def create_threshold_mask(inps):
if inps.dset:
print('read %s %s' % (inps.file, inps.dset))
else:
print('read %s' % (inps.file))
data, atr = readfile.read(inps.file, datasetName=inps.dset)
if len(data.shape) > 2:
raise Exception(
'Only 2D dataset is supported for threshold method, input is 3D')
length, width = int(atr['LENGTH']), int(atr['WIDTH'])
nanmask = ~np.isnan(data)
# row/column range for threshold operations
vmask = np.ones((length, width), dtype=np.bool_)
if inps.v_subset_x:
[vx0, vx1] = sorted(inps.v_subset_x)
vmask[:, :vx0] = 0
vmask[:, vx1:] = 0
if inps.v_subset_y:
[vy0, vy1] = sorted(inps.v_subset_y)
vmask[:vy0, :] = 0
vmask[vy1:, :] = 0
if inps.vroipoly:
from mintpy.utils import plot_ext
poly_mask = plot_ext.get_poly_mask(inps.file,
datasetName=inps.dset,
view_cmd=inps.view_cmd)
if poly_mask is not None:
vmask[poly_mask == 0] = 0
print(
'create initial mask with the same size as the input file and all = 1')
mask = np.ones((length, width), dtype=np.bool_)
# nan value
if not inps.keep_nan:
mask *= nanmask
print('all pixels with nan value = 0')
if inps.nonzero:
mask[data == 0.] = 0
print('exclude pixels with zero value')
# min threshold
if inps.vmin is not None:
mask[vmask] *= ~(data[vmask] < inps.vmin)
print('exclude pixels with value < %s' % str(inps.vmin))
# max threshold
if inps.vmax is not None:
mask[vmask] *= ~(data[vmask] > inps.vmax)
print('exclude pixels with value > %s' % str(inps.vmax))
# remove small pixel clusters
if inps.minpixels is not None:
from skimage.morphology import remove_small_objects
num_pixel = np.sum(mask)
mask = remove_small_objects(mask, inps.minpixels, connectivity=1)
print(
'exclude pixel clusters with size < %d pixels: remove %d pixels' %
(inps.minpixels, num_pixel - np.sum(mask)))
# remove pixels with large velocity STD
if inps.vstd:
if atr['FILE_TYPE'] != 'velocity':
raise ValueError(
'Input file MUST be a velocity file when using the --vstd option!'
)
data_std = readfile.read(inps.file, datasetName='velocityStd')[0]
mask[nanmask] *= (np.abs(data[nanmask]) >
(inps.vstd_num * data_std[nanmask]))
print(
'exclude pixels according to the formula: |velocity| > {} * velocityStd'
.format(inps.vstd_num))
# subset in Y
if inps.subset_y is not None:
y0, y1 = sorted(inps.subset_y)
mask[0:y0, :] = 0
mask[y1:length, :] = 0
print('exclude pixels with y OUT of [%d, %d]' % (y0, y1))
# subset in x
if inps.subset_x is not None:
x0, x1 = sorted(inps.subset_x)
mask[:, 0:x0] = 0
mask[:, x1:width] = 0
print('exclude pixels with x OUT of [%d, %d]' % (x0, x1))
# exclude circular area
if inps.ex_circle:
x, y, r = inps.ex_circle
cmask = ut.get_circular_mask(x, y, r, (length, width))
mask[cmask == 1] = 0
print('exclude pixels inside of circle defined as (x={}, y={}, r={})'.
format(x, y, r))
# include circular area
if inps.in_circle:
x, y, r = inps.in_circle
cmask = ut.get_circular_mask(x, y, r, (length, width))
mask[cmask == 0] = 0
print('exclude pixels outside of circle defined as (x={}, y={}, r={})'.
