def load_hyper_apples():
# read image hyper spectral
header_path = './data/SSM5x5-NIR_09580554_20160605171039.hdr'
filename = './data/SSM5x5-NIR_09580554_20160605171039.bsq'
h = read_envi_header(header_path)
p = gen_params(h)
p.filename = filename
img_hyper = BsqFile(p, h)
height, width, channels = img_hyper.shape
img_hyper = img_hyper[:, :, :]
img_hyper = np.array(img_hyper, dtype='f')
img_hyper /= img_hyper.max()
#import ipdb; ipdb.set_trace()
print(img_hyper.shape)
# read hyperspectral mask
path_hyper_mask = "./data/hyper_apples_labels.npy"
mask_hyper = np.load(path_hyper_mask)
print(mask_hyper.shape)
#
return img_hyper, mask_hyper, 197, 'hyper_apples'
def create_image(hdr_file, metadata=None, **kwargs):
'''
Creates an image file and ENVI header with a memmep array for write access.
Arguments:
`hdr_file` (str):
Header file (with ".hdr" extension) name with path.
`metadata` (dict):
Metadata to specify the image file format. The following parameters
(in ENVI header format) are required, if not specified via
corresponding keyword arguments: "bands", "lines", "samples",
and "data type".
Keyword Arguments:
`dtype` (numpy dtype or type string):
The numpy data type with which to store the image. For example,
to store the image in 16-bit unsigned integer format, the argument
could be any of `numpy.uint16`, "u2", "uint16", or "H". If this
keyword is given, it will override the "data type" parameter in
the `metadata` argument.
`force` (bool, False by default):
If the associated image file or header already exist and `force` is
True, the files will be overwritten; otherwise, if either of the
files exist, an exception will be raised.
`ext` (str):
The extension to use for the image file. If not specified, the
default extension ".img" will be used. If `ext` is an empty
string, the image file will have the same name as the header but
without the ".hdr" extension.
`interleave` (str):
Must be one of "bil", "bip", or "bsq". This keyword supercedes the
value of "interleave" in the metadata argument, if given. If no
interleave is specified (via keyword or `metadata`), "bip" is
assumed.
`shape` (tuple of integers):
Specifies the number of rows, columns, and bands in the image.
This keyword should be either of the form (R, C, B) or (R, C),
where R, C, and B specify the number or rows, columns, and bands,
respectively. If B is omitted, the number of bands is assumed to
be one. If this keyword is given, its values supercede the values
of "bands", "lines", and "samples" if they are present in the
`metadata` argument.
`offset` (integer, default 0):
The offset (in bytes) of image data from the beginning of the file.
This value supercedes the value of "header offset" in the metadata
argument (if given).
Returns:
`SpyFile` object:
To access a `numpy.memmap` for the returned `SpyFile` object, call
the `open_memmap` method of the returned object.
Examples:
Creating a new image from metadata::
>>> md = {'lines': 30,
'samples': 40,
'bands': 50,
'data type': 12}
>>> img = envi.create_image('new_image.hdr', md)
Creating a new image via keywords::
>>> img = envi.create_image('new_image2.hdr',
shape=(30, 40, 50),
dtype=np.uint16)
Writing to the new image using a memmap interface::
>>> # Set all band values for a single pixel to 100.
>>> mm = img.open_memmap(writable=True)
>>> mm[30, 30] = 100
'''
import numpy as np
import os
import spectral
force = kwargs.get('force', False)
img_ext = kwargs.get('ext', '.img')
memmap_mode = kwargs.get('memmap_mode', 'w+')
(hdr_file, img_file) = check_new_filename(hdr_file, img_ext, force)
default_metadata = {'header offset': 0, 'interleave': 'bip'}
if metadata is None:
metadata = default_metadata
else:
default_metadata.update(metadata)
metadata = default_metadata
# Keyword args supercede metadata dict
if 'shape' in kwargs:
shape = kwargs['shape']
metadata['lines'] = shape[0]
metadata['samples'] = shape[1]
if len(shape) == 3:
metadata['bands'] = shape[2]
else:
metadata['bands'] = 1
if 'offset' in kwargs:
metadata['header offset'] = kwargs['offset']
if 'dtype' in kwargs:
metadata['data type'] = dtype_to_envi[np.dtype(kwargs['dtype']).char]
if 'interleave' in kwargs:
metadata['interleave'] = kwargs['interleave']
metadata['byte order'] = spectral.byte_order
# Verify minimal set of parameters have been provided
if 'lines' not in metadata:
raise EnviException('Number of image rows is not defined.')
