def test_constructor(self):
"""Test that the constructor for PoseEstimation sets values as expected."""
pose_estimation_series = create_pose_series()
pe = PoseEstimation(
pose_estimation_series=pose_estimation_series,
description='Estimated positions of front paws using DeepLabCut.',
original_videos=['camera1.mp4', 'camera2.mp4'],
labeled_videos=['camera1_labeled.mp4', 'camera2_labeled.mp4'],
dimensions=[[640, 480], [1024, 768]],
scorer='DLC_resnet50_openfieldOct30shuffle1_1600',
source_software='DeepLabCut',
source_software_version='2.2b8',
nodes=['front_left_paw', 'front_right_paw'],
edges=[[0, 1]],
# devices=[self.nwbfile.devices['camera1'], self.nwbfile.devices['camera2']],
)
self.assertEqual(pe.name, 'PoseEstimation')
self.assertEqual(len(pe.pose_estimation_series), 2)
self.assertIs(pe.pose_estimation_series['front_left_paw'],
pose_estimation_series[0])
self.assertIs(pe.pose_estimation_series['front_right_paw'],
pose_estimation_series[1])
self.assertEqual(
pe.description,
'Estimated positions of front paws using DeepLabCut.')
self.assertEqual(pe.original_videos, ['camera1.mp4', 'camera2.mp4'])
self.assertEqual(pe.labeled_videos,
['camera1_labeled.mp4', 'camera2_labeled.mp4'])
self.assertEqual(pe.dimensions, [[640, 480], [1024, 768]])
self.assertEqual(pe.scorer, 'DLC_resnet50_openfieldOct30shuffle1_1600')
self.assertEqual(pe.source_software, 'DeepLabCut')
self.assertEqual(pe.source_software_version, '2.2b8')
self.assertEqual(pe.nodes, ['front_left_paw', 'front_right_paw'])
self.assertEqual(pe.edges, [[0, 1]])
def test_constructor(self):
"""Test that the constructor for PoseEstimation sets values as expected."""
pose_estimation_series = create_pose_series()
n_frames = pose_estimation_series[0].data.shape[0]
pose_grouping_series = PoseGroupingSeries(
name="Centroid",
timestamps=pose_estimation_series[0].timestamps,
data=np.random.rand(n_frames),
location=np.random.rand(n_frames, 3),
)
animal_identity_series = AnimalIdentitySeries(
name="Mouse1",
timestamps=pose_estimation_series[0].timestamps,
data=np.random.rand(n_frames),
)
pe = PoseEstimation(
pose_estimation_series=pose_estimation_series,
pose_grouping_series=[pose_grouping_series],
animal_identity_series=[animal_identity_series],
description='Estimated positions of front paws using DeepLabCut.',
original_videos=['camera1.mp4', 'camera2.mp4'],
labeled_videos=['camera1_labeled.mp4', 'camera2_labeled.mp4'],
dimensions=[[640, 480], [1024, 768]],
scorer='DLC_resnet50_openfieldOct30shuffle1_1600',
source_software='DeepLabCut',
source_software_version='2.2b8',
nodes=['front_left_paw', 'front_right_paw'],
edges=[[0, 1]],
# devices=[self.nwbfile.devices['camera1'], self.nwbfile.devices['camera2']],
)
self.assertEqual(pe.name, 'PoseEstimation')
self.assertEqual(len(pe.pose_estimation_series), 2)
self.assertIs(pe.pose_estimation_series['front_left_paw'],
pose_estimation_series[0])
self.assertIs(pe.pose_estimation_series['front_right_paw'],
pose_estimation_series[1])
self.assertIs(pe.pose_grouping_series["Centroid"],
pose_grouping_series)
self.assertIs(pe.animal_identity_series["Mouse1"],
animal_identity_series)
self.assertEqual(
pe.description,
'Estimated positions of front paws using DeepLabCut.')
self.assertEqual(pe.original_videos, ['camera1.mp4', 'camera2.mp4'])
self.assertEqual(pe.labeled_videos,
['camera1_labeled.mp4', 'camera2_labeled.mp4'])
self.assertEqual(pe.dimensions, [[640, 480], [1024, 768]])
self.assertEqual(pe.scorer, 'DLC_resnet50_openfieldOct30shuffle1_1600')
self.assertEqual(pe.source_software, 'DeepLabCut')
self.assertEqual(pe.source_software_version, '2.2b8')
self.assertEqual(pe.nodes, ['front_left_paw', 'front_right_paw'])
self.assertEqual(pe.edges, [[0, 1]])
def setUpContainer(self):
""" Return the test PoseEstimation to read/write """
pose_estimation_series = create_series()
pe = PoseEstimation(
pose_estimation_series=pose_estimation_series,
description='Estimated positions of front paws using DeepLabCut.',
original_videos=['camera1.mp4', 'camera2.mp4'],
labeled_videos=['camera1_labeled.mp4', 'camera2_labeled.mp4'],
dimensions=[[640, 480], [1024, 768]],
scorer='DLC_resnet50_openfieldOct30shuffle1_1600',
source_software='DeepLabCut',
source_software_version='2.2b8',
nodes=['front_left_paw', 'front_right_paw'],
edges=[[0, 1]],
# devices=[self.nwbfile.devices['camera1'], self.nwbfile.devices['camera2']],
)
return pe
def test_roundtrip(self):
"""
Add a PoseEstimation to an NWBFile, write it, read it, and test that the read object matches the original.
