def test_resize_complex_alg4(self):
n = Nansat(self.test_file_complex,
log_level=40,
mapper=self.default_mapper)
n.resize(0.5, resample_alg=4)
self.assertTrue(np.any(n[1].imag != 0))
def test_resize_by_height(self):
n = Nansat(self.test_file_gcps,
log_level=40,
mapper=self.default_mapper)
n.resize(height=500, resample_alg=1)
self.assertEqual(type(n[1]), np.ndarray)
def test_undo(self):
n1 = Nansat(self.test_file_stere, logLevel=40)
shape1 = n1.shape()
n1.resize(10)
n1.undo()
shape2 = n1.shape()
self.assertEqual(shape1, shape2)
def test_resize_complex_alg_average(self):
n = Nansat(self.test_file_complex, log_level=40, mapper=self.default_mapper)
with warnings.catch_warnings(record=True) as w:
n.resize(0.5, resample_alg=-1)
self.assertEqual(len(w), 1)
self.assertTrue(issubclass(w[-1].category, UserWarning))
self.assertIn('The imaginary parts of complex numbers '
'are lost when resampling by averaging ', str(w[-1].message))
def test_undo(self):
n1 = Nansat(self.test_file_stere, logLevel=40)
shape1 = n1.shape()
n1.resize(10)
n1.undo()
shape2 = n1.shape()
self.assertEqual(shape1, shape2)
def test_undo(self):
n1 = Nansat(self.test_file_stere, log_level=40, mapper=self.default_mapper)
shape1 = n1.shape()
n1.resize(10)
n1.undo()
shape2 = n1.shape()
self.assertEqual(shape1, shape2)
def feature_tracking(self,
bandName='sigma0_HV',
factor=0.5,
vmin=0,
vmax=0.013,
domainMargin=10,
maxDrift=20,
denoise=False,
dB=False,
**kwargs):
''' Find starting and ending point of drift using feature tracking '''
if denoise:
# open, denoise and reduce size
n1 = get_denoised_object(self.filename1, bandName, factor,
**kwargs)
n2 = get_denoised_object(self.filename2, bandName, factor,
**kwargs)
else:
# open and reduce size
n1 = Nansat(self.filename1)
n2 = Nansat(self.filename2)
n1.resize(factor, eResampleAlg=-1)
n2.resize(factor, eResampleAlg=-1)
# increase accuracy of coordinate transfomation
n1 = reproject_gcp_to_stere(n1)
n2 = reproject_gcp_to_stere(n2)
# get matrices with data
img1 = n1[bandName]
img2 = n2[bandName]
if not denoise and dB:
img1 = 10 * np.log10(img1)
img2 = 10 * np.log10(img2)
# convert to 0 - 255
img1 = get_uint8_image(img1, vmin, vmax)
img2 = get_uint8_image(img2, vmin, vmax)
# find many key points
kp1, descr1 = find_key_points(img1, **kwargs)
kp2, descr2 = find_key_points(img2, **kwargs)
# filter keypoints by Domain
kp1, descr1 = domain_filter(n1, kp1, descr1, n2, domainMargin)
kp2, descr2 = domain_filter(n2, kp2, descr2, n1, domainMargin)
# find coordinates of matching key points
x1, y1, x2, y2 = get_match_coords(kp1, descr1, kp2, descr2, **kwargs)
# filter out pair with too high drift
x1, y1, x2, y2 = max_drift_filter(n1, x1, y1, n2, x2, y2, maxDrift)
# filter out inconsistent pairs
x1, y1, x2, y2 = lstsq_filter(x1, y1, x2, y2, **kwargs)
return n1, img1, x1, y1, n2, img2, x2, y2
def test_resize_resize(self):
n = Nansat(self.test_file_gcps, log_level=40, mapper=self.default_mapper)
n.resize(0.1)
n.resize(10)
tmpfilename = os.path.join(self.tmp_data_path,
'nansat_resize_resize.png')
n.write_figure(tmpfilename, 2, clim='hist')
self.assertEqual(type(n[1]), np.ndarray)
def test_resize_resize(self):
n = Nansat(self.test_file_gcps, logLevel=40)
n.resize(0.1)
n.resize(10)
tmpfilename = os.path.join(ntd.tmp_data_path,
'nansat_resize_resize.png')
n.write_figure(tmpfilename, 2, clim='hist')
self.assertEqual(type(n[1]), np.ndarray)
def test_reproject_gcps_resize(self):
n1 = Nansat(self.test_file_stere, logLevel=40)
n2 = Nansat(self.test_file_gcps, logLevel=40)
n1.reproject(n2)
n1.resize(2)
tmpfilename = os.path.join(ntd.tmp_data_path,
'nansat_reproject_gcps_resize.png')
n1.write_figure(tmpfilename, 2, clim='hist')
self.assertEqual(n1.shape()[0], n2.shape()[0]*2)
self.assertEqual(n1.shape()[1], n2.shape()[1]*2)
self.assertEqual(type(n1[1]), np.ndarray)
def test_resize_complex_algAverage(self):
n = Nansat(self.test_file_complex, logLevel=40)
