def filters_eq(image, use_acceleration=True):
if not use_acceleration:
from pyradar.filters.frost import frost_filter
from pyradar.filters.kuan import kuan_filter
from pyradar.filters.lee import lee_filter
from pyradar.filters.lee_enhanced import lee_enhanced_filter
from pyradar.filters.mean import mean_filter
else:
from pyradar.filters.accelerated.frost import frost_filter
from pyradar.filters.accelerated.kuan import kuan_filter
from pyradar.filters.accelerated.lee import lee_filter
from pyradar.filters.accelerated.lee_enhanced import lee_enhanced_filter
from pyradar.filters.accelerated.mean import mean_filter
from pyradar.filters.median import median_filter
# filters parameters
winsize = 15 # window size
k_value1 = 2.0 # damping factor for frost
k_value2 = 1.0 # damping factor for lee enhanced
cu_value = 0.25 # coefficient of variation of noise
cu_lee_enhanced = 0.523 # coefficient of variation for lee enhanced of noise
cmax_value = 1.73 # max coefficient of variation for lee enhanced
# frost filter
tic('Frost')
image_frost = frost_filter(image,
damping_factor=k_value1,
win_size=winsize)
toc()
tic('Kuan')
image_kuan = kuan_filter(image, win_size=winsize, cu=cu_value)
toc()
tic('Lee')
image_lee = lee_filter(image, win_size=winsize, cu=cu_value)
toc()
tic('Lee_ench')
image_lee_enhanced = lee_enhanced_filter(image,
win_size=winsize,
k=k_value2,
cu=cu_lee_enhanced,
cmax=cmax_value)
toc()
show_image([(image, 'original'), (image_frost, 'Frost'),
(image_kuan, 'Kuan'), (image_lee, 'Lee'),
(image_lee_enhanced, 'lee_e')])
# kuan filter
# tic('Kuan')
# toc()
# show_image([(image, 'original'), (image_kuan, 'Kuan') ])
# exit()
# # lee filter
exit()
# lee enhanced filter
show_image(image_lee_enhanced, 'Enchanced Lee')
def naive_eq(image):
# get actual range
input_range = image.min(), image.max()
# set new range
output_range = 0, 255
# equalize image
image_eq = naive_equalize_image(image, input_range, output_range)
# save image in current directory
show_image(image_eq, 'Naive equalization')
def naive_eq(image):
# get actual range
input_range = image.min(), image.max()
# set new range
output_range = 0, 255
# equalize image
image_eq = naive_equalize_image(image, input_range, output_range)
# save image in current directory
show_image(image_eq, 'Naive equalization')
def filters_eq(image, use_acceleration=True):
if not use_acceleration:
from pyradar.filters.frost import frost_filter
from pyradar.filters.kuan import kuan_filter
from pyradar.filters.lee import lee_filter
from pyradar.filters.lee_enhanced import lee_enhanced_filter
from pyradar.filters.mean import mean_filter
else:
from pyradar.filters.accelerated.frost import frost_filter
from pyradar.filters.accelerated.kuan import kuan_filter
from pyradar.filters.accelerated.lee import lee_filter
from pyradar.filters.accelerated.lee_enhanced import lee_enhanced_filter
from pyradar.filters.accelerated.mean import mean_filter
from pyradar.filters.median import median_filter
# filters parameters
winsize = 15 # window size
k_value1 = 2.0 # damping factor for frost
k_value2 = 1.0 # damping factor for lee enhanced
cu_value = 0.25 # coefficient of variation of noise
cu_lee_enhanced = 0.523 # coefficient of variation for lee enhanced of noise
cmax_value = 1.73 # max coefficient of variation for lee enhanced
# frost filter
tic('Frost')
image_frost = frost_filter(image, damping_factor=k_value1, win_size=winsize)
toc()
tic('Kuan')
image_kuan = kuan_filter(image, win_size=winsize, cu=cu_value)
toc()
tic('Lee')
image_lee = lee_filter(image, win_size=winsize, cu=cu_value)
toc()
tic('Lee_ench')
image_lee_enhanced = lee_enhanced_filter(image, win_size=winsize, k=k_value2,
cu=cu_lee_enhanced, cmax=cmax_value)
toc()
show_image([(image, 'original'), (image_frost, 'Frost'), (image_kuan, 'Kuan'), (image_lee, 'Lee'),
(image_lee_enhanced, 'lee_e')])
# kuan filter
# tic('Kuan')
# toc()
# show_image([(image, 'original'), (image_kuan, 'Kuan') ])
# exit()
# # lee filter
exit()
# lee enhanced filter
show_image(image_lee_enhanced, 'Enchanced Lee')
def iso(image):
# this should be placed at the top with all the imports
from pyradar.classifiers.isodata import isodata_classification
from pyradar.core.equalizers import equalization_using_histogram
from pyradar.core.sar import save_image
params = {"K": 15, "I": 100, "P": 2, "THETA_M": 10, "THETA_S": 0.1,
"THETA_C": 2, "THETA_O": 0.01}
# run Isodata
class_image = isodata_classification(image, parameters=params)
# equalize class image to 0:255
class_image_eq = equalization_using_histogram(class_image)
show_image(class_image_eq, 'isodata_classification')
def kmeans(image):
from pyradar.classifiers.kmeans import kmeans_classification
# number of clusters
k = 4
# max number of iterations
iter_max = 1000
# run K-Means
class_image = kmeans_classification(image, k, iter_max)
# equalize class image to 0:255
class_image_eq = equalization_using_histogram(class_image)
# save it
show_image(class_image_eq, 'isodata_classification')
def kmeans(image):
from pyradar.classifiers.kmeans import kmeans_classification
# number of clusters
k = 4
# max number of iterations
iter_max = 1000
# run K-Means
class_image = kmeans_classification(image, k, iter_max)
# equalize class image to 0:255
class_image_eq = equalization_using_histogram(class_image)
# save it
show_image(class_image_eq, 'isodata_classification')
def iso(image):
# this should be placed at the top with all the imports
from pyradar.classifiers.isodata import isodata_classification
from pyradar.core.equalizers import equalization_using_histogram
from pyradar.core.sar import save_image
params = {
"K": 15,
"I": 100,
"P": 2,
"THETA_M": 10,
"THETA_S": 0.1,
"THETA_C": 2,
"THETA_O": 0.01
}
# run Isodata
class_image = isodata_classification(image, parameters=params)
# equalize class image to 0:255
class_image_eq = equalization_using_histogram(class_image)
show_image(class_image_eq, 'isodata_classification')
def eq(image):
# equalize img to 0:255
image_eq = equalization_using_histogram(image)
# save img in current directory
show_image(image_eq, 'Equalized image via Hist')
def dud(image):
show_image(image, 'isodata_classification')
def eq(image):
# equalize img to 0:255
image_eq = equalization_using_histogram(image)
# save img in current directory
show_image(image_eq, 'Equalized image via Hist')
def dud(image):
show_image(image, 'isodata_classification')
