def select_corner_knees(points: np.ndarray,
knees: np.ndarray,
t: float = .33) -> np.ndarray:
"""
Detect and keep the left upper corner knees points.
The detection method relies on a three point rectangle fitting and overlap.
Args:
points (np.ndarray): numpy array with the points (x, y)
knees (np.ndarray): knees indexes
t (float): overlap treshold (default 0.33)
Returns:
np.ndarray: the filtered knees
"""
filtered_knees = []
for i in range(len(knees)):
idx = knees[i]
if idx - 1 >= 0 and idx + 1 < len(points):
p0, p1, p2 = points[idx - 1:idx + 2]
corner0 = np.array([p0[0], p2[1]])
amin, amax = kr.rect(corner0, p1)
bmin, bmax = kr.rect(p0, p2)
p = kr.rect_overlap(amin, amax, bmin, bmax)
if p >= t:
filtered_knees.append(idx)
return np.array(filtered_knees)
def test_rect_overlap(self):
amin = np.array([2, 1])
amax = np.array([5, 5])
bmin = np.array([3, 2])
bmax = np.array([5, 7])
result = ranking.rect_overlap(amin, amax, bmin, bmax)
desired = 0.375
self.assertEqual(result, desired)
def test_lower_overlap_02(self):
points = np.array([[0,1],[1,0],[2,1]])
idx = 1
p0, p1, p2 = points[idx-1:idx+2]
corner0 = np.array([p2[0], p0[1]])
amin, amax = ranking.rect(corner0, p1)
bmin, bmax = ranking.rect(p0, p2)
result = ranking.rect_overlap(amin, amax, bmin, bmax)
desired = 0.0
self.assertEqual(result, desired)
def test_lower_overlap_03(self):
amin, amax = (np.array([782, 3.47059833e-01]), np.array([805, 3.49430416e-01]))
bmin, bmax = (np.array([759, 3.48911963e-01]), np.array([805, 3.49430416e-01]))
result = ranking.rect_overlap(amin, amax, bmin, bmax)
desired = 0.17945536158082412
self.assertEqual(result, desired)