def confidence_map2d(data,
alpha=1.5,
beta=90,
gamma=0.03,
spacing=None,
data_mode='B',
solver_mode="cg"):
"""2d confidence map
# Args
data: 3d numpy array, RF mode data，(height, width)
mode: string, 'RF' or 'B' mode data
alpha: float, distance(vertical) penalty
beta: float, Random walks parameter
gamma: float, horizontal penalty
"""
data = data.astype('float')
data = normalize_data(data)
if data_mode == "RF":
data = np.abs(hilbert2(data))
labels = np.zeros_like(data)
labels[0, :] = 1 # 探头元素
labels[-1, :] = 2 # shadow元素
conf_map = confidence_map(data,
labels,
alpha,
beta,
gamma,
True,
mode=solver_mode,
spacing=spacing)
conf_map = conf_map[0, :, :]
return conf_map
def test_hilbert2(self, num_samps):
cpu_sig = np.random.rand(num_samps, num_samps)
gpu_sig = cp.asarray(cpu_sig)
cpu_hilbert2 = signal.hilbert2(cpu_sig)
gpu_hilbert2 = cp.asnumpy(cusignal.hilbert2(gpu_sig))
assert array_equal(cpu_hilbert2, gpu_hilbert2)
def drawPinholeMap(self ):
sig = self.WidePinHole[0:300, 0, 0] - np.mean(self.WidePinHole[:, 0, 0])
evp = signal.hilbert2(sig)
envelope = np.abs(evp)
# envelope = np.real( evp)
print "evp max =", np.max(envelope), "arg max =", np.argmax(envelope)
plt.close("all")
print "drawCurrentPinhole"
# plt.gray()
# ax = plt.gca()
z = plt.axvline(x=frameNo, linewidth=2, color='r')
# ax.add_artist(z)
plt.plot(envelope, color='r')
plt.plot(-envelope, color='g')
plt.plot(sig)
# plt.plot(self.WidePinHole[:, 1, 0])
plt.show()
from cStringIO import StringIO
buffer_ = StringIO()
plt.savefig(buffer_, format="png")
buffer_.seek(0)
image = Image.open(buffer_)
from PIL.ImageQt import ImageQt
qimage = ImageQt(image)
buffer_.close()
return qimage
def measure_energy1_2d(f):
hil = hilbert2(f)
energy2d = np.sqrt(np.square(np.real(hil)) + np.square(np.imag(hil)))
#phibar = np.arctan(h/f)
phibar = np.arctan2(np.imag(hil), np.real(hil))
return (energy2d, phibar)
def measure_energy2_2d(f, pbflag=True):
h = np.imag(hilbert2(f))
#phibar = np.arctan(h/f)
phibar = np.arctan2(h, f)
energy2d = np.multiply((np.cos(phibar) - np.abs(np.sin(phibar))), f)
if (pbflag):
return (energy2d, phibar)
else:
return (energy2d)
def hilbert2(x, N=None):
return signal.hilbert2(x, N=N)
def cpu_version(self, cpu_sig):
return signal.hilbert2(cpu_sig)
def hilb2(x): return hilbert2(fDesign(x)) return hilb2
def filterData(R_Data, L_Data):
R_filt = dsp.hilbert2(R_Data)
return R_filt
def fvecfilt(fce):
return hilbert2(filter_design(sf, timeL, n.array(fce),
filtname=filtname, cycle=cycle, order=order,
axis=axis)(x))
def hilb2(x):
return hilbert2(fDesign(x))
