def _by_points(self,
case_name,
n,
*,
angle_min=0,
angle_max=359,
noise_error=0.0):
w, h = 300, 150
ellipse = shapes.Ellipse(w // 2, h // 2, w // 3, h // 3, 20)
img_contour = np.zeros((h, w), np.uint8)
ellipse.draw(img_contour, 255)
self.dump_debug_img(Path(case_name) / '0_ellipse.png', img_contour)
np.random.seed(239)
angles = np.random.randint(angle_min, angle_max, (n, ))
xys = np.float32(list(map(ellipse.calculate_point, angles)))
xys += np.random.random_sample((len(xys), 2)) * noise_error
img_points = img_contour // 3
draw_pixels(img_points, xys, 255)
self.dump_debug_img(Path(case_name) / '1_points.png', img_points)
estimation = fit_ellipse(xys)
estimation.draw(img_points, 255)
self.dump_debug_img(Path(case_name) / '2_estimation.png', img_points)
self.assertTrue(
np.all(np.abs(np.array(ellipse) - np.array(estimation)) < 3.0))
def _by_points(self, case_name, n, *,
angle_min=0, angle_max=359, noise_error=0.0):
w, h = 300, 150
ellipse = shapes.Ellipse(w // 2, h // 2, w // 3, h // 3, 20)
img_contour = np.zeros((h, w), np.uint8)
ellipse.draw(img_contour, 255)
self.dump_debug_img(Path(case_name) / '0_ellipse.png', img_contour)
np.random.seed(239)
angles = np.random.randint(angle_min, angle_max, (n,))
xys = np.float32(list(map(ellipse.calculate_point, angles)))
xys += np.random.random_sample((len(xys), 2)) * noise_error
img_points = img_contour // 3
draw_pixels(img_points, xys, 255)
self.dump_debug_img(Path(case_name) / '1_points.png', img_points)
estimation = fit_ellipse(xys)
estimation.draw(img_points, 255)
self.dump_debug_img(Path(case_name) / '2_estimation.png', img_points)
self.assertTrue(np.all(np.abs(np.array(ellipse) - np.array(estimation)) < 3.0))
def process_contour(self, contour):
points = contour.reshape(-1, 2)
found_ellipses = []
n = len(points)
mask = np.ones(n, np.bool)
if n < self._min_pixels:
return found_ellipses
for ellipse in self.ellipses:
err = self._distance_to_ellipse_as_circle(points, ellipse)
mask[err < self._err_threshold] = False
for from_i in range(0, len(points), self._min_pixels // 4):
number_to_sample = 3 * len(points) // 5
sub_points = points[from_i:][:number_to_sample]
sub_points = sub_points[mask[from_i:][:number_to_sample]]
if len(sub_points) < self._min_pixels // 2:
continue
max_inliers = 0
best_ellipse = None
for i in range(20):
indices = np.arange(len(sub_points))
np.random.shuffle(indices)
sample_points = sub_points[indices[:self._min_pixels // 2]]
assert len(sample_points) >= 5
ellipse = fit_ellipse(sample_points)
if not self._is_ok(ellipse):
continue
err = self._distance_to_ellipse_as_circle(points[mask], ellipse)
inliers_mask = err < self._err_threshold
inliers = inliers_mask.sum()
if inliers < self._min_pixels:
continue
if inliers > max_inliers:
max_inliers = inliers
best_ellipse = ellipse
if best_ellipse is not None:
err = self._distance_to_ellipse_as_circle(points[mask], best_ellipse)
inliers_mask = err < self._err_threshold
for i in range(3):
if inliers_mask.sum() < 5:
break
ellipse = fit_ellipse(points[mask][inliers_mask])
if not self._is_ok(ellipse):
break
err = self._distance_to_ellipse_as_circle(points[mask], ellipse)
inliers_mask = err < self._err_threshold
if inliers_mask.sum() < 5 or not self._is_ok(ellipse):
break
sectors_number = self._min_pixels // 2
covered_sectors_number = len(self._calculate_sectors(points[mask][inliers_mask], ellipse, sectors_number))
if covered_sectors_number / sectors_number < self._min_sectors_fraction:
continue
err = self._distance_to_ellipse_as_circle(points, ellipse)
mask[err < self._err_threshold] = False
self.ellipses.append(ellipse)
found_ellipses.append(ellipse)
return found_ellipses