n_hat=platform['n_hat'])
# Apply polar format algorithm to phase history data
# (Other options not available since platform position is unknown)
img_pf = imgTools.polar_format(phs_corr, platform, img_plane, taylor=30)
# Degrade image with random 10th order polynomial phase
coeff = (np.random.rand(10) - 0.5) * img_pf.shape[0]
x = np.linspace(-1, 1, img_pf.shape[0])
y = np.poly1d(coeff)(x)
slope, intercept, r_value, p_value, std_err = linregress(x, y)
line = slope * x + np.mean(y)
y = y - line
ph_err = np.tile(np.array([y]).T, (1, img_pf.shape[1]))
img_err = sig.ft(sig.ift(img_pf, ax=0) * np.exp(1j * ph_err), ax=0)
# Autofocus image
print('autofocusing')
img_af, af_ph = imgTools.autoFocus2(img_err, win='auto')
# img_af, af_ph = imgTools.autoFocus2(img_err, win = 0, win_params = [500,0.8])
# Output image
plt.figure()
plt.plot(x, y, x, af_ph)
plt.legend(['true error', 'estimated error'], loc='best')
plt.ylabel('Phase (radians)')
# Output image
plt.figure()
imgTools.imshow(img_af, dB_scale=[-45, 0])
platform = plat_dict()
#Create image plane dictionary
img_plane = imgTools.img_plane_dict(platform, aspect = 1)
#Simulate phase history, if needed
##############################################################################
nsamples = platform['nsamples']
npulses = platform['npulses']
x = img_plane['u']; y = img_plane['v']
points = [[0,0,0],
[0,-100,0],
[200,0,0]]
amplitudes = [1,1,1]
phs = phsTools.simulate_phs(platform, points, amplitudes)
##############################################################################
#Apply RVP correction
phs_corr = phsTools.RVP_correct(phs, platform)
#Demodulate phase history with constant reference, if needed
phs_fixed = phsTools.phs_to_const_ref(phs_corr, platform, upchirp = 1)
#Apply algorithm of choice to phase history data
img_pf = imgTools.polar_format(phs_corr, platform, img_plane, taylor = 17)
#img_wk = imgTools.omega_k(phs_fixed, platform, taylor = 17, upsample = 2)
#img_bp = imgTools.backprojection(phs_corr, platform, img_plane, taylor = 17, upsample = 2)
#Output image
imgTools.imshow(img_pf, dB_scale = [-25,0])
#Include SARIT toolset
from ritsar import phsRead
from ritsar import phsTools
from ritsar import imgTools
#Define directory containing *.au2 and *.phs files
directory = './data/Sandia/'
#Import phase history and create platform dictionary
[phs, platform] = phsRead.Sandia(directory)
#Correct for residual video phase
phs_corr = phsTools.RVP_correct(phs, platform)
#Import image plane dictionary from './parameters/img_plane'
img_plane = imgTools.img_plane_dict(platform,
res_factor=1.0,
n_hat=platform['n_hat'])
#Apply polar format algorithm to phase history data
#(Other options not available since platform position is unknown)
img_pf = imgTools.polar_format(phs_corr, platform, img_plane, taylor=30)
#Output image
imgTools.imshow(img_pf, [-45, 0])
if batch:
plt.savefig("Sandia_demo.png")
else:
plt.show()
from sys import path
path.append('../')
#Include SARIT toolset
from ritsar import phsRead
from ritsar import phsTools
from ritsar import imgTools
#Define directory containing *.au2 and *.phs files
directory = './data/DIRSIG/'
#Import phase history and create platform dictionary
[phs, platform] = phsRead.DIRSIG(directory)
#Correct for reisdual video phase
phs_corr = phsTools.RVP_correct(phs, platform)
