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_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 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
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_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))
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 'in' in CAMS:
CAMS += ['spot']
for cam in CAMS:
init_x_diff = np.copy(data[cam].x_diff[0])
init_y_diff = np.copy(data[cam].y_diff[0])
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
data[cam].time[i] -= init_time
data[cam].time[i] = data[cam].time[i].total_seconds() / 60.0
plot_stability(data[cam], cam)
plt.savefig(FOLDER + cam + '_stability.png')
plot_stability_binned(data[cam], cam, BIN_SIZE)
plt.savefig(FOLDER + cam + '_stability_binned.png')
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 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)