def getSDSSImage(ra,dec,radius=1.0,xsize=800,opt='GML',**kwargs):
"""
Download Sloan Digital Sky Survey images
http://skyserver.sdss3.org/dr9/en/tools/chart/chart.asp
radius (degrees)
opts: (G) Grid, (L) Label, P (PhotoObj), S (SpecObj), O (Outline), (B) Bounding Box,
(F) Fields, (M) Mask, (Q) Plates, (I) Invert
"""
import subprocess
import tempfile
url="http://skyservice.pha.jhu.edu/DR10/ImgCutout/getjpeg.aspx?"
scale = 2. * radius * 3600. / xsize
params=dict(ra=ra,dec=dec,
width=xsize,height=xsize,
scale=scale,opt=opt)
query='&'.join("%s=%s"%(k,v) for k,v in params.items())
tmp = tempfile.NamedTemporaryFile(suffix='.jpeg')
cmd='wget --progress=dot:mega -O %s "%s"'%(tmp.name,url+query)
subprocess.call(cmd,shell=True)
im = pylab.imread(tmp.name)
tmp.close()
return im
python类imread()的实例源码
EvaluationModel.py 文件源码
项目:A-Neural-Algorithm-of-Artistic-Style
作者: kbedi95
项目源码
文件源码
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def __init__(self, content_path, style_path):
# 4D representations of the given content and style images
self.content_image = utils.preprocess(plt.imread(content_path))[np.newaxis]
self.style_image = utils.preprocess(plt.imread(style_path))[np.newaxis]
# The session and graph used for evaluating the content and style of the
# given content and style images
self.evaluation_g = tf.Graph()
self.evaluation_sess = tf.Session(graph=self.evaluation_g)
# The outputs (:0) of the intermediate layers of the VGG16 model used to represent the
# content and style of an input to the model
self.content_layer = config["content_layer"]
self.style_layers = config["style_layers"]
with self.evaluation_g.as_default():
# Import the VGG16 ImageNet predictor model graph into the evaluation_g member variable
tf.import_graph_def(utils.get_vgg_model(), name="vgg")
# The input to the VGG16 predictor model is the output (:0) of the first operation of the graph
self.input_tensor = [op.name for op in self.evaluation_g.get_operations()][0] + ":0"
def __init__(self, lens_file=None, lens_psf_size=None, lens_grid_size=None):
if lens_file:
self.lens = True
grid = scipy.misc.imread(lens_file, flatten=True)
if np.max(grid) > 255:
grid /= 2**(16-1)
else:
grid /= 255
self.grid_gpu = cu.matrix_to_array(grid, 'C')
self.lens_psf_size = lens_psf_size
self.lens_grid_size = lens_grid_size
else:
self.lens = False
def getDSSImage(ra,dec,radius=1.0,xsize=800,**kwargs):
"""
Download Digitized Sky Survey images
https://archive.stsci.edu/cgi-bin/dss_form
https://archive.stsci.edu/cgi-bin/dss_search
Image is in celestial orientation (RA increases to the right)
https://archive.stsci.edu/dss/script_usage.html
ra (r) - right ascension
dec (d) - declination
equinox (e) - equinox (B1950 or J2000; default: J2000)
height (h) - height of image (arcminutes; default: 15.0)
width (w) - width of image (arcminutes; default: 15.0)
format (f) - image format (FITS or GIF; default: FITS)
compression (c) - compression (UNIX, GZIP, or NONE; default: NONE; compression
applies to FITS only)
version (v) - Which version of the survey to use:
1 - First Generation survey (garden variety)
2 - Second generation survey (incomplete)
3 - Check the 2nd generation; if no image is available,
then go to the 1st generation.
4 - The Quick V survey (whence came the Guide Stars Catalog;
used mostly for Phase II proposal submission)
save (s) - Save the file to disk instead of trying to display.
(ON (or anything) or not defined; default: not defined.)
"""
import subprocess
import tempfile
url="https://archive.stsci.edu/cgi-bin/dss_search?"
scale = 2.0 * radius * 60.
params=dict(ra='%.3f'%ra,dec='%.3f'%dec,width=scale,height=scale,
format='gif',version=1)
#v='poss2ukstu_red'
query='&'.join("%s=%s"%(k,v) for k,v in params.items())
tmp = tempfile.NamedTemporaryFile(suffix='.gif')
cmd='wget --progress=dot:mega -O %s "%s"'%(tmp.name,url+query)
subprocess.call(cmd,shell=True)
im = pylab.imread(tmp.name)
tmp.close()
if xsize: im = scipy.misc.imresize(im,size=(xsize,xsize))
return im
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