copy over repository

.dockerignore

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+.git
+Data
+.env
+docker

.gitignore

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+*.mp4
+#*.mov
+*~
+#*.json
+.ipynb_checkpoints
+__pycache__
+*.log
+*WithKeypoints.avi
+*_rotated.avi
+.env
+*.pth
+
+# Data
+*/Data/*
+Data/
+Examples/Data/*
+
+*.ini
+*.stats

Examples/batchDownloadData.py

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+"""
+---------------------------------------------------------------------------
+OpenCap: batchDownloadData.py
+---------------------------------------------------------------------------
+
+Copyright 2022 Stanford University and the Authors
+
+Author(s): Scott Uhlrich, Antoine Falisse
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not
+use this file except in compliance with the License. You may obtain a copy
+of the License at http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+
+
+This script dowloads data from multiple sessions from the database.
+
+
+You will need login credentials from app.opencap.ai to run this script. You 
+can quickly create credentials from the home page: https://app.opencap.ai. 
+We recommend first running the Examples/createAuthenticationEnvFile.py script
+prior to running this script. Otherwise, you will need to to login every time 
+you run the script.
+"""
+
+# %% Imports
+
+import sys
+import os
+sys.path.append(os.path.abspath('./..'))
+import utils
+
+# %% User inputs
+
+# Sessions to download. List of strings of identifiers (36-character) string
+# at the end of the session url app.opencap.ai/session/<session_id>
+session_ids = [
+               '23d52d41-69fe-47cf-8b60-838e4268dd50'
+              ] # list of session identifiers as strings
+
+# Local path to download sessions to
+downloadFolder = os.path.abspath(os.path.join(os.path.dirname(__file__),'..','Data'))
+
+# Name downloaded folder with subject name from web app (True), or the session_id (False)
+useSubjectIdentifierAsFolderName = True
+
+
+#%% Processing
+
+# Batch download. 
+for session_id in session_ids:
+    utils.downloadAndZipSession(session_id,justDownload=True,data_dir=downloadFolder,
+                          useSubjectNameFolder=useSubjectIdentifierAsFolderName)

Examples/changeSessionMetadata.py

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+"""
+---------------------------------------------------------------------------
+OpenCap: changeSessionMetadata.py
+---------------------------------------------------------------------------
+
+Copyright 2022 Stanford University and the Authors
+
+Author(s): Scott Uhlrich, Antoine Falisse
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not
+use this file except in compliance with the License. You may obtain a copy
+of the License at http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+
+
+This script allows you to change the metadata of the session. E.g., to change
+the pose estimator used when reprocessing data in the cloud. This is mostly for
+developer use.
+"""
+import os
+import sys
+sys.path.append(os.path.abspath('./..'))
+
+from utils import changeSessionMetadata
+
+session_ids = ['0d46adef-62cb-455f-9ff3-8116717cc2fe']
+
+# Dictionary of metadata fields to change.
+newMetadata = {'posemodel':'openpose'} # dict of meta fields to change
+# newMetadata= {'mass':83}
+
+changeSessionMetadata(session_ids,newMetadata)

Examples/createAuthenticationEnvFile.py

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+'''
+    ---------------------------------------------------------------------------
+    OpenCap: createAuthenticationEnvFile.py
+    ---------------------------------------------------------------------------
+
+    Copyright 2022 Stanford University and the Authors
+    
+    Author(s): Antoine Falisse, Scott Uhlrich
+    
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not
+    use this file except in compliance with the License. You may obtain a copy
+    of the License at http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software
+    distributed under the License is distributed on an "AS IS" BASIS,
+    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+    See the License for the specific language governing permissions and
+    limitations under the License.
+    
+    
+    Run this script to save your authentication token (for logging into webapp)
+    as an environment variable in a .env file. 
+    DO NOT CHANGE THE FILE NAME OF THE .env FILE, or your credentials will get pushed
+    to github with a commit, and anyone could get access to your data.
+    We recommend only saving this .env file on your own encrypted machine
+    and not while running on google collab.
+'''
+
+
+
+# Restart your terminal or IDE (eg Spyder) after running the script for the 
+# new environment variable to be loaded. 
+from utilsAuth import getToken
+import os
+
+getToken(saveEnvPath=os.path.abspath(os.path.join(os.path.dirname(__file__),'../')))

