def test_large_circle_50X50_no_noise_2(self):
folder_script=os.path.dirname(__file__)
filename_input="NoisyCircle_x_6_y_-30_r_118.162.png"
file_noisy_line=os.path.join(folder_script,"./data/",filename_input)
np_image=skimage.io.imread(file_noisy_line,as_gray=True)
lst_points=Util.create_points_from_numpyimage(np_image)
helper=BullockCircleFitting(lst_points)
result:CircleModel =helper.FindBestFittingCircle()
#
#Superimpose the new line over the image
#
folder_results=os.path.join(folder_script,"../out/")
count_of_files=len(os.listdir(folder_results))
filename_results=("%s.%d.png" % (__name__,count_of_files) )
file_result=os.path.join(folder_results,filename_results)
new_points=CircleModel.generate_points_from_circle(result)
np_superimposed=Util.superimpose_points_on_image(np_image,new_points,100,255,100)
skimage.io.imsave(file_result,np_superimposed)
delta=10
self.assertAlmostEquals(result.R, +118.0, delta=delta);
self.assertAlmostEquals(result.X, +06.0, delta=delta);
self.assertAlmostEquals(result.Y, -30.0, delta=delta);
pass
def test_3points_around_origin_unit_radius(self):
p1=Point(+1,0)
p2=Point(+0,1)
p3=Point(-1,0)
expected_list=list()
expected_list.append(p1)
expected_list.append(p2)
expected_list.append(p3)
helper=BullockCircleFitting(expected_list)
result:CircleModel =helper.FindBestFittingCircle()
delta=0.01
self.assertAlmostEquals(result.R, 1.0, delta=delta);
self.assertAlmostEquals(result.X, 0.0, delta=delta);
self.assertAlmostEquals(result.Y, 0.0, delta=delta);
def find_model_using_gradient_descent(self, modelhint: CircleModel,
points: List[Point]) -> CircleModel:
try:
gdhelper = BullockCircleFitting.BullockCircleFitting(points)
new_model = gdhelper.FindBestFittingCircle()
return new_model
except Exception as e:
print("Error while Gradient descent: %s" % (str(e)))
return None
pass
def test_constructor(self):
p1=Point(1,1)
p2=Point(2,2)
p3=Point(3,3)
expected_list=list()
expected_list.append(p1)
expected_list.append(p2)
expected_list.append(p3)
algo=BullockCircleFitting(expected_list)
self.assertIsNotNone(algo)
self.assertTrue (p1 in algo._points)
self.assertTrue (p2 in algo._points)
self.assertTrue (p3 in algo._points)
self.assertEqual(len(algo._points),3)
pass
def find_model_using_gradient_descent2(self, modelhint: CircleModel,
points: List[Point], lst_results):
new_model = None
try:
gdhelper = BullockCircleFitting.BullockCircleFitting(points)
new_model = gdhelper.FindBestFittingCircle()
except Exception as e:
#print("Error while Gradient descent: %s" % (str(e)))
pass
if (new_model == None):
return
new_inliers, goodness = self.get_inliers(new_model, [])
if (len(new_inliers) == 0):
return
result = (new_model, new_inliers, goodness)
lst_results.append(result)
return