All the examples in one place!
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from pyradar.utils.sar_debugger import take_snapshot
MAX_ITER = 1000
for iter in xrange(0, MAX_ITER):
image = some_algorithm(image)
take_snapshot(image, iteration_step=iter)
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# 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
save_image(IMG_DEST_DIR, "image_sar", image_eq)
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from pyradar.simulate.image_simulator import ImageSimulator
from pyradar.utils.timeutils import Timer
pylab.close()
timer = Timer()
width, height = 2000, 2000
gamma_ims = ImageSimulator(width, height)
k_ims = ImageSimulator(width, height)
noise_layer_ims = ImageSimulator(width, height)
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')
k_ims.plot_layer_histogram(layer_name='image_layer', filename='plot_k_img')
timer.stop_timer()
timer.calculate_time_elapsed(print_value=True)
#!/usr/bin/env python
# -*- coding: utf-8 -*-
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.median import median_filter
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)
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from pyradar.utils.system_info import get_system_info
from pyradar.utils.system_info import print_info
info = get_system_info()
print_info(info)
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# this should be placed at the top with all the imports
from pyradar.classifiers.isodata import isodata_classification
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(img, parameters=params)
# equalize class image to 0:255
class_image_eq = equalization_using_histogram(class_image)
# save it
save_image(IMG_DEST_DIR, "class_image_eq", class_image_eq)
# also save original image
image_eq = equalization_using_histogram(image)
# save it
save_image(IMG_DEST_DIR, "image_eq", image_eq)
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from pyradar.core.sar import create_dataset_from_path
from pyradar.core.sar import get_band_from_dataset
from pyradar.core.sar import get_geoinfo
from pyradar.core.sar import read_image_from_band
from pyradar.core.sar import save_image
from pyradar.core.equalizers import equalization_using_histogram
IMAGE_PATH = "./img_sar/DAT_01.001"
IMG_DEST_DIR = "."
# create dataset
dataset = create_dataset_from_path(IMAGE_PATH)
# get band from dataset
band = get_band_from_dataset(dataset)
# get geo info from dataset
geoinfo = get_geoinfo(dataset, cast_to_int=True)
#usually both values are zero
xoff = geoinfo['xoff']
yoff = geoinfo['yoff']
# window size in coord x
win_xsize = 128
# window size in coord y
win_ysize = 128
image = read_image_from_band(band, xoff, yoff, win_xsize, win_ysize)
#equalize img to 0:255
image_eq = equalization_using_histogram(image)
# save img in current directory
save_image(IMG_DEST_DIR, "image_sar", image_eq)
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# this should be placed at the top with all the imports
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
save_image(IMG_DEST_DIR, "class_image_eq", class_image_eq)
# also save original image
image_eq = equalization_using_histogram(image)
# save it
save_image(IMG_DEST_DIR, "image_eq", image_eq)
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import numpy as np
from pyradar.utils.statutils import compute_cfs
from pyradar.utils.statutils import calculate_pdf_for_pixel
from pyradar.utils.statutils import calculate_cdf_for_pixel
from pyradar.utils.statutils import compute_cdfs
arr = np.array([31, 49, 19, 62, 24, 45, 23, 51, 55, 60, 40, 35,
54, 26, 57, 37, 43, 65, 18, 41, 50, 56, 4, 54,
39, 52, 35, 51, 63, 42])
max_value = arr.max()
min_value = arr.min()
start, stop, step = int(min_value), int(max_value + 2), 1
histogram, bin_edge = np.histogram(arr, xrange(start, stop, step))
compute_cfs(histogram)
# >>> array([ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
# 1, 2, 3, 3, 3, 3, 4, 5, 5, 6, 6, 6, 6,
# 6, 7, 7, 7, 7, 9, 9, 10, 10, 11, 12, 13, 14,
# 15, 15, 16, 16, 16, 16, 17, 18, 20, 21, 21, 23, 24,
# 25, 26, 26, 26, 27, 27, 28, 29, 29, 30])
calculate_pdf_for_pixel(arr, histogram, bin_edge, 54)
# >>> 0.066666666666666666
calculate_pdf_for_pixel(arr, histogram, bin_edge, 20)
# >>> 0.0
calculate_pdf_for_pixel(arr, histogram, bin_edge, 18)
# >>> 0.033333333333333333
calculate_cdf_for_pixel(arr, histogram, bin_edge, 4)
# >>> 0.033333333333333333
calculate_cdf_for_pixel(arr, histogram, bin_edge, 50)
# >>> 0.59999999999999998
compute_cdfs(arr, histogram, bin_edge)
# >>> array([ 0.03333333, 0.03333333, 0.03333333, 0.03333333, 0.03333333,
# 0.03333333, 0.03333333, 0.03333333, 0.03333333, 0.03333333,
# 0.03333333, 0.03333333, 0.03333333, 0.03333333, 0.06666667,
# 0.1 , 0.1 , 0.1 , 0.1 , 0.13333333,
# 0.16666667, 0.16666667, 0.2 , 0.2 , 0.2 ,
# 0.2 , 0.2 , 0.23333333, 0.23333333, 0.23333333,
# 0.23333333, 0.3 , 0.3 , 0.33333333, 0.33333333,
# 0.36666667, 0.4 , 0.43333333, 0.46666667, 0.5 ,
# 0.5 , 0.53333333, 0.53333333, 0.53333333, 0.53333333,
# 0.56666667, 0.6 , 0.66666667, 0.7 , 0.7 ,
# 0.76666667, 0.8 , 0.83333333, 0.86666667, 0.86666667,
# 0.86666667, 0.9 , 0.9 , 0.93333333, 0.96666667,
# 0.96666667, 1. ])
#!/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)
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from pyradar.comparator.image_comparator import ImageComparator
from pyradar.examples.sample_matrixes import (numpy_image,
numpy_image1)
im = ImageComparator(numpy_image, numpy_image1)
print 'rmse1: ', im.compare_by('rmse1', None)
print 'rmse2: ', im.compare_by('rmse2', None)
print 'mae: ', im.compare_by('mae', None)
print 'pearson: ', im.compare_by('pearson', None)