def fun_rcdt_single(self, I):
# I: (width, height)
radoncdt = RadonCDT(self.thetas)
Ircdt = radoncdt.forward(x0_range,
self.template / np.sum(self.template),
x_range, I / np.sum(I), self.rm_edge)
return Ircdt
def fun_rcdt_single(self, I):
# I: (rows, columns)
radoncdt = RadonCDT(self.thetas)
template = np.ones(I.shape, dtype=I.dtype)
Ircdt = radoncdt.forward(x0_range, template / np.sum(template),
x_range, I / np.sum(I), self.rm_edge)
return Ircdt
def forward_seq(self, X, template):
self.template = template
radoncdt = RadonCDT(self.thetas)
x_hat = []
for i in range(X.shape[0]):
x_hat.append(
radoncdt.forward(x0_range,
self.template / np.sum(self.template),
x_range, X[i, :] / np.sum(X[i, :]),
self.rm_edge))
return np.asarray(x_hat)
input_size = int(sys.argv[1])
eps = 1e-6
x0_range = [0, 1]
x_range = [0, 1]
Rdown = 4 # downsample radon projections (w.r.t. angles)
theta = np.linspace(0, 176, 180 // Rdown)
radoncdt = RadonCDT(theta)
I = np.random.rand(input_size, input_size)
template = np.ones(I.shape, dtype=I.dtype)
high.start_counters([
events.PAPI_FP_OPS,
])
rcdt = radoncdt.forward(x0_range, template / np.sum(template), x_range,
I / np.sum(I), False)
print(high.stop_counters()[0] / 1e9)
def discrete_radon_transform(image, steps):
R = np.zeros((steps, image.shape[0]), dtype=np.float32)
for s in range(steps):
rotation = rotate(image, -s * 180 / steps).astype(np.float32)
R[s] = np.sum(rotation, axis=1)
return R
I = np.random.rand(input_size, input_size)
high.start_counters([
events.PAPI_FP_OPS,
])