gamma_params = {'scale': 2.0, 'shape': 3.0}
k_params = {'mean': 2.0, 'shape': 2.0}
noise_layer_params = {'df': 3}
gamma_ims.generate_image_layer(distribution='gamma', params=gamma_params)
k_ims.generate_image_layer(distribution='k', params=k_params)
noise_layer_ims.generate_noise_layer(distribution='chisquare', params=noise_layer_params)
# Make some noise!
gamma_ims.noise_layer = noise_layer_ims.noise_layer
k_ims.noise_layer = noise_layer_ims.noise_layer
gamma_ims.generate_noisy_layer()
k_ims.generate_noisy_layer()
timer.calculate_time_elapsed(print_value=True)
# Export the files:
gamma_ims.export_image_layer(layer_name='image_layer', filename='gamma_img_layer',
path_to='.')
k_ims.export_image_layer(layer_name='image_layer', filename='k_img_layer',
path_to='.')
gamma_ims.export_image_layer(layer_name='noisy_image', filename='gamma_noisy_img',
path_to='.')
k_ims.export_image_layer(layer_name='noisy_image', filename='k_noisy_img',
path_to='.')
timer.calculate_time_elapsed(print_value=True)
# Make a plot:
print 'Making a plot to "plot_img.png":'
pylab.close()
gamma_ims.plot_layer_histogram(layer_name='image_layer', filename='plot_gamma_img')
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"
gamma_params = {'scale': 2.0, 'shape': 3.0}
k_params = {'mean': 2.0, 'shape': 2.0}
noise_layer_params = {'df': 3}
gamma_ims.generate_image_layer(distribution='gamma', params=gamma_params)
k_ims.generate_image_layer(distribution='k', params=k_params)
noise_layer_ims.generate_noise_layer(distribution='chisquare',
params=noise_layer_params)
# Make some noise!
gamma_ims.noise_layer = noise_layer_ims.noise_layer
k_ims.noise_layer = noise_layer_ims.noise_layer
gamma_ims.generate_noisy_layer()
k_ims.generate_noisy_layer()
timer.calculate_time_elapsed(print_value=True)
# Export the files:
gamma_ims.export_image_layer(layer_name='image_layer',
filename='gamma_img_layer',
path_to='.')
k_ims.export_image_layer(layer_name='image_layer',
filename='k_img_layer',
path_to='.')
gamma_ims.export_image_layer(layer_name='noisy_image',
filename='gamma_noisy_img',
path_to='.')
k_ims.export_image_layer(layer_name='noisy_image',
filename='k_noisy_img',
path_to='.')
timer.calculate_time_elapsed(print_value=True)
# -*- coding: utf-8 -*- from pyradar.utils.timeutils import Timer # crea y arranca el timer simple_timer = Timer() # procedimiento que queremos medir result = function(arg1, arg2) # paramos el timer simple_timer.stop_timer() #imprimimos los resultados y los guardamos en diff diff = simple_timer.calculate_time_elapsed(print_value=True)
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"
#!/usr/bin/env python # -*- coding: utf-8 -*- from pyradar.utils.timeutils import Timer # crea y arranca el timer simple_timer = Timer() # procedimiento que queremos medir result = function(arg1, arg2) # paramos el timer simple_timer.stop_timer() #imprimimos los resultados y los guardamos en diff diff = simple_timer.calculate_time_elapsed(print_value=True)