reshape(
lines[:, 0, :].shape).copy()
lines_new_y = pts_at_T.cpu[:, 1].reshape(
lines[:, 0, :].shape).copy()
for line_new_x, line_new_y in zip(lines_new_x,
lines_new_y):
plt.plot(line_new_x, line_new_y, c, lw=lw)
else:
raise NotImplementedError
def __init__(self, nSuperpixels, s_std, i_std, prior_count, nChannels):
"""
Initilize the parameters for the superpixels:
The means are set to zeros at this point,
and will be set later in the first M step.
The space/color covariances (and their inverse), however, are being set
to initial values here.
We use a Inverse-Wishart prior on the space covariance
Arguments:
nSuperpixels: the number of superpixels to generate
s_std: should be fixed as nPixels_on_side
i_std: control the relative importance between RGB and location.
The smaller it is, bigger the RGB effect is / more irregular the superpixels are.
prior_count: determines the weight of Inverse-Wishart prior of space covariance(ex:1,5,10)
nChannels: the number of channels of the input image (gray:1, LAB/RGB: 3)
"""
if nChannels not in (1,3):
raise NotImplementedError(nChannels)
dim_i=nChannels
dim_s=2
self.dim_i=dim_i
self.dim_s=dim_s
self.nSuperpixels=nSuperpixels
self.s_std, self.i_std, self.prior_count = s_std,i_std,prior_count
mu_s = CpuGpuArray.zeros((nSuperpixels,dim_s))
mu_i = CpuGpuArray.zeros((nSuperpixels,dim_i))
Sigma_s = CpuGpuArray.zeros(shape = (nSuperpixels,dim_s,dim_s))
J_s = CpuGpuArray.zeros_like(Sigma_s)
Sigma_i = CpuGpuArray.zeros((nSuperpixels,dim_i,dim_i))
J_i = CpuGpuArray.zeros_like(Sigma_i)
logdet_Sigma_i = CpuGpuArray.zeros((nSuperpixels,1)) # scalars
logdet_Sigma_s = CpuGpuArray.zeros((nSuperpixels,1))
# start with unnormalized counts (uniform)
counts = np.ones(nSuperpixels,dtype=np.int32)
counts = CpuGpuArray(counts)
self.params = Bunch()
self.params.mu_i = mu_i
self.params.mu_s = mu_s
self.params.Sigma_i = Sigma_i
self.params.Sigma_s = Sigma_s
self.params.prior_sigma_s_sum = Sigma_s
self.params.J_i = J_i
self.params.J_s = J_s
self.params.logdet_Sigma_i = logdet_Sigma_i
self.params.logdet_Sigma_s = logdet_Sigma_s
self.params.counts = counts
# set those parameters related to covariance
self.initialize_params()
# intermediate arrays needed for the Gaussian parameter calculation on GPU
self.gpu_helper = Bunch()
self.gpu_helper.mu_i_helper = gpuarray.zeros((nSuperpixels,dim_i),dtype=np.int64)
self.gpu_helper.mu_s_helper = gpuarray.zeros((nSuperpixels,dim_s),dtype=np.int64)
self.gpu_helper.prior_sigma_s = self.params.prior_sigma_s_sum.gpu.copy()
self.gpu_helper.sigma_s_helper = gpuarray.zeros((nSuperpixels,3),dtype=np.int64)
self.gpu_helper.log_count_helper = gpuarray.zeros(nSuperpixels,dtype=np.double)
self.gpu_helper.non_NaN_count = gpuarray.zeros(nSuperpixels,dtype=np.int32)
self.gpu_helper.NaN_count = gpuarray.zeros(nSuperpixels,dtype=np.int32)
from of.utils import *
from of.gpu import CpuGpuArray
import of.plt
from cpab.cpa2d.TransformWrapper import TransformWrapper
from get_data_LFW import get_data
from disp import disp
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]
def __init__(self, nSuperpixels, s_std, i_std, prior_count, nChannels):
"""
Initilize the parameters for the superpixels:
The means are set to zeros at this point,
and will be set later in the first M step.
The space/color covariances (and their inverse), however, are being set
to initial values here.
We use a Inverse-Wishart prior on the space covariance
Arguments:
nSuperpixels: the number of superpixels to generate
s_std: should be fixed as nPixels_on_side
i_std: control the relative importance between RGB and location.
The smaller it is, bigger the RGB effect is / more irregular the superpixels are.
prior_count: determines the weight of Inverse-Wishart prior of space covariance(ex:1,5,10)
nChannels: the number of channels of the input image (gray:1, LAB/RGB: 3)
"""
if nChannels not in (1,3):
raise NotImplementedError(nChannels)
dim_i=nChannels
dim_s=2
self.dim_i=dim_i
self.dim_s=dim_s
self.nSuperpixels=nSuperpixels
self.s_std, self.i_std, self.prior_count = s_std,i_std,prior_count
mu_s = CpuGpuArray.zeros((nSuperpixels,dim_s))
mu_i = CpuGpuArray.zeros((nSuperpixels,dim_i))
Sigma_s = CpuGpuArray.zeros(shape = (nSuperpixels,dim_s,dim_s))
J_s = CpuGpuArray.zeros_like(Sigma_s)
Sigma_i = CpuGpuArray.zeros((nSuperpixels,dim_i,dim_i))
J_i = CpuGpuArray.zeros_like(Sigma_i)
logdet_Sigma_i = CpuGpuArray.zeros((nSuperpixels,1)) # scalars
logdet_Sigma_s = CpuGpuArray.zeros((nSuperpixels,1))
# start with unnormalized counts (uniform)
counts = np.ones(nSuperpixels,dtype=np.int32)
counts = CpuGpuArray(counts)
self.params = Bunch()
self.params.mu_i = mu_i
self.params.mu_s = mu_s
self.params.Sigma_i = Sigma_i
self.params.Sigma_s = Sigma_s
self.params.prior_sigma_s_sum = Sigma_s
self.params.J_i = J_i
self.params.J_s = J_s
self.params.logdet_Sigma_i = logdet_Sigma_i
self.params.logdet_Sigma_s = logdet_Sigma_s
self.params.counts = counts
# set those parameters related to covariance
self.initialize_params()
# intermediate arrays needed for the Gaussian parameter calculation on GPU
self.gpu_helper = Bunch()
self.gpu_helper.mu_i_helper = gpuarray.zeros((nSuperpixels,dim_i),dtype=np.int32)
self.gpu_helper.mu_s_helper = gpuarray.zeros((nSuperpixels,dim_s),dtype=np.int32)
self.gpu_helper.prior_sigma_s = self.params.prior_sigma_s_sum.gpu.copy()
self.gpu_helper.sigma_s_helper = gpuarray.zeros((nSuperpixels,3),dtype=np.int64)
self.gpu_helper.log_count_helper = gpuarray.zeros((nSuperpixels,1),dtype=np.double)
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
