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Repository: microsoft/SGN
Branch: master
Commit: 79aa0dd89e33
Files: 22
Total size: 1.9 MB
Directory structure:
gitextract_teiivr1p/
├── .gitignore
├── CODE_OF_CONDUCT.md
├── LICENSE
├── README.md
├── SECURITY.md
├── data/
│ └── ntu/
│ ├── get_raw_denoised_data.py
│ ├── get_raw_skes_data.py
│ ├── seq_transformation.py
│ └── statistics/
│ ├── camera.txt
│ ├── label.txt
│ ├── performer.txt
│ ├── replication.txt
│ ├── samples_with_missing_skeletons.txt
│ ├── setup.txt
│ └── skes_available_name.txt
├── data.py
├── fit.py
├── main.py
├── model.py
├── results/
│ └── NTU/
│ └── SGN/
│ ├── 0_best.pth
│ └── 1_best.pth
└── util.py
================================================
FILE CONTENTS
================================================
================================================
FILE: .gitignore
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================================================
FILE: CODE_OF_CONDUCT.md
================================================
# Microsoft Open Source Code of Conduct
This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
Resources:
- [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/)
- [Microsoft Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/)
- Contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with questions or concerns
================================================
FILE: LICENSE
================================================
MIT License
Copyright (c) Microsoft Corporation.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE
================================================
FILE: README.md
================================================
# Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition (SGN)
## Introduction
Skeleton-based human action recognition has attracted great interest thanks to the easy accessibility of the human skeleton data. Recently, there is a trend of using very deep feedforward neural networks to model the 3D coordinates of joints without considering the computational efficiency. In this work, we propose a simple yet effective semantics-guided neural network (SGN). We explicitly introduce the high level semantics of joints (joint type and frame index) into the network to enhance the feature representation capability. Intuitively, semantic information, i.e., the joint type and the frame index, together with dynamics (i.e., 3D coordinates) reveal the spatial and temporal configuration/structure of human body joints and are very important for action recognition.
In addition, we exploit the relationship of joints hierarchically through two modules, i.e., a joint-level module for modeling the correlations of joints in the same frame and a frame-level module for modeling the dependencies of frames by taking the joints in the same frame as a whole. A strong baseline is proposed to facilitate the study of this field. With an order of magnitude smaller model size than most previous works, SGN achieves the state-of-the-art performance.
<div align=center>
<img src="https://github.com/microsoft/SGN/blob/master/images/para.PNG" width = 50% height = 50% div align=center>
</div>
Figure 1: Comparisons of different methods on NTU60 (CS setting) in terms of accuracy and the number of parameters. Among these methods, the proposed SGN model achieves the best performance with an order of magnitude smaller model size.
## Framework
Figure 2: Framework of the proposed end-to-end Semantics-Guided Neural Network (SGN). It consists of a joint-level module and a frame-level module. In DR, we learn the dynamics representation of a joint by fusing the position and velocity information of a joint. Two types of semantics, i.e., joint type and frame index, are incorporated into the joint-level module and the frame-level module, respectively. To model the dependencies of joints in the joint-level module, we use three GCN layers. To model the dependencies of frames, we use two CNN layers.
## Prerequisites
The code is built with the following libraries:
- Python 3.6
- [Anaconda](https://www.anaconda.com/)
- [PyTorch](https://pytorch.org/) 1.3
## Data Preparation
We use the dataset of NTU60 RGB+D as an example for description. We need to first dowload the [NTU-RGB+D](https://github.com/shahroudy/NTURGB-D) dataset.
- Extract the dataset to ./data/ntu/nturgb+d_skeletons/
- Process the data
```bash
cd ./data/ntu
# Get skeleton of each performer
python get_raw_skes_data.py
# Remove the bad skeleton
python get_raw_denoised_data.py
# Transform the skeleton to the center of the first frame
python seq_transformation.py
```
## Training
```bash
# For the CS setting
python main.py --network SGN --train 1 --case 0
# For the CV setting
python main.py --network SGN --train 1 --case 1
```
## Testing
- Test the pre-trained models (./results/NTU/SGN/)
```bash
# For the CS setting
python main.py --network SGN --train 0 --case 0
# For the CV setting
python main.py --network SGN --train 0 --case 1
```
## Reference
This repository holds the code for the following paper:
[Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition](https://arxiv.org/abs/1904.01189). CVPR, 2020.
If you find our paper and repo useful, please cite our paper. Thanks!
```
@inproceedings{zhang2020semantics,
title={Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition},
author={Zhang, Pengfei and Lan, Cuiling and Zeng, Wenjun and Xing, Junliang and Xue, Jianru and Zheng, Nanning},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
year={2020},
}
```
## Contributing
This project welcomes contributions and suggestions. Most contributions require you to agree to a
Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us
the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.
When you submit a pull request, a CLA bot will automatically determine whether you need to provide
a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions
provided by the bot. You will only need to do this once across all repos using our CLA.
This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or
contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.
================================================
FILE: SECURITY.md
================================================
<!-- BEGIN MICROSOFT SECURITY.MD V0.0.5 BLOCK -->
## Security
Microsoft takes the security of our software products and services seriously, which includes all source code repositories managed through our GitHub organizations, which include [Microsoft](https://github.com/Microsoft), [Azure](https://github.com/Azure), [DotNet](https://github.com/dotnet), [AspNet](https://github.com/aspnet), [Xamarin](https://github.com/xamarin), and [our GitHub organizations](https://opensource.microsoft.com/).
If you believe you have found a security vulnerability in any Microsoft-owned repository that meets [Microsoft's definition of a security vulnerability](https://docs.microsoft.com/en-us/previous-versions/tn-archive/cc751383(v=technet.10)), please report it to us as described below.
## Reporting Security Issues
**Please do not report security vulnerabilities through public GitHub issues.**
Instead, please report them to the Microsoft Security Response Center (MSRC) at [https://msrc.microsoft.com/create-report](https://msrc.microsoft.com/create-report).
