def load_hdu(self,
hdu,
fobj=None,
naxispath=None,
inherit_primary_header=None):
if self.io is None:
# need image loader for the fromHDU() call below
raise ImageError("No IO loader defined")
self.clear_metadata()
# collect HDU header
ahdr = self.get_header()
self.io.fromHDU(hdu, ahdr)
# Set PRIMARY header
if inherit_primary_header is None:
inherit_primary_header = self.inherit_primary_header
else: # This ensures get_header() is consistent
self.inherit_primary_header = inherit_primary_header
save_primary_header = (self.save_primary_header
or inherit_primary_header)
if save_primary_header and (fobj is not None):
if self._primary_hdr is None:
self._primary_hdr = AstroHeader()
self.io.fromHDU(fobj[0], self._primary_hdr)
self.setup_data(hdu.data, naxispath=naxispath)
# Try to make a wcs object on the header
if hasattr(self, 'wcs') and self.wcs is not None:
self.wcs.load_header(hdu.header, fobj=fobj)
def imresize(self, data, new_wd, new_ht, method='bilinear'):
"""Scale an image in numpy array _data_ to the specified width and
height. A smooth scaling is preferred.
"""
old_ht, old_wd = data.shape[:2]
start_time = time.time()
if have_pilutil:
means = 'PIL'
zoom_x = float(new_wd) / float(old_wd)
zoom_y = float(new_ht) / float(old_ht)
if (old_wd >= new_wd) or (old_ht >= new_ht):
# data size is bigger, skip pixels
zoom = max(zoom_x, zoom_y)
else:
zoom = min(zoom_x, zoom_y)
newdata = imresize(data, zoom, interp=method)
else:
from ginga.BaseImage import ImageError
raise ImageError("No way to scale image smoothly")
end_time = time.time()
self.logger.debug("scaling (%s) time %.4f sec" % (
means, end_time - start_time))
return newdata
def save_file_as(self, filepath, data_np, header):
# TODO: save keyword metadata!
if not have_pil:
raise ImageError("Install 'pillow' to be able " "to save images")
img = PILimage.fromarray(data_np)
img.save(filepath)
def save_file_as(self, filepath, data_np, header):
if not have_pil:
from ginga.BaseImage import ImageError
raise ImageError("Install PIL to be able to save images")
# TODO: save keyword metadata!
imsave(filepath, data_np)
def _imload(self, filepath, kwds):
if not have_opencv:
raise ImageError("Install 'opencv' to be able " "to load images")
# First choice is OpenCv, because it supports high-bit depth
# multiband images
data_np = cv2.imread(filepath,
cv2.IMREAD_ANYDEPTH + cv2.IMREAD_ANYCOLOR)
# funky indexing because opencv returns BGR images,
# whereas PIL and others return RGB
if len(data_np.shape) >= 3 and data_np.shape[2] >= 3:
data_np = data_np[..., ::-1]
# OpenCv doesn't "do" image metadata, so we punt to piexif
# library (if installed)
self.piexif_getexif(filepath, kwds)
# OpenCv added a feature to do auto-orientation when loading
# (see https://github.com/opencv/opencv/issues/4344)
# So reset these values to prevent auto-orientation from
# happening later
kwds['Orientation'] = 1
kwds['Image Orientation'] = 1
# convert to working color profile, if can
if self.clr_mgr.can_profile():
data_np = self.clr_mgr.profile_to_working_numpy(data_np, kwds)
return data_np
def imresize(self, data, new_wd, new_ht, method='bilinear'):
"""Scale an image in numpy array _data_ to the specified width and
height. A smooth scaling is preferred.
