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 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')
# filters parameters
# window size
winsize = 9
# damping factor for frost
k_value1 = 2.0
# damping factor for lee enhanced
k_value2 = 1.0
# coefficient of variation of noise
cu_value = 0.25
# coefficient of variation for lee enhanced of noise
cu_lee_enhanced = 0.523
# max coefficient of variation for lee enhanced
cmax_value = 1.73
# frost filter
image_frost = frost_filter(image, damping_factor=k_value1, win_size=winsize)
# kuan filter
image_kuan = kuan_filter(image, win_size=winsize, cu=cu_value)
# lee filter
image_lee = lee_filter(image, win_size=winsize, cu=cu_value)
# lee enhanced filter
image_lee_enhanced = lee_enhanced_filter(image,
win_size=winsize,
k=k_value2,
cu=cu_lee_enhanced,
cmax=cmax_value)
# mean filter
image_mean = mean_filter(image, win_size=winsize)
# median filter
image_median = median_filter(image, win_size=winsize)
def test_all(self):
image_path = os.path.join(IMAGES, "DAT_01.001")
print image_path
dataset = create_dataset_from_path(image_path)
band = get_band_from_dataset(dataset)
geoinfo = get_geoinfo(dataset, cast_to_int=True)
xoff = geoinfo['xoff'] + 2000
yoff = geoinfo['yoff'] + 2000
## Parameters:
win_xsize = 100 # window size in coord x
win_ysize = 100 # window size in coord y
k = 1 # parameter of frost filter, ex: k=1 or k=10 or k=100
win_size = 3 # size of the window for the filter function
damping_factor = 1 # parameter of frost filter, ex: 1 or 10 or 1000
image = read_image_from_band(band, xoff, yoff, win_xsize, win_ysize)
# Try K-Means
kmean_timer = Timer()
n_classes = 8
iterations = 1000
class_image = kmeans_classification(image, n_classes, iterations)
kmean_timer.stop_timer()
kmean_timer.calculate_time_elapsed(print_value=True)
# Try Isodata
isodata_timer = Timer()
parameters={"K": 8, "I":1000}
class_image = isodata_classification(image,parameters=parameters )
isodata_timer.stop_timer()
isodata_timer.calculate_time_elapsed(print_value=True)
numerito = parameters["K"]
# Try the filters
filter_timer = Timer()
numerito = 11
cucito = 0.30
image_filtered = mean_filter(image, win_size=numerito)
image_filtered = median_filter(image,win_size)
image_filtered = frost_filter(image, damping_factor=1.0, win_size=11)
image_filtered = kuan_filter(image, win_size=7, cu=1.0)
image_filtered = lee_filter(image, win_size=numerito, cu=cucito)
image_filtered = lee_enhanced_filter(image, win_size=numerito, cu=cucito)
filter_timer.stop_timer()
diff = filter_timer.calculate_time_elapsed(print_value=True)
# Frost y K-Means
ventana = 7
damp = 1.0
#
parameters={"K" : 5, "I" : 100}
#
_timer = Timer()
#
image_filtered = frost_filter(image, damping_factor=damp, win_size=ventana)
class_image = isodata_classification(image_filtered, parameters)
#
#
_timer.stop_timer()
_timer.calculate_time_elapsed(print_value=True)
#
image_corrected = equalization_using_histogram(class_image)
save_image(IMG_DEST_DIR, "image_" + str(ventana) + "frostisodata" +
str(damp) + "c" + str(parameters["K"]), image_corrected)
#
#
# Equalize and save the images to files
image_corrected = equalization_using_histogram(class_image)
save_image(IMG_DEST_DIR, "image_isodata8", image_corrected)
#
image_original = equalization_using_histogram(image)
save_image(IMG_DEST_DIR, "image_original", image_original)
print "\a\a\a"
from pyradar.filters.mean import mean_filter
# filters parameters
# window size
winsize = 9
# damping factor for frost
k_value1 = 2.0
# damping factor for lee enhanced
k_value2 = 1.0
# coefficient of variation of noise
cu_value = 0.25
# coefficient of variation for lee enhanced of noise
cu_lee_enhanced = 0.523
# max coefficient of variation for lee enhanced
cmax_value = 1.73
# frost filter
image_frost = frost_filter(image, damping_factor=k_value1, win_size=winsize)
# kuan filter
image_kuan = kuan_filter(image, win_size=winsize, cu=cu_value)
# lee filter
image_lee = lee_filter(image, win_size=winsize, cu=cu_value)
# lee enhanced filter
image_lee_enhanced = lee_enhanced_filter(image, win_size=winsize, k=k_value2,
cu=cu_lee_enhanced, cmax=cmax_value)
