def test_otsu_camera_image():
camera = skimage.img_as_ubyte(data.camera())
assert 86 < threshold_otsu(camera) < 88
def test_otsu_lena_image():
img = skimage.img_as_ubyte(data.lena())
with expected_warnings(['grayscale']):
assert 140 < threshold_otsu(img) < 142
def test_bimodal_multiotsu_hist():
image = data.camera()
thr_otsu = threshold_otsu(image)
thr_multi = threshold_multiotsu(image, classes=2)
assert thr_otsu == thr_multi
def test_otsu_astro_image():
img = skimage.img_as_ubyte(data.astronaut())
with expected_warnings(['grayscale']):
assert 109 < threshold_otsu(img) < 111
def test_otsu_negative_int(self):
image = self.image - 2
assert threshold_otsu(image) == 0
def test_otsu_one_color_image():
img = np.ones((10, 10), dtype=np.uint8)
with pytest.raises(ValueError):
threshold_otsu(img)
def test_otsu_coins_image():
coins = skimage.img_as_ubyte(data.coins())
assert 106 < threshold_otsu(coins) < 108
def test_otsu_coins_image():
coins = util.img_as_ubyte(data.coins())
assert 106 < threshold_otsu(coins) < 108
def test_otsu_coins_image_as_float():
coins = util.img_as_float(data.coins())
assert 0.41 < threshold_otsu(coins) < 0.42
def test_otsu_camera_image_counts():
camera = util.img_as_ubyte(data.camera())
counts, bin_centers = histogram(camera.ravel(), 256, source_range='image')
assert 101 < threshold_otsu(hist=counts) < 103
def test_otsu_one_color_image_3d():
img = np.ones((10, 10, 10), dtype=np.uint8)
assert threshold_otsu(img) == 1
def test_otsu_camera_image_histogram():
camera = util.img_as_ubyte(data.camera())
hist = histogram(camera.ravel(), 256, source_range='image')
assert 101 < threshold_otsu(hist=hist) < 103
def test_otsu_camera_image():
camera = util.img_as_ubyte(data.camera())
assert 101 < threshold_otsu(camera) < 103
import scipy.misc
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
from skimage.measure import label
from scipy.misc.pilutil import Image
from skimage.measure import regionprops
from skimage.filters.thresholding import threshold_otsu
# opening the image and converting it to grayscale
a = Image.open('../Figures/objects.png').convert('L')
# a is converted to an ndarray
a = scipy.misc.fromimage(a)
# threshold value is determined by
# using Otsu's method
thresh = threshold_otsu(a)
# the pixels with intensity greater than
# theshold are kept
b = a > thresh
# labelling is performed on b
c = label(b)
# c is converted from an ndarray to an image
c1 = scipy.misc.toimage(c)
# c1 is saved as label_output.png
c1.save('../Figures/label_output.png')
# on the labelled image c, regionprops is performed
d = regionprops(c)
# the following command creates an empty plot of
# dimension 6 inch by 6 inch
def test_otsu_lena_image():
img = skimage.img_as_ubyte(data.lena())
assert 140 < threshold_otsu(img) < 142
def test_otsu_astro_image():
img = util.img_as_ubyte(data.astronaut())
with expected_warnings(['grayscale']):
assert 109 < threshold_otsu(img) < 111
def test_otsu_astro_image():
img = skimage.img_as_ubyte(data.astronaut())
assert 109 < threshold_otsu(img) < 111
def test_otsu_one_color_image():
img = np.ones((10, 10), dtype=np.uint8)
with testing.raises(ValueError):
threshold_otsu(img)
def test_otsu_camera_image():
camera = skimage.img_as_ubyte(data.camera())
assert 86 < threshold_otsu(camera) < 88
def test_otsu(self):
assert threshold_otsu(self.image) == 2
def test_otsu_coins_image_as_float():
coins = skimage.img_as_float(data.coins())
assert 0.41 < threshold_otsu(coins) < 0.42
def test_otsu_negative_int(self):
image = self.image - 2
assert threshold_otsu(image) == 0
def test_otsu(self):
assert threshold_otsu(self.image) == 2
def test_otsu_float_image(self):
image = np.float64(self.image)
assert 2 <= threshold_otsu(image) < 3
def test_otsu_float_image(self):
image = np.float64(self.image)
assert 2 <= threshold_otsu(image) < 3
def console_estimate_response_matrix():
# parse arguments
parser = argparse.ArgumentParser()
parser.add_argument(
'config',
type=str,
help=
'Name of the configuration file to use (don\'t include the path or extension).'
