def save_fft_plot(folder, tests, cam, labels, title):
print 'saving fft plot'
fft_info_list = []
for test in tests:
im_obj = FiberImage(object_file(folder, test, cam))
fft_info_list.append(im_obj.get_modal_noise(method='fft'))
min_wavelength = im_obj.pixel_size / im_obj.magnification * 2.0
max_wavelength = im_obj.get_fiber_radius(method='edge', units='microns')
plot_fft(fft_info_list,
labels=labels,
min_wavelength=min_wavelength,
max_wavelength=max_wavelength)
save_plot(folder + 'analysis/' + title + '/' + cam.upper() + '.png',
dpi=600)
save_plot(folder + 'analysis/' + title + '/' + cam.upper() + '.pdf',
dpi=600)
def save_modal_noise_data(folder, tests, cam, methods, title):
print 'saving modal noise data'
modal_noise_info = [['cam', 'test'] + methods]
for i, test in enumerate(tests):
im_obj = FiberImage(object_file(folder, test, cam))
modal_noise_info.append([cam, test])
for method in methods:
modal_noise = im_obj.get_modal_noise(method)
im_obj.save_object(object_file(folder, test, cam))
print cam, test, method, modal_noise
modal_noise_info[i + 1].append(modal_noise)
with open(folder + 'analysis/' + title + '/' + cam.upper() + ' Data.csv',
'wb') as f:
wr = csv.writer(f)
wr.writerows(modal_noise_info)
def main(folder=FOLDER,
cameras=CAMERAS,
num_images=NUM_IMAGES,
new_data=NEW_DATA):
for cam in cameras:
centers = []
for i in xrange(0, 300 - num_images, num_images):
object_file = cam + '_' + str(i).zfill(3) + '-' + str(
i + num_images).zfill(3) + '_obj.pkl'
if object_file not in os.listdir(folder) or new_data:
images = [
folder + cam + '_' + str(j).zfill(3) + '.fit'
for j in xrange(i, i + num_images)
]
ambient = [
folder + 'ambient/' + cam + '_' + str(j).zfill(3) + '.fit'
for j in xrange(min(10, num_images))
]
dark = [
folder + 'dark/' + cam + '_' + str(j).zfill(3) + '.fit'
for j in xrange(min(10, num_images))
]
im_obj = FiberImage(images,
ambient=ambient,
dark=dark,
camera=cam)
im_obj.save_object(folder + object_file)
object_file = folder + object_file
im_obj = FiberImage(object_file)
center = im_obj.get_fiber_center(
method='circle') - im_obj.get_fiber_centroid(method='full')
print center
centers.append(center)
im_obj.save_object(object_file)
plt.figure()
plt.subplot(211)
plt.plot([center.x for center in centers])
plt.subplot(212)
plt.plot([center.y for center in centers])
plt.show()
def save_objects(i, cam, method):
obj_file = cam + '_obj_' + str(i).zfill(3) + '.pkl'
if obj_file not in os.listdir(FOLDER):
print 'saving ' + cam + '_' + str(i).zfill(3)
im_file = FOLDER + cam + '_' + str(i).zfill(3) + '.fit'
obj = FiberImage(im_file, threshold=1000)
obj.set_fiber_center(method=method,
radius_tol=.03, radius_range=64,
center_tol=.03, center_range=64)
obj.set_fiber_centroid(method=method)
obj.save_object(FOLDER + obj_file)
def testMethod(method):
tol = 1
test_range = 1
factor = 1.0
y_err = []
x_err = []
d_err = []
for i in xrange(10):
image_height = 2000
image_width = 2000
x0 = image_width * 0.5 + np.random.rand()
y0 = image_height * 0.5 + np.random.rand()
radius = min(image_height, image_width) * 0.25 + np.random.rand()
amp = 10000
dark_level = 500
num_images = 1
num_darks = 10
mesh_grid = np.meshgrid(np.arange(image_width), np.arange(image_height))
dark_image = [dark_level * (np.random.rand(image_height, image_width) + 0.5) for i in xrange(num_darks)]
# noise = [0 for i in xrange(num_images)]
noise = [np.random.rand(image_height, image_width) - 0.5 for i in xrange(num_images)]
if method == 'edge' or method == 'radius':
test_array = amp * circle_array(mesh_grid, x0, y0, radius, res=10) + dark_level
test_array = [test_array + np.sqrt(test_array) * noise[i] * 30 for i in xrange(num_images)]
elif method == 'gaussian':
