def test_raster_should_get_array_block_one_band(self):
filename = 'data/RGB.byte.tif'
raster = Raster(filename)
array = raster.array_from_bands(2, block_win=(256, 256, 128, 128))
self.assertEqual(array.ndim, 2)
self.assertEqual(array.shape, (128, 128))
self.assertEqual(array.dtype, 'UInt8')
def test_raster_should_get_array_one_band(self):
filename = 'data/RGB.byte.tif'
raster = Raster(filename)
array = raster.array_from_bands(1)
self.assertEqual(array.ndim, 2)
self.assertEqual(array.shape, (718, 791))
self.assertEqual(array.dtype, 'UInt8')
def test_raster_should_get_array_block(self):
filename = 'data/RGB.byte.tif'
raster = Raster(filename)
array = raster.array_from_bands(block_win=(256, 256, 300, 300))
self.assertEqual(array.ndim, 3)
self.assertEqual(array.shape, (300, 300, 3))
self.assertEqual(array.dtype, 'UInt8')
def apply_mask(args):
mask_raster = Raster(args.mask)
for filename in args.raster:
raster = Raster(filename)
raster.apply_mask(mask_raster=mask_raster,
mask_value=args.mask_value,
set_value=args.set_value,
out_filename=args.out_file)
def lsms_segmentation(args):
raster = Raster(args.raster)
block_size = (args.block_width, args.block_height) \
if args.block_width and args.block_height \
else None
raster.lsms_segmentation(args.spatialr, args.spectralr, args.thres,
args.rangeramp, args.maxiter,
object_minsize=args.minsize,
block_size=block_size,
out_vector_filename=args.vector_file,
out_filename=args.out_file)
def lsms_segmentation(args):
raster = Raster(args.raster)
block_size = (args.block_width, args.block_height) \
if args.block_width and args.block_height \
else None
raster.lsms_segmentation(args.spatialr,
args.spectralr,
args.thres,
args.rangeramp,
args.maxiter,
object_minsize=args.minsize,
block_size=block_size,
out_vector_filename=args.vector_file,
out_filename=args.out_file)
def test_raster_should_set_date(self):
filename = 'data/RGB_unproj.byte.tif'
tmp_file = tempfile.NamedTemporaryFile(suffix='.tif')
shutil.copyfile(filename, tmp_file.name)
raster = Raster(tmp_file.name)
self.assertIsNone(raster.meta['date_time'])
dt = datetime(2014, 01, 01)
raster.date_time = dt
_check_image(self,
tmp_file.name,
u'GTiff',
791,
718,
3,
'Byte',
date_time=dt.strftime('%Y:%m:%d %H:%M:%S'))
self.assertEqual(raster.meta['date_time'], dt)
def test_raster_should_set_projection(self):
filename = 'data/RGB_unproj.byte.tif'
tmp_file = tempfile.NamedTemporaryFile(suffix='.tif')
shutil.copyfile(filename, tmp_file.name)
raster = Raster(tmp_file.name)
self.assertIsNone(raster.srs)
sr = osr.SpatialReference()
sr.ImportFromEPSG(4326)
raster.srs = sr
_check_image(self,
tmp_file.name,
u'GTiff',
791,
718,
3,
'Byte',
proj=sr.ExportToProj4())
self.assertTrue(raster.meta['srs'].IsSame(sr))
def test_raster_should_get_block_list(self):
filename = 'data/RGB.byte.tif'
raster = Raster(filename)
blocks = raster.block_windows()
self.assertEqual(blocks.next(), (0, 0, raster.meta['block_size'][0],
raster.meta['block_size'][1]))
last_block = None
length = 1
total_height = raster.block_size[1]
while True:
try:
last_block = blocks.next()
length += 1
total_height += last_block[3]
except StopIteration:
break
self.assertEqual(last_block, (0, 717, raster.meta['block_size'][0], 1))
self.assertEqual(length, 240)
self.assertEqual(total_height, raster.meta['height'])
def snow_brake_date():
# Command-line parameters
parser = argparse.ArgumentParser(
description="Compute a raster whose value for each pixel is the "
"date/time of snow break, given a list of temporally distinct, but "
"spatially identical, images.\n\n"
"That is, the value of each pixel will be the date/time of the first "
"image where the snow disappear on the pixel and does not appear again "
"in the following images.\n\n"
"Presence or absence of snow is evaluated from the Normalized "
"Difference Snow Index (NDSI) which is computed from green and "
"mid-infrared band."
