def main():
args = sys.argv[1:]
if len(args) < 3:
print >> sys.stderr, 'check_dist.py a.png b.png out.pfm'
sys.exit(1)
if use_c:
pfm_arg = args[2]
if os.path.splitext(pfm_arg)[1] == '.mat':
nnf = read_mat(pfm_arg)
if nnf.shape[2] != 3:
nnf = numpy.dstack(
(nnf, numpy.zeros((nnf.shape[0], nnf.shape[1]))))
pfm_arg = '_temp_dist.pfm'
pfm.writepfm(nnf, pfm_arg)
ans = subprocess.check_output('./patchtable check_dist %s %s %s' %
(args[0], args[1], pfm_arg),
shell=True)
sys.stdout.write(ans)
return
a = skimage.img_as_float(skimage.io.imread(args[0]))
b = skimage.img_as_float(skimage.io.imread(args[1]))
if os.path.splitext(args[2])[1] == '.pfm':
nnf = pfm.readpfm(args[2])
else:
nnf = read_mat(args[2])
beh = b.shape[0] - patch_w + 1
bew = b.shape[1] - patch_w + 1
nnf_h = a.shape[0] - patch_w + 1
nnf_w = a.shape[1] - patch_w + 1
dimg = numpy.zeros((nnf_h, nnf_w))
for ay in range(nnf_h):
for ax in range(nnf_w):
bx = nnf[ay, ax, 0]
by = nnf[ay, ax, 1]
assert 0 <= bx < bew, (bx, bew)
assert 0 <= by < beh, (by, beh)
apatch = a[ay:ay + patch_w, ax:ax + patch_w, :].flatten()
bpatch = b[by:by + patch_w, bx:bx + patch_w, :].flatten()
d = (apatch - bpatch)
d = numpy.sqrt(numpy.sum(d * d))
dimg[ay, ax] = d
#d_nnf = numpy.sqrt(nnf[ay,ax,2])
#delta = abs(d-d_nnf)
#if delta > 1e-4:
# print 'error:', ay, ax, d, dcorrect
#print '0,0 nnf:', nnf[0,0,:]
#print '0,0 correct distance:', dimg[0,0]
#print '%d,0 nnf:'%(nnf_w-1), nnf[0,nnf_w-1,:]
#print '%d,0 correct distance:'%(nnf_w-1), dimg[0,nnf_w-1]
print numpy.mean(dimg.flatten())
def main():
args = sys.argv[1:]
if len(args) < 3:
print >> sys.stderr, 'check_dist.py a.png b.png out.pfm'
sys.exit(1)
if use_c:
pfm_arg = args[2]
if os.path.splitext(pfm_arg)[1] == '.mat':
nnf = read_mat(pfm_arg)
if nnf.shape[2] != 3:
nnf = numpy.dstack((nnf, numpy.zeros((nnf.shape[0], nnf.shape[1]))))
pfm_arg = '_temp_dist.pfm'
pfm.writepfm(nnf, pfm_arg)
ans = subprocess.check_output('./patchtable check_dist %s %s %s' % (args[0], args[1], pfm_arg), shell=True)
sys.stdout.write(ans)
return
a = skimage.img_as_float(skimage.io.imread(args[0]))
b = skimage.img_as_float(skimage.io.imread(args[1]))
if os.path.splitext(args[2])[1] == '.pfm':
nnf = pfm.readpfm(args[2])
else:
nnf = read_mat(args[2])
beh = b.shape[0]-patch_w+1
bew = b.shape[1]-patch_w+1
nnf_h = a.shape[0]-patch_w+1
nnf_w = a.shape[1]-patch_w+1
dimg = numpy.zeros((nnf_h, nnf_w))
for ay in range(nnf_h):
for ax in range(nnf_w):
bx = nnf[ay,ax,0]
by = nnf[ay,ax,1]
assert 0 <= bx < bew, (bx, bew)
assert 0 <= by < beh, (by, beh)
apatch = a[ay:ay+patch_w, ax:ax+patch_w, :].flatten()
bpatch = b[by:by+patch_w, bx:bx+patch_w, :].flatten()
d = (apatch-bpatch)
d = numpy.sqrt(numpy.sum(d*d))
dimg[ay,ax] = d
#d_nnf = numpy.sqrt(nnf[ay,ax,2])
#delta = abs(d-d_nnf)
#if delta > 1e-4:
# print 'error:', ay, ax, d, dcorrect
#print '0,0 nnf:', nnf[0,0,:]
#print '0,0 correct distance:', dimg[0,0]
#print '%d,0 nnf:'%(nnf_w-1), nnf[0,nnf_w-1,:]
#print '%d,0 correct distance:'%(nnf_w-1), dimg[0,nnf_w-1]
print numpy.mean(dimg.flatten())
def make_prev_nnf():
prev_nnf_x = numpy.asarray(numpy.random.randint(bew, size=(aeh, aew)), float)
prev_nnf_y = numpy.asarray(numpy.random.randint(beh, size=(aeh, aew)), float)
if use_allowed:
for y in range(aeh):
for x in range(aew):
while not allowed[prev_nnf_y[y,x], prev_nnf_x[y,x], 0]:
prev_nnf_y[y,x] = random.randrange(beh)
prev_nnf_x[y,x] = random.randrange(bew)
prev_nnf_dist = numpy.zeros((aeh, aew))
prev_nnf = numpy.dstack((prev_nnf_x, prev_nnf_y, prev_nnf_dist))
prev_nnf_filename = 'prev_nnf.pfm'
pfm.writepfm(prev_nnf, prev_nnf_filename)
return prev_nnf_filename