"""
pose_estimation_series = create_series()
pe = PoseEstimation(
pose_estimation_series=pose_estimation_series,
description='Estimated positions of front paws using DeepLabCut.',
original_videos=['camera1.mp4', 'camera2.mp4'],
labeled_videos=['camera1_labeled.mp4', 'camera2_labeled.mp4'],
dimensions=[[640, 480], [1024, 768]],
scorer='DLC_resnet50_openfieldOct30shuffle1_1600',
source_software='DeepLabCut',
source_software_version='2.2b8',
nodes=['front_left_paw', 'front_right_paw'],
edges=[[0, 1]],
# devices=[self.nwbfile.devices['camera1'], self.nwbfile.devices['camera2']],
)
behavior_pm = self.nwbfile.create_processing_module(
name='behavior', description='processed behavioral data')
behavior_pm.add(pe)
with NWBHDF5IO(self.path, mode='w') as io:
io.write(self.nwbfile)
with NWBHDF5IO(self.path, mode='r', load_namespaces=True) as io:
read_nwbfile = io.read()
read_pe = read_nwbfile.processing['behavior']['PoseEstimation']
self.assertContainerEqual(read_pe, pe)
self.assertEqual(len(read_pe.pose_estimation_series), 2)
self.assertContainerEqual(
read_pe.pose_estimation_series['front_left_paw'],
pose_estimation_series[0])
self.assertContainerEqual(
read_pe.pose_estimation_series['front_right_paw'],
pose_estimation_series[1])
def test_example_usage():
import datetime
import numpy as np
from pynwb import NWBFile, NWBHDF5IO
from ndx_pose import PoseEstimationSeries, PoseEstimation
nwbfile = NWBFile(session_description='session_description',
identifier='identifier',
session_start_time=datetime.datetime.now(
datetime.timezone.utc))
camera1 = nwbfile.create_device(name='camera1',
description='left camera',
manufacturer='my manufacturer')
camera2 = nwbfile.create_device(name='camera2',
description='right camera',
manufacturer='my manufacturer')
data = np.random.rand(100, 3) # num_frames x (x, y, z)
timestamps = np.linspace(0, 10, num=100) # a timestamp for every frame
confidence = np.random.rand(100) # a confidence value for every frame
front_left_paw = PoseEstimationSeries(
name='front_left_paw',
description='Marker placed around fingers of front left paw.',
data=data,
unit='pixels',
reference_frame='(0,0,0) corresponds to ...',
timestamps=timestamps,
confidence=confidence,
confidence_definition='Softmax output of the deep neural network.',
)
data = np.random.rand(100, 2) # num_frames x (x, y)
timestamps = np.linspace(0, 10, num=100) # a timestamp for every frame
confidence = np.random.rand(100) # a confidence value for every frame
front_right_paw = PoseEstimationSeries(
name='front_right_paw',
description='Marker placed around fingers of front right paw.',
data=data,
unit='pixels',
reference_frame='(0,0,0) corresponds to ...',
timestamps=front_left_paw, # link to timestamps of front_left_paw
confidence=confidence,
confidence_definition='Softmax output of the deep neural network.',
)
pose_estimation_series = [front_left_paw, front_right_paw]
pe = PoseEstimation(
pose_estimation_series=pose_estimation_series,
description='Estimated positions of front paws using DeepLabCut.',
original_videos=['camera1.mp4', 'camera2.mp4'],
labeled_videos=['camera1_labeled.mp4', 'camera2_labeled.mp4'],
dimensions=np.array([[640, 480], [1024, 768]], dtype='uint8'),
scorer='DLC_resnet50_openfieldOct30shuffle1_1600',
source_software='DeepLabCut',
source_software_version='2.2b8',
nodes=['front_left_paw', 'front_right_paw'],
edges=np.array([[0, 1]], dtype='uint8'),
# devices=[camera1, camera2], # this is not yet supported
)
behavior_pm = nwbfile.create_processing_module(
name='behavior', description='processed behavioral data')
behavior_pm.add(pe)
path = 'test_pose.nwb'
with NWBHDF5IO(path, mode='w') as io:
io.write(nwbfile)
with NWBHDF5IO(path, mode='r', load_namespaces=True) as io:
read_nwbfile = io.read()
read_pe = read_nwbfile.processing['behavior']['PoseEstimation']
print(read_pe)