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
n.resize(0.5, eResampleAlg=-1)
self.assertTrue(len(w) == 1)
self.assertTrue(issubclass(w[-1].category, UserWarning))
self.assertTrue(
'The imaginary parts of complex numbers '
'are lost when resampling by averaging ' in str(w[-1].message))
def test_reproject_gcps_resize(self):
n1 = Nansat(self.test_file_stere, log_level=40, mapper=self.default_mapper)
n2 = Nansat(self.test_file_gcps, log_level=40, mapper=self.default_mapper)
n1.reproject(n2)
n1.resize(2)
tmpfilename = os.path.join(self.tmp_data_path,
'nansat_reproject_gcps_resize.png')
n1.write_figure(tmpfilename, 2, clim='hist')
self.assertEqual(n1.shape()[0], n2.shape()[0] * 2)
self.assertEqual(n1.shape()[1], n2.shape()[1] * 2)
self.assertEqual(type(n1[1]), np.ndarray)
def test_resize_complex_algAverage(self):
n = Nansat(self.test_file_complex, logLevel=40)
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
n.resize(0.5, eResampleAlg=-1)
self.assertTrue(len(w)==1)
self.assertTrue(issubclass(w[-1].category, UserWarning))
self.assertTrue(
'The imaginary parts of complex numbers ' \
'are lost when resampling by averaging '
in str(w[-1].message))
def save_ice_map(inp_filename, raw_filename, classifier_filename, threads,
source, quicklook, force):
""" Load texture features, apply classifier and save ice map """
# get filenames
out_filename = inp_filename.replace('_texture_features.npz',
'_classified_%s.tif' % source)
if os.path.exists(out_filename) and not force:
print('Processed file %s already exists.' % out_filename)
return out_filename
# import classifier
clf = pickle.load(open(classifier_filename, "rb"))
clf.n_jobs = threads
# get texture features
npz = np.load(inp_filename)
features = np.vstack([
npz['textureFeatures'].item()['HH'],
npz['textureFeatures'].item()['HV'],
])
imgSize = features.shape[1:]
features = features.reshape((26, np.prod(imgSize))).T
gpi = np.isfinite(features.sum(axis=1))
result = clf.predict(features[gpi, :])
classImage = np.ones(np.prod(imgSize)) * 255
classImage[gpi] = result
classImage = classImage.reshape(imgSize)
img_shape = classImage.shape
# open original file to get geometry
raw_nansat = Nansat(raw_filename)
# crop and resize original Nansat to match the ice map
raw_shape = raw_nansat.shape()
crop = [rshape % ishape for (rshape, ishape) in zip(raw_shape, img_shape)]
raw_nansat.crop(0, 0, raw_shape[1] - crop[1], raw_shape[0] - crop[0])
raw_nansat.resize(height=img_shape[0])
raw_nansat.reproject_gcps()
# create new Nansat object and add ice map
ice_map = Nansat.from_domain(domain=raw_nansat,
array=classImage.astype(np.uint8))
ice_map.set_metadata(raw_nansat.get_metadata())
ice_map.set_metadata('entry_title', 'S1_SAR_ICE_MAP')
ice_map = add_colortable(ice_map, source)
ice_map.export(out_filename, bands=[1], driver='GTiff')
if quicklook:
rgb = colorcode_array(classImage, source)
plt.imsave(out_filename.replace('.tif', '.png'), rgb)
return out_filename
def test_add_latlon_grids_number(self):
''' Should create figure with lon/lat gridlines given manually '''
tmpfilename = os.path.join(self.tmp_data_path,
'figure_latlon_grids_number.png')
n = Nansat(self.test_file_gcps, mapper=self.default_mapper)
n.resize(3)
b = n[1]
lon, lat = n.get_geolocation_grids()
f = Figure(b)
f.process(cmax=100, lonGrid=lon, latGrid=lat, lonTicks=7, latTicks=7)
f.save(tmpfilename)
self.assertEqual(type(f), Figure)
self.assertTrue(os.path.exists(tmpfilename))
def get_n(filename,
bandName='sigma0_HV',
factor=0.5,
vmin=-30,
vmax=-5,
denoise=False,
dB=True,
**kwargs):
''' Get Nansat object with image data scaled to UInt8
Parameters
----------
filename : str - input file name
bandName : str - name of band in the file
factor : float - subsampling factor
vmin : float - minimum allowed value in the band
vmax : float - maximum allowed value in the band
denoise : bool - apply denoising of sigma0 ?