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## Policy
Microsoft follows the principle of [Coordinated Vulnerability Disclosure](https://www.microsoft.com/en-us/msrc/cvd).
<!-- END MICROSOFT SECURITY.MD BLOCK -->
================================================
FILE: data/ntu/get_raw_denoised_data.py
================================================
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
import os
import os.path as osp
import numpy as np
import pickle
import logging
root_path = './'
raw_data_file = osp.join(root_path, 'raw_data', 'raw_skes_data.pkl')
save_path = osp.join(root_path, 'denoised_data')
if not osp.exists(save_path):
os.mkdir(save_path)
rgb_ske_path = osp.join(save_path, 'rgb+ske')
if not osp.exists(rgb_ske_path):
os.mkdir(rgb_ske_path)
actors_info_dir = osp.join(save_path, 'actors_info')
if not osp.exists(actors_info_dir):
os.mkdir(actors_info_dir)
missing_count = 0
noise_len_thres = 11
noise_spr_thres1 = 0.8
noise_spr_thres2 = 0.69754
noise_mot_thres_lo = 0.089925
noise_mot_thres_hi = 2
noise_len_logger = logging.getLogger('noise_length')
noise_len_logger.setLevel(logging.INFO)
noise_len_logger.addHandler(logging.FileHandler(osp.join(save_path, 'noise_length.log')))
noise_len_logger.info('{:^20}\t{:^17}\t{:^8}\t{}'.format('Skeleton', 'bodyID', 'Motion', 'Length'))
noise_spr_logger = logging.getLogger('noise_spread')
noise_spr_logger.setLevel(logging.INFO)
noise_spr_logger.addHandler(logging.FileHandler(osp.join(save_path, 'noise_spread.log')))
noise_spr_logger.info('{:^20}\t{:^17}\t{:^8}\t{:^8}'.format('Skeleton', 'bodyID', 'Motion', 'Rate'))
noise_mot_logger = logging.getLogger('noise_motion')
noise_mot_logger.setLevel(logging.INFO)
noise_mot_logger.addHandler(logging.FileHandler(osp.join(save_path, 'noise_motion.log')))
noise_mot_logger.info('{:^20}\t{:^17}\t{:^8}'.format('Skeleton', 'bodyID', 'Motion'))
fail_logger_1 = logging.getLogger('noise_outliers_1')
fail_logger_1.setLevel(logging.INFO)
fail_logger_1.addHandler(logging.FileHandler(osp.join(save_path, 'denoised_failed_1.log')))
fail_logger_2 = logging.getLogger('noise_outliers_2')
fail_logger_2.setLevel(logging.INFO)
fail_logger_2.addHandler(logging.FileHandler(osp.join(save_path, 'denoised_failed_2.log')))
missing_skes_logger = logging.getLogger('missing_frames')
missing_skes_logger.setLevel(logging.INFO)
missing_skes_logger.addHandler(logging.FileHandler(osp.join(save_path, 'missing_skes.log')))
missing_skes_logger.info('{:^20}\t{}\t{}'.format('Skeleton', 'num_frames', 'num_missing'))
missing_skes_logger1 = logging.getLogger('missing_frames_1')
missing_skes_logger1.setLevel(logging.INFO)
missing_skes_logger1.addHandler(logging.FileHandler(osp.join(save_path, 'missing_skes_1.log')))
missing_skes_logger1.info('{:^20}\t{}\t{}\t{}\t{}\t{}'.format('Skeleton', 'num_frames', 'Actor1',
'Actor2', 'Start', 'End'))
missing_skes_logger2 = logging.getLogger('missing_frames_2')
missing_skes_logger2.setLevel(logging.INFO)
missing_skes_logger2.addHandler(logging.FileHandler(osp.join(save_path, 'missing_skes_2.log')))
missing_skes_logger2.info('{:^20}\t{}\t{}\t{}'.format('Skeleton', 'num_frames', 'Actor1', 'Actor2'))
def denoising_by_length(ske_name, bodies_data):
"""
Denoising data based on the frame length for each bodyID.
Filter out the bodyID which length is less or equal than the predefined threshold.
"""
noise_info = str()
new_bodies_data = bodies_data.copy()
for (bodyID, body_data) in new_bodies_data.items():
length = len(body_data['interval'])
if length <= noise_len_thres:
noise_info += 'Filter out: %s, %d (length).\n' % (bodyID, length)
noise_len_logger.info('{}\t{}\t{:.6f}\t{:^6d}'.format(ske_name, bodyID,
body_data['motion'], length))
del bodies_data[bodyID]
if noise_info != '':
noise_info += '\n'
return bodies_data, noise_info
def get_valid_frames_by_spread(points):
"""
Find the valid (or reasonable) frames (index) based on the spread of X and Y.
:param points: joints or colors
"""
num_frames = points.shape[0]
valid_frames = []
for i in range(num_frames):
x = points[i, :, 0]
y = points[i, :, 1]
if (x.max() - x.min()) <= noise_spr_thres1 * (y.max() - y.min()): # 0.8
valid_frames.append(i)
return valid_frames
def denoising_by_spread(ske_name, bodies_data):
"""
Denoising data based on the spread of Y value and X value.
Filter out the bodyID which the ratio of noisy frames is higher than the predefined
threshold.
bodies_data: contains at least 2 bodyIDs
"""
noise_info = str()
denoised_by_spr = False # mark if this sequence has been processed by spread.
