def featurize_and_save(f,out_prefix,factor=1,postfactor=1,maxcols=None,lock=None):
'''
Featurize the video at path 'f'. But first, check if it exists on the dist
at the output path already, if so, do not compute it again, just load it.
lock is a semaphore (multiprocessing.Lock) in the case this is being called
from a pool of workers
This function handles both the prefactor and the postfactor parameters.
Be sure to invoke actionbank.py with the same -f and -g parameters if
you call it multiple times in the same experiment.
'_featurize.npz' is the format to save them in.
'''
oname = out_prefix + featurized_suffix
if not path.exists(oname):
print oname, "computing"
featurized = spotting.featurize_video(f,factor=factor,maxcols=maxcols,lock=lock)
if postfactor != 1:
featurized = spotting.call_resample_with_7D(featurized,postfactor)
of = gzip.open(oname,"wb")
np.save(of,featurized)
of.close()
else:
print oname, "skipping; already cached"
def add_to_bank(bankpath,newvideos):
''' Add video(s) as new templates to the bank at path bankpath. '''
if not path.isdir(newvideos):
(h,t) = path.split(newvideos)
print "adding %s\n"%(newvideos)
F = spotting.featurize_video(newvideos);
of = gzip.open(path.join(bankpath,t+".npy.gz"),"wb")
np.save(of,F)
of.close()
else:
files = os.listdir(newvideos)
for f in files:
F = spotting.featurize_video(path.join(newvideos,f));
(h,t) = path.split(f)
print "adding %s\n"%(t)
of = gzip.open(path.join(bankpath,t+".npy.gz"),"wb")
np.save(of,F)
of.close()