dB : bool - apply conversion to dB ?
**kwargs : parameters for get_denoised_object()
Returns
-------
n : Nansat object with one band scaled to UInt8
'''
if denoise:
# run denoising
n = get_denoised_object(filename, bandName, factor, **kwargs)
else:
# open data with Nansat and downsample
n = Nansat(filename)
if factor != 1:
n.resize(factor, resample_alg=-1)
# get matrix with data
img = n[bandName]
# convert to dB
if not denoise and dB:
img = 10 * np.log10(img)
# convert to 0 - 255
img = get_uint8_image(img, vmin, vmax)
nout = Nansat.from_domain(n, img, parameters={'name': bandName})
nout.set_metadata(n.get_metadata())
# improve geolocation accuracy
if len(nout.vrt.dataset.GetGCPs()) > 0:
nout.reproject_gcps()
nout.vrt.tps = True
return nout
def test_add_latlon_grids_number(self):
''' Should create figure with lon/lat gridlines given manually '''
tmpfilename = os.path.join(self.tmp_data_path, 'figure_latlon_grids_number.png')
n = Nansat(self.test_file_gcps, mapper=self.default_mapper)
n.resize(3)
b = n[1]
lon, lat = n.get_geolocation_grids()
f = Figure(b)
f.process(cmax=100, lonGrid=lon,
latGrid=lat,
lonTicks=7,
latTicks=7)
f.save(tmpfilename)
self.assertEqual(type(f), Figure)
self.assertTrue(os.path.exists(tmpfilename))
def get_n(filename,
bandName='sigma0_HV',
factor=0.5,
denoise=False,
dB=True,
mask_invalid=True,
landmask_border=20,
correct_hh=False,
correct_hh_factor=-0.27,
remove_spatial_mean=False,
vmin=None,
vmax=None,
pmin=10,
pmax=99,
**kwargs):
""" Get Nansat object with image data scaled to UInt8
Parameters
----------
filename : str
input file name
bandName : str
name of band in the file
factor : float
subsampling factor
denoise : bool
apply denoising of sigma0 ?
dB : bool
apply conversion to dB ?
mask_invalid : bool
mask invalid pixels (land, inf, etc) with 0 ?
landmask_border : int
border around landmask
correct_hh : bool
perform angular correction of sigma0_HH ?
correct_hh_factor : float
coefficient in the correction factor sigma0_HH_cor = sigma0_HH + correct_hh_factor * incidence_angle
remove_spatial_mean : bool
remove spatial mean from image ?