new_bodies_data = bodies_data.copy()
# for (bodyID, body_data) in bodies_data.items():
for (bodyID, body_data) in new_bodies_data.items():
if len(bodies_data) == 1:
break
valid_frames = get_valid_frames_by_spread(body_data['joints'].reshape(-1, 25, 3))
num_frames = len(body_data['interval'])
num_noise = num_frames - len(valid_frames)
if num_noise == 0:
continue
ratio = num_noise / float(num_frames)
motion = body_data['motion']
if ratio >= noise_spr_thres2: # 0.69754
del bodies_data[bodyID]
denoised_by_spr = True
noise_info += 'Filter out: %s (spread rate >= %.2f).\n' % (bodyID, noise_spr_thres2)
noise_spr_logger.info('%s\t%s\t%.6f\t%.6f' % (ske_name, bodyID, motion, ratio))
else: # Update motion
joints = body_data['joints'].reshape(-1, 25, 3)[valid_frames]
body_data['motion'] = min(motion, np.sum(np.var(joints.reshape(-1, 3), axis=0)))
noise_info += '%s: motion %.6f -> %.6f\n' % (bodyID, motion, body_data['motion'])
# TODO: Consider removing noisy frames for each bodyID
if noise_info != '':
noise_info += '\n'
return bodies_data, noise_info, denoised_by_spr
def denoising_by_motion(ske_name, bodies_data, bodies_motion):
"""
Filter out the bodyID which motion is out of the range of predefined interval
"""
# Sort bodies based on the motion, return a list of tuples
# bodies_motion = sorted(bodies_motion.items(), key=lambda x, y: cmp(x[1], y[1]), reverse=True)
bodies_motion = sorted(bodies_motion.items(), key=lambda x: x[1], reverse=True)
# Reserve the body data with the largest motion
denoised_bodies_data = [(bodies_motion[0][0], bodies_data[bodies_motion[0][0]])]
noise_info = str()
for (bodyID, motion) in bodies_motion[1:]:
if (motion < noise_mot_thres_lo) or (motion > noise_mot_thres_hi):
noise_info += 'Filter out: %s, %.6f (motion).\n' % (bodyID, motion)
noise_mot_logger.info('{}\t{}\t{:.6f}'.format(ske_name, bodyID, motion))
else:
denoised_bodies_data.append((bodyID, bodies_data[bodyID]))
if noise_info != '':
noise_info += '\n'
return denoised_bodies_data, noise_info
def denoising_bodies_data(bodies_data):
"""
Denoising data based on some heuristic methods, not necessarily correct for all samples.
Return:
denoised_bodies_data (list): tuple: (bodyID, body_data).
"""
ske_name = bodies_data['name']
bodies_data = bodies_data['data']
# Step 1: Denoising based on frame length.
bodies_data, noise_info_len = denoising_by_length(ske_name, bodies_data)
if len(bodies_data) == 1: # only has one bodyID left after step 1
return bodies_data.items(), noise_info_len
# Step 2: Denoising based on spread.
bodies_data, noise_info_spr, denoised_by_spr = denoising_by_spread(ske_name, bodies_data)
if len(bodies_data) == 1:
return bodies_data.items(), noise_info_len + noise_info_spr
bodies_motion = dict() # get body motion
for (bodyID, body_data) in bodies_data.items():
bodies_motion[bodyID] = body_data['motion']
# Sort bodies based on the motion
# bodies_motion = sorted(bodies_motion.items(), key=lambda x, y: cmp(x[1], y[1]), reverse=True)
bodies_motion = sorted(bodies_motion.items(), key=lambda x: x[1], reverse=True)
denoised_bodies_data = list()
for (bodyID, _) in bodies_motion:
denoised_bodies_data.append((bodyID, bodies_data[bodyID]))
return denoised_bodies_data, noise_info_len + noise_info_spr
# TODO: Consider denoising further by integrating motion method
# if denoised_by_spr: # this sequence has been denoised by spread
# bodies_motion = sorted(bodies_motion.items(), lambda x, y: cmp(x[1], y[1]), reverse=True)
# denoised_bodies_data = list()
# for (bodyID, _) in bodies_motion:
# denoised_bodies_data.append((bodyID, bodies_data[bodyID]))
# return denoised_bodies_data, noise_info
# Step 3: Denoising based on motion
# bodies_data, noise_info = denoising_by_motion(ske_name, bodies_data, bodies_motion)
# return bodies_data, noise_info
def get_one_actor_points(body_data, num_frames):
"""
Get joints and colors for only one actor.
For joints, each frame contains 75 X-Y-Z coordinates.
For colors, each frame contains 25 x 2 (X, Y) coordinates.
"""
joints = np.zeros((num_frames, 75), dtype=np.float32)
colors = np.ones((num_frames, 1, 25, 2), dtype=np.float32) * np.nan
start, end = body_data['interval'][0], body_data['interval'][-1]
joints[start:end + 1] = body_data['joints'].reshape(-1, 75)
colors[start:end + 1, 0] = body_data['colors']
return joints, colors
def remove_missing_frames(ske_name, joints, colors):
"""
Cut off missing frames which all joints positions are 0s
For the sequence with 2 actors' data, also record the number of missing frames for
actor1 and actor2, respectively (for debug).
"""
num_frames = joints.shape[0]
num_bodies = colors.shape[1] # 1 or 2
if num_bodies == 2: # DEBUG
missing_indices_1 = np.where(joints[:, :75].sum(axis=1) == 0)[0]
missing_indices_2 = np.where(joints[:, 75:].sum(axis=1) == 0)[0]
cnt1 = len(missing_indices_1)
cnt2 = len(missing_indices_2)
start = 1 if 0 in missing_indices_1 else 0
end = 1 if num_frames - 1 in missing_indices_1 else 0
if max(cnt1, cnt2) > 0:
if cnt1 > cnt2:
info = '{}\t{:^10d}\t{:^6d}\t{:^6d}\t{:^5d}\t{:^3d}'.format(ske_name, num_frames,
cnt1, cnt2, start, end)
missing_skes_logger1.info(info)
else:
info = '{}\t{:^10d}\t{:^6d}\t{:^6d}'.format(ske_name, num_frames, cnt1, cnt2)
missing_skes_logger2.info(info)
# Find valid frame indices that the data is not missing or lost
# For two-subjects action, this means both data of actor1 and actor2 is missing.
valid_indices = np.where(joints.sum(axis=1) != 0)[0] # 0-based index
missing_indices = np.where(joints.sum(axis=1) == 0)[0]
num_missing = len(missing_indices)
if num_missing > 0: # Update joints and colors
joints = joints[valid_indices]
colors[missing_indices] = np.nan
global missing_count
missing_count += 1
missing_skes_logger.info('{}\t{:^10d}\t{:^11d}'.format(ske_name, num_frames, num_missing))
return joints, colors
def get_bodies_info(bodies_data):
bodies_info = '{:^17}\t{}\t{:^8}\n'.format('bodyID', 'Interval', 'Motion')
for (bodyID, body_data) in bodies_data.items():
start, end = body_data['interval'][0], body_data['interval'][-1]
bodies_info += '{}\t{:^8}\t{:f}\n'.format(bodyID, str([start, end]), body_data['motion'])
return bodies_info + '\n'
def get_two_actors_points(bodies_data):
"""
Get the first and second actor's joints positions and colors locations.
