Download ARCTIC/shared-v1.0/script/internal/sequence_pilot_common.py from ParityLab/Hand-Datasets-Raw: direct link, hf CLI and curl.
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https://huggingface.co/datasets/ParityLab/Hand-Datasets-Raw/resolve/refs%2Fpr%2F7/ARCTIC/shared-v1.0/script/internal/sequence_pilot_common.py
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hf download hf://datasets/ParityLab/Hand-Datasets-Raw@refs/pr/7/ARCTIC/shared-v1.0/script/internal/sequence_pilot_common.py
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curl -L -o sequence_pilot_common.py https://huggingface.co/datasets/ParityLab/Hand-Datasets-Raw/resolve/refs%2Fpr%2F7/ARCTIC/shared-v1.0/script/internal/sequence_pilot_common.py
4.21 kB
| """Whole-selection conversion utilities. Never reads previous pilot outputs.""" | |
| from pilot_build import * | |
| import hashlib | |
| RELEASE='shared-sequence-v1.1' | |
| class View: | |
| def __init__(self,dataset,seq,cam,ids,size,source,kind='video',sampling='dense',split='unknown'): | |
| self.root=ROOT/dataset/RELEASE;self.p=self.root/'data'/seq/cam | |
| self.p.mkdir(parents=True,exist_ok=True);self.T=len(ids);self.source=source;self.checks=[] | |
| self.m={'schema_version':'hand-warehouse/1.1','dataset_id':dataset,'release_id':RELEASE,'sequence_id':seq,'camera_id':cam,'sample_type':kind,'num_frames':self.T,'hand_slots':['left','right'],'sampling':{'type':'single' if kind=='image' else sampling,'source_fps':None,'frames_ref':'camera/frames.jsonl'},'camera':{'model':'unknown','schema_id':None,'image_size':size},'components':{},'split':split} | |
| self.frames=[{'frame_index':i,'source_frame_id':str(fid),'timestamp_ns':None,'clock_domain':None,'source_locator':{**source,'source_frame_id':str(fid)}} for i,fid in enumerate(ids)] | |
| def add(self,name,path,schema,axes,valid,method='source values retained',**kw): | |
| vp=Path(path).with_name(Path(path).stem+'_valid.pt').as_posix() | |
| tensor(self.p/vp,valid) | |
| self.m['components'][name]={'available':True,'schema_id':schema,'data_ref':path,'axes':axes,'valid_ref':vp,'valid_axes':axes[:valid.ndim],'provenance':{'kind':'format_conversion','method':method},'source_locator':self.source,**kw} | |
| def array(self,name,path,schema,a,valid,axes,**kw): | |
| a=np.asarray(a,np.float32).copy();valid=np.asarray(valid,bool) | |
| assert a.shape[:valid.ndim]==valid.shape and a.shape[0]==self.T | |
| assert np.isfinite(a[valid]).all(),name | |
| a[~valid]=np.nan;tensor(self.p/path,a);self.add(name,path,schema,axes,valid,**kw) | |
| restored=torch.load(self.p/path,map_location='cpu',weights_only=True).numpy() | |
| for index in range(self.T):assert np.array_equal(restored[index],a[index],equal_nan=True) | |
| self.checks.append({'test':'tensor_serialization_exact','file':path,'shape':list(a.shape),'valid_count':int(valid.sum())}) | |
| def video(self,name,path,iterator,mask=False,**kw): | |
| w,h=self.m['camera']['image_size'];pix='gray16le' if mask else 'rgb24';dtype='<u2' if mask else 'uint8' | |
| dst=self.p/path;dst.parent.mkdir(parents=True,exist_ok=True);hashes=[] | |
| proc=subprocess.Popen([FF,'-v','error','-y','-f','rawvideo','-pixel_format',pix,'-video_size',f'{w}x{h}','-framerate','1','-i','pipe:0','-c:v','ffv1','-level','3','-g','1',str(dst)],stdin=subprocess.PIPE) | |
| try: | |
| for frame in iterator: | |
| a=np.asarray(frame,dtype=dtype);assert a.shape==((h,w) if mask else (h,w,3));b=a.tobytes();hashes.append(hashlib.sha256(b).hexdigest());proc.stdin.write(b) | |
| finally:proc.stdin.close() | |
| assert proc.wait()==0 and len(hashes)==self.T | |
| proc=subprocess.Popen([FF,'-v','error','-i',str(dst),'-f','rawvideo','-pix_fmt',pix,'pipe:1'],stdout=subprocess.PIPE) | |
| nbytes=w*h*(2 if mask else 3) | |
| for digest in hashes: | |
| b=proc.stdout.read(nbytes);assert len(b)==nbytes and hashlib.sha256(b).hexdigest()==digest,'video roundtrip' | |
| assert not proc.stdout.read(1);assert proc.wait()==0 | |
| self.checks.append({'test':'all_frames_pixel_exact_roundtrip','file':path,'frames':self.T}) | |
| self.add(name,path,'segment/uint16_labels/v1' if mask else 'rgb/uint8/v1',['T','height','width'] if mask else ['T','height','width','channel'],np.ones(self.T,bool),storage_fps=1,timebase_role='index_only',**kw) | |
| def intrinsic(self,K): | |
| self.m['camera'].update(model='pinhole',schema_id='camera/pinhole/v1') | |
| self.array('intrinsic','camera/intrinsic.pt','camera/pinhole/v1',np.broadcast_to(K,(self.T,3,3)),np.ones(self.T,bool),['T','row','column']) | |
| def finish(self): | |
| lines(self.p/'camera/frames.jsonl',self.frames);save(self.p/'metainfo.json',self.m) | |
| save(self.root/'doc'/f'{self.m["sequence_id"]}__{self.m["camera_id"]}.checks.json',{'checks':self.checks,'frames':self.T,'input':self.source}) | |
| print('Converted',self.m['dataset_id'],self.m['sequence_id'],self.m['camera_id'],self.T,flush=True) | |
| def snapshot(dataset,scripts): | |
| # Retained for old callers. The organized bundle must not be repopulated at runtime. | |
| (ROOT/dataset/RELEASE/'script').mkdir(parents=True,exist_ok=True) | |