def test_project():
N=100
x=modified_shepp_logan((N,N,3))[:,:,1]
x=numpy.array(x)
rp=ref.project(x)
ip=opencl.project(x)
assert is_equal(rp,ip)
def generate_phantom_sinogram(size, nang):
dir_name = get_strorage_directory(size, nang)
phantom_image = os.path.join(dir_name, 'phantom.png')
sinogramm_image = os.path.join(dir_name, 'sinogramm.png')
phantom_file = os.path.join(dir_name, 'phantom.txt')
sinogramm_file = os.path.join(dir_name, 'sinogramm.txt')
if not os.path.isdir(dir_name):
ph = phantom.modified_shepp_logan(shape=(size, size, 3))[:, :, 1]
ph = np.array(ph, dtype='float32')
angles = np.arange(0, 180, 180.0 / nang).astype('float32')
s, a = genrate_sinogam(ph, angles)
try:
os.mkdir(dir_name)
except OSError:
pass
imsave(phantom_image, ph, cmap=plt.cm.Greys_r)
imsave(sinogramm_image, s, cmap=plt.cm.Greys_r)
np.savetxt(phantom_file, ph)
np.savetxt(os.path.join(dir_name, 'ang.txt'), a)
np.savetxt(sinogramm_file, s)
return {'phantom': phantom_file, 'sinogramm': sinogramm_file,
'sinogramm_image': sinogramm_image, 'phantom_image': phantom_image}
def test_backproject():
N=50
x=modified_shepp_logan((N,N,3))[:,int(N/2),1]
x=numpy.array(x)
rbp=ref.back_project(x)
obp=ocv.back_project(x)
assert is_equal(rbp,obp)
def test_project():
N=50
x=modified_shepp_logan((N,N,3))[:,:,1]
x=numpy.array(x)
rp=ref.project(x)
op=ocv.project(x)
assert is_equal(rp,op)
def test_project():
N=50
x=modified_shepp_logan((N,N,3))[:,:,1]
x=numpy.array(x)
rp=ref.project(x)
ip=ispmd.project(x)
# import pylab
# pylab.figure()
# pylab.subplot(211)
# pylab.plot(rp)
# pylab.subplot(212)
# pylab.plot(ip)
# pylab.show()
assert is_equal(rp,ip)
def test_backproject():
N=50
x=modified_shepp_logan((N,N,3))[:,int(N/2),1]
x=numpy.array(x)
rbp=ref.back_project(x)
ibp=ispmd.back_project(x)
# pylab.figure()
# pylab.subplot(121)
# pylab.imshow(ibp)
# pylab.colorbar()
# pylab.subplot(122)
# pylab.imshow(rbp)
# pylab.colorbar()
# pylab.show()
assert is_equal(rbp,ibp)
def bencmark_backprojection(sizes):
bencmark_res={}
counts=10
functions={'ocv.back_project':ocv.back_project,
'ispmd.back_project':ispmd.back_project,
'ref.back_project':ref.back_project}
for size in sizes:
x=phantom.modified_shepp_logan((size,size,3))[:,:,1]
x=ref.project(x)
for func_name in functions:
func=functions[func_name]
t=time.time()
for c in range(counts):
y=func(x)
btime=(time.time()-t)/counts
if not func_name in bencmark_res:
bencmark_res[func_name]=[]
bencmark_res[func_name].append({'size':size,'time':btime})
return bencmark_res
def test_rotate_square():
N=501
x=modified_shepp_logan((N,N,3))[:,:,1]
x=numpy.array(x)
for angle in [0.0, 10.0,45.0,90.0,150.0,180.0,210.0]:
r_rot=ref.rotate_square(x,angle)
o_rot=ocv.rotate_square(x,angle)
mssim=MSSIM(r_rot,o_rot,16)
print angle,mssim.min()
if not (numpy.min(mssim)>0.90).all():
pylab.figure()
pylab.subplot(121)
pylab.imshow(mssim)
pylab.colorbar()
pylab.subplot(122)
pylab.imshow(r_rot-o_rot)
pylab.colorbar()
pylab.show()
assert False
def test_rotate_square():
N=501
x=modified_shepp_logan((N,N,3))[:,:,1]
x=numpy.array(x)
for angle in [0.0, 10.0,45.0,90.0,150.0,180.0,210.0]:
r_rot=ref.rotate_square(x,angle)
i_rot=ispmd.rotate_square(x,angle)
mssim=MSSIM(r_rot,i_rot,16)
mssim=scipy.ndimage.median_filter(mssim,3)
print angle,mssim.min()
if not (numpy.min(mssim)>0.9).all():
pylab.figure()
pylab.subplot(121)
pylab.imshow(mssim)
pylab.colorbar()
pylab.subplot(122)
pylab.imshow(r_rot-i_rot)
pylab.colorbar()
pylab.show()
assert False
def test_sart():
size = 1024
angles = numpy.arange(0, 180, 1.0, dtype="float32")
x = modified_shepp_logan((size, size, 3))[:, :, 1]
x = numpy.array(x)
sinogram, angles = genrate_sinogam_ref(x, angles)
t = time.time()
for i in range(32):
res = sart(sinogram, angles)
print "Tomographic reconstruction: " + str(time.time() - t)
import pylab
pylab.subplot(121)
pylab.imshow(x)
pylab.colorbar()
pylab.subplot(122)
pylab.imshow(res, vmin=0, vmax=1)
pylab.colorbar()
pylab.show()
def bencmark_rotation(sizes,angles):
bencmark_res={}
counts=10
functions={'ocv.rotate_square':ocv.rotate_square,
'ispmd.rotate_square':ispmd.rotate_square,
# 'ref.rotate_square':ref.rotate_square
}
for size in sizes:
x=phantom.modified_shepp_logan((size,size,3))[:,:,1]
x=numpy.array(x)
for func_name in functions:
func=functions[func_name]
for angle in angles:
t=time.time()
for c in range(counts):
y=func(x,angle)
btime=(time.time()-t)/counts
if not func_name in bencmark_res:
bencmark_res[func_name]=[]
bencmark_res[func_name].append({'size':size,'time':btime,'angle':angle})
return bencmark_res
