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'''
Code for handling files that are band sequential (BSQ).
'''
from __future__ import absolute_import, division, print_function, unicode_literals
import array
import logging
import numpy as np
import os
import sys
import spectral as spy
from ..utilities.python23 import typecode, tobytes, frombytes
from .spyfile import SpyFile, MemmapFile
byte_typecode = typecode('b')
class BsqFile(SpyFile, MemmapFile):
'''
A class to represent image files stored with bands sequential.
'''
def __init__(self, params, metadata=None):
self.interleave = spy.BSQ
if metadata is None:
metadata = {}
SpyFile.__init__(self, params, metadata)
self._memmap = self._open_memmap('r')
def _open_memmap(self, mode):
logger = logging.getLogger('spectral')
if (os.path.getsize(self.filename) < sys.maxsize):
try:
(R, C, B) = self.shape
return np.memmap(self.filename, dtype=self.dtype, mode=mode,
offset=self.offset, shape=(B, R, C))
except:
logger.debug('Unable to create memmap interface.')
return None
else:
return None
def read_band(self, band, use_memmap=True):
'''Reads a single band from the image.
Arguments:
`band` (int):
Index of band to read.
`use_memmap` (bool, default True):
Specifies whether the file's memmap interface should be used
to read the data. Setting this arg to True only has an effect
if a memmap is being used (i.e., if `img.using_memmap` is True).
Returns:
:class:`numpy.ndarray`
An `MxN` array of values for the specified band.
'''
if self._memmap is not None and use_memmap is True:
data = np.array(self._memmap[band, :, :])
if self.scale_factor != 1:
data = data / float(self.scale_factor)
return data
vals = array.array(byte_typecode)
offset = self.offset + band * self.sample_size * \
self.nrows * self.ncols
f = self.fid
# Pixel format is BSQ, so read the whole band at once.
f.seek(offset, 0)
vals.fromfile(f, self.nrows * self.ncols * self.sample_size)
arr = np.frombuffer(tobytes(vals), dtype=self.dtype)
arr = arr.reshape(self.nrows, self.ncols)
if self.scale_factor != 1:
return arr / float(self.scale_factor)
return arr
def read_bands(self, bands, use_memmap=False):
'''Reads multiple bands from the image.
Arguments:
`bands` (list of ints):
Indices of bands to read.
`use_memmap` (bool, default False):
Specifies whether the file's memmap interface should be used
to read the data. Setting this arg to True only has an effect
if a memmap is being used (i.e., if `img.using_memmap` is True).
Returns:
:class:`numpy.ndarray`
An `MxNxL` array of values for the specified bands. `M` and `N`
are the number of rows & columns in the image and `L` equals
len(`bands`).
'''
if self._memmap is not None and use_memmap is True:
data = np.array(self._memmap[bands, :, :]).transpose((1, 2, 0))
if self.scale_factor != 1:
data = data / float(self.scale_factor)
return data
f = self.fid
arr = np.zeros((self.nrows, self.ncols, len(bands)), dtype=self.dtype)
for j in range(len(bands)):
vals = array.array(byte_typecode)
offset = self.offset + (bands[j]) * self.sample_size \
* self.nrows * self.ncols
# Pixel format is BSQ, so read an entire band at time.
f.seek(offset, 0)
vals.fromfile(f, self.nrows * self.ncols * self.sample_size)
band = np.frombuffer(tobytes(vals), dtype=self.dtype)
arr[:, :, j] = band.reshape(self.nrows, self.ncols)
if self.scale_factor != 1:
return arr / float(self.scale_factor)
return arr
def read_pixel(self, row, col, use_memmap=True):
'''Reads the pixel at position (row,col) from the file.
Arguments:
`row`, `col` (int):
Indices of the row & column for the pixel
`use_memmap` (bool, default True):
Specifies whether the file's memmap interface should be used
to read the data. Setting this arg to True only has an effect
if a memmap is being used (i.e., if `img.using_memmap` is True).
Returns:
:class:`numpy.ndarray`
A length-`B` array, where `B` is the number of image bands.
'''
if self._memmap is not None and use_memmap is True:
data = np.array(self._memmap[:, row, col])
if self.scale_factor != 1:
data = data / float(self.scale_factor)
return data
vals = array.array(byte_typecode)
delta = self.sample_size * (self.nbands - 1)
offset = self.offset + row * self.nbands * self.ncols \
* self.sample_size + col * self.sample_size
f = self.fid
nPixels = self.nrows * self.ncols
ncols = self.ncols
sampleSize = self.sample_size
bandSize = sampleSize * nPixels
rowSize = sampleSize * self.ncols
for i in range(self.nbands):
f.seek(self.offset
+ i * bandSize
+ row * rowSize
+ col * sampleSize, 0)
vals.fromfile(f, sampleSize)
pixel = np.frombuffer(tobytes(vals), dtype=self.dtype)
if self.scale_factor != 1:
return pixel / float(self.scale_factor)
return pixel
def read_subregion(self, row_bounds, col_bounds, bands=None,
use_memmap=True):
'''
Reads a contiguous rectangular sub-region from the image.
Arguments:
`row_bounds` (2-tuple of ints):
(a, b) -> Rows a through b-1 will be read.
`col_bounds` (2-tuple of ints):
(a, b) -> Columnss a through b-1 will be read.
`bands` (list of ints):
Optional list of bands to read. If not specified, all bands
are read.
