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sas_tools.py
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sas_tools.py
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import numpy as np
import scipy.stats
import ritsar.signal as sig
from PIL import Image
#--------------------------------------------------------------------------------------
# Adapted from the autoFocus2 function originally written by Douglas Macdonald
# as part of the RITSAR Python package located at https://github.com/dm6718/RITSAR and
# https://github.com/dm6718/RITSAR/blob/master/ritsar/imgTools.py.
#
# We modified the phase gradient estimate to use the ML estimate from:
# Jakowatz, Charles V., and Daniel E. Wahl. "Eigenvector method for maximum-likelihood
# estimation of phase errors in synthetic-aperture-radar imagery."
# JOSA A 10.12 (1993): 2539-2546.
#
# Flag for shadow PGA from:
# Prater, et al. J Prater, D Bryner, and S Synnes. "SHADOW BASED PHASE GRADIENT
# AUTOFOCUS FOR SYNTHETIC APERTURE SONAR." 5th annual Institute of Acoustics
# SAS/SAR Conference. Lerici, Italy. 2023.
#
# Assumes SLC azimuth is vertical dimension and range increases left to right
# along the horizontal dimension.
#
# "np" is the numpy package.
# "sig" is the signal package from RITSAR.
#--------------------------------------------------------------------------------------
def pga(img, win = 'auto', win_params = [100, 0.5], shadow_pga = False):
#Derive parameters
npulses = int(img.shape[0])
nsamples = int(img.shape[1])
#Initialize loop variables
img_af = 1.0*img
max_iter = 30
af_ph = 0
rms = []
#Compute phase error and apply correction
for iii in range(max_iter):
#Find brightest azimuth sample in each range bin
if shadow_pga:
index = np.argsort(np.abs(img_af), axis=0)[0]
else:
index = np.argsort(np.abs(img_af), axis=0)[-1]
#Circularly shift image so max values line up
f = np.zeros(img.shape)+0j
for i in range(nsamples):
f[:,i] = np.roll(img_af[:,i], int(npulses/2-index[i]))
if win == 'auto':
#Compute window width
s = np.sum(f*np.conj(f), axis = -1)
s = 10*np.log10(s/s.max())
# For first iteration, use all azimuth data
if iii == 0:
width = npulses
# For second iteration, use half azimuth data
elif iii == 1:
width = npulses // 2
#For all other iterations, use twice the 10 dB threshold
else:
width = np.sum(s>-10)
window = np.arange(npulses/2-width/2,npulses/2+width/2)
else:
#Compute window width using win_params if win not set to 'auto'
width = int(win_params[0]*win_params[1]**iii)
window = np.arange(npulses/2-width/2,npulses/2+width/2)
if width<5:
break
window = window.astype('int')
#Window image
g = np.zeros(img.shape)+0j
g[window] = f[window]
#Fourier Transform
G = sig.ift(g, ax=0)
# ML method
phi_dot = np.angle(np.sum(np.conj(G[:-1, :]) * G[1:, :], axis=1))
phi = np.concatenate([[0], np.cumsum(phi_dot)])
phi = np.unwrap(phi)
#Remove linear trend
t = np.arange(0,nsamples)
slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(t,phi)
line = slope*t + intercept
phi = phi - line
if shadow_pga:
phi = -phi
rms.append(np.sqrt(np.mean(phi**2)))
if win == 'auto':
if rms[iii]<0.01:
break
#Apply correction
phi2 = np.tile(np.array([phi]).T,(1,nsamples))
IMG_af = sig.ift(img_af, ax=0)
IMG_af = IMG_af*np.exp(-1j*phi2)
img_af = sig.ft(IMG_af, ax=0)
#Store phase
af_ph += phi
print('number of iterations: {}'.format(iii+1))
return(img_af, np.flip(af_ph), rms)
#-----------------------------------------------------------------------------------------------------------------------------------------------------
# C Schlick Rational Tone Mapping Operator
# targetBrightness is 0,1.
def schlick(L, targetBrightness = 0.3, medianFlag = True):
L = np.squeeze(L)
if np.iscomplex(L).sum() > 0:
L = np.abs(L)
L = normalize(L.astype('float32'))
# determine b
if medianFlag:
m = np.median(L[np.where(L>0)])
else:
m = np.sqrt(np.sum(L**2) / (2*np.prod(L.shape)))
#
if np.isnan(m):
return np.zeros_like(L)
b = (targetBrightness - targetBrightness * m) / (m - targetBrightness * m)
b = np.clip(b, 1, 99999999)
# apply b
L = (b*L) / ((b-1)*L + 1 + 1e-9)
return L
#-----------------------------------------------------------------------------------------------------------------------------------------------------
def imwrite(mat, filename, normalize_data=True):
if mat.ndim == 3:
mat = mat[:,:,0:3]
if normalize_data:
mat = (normalize(mat)*255).astype('uint8')
else: # grayscale
if normalize_data:
mat = (normalize(mat)*255).astype('uint8')
else:
mat = (mat*255).astype('uint8')
#
img = Image.fromarray(mat)
img.save(filename)
return
#-----------------------------------------------------------------------------------------------------------------------------------------------------
# Normalizes array to [0,1]
def normalize(arr):
arr -= arr.min()
arr /= (arr.max() + 1e-9)
return arr
#-----------------------------------------------------------------------------------------------------------------------------------------------------
def get_fig_as_numpy(fig):
import io
buf = io.BytesIO()
fig.savefig(buf, format='png', bbox_inches='tight', pad_inches=0)
buf.seek(0)
# Open the image with PIL and convert to NumPy array
image = Image.open(buf)
image_array = np.array(image)
# Close the BytesIO object
buf.close()
# Resize the image to 256x256 using PIL
resized_image = Image.fromarray(image_array).resize((512, 512), Image.ANTIALIAS)
resized_image_array = np.array(resized_image)
return resized_image_array