"""
# TODO: take into account the method parameter
old_ht, old_wd = data.shape[:2]
start_time = time.time()
if have_opencv:
# First choice is OpenCv, because it supports high-bit depth
# multiband images
means = 'opencv'
newdata = cv2.resize(data,
dsize=(new_wd, new_ht),
interpolation=cv2.INTER_CUBIC)
elif have_pil:
means = 'PIL'
img = PILimage.fromarray(data)
img = img.resize((new_wd, new_ht), PILimage.BICUBIC)
newdata = np.array(img)
else:
raise ImageError("Install 'pillow' or 'opencv' to be able "
"to resize RGB images")
end_time = time.time()
self.logger.debug("scaling (%s) time %.4f sec" %
(means, end_time - start_time))
return newdata
def _imload(self, filepath, metadata):
if not have_pil:
raise ImageError("Install 'pillow' to be able "
"to load RGB images")
image = PILimage.open(filepath)
kwds = metadata.get('header', None)
if kwds is None:
kwds = Header()
metadata['header'] = kwds
try:
self._get_header(image, kwds)
except Exception as e:
self.logger.warning("Failed to get image metadata: {!r}".format(e))
# convert to working color profile, if can
if self.clr_mgr.can_profile():
image = self.clr_mgr.profile_to_working_pil(image, kwds)
# convert from PIL to numpy
data_np = np.array(image)
metadata['order'] = image.mode
return data_np
def save_file_as(self, filepath, data_np, header):
# TODO: save keyword metadata!
if not have_opencv:
raise ImageError("Install 'opencv' to be able " "to save images")
# First choice is OpenCv, because it supports high-bit depth
# multiband images
cv2.imwrite(filepath, data_np)
def load_file(self, filepath, numhdu=None, naxispath=None):
self.logger.debug("Loading file '%s' ..." % (filepath))
self.set(path=filepath)
fits_f = pyfits.open(filepath, 'readonly')
# this seems to be necessary now for some fits files...
try:
fits_f.verify('fix')
except Exception, e:
raise ImageError("Error loading fits file '%s': %s" %
(fitspath, str(e)))
def load_hdu(self,
hdu,
fobj=None,
naxispath=None,
inherit_primary_header=None):
if self.io is None:
# need image loader for the fromHDU() call below
raise ImageError("No IO loader defined")
self.clear_metadata()
# collect HDU header
ahdr = self.get_header()
self.io.fromHDU(hdu, ahdr)
# Set PRIMARY header
if inherit_primary_header is None:
inherit_primary_header = self.inherit_primary_header
if inherit_primary_header and (fobj is not None):
if self._primary_hdr is None:
self._primary_hdr = AstroHeader()
self.io.fromHDU(fobj[0], self._primary_hdr)
data = hdu.data
if data is None:
data = numpy.zeros((0, 0))
elif not isinstance(data, numpy.ndarray):
data = numpy.zeros((0, 0))
elif 0 in data.shape:
data = numpy.zeros((0, 0))
elif len(data.shape) < 2:
# Expand 1D arrays into 1xN array
data = data.reshape((1, data.shape[0]))
# this is a handle to the full data array
self._md_data = data
# this will get reset in set_naxispath() if array is
# multidimensional
self._data = data
if naxispath is None:
naxispath = []
# Set naxispath to drill down to 2D data slice
if len(naxispath) == 0:
naxispath = ([0] * (len(data.shape) - 2))
self.set_naxispath(naxispath)
# Try to make a wcs object on the header
self.wcs.load_header(hdu.header, fobj=fobj)
def _imload(self, filepath, metadata):
if not have_opencv:
raise ImageError("Install 'opencv' to be able "
"to load images")
# First choice is OpenCv, because it supports high-bit depth
# multiband images
data_np = cv2.imread(filepath,
cv2.IMREAD_ANYDEPTH + cv2.IMREAD_ANYCOLOR)
return self._process_opencv_array(data_np, metadata, filepath)
def _imresize(self, data, new_wd, new_ht, method='bilinear'):
# TODO: take into account the method parameter
if not have_opencv:
raise ImageError("Install 'opencv' to be able "
"to resize RGB images")
# First choice is OpenCv, because it supports high-bit depth
# multiband images
newdata = cv2.resize(data, dsize=(new_wd, new_ht),
interpolation=cv2.INTER_CUBIC)
return newdata
def _imresize(self, data, new_wd, new_ht, method='bilinear'):
# TODO: take into account the method parameter
if not have_pil:
raise ImageError("Install 'pillow' to be able "
"to resize RGB images")
img = PILimage.fromarray(data)
img = img.resize((new_wd, new_ht), PILimage.BICUBIC)
newdata = np.array(img)
return newdata
def save_file_as(self, filepath, data_np, header):
