update the config
This commit is contained in:
shuaiqing
parent
7168f51d42
commit
88ee221c10
42
config/mvmp/body/ballet_2person.yml
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42
config/mvmp/body/ballet_2person.yml
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data: config/datasets/mvimage.yml
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exp: config/mvmp/detect_match_triangulate_fitSMPL.yml
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data_opts:
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root: TO_BE_FILLED
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# subs: ['01', '03', '05', '07', '09', '11', '13', '15', '17', '19', '21', '23']
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subs: ['01', '04', '07', '10', '13', '16', '19', '22'] # Use 8 cameras
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subs_vis: ['01', '07', '13', '19']
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# subs_vis: [_all_]
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ranges: [0, 1100, 1]
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exp_opts:
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output: output/ballet_2person
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at_step:
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detect:
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args:
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min_length: 200
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max_length: 1000
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match:
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args:
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cfg_match:
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distance:
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threshold_track: 0.05
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triangulate:
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min_view: 3 # at least 3 views to triangulate
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min_view_body: 5 # at least 5 views to triangulate
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dist_max: 25 # pixel
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dist_track: 100 # 100mm；
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cfg_track:
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max_person: 2
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final_ranges: [[-3, -3, 0.], [3, 3, 3.]] # 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: 2
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at_final:
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collect:
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args:
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min_frame: 100
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fitting_each_person:
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args:
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stages:
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refine_poses:
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args:
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loss:
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smooth:
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weight: 5.
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43
config/mvmp/body/boxing.yml
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43
config/mvmp/body/boxing.yml
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data: config/datasets/mvimage.yml
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exp: config/mvmp/detect_match_triangulate_fitSMPL.yml
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data_opts:
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root: TO_BE_FILLED
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# subs: ['01', '03', '05', '07', '09', '11', '13', '15', '17', '19', '21', '23']
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subs: ['01', '04', '07', '10', '13', '16', '19', '22'] # Use 8 cameras
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subs_vis: ['01', '07', '13', '19'] # Visualize 4 cameras
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ranges: [0, 200, 1]
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exp_opts:
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output: output/boxing
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at_step:
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detect:
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args:
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min_length: 200
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max_length: 1000
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match:
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args:
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cfg_match:
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distance:
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threshold_track: 0.05
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triangulate:
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min_view: 3 # at least 3 views to triangulate
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min_view_body: 4 # at least 5 views to triangulate
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dist_max: 25 # pixel
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dist_track: 50 # 100mm；
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cfg_track:
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final_ranges: [[-3, -3, 0.], [3, 3, 3.]] # 最终的输出的range，仅用于输出的时候的筛选
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at_final:
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collect:
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args:
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min_frame: 100
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fitting_each_person:
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args:
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stages:
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refine_poses:
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args:
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loss:
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smooth:
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weight: 1.
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vis_render:
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args:
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view_list: [3]
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scale: 1.
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43
config/mvmp/body/soccer1_6.yml
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43
config/mvmp/body/soccer1_6.yml
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data: config/datasets/mvimage.yml
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exp: config/mvmp/detect_match_triangulate_fitSMPL.yml
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data_opts:
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root: TO_BE_FILLED
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subs: []
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subs_vis: ['1', '3', '5', '7']
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ranges: [0, 600, 1]
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exp_opts:
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output: output/soccer1_6
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at_step:
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detect:
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args:
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min_length: 100
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max_length: 1000
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match:
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args:
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cfg_match:
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distance:
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threshold_track: 0.05
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triangulate:
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min_view: 3 # at least 3 views to triangulate
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min_view_body: 3 # at least 5 views to triangulate
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dist_max: 25 # pixel
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dist_track: 100 # 100mm；
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cfg_track:
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final_ranges: [[-5, -5, 0.], [5, 5, 5.]] # 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: 6
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at_final:
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collect:
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args:
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min_frame: 100
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fitting_each_person:
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args:
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stages:
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refine_poses:
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args:
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loss:
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smooth:
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weight: 1.
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vis_render:
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args:
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view_list: [0, 2, 4, 6]
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scale: 0.5
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87
config/mvmp/detect_match_triangulate.yml
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87
config/mvmp/detect_match_triangulate.yml
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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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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
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121
config/mvmp/detect_match_triangulate_fitSMPL.yml
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121
config/mvmp/detect_match_triangulate_fitSMPL.yml
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@ -0,0 +1,121 @@
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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_pixel: 100 # 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5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: [[ 1, 0], [ 2, 1], [ 3, 2], [ 4, 3], [ 5, 1], [ 6, 5], [ 7, 6], [ 8, 1], [ 9, 8], [10, 9], [11, 10], [12, 8], [13, 12], [14, 13], [15, 0], [16, 0], [17, 15], [18, 16], [19, 14], [20, 19], [21, 14], [22, 11], [23, 22], [24, 11]]
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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
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107
config/mvmp/meta_fit_SMPL.yml
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107
config/mvmp/meta_fit_SMPL.yml
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init_params: # 初始化姿态
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module: myeasymocap.operations.init.InitParams
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key_from_data: [keypoints3d]
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args:
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num_poses: 69
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num_shapes: 10
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fitShape: # 这一步需要根据骨长优化一下SMPL的shape参数
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module: myeasymocap.operations.optimizer.Optimizer
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key_from_data: [keypoints3d]
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key_from_previous: [model, params] # 这一步优化所使用的model，是一个可调用的函数，负责把params的输入变成输出，而不用考虑其他，与SMPL model是不一样的
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args:
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optimizer_args: {optim_type: lbfgs}
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optimize_keys: [shapes]
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loss:
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k3d:
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weight: 1000.
