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()
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)
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
+ data_dict[camera][test]['y0']**2)
for i, time in enumerate(data_dict[camera][test]['time']):
data_dict[camera][test]['time'][i] = time.total_seconds()
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
data[cam].y_diff[i] -= avg_y_diff
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()
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)
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_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)