"Dates will be in numeric format (eg. Apr 25, 2013 (midnight) "
"-> 1366840800.0)")
parser.add_argument("rasters", nargs='+',
help="List of input images")
parser.add_argument("-g", "--idx_green", type=int, default=3,
help="Index of a green band, common to all images "
"(default: 3)")
parser.add_argument("-mir", "--idx_mir", type=int, default=6,
help="Index of a mid-infrared band, common to all "
"images (default: 6)")
parser.add_argument("-o", "--out_file", default='./snow_break.tif',
help="Path to the output file (default: "
"'snow_break.tif' in the current folder)")
args = parser.parse_args()
# Perform the actual work
# Compute all NDSIs, read them as NumPy arrays, and stack them into one
# array
rasters = [Raster(filename) for filename in args.rasters]
rasters.sort(key=lambda raster: raster.meta['datetime'])
for raster in rasters:
assert raster.meta['datetime'] is not None, "Raster has no "
"TIFFTAG_DATETIME metatadata: {}".format(raster.filename)
ndsis = [raster.mndwi('{}.mndwi.tif'.format(
os.path.basename(os.path.splitext(raster.filename)[0])),
idx_green=args.idx_green,
idx_mir=args.idx_mir)
for raster in rasters]
arrays = [ndsi.array() for ndsi in ndsis]
stack_array = np.dstack(arrays)
# List of dates
dates = [ndsi.meta['datetime'] for ndsi in ndsis]
# Create an empty array of correct size
ndsi0 = ndsis[0]
width = ndsi0.meta['width']
height = ndsi0.meta['height']
out_array = np.empty((height, width), dtype=np.float64)
# Find the snow-brake date
_under_threshold_date(stack_array, dates, out_array)
def compute_temporal_stats(args):
rasters = [Raster(filename) for filename in args.raster]
kw = {}
kw['out_filename'] = args.out_file
if args.band_idx is not None:
kw['band_idx'] = args.band_idx
if args.stat is not None:
kw['stats'] = list(args.stat)
if args.date_from is not None:
kw['date2float'] = partial(_days_between, args.date_from)
temporal_stats(*rasters, **kw)
def test_raster_should_get_block_list_with_given_size(self):
filename = 'data/RGB.byte.tif'
raster = Raster(filename)
xsize = 7
ysize = 2
lastx = raster.meta['width'] - xsize
lasty = raster.meta['height'] - ysize
number_blocks = \
raster.meta['width'] / xsize * raster.meta['height'] / ysize
blocks = raster.block_windows(block_size=(xsize, ysize))
self.assertEqual(blocks.next(), (0, 0, xsize, ysize))
last_block = None
length = 1
while True:
try:
last_block = blocks.next()
length += 1
except StopIteration:
break
self.assertEqual(last_block, (lastx, lasty, xsize, ysize))
self.assertEqual(length, number_blocks)
def test_concatenate_should_work_when_same_size_same_proj(self):
rasters = [Raster(os.path.join(self.folder, filename))
for filename in os.listdir(self.folder)
if filename.startswith('l8_')
and filename.endswith('.tif')]
out_file = tempfile.NamedTemporaryFile(suffix='.tif')
concatenate_rasters(*rasters, out_filename=out_file.name)
_check_image(tester=self,
filename=out_file.name,
driver=u'GTiff',
width=rasters[0].meta['width'],
height=rasters[0].meta['height'],
number_bands=rasters[0].meta['count'] * 8,
dtype=rasters[0].meta['dtype'].ustr_dtype,
proj=rasters[0].meta['srs'].ExportToProj4())
def stat_to_classification(args):
#Get the sample-feature matrix from the roi
X, Y = cla.get_samples_from_roi(args.label_file, args.roi_file,
args.stat_file)
#Get the sample-feature matrix from the labeled raster
X_label, reverse = cla.get_samples_from_label_img(args.label_file,
args.stat_file)
#Split the roi into two dataset
X_train, X_test, Y_train, Y_test = train_test_split(X, Y,train_size =\
args.train_size)
#set some parameters
