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Replace numpy with math for scalar functions and remove Dfun from ellipse residuals for speedup

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Johannes Schönberger
2013-03-01 09:50:26 +01:00
committed by Johannes Schönberger
parent 444d51ceb7
commit dd714b910c
1 changed files with 28 additions and 22 deletions
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skimage/measure/fit.py
+28 -22
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@@ -1,3 +1,4 @@
import math
import numpy as np
from scipy import optimize
@@ -56,7 +57,7 @@ class LineModel(BaseModel):
# angle perpendicular to line angle
theta = (theta + np.pi / 2) % np.pi
# line always passes through mean
dist = X0[0] * np.cos(theta) + X0[1] * np.sin(theta)
dist = X0[0] * math.cos(theta) + X0[1] * math.sin(theta)
self._params = (dist, theta)
@@ -82,7 +83,7 @@ class LineModel(BaseModel):
x = data[:, 0]
y = data[:, 1]
return dist - (x * np.cos(theta) + y * np.sin(theta))
return dist - (x * math.cos(theta) + y * math.sin(theta))
@classmethod
def is_degenerate(cls, data):
@@ -122,7 +123,7 @@ class LineModel(BaseModel):
if params is None:
params = self._params
dist, theta = params
return (dist - y * np.cos(theta)) / np.cos(theta)
return (dist - y * math.cos(theta)) / math.cos(theta)
def predict_y(self, x, params=None):
'''Predict y-coordinates using the estimated model.
@@ -144,7 +145,7 @@ class LineModel(BaseModel):
if params is None:
params = self._params
dist, theta = params
return (dist - x * np.cos(theta)) / np.sin(theta)
return (dist - x * math.cos(theta)) / math.sin(theta)
class CircleModel(BaseModel):
@@ -343,8 +344,8 @@ class EllipseModel(BaseModel):
ct = np.cos(t)
st = np.sin(t)
ctheta = np.cos(theta)
stheta = np.sin(theta)
ctheta = math.cos(theta)
stheta = math.sin(theta)
# derivatives for fx, fy in the following order:
# xc, yc, a, b, theta, t_i
@@ -393,26 +394,31 @@ class EllipseModel(BaseModel):
xc, yc, a, b, theta = self._params
ctheta = math.cos(theta)
stheta = math.sin(theta)
x = data[:, 0]
y = data[:, 1]
N = data.shape[0]
def fun(t, xi, yi):
xt, yt = self.predict_xy(t)
ct = math.cos(t)
st = math.sin(t)
xt = xc + a * ctheta * ct - b * stheta * st
yt = yc + a * stheta * ct + b * ctheta * st
return (xi - xt)**2 + (yi - yt)**2
def Dfun(t, xi, yi):
xt, yt = self.predict_xy(t)
ct = np.cos(t)
st = np.sin(t)
ctheta = np.cos(theta)
stheta = np.sin(theta)
dfx_t = - 2 * (xi - xt) * (- a * ctheta * st
- b * stheta * ct)
dfy_t = - 2 * (yi - yt) * (- a * stheta * st
+ b * ctheta * ct)
return dfx_t + dfy_t
# def Dfun(t, xi, yi):
# ct = math.cos(t)
# st = math.sin(t)
# xt = xc + a * ctheta * ct - b * stheta * st
# yt = yc + a * stheta * ct + b * ctheta * st
# dfx_t = - 2 * (xi - xt) * (- a * ctheta * st
# - b * stheta * ct)
# dfy_t = - 2 * (yi - yt) * (- a * stheta * st
# + b * ctheta * ct)
# return [dfx_t + dfy_t]
residuals = np.empty((N, ), dtype=np.double)
@@ -423,8 +429,8 @@ class EllipseModel(BaseModel):
for i in range(N):
xi = x[i]
yi = y[i]
t, _ = optimize.leastsq(fun, t0[i], args=(xi, yi), Dfun=Dfun,
col_deriv=True)
# faster without Dfun, because of the python overhead
t, _ = optimize.leastsq(fun, t0[i], args=(xi, yi))
residuals[i] = np.sqrt(fun(t, xi, yi))
return residuals
@@ -473,8 +479,8 @@ class EllipseModel(BaseModel):
ct = np.cos(t)
st = np.sin(t)
ctheta = np.cos(theta)
stheta = np.sin(theta)
ctheta = math.cos(theta)
stheta = math.sin(theta)
x = xc + a * ctheta * ct - b * stheta * st
y = yc + a * stheta * ct + b * ctheta * st