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
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
+ data_dict[camera][test]['y0']**2)
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
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]) -