/usr/lib/python2.7/dist-packages/pyresample/image.py is in python-pyresample 1.1.0-1.
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#
#Copyright (C) 2010 Esben S. Nielsen
#
#This program is free software: you can redistribute it and/or modify
#it under the terms of the GNU General Public License as published by
#the Free Software Foundation, either version 3 of the License, or
#(at your option) any later version.
#
#This program 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 General Public License for more details.
#
#You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
"""Handles resampling of images with assigned geometry definitions"""
import numpy as np
import geometry, grid, kd_tree
class ImageContainer(object):
"""Holds image with geometry definition.
Allows indexing with linesample arrays.
:Parameters:
image_data : numpy array
Image data
geo_def : object
Geometry definition
fill_value : {int, None} optional
Set undetermined pixels to this value.
If fill_value is None a masked array is returned
with undetermined pixels masked
nprocs : int, optional
Number of processor cores to be used
:Attributes:
image_data : numpy array
Image data
geo_def : object
Geometry definition
fill_value : {int, None}
Resample result fill value
nprocs : int
Number of processor cores to be used for geometry operations
"""
def __init__(self, image_data, geo_def, fill_value=0, nprocs=1):
if not isinstance(image_data, (np.ndarray, np.ma.core.MaskedArray)):
raise TypeError('image_data must be either an ndarray'
' or a masked array')
elif ((image_data.ndim > geo_def.ndim + 1) or
(image_data.ndim < geo_def.ndim)):
raise ValueError(('Unexpected number of dimensions for '
'image_data: ') % image_data.ndim)
for i, size in enumerate(geo_def.shape):
if image_data.shape[i] != size:
raise ValueError(('Size mismatch for image_data. Expected '
'size %s for dimension %s and got %s') %
(size, i, image_data.shape[i]))
self.shape = geo_def.shape
self.size = geo_def.size
self.ndim = geo_def.ndim
self.image_data = image_data
if image_data.ndim > geo_def.ndim:
self.channels = image_data.shape[-1]
else:
self.channels = 1
self.geo_def = geo_def
self.fill_value = fill_value
self.nprocs = nprocs
def __str__(self):
return 'Image:\n %s'%self.image_data.__str__()
def __repr__(self):
return self.image_data.__repr__()
def resample(self, target_geo_def):
"""Base method for resampling"""
raise NotImplementedError('Method "resample" is not implemented '
'in class %s' % self.__class__.__name__)
def get_array_from_linesample(self, row_indices, col_indices):
"""Samples from image based on index arrays.
:Parameters:
row_indices : numpy array
Row indices. Dimensions must match col_indices
col_indices : numpy array
Col indices. Dimensions must match row_indices
:Returns:
image_data : numpy_array
Resampled image data
"""
if self.geo_def.ndim != 2:
raise TypeError('Resampling from linesamples only makes sense '
'on 2D data')
return grid.get_image_from_linesample(row_indices, col_indices,
self.image_data,
self.fill_value)
def get_array_from_neighbour_info(self, *args, **kwargs):
"""Base method for resampling from preprocessed data."""
raise NotImplementedError('Method "get_array_from_neighbour_info" is '
'not implemented in class %s' %
self.__class__.__name__)
class ImageContainerQuick(ImageContainer):
"""Holds image with area definition. '
Allows quick resampling within area.
:Parameters:
image_data : numpy array
Image data
geo_def : object
Area definition as AreaDefinition object
fill_value : {int, None} optional
Set undetermined pixels to this value.
If fill_value is None a masked array is returned
with undetermined pixels masked
nprocs : int, optional
Number of processor cores to be used for geometry operations
segments : {int, None}
Number of segments to use when resampling.
If set to None an estimate will be calculated
:Attributes:
image_data : numpy array
Image data
geo_def : object
Area definition as AreaDefinition object
fill_value : {int, None}
Resample result fill value
If fill_value is None a masked array is returned
with undetermined pixels masked
nprocs : int
Number of processor cores to be used
segments : {int, None}
Number of segments to use when resampling
"""
def __init__(self, image_data, geo_def, fill_value=0, nprocs=1,
segments=None):
if not isinstance(geo_def, geometry.AreaDefinition):
raise TypeError('area_def must be of type '
'geometry.AreaDefinition')
super(ImageContainerQuick, self).__init__(image_data, geo_def,
fill_value=fill_value,
nprocs=nprocs)
self.segments = segments
def resample(self, target_area_def):
"""Resamples image to area definition using nearest neighbour
approach in projection coordinates.
:Parameters:
target_area_def : object
Target area definition as AreaDefinition object
:Returns:
image_container : object
ImageContainerQuick object of resampled area
"""
resampled_image = grid.get_resampled_image(target_area_def,
self.geo_def,
self.image_data,
fill_value=self.fill_value,
nprocs=self.nprocs,
segments=self.segments)
return ImageContainerQuick(resampled_image, target_area_def,
fill_value=self.fill_value,
nprocs=self.nprocs, segments=self.segments)
class ImageContainerNearest(ImageContainer):
"""Holds image with geometry definition.
Allows nearest neighbour resampling to new geometry definition.
:Parameters:
image_data : numpy array
Image data
geo_def : object
Geometry definition
radius_of_influence : float
Cut off distance in meters
epsilon : float, optional
Allowed uncertainty in meters. Increasing uncertainty
reduces execution time
fill_value : {int, None} optional
Set undetermined pixels to this value.
If fill_value is None a masked array is returned
with undetermined pixels masked
reduce_data : bool, optional
Perform coarse data reduction before resampling in order
to reduce execution time
nprocs : int, optional
Number of processor cores to be used for geometry operations
segments : {int, None}
Number of segments to use when resampling.
If set to None an estimate will be calculated
:Attributes:
image_data : numpy array
Image data
geo_def : object
Geometry definition
radius_of_influence : float
Cut off distance in meters
epsilon : float
Allowed uncertainty in meters
fill_value : {int, None}
Resample result fill value
reduce_data : bool
Perform coarse data reduction before resampling
nprocs : int
Number of processor cores to be used
segments : {int, None}
Number of segments to use when resampling
"""
def __init__(self, image_data, geo_def, radius_of_influence, epsilon=0,
fill_value=0, reduce_data=True, nprocs=1, segments=None):
super(ImageContainerNearest, self).__init__(image_data, geo_def,
fill_value=fill_value,
nprocs=nprocs)
self.radius_of_influence = radius_of_influence
self.epsilon = epsilon
self.reduce_data = reduce_data
self.segments = segments
def resample(self, target_geo_def):
"""Resamples image to area definition using nearest neighbour
approach
:Parameters:
target_geo_def : object
Target geometry definition
:Returns:
image_container : object
ImageContainerNearest object of resampled geometry
"""
if self.image_data.ndim > 2 and self.ndim > 1:
image_data = self.image_data.reshape(self.image_data.shape[0] *
self.image_data.shape[1],
self.image_data.shape[2])
else:
image_data = self.image_data.ravel()
resampled_image = \
kd_tree.resample_nearest(self.geo_def,
image_data,
target_geo_def,
self.radius_of_influence,
epsilon=self.epsilon,
fill_value=self.fill_value,
nprocs=self.nprocs,
reduce_data=self.reduce_data,
segments=self.segments)
return ImageContainerNearest(resampled_image, target_geo_def,
self.radius_of_influence,
epsilon=self.epsilon,
fill_value=self.fill_value,
reduce_data=self.reduce_data,
nprocs=self.nprocs,
segments=self.segments)
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