`use_memmap` (bool, default True):
Specifies whether the file's memmap interface should be used
to read the data. Setting this arg to True only has an effect
if a memmap is being used (i.e., if `img.using_memmap` is True).
Returns:
:class:`numpy.ndarray`
An `MxNxL` array.
'''
if self._memmap is not None and use_memmap is True:
if bands is None:
data = np.array(self._memmap[:, row_bounds[0]: row_bounds[1],
col_bounds[0]: col_bounds[1]])
else:
data = np.array(
self._memmap[bands, row_bounds[0]: row_bounds[1],
col_bounds[0]: col_bounds[1]])
data = data.transpose((1, 2, 0))
if self.scale_factor != 1:
data = data / float(self.scale_factor)
return data
nSubRows = row_bounds[1] - row_bounds[0] # Rows in sub-image
nSubCols = col_bounds[1] - col_bounds[0] # Cols in sub-image
f = self.fid
f.seek(self.offset, 0)
# Increments between bands
if bands is None:
# Read all bands.
bands = list(range(self.nbands))
arr = np.zeros((nSubRows, nSubCols, len(bands)), dtype=self.dtype)
nrows = self.nrows
ncols = self.ncols
sampleSize = self.sample_size
bandSize = nrows * ncols * sampleSize
colStartOffset = col_bounds[0] * sampleSize
rowSize = ncols * sampleSize
rowStartOffset = row_bounds[0] * rowSize
nSubBands = len(bands)
# Pixel format is BSQ
for i in bands:
vals = array.array(byte_typecode)
bandOffset = i * bandSize
for j in range(row_bounds[0], row_bounds[1]):
f.seek(self.offset
+ bandOffset
+ j * rowSize
+ colStartOffset, 0)
vals.fromfile(f, nSubCols * sampleSize)
subArray = np.frombuffer(tobytes(vals),
dtype=self.dtype).reshape((nSubRows,
nSubCols))
arr[:, :, i] = subArray
if self.scale_factor != 1:
return arr / float(self.scale_factor)
return arr
def read_subimage(self, rows, cols, bands=None, use_memmap=False):
'''
Reads arbitrary rows, columns, and bands from the image.
Arguments:
`rows` (list of ints):
Indices of rows to read.
`cols` (list of ints):
Indices of columns to read.
`bands` (list of ints):
Optional list of bands to read. If not specified, all bands
are read.
`use_memmap` (bool, default False):
Specifies whether the file's memmap interface should be used
to read the data. Setting this arg to True only has an effect
if a memmap is being used (i.e., if `img.using_memmap` is True).
Returns:
:class:`numpy.ndarray`
An `MxNxL` array, where `M` = len(`rows`), `N` = len(`cols`),
and `L` = len(bands) (or # of image bands if `bands` == None).
'''
if self._memmap is not None and use_memmap is True:
if bands is None:
data = np.array(self._memmap[:].take(rows, 1).take(cols, 2))
else:
data = np.array(
self._memmap.take(bands, 0).take(rows, 1).take(cols, 2))
data = data.transpose((1, 2, 0))
if self.scale_factor != 1:
data = data / float(self.scale_factor)
return data
nSubRows = len(rows) # Rows in sub-image
nSubCols = len(cols) # Cols in sub-image
d_col = self.sample_size
d_band = d_col * self.ncols
d_row = d_band * self.nbands
f = self.fid
f.seek(self.offset, 0)
# Increments between bands
if bands is None:
# Read all bands.
bands = list(range(self.nbands))
nSubBands = len(bands)
arr = np.zeros((nSubRows, nSubCols, nSubBands), dtype=self.dtype)
offset = self.offset
vals = array.array(byte_typecode)
nrows = self.nrows
ncols = self.ncols
sampleSize = self.sample_size
bandSize = nrows * ncols * sampleSize
sampleSize = self.sample_size
rowSize = ncols * sampleSize
# Pixel format is BSQ
for i in bands:
bandOffset = offset + i * bandSize
for j in rows:
rowOffset = j * rowSize
for k in cols:
f.seek(bandOffset
+ rowOffset
+ k * sampleSize, 0)
vals.fromfile(f, sampleSize)
arr = np.frombuffer(tobytes(vals), dtype=self.dtype)
arr = arr.reshape(nSubBands, nSubRows, nSubCols)
arr = np.transpose(arr, (1, 2, 0))
if self.scale_factor != 1:
return arr / float(self.scale_factor)
return arr
def read_datum(self, i, j, k, use_memmap=True):
'''Reads the band `k` value for pixel at row `i` and column `j`.
Arguments:
`i`, `j`, `k` (integer):
Row, column and band index, respectively.
`use_memmap` (bool, default True):
Specifies whether the file's memmap interface should be used
to read the data. Setting this arg to True only has an effect
if a memmap is being used (i.e., if `img.using_memmap` is True).
Using this function is not an efficient way to iterate over bands or
pixels. For such cases, use readBands or readPixel instead.
'''
if self._memmap is not None and use_memmap is True:
datum = self._memmap[k, i, j]
if self.scale_factor != 1:
datum /= float(self.scale_factor)
return datum
nrows = self.nrows
ncols = self.ncols
sampleSize = self.sample_size
self.fid.seek(self.offset
+ (k * nrows * ncols
+ i * ncols
+ j) * sampleSize, 0)
vals = array.array(byte_typecode)
vals.fromfile(self.fid, sampleSize)
arr = np.frombuffer(tobytes(vals), dtype=self.dtype)
return arr.tolist()[0] / float(self.scale_factor)
|