stat = Raster(args.stat_file)
out_filename = os.path.join(args.dir, args.out_file)
#Initialize the classifier, train it and perform it
Y_predict = cla.decision_tree(X_train,
Y_train,
X_test,
X_label,
reverse,
stat,
out_filename,
ext=args.format)
#Compute the classification metrics and the confusion matrix
cm, report, accuracy = cla.pred_error_metrics(Y_predict, Y_test,
target_names = \
args.target_names)
#Print the results
print "confusion matrix\n", cm
print report
print "OA :", accuracy
# Show confusion matrix in a separate window
plt.matshow(cm)
plt.title('Confusion matrix')
plt.colorbar()
plt.ylabel('True label')
plt.xlabel('Predicted label')
plt.show()
def test_raster_should_get_attr_values(self):
filename = 'data/l8_20130425.tif'
raster = Raster(filename)
self.assertEqual(raster.filename, filename)
self.assertEqual(raster.driver.gdal_name, 'GTiff')
self.assertEqual(raster.width, 66)
self.assertEqual(raster.height, 56)
self.assertEqual(raster.count, 7)
self.assertEqual(raster.dtype.lstr_dtype, 'int16')
self.assertEqual(raster.block_size, (66, 8))
self.assertEqual(raster.date_time, datetime(2013, 04, 25))
self.assertEqual(raster.gdal_extent,
((936306.723651, 6461635.694121),
(936306.723651, 6459955.694121),
(938286.723651, 6461635.694121),
(938286.723651, 6459955.694121)))
self.assertEqual(
raster.meta['srs'].ExportToWkt(),
'PROJCS["RGF93 / Lambert-93",GEOGCS["RGF93",'
'DATUM["Reseau_Geodesique_Francais_1993",'
'SPHEROID["GRS 1980",6378137,298.2572221010042,'
'AUTHORITY["EPSG","7019"]],AUTHORITY["EPSG","6171"]],'
'PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433],'
'AUTHORITY["EPSG","4171"]],'
'PROJECTION["Lambert_Conformal_Conic_2SP"],'
'PARAMETER["standard_parallel_1",49],'
'PARAMETER["standard_parallel_2",44],'
'PARAMETER["latitude_of_origin",46.5],'
'PARAMETER["central_meridian",3],'
'PARAMETER["false_easting",700000],'
'PARAMETER["false_northing",6600000],UNIT["metre",1,'
'AUTHORITY["EPSG","9001"]],AUTHORITY["EPSG","2154"]]')
self.assertEqual(raster.meta['transform'], (936306.723651,
30.0,
0.0,
6461635.694121,
0.0,
-30.0))
def test_concatenate_should_raise_assertion_error_if_not_same_proj(self):
rasters = [Raster(os.path.join(self.folder, 'RGB.byte.tif')),
Raster(os.path.join(self.folder, 'RGB_unproj.byte.tif'))]
out_file = tempfile.NamedTemporaryFile(suffix='.tif')
self.assertRaises(AssertionError, concatenate_rasters, *rasters,
out_filename=out_file.name)
def label_stats(args):
raster = Raster(args.raster)
label_raster = Raster(args.label_file)
raster.label_stats(args.stats,
label_raster=label_raster,
out_filename=args.out_file)
def main():
raster = Raster(IMG)
raster.ndvi(4, 5, '/tmp/l8_20130714_ndvi.tif')
raster.ndmi(5, 6, '/tmp/l8_20130714_ndwi.tif')
raster.ndsi(3, 6, '/tmp/l8_20130714_mndwi.tif',)
def compute_temporal_stats():
# Command-line parameters
parser = argparse.ArgumentParser(
description="Compute pixel-wise statistics on radiometric indices from "
"a given list of multitemporal, but spatially identical, images.\n\n"
"Output is a multi-band image where each band contains a result (eg. "
"maximum, mean). For results taken from original values (eg. maximum), "
"there is an additional band which gives the date/time at which the "
"result has been found in numeric format (eg. maximum has occured on "
"Apr 25, 2013 (midnight) -> 1366840800.0)")
parser.add_argument("rasters", nargs='+', help="List of input images")
parser.add_argument("-b",
"--idx_blue",
type=int,
default=2,
help="Index of a blue band, common to all images "
"(default: 2)")
parser.add_argument("-g",
