# ndx-pose Extension for NWB
This is a work in progress and not yet ready for public usage.
## Installation
TBD
## Usage
```python
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)
```
## Contributors
- @rly
- @bendichter
- @AlexEMG
This extension was created using [ndx-template](https://github.com/nwb-extensions/ndx-template).
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"description": "# ndx-pose Extension for NWB\n\nThis is a work in progress and not yet ready for public usage.\n\n## Installation\n\nTBD\n\n## Usage\n```python\nimport datetime\nimport numpy as np\nfrom pynwb import NWBFile, NWBHDF5IO\nfrom ndx_pose import PoseEstimationSeries, PoseEstimation\n\nnwbfile = NWBFile(\n session_description='session_description',\n identifier='identifier',\n session_start_time=datetime.datetime.now(datetime.timezone.utc)\n)\n\ncamera1 = nwbfile.create_device(\n name='camera1',\n description='left camera',\n manufacturer='my manufacturer'\n)\ncamera2 = nwbfile.create_device(\n name='camera2',\n description='right camera',\n manufacturer='my manufacturer'\n)\n\ndata = np.random.rand(100, 3) # num_frames x (x, y, z)\ntimestamps = np.linspace(0, 10, num=100) # a timestamp for every frame\nconfidence = np.random.rand(100) # a confidence value for every frame\nfront_left_paw = PoseEstimationSeries(\n name='front_left_paw',\n description='Marker placed around fingers of front left paw.',\n data=data,\n unit='pixels',\n reference_frame='(0,0,0) corresponds to ...',\n timestamps=timestamps,\n confidence=confidence,\n confidence_definition='Softmax output of the deep neural network.',\n)\n\ndata = np.random.rand(100, 2) # num_frames x (x, y)\ntimestamps = np.linspace(0, 10, num=100) # a timestamp for every frame\nconfidence = np.random.rand(100) # a confidence value for every frame\nfront_right_paw = PoseEstimationSeries(\n name='front_right_paw',\n description='Marker placed around fingers of front right paw.',\n data=data,\n unit='pixels',\n reference_frame='(0,0,0) corresponds to ...',\n timestamps=front_left_paw, # link to timestamps of front_left_paw\n confidence=confidence,\n confidence_definition='Softmax output of the deep neural network.',\n)\n\npose_estimation_series = [front_left_paw, front_right_paw]\n\npe = PoseEstimation(\n pose_estimation_series=pose_estimation_series,\n description='Estimated positions of front paws using DeepLabCut.',\n original_videos=['camera1.mp4', 'camera2.mp4'],\n labeled_videos=['camera1_labeled.mp4', 'camera2_labeled.mp4'],\n dimensions=np.array([[640, 480], [1024, 768]], dtype='uint8'),\n scorer='DLC_resnet50_openfieldOct30shuffle1_1600',\n source_software='DeepLabCut',\n source_software_version='2.2b8',\n nodes=['front_left_paw', 'front_right_paw'],\n edges=np.array([[0, 1]], dtype='uint8'),\n # devices=[camera1, camera2], # this is not yet supported\n)\n\nbehavior_pm = nwbfile.create_processing_module(\n name='behavior',\n description='processed behavioral data'\n)\nbehavior_pm.add(pe)\n\npath = 'test_pose.nwb'\nwith NWBHDF5IO(path, mode='w') as io:\n io.write(nwbfile)\n\nwith NWBHDF5IO(path, mode='r', load_namespaces=True) as io:\n read_nwbfile = io.read()\n read_pe = read_nwbfile.processing['behavior']['PoseEstimation']\n print(read_pe)\n```\n\n\n## Contributors\n- @rly\n- @bendichter\n- @AlexEMG\n\nThis extension was created using [ndx-template](https://github.com/nwb-extensions/ndx-template).\n\n\n",
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