test_array = gaussian_array(mesh_grid, x0, y0, radius, amp, dark_level).reshape(image_height, image_width)
test_array = [test_array + np.sqrt(test_array) * noise[i] * 30 for i in xrange(num_images)]
im_obj = FiberImage(test_array, dark=dark_image, magnification=1, pixel_size=1)
center_y, center_x, diameter = im_obj.get_fiber_data(method=method, units='pixels',
tol=tol, test_range=test_range)
y_err.append(center_y - y0)
x_err.append(center_x - x0)
d_err.append(diameter - radius*2)
return y_err, x_err, d_err
def save_objects(i, cam, method):
obj_file = cam + '_obj_' + str(i).zfill(3) + '.pkl'
if obj_file not in os.listdir(FOLDER):
print 'saving ' + cam + '_' + str(i).zfill(3)
im_file = FOLDER + cam + '_' + str(i).zfill(3) + '.fit'
obj = FiberImage(im_file)
obj.set_fiber_center(method=method, **OPTIONS)
obj.set_fiber_centroid(method=method, **OPTIONS)
obj.save_object(FOLDER + obj_file)
def save_baseline_object(folder,
test,
cam,
best_test,
fiber_method='edge',
kernel=None):
print 'saving new baseline object'
im_obj = FiberImage(object_file(folder, best_test, cam))
baseline = baseline_image(im_obj,
stdev=im_obj.get_dark_image().std(),
fiber_method=fiber_method,
kernel_size=kernel)
baseline_obj = FiberImage(baseline,
camera=cam,
pixel_size=im_obj.pixel_size)
baseline_obj.save_image(image_file(folder, test, cam))
baseline_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 input_objects(folder, in_f):
if NEW_OBJECTS:
for f in in_f:
print 'Saving ' + folder + 'input_' + str(f)
im_obj = FiberImage(input_files(folder, f),
dark=dark_files(folder),
ambient=ambient_files(folder),
input_fnum=f,
threshold=FRD_CALIBRATION_THRESHOLD,
camera='ff')
im_obj.save_object(folder + 'input_' + str(f) + '/ff_object.pkl')
im_obj.save_image(folder + 'input_' + str(f) + '/ff_corrected.fit')
return [folder + 'input_' + str(f) + '/ff_object.pkl' for f in in_f]
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 set_new_data(folder, test, cam, methods, fiber_method='edge', kernel=None):
im_obj = FiberImage(object_file(folder, test, cam))
radius_factor = None
if 'rectang' in test:
radius_factor = 0.3
print 'setting new data'
for method in methods:
print 'setting method ' + method
im_obj.set_modal_noise(method,
fiber_method=fiber_method,
kernel_size=kernel,
radius_factor=radius_factor)
im_obj.save_object(object_file(folder, test, cam))
print
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()
in_objs = [FOLDER + pos + '/in_object.pkl' for pos in POSITIONS]
nf_objs = [FOLDER + pos + '/nf_object.pkl' for pos in POSITIONS]
nf_scrambling = scrambling_gain(in_objs, nf_objs, input_method='rectangle', output_method='rectangle')
print nf_scrambling.in_d
print nf_scrambling.out_d
print nf_scrambling.scrambling_gain
print 'Minimum NF scrambling:', min(nf_scrambling.scrambling_gain)
print 'Maximum NF scrambling:', max(nf_scrambling.scrambling_gain)
plot_scrambling_gain_input_output(nf_scrambling, 'NF Centroid Shift')
name = 'Near Field'
method = 'radius'
if camera == 'ff':
name = 'Far Field'
method = 'gaussian'
print name
data_dict[camera] = {}
for test in TESTS:
data_dict[camera][test] = deepcopy(base_dict)
for i in xrange(NUM_IMAGES):
data_FOLDER = FOLDER[test] + camera + '_' + str(i).zfill(
3) + '_data.p'
obj = FiberImage(image_input=None, image_data=data_FOLDER)
y0, x0, diameter = obj.get_fiber_data(method=method,
units='microns')
if camera == 'in':
gauss_y, gauss_x = obj.get_fiber_center(method='gaussian',
units='microns')
data_dict[camera][test]['x0'].append(gauss_x - x0)
data_dict[camera][test]['y0'].append(gauss_y - y0)
else:
data_dict[camera][test]['x0'].append(x0)
data_dict[camera][test]['y0'].append(y0)