"--idx_green",
type=int,
default=3,
help="Index of a green band, common to all images "
"(default: 3)")
parser.add_argument("-r",
"--idx_red",
type=int,
default=4,
help="Index of a red band, common to all images "
"(default: 4)")
parser.add_argument("-nir",
"--idx_nir",
type=int,
default=5,
help="Index of a near-infrared band, common to all "
"images (default: 5)")
parser.add_argument("-mir",
"--idx_mir",
type=int,
default=6,
help="Index of a mid-infrared band, common to all "
"images (default: 6)")
parser.add_argument("-d",
"--date-from",
type=valid_date,
help="Date from which to compute statis "
"the input images and whose values are dates in "
"numeric format. If specified, statistical dates are "
"computed relative to the dates in this raster")
parser.add_argument(
"-t",
"--time_raster",
help="Path to a mono-band raster with same extent than "
"the input images and whose values are dates in "
"numeric format. If specified, statistical dates are "
"computed relative to the dates in this raster")
parser.add_argument(
"-o",
"--out_file",
default='./stats.tif',
help="Path to the output file (default: 'stats.tif' in "
"the current folder)")
args = parser.parse_args()
# Do the actual work
rasters = [Raster(filename) for filename in args.rasters]
ndvis = [
raster.ndvi(idx_red=args.idx_red,
idx_nir=args.idx_nir,
'{}.ndvi.tif'.format(
os.path.basename(os.path.splitext(
raster.filename)[0]))) for raster in rasters
]
ndwis = [
raster.ndwi(idx_nir=args.idx_nir,
idx_mir=args.idx_mir,
'{}.ndwi.tif'.format(
os.path.basename(os.path.splitext(
raster.filename)[0]))) for raster in rasters
]
mndwis = [
raster.mndwi(idx_green=args.idx_green,
idx_mir=args.idx_mir,
'{}.mndwi.tif'.format(
os.path.basename(
os.path.splitext(raster.filename)[0])))
for raster in rasters
]
temporal_stats(ndvis,
'stats.ndvi.tif',
'GTiff',
stats=['min', 'max', 'range'])
temporal_stats(ndwis,
'stats.ndwi.tif',
'GTiff',
stats=['min', 'max', 'range'])
temporal_stats(mndwis,
'stats.mndwi.tif',
'GTiff',
stats=['min', 'max', 'range'])
def label_stats(args):
raster = Raster(args.raster)
label_raster = Raster(args.label_file)
raster.label_stats(args.stats, label_raster=label_raster,
out_filename=args.out_file)
def main():
raster = Raster(IMG)
raster.ndvi(3, 4, '/tmp/spot6_ndvi.tif')
def pan_sharpen(args):
raster = Raster(args.raster)
pan_raster = Raster(args.pan_file)
raster.fusion(pan_raster, out_filename=args.out_file)
def remove_bands(args):
for filename in args.raster:
raster = Raster(filename)
raster.remove_bands(*args.idxs, out_filename=args.out_file)
def main():
raster = Raster(IMG)
raster.ndvi(3, 4, '/tmp/spot6_ndvi.tif')
# -*- coding: utf-8 -*-
"""
Created on Mon Feb 9 14:13:31 2015
@author: sig
"""
from ymraster import Raster, write_file, get_samples_from_roi
from sklearn import tree
from sklearn.externals.six import StringIO
import pydot #TODO install the module
rst_file = "data/new_set2/Spot6_MS_31072013_masked.tif"
rst = Raster(rst_file)
rst_array = rst.array()
roi_file =""
label_file = ""
stat_file = ""
statX_array, statY_array = get_samples_from_roi(label_file,roi_file,
stat_file )
clf = tree.DecisionTreeClassifier()
clf = clf.fit(statX_array, statY_array)
classif = clf.predict(rst_array)
dot_data = StringIO()
tree.export_graphviz(clf, out_file=dot_data)
graph = pydot.graph_from_dot_data(dot_data.getvalue())
def setUp(self):
self.raster = Raster('data/l8_20130714.tif')
def concatenate(args):
rasters = [Raster(raster) for raster in args.raster]
concatenate_rasters(*rasters, out_filename=args.out_file)
def ndwi(args):