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module: myeasymocap.operations.loss.LimbLength
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key_from_output: [keypoints]
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key_from_infos: [keypoints3d]
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args:
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kintree: [[8, 1], [2, 5], [2, 3], [5, 6], [3, 4], [6, 7], [2, 3], [5, 6], [3, 4], [6, 7], [2, 3], [5, 6], [3, 4], [6, 7], [1, 0], [9, 12], [9, 10], [10, 11], [12, 13],[13, 14]]
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regshape:
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weight: 0.1
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module: myeasymocap.operations.loss.RegLoss
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key_from_output: [shapes]
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key_from_infos: [] # TODO: 根据2D的置信度来计算smooth权重
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args:
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key: shapes
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norm: l2
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init_RT: # 这一步中，首先将SMPL参数的shape参数进行整段平均。重新优化更新RT参数
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module: myeasymocap.operations.optimizer.Optimizer
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key_from_data: [keypoints3d]
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key_from_previous: [model, params] # 这一步优化所使用的model，是一个可调用的函数，负责把params的输入变成输出，而不用考虑其他，与SMPL model是不一样的
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# 这样设计的目的是对于一些不只是SMPL本身的模型，可以在外面套一层接口
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# model是一个纯函数，用来进行可视化
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args:
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optimizer_args: {optim_type: lbfgs}
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optimize_keys: [Th, Rh]
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loss:
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k3d:
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weight: 100.
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module: myeasymocap.operations.loss.Keypoints3D
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key_from_output: [keypoints]
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key_from_infos: [keypoints3d]
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args:
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norm: l2
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index_est: [2, 5, 9, 12]
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index_gt: [2, 5, 9, 12]
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smooth:
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weight: 1.
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module: myeasymocap.operations.loss.Smooth
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key_from_output: [Th, keypoints]
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key_from_infos: [] # TODO: 根据2D的置信度来计算smooth权重
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args:
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keys: [keypoints, Th]
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smooth_type: [Linear, Linear] # 这个depth似乎需要相机参数进行转换
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norm: [l2, l2]
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order: [2, 2]
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weights: [10., 100.]
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window_weight: [0.5, 0.3, 0.1, 0.1]
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refine_poses:
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repeat: 2
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module: myeasymocap.operations.optimizer.Optimizer
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key_from_data: [keypoints3d]
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key_from_previous: [model, params]
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args:
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optimizer_args: {optim_type: lbfgs}
|
||||
optimize_keys: [[poses, Rh, Th], [poses, shapes, Rh, Th]]
|
||||
loss:
|
||||
k3d:
|
||||
weight: 1000.
|
||||
module: myeasymocap.operations.loss.Keypoints3D
|
||||
key_from_output: [keypoints]
|
||||
key_from_infos: [keypoints3d]
|
||||
args:
|
||||
norm: l2
|
||||
norm_info: 0.02
|
||||
ranges_est: [0, 25]
|
||||
ranges_gt: [0, 25]
|
||||
smooth:
|
||||
weight: 1.
|
||||
module: myeasymocap.operations.loss.Smooth
|
||||
key_from_output: [poses, Th, keypoints]
|
||||
key_from_infos: [] # TODO: 根据2D的置信度来计算smooth权重
|
||||
args:
|
||||
keys: [Th, poses, keypoints]
|
||||
smooth_type: [Linear, Linear, Linear] # 这个depth似乎需要相机参数进行转换
|
||||
norm: [l2, l2, l2]
|
||||
order: [2, 2, 2]
|
||||
weights: [10., 10., 10.,]
|
||||
window_weight: [0.5, 0.3, 0.1, 0.1]
|
||||
prior:
|
||||
weight: 0.1
|
||||
module: easymocap.multistage.gmm.GMMPrior
|
||||
key_from_output: [poses]
|
||||
key_from_infos: []
|
||||
args:
|
||||
start: 0
|
||||
end: 69
|
||||
regshape:
|
||||
weight: 0.1
|
||||
module: myeasymocap.operations.loss.RegLoss
|
||||
key_from_output: [shapes]
|
||||
key_from_infos: [] # TODO: 根据2D的置信度来计算smooth权重
|
||||
args:
|
||||
key: shapes
|
||||
norm: l2
|
||||
@ -145,6 +145,7 @@ class StageForFittingEach:
|
||||
for key, stage in self.stages.items():
|
||||
for iter_ in range(self.stages_args[key].get('repeat', 1)):
|
||||
inputs = {}
|
||||
stage.iter = iter_
|
||||
for k in self.stages_args[key].get('key_from_data', []):
|
||||
inputs[k] = result[k]
|
||||
for k in self.stages_args[key].get('key_from_previous', []):
|
||||
|
||||
Loading…
Reference in New Issue
Block a user