Update README.md
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README.md
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README.md
@ -107,7 +107,7 @@ Results are stored as .trc files in the `Demo/pose-3d` directory.
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1. Find your `Pose2Sim\Empty_project`, copy-paste it where you like and give it the name of your choice.
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2. Edit the `User\Config.toml` file as needed, **especially regarding the path to your project**.
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3. Populate the `raw-2d`folder with your camera images or videos.
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3. Populate the `raw-2d`folder with your videos.
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<pre>
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Project
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@ -119,9 +119,9 @@ Results are stored as .trc files in the `Demo/pose-3d` directory.
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│ └──IK_Setup_Pose2Sim_Body25b.xml
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│
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├── <b>raw-2d
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│ ├──raw_cam1_img
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│ ├──vid_cam1.mp4 (or other extension)
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│ ├──...
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│ └──raw_camN_img</b>
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│ └──vid_camN.mp4</b>
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│
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└──User
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└──Config.toml
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@ -135,7 +135,7 @@ The accuracy and robustness of Pose2Sim have been thoroughly assessed only with
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* Open a command prompt in your **OpenPose** directory. \
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Launch OpenPose for each raw image folder:
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```
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bin\OpenPoseDemo.exe --model_pose BODY_25B --image_dir <PATH_TO_PROJECT_DIR>\raw-2d\raw_cam1_img --write_json <PATH_TO_PROJECT_DIR>\pose-2d\pose_cam1_json
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bin\OpenPoseDemo.exe --model_pose BODY_25B --video <PATH_TO_PROJECT_DIR>\raw-2d\vid_cam1.mp4 --write_json <PATH_TO_PROJECT_DIR>\pose-2d\pose_cam1_json
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```
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* The [BODY_25B model](https://github.com/CMU-Perceptual-Computing-Lab/openpose_train/tree/master/experimental_models) has more accurate results than the standard BODY_25 one and has been extensively tested for Pose2Sim. \
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You can also use the [BODY_135 model](https://github.com/CMU-Perceptual-Computing-Lab/openpose_train/tree/master/experimental_models), which allows for the evaluation of pronation/supination, wrist flexion, and wrist deviation.\
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@ -174,7 +174,7 @@ If you need to detect specific points on a human being, an animal, or an object,
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4. Create an OpenSim model if you need 3D joint angles.
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#### With AlphaPose:
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[AlphaPose](https://github.com/MVIG-SJTU/AlphaPose) is slightly less renowned than OpenPose and not as easy to run on non-Linux machines, but its accuracy is comparable. As a top-down approach (unlike OpenPose which is bottom-up), it is faster on single-person detection, but slower on multi-person detection.
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[AlphaPose](https://github.com/MVIG-SJTU/AlphaPose) is one of the main competitors of OpenPose, and its accuracy is comparable. As a top-down approach (unlike OpenPose which is bottom-up), it is faster on single-person detection, but slower on multi-person detection.
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* Install and run AlphaPose on your videos (more intruction on their repository)
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* Translate the AlphaPose single json file to OpenPose frame-by-frame files (with `AlphaPose_to_OpenPose.py` script, see [Utilities](#utilities)):
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```
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@ -207,9 +207,9 @@ N.B.: Markers are not needed in Pose2Sim and were used here for validation
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│ └──pose_camN_json</i></b>
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│
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├── raw-2d
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│ ├──raw_cam1_img
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│ ├──...
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│ └──raw_camN_img
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│ ├──vid_cam1.mp4
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│ ├──...
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│ └──vid_camN.mp4
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│
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└──User
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└──Config.toml
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@ -267,9 +267,9 @@ Output:\
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│ └──pose_camN_json
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│
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├── raw-2d
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│ ├──raw_cam1_img
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│ ├──...
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│ └──raw_camN_img
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│ ├──vid_cam1.mp4
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│ ├──...
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│ └──vid_camN.mp4
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│
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└──User
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└──Config.toml
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@ -321,9 +321,9 @@ Output:\
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│ └──tracked_camN_json</i></b>
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│
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├── raw-2d
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│ ├──raw_cam1_img
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│ ├──...
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│ └──raw_camN_img
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│ ├──vid_cam1.mp4
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│ ├──...
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│ └──vid_camN.mp4
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│
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└──User
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└──Config.toml
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@ -379,9 +379,9 @@ Output:\
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└──Pose-3d.trc</i></b>>
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│
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├── raw-2d
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│ ├──raw_cam1_img
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│ ├──...
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│ └──raw_camN_img
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│ ├──vid_cam1.mp4
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│ ├──...
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│ └──vid_camN.mp4
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│
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└──User
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└──Config.toml
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@ -440,9 +440,9 @@ Output:\
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│ └──Pose-3d-filtered.trc</i></b>
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│
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├── raw-2d
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│ ├──raw_cam1_img
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│ ├──...
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│ └──raw_camN_img
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│ ├──vid_cam1.mp4
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│ ├──...
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│ └──vid_camN.mp4
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│
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└──User
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└──Config.toml
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@ -525,9 +525,9 @@ Note that it is easier to install on Python 3.7 and with OpenSim 4.2.
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│ └──Pose-3d-filtered.trc
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│
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├── raw-2d
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│ ├──raw_cam1_img
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│ ├──...
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│ └──raw_camN_img
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│ ├──vid_cam1.mp4
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│ ├──...
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│ └──vid_camN.mp4
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│
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└──User
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└──Config.toml
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