# Arguments:
bodies_data (dict): 3 key-value pairs: 'name', 'data', 'num_frames'.
bodies_data['data'] is also a dict, while the key is bodyID, the value is
the corresponding body_data which is also a dict with 4 keys:
- joints: raw 3D joints positions. Shape: (num_frames x 25, 3)
- colors: raw 2D color locations. Shape: (num_frames, 25, 2)
- interval: a list which records the frame indices.
- motion: motion amount
# Return:
joints, colors.
"""
ske_name = bodies_data['name']
label = int(ske_name[-2:])
num_frames = bodies_data['num_frames']
bodies_info = get_bodies_info(bodies_data['data'])
bodies_data, noise_info = denoising_bodies_data(bodies_data) # Denoising data
bodies_info += noise_info
bodies_data = list(bodies_data)
if len(bodies_data) == 1: # Only left one actor after denoising
if label >= 50: # DEBUG: Denoising failed for two-subjects action
fail_logger_2.info(ske_name)
bodyID, body_data = bodies_data[0]
joints, colors = get_one_actor_points(body_data, num_frames)
bodies_info += 'Main actor: %s' % bodyID
else:
if label < 50: # DEBUG: Denoising failed for one-subject action
fail_logger_1.info(ske_name)
joints = np.zeros((num_frames, 150), dtype=np.float32)
colors = np.ones((num_frames, 2, 25, 2), dtype=np.float32) * np.nan
bodyID, actor1 = bodies_data[0] # the 1st actor with largest motion
start1, end1 = actor1['interval'][0], actor1['interval'][-1]
joints[start1:end1 + 1, :75] = actor1['joints'].reshape(-1, 75)
colors[start1:end1 + 1, 0] = actor1['colors']
actor1_info = '{:^17}\t{}\t{:^8}\n'.format('Actor1', 'Interval', 'Motion') + \
'{}\t{:^8}\t{:f}\n'.format(bodyID, str([start1, end1]), actor1['motion'])
del bodies_data[0]
actor2_info = '{:^17}\t{}\t{:^8}\n'.format('Actor2', 'Interval', 'Motion')
start2, end2 = [0, 0] # initial interval for actor2 (virtual)
while len(bodies_data) > 0:
bodyID, actor = bodies_data[0]
start, end = actor['interval'][0], actor['interval'][-1]
if min(end1, end) - max(start1, start) <= 0: # no overlap with actor1
joints[start:end + 1, :75] = actor['joints'].reshape(-1, 75)
colors[start:end + 1, 0] = actor['colors']
actor1_info += '{}\t{:^8}\t{:f}\n'.format(bodyID, str([start, end]), actor['motion'])
# Update the interval of actor1
start1 = min(start, start1)
end1 = max(end, end1)
elif min(end2, end) - max(start2, start) <= 0: # no overlap with actor2
joints[start:end + 1, 75:] = actor['joints'].reshape(-1, 75)
colors[start:end + 1, 1] = actor['colors']
actor2_info += '{}\t{:^8}\t{:f}\n'.format(bodyID, str([start, end]), actor['motion'])
# Update the interval of actor2
start2 = min(start, start2)
end2 = max(end, end2)
del bodies_data[0]
bodies_info += ('\n' + actor1_info + '\n' + actor2_info)
with open(osp.join(actors_info_dir, ske_name + '.txt'), 'w') as fw:
fw.write(bodies_info + '\n')
return joints, colors
def get_raw_denoised_data():
"""
Get denoised data (joints positions and color locations) from raw skeleton sequences.
For each frame of a skeleton sequence, an actor's 3D positions of 25 joints represented
by an 2D array (shape: 25 x 3) is reshaped into a 75-dim vector by concatenating each
3-dim (x, y, z) coordinates along the row dimension in joint order. Each frame contains
two actor's joints positions constituting a 150-dim vector. If there is only one actor,
then the last 75 values are filled with zeros. Otherwise, select the main actor and the
second actor based on the motion amount. Each 150-dim vector as a row vector is put into
a 2D numpy array where the number of rows equals the number of valid frames. All such
2D arrays are put into a list and finally the list is serialized into a cPickle file.
For the skeleton sequence which contains two or more actors (mostly corresponds to the
last 11 classes), the filename and actors' information are recorded into log files.
For better understanding, also generate RGB+skeleton videos for visualization.
"""
with open(raw_data_file, 'rb') as fr: # load raw skeletons data
raw_skes_data = pickle.load(fr)
num_skes = len(raw_skes_data)
print('Found %d available skeleton sequences.' % num_skes)
raw_denoised_joints = []
raw_denoised_colors = []
frames_cnt = []
for (idx, bodies_data) in enumerate(raw_skes_data):
ske_name = bodies_data['name']
print('Processing %s' % ske_name)
num_bodies = len(bodies_data['data'])
if num_bodies == 1: # only 1 actor
num_frames = bodies_data['num_frames']
body_data = list(bodies_data['data'].values())[0]
joints, colors = get_one_actor_points(body_data, num_frames)
else: # more than 1 actor, select two main actors
joints, colors = get_two_actors_points(bodies_data)
# Remove missing frames
joints, colors = remove_missing_frames(ske_name, joints, colors)
