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# Copyright (c) OpenMMLab. All rights reserved.
from typing import List, Union
from unittest import TestCase
import numpy as np
from mmcv import is_list_of
from mmpose.core.post_processing.smoother import Smoother
class TestSmoother(TestCase):
def build_smoother(self):
smoother = Smoother(
'configs/_base_/filters/gaussian.py', keypoint_dim=2)
return smoother
def build_pose_results(self,
num_target: Union[int, List[int]],
num_frame: int = -1,
has_track_id: bool = True):
keypoint_shape = (17, 2)
results = []
if isinstance(num_target, list):
num_frame = len(num_target)
else:
assert num_frame >= 0
num_target = [num_target] * num_frame
for n in num_target:
results_t = []
for idx in range(n):
result = dict(keypoints=np.random.rand(*keypoint_shape))
if has_track_id:
result['track_id'] = str(idx)
results_t.append(result)
results.append(results_t)
return results
def test_corner_cases(self):
# Test empty input
smoother = self.build_smoother()
results = []
with self.assertWarnsRegex(UserWarning,
'Smoother received empty result.'):
_ = smoother.smooth(results)
# Test inconsistent tracked poses
smoother = self.build_smoother()
results = self.build_pose_results(num_target=[1, 2], has_track_id=True)
with self.assertRaisesRegex(ValueError, 'Inconsistent track ids'):
_ = smoother.smooth(results)
# Test inconsistent untracked poses
smoother = self.build_smoother()
results = self.build_pose_results(
num_target=[1, 2], has_track_id=False)
with self.assertRaisesRegex(ValueError, 'Inconsistent target number'):
_ = smoother.smooth(results)
def test_smooth_online_with_trackid(self):
smoother = self.build_smoother()
num_target = [2] * 10 + [3] * 10
results = self.build_pose_results(
num_target=num_target, has_track_id=True)
for results_t in results:
smoothed_results_t = smoother.smooth(results_t)
# Sort by track_id
results_t.sort(key=lambda x: x['track_id'])
smoothed_results_t.sort(key=lambda x: x['track_id'])
# Check the output is non-nested list
self.assertTrue(is_list_of(smoothed_results_t, dict))
# Check the target number in the frame is correct
self.assertEqual(len(smoothed_results_t), len(results_t))
for result, smoothed_result in zip(results_t, smoothed_results_t):
# Check the target_id is correct
self.assertEqual(result['track_id'],
smoothed_result['track_id'])
# Check the pose shape is correct
self.assertEqual(result['keypoints'].shape,
smoothed_result['keypoints'].shape)
def test_smooth_online_wo_trackid(self):
smoother = self.build_smoother()
num_target = [2] * 10 + [3] * 10
results = self.build_pose_results(
num_target=num_target, has_track_id=False)
for results_t in results:
smoothed_results_t = smoother.smooth(results_t)
# Check the output is non-nested list
self.assertTrue(is_list_of(smoothed_results_t, dict))
# Check the target number in the frame is correct
self.assertEqual(len(smoothed_results_t), len(results_t))
for result, smoothed_result in zip(results_t, smoothed_results_t):
# Check the pose shape is correct
self.assertEqual(result['keypoints'].shape,
smoothed_result['keypoints'].shape)
def test_smooth_offline_with_trackid(self):
smoother = self.build_smoother()
results = self.build_pose_results(
num_target=2, num_frame=20, has_track_id=True)
smoothed_results = smoother.smooth(results)
for results_t, smoothed_results_t in zip(results, smoothed_results):
# Sort by track_id
results_t.sort(key=lambda x: x['track_id'])
smoothed_results_t.sort(key=lambda x: x['track_id'])
# Check the output is non-nested list
self.assertTrue(is_list_of(smoothed_results_t, dict))
# Check the target number in the frame is correct
self.assertEqual(len(smoothed_results_t), len(results_t))
for result, smoothed_result in zip(results_t, smoothed_results_t):
# Check the target_id is correct
self.assertEqual(result['track_id'],
smoothed_result['track_id'])
# Check the pose shape is correct
self.assertEqual(result['keypoints'].shape,
smoothed_result['keypoints'].shape)
def test_smooth_offline_wo_trackid(self):
smoother = self.build_smoother()
results = self.build_pose_results(
num_target=2, num_frame=20, has_track_id=False)
smoothed_results = smoother.smooth(results)
for results_t, smoothed_results_t in zip(results, smoothed_results):
# Check the output is non-nested list
self.assertTrue(is_list_of(smoothed_results_t, dict))
# Check the target number in the frame is correct
self.assertEqual(len(smoothed_results_t), len(results_t))
for result, smoothed_result in zip(results_t, smoothed_results_t):
# Check the pose shape is correct
self.assertEqual(result['keypoints'].shape,
smoothed_result['keypoints'].shape)
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