#Demodulate phase history with constant reference, if needed
phs_fixed = phsTools.phs_to_const_ref(phs_corr, platform, upchirp = 1)
#Import image plane dictionary from './parameters/img_plane'
img_plane = imgTools.img_plane_dict(platform, res_factor = 1.0, aspect = 1.0)
#Apply polar format algorithm to phase history data
img_wk = imgTools.omega_k(phs_fixed, platform, taylor = 13, upsample = 2)
#img_bp = imgTools.backprojection(phs_corr, platform, img_plane, taylor = 13, upsample = 6)
#img_pf = imgTools.polar_format(phs_corr, platform, img_plane, taylor = 13)
#Output image
imgTools.imshow(img_wk, [-20,0])
##############################################################################
#Add include directories to default path list
from sys import path
path.append('../')
#Include SARIT toolset
from ritsar import phsRead
from ritsar import phsTools
from ritsar import imgTools
#Define directory containing *.au2 and *.phs files
directory = './data/Sandia/'
#Import phase history and create platform dictionary
[phs, platform] = phsRead.Sandia(directory)
#Correct for residual video phase
phs_corr = phsTools.RVP_correct(phs, platform)
#Import image plane dictionary from './parameters/img_plane'
img_plane = imgTools.img_plane_dict(platform,
res_factor = 1.0, n_hat = platform['n_hat'])
#Apply polar format algorithm to phase history data
#(Other options not available since platform position is unknown)
img_pf = imgTools.polar_format(phs_corr, platform, img_plane, taylor = 30)
#Output image
imgTools.imshow(img_pf, [-45,0])
#Include SARIT toolset
from ritsar import phsRead
from ritsar import imgTools
#Define top level directory containing *.mat file
#and choose polarization and starting azimuth
pol = 'HH'
directory = './data/AFRL/pass1'
start_az = 1
#Import phase history and create platform dictionary
[phs, platform] = phsRead.AFRL(directory, pol, start_az, n_az=3)
#Create image plane dictionary
img_plane = imgTools.img_plane_dict(platform,
res_factor=1.4,
upsample=True,
aspect=1.0)
#Apply algorithm of choice to phase history data
img_bp = imgTools.backprojection(phs,
platform,
img_plane,
taylor=20,
upsample=6)
#img_pf = imgTools.polar_format(phs, platform, img_plane, taylor = 20)
#Output image
imgTools.imshow(img_bp, dB_scale=[-30, 0])
plt.title('Backprojection')
fbp_time = time() - start
# Fast-factorized backprojection with multi-processing
start = time()
img_FFBP = imgTools.FFBPmp(phs, platform, img_plane, taylor=17, factor_max=2)
fbpmp_time = time() - start
# Output image
u = img_plane['u'];
v = img_plane['v']
extent = [u.min(), u.max(), v.min(), v.max()]
plt.subplot(2, 1, 1)
plt.title('Full Backprojection \n \
Runtime = %i s' % bp_time)
imgTools.imshow(img_bp, dB_scale=[-30, 0], extent=extent)
plt.xlabel('meters');
plt.ylabel('meters')
plt.subplot(2, 2, 3)
plt.title('Fast Factorized Backprojection \n w/o multi-processing \n \
Runtime = %i s' % fbp_time)
imgTools.imshow(img_FFBP, dB_scale=[-30, 0], extent=extent)
plt.xlabel('meters');
plt.ylabel('meters')
plt.subplot(2, 2, 4)
plt.title('Fast Factorized Backprojection \n w/ multi-processing \n \
Runtime = %i s' % fbpmp_time)
imgTools.imshow(img_FFBP, dB_scale=[-30, 0], extent=extent)
plt.xlabel('meters');
img_bp = imgTools.backprojection(phs, platform, img_plane, taylor = 17, upsample = 2)