num_frames = joints.shape[0] # Update
# Visualize selected actors' skeletons on RGB videos.
raw_denoised_joints.append(joints)
raw_denoised_colors.append(colors)
frames_cnt.append(num_frames)
if (idx + 1) % 1000 == 0:
print('Processed: %.2f%% (%d / %d), ' % \
(100.0 * (idx + 1) / num_skes, idx + 1, num_skes) + \
'Missing count: %d' % missing_count)
raw_skes_joints_pkl = osp.join(save_path, 'raw_denoised_joints.pkl')
with open(raw_skes_joints_pkl, 'wb') as f:
pickle.dump(raw_denoised_joints, f, pickle.HIGHEST_PROTOCOL)
raw_skes_colors_pkl = osp.join(save_path, 'raw_denoised_colors.pkl')
with open(raw_skes_colors_pkl, 'wb') as f:
pickle.dump(raw_denoised_colors, f, pickle.HIGHEST_PROTOCOL)
frames_cnt = np.array(frames_cnt, dtype=np.int)
np.savetxt(osp.join(save_path, 'frames_cnt.txt'), frames_cnt, fmt='%d')
print('Saved raw denoised positions of {} frames into {}'.format(np.sum(frames_cnt),
raw_skes_joints_pkl))
print('Found %d files that have missing data' % missing_count)
if __name__ == '__main__':
get_raw_denoised_data()
================================================
FILE: data/ntu/get_raw_skes_data.py
================================================
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
import os.path as osp
import os
import numpy as np
import pickle
import logging
def get_raw_bodies_data(skes_path, ske_name, frames_drop_skes, frames_drop_logger):
"""
Get raw bodies data from a skeleton sequence.
Each body's data is a dict that contains the following keys:
- joints: raw 3D joints positions. Shape: (num_frames x 25, 3)
- colors: raw 2D color locations. Shape: (num_frames, 25, 2)
- interval: a list which stores the frame indices of this body.
- motion: motion amount (only for the sequence with 2 or more bodyIDs).
Return:
a dict for a skeleton sequence with 3 key-value pairs:
- name: the skeleton filename.
- data: a dict which stores raw data of each body.
- num_frames: the number of valid frames.
"""
ske_file = osp.join(skes_path, ske_name + '.skeleton')
assert osp.exists(ske_file), 'Error: Skeleton file %s not found' % ske_file
# Read all data from .skeleton file into a list (in string format)
print('Reading data from %s' % ske_file[-29:])
with open(ske_file, 'r') as fr:
str_data = fr.readlines()
num_frames = int(str_data[0].strip('\r\n'))
frames_drop = []
bodies_data = dict()
valid_frames = -1 # 0-based index
current_line = 1
for f in range(num_frames):
num_bodies = int(str_data[current_line].strip('\r\n'))
current_line += 1
if num_bodies == 0: # no data in this frame, drop it
frames_drop.append(f) # 0-based index
continue
valid_frames += 1
joints = np.zeros((num_bodies, 25, 3), dtype=np.float32)
colors = np.zeros((num_bodies, 25, 2), dtype=np.float32)
for b in range(num_bodies):
bodyID = str_data[current_line].strip('\r\n').split()[0]
current_line += 1
num_joints = int(str_data[current_line].strip('\r\n')) # 25 joints
current_line += 1
for j in range(num_joints):
temp_str = str_data[current_line].strip('\r\n').split()
joints[b, j, :] = np.array(temp_str[:3], dtype=np.float32)
colors[b, j, :] = np.array(temp_str[5:7], dtype=np.float32)
current_line += 1
if bodyID not in bodies_data: # Add a new body's data
body_data = dict()
body_data['joints'] = joints[b] # ndarray: (25, 3)
body_data['colors'] = colors[b, np.newaxis] # ndarray: (1, 25, 2)
body_data['interval'] = [valid_frames] # the index of the first frame
else: # Update an already existed body's data
body_data = bodies_data[bodyID]
# Stack each body's data of each frame along the frame order
body_data['joints'] = np.vstack((body_data['joints'], joints[b]))
body_data['colors'] = np.vstack((body_data['colors'], colors[b, np.newaxis]))
pre_frame_idx = body_data['interval'][-1]
body_data['interval'].append(pre_frame_idx + 1) # add a new frame index
bodies_data[bodyID] = body_data # Update bodies_data
num_frames_drop = len(frames_drop)
assert num_frames_drop < num_frames, \
'Error: All frames data (%d) of %s is missing or lost' % (num_frames, ske_name)
if num_frames_drop > 0:
frames_drop_skes[ske_name] = np.array(frames_drop, dtype=np.int)
frames_drop_logger.info('{}: {} frames missed: {}\n'.format(ske_name, num_frames_drop,
frames_drop))
# Calculate motion (only for the sequence with 2 or more bodyIDs)
if len(bodies_data) > 1:
for body_data in bodies_data.values():
body_data['motion'] = np.sum(np.var(body_data['joints'], axis=0))
return {'name': ske_name, 'data': bodies_data, 'num_frames': num_frames - num_frames_drop}
def get_raw_skes_data():
# # save_path = './data'
# # skes_path = '/data/pengfei/NTU/nturgb+d_skeletons/'
# stat_path = osp.join(save_path, 'statistics')
#
# skes_name_file = osp.join(stat_path, 'skes_available_name.txt')
# save_data_pkl = osp.join(save_path, 'raw_skes_data.pkl')
# frames_drop_pkl = osp.join(save_path, 'frames_drop_skes.pkl')
#
# frames_drop_logger = logging.getLogger('frames_drop')
# frames_drop_logger.setLevel(logging.INFO)
# frames_drop_logger.addHandler(logging.FileHandler(osp.join(save_path, 'frames_drop.log')))
# frames_drop_skes = dict()
skes_name = np.loadtxt(skes_name_file, dtype=str)
num_files = skes_name.size
print('Found %d available skeleton files.' % num_files)
raw_skes_data = []
frames_cnt = np.zeros(num_files, dtype=np.int)
for (idx, ske_name) in enumerate(skes_name):
bodies_data = get_raw_bodies_data(skes_path, ske_name, frames_drop_skes, frames_drop_logger)
raw_skes_data.append(bodies_data)
frames_cnt[idx] = bodies_data['num_frames']
if (idx + 1) % 1000 == 0:
print('Processed: %.2f%% (%d / %d)' % \
(100.0 * (idx + 1) / num_files, idx + 1, num_files))
with open(save_data_pkl, 'wb') as fw:
pickle.dump(raw_skes_data, fw, pickle.HIGHEST_PROTOCOL)
np.savetxt(osp.join(save_path, 'raw_data', 'frames_cnt.txt'), frames_cnt, fmt='%d')
print('Saved raw bodies data into %s' % save_data_pkl)
print('Total frames: %d' % np.sum(frames_cnt))
with open(frames_drop_pkl, 'wb') as fw:
pickle.dump(frames_drop_skes, fw, pickle.HIGHEST_PROTOCOL)
if __name__ == '__main__':
save_path = './'
skes_path = './nturgb+d_skeletons/'
stat_path = osp.join(save_path, 'statistics')
if not osp.exists('./raw_data'):
os.makedirs('./raw_data')
skes_name_file = osp.join(stat_path, 'skes_available_name.txt')
save_data_pkl = osp.join(save_path, 'raw_data', 'raw_skes_data.pkl')
frames_drop_pkl = osp.join(save_path, 'raw_data', 'frames_drop_skes.pkl')
frames_drop_logger = logging.getLogger('frames_drop')
frames_drop_logger.setLevel(logging.INFO)
frames_drop_logger.addHandler(logging.FileHandler(osp.join(save_path, 'raw_data', 'frames_drop.log')))
frames_drop_skes = dict()
get_raw_skes_data()
with open(frames_drop_pkl, 'wb') as fw:
pickle.dump(frames_drop_skes, fw, pickle.HIGHEST_PROTOCOL)
================================================
FILE: data/ntu/seq_transformation.py
================================================
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
import os
import os.path as osp
import numpy as np
import pickle
import logging
import h5py
from sklearn.model_selection import train_test_split
root_path = './'
stat_path = osp.join(root_path, 'statistics')
setup_file = osp.join(stat_path, 'setup.txt')
camera_file = osp.join(stat_path, 'camera.txt')
performer_file = osp.join(stat_path, 'performer.txt')
replication_file = osp.join(stat_path, 'replication.txt')
label_file = osp.join(stat_path, 'label.txt')
skes_name_file = osp.join(stat_path, 'skes_available_name.txt')
denoised_path = osp.join(root_path, 'denoised_data')
raw_skes_joints_pkl = osp.join(denoised_path, 'raw_denoised_joints.pkl')
frames_file = osp.join(denoised_path, 'frames_cnt.txt')
save_path = './'
if not osp.exists(save_path):
os.mkdir(save_path)
def remove_nan_frames(ske_name, ske_joints, nan_logger):
num_frames = ske_joints.shape[0]
valid_frames = []
for f in range(num_frames):
if not np.any(np.isnan(ske_joints[f])):
valid_frames.append(f)
else:
nan_indices = np.where(np.isnan(ske_joints[f]))[0]
nan_logger.info('{}\t{:^5}\t{}'.format(ske_name, f + 1, nan_indices))
return ske_joints[valid_frames]
def seq_translation(skes_joints):
for idx, ske_joints in enumerate(skes_joints):
num_frames = ske_joints.shape[0]
num_bodies = 1 if ske_joints.shape[1] == 75 else 2
if num_bodies == 2:
missing_frames_1 = np.where(ske_joints[:, :75].sum(axis=1) == 0)[0]
missing_frames_2 = np.where(ske_joints[:, 75:].sum(axis=1) == 0)[0]
cnt1 = len(missing_frames_1)
cnt2 = len(missing_frames_2)
i = 0 # get the "real" first frame of actor1
while i < num_frames:
if np.any(ske_joints[i, :75] != 0):
break
i += 1
origin = np.copy(ske_joints[i, 3:6]) # new origin: joint-2
for f in range(num_frames):
if num_bodies == 1:
ske_joints[f] -= np.tile(origin, 25)
else: # for 2 actors
ske_joints[f] -= np.tile(origin, 50)
if (num_bodies == 2) and (cnt1 > 0):
ske_joints[missing_frames_1, :75] = np.zeros((cnt1, 75), dtype=np.float32)
if (num_bodies == 2) and (cnt2 > 0):
ske_joints[missing_frames_2, 75:] = np.zeros((cnt2, 75), dtype=np.float32)
skes_joints[idx] = ske_joints # Update
return skes_joints
def frame_translation(skes_joints, skes_name, frames_cnt):
nan_logger = logging.getLogger('nan_skes')
nan_logger.setLevel(logging.INFO)
nan_logger.addHandler(logging.FileHandler("./nan_frames.log"))
nan_logger.info('{}\t{}\t{}'.format('Skeleton', 'Frame', 'Joints'))
for idx, ske_joints in enumerate(skes_joints):
num_frames = ske_joints.shape[0]
# Calculate the distance between spine base (joint-1) and spine (joint-21)
j1 = ske_joints[:, 0:3]
j21 = ske_joints[:, 60:63]
dist = np.sqrt(((j1 - j21) ** 2).sum(axis=1))
for f in range(num_frames):
origin = ske_joints[f, 3:6] # new origin: middle of the spine (joint-2)
if (ske_joints[f, 75:] == 0).all():
ske_joints[f, :75] = (ske_joints[f, :75] - np.tile(origin, 25)) / \
dist[f] + np.tile(origin, 25)
else:
ske_joints[f] = (ske_joints[f] - np.tile(origin, 50)) / \
dist[f] + np.tile(origin, 50)
ske_name = skes_name[idx]
ske_joints = remove_nan_frames(ske_name, ske_joints, nan_logger)
frames_cnt[idx] = num_frames # update valid number of frames
skes_joints[idx] = ske_joints
return skes_joints, frames_cnt
def align_frames(skes_joints, frames_cnt):
"""
Align all sequences with the same frame length.
