def from_center(self, center, width = None, nsamples = all):
"""Initialize worm model from center line and width
Arguments:
center_line (nx2 array): points of center line
width (array or None): width of worm at reference points, if None use initial guess
nsamples (int or all): number of intermediate samples points
"""
theta, orientation, xy, length = wormgeo.theta_from_center_discrete(center, npoints = self.npoints,
nsamples = nsamples, resample=False, smooth=0);
#points = np.linspace(0,1,self.npoints-2);
#self.theta.set_parameter_from_values(theta, points = points);
self.theta.set_values(theta);
self.xy = xy;
self.orientation = orientation;
self.length = length;
if width is not None:
#points = np.linspace(0,1,width.shape[0]);
#self.width.set_parameter_from_values(width, points = points);
self.width.set_values(width);
def from_center(self, center, width=None, nsamples=all):
"""Initialize worm model from center line and width
Arguments:
center_line (nx2 array): points of center line
width (array or None): width of worm at reference points, if None use initial guess
nsamples (int or all): number of intermediate samples points
"""
theta, orientation, xy, length = wormgeo.theta_from_center_discrete(
center,
npoints=self.npoints,
nsamples=nsamples,
resample=False,
smooth=0)
#points = np.linspace(0,1,self.npoints-2);
#self.theta.set_parameter_from_values(theta, points = points);
self.theta = theta
self.xy = xy
self.orientation = orientation
self.length = length
if width is not None:
#points = np.linspace(0,1,width.shape[0]);
#self.width.set_parameter_from_values(width, points = points);
self.width = width
def from_shape(self,
left,
right,
center=None,
width=None,
nsamples=all,
nneighbours=all):
"""Initialize worm model from the left and right border lines
Arguments:
left (nx2 array): points of left border line
right (nx2 array): points of right border line
center (nx2 array): optional center line
width (n array): optional width of the worm
nsamples (int): number of intermediate sample points
"""
# get center line and width profile
if width is None:
with_width = True
else:
with_width = False
if center is None or width is None:
cw = wormgeo.center_from_sides_discrete(left,
right,
with_width=with_width,
nsamples=nsamples,
npoints=nsamples,
resample=False,
smooth=0,
nneighbours=nneighbours,
method='projection')
if width is None:
center, width = cw
else:
center = cw
npoints = self.theta.npoints
theta, orientation, xy, length = wormgeo.theta_from_center_discrete(
center,
npoints=npoints,
nsamples=nsamples,
resample=False,
smooth=0)
# non uniform version
#oints = np.linspace(0,1,npoints-2);
#self.theta.set_parameter_from_values(theta, points = points);
#points = np.linspace(0,1,nsamples);
#self.width.set_parameter_from_values(width, points = points);
#theta and width uniform
self.theta.set_values(theta)
self.width.set_values(width)
self.xy = xy
self.orientation = orientation
self.length = length
def curve(self, mode_amplitudes):
"""Change curvature properties of the worm
Arguments:
mode_amplitudes (number or array): additional power in the first fourier modes of the worms angles
"""
#changes curvature by the mode amplitudes;
#cos = np.cos(self.theta); -> ok to use theta directly
theta, orientation, xy, length = wormgeo.theta_from_center_discrete(self.center);
t = np.fft.rfft(theta);
mode_amplitudes = np.array(mode_amplitudes);
t[:mode_amplitudes.shape[0]] += mode_amplitudes;
theta = np.fft.irfft(t, n = self.npoints-2);
self.center = wormgeo.center_from_theta_discrete(theta, orientation, xy, length);
def curve(self, mode_amplitudes):
"""Change curvature properties of the worm
Arguments:
mode_amplitudes (number or array): additional power in the first fourier modes of the worms angles
"""
#changes curvature by the mode amplitudes;
#cos = np.cos(self.theta); -> ok to use theta directly
theta, orientation, xy, length = wormgeo.theta_from_center_discrete(
self.center)
t = np.fft.rfft(theta)
mode_amplitudes = np.array(mode_amplitudes)
t[:mode_amplitudes.shape[0]] += mode_amplitudes
theta = np.fft.irfft(t, n=self.npoints - 2)
self.center = wormgeo.center_from_theta_discrete(
theta, orientation, xy, length)
def bend(self, bend, exponent = 4, head = True):
"""Change curvature properties of the worm
Arguments:
bend (number): bending amplitude
exponent (number): expoential modulation of the bending
head (bool): if True bend head side otherwise tail side
"""
#head tail bend profile
theta, orientation, xy, length = wormgeo.theta_from_center_discrete(self.center);
n2 = theta.shape[0]//2;
if head:
theta[:n2-1] += bend * np.exp(-exponent * np.linspace(0,1,n2-1));
else:
theta[-(n2-1):] += bend * np.exp(-exponent * np.linspace(1,0,n2-1));
self.center = wormgeo.center_from_theta_discrete(theta, orientation, xy, length);
def from_shape(self, left, right, center = None, width = None, nsamples = all, nneighbours = all):
"""Initialize worm model from the left and right border lines
Arguments:
left (nx2 array): points of left border line
right (nx2 array): points of right border line
center (nx2 array): optional center line
width (n array): optional width of the worm
nsamples (int): number of intermediate sample points
"""
# get center line and width profile
if width is None:
with_width = True;
else:
with_width = False;
if center is None or width is None:
cw = wormgeo.center_from_sides_discrete(left, right, with_width = with_width,
nsamples = nsamples, npoints = nsamples, resample = False,
smooth=0, nneighbours=nneighbours,
method='projection');
if width is None:
center, width = cw;
else:
center = cw;
npoints = self.theta.npoints;
theta, orientation, xy, length = wormgeo.theta_from_center_discrete(center, npoints = npoints,
nsamples = nsamples, resample=False, smooth=0);
# non uniform version
#oints = np.linspace(0,1,npoints-2);
#self.theta.set_parameter_from_values(theta, points = points);
#points = np.linspace(0,1,nsamples);
#self.width.set_parameter_from_values(width, points = points);
#theta and width uniform
self.theta.set_values(theta);
self.width.set_values(width);
self.xy = xy;
self.orientation = orientation;
self.length = length;
def bend(self, bend, exponent=4, head=True):
"""Change curvature properties of the worm
Arguments:
bend (number): bending amplitude
exponent (number): expoential modulation of the bending
head (bool): if True bend head side otherwise tail side
"""
#head tail bend profile
theta, orientation, xy, length = wormgeo.theta_from_center_discrete(
self.center)
n2 = theta.shape[0] // 2
if head:
theta[:n2 -
1] += bend * np.exp(-exponent * np.linspace(0, 1, n2 - 1))
else:
theta[-(n2 - 1):] += bend * np.exp(
-exponent * np.linspace(1, 0, n2 - 1))
self.center = wormgeo.center_from_theta_discrete(
theta, orientation, xy, length)
g = tf.Graph()
sess = tf.Session();
#%%
fids = range(300000, 400000, 1000)[:batch_size];
img = [exp.image_data(frame = f, smooth = True) for f in fids];
imgc = [to_color(i) for i in img];
img_pre = np.array([vgg.preprocess(i, mean_pixel) for i in imgc])
features = net['relu5_4'].eval(feed_dict={image: img_pre}, session = sess)
features_flat = np.reshape(features, (batch_size, -1));
centers =[exp.shape_data(frame = f)[:2,:] for f in fids];
thetas = np.array([wgeo.theta_from_center_discrete(c.T)[0] for c in centers]);
#%% Regression
from sklearn import linear_model
from sklearn.metrics import mean_squared_error, r2_score
model = linear_model.LinearRegression()
model.fit(features_flat, thetas)
#%% Test
fids_test = range(300500, 400500, 1000)[:batch_size];
fids = range(600000, 600000+40000); #len(data_shape));
ds = data_shape[fids, :2,:];
#ds = np.reshape(ds, (ds.shape[0], -1));
print ds.shape
#%% calculate
import worm.geometry as wgeo
data_theta = np.zeros((len(ds), data_shape.shape[-1]-2));
for i in range(len(ds)):
if i%100==0:
print('%d/%d' % (i,len(ds)));
theta,a,b,c = wgeo.theta_from_center_discrete(ds[i].T);
data_theta[i,:] = theta;
#%%
#%%
import sklearn.manifold as sl;
n_components = 2;
metric = 'euclidean';
tsne = sl.TSNE(n_components=n_components, init = 'pca', random_state = 10, metric = metric)
Y = tsne.fit_transform(data_theta)
#%% tsne of shapes
def theta(self): return wormgeo.theta_from_center_discrete(self.center);
g = tf.Graph()
sess = tf.Session()
#%%
fids = range(300000, 400000, 1000)[:batch_size]
img = [exp.image_data(frame=f, smooth=True) for f in fids]
imgc = [to_color(i) for i in img]
img_pre = np.array([vgg.preprocess(i, mean_pixel) for i in imgc])
features = net['relu5_4'].eval(feed_dict={image: img_pre}, session=sess)
features_flat = np.reshape(features, (batch_size, -1))
centers = [exp.shape_data(frame=f)[:2, :] for f in fids]
thetas = np.array([wgeo.theta_from_center_discrete(c.T)[0] for c in centers])
#%% Regression
from sklearn import linear_model
from sklearn.metrics import mean_squared_error, r2_score
model = linear_model.LinearRegression()
model.fit(features_flat, thetas)
#%% Test
fids_test = range(300500, 400500, 1000)[:batch_size]
img = [exp.image_data(frame=f, smooth=True) for f in fids_test]
imgc = [to_color(i) for i in img]
def theta(self):
return wormgeo.theta_from_center_discrete(self.center)