def fit_circle(points_l, scale, debug=False):
"""Get information about a circle from a list of points `points_l`.
This may involve fitting a circle or ellipse to a set of points?
pip install circle-fit
https://github.com/AlliedToasters/circle-fit
Assuing for now that points_l contains a list of (x,y,z) points, so we
take only (x,y) and scale according to `scale`. Both methods return a
tuple of four values:
xc: x-coordinate of solution center (float)
yc: y-coordinate of solution center (float)
R: Radius of solution (float)
variance or residual (float)
These methods should be identical if we're querying this with actual
circles. Returning the second one for now.
"""
from circle_fit import hyper_fit, least_squares_circle
data = [(item[0] * scale, item[1] * scale) for item in points_l]
data = np.array(data)
circle_1 = hyper_fit(data)
circle_2 = least_squares_circle(data)
xc_1, yc_1, r_1, _ = circle_1
xc_2, yc_2, r_2, _ = circle_2
if debug:
print(f'(hyperfit) rad {r_1:0.4f}, center ({xc_1:0.4f},{yc_1:0.4f})')
print(f'(least-sq) rad {r_2:0.4f}, center ({xc_2:0.4f},{yc_2:0.4f})')
return circle_2
def detect_fit_cercle(myimg, y1, y2): #obsolete
edgeX = []
edgeY = []
cercle_edge = []
for i in range(y1 + 10, y2 - 10):
li = myimg[i, :-5]
li_filter = savgol_filter(li, 51, 3)
li_gr = np.gradient(li_filter)
a = np.where((abs(li_gr) > 80))
if i == 650:
plt.plot(li_gr)
plt.show()
s = a[0]
if s.size != 0:
c_x1 = s[0]
c_x2 = s[-1]
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])
#best fit du cercle centre, radius, rms
CercleFit = cf.hyper_fit(cercle_edge)
#print (CercleFit)
#calcul des x,y cercle
cy = CercleFit[1]
cx = CercleFit[0]
radius = CercleFit[2]
cercle = []
for y in range(y1, y2):
x = ((radius * radius - ((y - cy) * (y - cy)))**0.5)
xa = cx - x
cercle.append([xa, y])
xb = cx + x
cercle.append([xb, y])
# plot cercle sur image
np_m = np.asarray(cercle_edge)
xm, ym = np_m.T
np_cercle = np.asarray(cercle)
xc, yc = np_cercle.T
plt.imshow(myimg)
plt.scatter(xm, ym, s=0.1, marker='.', edgecolors=('red'))
plt.scatter(xc, yc, s=0.1, marker='.', edgecolors=('green'))
plt.show()
return CercleFit
def fit(points):
center_x, center_y, radius, _ = hyper_fit(points)
return Circle(Point(center_x, center_y), radius)
def test_hyper_fit(self):
circle = hyper_fit(self.data)
circle = hyper_fit(self.numpy_data)