format(x, y, r))
# interactively select polygonal region of interest (ROI)
if inps.roipoly:
from mintpy.utils import plot_ext
poly_mask = plot_ext.get_poly_mask(inps.file,
datasetName=inps.dset,
view_cmd=inps.view_cmd)
if poly_mask is not None:
mask *= poly_mask
# base mask
if inps.base_file:
# read base mask file
base_data = readfile.read(inps.base_file,
datasetName=inps.base_dataset)[0]
if len(base_data.shape) == 3:
base_data = np.sum(base_data, axis=0)
# apply base mask
mask[base_data == float(inps.base_value)] = 0
# message
msg = 'exclude pixels in base file {} '.format(
os.path.basename(inps.base_file))
if inps.base_dataset:
msg += 'dataset {} '.format(inps.base_dataset)
msg += 'with value == {}'.format(inps.base_value)
print(msg)
# revert
if inps.revert:
temp = np.array(mask, dtype=np.bool_)
mask[temp == True] = False
mask[temp == False] = True
del temp
# Write mask file
atr['FILE_TYPE'] = 'mask'
writefile.write(mask, out_file=inps.outfile, metadata=atr)
return inps.outfile
def create_threshold_mask(inps):
if inps.dset:
print('read %s %s' % (inps.file, inps.dset))
else:
print('read %s' % (inps.file))
data, atr = readfile.read(inps.file, datasetName=inps.dset)
if len(data.shape) > 2:
raise Exception('Only 2D dataset is supported for threshold method, input is 3D')
length, width = int(atr['LENGTH']), int(atr['WIDTH'])
nanmask = ~np.isnan(data)
print('create initial mask with the same size as the input file and all = 1')
mask = np.ones((length, width), dtype=np.bool_)
# nan value
if not inps.keep_nan:
mask *= nanmask
print('all pixels with nan value = 0')
if inps.nonzero:
mask[nanmask] *= ~(data[nanmask] == 0.)
print('exclude pixels with zero value')
# min threshold
if inps.vmin is not None:
mask[nanmask] *= ~(data[nanmask] < inps.vmin)
print('exclude pixels with value < %s' % str(inps.vmin))
# max threshold
if inps.vmax is not None:
mask[nanmask] *= ~(data[nanmask] > inps.vmax)
print('exclude pixels with value > %s' % str(inps.vmax))
# subset in Y
if inps.subset_y is not None:
y0, y1 = sorted(inps.subset_y)
mask[0:y0, :] = 0
mask[y1:length, :] = 0
print('exclude pixels with y OUT of [%d, %d]' % (y0, y1))
# subset in x
if inps.subset_x is not None:
x0, x1 = sorted(inps.subset_x)
mask[:, 0:x0] = 0
mask[:, x1:width] = 0
print('exclude pixels with x OUT of [%d, %d]' % (x0, x1))
# exclude circular area
if inps.ex_circle:
x, y, r = inps.ex_circle
cmask = ut.get_circular_mask(x, y, r, (length, width))
mask[cmask == 1] = 0
print('exclude pixels inside of circle defined as (x={}, y={}, r={})'.format(x, y, r))
# include circular area
if inps.in_circle:
x, y, r = inps.in_circle
cmask = ut.get_circular_mask(x, y, r, (length, width))
mask[cmask == 0] = 0
print('exclude pixels outside of circle defined as (x={}, y={}, r={})'.format(x, y, r))
# interactively select polygonal region of interest (ROI)
if inps.roipoly:
from mintpy.utils import plot_ext
poly_mask = plot_ext.get_poly_mask(inps.file, datasetName=inps.dset)
if poly_mask is not None:
mask *= poly_mask
# base mask
if inps.base_file:
# read base mask file
base_data = readfile.read(inps.base_file, datasetName=inps.base_dataset)[0]
if len(base_data.shape) == 3:
base_data = np.sum(base_data, axis=0)
# apply base mask
mask[base_data == float(inps.base_value)] = 0
# message
msg = 'exclude pixels in base file {} '.format(os.path.basename(inps.base_file))
if inps.base_dataset:
msg += 'dataset {} '.format(inps.base_dataset)
msg += 'with value == {}'.format(inps.base_value)
print(msg)
# revert
if inps.revert:
temp = np.array(mask, dtype=np.bool_)
mask[temp == True] = False
mask[temp == False] = True
del temp
# Write mask file
atr['FILE_TYPE'] = 'mask'
writefile.write(mask, out_file=inps.outfile, metadata=atr)
return inps.outfile