)
parser.add_argument(
'--fits_file',
type=str,
default=None,
help=
'Explicitly specify the fits file to process. If not given, defaults to latest interaction matrix in the calib directory.'
)
parser.add_argument('--nproc',
type=int,
default=2,
help='Number of processes to spawn. [Default: 2]')
#parser.add_argument('--paramfunc','-p', type=str, default=None, help='Function from which to define fitting parameters. Specified as module.function Default: get_magaox_fitting_params')
parser.add_argument(
'--override',
'-o',
type=str,
default=None,
nargs='+',
help=
'Space-delimited list of config parameters to override, set as section.option=value'
)
args = parser.parse_args()
# get parameters from config file
config_params = Configuration(args.config)
# update with override args, if any
if args.override is not None:
override = parse_override_args(args.override)
config_params.update_from_dict(override)
validate_calibration_directory(config_params)
calib_path = config_params.get_param('calibration', 'path', str)
sympath_in = path.join(calib_path, 'measrespM.fits')
# find and read in fits cube
if args.fits_file is not None:
pathname = args.fits_file
else:
pathname = sympath_in
with fits.open(pathname) as f:
image_cube = f[0].data
logger.info(f'Performing estimation for {image_cube.shape} FITS file.')
# get fitting params
calib_func = config_params.get_param('calibration', 'function', str)
mname, fname = calib_func.rsplit('.', 1)
mod = import_module(mname)
param_func = getattr(mod, fname)
# get default steps
config_steps = config_params.get_param('calibration', 'default_steps', str)
mname, fname = config_steps.rsplit('.', 1)
mod = import_module(mname)
default_steps = getattr(mod, fname)
# get default steps (no xy)
config_steps = config_params.get_param('calibration', 'default_steps_noxy',
str)
mname, fname = config_steps.rsplit('.', 1)
mod = import_module(mname)
default_steps_noxy = getattr(mod, fname)
fitting_params = param_func(
camera=config_params.get_param('camera', 'name', str),
wfilter=config_params.get_param('diversity', 'wfilter', str),
mask=config_params.get_param('estimation', 'pupil', str),
N=config_params.get_param('estimation', 'N', int),
divtype=config_params.get_param('diversity', 'type', str),
divvals=config_params.get_param('diversity', 'values', float),
npad=config_params.get_param('estimation', 'npad', int),
nzernikes=config_params.get_param('estimation', 'nzernike', int),
)
estdict = estimate_response_matrix(
image_cube,
fitting_params,
processes=config_params.get_param('estimation', 'nproc', int),
gpus=config_params.get_param('estimation', 'gpus', int),
fix_xy_to_first=config_params.get_param('interaction',
'fix_xy_to_first', bool),
default_steps=default_steps,
steps_noxy=default_steps_noxy)
pupil = fitting_params['pupil_analytic'].astype(bool)
estrespM = np.asarray(estdict['phase']) * pupil
# amplitude thresholding
amp = np.mean(estdict['amp'], axis=0)
amp_norm = amp / np.mean(amp[pupil])
thresh_amp = threshold_otsu(amp_norm)
threshold = config_params.get_param('control', 'ampthreshold', float)
amp_mask = amp_norm > (thresh_amp * threshold)
estrespM *= amp_mask
date = datetime.now().strftime("%Y%m%d-%H%M%S")
outname = path.join(calib_path, 'estrespM', f'estrespM_{date}.fits')
# write phases out to file
fits.writeto(outname, estrespM)
# replace symlink
sympath_out = path.join(calib_path, 'estrespM.fits')
replace_symlink(sympath_out, outname)