vmin : float or None
minimum value to convert to 1
vmax : float or None
maximum value to convert to 255
pmin : float
lower percentile for data scaling if vmin is None
pmax : float
upper percentile for data scaling if vmax is None
**kwargs : dummy parameters for
get_denoised_object()
Returns
-------
n : Nansat object with one band scaled to UInt8
"""
if denoise:
# run denoising
n = get_denoised_object(filename, bandName, factor, **kwargs)
else:
# open data with Nansat and downsample
n = Nansat(filename)
if factor != 1:
n.resize(factor, resample_alg=-1)
# get matrix with data
img = n[bandName]
# convert to dB
if not denoise and dB:
img[img <= 0] = np.nan
img = 10 * np.log10(img)
if correct_hh:
img = hh_angular_correction(n, img, bandName, correct_hh_factor)
if mask_invalid:
mask = get_invalid_mask(img, n, landmask_border)
img[mask] = np.nan
if remove_spatial_mean:
img -= get_spatial_mean(img)
# convert to 0 - 255
img = get_uint8_image(img, vmin, vmax, pmin, pmax)
# create Nansat with one band only
nout = Nansat.from_domain(n, img, parameters={'name': bandName})
nout.set_metadata(n.get_metadata())
# improve geolocation accuracy
if len(nout.vrt.dataset.GetGCPs()) > 0:
nout.reproject_gcps()
nout.vrt.tps = True
return nout
# Fill gaps: extrapolate valid values into 2 pixels border using nearest neighbour
# get distance and indices of nearest neighbours
dst, ind = nd.distance_transform_edt(np.isnan(sssOrig),
return_distances=True,
return_indices=True)
# erase row,col indeces further than 2 pixels
ind[0][dst > 2] = 0
ind[1][dst > 2] = 0
# fill gaps
sssExtra = sssOrig[tuple(ind)]
# Create a Nansat object from matrix with extrapolated product
nExtra = Nansat(domain=nOrig, array=sssExtra)
# Increase resolution
nExtra.resize(10, eResampleAlg=1)
# Get upscaled SSS
sssUpscaled = nExtra[1]
# Plot all SSS for comparison
for sss, sssName in [(sssOrig, 'sss0.png'),
(sssExtra, 'sss1.png'),
(sssUpscaled, 'sss2.png')]:
f = plt.figure(figsize=(10,5))
plt.imshow(sss, vmin=20, vmax=38, interpolation='nearest')
plt.gca().get_xaxis().set_visible(False)
plt.gca().get_yaxis().set_visible(False)
plt.savefig(sssName, bbox_inches='tight', pad_inches=0.0, dpi=300)
plt.close('all')
'info': 'copy from the 1st band array'})
# print band list
n.list_bands()
# get GDAL raster band (2nd band)
band = n.get_GDALRasterBand(bandID=2)
# Get band data and do some operations
# -- Get data from 1st band as numpy array
a = n[1]
# -- Plot the array (pyplot image is save to a PNG file)
plt.imshow(a);plt.colorbar();plt.savefig(oFileName + '01_imshow.png');plt.close()
# -- Save as Matlab file
savemat(oFileName + '01.mat', {'band_1': a})
# Resize the data to 50%
n.resize(0.5)
# make simple indexed image from 1st band with default colormap
n.write_figure(oFileName + '02.png', clim='hist')
# undo resize
n.resize()
# Resize the data to 50% using CubicSpline
n.resize_lite(0.5, eResampleAlg=3)
# make simple indexed image from 1st band with default colormap
n.write_figure(oFileName + '02CubicSpline.png', clim='hist')
# undo resize
n.resize()
# make image with map of the file location
n.write_map(oFileName + '04_map.png')
savemat(oPath + fileName + '.mat', {'band_1': a})
# make simple indexed image from 1st band
n.write_figure(oPath + fileName + '.png')
# make RGB image from bands 6,5,2 with brightness correction
n.write_figure(oPath + fileName + '_rgb.png', bands=[6,5,2], clim='hist', ratio=0.7, logarithm=True, gamma=3)
# make indexed image with legend
n.write_figure(oPath + fileName + '_legend.png', bands='radiance_900', clim='hist', ratio=0.7, legend=True, titleString='NANSAT Tutorial', fontSize=40)
# make small preview in three steps:
# 1. Reduce size of the Nansat object ten times
# 2. Make simple grayscaled image with brightness correction
# 3. Resize back to original resolution
n.resize(0.1)
n.write_figure(oPath + fileName + '_preview.png', clim='hist', ratio=0.7, cmapName='gray')
n.resize()
# make KML file with image borders (to be opened in Googe Earth)
n.write_kml(kmlFileName=oPath + fileName + '_preview.kml')
# make image with map of the file location
n.write_map(oPath + fileName + '_map.png')
