forked from open-mmlab/mmpose
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathtest_post_processing.py
94 lines (74 loc) · 3.17 KB
/
test_post_processing.py
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
# Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
from numpy.testing import assert_array_almost_equal
from mmpose.core import (affine_transform, flip_back, fliplr_joints,
fliplr_regression, get_affine_transform, rotate_point,
transform_preds)
def test_affine_transform():
pt = np.array([0, 1])
trans = np.array([[1, 0, 1], [0, 1, 0]])
result = affine_transform(pt, trans)
assert_array_almost_equal(result, np.array([1, 1]), decimal=4)
assert isinstance(result, np.ndarray)
def test_rotate_point():
src_point = np.array([0, 1])
rot_rad = np.pi / 2.
result = rotate_point(src_point, rot_rad)
assert_array_almost_equal(result, np.array([-1, 0]), decimal=4)
assert isinstance(result, list)
def test_fliplr_joints():
joints = np.array([[0, 0, 0], [1, 1, 0]])
joints_vis = np.array([[1], [1]])
joints_flip, _ = fliplr_joints(joints, joints_vis, 5, [[0, 1]])
res = np.array([[3, 1, 0], [4, 0, 0]])
assert_array_almost_equal(joints_flip, res)
def test_flip_back():
heatmaps = np.random.random([1, 2, 32, 32])
flipped_heatmaps = flip_back(heatmaps, [[0, 1]])
heatmaps_new = flip_back(flipped_heatmaps, [[0, 1]])
assert_array_almost_equal(heatmaps, heatmaps_new)
heatmaps = np.random.random([1, 2, 32, 32])
flipped_heatmaps = flip_back(heatmaps, [[0, 1]])
heatmaps_new = flipped_heatmaps[..., ::-1]
assert_array_almost_equal(heatmaps[:, 0], heatmaps_new[:, 1])
assert_array_almost_equal(heatmaps[:, 1], heatmaps_new[:, 0])
ori_heatmaps = heatmaps.copy()
# test in-place flip
heatmaps = heatmaps[:, :, :, ::-1]
assert_array_almost_equal(ori_heatmaps[:, :, :, ::-1], heatmaps)
def test_transform_preds():
coords = np.random.random([2, 2])
center = np.array([50, 50])
scale = np.array([100 / 200.0, 100 / 200.0])
size = np.array([100, 100])
result = transform_preds(coords, center, scale, size)
assert_array_almost_equal(coords, result)
coords = np.random.random([2, 2])
center = np.array([50, 50])
scale = np.array([100 / 200.0, 100 / 200.0])
size = np.array([101, 101])
result = transform_preds(coords, center, scale, size, use_udp=True)
assert_array_almost_equal(coords, result)
def test_get_affine_transform():
center = np.array([50, 50])
scale = np.array([100 / 200.0, 100 / 200.0])
size = np.array([100, 100])
result = get_affine_transform(center, scale, 0, size)
trans = np.array([[1, 0, 0], [0, 1, 0]])
assert_array_almost_equal(trans, result)
def test_flip_regression():
coords = np.random.rand(3, 3)
flip_pairs = [[1, 2]]
root = coords[:1]
coords_flipped = coords.copy()
coords_flipped[1] = coords[2]
coords_flipped[2] = coords[1]
coords_flipped[..., 0] = 2 * root[..., 0] - coords_flipped[..., 0]
# static mode
res_static = fliplr_regression(
coords, flip_pairs, center_mode='static', center_x=root[0, 0])
assert_array_almost_equal(res_static, coords_flipped)
# root mode
res_root = fliplr_regression(
coords, flip_pairs, center_mode='root', center_index=0)
assert_array_almost_equal(res_root, coords_flipped)