data_dict[camera][test]['diameter'].append(diameter)
data_dict[camera][test]['time'].append(
obj.get_image_info('date_time'))
for prop in ['x0', 'y0', 'diameter', 'time']:
'0.1Hz 160mm 80s',
'1.0Hz 160mm 1s',
'1.0Hz 160mm 10s',
'baseline']
# def object_file(folder, test, cam):
# return folder + test + '/' + cam + '_obj.pkl'
if __name__ == '__main__':
for cam in CAMS:
# save_modal_noise_data(FOLDERS, TESTS, cam, labels, METHODS, TITLES)
modal_noise = []
for title, folder, test in zip(TITLES, FOLDERS, TESTS):
modal_noise.append([])
for t in test:
# mn = []
# for im in xrange(10):
# im_obj = FiberImage(object_file(folder + t, cam, 1, im))
# mn.append(im_obj.get_modal_noise(method=METHOD))
# mn = np.array(mn).mean()
im_obj = FiberImage(object_file(folder + t, cam, 10, 0))
mn = im_obj.get_modal_noise(method=METHOD)
modal_noise[-1].append(mn)
print t, mn
plot_modal_noise(modal_noise, plot_type='bar', bar_labels=LABELS, labels=TITLES, method=METHOD)
save_plot(FOLDER + 'analysis/' + TITLE + ' ' + cam.upper() + ' ' + METHOD + ' SNR.png')
# save_plot(FOLDER + 'analysis/' + TITLE + ' ' + cam.upper() + ' ' + METHOD + ' SNR.pdf')
amp = 40000
fft_list = []
freq_list = []
label_list = []
perfect_image = amp * circle_array(mesh_grid, height / 2, width / 2,
0.8 * min(height, width))
# perfect_image = gaussian_array(mesh_grid, height/2, width/2, 0.8*min(height,width), amp, 0).reshape(height, width)
for factor in [0.1, 0.2, 0.4, 0.8, 1.0]:
test_image = perfect_image * factor
baseline_image = np.zeros_like(test_image)
for i in xrange(10):
baseline_image += np.random.poisson(test_image) / 10
baseline_obj = FiberImage(baseline_image, pixel_size=3.45, camera='nf')
fft, freq = modal_noise(baseline_obj,
method='fft',
output='array',
radius_factor=1.0,
show_image=False)
fft_list.append(fft)
freq_list.append(freq)
label_list.append('baseline_' + str(factor))
plot_fft(freq_list[:-1], [fft_list[i] - fft_list[-1] for i in xrange(4)],
labels=label_list[:-1],
title='Modal Noise Baseline Test')
show_plot()
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')
if CASE == 4:
TITLE = 'Amplitude vs Frequency NSR'
FOLDER += 'amp_freq_200um/'
TITLES = ['']
FOLDERS = [FOLDER]
TESTS = [[
'agitated_5volts_40mm_8s', 'agitated_30volts_40mm_1s',
'agitated_5volts_160mm_8s', 'agitated_30volts_160mm_1s', 'baseline'
]]
LABELS = [
'0.1Hz 40mm', '1.0Hz 40mm', '0.1Hz 160mm', '1.0Hz 160mm', 'LED source'
]
def object_file(folder, test, cam):
return folder + test + '/' + cam + '_obj.pkl'
if __name__ == '__main__':
for cam in CAMS:
modal_noise = []
for title, folder, test in zip(TITLES, FOLDERS, TESTS):
modal_noise.append([])
for t in test:
im_obj = FiberImage(object_file(folder, t, cam))
modal_noise[-1].append(im_obj.get_modal_noise(method=METHOD))
plot_modal_noise(modal_noise, LABELS, TITLES, method=METHOD)
save_plot(FOLDER + 'analysis/' + TITLE + '/' + cam.upper() + '.png')
save_plot(FOLDER + 'analysis/' + TITLE + '/' + cam.upper() + '.pdf')
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 testMethod(method):
tol = 1
test_range = 1
factor = 1.0
y_err = []
x_err = []
d_err = []
for i in xrange(10):
image_height = 2000
image_width = 2000
x0 = image_width * 0.5 + np.random.rand()
y0 = image_height * 0.5 + np.random.rand()
radius = min(image_height, image_width) * 0.25 + np.random.rand()
amp = 10000
dark_level = 500
num_images = 1
num_darks = 10
mesh_grid = np.meshgrid(np.arange(image_width),
np.arange(image_height))
dark_image = [
dark_level * (np.random.rand(image_height, image_width) + 0.5)
for i in xrange(num_darks)
]
# noise = [0 for i in xrange(num_images)]
noise = [
np.random.rand(image_height, image_width) - 0.5
for i in xrange(num_images)
]