# Make image, reprojected onto map of Northern Europe in three steps:
# 1. Create Domain object. It describes the desired grid of reprojected image:
# projection, resolution, size, etc. In this case it is geographic projection;
# -10 - 30 E, 50 - 70 W; 2000 x 2000 pixels
# 2. Reproject the Nansat object
# 3. Make simple image
gamma=3)
# make indexed image with legend
n.write_figure(oPath + fileName + '_legend.png',
bands='radiance_900',
clim='hist',
ratio=0.7,
legend=True,
titleString='NANSAT Tutorial',
fontSize=40)
# make small preview in three steps:
# 1. Reduce size of the Nansat object ten times
# 2. Make simple grayscaled image with brightness correction
# 3. Resize back to original resolution
n.resize(0.1)
n.write_figure(oPath + fileName + '_preview.png',
clim='hist',
ratio=0.7,
cmapName='gray')
n.resize()
# make KML file with image borders (to be opened in Googe Earth)
n.write_kml(kmlFileName=oPath + fileName + '_preview.kml')
# make image with map of the file location
n.write_map(oPath + fileName + '_map.png')
# Make image, reprojected onto map of Northern Europe in three steps:
# 1. Create Domain object. It describes the desired grid of reprojected image:
# projection, resolution, size, etc. In this case it is geographic projection;
def test_resize_eResampleAlg_is_given(self):
n = Nansat(self.test_file_gcps, log_level=40, mapper=self.default_mapper)
n.resize(pixelsize=500, eResampleAlg=5)
# Fill gaps: extrapolate valid values into 2 pixels border using nearest neighbour
# get distance and indices of nearest neighbours
dst, ind = nd.distance_transform_edt(np.isnan(sssOrig),
return_distances=True,
return_indices=True)
# erase row,col indeces further than 2 pixels
ind[0][dst > 2] = 0
ind[1][dst > 2] = 0
# fill gaps
sssExtra = sssOrig[tuple(ind)]
# Create a Nansat object from matrix with extrapolated product
nExtra = Nansat(domain=nOrig, array=sssExtra)
# Increase resolution
nExtra.resize(10, eResampleAlg=1)
# Get upscaled SSS
sssUpscaled = nExtra[1]
# Plot all SSS for comparison
for sss, sssName in [(sssOrig, 'sss0.png'), (sssExtra, 'sss1.png'),
(sssUpscaled, 'sss2.png')]:
f = plt.figure(figsize=(10, 5))
plt.imshow(sss, vmin=20, vmax=38, interpolation='nearest')
plt.gca().get_xaxis().set_visible(False)
plt.gca().get_yaxis().set_visible(False)
plt.savefig(sssName, bbox_inches='tight', pad_inches=0.0, dpi=300)
plt.close('all')
def test_resize_complex_alg4(self):
n = Nansat(self.test_file_complex, logLevel=40)
n.resize(0.5, eResampleAlg=4)
self.assertTrue(np.any(n[1].imag != 0))
def test_resize_by_height(self):
n = Nansat(self.test_file_gcps, logLevel=40)
n.resize(height=500, eResampleAlg=1)
self.assertEqual(type(n[1]), np.ndarray)
def test_resize_by_factor(self):
n = Nansat(self.test_file_gcps, logLevel=40)
n.resize(0.5, eResampleAlg=1)
self.assertEqual(type(n[1]), np.ndarray)
def test_resize_by_factor(self):
n = Nansat(self.test_file_gcps, logLevel=40)
n.resize(0.5, eResampleAlg=1)
self.assertEqual(type(n[1]), np.ndarray)
def test_resize_complex_alg4(self):
n = Nansat(self.test_file_complex, logLevel=40)
n.resize(0.5, eResampleAlg=4)
self.assertTrue(np.any(n[1].imag!=0))
def test_resize_by_height(self):
n = Nansat(self.test_file_gcps, logLevel=40)
n.resize(height=500, eResampleAlg=1)
self.assertEqual(type(n[1]), np.ndarray)
# add logo to image to the lower left corner # (make sure file is in the current folder) n.write_figure(oFileName + '_logo.png', logoFileName='nansat_logo_s.png', logoLocation=[10, -10], logoSize=[70, 70]) # write figure with lat/lon grid # 1. Get lat/lon arrays from Nansat object (may take some time) # 2. Make figure with lat/lon grids lonGrid, latGrid = n.get_geolocation_grids() n.write_figure(oFileName + '_latlon.png', latGrid=latGrid, lonGrid=lonGrid, latlonGridSpacing=10, latlonLabels=10) # make small preview # 1. Reduce size of the Nansat object ten times # 2. Make simple grayscaled image with brightness correction # 3. Resize back to original resolution n.resize(0.1) n.write_figure(oFileName + '_preview.png', clim='hist', cmapName='gray') n.resize() # enlarge the image with bicubic-spline interpolation n.resize(2, eResampleAlg=3) n.write_figure(oFileName + '_large.png', clim='hist', cmapName='gray') n.resize() # make KML file with image borders (to be opened in Googe Earth) n.write_kml(kmlFileName=oFileName + '_preview.kml') # make image with map of the file location n.write_map(oFileName + '_map.png')
