def save_new_object(folder,
test,
cam,
ambient_folder='ambient/',
dark_folder='dark/'):
print 'saving new object'
images = image_list(folder + test + '/' + cam + '_')
ambient = image_list(folder + ambient_folder + cam + '_')
dark = image_list(folder + dark_folder + cam + '_')
im_obj = FiberImage(images, dark=dark, ambient=ambient, camera=cam)
im_obj.save_image(image_file(folder, test, cam))
im_obj.save_object(object_file(folder, test, cam))
def main():
num_images = 1
interval = 1
max_image = 10
cam = 'nf'
# folder = '../data/modal_noise/Kris_data/rectangular_100x300um/baseline/'
# folder = '../data/modal_noise/rv_error/coupled_ag_new/'
folder = '../data/modal_noise/rec_fiber_freq_tests/agitated_15volts_120mm_2s/'
# folder = '../data/stability/2017-03-19 Stability Test/circular_200um/'
# ambient = image_list(folder + '../ambient_2s/' + cam + '_')
ambient = None
# dark = image_list(folder + 'dark/' + cam + '_')
dark = None
# with imageio.get_writer(folder + cam + '_' + str(num_images) + '.gif', mode='I') as writer:
with imageio.get_writer(folder + cam + '_snr.gif', mode='I') as writer:
for i in xrange(0, max_image, interval):
# images = image_list(folder + cam + '_', num=num_images, start=i)
images = image_list(folder + cam + '_', num=i+1, start=0)
image = FiberImage(images, ambient=ambient, dark=dark, camera=cam)
print images[-1]
writer.append_data(image.get_image())
def output_files(folder, f):
return image_list(folder + 'output_' + str(f) + '/ff_')
def ambient_files(folder):
return image_list(folder + 'ambient/ff_')
def dark_files(folder):
return image_list(folder + 'dark/ff_')
def image_list_frd(image_name, f_ratios, **kwargs):
return [
image_list(image_name + str(f) + '/im_', **kwargs) for f in f_ratios
]
from fiber_properties import FiberImage, image_list, show_image_array
folder = '../data/scrambling/2016-08-05 Prototype Core Extension 1/'
dark_folder = folder + 'Dark/'
ambient_folder = folder + 'Ambient/'
in_images = image_list(ambient_folder + 'in_')
nf_images = image_list(ambient_folder + 'nf_')
ff_images = image_list(ambient_folder + 'ff_')
print 'input'
for image in in_images:
print FiberImage(image, camera='in',
ambient=in_images).get_fiber_centroid(method='full',
units='pixels')
print
print 'near field'
for image in nf_images:
nf_obj = FiberImage(image, camera='nf')
print nf_obj.get_fiber_centroid(method='full', units='pixels')
print
print 'far field'
for image in ff_images:
ff_obj = FiberImage(image, camera='ff')
print ff_obj.get_fiber_centroid(method='full', units='pixels')
from fiber_properties import FiberImage, image_list
obj = FiberImage(image_list('pos_1/nf_'),
ambient=image_list('../ambient/nf_'),
camera='ff')
print obj.get_fiber_center(method='rectangle', show_image=True)
from fiber_properties import (scrambling_gain, image_list,
plot_scrambling_gain_input_output,
plot_scrambling_gain, save_plot, show_plots,
load_image_object, FiberImage)
NEW_DATA = False
FOLDER = '../data/EXPRES/rectangular_132/scrambling/'
POSITIONS = ['pos_1', 'pos_2', 'pos_3', 'pos_4', 'pos_5']
if __name__ == '__main__':
if NEW_DATA:
from fiber_properties import FiberImage
in_dark = image_list(FOLDER + '../dark/in_')
in_ambient = image_list(FOLDER + '../ambient/in_')
nf_dark = image_list(FOLDER + '../dark/nf_')
nf_ambient = image_list(FOLDER + '../ambient/nf_')
ff_dark = image_list(FOLDER + '../dark/ff_')
ff_ambient = image_list(FOLDER + '../ambient/ff_')
for pos in POSITIONS:
print 'Initializing ' + pos
in_images = image_list(FOLDER + pos + '/in_')
nf_images = image_list(FOLDER + pos + '/nf_')
ff_images = image_list(FOLDER + pos + '/ff_')
