457 lines
17 KiB
Python
457 lines
17 KiB
Python
#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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'''
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###########################################################################
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## POSE2SIM ##
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###########################################################################
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This repository offers a way to perform markerless kinematics, and gives an
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example workflow from an Openpose input to an OpenSim result.
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It offers tools for:
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- 2D pose estimation,
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- Cameras calibration,
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- Tracking the person of interest,
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- Robust triangulation,
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- Filtration.
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It has been tested on Windows 10 but should work similarly on Linux.
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Please subscribe to this issue if you wish to be notified of the code release.
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See https://github.com/perfanalytics/pose2sim
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Installation:
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# Open Anaconda prompt. Type:
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# - conda create -n Pose2Sim python=3.7 tensorflow-gpu=1.13.1
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# - conda activate Pose2Sim
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# - conda install Pose2Sim
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Usage:
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# First run Pose estimation and organize your directories (see Readme.md)
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from Pose2Sim import Pose2Sim
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Pose2Sim.calibration()
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Pose2Sim.personAssociation()
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Pose2Sim.triangulation()
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Pose2Sim.filtering()
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# Then run OpenSim (see Readme.md)
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'''
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## INIT
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import toml
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import os
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import time
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from copy import deepcopy
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import logging, logging.handlers
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## AUTHORSHIP INFORMATION
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__author__ = "David Pagnon"
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__copyright__ = "Copyright 2021, Pose2Sim"
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__credits__ = ["David Pagnon"]
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__license__ = "BSD 3-Clause License"
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__version__ = "0.4"
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__maintainer__ = "David Pagnon"
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__email__ = "contact@david-pagnon.com"
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__status__ = "Development"
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## FUNCTIONS
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def setup_logging(session_dir):
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'''
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Create logging file and stream handlers
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'''
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with open(os.path.join(session_dir, 'logs.txt'), 'a+') as log_f: pass
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logging.basicConfig(format='%(message)s', level=logging.INFO,
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handlers = [logging.handlers.TimedRotatingFileHandler(os.path.join(session_dir, 'logs.txt'), when='D', interval=7), logging.StreamHandler()])
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def recursive_update(dict_to_update, dict_with_new_values):
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'''
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Update nested dictionaries without overwriting existing keys in any level of nesting
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Example:
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dict_to_update = {'key': {'key_1': 'val_1', 'key_2': 'val_2'}}
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dict_with_new_values = {'key': {'key_1': 'val_1_new'}}
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returns {'key': {'key_1': 'val_1_new', 'key_2': 'val_2'}}
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while dict_to_update.update(dict_with_new_values) would return {'key': {'key_1': 'val_1_new'}}
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'''
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for key, value in dict_with_new_values.items():
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if key in dict_to_update and isinstance(value, dict) and isinstance(dict_to_update[key], dict):
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# Recursively update nested dictionaries
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dict_to_update[key] = recursive_update(dict_to_update[key], value)
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else:
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# Update or add new key-value pairs
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dict_to_update[key] = value
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return dict_to_update
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def determine_level(config_dir):
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'''
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Determine the level at which the function is called.
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Level = 1: Trial folder
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Level = 2: Participant folder
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Level = 3: Session folder
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'''
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len_paths = [len(root.split(os.sep)) for root,dirs,files in os.walk(config_dir) if 'Config.toml' in files]
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level = max(len_paths) - min(len_paths) + 1
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return level
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def read_config_files(config):
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'''
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Read Session, Participant, and Trial configuration files,
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and output a dictionary with all the parameters.