bp_time = time()-start
#Fast-factorized backprojection
start = time()
img_FFBP = imgTools.FFBP(phs, platform, img_plane, taylor = 17, factor_max = 2)
fbp_time = time()-start
#Output image
u = img_plane['u']; v = img_plane['v']
extent = [u.min(), u.max(), v.min(), v.max()]
plt.subplot(1,2,1)
plt.title('Full Backprojection \n \
Runtime = %i s'%bp_time)
imgTools.imshow(img_bp, dB_scale = [-30,0], extent = extent)
plt.xlabel('meters'); plt.ylabel('meters')
plt.subplot(1,2,2)
plt.title('Fast Factorized Backprojection \n \
Runtime = %i s'%fbp_time)
imgTools.imshow(img_FFBP, dB_scale = [-30,0], extent = extent)
plt.xlabel('meters'); plt.ylabel('meters')
plt.tight_layout()
'''
#DIRSIG DSBP demo
###############################################################################
#Define directory containing *.au2 and *.phs files
directory = './data/DIRSIG/'
#Import phase history and create platform dictionary
#Create platform dictionary platform = plat_dict() #Create image plane dictionary img_plane = imgTools.img_plane_dict(platform, aspect=1) #Simulate phase history, if needed ############################################################################## nsamples = platform['nsamples'] npulses = platform['npulses'] x = img_plane['u'] y = img_plane['v'] points = [[0, 0, 0], [0, -100, 0], [200, 0, 0]] amplitudes = [1, 1, 1] phs = phsTools.simulate_phs(platform, points, amplitudes) ############################################################################## #Apply RVP correction phs_corr = phsTools.RVP_correct(phs, platform) #Demodulate phase history with constant reference, if needed phs_fixed = phsTools.phs_to_const_ref(phs_corr, platform, upchirp=1) #Apply algorithm of choice to phase history data img_pf = imgTools.polar_format(phs_corr, platform, img_plane, taylor=17) #img_wk = imgTools.omega_k(phs_fixed, platform, taylor = 17, upsample = 2) #img_bp = imgTools.backprojection(phs_corr, platform, img_plane, taylor = 17, upsample = 2) #Output image imgTools.imshow(img_pf, dB_scale=[-25, 0])
center=[200, 0, 0],
size=[N, N],
n=8)
#Output image
du = img_plane['du']
dv = img_plane['dv']
#u = img_plane['u']; v = img_plane['v']
u = np.arange(-N / 2, N / 2) * du
v = np.arange(-N / 2, N / 2) * dv
extent = [u.min(), u.max(), v.min(), v.max()]
plt.subplot(1, 2, 1)
plt.title('Full Backprojection')
imgTools.imshow(img_bp[1024 - N / 2:1024 + N / 2, 1315 - N / 2:1315 + N / 2],
dB_scale=[-25, 0],
extent=extent)
plt.xlabel('meters')
plt.ylabel('meters')
plt.subplot(1, 2, 2)
plt.title('Digital Spotlight Backprojection')
imgTools.imshow(img_DSBP, dB_scale=[-25, 0], extent=extent)
plt.xlabel('meters')
plt.ylabel('meters')
plt.tight_layout()
#%%
#AFRL DSBP demo
###############################################################################
#Apply algorithm of choice to phase history data
img_bp = imgTools.backprojection(phs, platform, img_plane, taylor = 17, upsample = 2)
N = 128
img_DSBP = imgTools.DSBP(phs, platform, img_plane, center = [-15-0.6,22-0.4,0], size = [N,N])
#Output image
du = img_plane['du']; dv = img_plane['dv']
#u = img_plane['u']; v = img_plane['v']
u = np.arange(-N/2,N/2)*du
v = np.arange(-N/2,N/2)*dv
extent = [u.min(), u.max(), v.min(), v.max()]
plt.subplot(1,2,1)
plt.title('Full Backprojection')
