def test_imread_collection_single_MEF():
io.use_plugin('fits')
testfile = os.path.join(data_dir, 'multi.fits')
ic1 = io.imread_collection(testfile)
ic2 = io.ImageCollection([(testfile, 1), (testfile, 2), (testfile, 3)],
load_func=fplug.FITSFactory)
assert _same_ImageCollection(ic1, ic2)
def test_imread_collection_single_MEF():
io.use_plugin('fits')
testfile = os.path.join(data_dir, 'multi.fits')
ic1 = io.imread_collection(testfile)
ic2 = io.ImageCollection([(testfile, 1), (testfile, 2), (testfile, 3)],
load_func=fplug.FITSFactory)
assert _same_ImageCollection(ic1, ic2)
def test_imread_collection_MEF_and_simple():
io.use_plugin('fits')
testfile1 = os.path.join(data_dir, 'multi.fits')
testfile2 = os.path.join(data_dir, 'simple.fits')
ic1 = io.imread_collection([testfile1, testfile2])
ic2 = io.ImageCollection([(testfile1, 1), (testfile1, 2),
(testfile1, 3), (testfile2, 0)],
load_func=fplug.FITSFactory)
assert _same_ImageCollection(ic1, ic2)
def test_imread_collection_MEF_and_simple():
io.use_plugin('fits')
testfile1 = os.path.join(data_dir, 'multi.fits')
testfile2 = os.path.join(data_dir, 'simple.fits')
ic1 = io.imread_collection([testfile1, testfile2])
ic2 = io.ImageCollection([(testfile1, 1), (testfile1, 2), (testfile1, 3),
(testfile2, 0)],
load_func=fplug.FITSFactory)
assert _same_ImageCollection(ic1, ic2)
def test_fits_plugin_import():
# Make sure we get an import exception if PyFITS isn't there
# (not sure how useful this is, but it ensures there isn't some other
# error when trying to load the plugin)
try:
io.use_plugin('fits')
except ImportError:
assert pyfits_available == False
else:
assert pyfits_available == True
def test_fits_plugin_import():
# Make sure we get an import exception if PyFITS isn't there
# (not sure how useful this is, but it ensures there isn't some other
# error when trying to load the plugin)
try:
io.use_plugin('fits')
except ImportError:
assert pyfits_available == False
else:
assert pyfits_available == True
def main():
import scikits.image.io as io
import sys
if len(sys.argv) != 2:
print "Usage: scivi <image-file>"
sys.exit(-1)
io.use_plugin("qt")
io.imshow(io.imread(sys.argv[1]), fancy=True)
io.show()
def main():
import scikits.image.io as io
import sys
if len(sys.argv) != 2:
print "Usage: scivi <image-file>"
sys.exit(-1)
io.use_plugin('qt')
io.imshow(io.imread(sys.argv[1]), fancy=True)
io.show()
def main():
import scikits.image.io as io
import sys
if len(sys.argv) < 2:
print "Usage: scivi <image-file> [<flip-file>]"
sys.exit(-1)
io.use_plugin('qt2')
im = io.imread(sys.argv[1])
flip = None
if len(sys.argv) > 2:
flip = io.imread(sys.argv[2])
io.imshow(im, flip=flip, fancy=True)
io.show()
#!/usr/bin/env python
# From EPD webinar
#
import sys;
from itertools import izip;
import numpy as np;
import scipy.ndimage as ndi;
from PIL import Image,ImageDraw;
from scikits.image import io;
io.use_plugin('qt');
def hough(img,theta=None):
"""
Apply a linear hough transform to the input image
Input:
- img <ndarray> : Thresholded input image (ndim=2,dtype=float)
- theta <ndarray> : Angle range to search in trsnform space. (ndim=1,dtype=float)
If None, defaults to (-pi/2,pi/2)
Output:
- hough <ndarray> : The hough transform coefficients (ndim=2,dtype=float)
- dists <ndarray> : Distance values (ndim=1,dtype=float)
- theta <ndarray> : Angle values (ndim=1,dtype=float)
---
"""
import os.path
import numpy as np
from numpy.testing import *
from tempfile import NamedTemporaryFile
from scikits.image import data_dir
from scikits.image.io import imread, imsave, use_plugin
from scikits.image.io._plugins.pil_plugin import _palette_is_grayscale
use_plugin('pil')
def test_imread_flatten():
# a color image is flattened
img = imread(os.path.join(data_dir, 'color.png'), flatten=True)
assert img.ndim == 2
assert img.dtype == np.float64
img = imread(os.path.join(data_dir, 'camera.png'), flatten=True)