"""
num_skes = len(skes_joints)
max_num_frames = frames_cnt.max() # 300
aligned_skes_joints = np.zeros((num_skes, max_num_frames, 150), dtype=np.float32)
for idx, ske_joints in enumerate(skes_joints):
num_frames = ske_joints.shape[0]
num_bodies = 1 if ske_joints.shape[1] == 75 else 2
if num_bodies == 1:
aligned_skes_joints[idx, :num_frames] = np.hstack((ske_joints,
np.zeros_like(ske_joints)))
else:
aligned_skes_joints[idx, :num_frames] = ske_joints
return aligned_skes_joints
def one_hot_vector(labels):
num_skes = len(labels)
labels_vector = np.zeros((num_skes, 60))
for idx, l in enumerate(labels):
labels_vector[idx, l] = 1
return labels_vector
def split_train_val(train_indices, method='sklearn', ratio=0.05):
"""
Get validation set by splitting data randomly from training set with two methods.
In fact, I thought these two methods are equal as they got the same performance.
"""
if method == 'sklearn':
return train_test_split(train_indices, test_size=ratio, random_state=10000)
else:
np.random.seed(10000)
np.random.shuffle(train_indices)
val_num_skes = int(np.ceil(0.05 * len(train_indices)))
val_indices = train_indices[:val_num_skes]
train_indices = train_indices[val_num_skes:]
return train_indices, val_indices
def split_dataset(skes_joints, label, performer, camera, evaluation, save_path):
train_indices, test_indices = get_indices(performer, camera, evaluation)
m = 'sklearn' # 'sklearn' or 'numpy'
# Select validation set from training set
train_indices, val_indices = split_train_val(train_indices, m)
# Save labels and num_frames for each sequence of each data set
train_labels = label[train_indices]
val_labels = label[val_indices]
test_labels = label[test_indices]
# Save data into a .h5 file
h5file = h5py.File(osp.join(save_path, 'NTU_%s.h5' % (evaluation)), 'w')
# Training set
h5file.create_dataset('x', data=skes_joints[train_indices])
train_one_hot_labels = one_hot_vector(train_labels)
h5file.create_dataset('y', data=train_one_hot_labels)
# Validation set
h5file.create_dataset('valid_x', data=skes_joints[val_indices])
val_one_hot_labels = one_hot_vector(val_labels)
h5file.create_dataset('valid_y', data=val_one_hot_labels)
# Test set
h5file.create_dataset('test_x', data=skes_joints[test_indices])
test_one_hot_labels = one_hot_vector(test_labels)
h5file.create_dataset('test_y', data=test_one_hot_labels)
h5file.close()
def get_indices(performer, camera, evaluation='CS'):
test_indices = np.empty(0)
train_indices = np.empty(0)
if evaluation == 'CS': # Cross Subject (Subject IDs)
train_ids = [1, 2, 4, 5, 8, 9, 13, 14, 15, 16,
17, 18, 19, 25, 27, 28, 31, 34, 35, 38]
test_ids = [3, 6, 7, 10, 11, 12, 20, 21, 22, 23,
24, 26, 29, 30, 32, 33, 36, 37, 39, 40]
# Get indices of test data
for idx in test_ids:
temp = np.where(performer == idx)[0] # 0-based index
test_indices = np.hstack((test_indices, temp)).astype(np.int)
# Get indices of training data
for train_id in train_ids:
temp = np.where(performer == train_id)[0] # 0-based index
train_indices = np.hstack((train_indices, temp)).astype(np.int)
else: # Cross View (Camera IDs)
train_ids = [2, 3]
test_ids = 1
# Get indices of test data
temp = np.where(camera == test_ids)[0] # 0-based index
test_indices = np.hstack((test_indices, temp)).astype(np.int)
# Get indices of training data
for train_id in train_ids:
temp = np.where(camera == train_id)[0] # 0-based index
train_indices = np.hstack((train_indices, temp)).astype(np.int)
return train_indices, test_indices
if __name__ == '__main__':
camera = np.loadtxt(camera_file, dtype=np.int) # camera id: 1, 2, 3
performer = np.loadtxt(performer_file, dtype=np.int) # subject id: 1~40
label = np.loadtxt(label_file, dtype=np.int) - 1 # action label: 0~59
frames_cnt = np.loadtxt(frames_file, dtype=np.int) # frames_cnt
skes_name = np.loadtxt(skes_name_file, dtype=np.string_)
with open(raw_skes_joints_pkl, 'rb') as fr:
skes_joints = pickle.load(fr) # a list
skes_joints = seq_translation(skes_joints)
skes_joints = align_frames(skes_joints, frames_cnt) # aligned to the same frame length
evaluations = ['CS', 'CV']
for evaluation in evaluations:
split_dataset(skes_joints, label, performer, camera, evaluation, save_path)
================================================
FILE: data/ntu/statistics/camera.txt
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================================================
FILE: data/ntu/statistics/label.txt
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gitextract_teiivr1p/ ├── .gitignore ├── CODE_OF_CONDUCT.md ├── LICENSE ├── README.md ├── SECURITY.md ├── data/ │ └── ntu/ │ ├── get_raw_denoised_data.py │ ├── get_raw_skes_data.py │ ├── seq_transformation.py │ └── statistics/ │ ├── camera.txt │ ├── label.txt │ ├── performer.txt │ ├── replication.txt │ ├── samples_with_missing_skeletons.txt │ ├── setup.txt │ └── skes_available_name.txt ├── data.py ├── fit.py ├── main.py ├── model.py ├── results/ │ └── NTU/ │ └── SGN/ │ ├── 0_best.pth │ └── 1_best.pth └── util.py
SYMBOL INDEX (80 symbols across 8 files)
FILE: data.py
class NTUDataset (line 22) | class NTUDataset(Dataset):
method __init__ (line 23) | def __init__(self, x, y):
method __len__ (line 27) | def __len__(self):
method __getitem__ (line 30) | def __getitem__(self, index):
class NTUDataLoaders (line 33) | class NTUDataLoaders(object):
method __init__ (line 34) | def __init__(self, dataset ='NTU', case = 0, aug = 1, seg = 30):
method get_train_loader (line 44) | def get_train_loader(self, batch_size, num_workers):
method get_val_loader (line 54) | def get_val_loader(self, batch_size, num_workers):
method get_test_loader (line 65) | def get_test_loader(self, batch_size, num_workers):
method get_train_size (line 70) | def get_train_size(self):
method get_val_size (line 73) | def get_val_size(self):