imgTools.imshow(img_bp[177-N/2:177+N/2,202-N/2:202+N/2], dB_scale = [-25,0], extent = extent)
plt.xlabel('meters'); plt.ylabel('meters')
plt.subplot(1,2,2)
plt.title('Digital Spotlight Backprojection')
imgTools.imshow(img_DSBP, dB_scale = [-25,0], extent = extent)
plt.xlabel('meters'); plt.ylabel('meters')
plt.tight_layout()
'''
#DIRSIG DSBP demo
###############################################################################
#Define directory containing *.au2 and *.phs files
directory = './data/DIRSIG/'
#Import phase history and create platform dictionary
[phs, platform] = phsRead.DIRSIG(directory)
img_plane,
center=[-15 - 0.6, 22 - 0.4, 0],
size=[N, N])
#Output image
du = img_plane['du']
dv = img_plane['dv']
#u = img_plane['u']; v = img_plane['v']
u = np.arange(-N / 2, N / 2) * du
v = np.arange(-N / 2, N / 2) * dv
extent = [u.min(), u.max(), v.min(), v.max()]
plt.subplot(1, 2, 1)
plt.title('Full Backprojection')
imgTools.imshow(img_bp[177 - N // 2:177 + N // 2, 202 - N // 2:202 + N // 2],
dB_scale=[-25, 0],
extent=extent)
plt.xlabel('meters')
plt.ylabel('meters')
plt.subplot(1, 2, 2)
plt.title('Digital Spotlight Backprojection')
imgTools.imshow(img_DSBP, dB_scale=[-25, 0], extent=extent)
plt.xlabel('meters')
plt.ylabel('meters')
plt.tight_layout()
if batch:
plt.savefig("DSBP_demo.png")
else:
plt.show()
'''
img_plane = imgTools.img_plane_dict(platform,
res_factor = 1.0, n_hat = platform['n_hat'])
#Apply polar format algorithm to phase history data
#(Other options not available since platform position is unknown)
img_pf = imgTools.polar_format(phs_corr, platform, img_plane, taylor = 30)
#Degrade image with random 10th order polynomial phase
coeff = (np.random.rand(10)-0.5)*img_pf.shape[0]
x = np.linspace(-1,1,img_pf.shape[0])
y = np.poly1d(coeff)(x)
slope, intercept, r_value, p_value, std_err = linregress(x,y)
line = slope*x+np.mean(y)
y = y-line
ph_err = np.tile(np.array([y]).T,(1,img_pf.shape[1]))
img_err = sig.ft(sig.ift(img_pf,ax=0)*np.exp(1j*ph_err),ax=0)
#Autofocus image
print('autofocusing')
img_af, af_ph = imgTools.autoFocus2(img_err, win = 'auto')
#img_af, af_ph = imgTools.autoFocus2(img_err, win = 0, win_params = [500,0.8])
#Output image
plt.figure()
plt.plot(x,y,x,af_ph); plt.legend(['true error','estimated error'], loc = 'best')
plt.ylabel('Phase (radians)')
#Output image
plt.figure()
imgTools.imshow(img_af, dB_scale = [-45,0])
#Add include directories to default path list
from sys import path
path.append('../')
#Include standard library dependencies
import matplotlib.pylab as plt
#Include SARIT toolset
from ritsar import phsRead
from ritsar import imgTools
#Define top level directory containing *.mat file
#and choose polarization and starting azimuth
pol = 'HH'
directory = './data/AFRL/pass1'
start_az = 1
#Import phase history and create platform dictionary
[phs, platform] = phsRead.AFRL(directory, pol, start_az, n_az = 3)
#Create image plane dictionary
img_plane = imgTools.img_plane_dict(platform, res_factor = 1.4, upsample = True, aspect = 1.0)
#Apply algorithm of choice to phase history data
img_bp = imgTools.backprojection(phs, platform, img_plane, taylor = 20, upsample = 6)
#img_pf = imgTools.polar_format(phs, platform, img_plane, taylor = 20)
#Output image
imgTools.imshow(img_bp, dB_scale = [-30,0])
plt.title('Backprojection')