elif 'samples' not in metadata:
raise EnviException('Number of image columns is not defined.')
elif 'bands' not in metadata:
raise EnviException('Number of image bands is not defined.')
elif 'samples' not in metadata:
raise EnviException('Number of image columns is not defined.')
elif 'data type' not in metadata:
raise EnviException('Image data type is not defined.')
params = gen_params(metadata)
dt = np.dtype(params.dtype).char
_validate_dtype(dt)
params.filename = img_file
is_library = False
if metadata.get('file type') == 'ENVI Spectral Library':
is_library = True
raise NotImplementedError('ENVI Spectral Library cannot be created ')
# Create the appropriate object type -> the memmap (=image) will be
# created on disk
inter = metadata["interleave"]
(R, C, B) = (params.nrows, params.ncols, params.nbands)
if inter.lower() not in ['bil', 'bip', 'bsq']:
raise ValueError('Invalid interleave specified: %s.' % str(inter))
if inter.lower() == 'bil':
from spectral.io.bilfile import BilFile
memmap = np.memmap(img_file,
dtype=dt,
mode=memmap_mode,
offset=params.offset,
shape=(R, B, C))
img = BilFile(params, metadata)
img._memmap = memmap
elif inter.lower() == 'bip':
from spectral.io.bipfile import BipFile
memmap = np.memmap(img_file,
dtype=dt,
mode=memmap_mode,
offset=params.offset,
shape=(R, C, B))
img = BipFile(params, metadata)
img._memmap = memmap
else:
from spectral.io.bsqfile import BsqFile
memmap = np.memmap(img_file,
dtype=dt,
mode=memmap_mode,
offset=params.offset,
shape=(B, R, C))
img = BsqFile(params, metadata)
img._memmap = memmap
# Write the header file after the image to assure write success
write_envi_header(hdr_file, metadata, is_library=is_library)
return img
def open(file, image=None):
'''
Opens an image or spectral library with an associated ENVI HDR header file.
Arguments:
`file` (str):
Name of the header file for the image.
`image` (str):
Optional name of the associated image data file.
Returns:
:class:`spectral.SpyFile` or :class:`spectral.io.envi.SpectralLibrary`
object.
Raises:
TypeError, EnviDataFileNotFoundError
If the specified file is not found in the current directory, all
directories listed in the SPECTRAL_DATA environment variable will be
searched until the file is found. Based on the name of the header file,
this function will search for the image file in the same directory as the
header, looking for a file with the same name as the header but different
extension. Extensions recognized are .img, .dat, .sli, and no extension.
Capitalized versions of the file extensions are also searched.
'''
import os
from .spyfile import find_file_path
import numpy
import spectral
header_path = find_file_path(file)
h = read_envi_header(header_path)
check_compatibility(h)
p = gen_params(h)
inter = h["interleave"]
# Validate image file name
if not image:
# Try to determine the name of the image file
(header_path_title, header_ext) = os.path.splitext(header_path)
if header_ext.lower() == '.hdr':
exts = [ext.lower() for ext in KNOWN_EXTS] + [inter.lower()]
exts = [''] + exts + [ext.upper() for ext in exts]
for ext in exts:
if len(ext) == 0:
testname = header_path_title
else:
testname = header_path_title + '.' + ext
if os.path.isfile(testname):
image = testname
break
if not image:
msg = 'Unable to determine the ENVI data file name for the ' \
'given header file. You can specify the data file by passing ' \
'its name as the optional `image` argument to envi.open.'
