out_fname = "patches-%d-dog" % size
if options.mf:
out_fname += "-mf"
if options.norm:
out_fname += "-norm"
if options.varnorm:
out_fname += "-varnorm"
#
print "Input file: %s" % in_fname
print "Output file: %s" % out_fname
print "# of patches: %d" % N_patches
print "Patch size : %d x %d" % (size, size)
# Create output file
tbl_out = AutoTable(out_fname + ".h5")
# Size magic
left = (oversize // 2) - (size // 2)
right = left + size
#============================================================
# Start to do some real work
batch_size = 1000
dog = DoG(1., 3., 9)
for n in xrange(0, N_patches):
if n % batch_size == 0:
dlog.progress("Preprocessing...", n / N_patches)
P = in_oversized[n, :, :]
out_fname = "patches-%d-dog" % size
if options.mf:
out_fname += "-mf"
if options.norm:
out_fname += "-norm"
if options.varnorm:
out_fname += "-varnorm"
#
print "Input file: %s" % in_fname
print "Output file: %s" % out_fname
print "# of patches: %d" % N_patches
print "Patch size : %d x %d" % (size, size)
# Create output file
tbl_out = AutoTable(out_fname+".h5")
# Size magic
left = (oversize // 2)-(size //2)
right = left + size
#============================================================
# Start to do some real work
batch_size = 1000
dog = DoG(1., 3., 9)
for n in xrange(0, N_patches):
if n % batch_size == 0:
dlog.progress("Preprocessing...", n/N_patches)
P = in_oversized[n,:,:]
out_fname = "patches-%d-zca3" % size
if options.mf:
out_fname += "-mf"
if options.norm:
out_fname += "-norm"
if options.varnorm:
out_fname += "-varnorm"
#
print "Input file: %s" % in_fname
print "Output file: %s" % out_fname
print "# of patches: %d" % N_patches
print "Patch size : %d x %d" % (size, size)
# Create output file
tbl_out = AutoTable(out_fname + ".h5")
# Internal parameters
batch_size = 1000
epsilon = 1e-3
D = size**2
dim = D
#============================================================
# Start to do some real work
dlog.progress("Loading patches...")
P = in_patches[:N_patches, :, :].reshape(N_patches, -1)
P_mean = P.mean(axis=0)
# Create covariance matrix
out_fname = "patches-%d-zca3" % size
if options.mf:
out_fname += "-mf"
if options.norm:
out_fname += "-norm"
if options.varnorm:
out_fname += "-varnorm"
#
print "Input file: %s" % in_fname
print "Output file: %s" % out_fname
print "# of patches: %d" % N_patches
print "Patch size : %d x %d" % (size, size)
# Create output file
tbl_out = AutoTable(out_fname+".h5")
# Internal parameters
batch_size = 1000
epsilon = 1e-3
D = size**2
dim = D
#============================================================
# Start to do some real work
dlog.progress("Loading patches...")
P = in_patches[:N_patches,:,:].reshape(N_patches, -1)
P_mean = P.mean(axis=0)
# Create covariance matrix
#=============================================================================
if __name__ == "__main__":
if len(sys.argv) != 3:
print "Usage: %s <images.h5> <size>" % sys.argv[0]
exit(1)
images_fname = sys.argv[1]
size = int(sys.argv[2])
oversize = 2*size
N_patches = 1000000
min_var = 0.0001
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()
import pulp.preproc.image as pri
#=============================================================================
if __name__ == "__main__":
if len(sys.argv) != 3:
print "Usage: %s <images.h5> <size>" % sys.argv[0]
exit(1)
images_fname = sys.argv[1]
size = int(sys.argv[2])
oversize = 2 * size
N_patches = 1000000
min_var = 0.0001
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()
