plt.title('Fast Factorized Backprojection \n w/ multi-processing \n \
Runtime = %i s'%fbpmp_time)
imgTools.imshow(img_FFBP, dB_scale = [-25,0], extent = extent)
plt.xlabel('meters'); plt.ylabel('meters')
plt.tight_layout()
'''
# AFRL DSBP demo
###############################################################################
# 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=4)
# Create image plane dictionary
img_plane = imgTools.img_plane_dict(platform, res_factor=1.0, upsample=True, aspect=1.0)
# full backprojection
start = time()
img_bp = imgTools.backprojection(phs, platform, img_plane, taylor=17, upsample=2)
bp_time = time() - start
# Fast-factorized backprojection without multi-processing
start = time()
img_FFBP = imgTools.FFBP(phs, platform, img_plane, taylor=17, factor_max=2)
fbp_time = time() - start
# Fast-factorized backprojection with multi-processing
#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, start_az, pol=pol, 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,
prnt=50)
#img_pf = imgTools.polar_format(phs, platform, img_plane, taylor = 20)