method get_test_size (line 76) | def get_test_size(self):
method create_datasets (line 79) | def create_datasets(self):
method collate_fn_fix_train (line 102) | def collate_fn_fix_train(self, batch):
method collate_fn_fix_val (line 134) | def collate_fn_fix_val(self, batch):
method collate_fn_fix_test (line 147) | def collate_fn_fix_test(self, batch):
method Tolist_fix (line 161) | def Tolist_fix(self, joints, y, train = 1):
method sub_seq (line 177) | def sub_seq(self, seqs, seq , train = 1):
class AverageMeter (line 209) | class AverageMeter(object):
method __init__ (line 211) | def __init__(self):
method reset (line 214) | def reset(self):
method update (line 220) | def update(self, val, n=1):
function turn_two_to_one (line 227) | def turn_two_to_one(seq):
function _rot (line 239) | def _rot(rot):
function _transform (line 262) | def _transform(x, theta):
FILE: data/ntu/get_raw_denoised_data.py
function denoising_by_length (line 71) | def denoising_by_length(ske_name, bodies_data):
function get_valid_frames_by_spread (line 92) | def get_valid_frames_by_spread(points):
function denoising_by_spread (line 108) | def denoising_by_spread(ske_name, bodies_data):
function denoising_by_motion (line 149) | def denoising_by_motion(ske_name, bodies_data, bodies_motion):
function denoising_bodies_data (line 174) | def denoising_bodies_data(bodies_data):
function get_one_actor_points (line 223) | def get_one_actor_points(body_data, num_frames):
function remove_missing_frames (line 238) | def remove_missing_frames(ske_name, joints, colors):
function get_bodies_info (line 281) | def get_bodies_info(bodies_data):
function get_two_actors_points (line 290) | def get_two_actors_points(bodies_data):
function get_raw_denoised_data (line 367) | def get_raw_denoised_data():
FILE: data/ntu/get_raw_skes_data.py
function get_raw_bodies_data (line 10) | def get_raw_bodies_data(skes_path, ske_name, frames_drop_skes, frames_dr...
function get_raw_skes_data (line 94) | def get_raw_skes_data():
FILE: data/ntu/seq_transformation.py
function remove_nan_frames (line 31) | def remove_nan_frames(ske_name, ske_joints, nan_logger):
function seq_translation (line 44) | def seq_translation(skes_joints):
function frame_translation (line 79) | def frame_translation(skes_joints, skes_name, frames_cnt):
function align_frames (line 109) | def align_frames(skes_joints, frames_cnt):
function one_hot_vector (line 130) | def one_hot_vector(labels):
function split_train_val (line 139) | def split_train_val(train_indices, method='sklearn', ratio=0.05):
function split_dataset (line 156) | def split_dataset(skes_joints, label, performer, camera, evaluation, sav...
function get_indices (line 185) | def get_indices(performer, camera, evaluation='CS'):
FILE: fit.py
function add_fit_args (line 3) | def add_fit_args(parser):
FILE: main.py
function main (line 42) | def main():
function train (line 153) | def train(train_loader, model, criterion, optimizer, epoch):
function validate (line 183) | def validate(val_loader, model, criterion):
function test (line 203) | def test(test_loader, model, checkpoint, lable_path, pred_path):
function accuracy (line 235) | def accuracy(output, target):
function save_checkpoint (line 244) | def save_checkpoint(state, filename='checkpoint.pth.tar', is_best=False):
function get_n_params (line 249) | def get_n_params(model):
class LabelSmoothingLoss (line 258) | class LabelSmoothingLoss(nn.Module):
method __init__ (line 259) | def __init__(self, classes, smoothing=0.0, dim=-1):
method forward (line 266) | def forward(self, pred, target):
FILE: model.py
class SGN (line 7) | class SGN(nn.Module):
method __init__ (line 8) | def __init__(self, num_classes, dataset, seg, args, bias = True):
method forward (line 49) | def forward(self, input):
method one_hot (line 79) | def one_hot(self, bs, spa, tem):
class norm_data (line 92) | class norm_data(nn.Module):
method __init__ (line 93) | def __init__(self, dim= 64):
method forward (line 98) | def forward(self, x):
class embed (line 105) | class embed(nn.Module):
method __init__ (line 106) | def __init__(self, dim = 3, dim1 = 128, norm = True, bias = False):
method forward (line 125) | def forward(self, x):
class cnn1x1 (line 129) | class cnn1x1(nn.Module):
method __init__ (line 130) | def __init__(self, dim1 = 3, dim2 =3, bias = True):
method forward (line 134) | def forward(self, x):
class local (line 138) | class local(nn.Module):
method __init__ (line 139) | def __init__(self, dim1 = 3, dim2 = 3, bias = False):
method forward (line 149) | def forward(self, x1):
class gcn_spa (line 161) | class gcn_spa(nn.Module):
method __init__ (line 162) | def __init__(self, in_feature, out_feature, bias = False):
method forward (line 170) | def forward(self, x1, g):
class compute_g_spa (line 178) | class compute_g_spa(nn.Module):
method __init__ (line 179) | def __init__(self, dim1 = 64 *3, dim2 = 64*3, bias = False):
method forward (line 187) | def forward(self, x1):
FILE: util.py
function make_dir (line 11) | def make_dir(dataset):
function get_num_classes (line 22) | def get_num_classes(dataset):
Condensed preview — 22 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (2,294K chars).
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"preview": "# Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition (SGN)\n\n## Introduction\n\nSkeleto"
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]
// ... and 2 more files (download for full content)
About this extraction
This page contains the full source code of the microsoft/SGN GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 22 files (1.9 MB), approximately 1.2M tokens, and a symbol index with 80 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.