if method == 'edge' or method == 'radius':
test_array = amp * circle_array(mesh_grid, x0, y0, radius,
res=10) + dark_level
test_array = [
test_array + np.sqrt(test_array) * noise[i] * 30
for i in xrange(num_images)
]
elif method == 'gaussian':
test_array = gaussian_array(mesh_grid, x0, y0, radius, amp,
dark_level).reshape(
image_height, image_width)
test_array = [
test_array + np.sqrt(test_array) * noise[i] * 30
for i in xrange(num_images)
]
im_obj = FiberImage(test_array,
dark=dark_image,
magnification=1,
pixel_size=1)
center_y, center_x, diameter = im_obj.get_fiber_data(
method=method, units='pixels', tol=tol, test_range=test_range)
y_err.append(center_y - y0)
x_err.append(center_x - x0)
d_err.append(diameter - radius * 2)
return y_err, x_err, d_err
from fiber_properties import gaussian_fit, gaussian_array, FiberImage, plot_overlaid_cross_sections
import numpy as np
import matplotlib.pyplot as plt
# image = np.meshgrid(np.arange(100), np.arange(100))
# coeffs = np.random.normal(size=5) * 15 + 50
# print coeffs
# image = gaussian_array(image, *coeffs).reshape(100,100).astype('float64')
# image += np.random.normal(size=image.shape)
# im_obj = FiberImage('../data/modal_noise/Kris_data/rectangular_100x300um/coupled_agitation/ff_corrected.fit')
im_obj = FiberImage(
'../data/stability/2017-03-19 Stability Test/circular_200um/in_100.fit',
camera='in')
image = im_obj.get_image()
center = im_obj.get_fiber_center()
plt.figure(1)
plt.imshow(image)
# fit, output = gaussian_fit(image, full_output=True)
# print output
fit = im_obj.get_gaussian_fit()
plt.figure(2)
plt.imshow(fit)
plot_overlaid_cross_sections(image, fit, center)
plt.show()
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 FiberImage # image = 'C:/Libraries/Box Sync/ExoLab/Fiber_Characterization/Image Analysis/data/' \ # + 'modal_noise/coupled_fibers/200-200um_test2/agitated_both/nf_obj.pkl' image = 'C:/Libraries/Box Sync/ExoLab/Fiber_Characterization/Image Analysis/data/modal_noise/Kris_data/rectangular_100x300um/coupled_agitation/nf_obj.pkl' im_obj = FiberImage(image) # im_obj.get_modal_noise(method='fft', new=True, show_image=True) im_obj.set_modal_noise(method='filter', new=True, show_image=False, kernel_size=31) # print im_obj.get_modal_noise(method='filter') im_obj.save_object(image)
from fiber_properties import gaussian_fit, gaussian_array, FiberImage, plot_overlaid_cross_sections
import numpy as np
import matplotlib.pyplot as plt
# image = np.meshgrid(np.arange(100), np.arange(100))
# coeffs = np.random.normal(size=5) * 15 + 50
# print coeffs
# image = gaussian_array(image, *coeffs).reshape(100,100).astype('float64')
# image += np.random.normal(size=image.shape)
# im_obj = FiberImage('../data/modal_noise/Kris_data/rectangular_100x300um/coupled_agitation/ff_corrected.fit')
im_obj = FiberImage('../data/stability/2017-03-19 Stability Test/circular_200um/in_100.fit', camera='in')
image = im_obj.get_image()
center = im_obj.get_fiber_center()
plt.figure(1)
plt.imshow(image)
# fit, output = gaussian_fit(image, full_output=True)
# print output
fit = im_obj.get_gaussian_fit()
plt.figure(2)
plt.imshow(fit)
plot_overlaid_cross_sections(image, fit, center)
plt.show()
import time
from scipy.signal import medfilt2d, order_filter
from scipy.ndimage.filters import median_filter, gaussian_filter, convolve
import numpy as np
from fiber_properties import FiberImage, filter_image, plot_image, show_plots, crop_image, plot_overlaid_cross_sections, Pixel
import pstats, cProfile
kernel = 51
# a = 2**16 * np.random.rand(100, 100)
# a = FiberImage('../data/modal_noise/Kris_data/rectangular_100x300um/unagitated/nf_corrected.fit').get_image()
im_obj = FiberImage('../data/modal_noise/coupled_fibers/200-200um_test2/agitated_both/nf_corrected.fit', threshold=1000)