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'''
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if type(config)==dict:
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level = 3 # log_dir = os.getcwd()
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config_dicts = [config]
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if config_dicts[0].get('project').get('project_dir') == None:
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raise ValueError('Please specify the project directory in config_dict:\n \
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config_dict.get("project").update({"project_dir":"<YOUR_PROJECT_DIRECTORY>"})')
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else:
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# if launched without an argument, config == None, else it is the path to the config directory
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config_dir = ['.' if config == None else config][0]
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level = determine_level(config_dir)
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# Trial level
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if level == 1:
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session_config_dict = toml.load(os.path.join(config_dir, '..','..','Config.toml'))
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participant_config_dict = toml.load(os.path.join(config_dir, '..','Config.toml'))
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trial_config_dict = toml.load(os.path.join(config_dir, 'Config.toml'))
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session_config_dict = recursive_update(session_config_dict,participant_config_dict)
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session_config_dict = recursive_update(session_config_dict,trial_config_dict)
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session_config_dict.get("project").update({"project_dir":config_dir})
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config_dicts = [session_config_dict]
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# Participant level
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if level == 2:
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session_config_dict = toml.load(os.path.join(config_dir, '..','Config.toml'))
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participant_config_dict = toml.load(os.path.join(config_dir, 'Config.toml'))
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config_dicts = []
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# Create config dictionaries for all trials of the participant
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for (root,dirs,files) in os.walk(config_dir):
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if 'Config.toml' in files and root != config_dir:
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trial_config_dict = toml.load(os.path.join(root, files[0]))
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# deep copy, otherwise session_config_dict is modified at each iteration within the config_dicts list
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temp_dict = deepcopy(session_config_dict)
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temp_dict = recursive_update(temp_dict,participant_config_dict)
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temp_dict = recursive_update(temp_dict,trial_config_dict)
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temp_dict.get("project").update({"project_dir":os.path.join(config_dir, os.path.relpath(root))})
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if not os.path.basename(root) in temp_dict.get("project").get('exclude_from_batch'):
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config_dicts.append(temp_dict)
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# Session level
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if level == 3:
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session_config_dict = toml.load(os.path.join(config_dir, 'Config.toml'))
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config_dicts = []
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# Create config dictionaries for all trials of all participants of the session
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for (root,dirs,files) in os.walk(config_dir):
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if 'Config.toml' in files and root != config_dir:
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# participant
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if determine_level(root) == 2:
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participant_config_dict = toml.load(os.path.join(root, files[0]))
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# trial
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elif determine_level(root) == 1:
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trial_config_dict = toml.load(os.path.join(root, files[0]))
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# deep copy, otherwise session_config_dict is modified at each iteration within the config_dicts list
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temp_dict = deepcopy(session_config_dict)
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temp_dict = recursive_update(temp_dict,participant_config_dict)
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temp_dict = recursive_update(temp_dict,trial_config_dict)
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temp_dict.get("project").update({"project_dir":os.path.join(config_dir, os.path.relpath(root))})
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if not os.path.relpath(root) in [os.path.relpath(p) for p in temp_dict.get("project").get('exclude_from_batch')]:
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config_dicts.append(temp_dict)
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return level, config_dicts
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def base_params(config_dict, level):
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'''
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Retrieve sequence name and frames to be analyzed.
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'''
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project_dir = os.getcwd()
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frame_range = config_dict.get('project').get('frame_range')
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seq_name = os.path.basename(project_dir)
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frames = ["all frames" if frame_range == [] else f"frames {frame_range[0]} to {frame_range[1]}"][0]
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log_dir = os.path.realpath([os.getcwd() if level==3 else os.path.join(os.getcwd(), '..') if level==2 else os.path.join(os.getcwd(), '..', '..')][0])
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with open(os.path.join(log_dir, 'logs.txt'), 'a+') as log_f: pass
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logging.basicConfig(format='%(message)s', level=logging.INFO,
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handlers = [logging.handlers.TimedRotatingFileHandler(os.path.join(log_dir, 'logs.txt'), when='D', interval=7), logging.StreamHandler()])
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return project_dir, seq_name, frames
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def calibration(config=None):
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'''
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Cameras calibration from checkerboards or from qualisys files.
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config can be a dictionary,
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or a trial, participant, or session directory path,
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or the function can be called without an argument, in which case it is the current directory.
