def bessel_rotate(image_org, theta, mask = False, smooth = False, mode = 1):
image = image_org.copy()
if(mask):
image = circle_mask(image, smooth, mode)
Ib = np.zeros(image.shape)
theta = to_radian(theta)
s = (image.shape[0]-1)/2.
x = np.linspace(-s, s, image.shape[1])
y = np.linspace(-s, s, image.shape[0])
xx, yy = np.meshgrid(x,y)
rM = np.array([[np.cos(theta), -np.sin(theta)],[np.sin(theta), np.cos(theta)]])
for i in np.arange(-s,s+1):
for j in np.arange(-s,s+1):
new_x = np.dot(rM, np.array([i,j]))
if(np.sum(abs(np.round(new_x,5))>s)):
Ib[i+s,j+s] = 0
else:
R = np.sqrt((xx-new_x[1])**2 + (yy-new_x[0])**2)
mask_R = (R == 0)
Bess = np.zeros(R.shape)
Bess[~mask_R] = scipy.special.j1(np.pi*R[~mask_R])*hann(R[~mask_R],image.shape[0]*mode)/(np.pi*R[~mask_R])
Bess[mask_R] = 0.5
Bess = Bess/np.sum(Bess)
tmp = image*Bess
Ib[i+s,j+s] = np.sum(tmp) #np.round(np.sum(tmp),10)
return Ib
def bessel_rotate_halton(image, theta, x1, y1):
Ib = []
theta = to_radian(theta)
s = (image.shape[0]-1)/2.
rM = np.array([[np.cos(theta), -np.sin(theta)],[np.sin(theta), np.cos(theta)]])
x = []
for i in np.arange(-s,s+1):
for j in np.arange(-s,s+1):
x.append(np.dot(rM, np.array([i,j])))
x = np.array(x)
for idx in xrange(len(x1)):
R = np.sqrt((x[:,1]-x1[idx])**2 + (x[:,0]-y1[idx])**2)
mask_R = (R == 0)
Bess = np.zeros(R.shape)
Bess[~mask_R] = scipy.special.j1(np.pi*R[~mask_R])*hann(R[~mask_R],image.shape[0])/(np.pi*R[~mask_R])
#Bess[~mask_R] = scipy.special.j1(np.pi*R[~mask_R])/(np.pi*R[~mask_R])
Bess[mask_R] = 0.5
Bess = Bess/np.sum(Bess)
tmp = image.ravel()*Bess
Ib.append(np.sum(tmp))
return np.array(Ib)
import numpy as np from utils import hann, linear_2D_array fft_size = 512 # fft size fft_hop = 8 # hop size in stft fft_zp = 512 # zero padding in stft analysis_window = np.concatenate((hann(fft_size), np.zeros(fft_zp))) t_cut = 0.83 # length in [s] 用于消除无信号的片段 # 仿真参数 Fs = 8000 t0 = 1./(Fs*np.pi*1e-2) # 开始时间 absorption = 0.1 # 墙面的吸收程度 0-1, 越小表述多径效应越明显 max_order_sim = 2 sigma2_n = 5e-7 # 环境噪声 # 麦克风阵列的几何结构 mic1 = np.array([2, 1.5]) # 坐标 M = 8 # 麦克风数量 d = 0.08 # 麦克风间隔 8cm phi = 0. # 摆放角度为水平 max_order_design = 1 # 仿真相关的参数 Lg_t = 0.100 # 滤波器的窗口长度 时域上为0.1s Lg = np.ceil(Lg_t*Fs) # 滤波器的窗口长度 具体的阶数 delay = 0.050 # 仿真beamforming的延迟 N = 1024
