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0efc562 | 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 | # Copyright (c) OpenMMLab. All rights reserved.
from copy import deepcopy
from unittest import TestCase
import numpy as np
from mmpose.evaluation.functional import (transform_ann, transform_pred,
transform_sigmas)
class TestKeypointEval(TestCase):
def test_transform_sigmas(self):
mapping = [(3, 0), (6, 1), (16, 2), (5, 3)]
num_keypoints = 5
sigmas = np.random.rand(17)
new_sigmas = transform_sigmas(sigmas, num_keypoints, mapping)
self.assertEqual(len(new_sigmas), 5)
for i, j in mapping:
self.assertEqual(sigmas[i], new_sigmas[j])
def test_transform_ann(self):
mapping = [(3, 0), (6, 1), (16, 2), (5, 3)]
num_keypoints = 5
kpt_info = dict(
num_keypoints=17,
keypoints=np.random.randint(3, size=(17 * 3, )).tolist())
kpt_info_copy = deepcopy(kpt_info)
_ = transform_ann(kpt_info, num_keypoints, mapping)
self.assertEqual(kpt_info['num_keypoints'], 5)
self.assertEqual(len(kpt_info['keypoints']), 15)
for i, j in mapping:
self.assertListEqual(kpt_info_copy['keypoints'][i * 3:i * 3 + 3],
kpt_info['keypoints'][j * 3:j * 3 + 3])
def test_transform_pred(self):
mapping = [(3, 0), (6, 1), (16, 2), (5, 3)]
num_keypoints = 5
kpt_info = dict(
num_keypoints=17,
keypoints=np.random.randint(3, size=(
1,
17,
3,
)),
keypoint_scores=np.ones((1, 17)))
_ = transform_pred(kpt_info, num_keypoints, mapping)
self.assertEqual(kpt_info['num_keypoints'], 5)
self.assertEqual(len(kpt_info['keypoints']), 1)
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