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'''
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from Pose2Sim.calibration import calibrate_cams_all
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level, config_dicts = read_config_files(config)
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config_dict = config_dicts[0]
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session_dir = os.path.realpath([os.getcwd() if level==3 else os.path.join(os.getcwd(), '..') if level==2 else os.path.join(os.getcwd(), '..', '..')][0])
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config_dict.get("project").update({"project_dir":session_dir})
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# Set up logging
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setup_logging(session_dir)
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# Path to the calibration directory
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calib_dir = [os.path.join(session_dir, c) for c in os.listdir(session_dir) if ('Calib' or 'calib') in c][0]
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logging.info("\n\n---------------------------------------------------------------------")
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logging.info("Camera calibration")
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logging.info("---------------------------------------------------------------------")
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logging.info(f"\nCalibration directory: {calib_dir}")
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start = time.time()
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calibrate_cams_all(config_dict)
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end = time.time()
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logging.info(f'Calibration took {end-start:.2f} s.')
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def poseEstimation(config=None):
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'''
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Estimate pose using BlazePose, OpenPose, AlphaPose, or DeepLabCut.
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config can either be a path or a dictionary (for batch processing)
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'''
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raise NotImplementedError('This has not been integrated yet. \nPlease read README.md for further explanation')
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# TODO
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from Pose2Sim.poseEstimation import pose_estimation_all
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if type(config)==dict:
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config_dict = config
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else:
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config_dict = read_config_files(config)
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project_dir, seq_name, frames = base_params(config_dict)
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logging.info("\n\n---------------------------------------------------------------------")
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logging.info("Pose estimation")
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logging.info("---------------------------------------------------------------------")
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logging.info(f"\nProject directory: {project_dir}")
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start = time.time()
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pose_estimation_all(config_dict)
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end = time.time()
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logging.info(f'Pose estimation took {end-start:.2f} s.')
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def synchronization(config=None):
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'''
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Synchronize cameras if needed.
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config can either be a path or a dictionary (for batch processing)
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'''
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raise NotImplementedError('This has not been integrated yet. \nPlease read README.md for further explanation')
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#TODO
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from Pose2Sim.synchronization import synchronize_cams_all
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if type(config)==dict:
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config_dict = config
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else:
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config_dict = read_config_files(config)
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project_dir, seq_name, frames = base_params(config_dict)
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logging.info("\n\n---------------------------------------------------------------------")
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logging.info("Camera synchronization")
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logging.info("---------------------------------------------------------------------")
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logging.info(f"\nProject directory: {project_dir}")
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start = time.time()
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synchronize_cams_all(config_dict)
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end = time.time()
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logging.info(f'Synchronization took {end-start:.2f} s.')
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def personAssociation(config=None):
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'''
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Tracking of the person of interest in case of multiple persons detection.
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Needs a calibration file.
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config can either be a path or a dictionary (for batch processing)
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'''
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from Pose2Sim.personAssociation import track_2d_all
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if type(config)==dict:
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level = 3 # log_dir = os.getcwd()
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config_dict = config
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if config_dict.get('project').get('project_dir') == None:
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raise ValueError('Please specify the project directory in config_dict:\n \
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config_dict.get("project").update({"project_dir":"<YOUR_PROJECT_DIRECTORY>"})')
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else:
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# Determine the level at which the function is called (session:3, participant:2, trial:1)
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level = determine_level()
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config_dicts = read_config_files(level)
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# Set up logging
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session_dir = os.path.realpath(os.path.join(config_dicts[0].get('project').get('project_dir'), '..', '..'))
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setup_logging(session_dir)
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# Batch process all trials
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for config_dict in config_dicts:
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project_dir, seq_name, frames = base_params(config_dict)
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logging.info("\n\n---------------------------------------------------------------------")
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logging.info(f"Tracking of the person of interest for {seq_name}, for {frames}.")
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logging.info("---------------------------------------------------------------------")
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logging.info(f"\nProject directory: {project_dir}")
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start = time.time()
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track_2d_all(config_dict)
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end = time.time()
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logging.info(f'Tracking took {end-start:.2f} s.')