Examples/reprocessSessions.py

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+"""
+---------------------------------------------------------------------------
+OpenCap: reprocessSessions.py
+---------------------------------------------------------------------------
+
+Copyright 2022 Stanford University and the Authors
+
+Author(s): Scott Uhlrich, Antoine Falisse
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not
+use this file except in compliance with the License. You may obtain a copy
+of the License at http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+
+
+This script reprocesses OpenCap trials collected with the web application at
+app.opencap.ai. You can specify multiple sessions to reprocess, or specific
+trials within a session to reprocess. Data will (optionally) be saved locally
+in your <repositoryDirectory>/Data/<session_id> folder. 
+
+
+This script is useful for processing a session with more accurate and higher 
+resolution pose estimation settings It is also useful for debugging failed trials.
+
+
+The example session is publicly available, but you will need login credentials
+from app.opencap.ai to run this script. You can quickly create credentials
+from the home page: https://app.opencap.ai. We recommend first running the 
+Examples/createAuthenticationEnvFile.py script prior to running this script. Otherwise,
+you will need to to login every time you run the script.
+
+
+You can view the example session at:
+https://app.opencap.ai/session/23d52d41-69fe-47cf-8b60-838e4268dd50
+"""
+
+import os
+import sys
+sys.path.append(os.path.abspath('./..'))
+
+from utilsServer import batchReprocess
+from utils import getTrialNameIdMapping
+from utilsAPI import getAPIURL
+from utilsAuth import getToken
+
+API_URL = getAPIURL()
+API_TOKEN = getToken()
+
+# %% User inputs.
+# Enter the identifier(s) of the session(s) you want to reprocess. This is a list of one
+# or more session identifiers. The identifier is found as the 36-character string at the
+# end of the session url: app.opencap.ai/session/<session_id>
+session_ids = ['23d52d41-69fe-47cf-8b60-838e4268dd50']
+
+
+# Select which trials to reprocess. You can reprocess all trials in the session 
+# by entering None in all fields below. The correct calibration and static
+# trials will be automatically selected if None, and all dynamic trials will be
+# processed if None. If you do not want to process one of the trial types, 
+# enter []. If you specify multiple sessions above, all of the fields
+# below must be None or []. If you selected only one session_id above, you may
+# select specific reprocessed. Only one trial (str) is allowed for calib_id and
+# static_id. A list of strings is allowed for dynamic_ids.
+
+calib_id = []  # None (auto-selected trial), [] (skip), or string of specific trial_id
+static_id = [] # None (auto-selected trial), [] (skip), or string of specific trial_id
+dynamic_ids = None # None (all dynamic trials), [] (skip), or list of trial_id strings
+
+
+# # Optional: Uncomment this section to create a list of dynamic_ids to 
+# # reprocess from the trial names. This overwrites previous selection.
+
+# trialNames = getTrialNameIdMapping(session_ids[0])
+# dynamic_trialNames = ['squat'] # Enter a list of dynamic trial names to be reprocessed
+# dynamic_ids = [trialNames[name]['id'] for name in dynamic_trialNames]
+# print ('You are reprocessing specific dynamic trials by name')
+
+
+# The resolution at which the videos are processed by OpenPose can be adjusted.
+# The finer the resolution the more accurate the results (typically) but also
+# the more GPU memory is required and the more time it takes to process the video.
+# OpenCap supports the following four resolutionPoseDetection options (ordered
+# from lower to higher resolution/required memory):
+#   - 'default': 1x368 resolution, default in OpenPose (we were able to run with a GPU with 4GB memory).
+#   - '1x736': 1x736 resolution, default in OpenCap (we were able to run with a GPU with 4GB memory).
+#   - '1x736_2scales': 1x736 resolution with 2 scales (gap = 0.75). (may help with people larger in the frame. (we were able to run with a GPU with 8GB memory)
+#       - Please visit https://github.com/CMU-Perceptual-Computing-Lab/openpose/blob/master/include/openpose/flags.hpp#L112
+#         to learn more about scales. The conversation in this issue is also
+#         relevant: https://github.com/CMU-Perceptual-Computing-Lab/openpose/issues/622
+#   - '1x1008_4scales': 1x1008 resolution with 4 scales (gap = 0.25). (we were only able to run with a GPU with 24GB memory)
+#       - This is the highest resolution/settings we could use with a 24GB
+#         GPU without running into memory issues.
+resolutionPoseDetection = 'default'
+
+
+# Set deleteLocalFolder to False to keep a local copy of the data. If you are 
+# reprocessing a session that you collected, data will get written to the database
+# regardless of your selection. If True, the local copy will be deleted.
+deleteLocalFolder = False
+
+
+# %% Process data.
+batchReprocess(session_ids,calib_id,static_id,dynamic_ids,
+               resolutionPoseDetection=resolutionPoseDetection,
+               deleteLocalFolder=deleteLocalFolder)

MarkerAugmenter/LSTM/v0.0/model.json

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+{"class_name": "Sequential", "config": {"name": "sequential_1", "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": [null, null, 59], "dtype": "float32", "sparse": false, "ragged": false, "name": "lstm_3_input"}}, {"class_name": "LSTM", "config": {"name": "lstm_3", "trainable": true, "batch_input_shape": [null, null, 59], "dtype": "float32", "return_sequences": true, "return_state": false, "go_backwards": false, "stateful": false, "unroll": false, "time_major": false, "units": 144, "activation": "tanh", "recurrent_activation": "sigmoid", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "recurrent_initializer": {"class_name": "Orthogonal", "config": {"gain": 1.0, "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "unit_forget_bias": true, "kernel_regularizer": null, "recurrent_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "recurrent_constraint": null, "bias_constraint": null, "dropout": 0.0, "recurrent_dropout": 0.0, "implementation": 2}}, {"class_name": "LSTM", "config": {"name": "lstm_4", "trainable": true, "dtype": "float32", "return_sequences": true, "return_state": false, "go_backwards": false, "stateful": false, "unroll": false, "time_major": false, "units": 144, "activation": "tanh", "recurrent_activation": "sigmoid", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "recurrent_initializer": {"class_name": "Orthogonal", "config": {"gain": 1.0, "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "unit_forget_bias": true, "kernel_regularizer": null, "recurrent_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "recurrent_constraint": null, "bias_constraint": null, "dropout": 0.0, "recurrent_dropout": 0.0, "implementation": 2}}, {"class_name": "TimeDistributed", "config": {"name": "time_distributed_1", "trainable": true, "dtype": "float32", "layer": {"class_name": "Dense", "config": {"name": "dense_1", "trainable": true, "dtype": "float32", "units": 129, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}}}]}, "keras_version": "2.6.0", "backend": "tensorflow"}

