120 lines
4.2 KiB
YAML
120 lines
4.2 KiB
YAML
module: myeasymocap.stages.basestage.MultiStage
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args:
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output: output/detect_match_triangulate
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keys_keep: [cameras, imgnames]
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at_step:
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detect:
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module: myeasymocap.backbone.yolo.yolo.MultiPerson # Use YOLO to detect multi-person
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key_from_data: [images, imgnames]
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args:
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model: yolov5m
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name: person
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min_length: 150 # this two threshold control the wanted bboxes
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max_length: 1000
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# keypoints2d:
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# module: myeasymocap.backbone.hrnet.myhrnet.MyHRNet
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# key_from_data: [images, imgnames]
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# key_from_previous: [bbox]
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# key_keep: []
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# args:
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# ckpt: data/models/pose_hrnet_w48_384x288.pth
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# single_person: False # This flag controls the function to detect all keypoints
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keypoints2d:
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module: myeasymocap.backbone.vitpose.vit_moe.MyViT
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key_from_data: [images, imgnames]
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key_from_previous: [bbox]
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key_keep: []
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args:
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ckpt: data/models/vitpose+_base.pth
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single_person: False # This flag controls the function to detect all keypoints
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vis_2d:
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module: myeasymocap.io.vis.Vis2D
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skip: False
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key_from_data: [images]
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key_from_previous: [keypoints, bbox]
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args:
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name: vis_keypoints2d
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scale: 0.5
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match:
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module: myeasymocap.operations.match_base.MatchAndTrack
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key_from_data: [cameras, meta]
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key_from_previous: [keypoints]
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args:
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cfg_match:
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min_conf: 0.3
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min_joints: 9
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distance:
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mode: epipolar
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threshold: 0.05 # 用于控制匹配的内点阈值
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threshold_track: 0.05 # track的匹配的内点阈值
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min_common_joints: 9
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cfg_svt:
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debug: 0
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maxIter: 10
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w_sparse: 0.1
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w_rank: 50
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tol: 0.0001
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aff_min: 0.3
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triangulate:
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min_view: 3 # min view when triangulate each points
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min_view_body: 3 # min visible view of the body
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min_conf_3d: 0.1
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dist_max: 50 # pixel
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dist_track: 100 # mm
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cfg_track:
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max_person: 100
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max_missing: 3 # 最多丢失3帧就要删除
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final_ranges: [[-10000, -10000, -10000], [10000, 10000, 10000]] # 最终的输出的range，仅用于输出的时候的筛选
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final_max_person: 100
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kintree: [[2, 3], [5, 6], [3, 4], [6, 7], [11, 22], [22, 23], [11, 24], [14, 19], [19, 20], [14, 21]]
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vis_kpts3d:
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module: myeasymocap.io.vis.Vis3D
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key_from_data: [images, cameras]
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key_from_previous: [results] # 用于最后的一起优化
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args:
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scale: 0.5
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lw_factor: 10
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at_final:
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write_raw: # write the raw 3d keypoints
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module: myeasymocap.io.write.WriteAll
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key_from_data: [results, meta]
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args:
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name: keypoints3d_raw
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collect: # split the results of each frame to each person
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module: myeasymocap.stages.collect.CollectMultiPersonMultiFrame
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key_from_data: [keypoints3d, pids]
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args:
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key: keypoints3d
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min_frame: 20
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load_body_model: # 载入身体模型
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module: myeasymocap.io.model.SMPLLoader
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args:
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model_path: models/pare/data/body_models/smpl/SMPL_NEUTRAL.pkl # load PARE model
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regressor_path: models/J_regressor_body25.npy
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# # 这个模块返回两个内容：body_model, model; 其中的body_model是用来进行可视化的
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fitting_each_person:
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module: myeasymocap.stages.basestage.StageForFittingEach
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key_from_previous: [model, results]
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key_from_data: []
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args:
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stages: _file_/config/mvmp/meta_fit_SMPL.yml
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keys_keep: [params]
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write:
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module: myeasymocap.io.write.WriteSMPL
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key_from_data: [meta]
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key_from_previous: [results, model]
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args:
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name: smpl
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vis_render:
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module: myeasymocap.io.vis3d.RenderAll_multiview
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key_from_data: [meta, cameras, imgnames]
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key_from_previous: [results, body_model]
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args:
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backend: pyrender
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view_list: [0]
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scale: 0.5
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make_video:
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module: myeasymocap.io.video.MakeVideo
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args:
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fps: 60
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keep_image: False |