# check that flattening does not occur for an image that is grey already.
assert np.sctype2char(img.dtype) in np.typecodes['AllInteger']
def test_imread_palette():
img = imread(os.path.join(data_dir, 'palette_gray.png'))
assert img.ndim == 2
img = imread(os.path.join(data_dir, 'palette_color.png'))
assert img.ndim == 3
def test_palette_is_gray():
from PIL import Image
gray = Image.open(os.path.join(data_dir, 'palette_gray.png'))
assert _palette_is_grayscale(gray)
def test_imread_simple():
io.use_plugin('fits')
testfile = os.path.join(data_dir, 'simple.fits')
img = io.imread(testfile)
assert np.all(img==pyfits.getdata(testfile, 0))
import os.path
import numpy as np
from numpy.testing import *
from tempfile import NamedTemporaryFile
from scikits.image import data_dir
from scikits.image.io import imread, imsave, use_plugin
from scikits.image.io._plugins.pil_plugin import _palette_is_grayscale
use_plugin('pil')
def test_imread_flatten():
# a color image is flattened
img = imread(os.path.join(data_dir, 'color.png'), flatten=True)
assert img.ndim == 2
assert img.dtype == np.float64
img = imread(os.path.join(data_dir, 'camera.png'), flatten=True)
# check that flattening does not occur for an image that is grey already.
assert np.sctype2char(img.dtype) in np.typecodes['AllInteger']
def test_imread_palette():
img = imread(os.path.join(data_dir, 'palette_gray.png'))
assert img.ndim == 2
img = imread(os.path.join(data_dir, 'palette_color.png'))
assert img.ndim == 3
def test_palette_is_gray():
def test_imread_simple():
io.use_plugin('fits')
testfile = os.path.join(data_dir, 'simple.fits')
img = io.imread(testfile)
assert np.all(img == pyfits.getdata(testfile, 0))
def test_imread_MEF():
io.use_plugin('fits')
testfile = os.path.join(data_dir, 'multi.fits')
img = io.imread(testfile)
assert np.all(img == pyfits.getdata(testfile, 1))
import os
import scikits.image as si
import scikits.image.io as sio
sio.use_plugin('matplotlib', 'imshow')
sio.use_plugin('freeimage', 'imread')
img = sio.imread(os.path.join(si.data_dir, 'color.png'))
sio.imshow(img)
sio.show()
def test_imread_MEF():
io.use_plugin('fits')
testfile = os.path.join(data_dir, 'multi.fits')
img = io.imread(testfile)
assert np.all(img==pyfits.getdata(testfile, 1))
from numpy.testing import *
from scikits.image import io
from scikits.image.io._plugins import plugin
from numpy.testing.decorators import skipif
from copy import deepcopy
try:
io.use_plugin('pil')
PIL_available = True
priority_plugin = 'pil'
except ImportError:
PIL_available = False
try:
io.use_plugin('freeimage')
FI_available = True
priority_plugin = 'freeimage'
except OSError:
FI_available = False
def setup_module(self):
self.backup_plugin_store = deepcopy(plugin.plugin_store)
plugin.use('test') # see ../_plugins/test_plugin.py
def teardown_module(self):
plugin.plugin_store = self.backup_plugin_store
class TestPlugin:
