revert to model.predict(outputs)
This commit is contained in:
davidpagnon
parent
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@ -3,40 +3,39 @@
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'''
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###########################################################################
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## POSE2SIM ##
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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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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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- Marker augmentation,
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- OpenSim scaling and inverse kinematics
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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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- Marker augmentation,
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- OpenSim scaling and inverse kinematics
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It has been tested on Windows, Linux and MacOS, and works for any Python version >= 3.8
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It has been tested on Windows, Linux and MacOS, and works for any Python version >= 3.8
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Installation:
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# Open Anaconda prompt. Type:
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# - conda create -n Pose2Sim python=3.8
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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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Pose2Sim.markerAugmentation()
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# Then run OpenSim (see Readme.md)
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Installation:
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# Open Anaconda prompt. Type:
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# - conda create -n Pose2Sim python=3.8
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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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Pose2Sim.markerAugmentation()
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# Then run OpenSim (see Readme.md)
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'''
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@ -3,27 +3,27 @@
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'''
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###########################################################################
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## CAMERAS CALIBRATION ##
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###########################################################################
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###########################################################################
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## CAMERAS CALIBRATION ##
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###########################################################################
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Use this module to calibrate your cameras and save results to a .toml file.
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Use this module to calibrate your cameras and save results to a .toml file.
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It either converts a Qualisys calibration .qca.txt file,
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Or calibrates cameras from checkerboard images.
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It either converts a Qualisys calibration .qca.txt file,
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Or calibrates cameras from checkerboard images.
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Checkerboard calibration is based on
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https://docs.opencv.org/3.4.15/dc/dbb/tutorial_py_calibration.html.
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/!\ Beware that corners must be detected on all frames, or else extrinsic
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parameters may be wrong. Set show_corner_detection to 1 to verify.
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Checkerboard calibration is based on
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https://docs.opencv.org/3.4.15/dc/dbb/tutorial_py_calibration.html.
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/!\ Beware that corners must be detected on all frames, or else extrinsic
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parameters may be wrong. Set show_corner_detection to 1 to verify.
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INPUTS:
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- a calibration file in the 'calibration' folder (.qca.txt extension)
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- OR folders 'calibration\intrinsics' (populated with video or about 30 images) and 'calibration\extrinsics' (populated with video or one image)
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- a Config.toml file in the 'User' folder
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INPUTS:
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- a calibration file in the 'calibration' folder (.qca.txt extension)
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- OR folders 'calibration\intrinsics' (populated with video or about 30 images) and 'calibration\extrinsics' (populated with video or one image)
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- a Config.toml file in the 'User' folder
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OUTPUTS:
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- a calibration file in the 'calibration' folder (.toml extension)
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OUTPUTS:
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- a calibration file in the 'calibration' folder (.toml extension)
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'''
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# TODO: DETECT WHEN WINDOW IS CLOSED
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@ -3,12 +3,11 @@
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'''
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###########################################################################
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## OTHER SHARED UTILITIES ##
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###########################################################################
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Functions shared between modules, and other utilities
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###########################################################################
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## OTHER SHARED UTILITIES ##
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###########################################################################
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Functions shared between modules, and other utilities
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'''
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## INIT
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@ -3,22 +3,21 @@
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'''
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###########################################################################
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## FILTER 3D COORDINATES ##
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###########################################################################
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###########################################################################
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## FILTER 3D COORDINATES ##
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###########################################################################
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Filter trc 3D coordinates.
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Filter trc 3D coordinates.
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Available filters: Butterworth, Butterworth on speed, Gaussian, LOESS, Median
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Set your parameters in Config.toml
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Available filters: Butterworth, Butterworth on speed, Gaussian, LOESS, Median
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Set your parameters in Config.toml
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INPUTS:
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- a trc file
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- filtering parameters in Config.toml
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OUTPUT:
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- a filtered trc file
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INPUTS:
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- a trc file
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- filtering parameters in Config.toml
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OUTPUT:
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- a filtered trc file
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'''
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@ -17,7 +17,6 @@ INPUTS:
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OUTPUT:
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- a filtered trc file
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'''
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@ -186,7 +185,7 @@ def augmentTRC(config_dict):
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json_file.close()
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model = tf.keras.models.model_from_json(pretrainedModel_json)
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model.load_weights(os.path.join(augmenterModelDir, "weights.h5"))
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outputs = model(inputs)
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outputs = model.predict(inputs)
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tf.keras.backend.clear_session()
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# %% Post-process outputs.
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@ -3,28 +3,27 @@
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'''
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###########################################################################
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## TRACKING OF PERSON OF INTEREST ##
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###########################################################################
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###########################################################################
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## TRACKING OF PERSON OF INTEREST ##
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###########################################################################
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Openpose detects all people in the field of view.
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Which is the one of interest?
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Openpose detects all people in the field of view.
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Which is the one of interest?
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This module tries all possible triangulations of a chosen anatomical
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point. If "multi_person" mode is not used, it chooses the person for
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whom the reprojection error is smallest. Otherwise, it selects all
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persons with a reprojection error smaller than a threshold, and then
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associates them across time frames by minimizing the displacement speed.