raise EnviDataFileNotFoundError(msg)
else:
image = find_file_path(image)
p.filename = image
if h.get('file type') == 'ENVI Spectral Library':
# File is a spectral library
data = numpy.fromfile(p.filename, p.dtype, p.ncols * p.nrows)
data.shape = (p.nrows, p.ncols)
return SpectralLibrary(data, h, p)
# Create the appropriate object type for the interleave format.
inter = h["interleave"]
if inter == 'bil' or inter == 'BIL':
from spectral.io.bilfile import BilFile
img = BilFile(p, h)
elif inter == 'bip' or inter == 'BIP':
from spectral.io.bipfile import BipFile
img = BipFile(p, h)
else:
from spectral.io.bsqfile import BsqFile
img = BsqFile(p, h)
img.scale_factor = float(h.get('reflectance scale factor', 1.0))
# Add band info
if 'wavelength' in h:
try:
img.bands.centers = [float(b) for b in h['wavelength']]
except:
pass
if 'fwhm' in h:
try:
img.bands.bandwidths = [float(f) for f in h['fwhm']]
except:
pass
img.bands.band_unit = h.get('wavelength units', None)
if 'bbl' in h:
try:
h['bbl'] = [int(float(b)) for b in h['bbl']]
except:
print('Unable to parse bad band list (bbl) in header as integers.')
return img
def open(file, image=None):
'''
Opens an image or spectral library with an associated ENVI HDR header file.
Arguments:
`file` (str):
Name of the header file for the image.
`image` (str):
Optional name of the associated image data file.
Returns:
:class:`spectral.SpyFile` or :class:`spectral.io.envi.SpectralLibrary`
object.
Raises:
TypeError, IOError.
If the specified file is not found in the current directory, all
directories listed in the SPECTRAL_DATA environment variable will be
searched until the file is found. Based on the name of the header file,
this function will search for the image file in the same directory as the
header, looking for a file with the same name as the header but different
extension. Extensions recognized are .img, .dat, .sli, and no extension.
Capitalized versions of the file extensions are also searched.
'''
import os
from .spyfile import find_file_path
import numpy
import spectral
headerPath = find_file_path(file)
h = read_envi_header(headerPath)
p = gen_params(h)
inter = h["interleave"]
# Validate image file name
if not image:
# Try to determine the name of the image file
headerDir = os.path.split(headerPath)
if headerPath[-4:].lower() == '.hdr':
headerPathTitle = headerPath[:-4]
exts = ['', 'img', 'IMG', 'dat', 'DAT', 'sli', 'SLI', 'hyspex'] +\
[inter.lower(), inter.upper()]
for ext in exts:
if len(ext) == 0:
testname = headerPathTitle
else:
testname = headerPathTitle + '.' + ext
if os.path.isfile(testname):
image = testname
break
if not image:
raise IOError('Unable to determine image file name.')
else:
image = find_file_path(image)
p.filename = image
if h.get('file type') == 'ENVI Spectral Library':
# File is a spectral library
data = numpy.fromfile(p.filename, p.dtype, p.ncols * p.nrows)
data.shape = (p.nrows, p.ncols)
return SpectralLibrary(data, h, p)
# Create the appropriate object type for the interleave format.
inter = h["interleave"]
if inter == 'bil' or inter == 'BIL':
from spectral.io.bilfile import BilFile
img = BilFile(p, h)
elif inter == 'bip' or inter == 'BIP':
from spectral.io.bipfile import BipFile
img = BipFile(p, h)
else:
from spectral.io.bsqfile import BsqFile
img = BsqFile(p, h)
img.scale_factor = float(h.get('reflectance scale factor', 1.0))
# Add band info
if 'wavelength' in h:
try:
img.bands.centers = [float(b) for b in h['wavelength']]
except:
pass
if 'fwhm' in h:
try:
img.bands.bandwidths = [float(f) for f in h['fwhm']]
except:
pass
img.bands.band_unit = h.get('wavelength units', "")
img.bands.band_quantity = "Wavelength"
return img
from spectral.io.bsqfile import BsqFile from spectral.io.envi import * import numpy as np import cv2 from spectral import * # read image hyper spectral header_path = './data/SSM5x5-NIR_09580554_20160605171039.hdr' filename = './data/SSM5x5-NIR_09580554_20160605171039.bsq' h = read_envi_header(header_path) p = gen_params(h) p.filename = filename img_hyper = BsqFile(p, h) height, width, channels = img_hyper.shape img_hyper = img_hyper[:, :, :] print(img_hyper.shape) # read hyperspectral mask path_hyper_mask = "./data/hyper_apples_labels.npy" mask_hyper = np.load(path_hyper_mask) print(mask_hyper.shape) #read png image path_rgb_image = "./data/rgb_apples.png" img_rgb = cv2.imread(path_rgb_image) print(img_rgb.shape)