# TODO: save keyword metadata!
if not have_pil:
raise ImageError("Install 'pillow' to be able "
"to save images")
img = PILimage.fromarray(data_np)
# pillow is not happy saving images to JPG with an alpha channel
img = img.convert('RGB')
img.save(filepath)
def save_file_as(self, filepath, data_np, header):
# TODO: save keyword metadata!
if have_opencv:
# First choice is OpenCv, because it supports high-bit depth
# multiband images
cv2.imwrite(filepath, data_np)
elif have_pil:
img = PILimage.fromarray(data_np)
img.save(filepath)
else:
raise ImageError("Install 'pillow' or 'opencv' to be able "
"to save images")
def _imload(self, filepath, kwds):
"""Load an image file, guessing the format, and return a numpy
array containing an RGB image. If EXIF keywords can be read
they are returned in the dict _kwds_.
"""
start_time = time.time()
typ, enc = mimetypes.guess_type(filepath)
if not typ:
typ = 'image/jpeg'
typ, subtyp = typ.split('/')
self.logger.debug("MIME type is %s/%s" % (typ, subtyp))
if (typ == 'image') and (subtyp in ('x-portable-pixmap',
'x-portable-greymap')):
# Special opener for PPM files, preserves high bit depth
means = 'built-in'
data_np = open_ppm(filepath)
elif have_pil:
means = 'PIL'
image = PILimage.open(filepath)
try:
if hasattr(image, '_getexif'):
info = image._getexif()
if info is not None:
for tag, value in info.items():
kwd = TAGS.get(tag, tag)
kwds[kwd] = value
except Exception as e:
self.logger.warning("Failed to get image metadata: %s" % (str(e)))
# convert to working color profile, if can
if self.clr_mgr.can_profile():
image = self.clr_mgr.profile_to_working_pil(image, kwds)
# convert from PIL to numpy
data_np = np.array(image)
else:
from ginga.BaseImage import ImageError
raise ImageError("No way to load image format '%s/%s'" % (
typ, subtyp))
end_time = time.time()
self.logger.debug("loading (%s) time %.4f sec" % (
means, end_time - start_time))
return data_np
def set_naxispath(self, naxispath):
"""Choose a slice out of multidimensional data.
"""
revnaxis = list(naxispath)
revnaxis.reverse()
# construct slice view and extract it
view = revnaxis + [slice(None), slice(None)]
data = self.get_mddata()[view]
if len(data.shape) != 2:
raise ImageError(
"naxispath does not lead to a 2D slice: {}".format(naxispath))
self.naxispath = naxispath
self.revnaxis = revnaxis
self.set_data(data)
def _imload(self, filepath, kwds):
if not have_pil:
raise ImageError("Install 'pillow' to be able "
"to load RGB images")
image = PILimage.open(filepath)
kwds = {}
try:
self._get_header(image, kwds)
except Exception as e:
self.logger.warning("Failed to get image metadata: %s" % (str(e)))
# convert to working color profile, if can
if self.clr_mgr.can_profile():
image = self.clr_mgr.profile_to_working_pil(image, kwds)
# convert from PIL to numpy
data_np = np.array(image)
return data_np
def set_naxispath(self, naxispath):
"""Choose a slice out of multidimensional data.
"""
revnaxis = list(naxispath)
revnaxis.reverse()
# construct slice view and extract it
ndim = min(self.ndim, 2)
view = tuple(revnaxis + [slice(None)] * ndim)
data = self.get_mddata()[view]
if len(data.shape) not in (1, 2):
raise ImageError(
"naxispath does not lead to a 1D or 2D slice: {}".format(
naxispath))
self.naxispath = naxispath
self.revnaxis = revnaxis
self.set_data(data)
def open_file(self, filespec, **kwargs):
info = iohelper.get_fileinfo(filespec)
if not info.ondisk:
raise ImageError("File does not appear to be on disk: %s" %
(info.url))
self.fileinfo = info
filepath = info.filepath
self._path = filepath
self.rgb_f = PILimage.open(filepath)
idx = 0
extver_db = {}
self.hdu_info = []
self.hdu_db = {}
numframes = getattr(self.rgb_f, 'n_frames', 1)
self.logger.info("number of frames: {}".format(numframes))
for idx in range(numframes):
name = "frame{}".format(idx)
extver = 0
# prepare a record of pertinent info about the HDU for
# lookups by numerical index or (NAME, EXTVER)
d = Bunch.Bunch(index=idx,
name=name,
extver=extver,
dtype='uint8',
htype='N/A')
self.hdu_info.append(d)
# different ways of accessing this HDU:
# by numerical index
self.hdu_db[idx] = d
# by (hduname, extver)
key = (name, extver)
if key not in self.hdu_db:
self.hdu_db[key] = d
self.extver_db = extver_db
return self
def _imresize(self, data, new_wd, new_ht, method='bilinear'):
"""Scale an image in numpy array _data_ to the specified width and
height. A smooth scaling is preferred.