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This module tries all possible triangulations of a chosen anatomical
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point. If "multi_person" mode is not used, it chooses the person for
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whom the reprojection error is smallest. Otherwise, it selects all
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persons with a reprojection error smaller than a threshold, and then
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associates them across time frames by minimizing the displacement speed.
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INPUTS:
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- a calibration file (.toml extension)
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- json files from each camera folders with several detected persons
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- a Config.toml file
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- a skeleton model
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OUTPUTS:
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- json files for each camera with only one person of interest
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INPUTS:
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- a calibration file (.toml extension)
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- json files from each camera folders with several detected persons
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- a Config.toml file
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- a skeleton model
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OUTPUTS:
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- json files for each camera with only one person of interest
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'''
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@ -3,23 +3,23 @@
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'''
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###########################################################################
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## SKELETONS DEFINITIONS ##
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###########################################################################
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###########################################################################
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## SKELETONS DEFINITIONS ##
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###########################################################################
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The definition and hierarchy of the following skeletons are available:
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- OpenPose BODY_25B, BODY_25, BODY_135, COCO, MPII
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- Mediapipe BLAZEPOSE
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- AlphaPose HALPE_26, HALPE_68, HALPE_136, COCO_133, COCO, MPII
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(for COCO and MPII, AlphaPose must be run with the flag "--format cmu")
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- DeepLabCut CUSTOM: the skeleton will be defined in Config.toml
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The definition and hierarchy of the following skeletons are available:
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- OpenPose BODY_25B, BODY_25, BODY_135, COCO, MPII
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- Mediapipe BLAZEPOSE
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- AlphaPose HALPE_26, HALPE_68, HALPE_136, COCO_133, COCO, MPII
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(for COCO and MPII, AlphaPose must be run with the flag "--format cmu")
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- DeepLabCut CUSTOM: the skeleton will be defined in Config.toml
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N.B.: Not all face and hand keypoints are reported in the skeleton architecture,
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since some are redundant for the orientation of some bodies.
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N.B.: Not all face and hand keypoints are reported in the skeleton architecture,
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since some are redundant for the orientation of some bodies.
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N.B.: The corresponding OpenSim model files are provided in the "Pose2Sim\Empty project" folder.
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If you wish to use any other, you will need to adjust the markerset in the .osim model file,
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as well as in the scaling and IK setup files.
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N.B.: The corresponding OpenSim model files are provided in the "Pose2Sim\Empty project" folder.
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If you wish to use any other, you will need to adjust the markerset in the .osim model file,
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as well as in the scaling and IK setup files.
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'''
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## INIT
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'''
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###########################################################################
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## ROBUST TRIANGULATION OF 2D COORDINATES ##
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###########################################################################
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###########################################################################
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## ROBUST TRIANGULATION OF 2D COORDINATES ##
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###########################################################################
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This module triangulates 2D json coordinates and builds a .trc file readable
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by OpenSim.
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This module triangulates 2D json coordinates and builds a .trc file readable
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by OpenSim.
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The triangulation is weighted by the likelihood of each detected 2D keypoint
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(if they meet the likelihood threshold). If the reprojection error is above a
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threshold, right and left sides are swapped; if it is still above, a camera
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is removed for this point and this frame, until the threshold is met. If more
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cameras are removed than a predefined minimum, triangulation is skipped for
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the point and this frame. In the end, missing values are interpolated.
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The triangulation is weighted by the likelihood of each detected 2D keypoint
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(if they meet the likelihood threshold). If the reprojection error is above a
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threshold, right and left sides are swapped; if it is still above, a camera
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is removed for this point and this frame, until the threshold is met. If more
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cameras are removed than a predefined minimum, triangulation is skipped for
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the point and this frame. In the end, missing values are interpolated.
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In case of multiple subjects detection, make sure you first run the
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personAssociation module.
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In case of multiple subjects detection, make sure you first run the
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personAssociation module.
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INPUTS:
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- a calibration file (.toml extension)
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- json files for each camera with only one person of interest
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- a Config.toml file
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- a skeleton model
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OUTPUTS:
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- a .trc file with 3D coordinates in Y-up system coordinates
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INPUTS:
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- a calibration file (.toml extension)
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- json files for each camera with only one person of interest
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- a Config.toml file
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- a skeleton model
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OUTPUTS:
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- a .trc file with 3D coordinates in Y-up system coordinates
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'''
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@ -44,7 +43,7 @@ import cv2
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import toml
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from tqdm import tqdm
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from scipy import interpolate
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from collections import Counter, OrderedDict
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from collections import Counter
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from anytree import RenderTree
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from anytree.importer import DictImporter
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import logging
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@ -762,7 +761,7 @@ def triangulate_all(config):
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try:
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Q_tot[n] = Q_tot[n].apply(interpolate_zeros_nans, axis=0, args = [interp_gap_smaller_than, interpolation_kind])
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except:
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logging.info(f'Interpolation was not possible for person {n}. This means that the not enough points are available, which is often due to a bad calibration.')
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logging.info(f'Interpolation was not possible for person {n}. This means that not enough points are available, which is often due to a bad calibration.')
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# Q_tot.replace(np.nan, 0, inplace=True)
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# Create TRC file
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