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import numpy as np
from EllipsoidFit import ellipsoidFit
import pickle
from scipy.interpolate import interp1d
from scipy.optimize import minimize
from tqdm import tqdm
import matplotlib.pyplot as plt
from time import sleep
def cumulative_interp(k):
c = np.cumsum(k, axis=0)
c_interp_0 = interp1d(range(0, len(k)), c[:,0], kind='linear', fill_value='extrapolate')
c_interp_1 = interp1d(range(0, len(k)), c[:,1], kind='linear', fill_value='extrapolate')
return (c_interp_0, c_interp_1)
def calculate_drift(E):
v, radii, center = E
A, B, C, D, E, F = v
sx = (D*F - B*E) / (A*B - D**2)
sy = (D*E - A*F) / (A*B - D**2)
return sx, sy
def driftEstimate(ellipsoids, z_bounds, drift_spread=7, minimum_N=2):
drift_parameters = []
for E in ellipsoids:
v, radii, center = E
A, B, C, D, E, F = v
sx = (D*F - B*E) / (A*B - D**2)
sy = (D*E - A*F) / (A*B - D**2)
drift_parameters.append([sx, sy, center[0], center[1], center[2]])
z_lower, z_upper = z_bounds
drift_bins = [[] for i in range(z_lower, z_upper)]
drift_components = []
for param in drift_parameters:
drift_component = param[:2]
center_z = param[4]
center_k = int(round(center_z))
drift_components.append(drift_component)
for k in range(center_k - drift_spread, center_k + drift_spread + 1):
if k >= z_lower and k < z_upper:
k_array = k - z_lower
drift_bins[k_array].append(drift_component)
drift_bins_means = [np.mean(np.array(bin_group), axis=0) if len(bin_group) >= minimum_N else np.array([0, 0]) for bin_group in drift_bins]
drift_bins_std = [np.std(np.array(bin_group), axis=0) if len(bin_group) >= minimum_N else np.array([-1, -1]) for bin_group in drift_bins]
drift_bins_N = [len(bin_group) for bin_group in drift_bins]
# Interpolation, filling missing values etc., here
drift_estimate = np.array(drift_bins_means)
drift_std = np.array(drift_bins_std)
drift_bins_N = np.array(drift_bins_N)
return drift_estimate, drift_std, drift_bins_N, drift_components
def drift_points(points, c_interp):
new_points = []
c_interp_0, c_interp_1 = c_interp
for X in points:
new_X = np.copy(X)
new_X[:,0] += c_interp_0(X[:,2])
new_X[:,1] += c_interp_1(X[:,2])
new_points.append(new_X)
return new_points
def driftEstimate2(points, z_bounds):
drift_parameters = []
n = z_bounds[1] - z_bounds[0]
k = np.zeros((2*(n-1), 1), dtype=np.float32)
def callback(xk):
print(xk[:10:2])
def energy(x):
k = np.empty((n, 2), dtype=np.float32)
k[0,0] = 0
k[0,1] = 0
k[1:,:] = x.reshape((n-1, 2))
c_interp = cumulative_interp(k)
new_points = drift_points(points, c_interp)
energy = 0
for X in new_points:
if X.shape[0] >= 10:
E = ellipsoidFit(X)
sx, sy = calculate_drift(E)
print(sx, sy)
energy += abs(sx)**2 + abs(sy)**2
return energy
res = minimize(energy, k, callback=callback)
x = np.zeros(2*n, dtype=np.float32)
x[2:] = res.x
return x.reshape((n, 2))
def driftEstimate3(points, z_bounds):
drift_parameters = []
n = z_bounds[1] - z_bounds[0]
k = np.zeros((n, 2), dtype=np.float32)
n_passes = 1
for i in tqdm(range(n_passes), desc='n passes'):
print('k before:', k)
for j in tqdm(range(1, n), desc='slices'):
def energy(x):
k_in = np.copy(k)
k_in[j,:] = x
c_interp = cumulative_interp(k_in)
new_points = drift_points(points, c_interp)
energy = 0
for X in new_points:
if X.shape[0] >= 10:
E = ellipsoidFit(X)
sx, sy = calculate_drift(E)
energy += sx**2 + sy**2
return energy
#plt.plot(np.linspace(-0.4, 0.4, 15), [energy([x,0]) for x in np.linspace(-0.4, 0.4, 15)])
#plt.show()
res = minimize(energy, k[j,:])
k[j,:] = res.x
print('k after',i, ':', k)
return k
if __name__ == '__main__':
if False:
import matplotlib.pyplot as plt
data_path = '/home/dith/arngorf/code/fibsem_tools/data/image_transforms/synthetic_linear/'
transforms_path = data_path + 'transforms.txt'
points_path = data_path + 'vesicle_ellipsoid_manual_points_fixed.txt'
true_drift = []
with open(transforms_path, 'r') as f:
for line in f.readlines():
formated_line = list(map(float, line.rstrip().split(' ')))
true_drift.append([formated_line[0], formated_line[1]])
true_drift = np.array(true_drift)
vesicle_points = []
cur_vidx = -1
with open(points_path, 'r') as f:
for line in f.readlines():
fline = line.rstrip().split(' ')
vidx, x, y, z = int(fline[0]), float(fline[1]), float(fline[2]), float(fline[3])
if vidx != cur_vidx:
cur_vidx = vidx
if len(vesicle_points) > 0:
vesicle_points[-1] = np.array(vesicle_points[-1])
vesicle_points.append([])
vesicle_points[-1].append([x,y,z])
vesicle_points[-1] = np.array(vesicle_points[-1])
ellipsoids = []
for X in vesicle_points:
if X.shape[0] >= 10:
E = ellipsoidFit(X)
ellipsoids.append(E)
results = driftEstimate(ellipsoids, (0, 200))
results2 = driftEstimate3(vesicle_points, (0, 200))
plt.subplot(2,1,1)
plt.plot(true_drift[:,0], label='true', color='black', ls='--')
plt.plot(results[0][:,1], label='original')
plt.plot(results2[:,1], label='new')
plt.xlim(0,199)
plt.subplot(2,1,2)
plt.plot(true_drift[:,1], label='true', color='black', ls='--')
plt.plot(results[0][:,0], label='original')
plt.plot(results2[:,0], label='new')
plt.xlim(0,199)
plt.show()
sx = 0.25
sy = 0.0
a = 5
b = 5
c = 5
vesicle_test_points = []
for vidx in range(10):
vesicle_test_points.append([])
for u in np.linspace(0.1234, 2*np.pi, 9)[:-1]:
for v in np.linspace(0.1234, np.pi, 9)[:-1]:
x = a * np.cos(u) * np.sin(v) + np.random.uniform(-0.2, 0.2)
y = b * np.sin(u) * np.sin(v) + np.random.uniform(-0.2, 0.2)
z = c * np.cos(v) + np.random.uniform(-0.2, 0.2)
z += 3 + vidx
x += sx * z
y += sy * z
vesicle_test_points[-1].append([x,y,z])
vesicle_test_points[-1] = np.array(vesicle_test_points[-1])
X = np.round(vesicle_test_points[0],4)
print(X)
E = ellipsoidFit(X)
E_X_sx, E_X_sy = calculate_drift(E)
for i in range(16):
plt.subplot(4,4,i+1)
k = np.ones((10,2))
k[:,0] = -2 + i*0.25
k[:,1] = 0
#k[4,0] = 2
c_interp = cumulative_interp(k)
new_points = drift_points([X], c_interp)
plt.scatter(X[:,0], X[:,2], label='before, sx = ' + str(E_X_sx), marker='o')
Y = new_points[-1]
print(Y)
E = ellipsoidFit(Y)
E_Y_sx, E_Y_sy = calculate_drift(E)
plt.scatter(Y[:,0], Y[:,2], label='after, sx = ' + str(E_Y_sx), marker='x')
plt.legend()
plt.show()
sxs = np.linspace(-3, 3, 1000)
sxs_fit_sq = []
for sx in sxs:
k = np.ones((10,2))
k[:,0] = sx
k[:,1] = 0
#k[4,0] = 2
c_interp = cumulative_interp(k)
new_points = drift_points([X], c_interp)
Y = new_points[-1]
E = ellipsoidFit(Y)
E_Y_sx, E_Y_sy = calculate_drift(E)
sxs_fit_sq.append(E_Y_sx**2)
plt.plot(sxs, sxs_fit_sq)
plt.show()
r = driftEstimate3(vesicle_test_points, (0, 6))
print(np.round(r,2))