MarkerAugmenter/LSTM/v0.1_lower/model.json

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+{"class_name": "Sequential", "config": {"name": "sequential_1", "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": [null, null, 47], "dtype": "float32", "sparse": false, "ragged": false, "name": "lstm_3_input"}}, {"class_name": "LSTM", "config": {"name": "lstm_3", "trainable": true, "batch_input_shape": [null, null, 47], "dtype": "float32", "return_sequences": true, "return_state": false, "go_backwards": false, "stateful": false, "unroll": false, "time_major": false, "units": 128, "activation": "tanh", "recurrent_activation": "sigmoid", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "recurrent_initializer": {"class_name": "Orthogonal", "config": {"gain": 1.0, "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "unit_forget_bias": true, "kernel_regularizer": null, "recurrent_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "recurrent_constraint": null, "bias_constraint": null, "dropout": 0.0, "recurrent_dropout": 0.0, "implementation": 2}}, {"class_name": "LSTM", "config": {"name": "lstm_4", "trainable": true, "dtype": "float32", "return_sequences": true, "return_state": false, "go_backwards": false, "stateful": false, "unroll": false, "time_major": false, "units": 128, "activation": "tanh", "recurrent_activation": "sigmoid", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "recurrent_initializer": {"class_name": "Orthogonal", "config": {"gain": 1.0, "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "unit_forget_bias": true, "kernel_regularizer": null, "recurrent_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "recurrent_constraint": null, "bias_constraint": null, "dropout": 0.0, "recurrent_dropout": 0.0, "implementation": 2}}, {"class_name": "TimeDistributed", "config": {"name": "time_distributed_1", "trainable": true, "dtype": "float32", "layer": {"class_name": "Dense", "config": {"name": "dense_1", "trainable": true, "dtype": "float32", "units": 105, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}}}]}, "keras_version": "2.6.0", "backend": "tensorflow"}

MarkerAugmenter/LSTM/v0.1_upper/model.json

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+{"class_name": "Sequential", "config": {"name": "sequential_1", "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": [null, null, 23], "dtype": "float32", "sparse": false, "ragged": false, "name": "lstm_4_input"}}, {"class_name": "LSTM", "config": {"name": "lstm_4", "trainable": true, "batch_input_shape": [null, null, 23], "dtype": "float32", "return_sequences": true, "return_state": false, "go_backwards": false, "stateful": false, "unroll": false, "time_major": false, "units": 80, "activation": "tanh", "recurrent_activation": "sigmoid", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "recurrent_initializer": {"class_name": "Orthogonal", "config": {"gain": 1.0, "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "unit_forget_bias": true, "kernel_regularizer": null, "recurrent_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "recurrent_constraint": null, "bias_constraint": null, "dropout": 0.0, "recurrent_dropout": 0.0, "implementation": 2}}, {"class_name": "LSTM", "config": {"name": "lstm_5", "trainable": true, "dtype": "float32", "return_sequences": true, "return_state": false, "go_backwards": false, "stateful": false, "unroll": false, "time_major": false, "units": 80, "activation": "tanh", "recurrent_activation": "sigmoid", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "recurrent_initializer": {"class_name": "Orthogonal", "config": {"gain": 1.0, "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "unit_forget_bias": true, "kernel_regularizer": null, "recurrent_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "recurrent_constraint": null, "bias_constraint": null, "dropout": 0.0, "recurrent_dropout": 0.0, "implementation": 2}}, {"class_name": "LSTM", "config": {"name": "lstm_6", "trainable": true, "dtype": "float32", "return_sequences": true, "return_state": false, "go_backwards": false, "stateful": false, "unroll": false, "time_major": false, "units": 80, "activation": "tanh", "recurrent_activation": "sigmoid", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "recurrent_initializer": {"class_name": "Orthogonal", "config": {"gain": 1.0, "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "unit_forget_bias": true, "kernel_regularizer": null, "recurrent_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "recurrent_constraint": null, "bias_constraint": null, "dropout": 0.0, "recurrent_dropout": 0.0, "implementation": 2}}, {"class_name": "TimeDistributed", "config": {"name": "time_distributed_1", "trainable": true, "dtype": "float32", "layer": {"class_name": "Dense", "config": {"name": "dense_1", "trainable": true, "dtype": "float32", "units": 24, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}}}]}, "keras_version": "2.6.0", "backend": "tensorflow"}