forked from PyRadar/pyradar
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test.py
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test.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright 2012 - 2013
# Matías Herranz <matiasherranz@gmail.com>
# Joaquín Tita <joaquintita@gmail.com>
#
# hhttps://github.com/PyRadar/pyradar
#
# This library is free software; you can redistribute it and/or
# modify it under the terms of the GNU Lesser General Public
# License as published by the Free Software Foundation; either
# version 3 of the License, or (at your option) any later version.
#
# This library is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public
# License along with this library. If not, see <http://www.gnu.org/licenses/>.
import os, shutil, zipfile, unittest
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
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
from pyradar.utils.timeutils import Timer
from pyradar.classifiers.kmeans import kmeans_classification
from pyradar.classifiers.isodata import isodata_classification
#===============================================================================
# CONSTANTS
#===============================================================================
try:
ROOT = os.path.dirname(os.path.abspath(__file__.decode('utf-8')))
except:
ROOT = "."
TEST_ZIPS = os.path.join(ROOT, 'testpkg')
IMAGES = os.path.join(ROOT, "_test_images")
IMG_DEST_DIR = os.path.join(ROOT, '_test_output')
TEMP_DIR_DISCLAIMER = os.path.join(IMG_DEST_DIR, "warning.txt")
#===============================================================================
# PREPARE DIRS
#===============================================================================
if os.path.exists(IMG_DEST_DIR):
shutil.rmtree(IMG_DEST_DIR)
os.mkdir(IMG_DEST_DIR)
with open(TEMP_DIR_DISCLAIMER, "w") as fp:
fp.write("WARNING:\n\t THIS FOLDER IS DETROYED IN EVERY TEST")
if not os.path.exists(IMAGES):
import sys
print "Extracting files to zips, It's only the first time"
print "please wait..."
for dname, _, fnames in os.walk(TEST_ZIPS):
for fname in fnames:
fpath = os.path.join(dname, fname)
with zipfile.ZipFile(fpath, "r") as z:
z.extractall(IMAGES)
#===============================================================================
# TESTS
#===============================================================================
class TestAll(unittest.TestCase):
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"
#===============================================================================
# MAIN
#===============================================================================
if __name__ == "__main__":
unittest.main(verbosity=2)