Spaces:
Running
Running
File size: 3,086 Bytes
a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 |
import numpy as np
def line_to_border(line, size):
# line:(a,b,c), ax+by+c=0
# size:(W,H)
H, W = size[1], size[0]
a, b, c = line[0], line[1], line[2]
epsa = 1e-8 if a >= 0 else -1e-8
epsb = 1e-8 if b >= 0 else -1e-8
intersection_list = []
y_left = -c / (b + epsb)
y_right = (-c - a * (W - 1)) / (b + epsb)
x_top = -c / (a + epsa)
x_down = (-c - b * (H - 1)) / (a + epsa)
if y_left >= 0 and y_left <= H - 1:
intersection_list.append([0, y_left])
if y_right >= 0 and y_right <= H - 1:
intersection_list.append([W - 1, y_right])
if x_top >= 0 and x_top <= W - 1:
intersection_list.append([x_top, 0])
if x_down >= 0 and x_down <= W - 1:
intersection_list.append([x_down, H - 1])
if len(intersection_list) != 2:
return None
intersection_list = np.asarray(intersection_list)
return intersection_list
def find_point_in_line(end_point):
x_span, y_span = (
end_point[1, 0] - end_point[0, 0],
end_point[1, 1] - end_point[0, 1],
)
mv = np.random.uniform()
point = np.asarray([end_point[0, 0] + x_span * mv, end_point[0, 1] + y_span * mv])
return point
def epi_line(point, F):
homo = np.concatenate([point, np.ones([len(point), 1])], axis=-1)
epi = np.matmul(homo, F.T)
return epi
def dis_point_to_line(line, point):
homo = np.concatenate([point, np.ones([len(point), 1])], axis=-1)
dis = line * homo
dis = dis.sum(axis=-1) / (np.linalg.norm(line[:, :2], axis=-1) + 1e-8)
return abs(dis)
def SGD_oneiter(F1, F2, size1, size2):
H1, W1 = size1[1], size1[0]
factor1 = 1 / np.linalg.norm(size1)
factor2 = 1 / np.linalg.norm(size2)
p0 = np.asarray([(W1 - 1) * np.random.uniform(), (H1 - 1) * np.random.uniform()])
epi1 = epi_line(p0[np.newaxis], F1)[0]
border_point1 = line_to_border(epi1, size2)
if border_point1 is None:
return -1
p1 = find_point_in_line(border_point1)
epi2 = epi_line(p0[np.newaxis], F2)
d1 = dis_point_to_line(epi2, p1[np.newaxis])[0] * factor2
epi3 = epi_line(p1[np.newaxis], F2.T)
d2 = dis_point_to_line(epi3, p0[np.newaxis])[0] * factor1
return (d1 + d2) / 2
def compute_SGD(F1, F2, size1, size2):
np.random.seed(1234)
N = 1000
max_iter = N * 10
count, sgd = 0, 0
for i in range(max_iter):
d1 = SGD_oneiter(F1, F2, size1, size2)
if d1 < 0:
continue
d2 = SGD_oneiter(F2, F1, size1, size2)
if d2 < 0:
continue
count += 1
sgd += (d1 + d2) / 2
if count == N:
break
if count == 0:
return 1
else:
return sgd / count
def compute_inlier_rate(x1, x2, size1, size2, F_gt, th=0.003):
t1, t2 = np.linalg.norm(size1) * th, np.linalg.norm(size2) * th
epi1, epi2 = epi_line(x1, F_gt), epi_line(x2, F_gt.T)
dis1, dis2 = dis_point_to_line(epi1, x2), dis_point_to_line(epi2, x1)
mask_inlier = np.logical_and(dis1 < t2, dis2 < t1)
return mask_inlier.mean() if len(mask_inlier) != 0 else 0
|