P_ = P_[left:right, left:right]
# Normalize and mean-free
if options.mf:
P_ -= P_.mean()
if options.norm:
P_max = max(P_.max(), -P_.min())
P_ /= (P_max + 1e-5)
if options.varnorm:
P_var = np.var(P_)
P_ /= (np.sqrt(P_var) + 1e-5)
tbl_out.append("patches", P_)
in_h5.close()
tbl_out.close()
exit(0)
#============================================================
# Safe debug-output
zoom = 6
grid = U.transpose().reshape((D, size, size))
img = tiled_gfs(grid, sym_cm=False, global_cm=True)
img = img.resize((zoom * img.size[0], zoom * img.size[1]))
img.save(out_fname + "-components.png")
grid = P[:100, :, :]
img = tiled_gfs(grid, sym_cm=False, global_cm=False)
img = img.resize((zoom * img.size[0], zoom * img.size[1]))
img.save(out_fname + "-orig.png")
out_fname = "patches-%d" % size
out_tbl = AutoTable(out_fname+".h5")
images_h5 = tables.openFile(images_fname, "r")
images = images_h5.root.images
N_images = images.shape[0]
#ppi = (N_patches // N_images // 10) + 1
ppi = 4
for n in xrange(N_patches):
if n % 1000 == 0:
dlog.progress("Extracting patch %d" % n, n/N_patches)
if n % ppi == 0:
while True:
img = images[np.random.randint(N_images)]
img = img / img.max()
oversized_batch = pri.extract_patches_from_single_image(img, (oversize, oversize), ppi)
patches_batch = oversized_batch[:, (size//2):(size//2+size), (size//2):(size//2+size)]
variance = np.var( patches_batch.reshape([ppi, -1] ), axis=1)
if np.alltrue(variance > min_var):
break
out_tbl.append('oversized', oversized_batch[n%ppi])
out_tbl.append('patches', patches_batch[n%ppi])
out_tbl.close()
P_ = P_[left:right, left:right]
# Normalize and mean-free
if options.mf:
P_ -= P_.mean()
if options.norm:
P_max = max(P_.max(), -P_.min())
P_ /= (P_max+1e-5)
if options.varnorm:
P_var = np.var(P_)
P_ /= (np.sqrt(P_var)+1e-5)
tbl_out.append("patches", P_)
in_h5.close()
tbl_out.close()
exit(0)
#============================================================
# Safe debug-output
zoom = 6
grid = U.transpose().reshape( (D, size, size) )
img = tiled_gfs(grid, sym_cm=False, global_cm=True)
img = img.resize( (zoom*img.size[0], zoom*img.size[1]) )
img.save(out_fname+"-components.png")
grid = P[:100,:,:]
img = tiled_gfs(grid, sym_cm=False, global_cm=False)
img = img.resize( (zoom*img.size[0], zoom*img.size[1]) )
img.save(out_fname+"-orig.png")
out_tbl = AutoTable(out_fname + ".h5")
images_h5 = tables.openFile(images_fname, "r")
images = images_h5.root.images
N_images = images.shape[0]
#ppi = (N_patches // N_images // 10) + 1
ppi = 4
for n in xrange(N_patches):
if n % 1000 == 0:
dlog.progress("Extracting patch %d" % n, n / N_patches)
if n % ppi == 0:
while True:
img = images[np.random.randint(N_images)]
img = img / img.max()
oversized_batch = pri.extract_patches_from_single_image(
img, (oversize, oversize), ppi)
patches_batch = oversized_batch[:,
(size // 2):(size // 2 + size),
(size // 2):(size // 2 + size)]
variance = np.var(patches_batch.reshape([ppi, -1]), axis=1)
if np.alltrue(variance > min_var):
break
out_tbl.append('oversized', oversized_batch[n % ppi])
out_tbl.append('patches', patches_batch[n % ppi])
out_tbl.close()
