File size: 3,727 Bytes
3e56ede | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 | """Read small existing native annotations; do not convert or download data."""
from pilot_build import *
def shape_tree(x, depth=0):
if isinstance(x,(np.ndarray,torch.Tensor)):
if isinstance(x,np.ndarray) and x.dtype==object and x.shape==():return shape_tree(x.item(),depth)
return {'shape':list(x.shape),'dtype':str(x.dtype)}
if isinstance(x,dict):
if len(x)>30:return {'count':len(x),'first_key':str(next(iter(x))),'first_value':shape_tree(next(iter(x.values())),depth+1)}
return {str(k):shape_tree(v,depth+1) for k,v in x.items()}
if isinstance(x,(list,tuple)):
try:
a=np.asarray(x)
if a.dtype.kind in 'biuf':return {'shape':list(a.shape),'dtype':str(a.dtype)}
except (ValueError,TypeError):pass
return {'count':len(x),'first':shape_tree(x[0],depth+1) if x else None}
return x if isinstance(x,(str,int,float,bool)) or x is None else type(x).__name__
def native_npy(p):
with p.open('rb') as f:
version=np.lib.format.read_magic(f)
if version==(1,0):_,_,dtype=np.lib.format.read_array_header_1_0(f)
else:_,_,dtype=np.lib.format.read_array_header_2_0(f)
if dtype.hasobject:return Restricted(f,encoding='latin1').load()
return np.load(p,allow_pickle=False)
def main():
rows=[]
def add(ds,p,x,detail=None):rows.append({'dataset':ds,'path':str(p.relative_to(WORK)).replace('\\','/'),'fields':shape_tree(x),'detail':detail})
p=next((WORK/'tmp/pilot-dex').rglob('labels_000001.npz'))
with np.load(p,allow_pickle=False) as z:add('Dex-YCB',p,{k:z[k] for k in z.files})
p=next((ROOT/'VISOR/raw/data').rglob('P01_03.json'));x=load(p);frames=x['video_annotations'];keys=sorted({k for f in frames for a in f['annotations'] for k in a})
add('VISOR',p,frames[0],{'top_level_keys':list(x),'annotation_keys_all_local_frames':keys,'frames':len(frames),'image_keys':sorted({k for f in frames for k in f['image']})})
for folder in ['hand_pose','hand_pose_mano','cam_pose']:
p=ROOT/'H2O/raw/data/local_subset/subject4_ego/h1/0/cam4'/folder/'000000.txt';add('H2O',p,np.loadtxt(p))
p=next((ROOT/'HOI4D/raw/data').rglob('*.pickle'));add('HOI4D',p,read_pickle(p))
for name in ['training_K.json','training_mano.json','training_scale.json']:
p=next((ROOT/'FreiHAND/raw/data').rglob(name));x=load(p);add('FreiHAND',p,x[0],{'rows':len(x)})
p=next((ROOT/'RHD/raw/data').rglob('anno_evaluation.pickle'));x=read_pickle(p);a=x[0];add('RHD',p,a,{'records':len(x),'visibility_values':np.unique(a['uv_vis'][:,2]).tolist(),'invisible_points_first_sample':int((a['uv_vis'][:,2]==0).sum())})
p=next((ROOT/'ARCTIC/raw/data').rglob('*.mano.npy'));add('ARCTIC',p,native_npy(p))
p=next((ROOT/'ARCTIC/raw/data').rglob('*.egocam.dist.npy'));x=native_npy(p).item();add('ARCTIC',p,x,{'dist8':np.asarray(x['dist8']).tolist()})
p=next((ROOT/'OakInk2/raw/data').rglob('*.pkl'));x=read_pickle(p);add('OakInk2',p,x,{'frame_ids_first':x['frame_id_list'][:5],'mocap_ids_first':x['mocap_frame_id_list'][:5],'object_ids':x['obj_list']})
p=next((ROOT/'ReInterHand/raw/data').rglob('*_left.json'));add('ReInterHand',p,load(p))
p=OLD/'HOT3D/P0001_10a27bf7/mano_hand_pose_trajectory.jsonl';add('HOT3D',p,json.loads(p.open().readline()))
p=next((ROOT/'HOT3D/raw/data').glob('*ground_truth.zip'))
with zipfile.ZipFile(p) as z:
headers={}
for name in z.namelist():
if name.endswith('.csv'):
with z.open(name) as f:headers[name]=f.readline().decode().strip()
add('HOT3D',p,headers)
save(WORK/'docs/source-field-audit-evidence.json',{'purpose':'native field shape probe; not a full conversion or whole-dataset validation','records':rows})
print(json.dumps({'datasets':sorted({r['dataset'] for r in rows}),'records':len(rows),'output':'docs/source-field-audit-evidence.json'},indent=2))
if __name__=='__main__':main()
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