radius = im_obj.get_fiber_radius(method='edge')
center = im_obj.get_fiber_center(method='edge')
a, new_center = crop_image(im_obj.get_image(), center, radius*1.1)
plot_image(a)
start = time.time()
b = filter_image(a, kernel, square=False)
end = time.time()
print(end-start)
plot_image(b)
# start = time.time()
# c = filter_image(a, kernel, quick=False, cython=False, zero_fill=True)
# end = time.time()
# print(end-start)
# plot_image(c)
# start = time.time()
# d = filter_image(a, kernel, quick=False, cython=True, zero_fill=False)
method = FF_METHOD
if NEW_DATA:
if PARALLELIZE:
pool = Pool(processes=PROCESSES)
pool.map(partial(save_objects, cam=cam, method=method),
range(NUM_IMAGES))
else:
for i in xrange(NUM_IMAGES):
save_objects(i, cam, method)
for i in xrange(NUM_IMAGES):
obj_file = cam + '_obj_' + str(i).zfill(3) + '.pkl'
print 'loading ' + cam + '_' + str(i).zfill(3)
obj = FiberImage(FOLDER + obj_file)
data[cam].center.append(obj.get_fiber_center(method=method, **OPTIONS))
data[cam].centroid.append(obj.get_fiber_centroid(method=method, **OPTIONS))
data[cam].x_diff.append(data[cam].centroid[-1].x - data[cam].center[-1].x)
data[cam].y_diff.append(data[cam].centroid[-1].y - data[cam].center[-1].y)
# data[cam].x_diff.append(data[cam].centroid[-1].x)
# data[cam].y_diff.append(data[cam].centroid[-1].y)
data[cam].diameter.append(obj.get_fiber_diameter(method=method, **OPTIONS))
data[cam].time.append(obj.date_time)
obj.save_object(FOLDER + obj_file)
avg_x_diff = np.median(data[cam].x_diff)
avg_y_diff = np.median(data[cam].y_diff)
init_time = np.copy(data[cam].time[0])
for i in xrange(NUM_IMAGES):
data[cam].x_diff[i] -= avg_x_diff
from fiber_properties import FiberImage
# image = 'C:/Libraries/Box Sync/ExoLab/Fiber_Characterization/Image Analysis/data/' \
# + 'modal_noise/coupled_fibers/200-200um_test2/agitated_both/nf_obj.pkl'
image = 'C:/Libraries/Box Sync/ExoLab/Fiber_Characterization/Image Analysis/data/modal_noise/Kris_data/rectangular_100x300um/coupled_agitation/nf_obj.pkl'
im_obj = FiberImage(image)
# im_obj.get_modal_noise(method='fft', new=True, show_image=True)
im_obj.set_modal_noise(method='filter',
new=True,
show_image=False,
kernel_size=31)
# print im_obj.get_modal_noise(method='filter')
im_obj.save_object(image)
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_'), ff_dark, ff_ambient)
ff_baseline = FiberImage(ff_agitated.get_gaussian_fit(),
pixel_size=ff_agitated.get_pixel_size(),
magnification=1,
camera='ff')
from fiber_properties import polynomial_fit, polynomial_array, FiberImage, crop_image, plot_overlaid_cross_sections
import numpy as np
import matplotlib.pyplot as plt
# image = np.meshgrid(np.arange(100), np.arange(100))
# coeffs = np.random.rand(10) * 10.0
# print coeffs
# image = polynomial_array(image, *coeffs).reshape(100,100).astype('float64')
# image += np.random.normal(size=image.shape)*5.0
im_obj = FiberImage('../data/modal_noise/Kris_data/rectangular_100x300um/coupled_agitation/ff_corrected.fit')
image = im_obj.get_image()
center = im_obj.get_fiber_center()
# radius = im_obj.get_fiber_radius()
# image, center = crop_image(image, center=center, radius=radius/2)
plt.figure(1)
plt.imshow(image)
# fit = polynomial_fit(image, 6)
fit = im_obj.get_polynomial_fit()
plt.figure(2)
plt.imshow(fit)
plot_overlaid_cross_sections(image, fit, center)
plt.show()
if camera == 'nf':
name = 'Near Field'
method = 'radius'
if camera == 'ff':
name = 'Far Field'
method = 'gaussian'
print name
data_dict[camera] = {}
for test in TESTS:
data_dict[camera][test] = deepcopy(base_dict)
for i in xrange(NUM_IMAGES):