# mean filter
image_mean = mean_filter(image, win_size=winsize)
# median filter
image_median = median_filter(image, win_size=winsize)
def write_image(img, filename):
"""
Write img array to a file with the given filename
Args:
img (Band)
filename (str)
"""
x_size = img.shape[1]
y_size = img.shape[0]
dataset = driver.Create(filename, x_size, y_size)
dataset.GetRasterBand(1).WriteArray(img)
# load original image
dataset = gdal.Open('img/mozambique-after-subset.tif')
band = dataset.GetRasterBand(1)
img = band.ReadAsArray()
# lee filter
img_lee = lee_filter(img, win_size=3)
write_image(img_lee.astype(np.uint8), 'img/mozambique-after-subset-lee.tif')
# frost filter
img_frost = frost_filter(img, win_size=3)
write_image(img_frost.astype(np.uint8),
'img/mozambique-after-subset-frost.tif')
# kuan filter
img_kuan = kuan_filter(img, win_size=3)
write_image(img_kuan.astype(np.uint8), 'img/mozambique-after-subset-kuan.tif')
def test_all(self):
image_path = os.path.join(IMAGES, "DAT_01.001")
print image_path
dataset = create_dataset_from_path(image_path)
band = get_band_from_dataset(dataset)
geoinfo = get_geoinfo(dataset, cast_to_int=True)
xoff = geoinfo['xoff'] + 2000
yoff = geoinfo['yoff'] + 2000
## Parameters:
win_xsize = 100 # window size in coord x
win_ysize = 100 # window size in coord y
k = 1 # parameter of frost filter, ex: k=1 or k=10 or k=100
win_size = 3 # size of the window for the filter function
damping_factor = 1 # parameter of frost filter, ex: 1 or 10 or 1000
image = read_image_from_band(band, xoff, yoff, win_xsize, win_ysize)
# Try K-Means
kmean_timer = Timer()
n_classes = 8
iterations = 1000
class_image = kmeans_classification(image, n_classes, iterations)
kmean_timer.stop_timer()
kmean_timer.calculate_time_elapsed(print_value=True)
# Try Isodata
isodata_timer = Timer()
parameters={"K": 8, "I":1000}
class_image = isodata_classification(image,parameters=parameters )
isodata_timer.stop_timer()
isodata_timer.calculate_time_elapsed(print_value=True)
numerito = parameters["K"]
# Try the filters
filter_timer = Timer()
numerito = 11
cucito = 0.30
image_filtered = mean_filter(image, win_size=numerito)
image_filtered = median_filter(image,win_size)
image_filtered = frost_filter(image, damping_factor=1.0, win_size=11)
image_filtered = kuan_filter(image, win_size=7, cu=1.0)
image_filtered = lee_filter(image, win_size=numerito, cu=cucito)
image_filtered = lee_enhanced_filter(image, win_size=numerito, cu=cucito)
filter_timer.stop_timer()
diff = filter_timer.calculate_time_elapsed(print_value=True)
# Frost y K-Means
ventana = 7
damp = 1.0
#
parameters={"K" : 5, "I" : 100}
#
_timer = Timer()
#
image_filtered = frost_filter(image, damping_factor=damp, win_size=ventana)
class_image = isodata_classification(image_filtered, parameters)
#
#
_timer.stop_timer()
_timer.calculate_time_elapsed(print_value=True)
#
image_corrected = equalization_using_histogram(class_image)
save_image(IMG_DEST_DIR, "image_" + str(ventana) + "frostisodata" +
str(damp) + "c" + str(parameters["K"]), image_corrected)
#
#
# Equalize and save the images to files
image_corrected = equalization_using_histogram(class_image)
save_image(IMG_DEST_DIR, "image_isodata8", image_corrected)
#
image_original = equalization_using_histogram(image)
save_image(IMG_DEST_DIR, "image_original", image_original)
print "\a\a\a"
driver = gdal.GetDriverByName('GTiff')
def write_image(img, filename):
"""
Write img array to a file with the given filename
Args:
img (Band)
filename (str)
"""
x_size = img.shape[1]
y_size = img.shape[0]
dataset = driver.Create(filename, x_size, y_size)
dataset.GetRasterBand(1).WriteArray(img)
# load original image
dataset = gdal.Open('img/mozambique-after-subset.tif')
band = dataset.GetRasterBand(1)
img = band.ReadAsArray()
# lee filter
img_lee = lee_filter(img, win_size=3)
write_image(img_lee.astype(np.uint8), 'img/mozambique-after-subset-lee.tif')
# frost filter
img_frost = frost_filter(img, win_size=3)
write_image(img_frost.astype(np.uint8), 'img/mozambique-after-subset-frost.tif')
# kuan filter
img_kuan = kuan_filter(img, win_size=3)
write_image(img_kuan.astype(np.uint8), 'img/mozambique-after-subset-kuan.tif')