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session_dir = os.path.realpath([os.getcwd() if level==3 else os.path.join(os.getcwd(), '..') if level==2 else os.path.join(os.getcwd(), '..', '..')][0])
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config_dict.get("project").update({"project_dir":session_dir})
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# Path to the calibration directory
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calib_dir = [os.path.join(session_dir, c) for c in os.listdir(session_dir) if ('Calib' or 'calib') in c][0]
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logging.info("\n\n---------------------------------------------------------------------")
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logging.info("Camera calibration")
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logging.info("---------------------------------------------------------------------")
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logging.info(f"\nCalibration directory: {calib_dir}")
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start = time.time()
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calibrate_cams_all(config_dict)
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end = time.time()
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logging.info(f'Calibration took {end-start:.2f} s.')
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def triangulation(config=None):
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'''
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Robust triangulation of 2D points coordinates.
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config can either be a path or a dictionary (for batch processing)
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'''
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from Pose2Sim.triangulation import triangulate_all
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if type(config)==dict:
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config_dict = config
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else:
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config_dict = read_config_files(config)
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project_dir, seq_name, frames = base_params(config_dict)
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logging.info("\n\n---------------------------------------------------------------------")
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logging.info(f"Triangulation of 2D points for {seq_name}, for {frames}.")
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logging.info("---------------------------------------------------------------------")
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logging.info(f"\nProject directory: {project_dir}")
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start = time.time()
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triangulate_all(config_dict)
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end = time.time()
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logging.info(f'Triangulation took {end-start:.2f} s.')
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def filtering(config=None):
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'''
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Filter trc 3D coordinates.
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config can either be a path or a dictionary (for batch processing)
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'''
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from Pose2Sim.filtering import filter_all
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if type(config)==dict:
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config_dict = config
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else:
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config_dict = read_config_files(config)
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project_dir, seq_name, frames = base_params(config_dict)
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logging.info("\n\n---------------------------------------------------------------------")
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logging.info(f"Filtering 3D coordinates for {seq_name}, for {frames}.")
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logging.info("---------------------------------------------------------------------")
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logging.info(f"\nProject directory: {project_dir}")
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filter_all(config_dict)
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def scalingModel(config=None):
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'''
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Uses OpenSim to scale a model based on a static 3D pose.
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config can either be a path or a dictionary (for batch processing)
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'''
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raise NotImplementedError('This has not been integrated yet. \nPlease read README.md for further explanation')
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# TODO
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from Pose2Sim.scalingModel import scale_model_all
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if type(config)==dict:
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config_dict = config
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else:
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config_dict = read_config_files(config)
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project_dir, seq_name, frames = base_params(config_dict)
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logging.info("\n\n---------------------------------------------------------------------")
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logging.info("Scaling model")
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logging.info("---------------------------------------------------------------------")
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logging.info(f"\nProject directory: {project_dir}")
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start = time.time()
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scale_model_all(config_dict)
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end = time.time()
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logging.info(f'Model scaling took {end-start:.2f} s.')
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def inverseKinematics(config=None):
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'''
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Uses OpenSim to perform inverse kinematics.
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config can either be a path or a dictionary (for batch processing)
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'''
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raise NotImplementedError('This has not been integrated yet. \nPlease read README.md for further explanation')
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# TODO
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from Pose2Sim.inverseKinematics import inverse_kinematics_all
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if type(config)==dict:
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config_dict = config
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else:
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config_dict = read_config_files(config)
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project_dir, seq_name, frames = base_params(config_dict)
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logging.info("\n\n---------------------------------------------------------------------")
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logging.info("Inverse kinematics")
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logging.info("---------------------------------------------------------------------")
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logging.info(f"\nProject directory: {project_dir}")
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start = time.time()
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inverse_kinematics_all(config_dict)
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end = time.time()
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logging.info(f'Inverse kinematics took {end-start:.2f} s.') |