data_FOLDER = FOLDER[test] + camera + '_' + str(i).zfill(3) + '_data.p'
obj = FiberImage(image_input=None, image_data=data_FOLDER)
y0, x0, diameter = obj.get_fiber_data(method=method, units='microns')
if camera == 'in':
gauss_y, gauss_x = obj.get_fiber_center(method='gaussian', units='microns')
data_dict[camera][test]['x0'].append(gauss_x-x0)
data_dict[camera][test]['y0'].append(gauss_y-y0)
else:
data_dict[camera][test]['x0'].append(x0)
data_dict[camera][test]['y0'].append(y0)
data_dict[camera][test]['diameter'].append(diameter)
data_dict[camera][test]['time'].append(obj.get_image_info('date_time'))
for prop in ['x0', 'y0', 'diameter', 'time']:
data_dict[camera][test][prop] = (np.array(data_dict[camera][test][prop])
- data_dict[camera][test][prop][0])
data_dict[camera][test]['r0'] = np.sqrt(data_dict[camera][test]['x0']**2
from fiber_properties import polynomial_fit, polynomial_array, FiberImage, crop_image, plot_overlaid_cross_sections
import numpy as np
import matplotlib.pyplot as plt
# image = np.meshgrid(np.arange(100), np.arange(100))
# coeffs = np.random.rand(10) * 10.0
# print coeffs
# image = polynomial_array(image, *coeffs).reshape(100,100).astype('float64')
# image += np.random.normal(size=image.shape)*5.0
im_obj = FiberImage('../data/modal_noise/Kris_data/rectangular_100x300um/coupled_agitation/ff_corrected.fit')
image = im_obj.get_image()
center = im_obj.get_fiber_center()
# radius = im_obj.get_fiber_radius()
# image, center = crop_image(image, center=center, radius=radius/2)
plt.figure(1)
plt.imshow(image)
# fit = polynomial_fit(image, 6)
fit = im_obj.get_polynomial_fit()
plt.figure(2)
plt.imshow(fit)
plot_overlaid_cross_sections(image, fit, center)
plt.show()
method = FF_METHOD
if NEW_DATA:
if PARALLELIZE:
pool = Pool(processes=PROCESSES)
pool.map(partial(save_objects, cam=cam, method=method),
range(NUM_IMAGES))
else:
for i in xrange(NUM_IMAGES):
save_objects(i, cam, method)
for i in xrange(NUM_IMAGES):
obj_file = cam + '_obj_' + str(i).zfill(3) + '.pkl'
print 'loading ' + cam + '_' + str(i).zfill(3)
obj = FiberImage(FOLDER + obj_file)
data[cam].center.append(obj.get_fiber_center(method=method, units='microns'))
data[cam].centroid.append(obj.get_fiber_centroid(method=method, units='microns'))
data[cam].x_diff.append(data[cam].centroid[-1].x - data[cam].center[-1].x)
data[cam].y_diff.append(data[cam].centroid[-1].y - data[cam].center[-1].y)
data[cam].diameter.append(obj.get_fiber_diameter(method=method, units='microns'))
data[cam].time.append(obj.date_time)
obj.save_object(FOLDER + obj_file)
if cam == 'in':
data['spot'].center.append(obj.get_fiber_center(method='gaussian', units='microns'))
data['spot'].centroid.append(obj.get_fiber_centroid(method='gaussian', units='microns'))
data['spot'].x_diff.append(data['spot'].center[-1].x - data[cam].center[-1].x)
data['spot'].y_diff.append(data['spot'].center[-1].y - data[cam].center[-1].y)
data['spot'].diameter.append(obj.get_fiber_diameter(method='gaussian', units='microns'))
data['spot'].time.append(obj.date_time)
obj.save_object(FOLDER + obj_file)
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, circle_array
import numpy as np
mesh_grid = np.meshgrid(np.arange(1000), np.arange(1000))
base = 50000.0 * circle_array(mesh_grid, 500, 500, 50, res=16) + 400.0
dark = np.random.normal(base, 5.0 * np.sqrt(base)).astype('float64')
# dark = np.random.poisson(25.0*base).astype('float64') / 25.0
FiberImage(dark).save_image('noise_test.fit')
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_'),
ff_dark, ff_ambient)
ff_baseline = FiberImage(ff_agitated.get_gaussian_fit(),
pixel_size=ff_agitated.get_pixel_size(),
magnification=1, camera='ff')
print()