for filename in args.in_list:
raster = Raster(filename)
raster.ndwi(args.idx_nir, args.idx_mir, out_filename=args.out_file)
def mndwi(args):
for filename in args.in_list:
raster = Raster(filename)
raster.mndwi(args.idx_green, args.idx_mir, out_filename=args.out_file)
class TestOtbFunctions(unittest.TestCase):
def setUp(self):
self.raster = Raster('data/l8_20130714.tif')
def test_should_remove_band(self):
out_file = tempfile.NamedTemporaryFile(suffix='.tif')
result = self.raster.remove_bands(6, out_filename=out_file.name)
self.assertEqual(result.meta['count'], self.raster.meta['count'] - 1)
_check_image(tester=self,
filename=out_file.name,
driver=u'GTiff',
width=self.raster.meta['width'],
height=self.raster.meta['height'],
number_bands=self.raster.meta['count'] - 1,
dtype=self.raster.meta['dtype'].ustr_dtype,
proj=self.raster.meta['srs'].ExportToProj4())
def test_should_compute_ndvi(self):
out_file = tempfile.NamedTemporaryFile(suffix='.tif')
self.raster.ndvi(red_idx=4, nir_idx=5, out_filename=out_file.name)
_check_image(tester=self,
filename=out_file.name,
driver=u'GTiff',
width=self.raster.meta['width'],
height=self.raster.meta['height'],
number_bands=1,
dtype='Float32',
date_time=self.raster.meta['date_time'],
proj=self.raster.meta['srs'].ExportToProj4())
def test_should_compute_ndwi(self):
out_file = tempfile.NamedTemporaryFile(suffix='.tif')
self.raster.ndwi(nir_idx=4, mir_idx=5, out_filename=out_file.name)
_check_image(tester=self,
filename=out_file.name,
driver=u'GTiff',
width=self.raster.meta['width'],
height=self.raster.meta['height'],
number_bands=1,
dtype='Float32',
date_time=self.raster.meta['date_time'],
proj=self.raster.meta['srs'].ExportToProj4())
def test_should_compute_mndwi(self):
out_file = tempfile.NamedTemporaryFile(suffix='.tif')
self.raster.mndwi(green_idx=4, mir_idx=5, out_filename=out_file.name)
_check_image(tester=self,
filename=out_file.name,
driver=u'GTiff',
width=self.raster.meta['width'],
height=self.raster.meta['height'],
number_bands=1,
dtype='Float32',
date_time=self.raster.meta['date_time'],
proj=self.raster.meta['srs'].ExportToProj4())
def test_lsms_segmentation_should_compute_segmented_image(self):
out_file = tempfile.NamedTemporaryFile(suffix='.tif')
out_vector_filename = os.path.join(
tempfile.gettempdir(), 'labels.shp')
out_raster = self.raster.lsms_segmentation(
spatialr=5,
ranger=15,
thres=0.1,
rangeramp=0,
maxiter=5,
object_minsize=10,
out_vector_filename=out_vector_filename,
out_filename=out_file.name)
# Output raster should have same size, same proj, 1 band
_check_image(tester=self,
filename=out_file.name,
driver=u'GTiff',
width=self.raster.meta['width'],
height=self.raster.meta['height'],
number_bands=1,
dtype='Float32',
proj=self.raster.meta['srs'].ExportToProj4())
# Output vector should have same number of polygon than label
array = out_raster.array_from_bands()
number_labels = len(np.unique(array))
ds = ogr.Open(out_vector_filename)
layer = ds.GetLayer(0)
self.assertEqual(layer.GetFeatureCount(), number_labels)
def tearDown(self):
tmpdir = tempfile.gettempdir()
tmpfilenames = [filename
for filename in os.listdir(tmpdir)
if filename.endswith('.tif.aux.xml')
or filename.startswith('segmented_merged.')]
for filename in tmpfilenames:
os.remove(os.path.join(tmpdir, filename))
def ndvi(args):
for filename in args.in_list:
raster = Raster(filename)
raster.ndvi(args.idx_red, args.idx_nir, out_filename=args.out_file)
def pan_sharpen(args):
raster = Raster(args.raster)
pan_raster = Raster(args.pan_file)
raster.fusion(pan_raster, out_filename=args.out_file)