FiberImage(in_images, in_dark, in_ambient, camera='in').save()
FiberImage(nf_images, nf_dark, nf_ambient, camera='nf').save()
FiberImage(ff_images, ff_dark, ff_ambient, camera='ff').save()
from fiber_properties import FiberImage, image_list
obj = FiberImage(image_list('pos_1/nf_'),
ambient=image_list('../ambient/nf_'),
camera='ff')
print obj.get_fiber_center(method='rectangle', show_image=True)
def output_files(folder, f):
return image_list(folder + 'Output ' + str(f) + '/im_')
def input_files(folder, f):
return image_list(folder + 'Input ' + str(f) + '/im_')
def ambient_files(folder):
return image_list(folder + 'Ambient/im_')
def dark_files(folder):
return image_list(folder + 'Dark/im_')
#==== Modal Noise Test =======================================================#
#=============================================================================#
if __name__ == '__main__':
from fiber_properties import FiberImage, image_list, plot_fft
base_folder = '../data/Modal Noise Measurements/2016-07-26/'
ambient_folder = base_folder + 'ambient/600um/'
dark_folder = base_folder + 'dark/'
agitated_folder = base_folder + 'images/600um/agitated/'
unagitated_folder = base_folder + 'images/600um/unagitated/'
nf = {}
ff = {}
nf_dark = image_list(dark_folder + 'nf_dark_')
nf_ambient = image_list(ambient_folder + 'nf_ambient_')
ff_dark = image_list(dark_folder + 'ff_dark_')
ff_ambient = image_list(ambient_folder + 'ff_ambient_')
nf_agitated = FiberImage(image_list(agitated_folder + 'nf_agitated_'),
nf_dark, nf_ambient)
nf_unagitated = FiberImage(
image_list(unagitated_folder + 'nf_unagitated_'), nf_dark, nf_ambient)
nf_baseline = FiberImage(nf_agitated.get_tophat_fit(),
pixel_size=nf_agitated.get_pixel_size(),
threshold=0.1,
camera='nf')
ff_agitated = FiberImage(image_list(agitated_folder + 'ff_agitated_'),
ff_dark, ff_ambient)
from fiber_properties import (scrambling_gain, image_list,
plot_scrambling_gain_input_output,
plot_scrambling_gain, save_plot, show_plots,
load_image_object, FiberImage)
NEW_DATA = False
FOLDER = '../data/EXPRES/rectangular_132/scrambling/'
POSITIONS = ['pos_1', 'pos_2', 'pos_3', 'pos_4', 'pos_5']
if __name__ == '__main__':
if NEW_DATA:
from fiber_properties import FiberImage
in_dark = image_list(FOLDER + '../dark/in_')
in_ambient = image_list(FOLDER + '../ambient/in_')
nf_dark = image_list(FOLDER + '../dark/nf_')
nf_ambient = image_list(FOLDER + '../ambient/nf_')
ff_dark = image_list(FOLDER + '../dark/ff_')
ff_ambient = image_list(FOLDER + '../ambient/ff_')
for pos in POSITIONS:
print 'Initializing ' + pos
in_images = image_list(FOLDER + pos + '/in_')
nf_images = image_list(FOLDER + pos + '/nf_')
ff_images = image_list(FOLDER + pos + '/ff_')
FiberImage(in_images, in_dark, in_ambient, camera='in').save()
FiberImage(nf_images, nf_dark, nf_ambient, camera='nf').save()
FiberImage(ff_images, ff_dark, ff_ambient, camera='ff').save()
from fiber_properties import (scrambling_gain, image_list,
plot_scrambling_gain_input_output,
plot_scrambling_gain, save_plot, show_plots,
load_image_object, FiberImage)
if __name__ == '__main__':
NEW_DATA = False
folder = '../data/scrambling/2016-08-05 Prototype Core Extension 1/'
if NEW_DATA:
from fiber_properties import FiberImage
in_dark = image_list(folder + 'Dark/in_')
in_ambient = image_list(folder + 'Ambient/in_')
nf_dark = image_list(folder + 'Dark/nf_')
nf_ambient = image_list(folder + 'Ambient/nf_')
ff_dark = image_list(folder + 'Dark/ff_')
ff_ambient = image_list(folder + 'Ambient/ff_')
for shift in ['00', '05', '10', '15', '20', '25', '30']:
print 'Initializing Shift ' + shift
in_images = image_list(folder + 'Shift_' + shift + '/in_')
nf_images = image_list(folder + 'Shift_' + shift + '/nf_')
ff_images = image_list(folder + 'Shift_' + shift + '/ff_')
FiberImage(in_images, in_dark, in_ambient, camera='in').save()
FiberImage(nf_images, nf_dark, nf_ambient, camera='nf').save()
FiberImage(ff_images, ff_dark, ff_ambient, camera='ff').save()
shifts = ['00', '05', '10', '15', '20', '25', '30']
from fiber_properties import (scrambling_gain, image_list,
plot_scrambling_gain_input_output,
plot_scrambling_gain, save_plot, show_plots,
load_image_object, FiberImage)
if __name__ == '__main__':
NEW_DATA = False
folder = '../data/scrambling/2016-08-05 Prototype Core Extension 1/'
if NEW_DATA:
from fiber_properties import FiberImage
in_dark = image_list(folder + 'Dark/in_')
in_ambient = image_list(folder + 'Ambient/in_')
nf_dark = image_list(folder + 'Dark/nf_')
nf_ambient = image_list(folder + 'Ambient/nf_')
ff_dark = image_list(folder + 'Dark/ff_')
ff_ambient = image_list(folder + 'Ambient/ff_')
for shift in ['00', '05', '10', '15', '20', '25', '30']:
print 'Initializing Shift ' + shift
in_images = image_list(folder + 'Shift_' + shift + '/in_')
nf_images = image_list(folder + 'Shift_' + shift + '/nf_')
ff_images = image_list(folder + 'Shift_' + shift + '/ff_')
FiberImage(in_images, in_dark, in_ambient, camera='in').save()
FiberImage(nf_images, nf_dark, nf_ambient, camera='nf').save()
FiberImage(ff_images, ff_dark, ff_ambient, camera='ff').save()
shifts = ['00', '05', '10', '15', '20', '25', '30']
from fiber_properties import FiberImage, image_list, show_image_array
folder = '../data/scrambling/2016-08-05 Prototype Core Extension 1/'
dark_folder = folder + 'Dark/'
ambient_folder = folder + 'Ambient/'
in_images = image_list(ambient_folder + 'in_')
nf_images = image_list(ambient_folder + 'nf_')
ff_images = image_list(ambient_folder + 'ff_')
print 'input'
for image in in_images:
print FiberImage(image, camera='in', ambient=in_images).get_fiber_centroid(method='full', units='pixels')
print
print 'near field'
for image in nf_images:
nf_obj = FiberImage(image, camera='nf')
print nf_obj.get_fiber_centroid(method='full', units='pixels')
print
print 'far field'
for image in ff_images:
ff_obj = FiberImage(image, camera='ff')
print ff_obj.get_fiber_centroid(method='full', units='pixels')
#==== Modal Noise Test =======================================================#
#=============================================================================#
if __name__ == '__main__':
from fiber_properties import FiberImage, image_list, plot_fft
base_folder = '../data/Modal Noise Measurements/2016-07-26/'
ambient_folder = base_folder + 'ambient/600um/'
dark_folder = base_folder + 'dark/'
agitated_folder = base_folder + 'images/600um/agitated/'
unagitated_folder = base_folder + 'images/600um/unagitated/'
nf = {}
ff = {}
nf_dark = image_list(dark_folder + 'nf_dark_')
nf_ambient = image_list(ambient_folder + 'nf_ambient_')
ff_dark = image_list(dark_folder + 'ff_dark_')
ff_ambient = image_list(ambient_folder + 'ff_ambient_')
nf_agitated = FiberImage(image_list(agitated_folder + 'nf_agitated_'),
nf_dark, nf_ambient)
nf_unagitated = FiberImage(image_list(unagitated_folder + 'nf_unagitated_'),
nf_dark, nf_ambient)
nf_baseline = FiberImage(nf_agitated.get_tophat_fit(),
pixel_size=nf_agitated.get_pixel_size(),
threshold=0.1, camera='nf')
ff_agitated = FiberImage(image_list(agitated_folder + 'ff_agitated_'),
ff_dark, ff_ambient)
ff_unagitated = FiberImage(image_list(unagitated_folder + 'ff_unagitated_'),