"""
old_ht, old_wd = data.shape[:2]
start_time = time.time()
if have_qtimage:
# QImage method is slightly faster and gives a smoother looking
# result than PIL
means = 'QImage'
qimage = numpy2qimage(data)
qimage = qimage.scaled(
new_wd, new_ht, transformMode=QtCore.Qt.SmoothTransformation)
newdata = qimage2numpy(qimage)
elif have_pilutil:
means = 'PIL'
zoom_x = float(new_wd) / float(old_wd)
zoom_y = float(new_ht) / float(old_ht)
if (old_wd >= new_wd) or (old_ht >= new_ht):
# data size is bigger, skip pixels
zoom = max(zoom_x, zoom_y)
else:
zoom = min(zoom_x, zoom_y)
newdata = pilutil.imresize(data, zoom, interp=method)
else:
raise ImageError("No way to scale image smoothly")
end_time = time.time()
self.logger.debug("scaling (%s) time %.4f sec" %
(means, end_time - start_time))
return newdata
except Exception, e:
self.logger.error(
"Error converting from embedded color profile: %s" %
(str(e)))
self.logger.warn("Leaving image unprofiled.")
data_np = numpy.array(image)
elif have_qtimage:
means = 'QImage'
qimage = QImage()
qimage.load(filepath)
data_np = qimage2numpy(qimage)
else:
raise ImageError("No way to load image format '%s/%s'" %
(typ, subtyp))
end_time = time.time()
self.logger.debug("loading (%s) time %.4f sec" %
(means, end_time - start_time))
return data_np
def imload(self, filepath, kwds):
return self._imload(filepath, kwds)
def _imresize(self, data, new_wd, new_ht, method='bilinear'):
"""Scale an image in numpy array _data_ to the specified width and
height. A smooth scaling is preferred.
"""
old_ht, old_wd = data.shape[:2]
start_time = time.time()
def _imload(self, filepath, kwds):
"""Load an image file, guessing the format, and return a numpy
array containing an RGB image. If EXIF keywords can be read
they are returned in the dict _kwds_.
"""
start_time = time.time()
typ, enc = mimetypes.guess_type(filepath)
if not typ:
typ = 'image/jpeg'
typ, subtyp = typ.split('/')
self.logger.debug("MIME type is %s/%s" % (typ, subtyp))
data_loaded = False
if have_opencv and subtyp not in ['gif']:
# First choice is OpenCv, because it supports high-bit depth
# multiband images
means = 'opencv'
data_np = cv2.imread(filepath,
cv2.IMREAD_ANYDEPTH + cv2.IMREAD_ANYCOLOR)
if data_np is not None:
data_loaded = True
# funky indexing because opencv returns BGR images,
# whereas PIL and others return RGB
if len(data_np.shape) >= 3 and data_np.shape[2] >= 3:
data_np = data_np[..., ::-1]
# OpenCv doesn't "do" image metadata, so we punt to piexif
# library (if installed)
self.piexif_getexif(filepath, kwds)
# OpenCv added a feature to do auto-orientation when loading
# (see https://github.com/opencv/opencv/issues/4344)
# So reset these values to prevent auto-orientation from
# happening later
kwds['Orientation'] = 1
kwds['Image Orientation'] = 1
# convert to working color profile, if can
if self.clr_mgr.can_profile():
data_np = self.clr_mgr.profile_to_working_numpy(
data_np, kwds)
if not data_loaded and have_pil:
means = 'PIL'
image = PILimage.open(filepath)
try:
if hasattr(image, '_getexif'):
info = image._getexif()
if info is not None:
for tag, value in info.items():
kwd = TAGS.get(tag, tag)
kwds[kwd] = value
elif have_exif:
self.piexif_getexif(image.info["exif"], kwds)
else:
self.logger.warning(
"Please install 'piexif' module to get image metadata")
except Exception as e:
self.logger.warning("Failed to get image metadata: %s" %
(str(e)))
# convert to working color profile, if can
if self.clr_mgr.can_profile():
image = self.clr_mgr.profile_to_working_pil(image, kwds)
# convert from PIL to numpy
data_np = np.array(image)
if data_np is not None:
data_loaded = True
if (not data_loaded and (typ == 'image')
and (subtyp in ('x-portable-pixmap', 'x-portable-greymap'))):
# Special opener for PPM files, preserves high bit depth
means = 'built-in'
data_np = open_ppm(filepath)
if data_np is not None:
data_loaded = True
if not data_loaded:
raise ImageError("No way to load image format '%s/%s'" %
(typ, subtyp))
end_time = time.time()
self.logger.debug("loading (%s) time %.4f sec" %
(means, end_time - start_time))
return data_np
def load_file(self, filespec, **kwargs):
if self.io is None:
raise ImageError("No IO loader defined")
self.io.load_file(filespec, dstobj=self, **kwargs)
class AstroImage(BaseImage):
"""
Abstraction of an astronomical data (image).
NOTE: this module is NOT thread-safe!
"""
def __init__(self,
data_np=None,
metadata=None,
wcsclass=None,
logger=None):
if not wcsclass:
wcsclass = wcs.WCS
self.wcs = wcsclass()
BaseImage.__init__(self,
data_np=data_np,
metadata=metadata,
logger=logger)
self.iqcalc = iqcalc.IQCalc(logger=logger)
def load_hdu(self, hdu, fobj=None, naxispath=None):
data = hdu.data
if len(data.shape) < 2:
# Expand 1D arrays into 1xN array
data = data.reshape((1, data.shape[0]))
else:
if not naxispath:
naxispath = ([0] * (len(data.shape) - 2))
for idx in naxispath:
data = data[idx]
self.set_data(data)
# Load in FITS header
self.update_keywords(hdu.header)
# Preserve the ordering of the FITS keywords in the FITS file
keyorder = [key for key, val in hdu.header.items()]
self.set(keyorder=keyorder)
# Try to make a wcs object on the header
self.wcs.load_header(hdu.header, fobj=fobj)
def load_file(self, filepath, numhdu=None, naxispath=None):
self.logger.debug("Loading file '%s' ..." % (filepath))
self.set(path=filepath)
fits_f = pyfits.open(filepath, 'readonly')
# this seems to be necessary now for some fits files...
try:
fits_f.verify('fix')
except Exception, e:
raise ImageError("Error loading fits file '%s': %s" %
(fitspath, str(e)))
if numhdu == None:
found_valid_hdu = False
for i in range(len(fits_f)):
hdu = fits_f[i]
if hdu.data == None:
# compressed FITS file or non-pixel data hdu?
continue
if not isinstance(hdu.data, numpy.ndarray):
# We need to open a numpy array
continue
#print "data type is %s" % hdu.data.dtype.kind
# Looks good, let's try it
found_valid_hdu = True
break
if not found_valid_hdu:
raise ImageError(
"No data HDU found that Ginga can open in '%s'" %
(filepath))
else:
hdu = fits_f[numhdu]
self.load_hdu(hdu, fobj=fits_f, naxispath=naxispath)
# Set the name to the filename (minus extension) if no name
# currently exists for this image
name = self.get('name', None)
if name == None:
dirpath, filename = os.path.split(filepath)
name, ext = os.path.splitext(filename)
self.set(name=name)
fits_f.close()
def get_data_xy(self, x, y):
data = self.get_data()
assert (x >= 0) and (y >= 0), \
ImageError("Indexes out of range: (x=%d, y=%d)" % (
x, y))
return data[y, x]
def save_file_as(self, filepath):
if not have_pil:
raise ImageError("Install PIL to be able to save images")
data = self.get_data()
imsave(filepath, data)
