def fit_ellipse(myimg, X, disp_log):
debug_graphics = False
#disp_log=True
EllipseFit = []
reg = el.LsqEllipse().fit(X)
center, width, height, phi = reg.as_parameters()
EllipseFit = [center, width, height, phi]
#section=((baryY-center[1])/center[1])
XE = reg.return_fit(n_points=2000)
if disp_log or debug_graphics:
print()
print(f'center: {center[0]:.3f}, {center[1]:.3f}')
print(f'width: {width*2:.3f}')
print(f'height: {height*2:.3f}')
print(f'phi: {np.rad2deg(phi):.3f}')
if debug_graphics:
plt.imshow(myimg)
# plot ellipse in blue
ellipse = Ellipse(xy=center,
width=2 * width,
height=2 * height,
angle=np.rad2deg(phi),
edgecolor='b',
fc='None',
lw=1,
label='Fit',
zorder=2)
# plot edges on image as red dots
np_m = np.asarray(X)
xm, ym = np_m.T
plt.scatter(xm, ym, s=0.1, marker='.', edgecolors=('red'))
ax = plt.gca()
ax.add_patch(ellipse)
plt.show()
return EllipseFit, XE
#https://github.com/bdhammel/least-squares-ellipse-fitting
#To install: pip install lsq-ellipse
import pandas as pd
import math
import ellipse as el
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.patches import Ellipse
# X1, X2 = el.make_test_ellipse()
df = pd.read_csv("outputData.txt", sep=" ")
df.columns = ["x", "y", "z"]
X = np.array(list(zip(df["x"], df["y"])))
reg = el.LsqEllipse().fit(X)
center, width, height, phi = reg.as_parameters()
print("Ellipse Parameter:", center, width, height, phi)
plt.close('all')
fig = plt.figure(figsize=(6, 6))
ax = fig.add_subplot(111)
ax.axis('equal')
ax.plot(df["x"], df["y"], 'ro', label='Messungen', zorder=1)
ellipse = Ellipse(xy=center,
width=2 * width,
height=2 * height,
angle=np.rad2deg(phi),
edgecolor='b',
fc='None',
def best_fit(self, points):
r"""Find ellipse of best fit for points
This function computes the ellipse of best fit for a set of points.
It calls the `least-squares-ellipse-fitting`_ package, which implements
a published fitting algorithm in Python. [#halir]_
The current instance of the class is used as an initial guess for
the ellipse of best fit. Since an ellipse can be expressed multiple
ways (e.g. rotate 90 degrees and flip the axes), this initial guess
is used to choose from the multiple parameter sets.
Args:
points (list or numpy.ndarray): An Nx2 list of points to fit.
Returns:
.Ellipse: An instance of the class that best fits the points.
.. _`least-squares-ellipse-fitting`: https://github.com/bdhammel/least-squares-ellipse-fitting
.. [#halir] Halir, R., Flusser, J., "Numerically Stable Direct Least
Squares Fitting of Ellipses," *6th International Conference in Central
Europe on Computer Graphics and Visualization*, Vol. 98, 1998.
(http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.1.7559&rep=rep1&type=pdf)
""" # NOQA: E501
pts = np.array(points)
pt_cen = pts.mean(axis=0)
pts -= pt_cen.reshape(1, -1)
reg = lsqel.LsqEllipse().fit(pts)
center, width, height, phi = reg.as_parameters()
xc, yc = center
xc += pt_cen[0]
yc += pt_cen[1]
# Find pair closest to self
s = np.sin(phi)
c = np.cos(phi)
rot = np.array([[c, -s], [s, c]])
x_ax_seed = np.array(self.matrix)[:, 0]
x_dot, y_dot = rot.T.dot(x_ax_seed)
if np.abs(x_dot) > np.abs(y_dot):
if x_dot > 0:
x_ax_fit = rot[:, 0]
else:
x_ax_fit = -rot[:, 0]
a = width
b = height
else:
if y_dot > 0:
x_ax_fit = rot[:, 1]
else:
x_ax_fit = -rot[:, 1]
a = height
b = width
ang_diff = np.arcsin(np.cross(x_ax_seed, x_ax_fit))
ang_rad = self.angle_rad + ang_diff
return type(self)(center=(xc, yc), a=a, b=b, angle_rad=ang_rad)
def detect_fit_ellipse(myimg, y1, y2, zexcl):
edgeX = []
edgeY = []
cercle_edge = []
#detect si pas de limbes droits et/ou gauche
print()
y1, y2 = detect_bord(myimg, axis=1, offset=5) # bords verticaux
x1, x2 = detect_bord(myimg, axis=0, offset=5) # bords horizontaux
toprint = 'Position X des limbes droit et gauche x1, x2 : ' + str(
x1) + ' ' + str(x2)
mylog.append(toprint + '\n')
print(toprint)
iw = myimg.shape[1]
TailleX = int(x2 - x1)
if TailleX + 10 < int(iw / 5) or TailleX + 10 > int(iw * .99):
toprint = 'Pas de limbe solaire pour determiner la geometrie'
print(toprint)
mylog.append(toprint + '\n')
toprint = 'Reprendre les traitements en manuel avec ISIS'
print(toprint)
mylog.append(toprint + '\n')
#print(TailleX, iw)
ratio = 0.5
EllipseFit = [0, 0, ratio, 1, 0]
section = 0
zone_fit = abs(y2 - y1)
ze = int(zexcl * zone_fit)
for i in range(y1 + ze, y2 - ze):
li = myimg[i, :-5]
#li_filter=savgol_filter(li,31,3)
li_filter = gaussian_filter1d(li, 11)
li_gr = np.gradient(li_filter)
#if i==650:
#plt.plot(li)
#plt.plot(li_gr)
#plt.show()
a = np.where((abs(li_gr) > 80))
s = a[0]
if s.size != 0:
c_x1 = s[0] + 10
c_x2 = s[-1] - 10
edgeX.append(c_x1)
edgeY.append(i)
edgeX.append(c_x2)
edgeY.append(i)
cercle_edge.append([c_x1, i])
cercle_edge.append([c_x2, i])
zy = np.mean(edgeY)
X = np.array(list(zip(edgeX, edgeY)))
reg = el.LsqEllipse().fit(X)
center, width, height, phi = reg.as_parameters()
EllipseFit = [center, width, height, phi]
r = height / width
section = ((zy - center[1]) / center[1])
print(f'center: {center[0]:.3f}, {center[1]:.3f}')
print(f'width: {width*2:.3f}')
print(f'height: {height*2:.3f}')
#print(f'phi: {np.rad2deg(phi):.3f}')
print(f'SY/SX ellipse: {r:.3f}')
# print(f'Section: {section:.3f}')
"""
plt.imshow(myimg)
#plt.plot(edgeX, edgeY, 'ro', zorder=1)
ellipse = Ellipse(
xy=center, width=2*width, height=2*height, angle=np.rad2deg(phi),
edgecolor='b', fc='None', lw=1, label='Fit', zorder=2)
# plot cercle sur image
np_m=np.asarray(cercle_edge)
xm,ym=np_m.T
plt.scatter(xm,ym,s=0.1, marker='.', edgecolors=('red'))
ax=plt.gca()
ax.add_patch(ellipse)
plt.show()
"""
return EllipseFit, section
