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davidpagnon 2024-07-10 14:51:34 +02:00
parent b31215c8c0
commit e21c763614
2 changed files with 22 additions and 22 deletions
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22
Pose2Sim/Demo_MultiPerson/Config.toml
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@ -34,17 +34,17 @@ exclude_from_batch = [] # List of trials to be excluded from batch analysis, ['<
[pose]
vid_img_extension = 'mp4' # any video or image extension
pose_model = 'HALPE_26' #With RTMLib: HALPE_26 (default, body and feet), COCO_133 (body, feet, hands), COCO_17 (body)
# /!\ Only RTMPose is natively embeded in Pose2Sim. For all other pose estimation methods, you will have to run them yourself, and then refer to the documentation to convert the files if needed
#With MMPose: HALPE_26 (default), COCO_133, COCO_17, CUSTOM. See example at the end of the file
#With openpose: BODY_25B, BODY_25, BODY_135, COCO, MPII
#With mediapipe: BLAZEPOSE
#With alphapose: HALPE_26, HALPE_68, HALPE_136, COCO_133
#With deeplabcut: CUSTOM. See example at the end of the file
mode = 'balanced' # 'lightweight', 'balanced', 'performance'
det_frequency = 1 # Run person detection only every N frames, and inbetween track previously detected bounding boxes (still run keypoint detection on all frame).
#Equal to or greater than 1, can be as high as you want in simple uncrowded cases. Much faster, but might be less accurate.
tracking = true # Gives consistent person ID across frames. Slightly slower but might facilitate synchronization if other people are in the background
pose_model = 'HALPE_26' #With RTMLib: HALPE_26 (body and feet, default), COCO_133 (body, feet, hands), COCO_17 (body)
# /!\ Only RTMPose is natively embeded in Pose2Sim. For all other pose estimation methods, you will have to run them yourself, and then refer to the documentation to convert the files if needed
#With MMPose: HALPE_26, COCO_133, COCO_17, CUSTOM. See CUSTOM example at the end of the file
#With openpose: BODY_25B, BODY_25, BODY_135, COCO, MPII
#With mediapipe: BLAZEPOSE
#With alphapose: HALPE_26, HALPE_68, HALPE_136, COCO_133
#With deeplabcut: CUSTOM. See example at the end of the file
mode = 'performance' # 'lightweight', 'balanced', 'performance'
det_frequency = 1 # Run person detection only every N frames, and inbetween track previously detected bounding boxes (keypoint detection is still run on all frames).
# Equal to or greater than 1, can be as high as you want in simple uncrowded cases. Much faster, but might be less accurate.
tracking = false # Gives consistent person ID across frames. Slightly slower but might facilitate synchronization if other people are in the background
display_detection = true
save_video = 'to_video' # 'to_video' or 'to_images', 'none', or ['to_video', 'to_images']
output_format = 'openpose' # 'openpose', 'mmpose', 'deeplabcut', 'none' or a list of them # /!\ only 'openpose' is supported for now

22
Pose2Sim/Demo_SinglePerson/Config.toml
View File

@ -34,17 +34,17 @@ exclude_from_batch = [] # List of trials to be excluded from batch analysis, ['<
[pose]
vid_img_extension = 'mp4' # any video or image extension
pose_model = 'HALPE_26' #With RTMLib: HALPE_26 (default, body and feet), COCO_133 (body, feet, hands), COCO_17 (body)
# /!\ Only RTMPose is natively embeded in Pose2Sim. For all other pose estimation methods, you will have to run them yourself, and then refer to the documentation to convert the files if needed
#With MMPose: HALPE_26 (default), COCO_133, COCO_17, CUSTOM. See example at the end of the file
#With openpose: BODY_25B, BODY_25, BODY_135, COCO, MPII
#With mediapipe: BLAZEPOSE
#With alphapose: HALPE_26, HALPE_68, HALPE_136, COCO_133
#With deeplabcut: CUSTOM. See example at the end of the file
mode = 'balanced' # 'lightweight', 'balanced', 'performance'
det_frequency = 1 # Run person detection only every N frames, and inbetween track previously detected bounding boxes (still run keypoint detection on all frame).
#Equal to or greater than 1, can be as high as you want in simple uncrowded cases. Much faster, but might be less accurate.
tracking = true # Gives consistent person ID across frames. Slightly slower but might facilitate synchronization if other people are in the background
pose_model = 'HALPE_26' #With RTMLib: HALPE_26 (body and feet, default), COCO_133 (body, feet, hands), COCO_17 (body)
# /!\ Only RTMPose is natively embeded in Pose2Sim. For all other pose estimation methods, you will have to run them yourself, and then refer to the documentation to convert the files if needed
#With MMPose: HALPE_26, COCO_133, COCO_17, CUSTOM. See CUSTOM example at the end of the file
#With openpose: BODY_25B, BODY_25, BODY_135, COCO, MPII
#With mediapipe: BLAZEPOSE
#With alphapose: HALPE_26, HALPE_68, HALPE_136, COCO_133
#With deeplabcut: CUSTOM. See example at the end of the file
mode = 'performance' # 'lightweight', 'balanced', 'performance'
det_frequency = 1 # Run person detection only every N frames, and inbetween track previously detected bounding boxes (keypoint detection is still run on all frames).
# Equal to or greater than 1, can be as high as you want in simple uncrowded cases. Much faster, but might be less accurate.
tracking = false # Gives consistent person ID across frames. Slightly slower but might facilitate synchronization if other people are in the background
display_detection = true
save_video = 'to_video' # 'to_video' or 'to_images', 'none', or ['to_video', 'to_images']
output_format = 'openpose' # 'openpose', 'mmpose', 'deeplabcut', 'none' or a list of them # /!\ only 'openpose' is supported for now