def main():
return
path_to_db = 'sqlite://///disk2/novemberExperiments/experiments_youtube/patches_nn_hash.db'
mani = Tube_Manipulator(path_to_db)
mani_hash = TubeHash_Manipulator(path_to_db)
mani.openSession()
deep_features_path_all = mani.select((Tube.deep_features_path, ),
distinct=True)
deep_features_path_all = [x[0] for x in deep_features_path_all]
print len(deep_features_path_all)
# print deep_features_path_all[:10]
mani_hash.openSession()
for idx_deep_features_path, deep_features_path in enumerate(
deep_features_path_all[11:]):
t = time.time()
hash_file = deep_features_path[:-4] + '_hash.npy'
print hash_file
idx_info = mani.select(
(Tube.idx, Tube.deep_features_idx),
(Tube.deep_features_path == deep_features_path, ))
# idx_all,deep_features_idx_all=zip(*idx_info);
hash_vals = np.load(hash_file)
# print len(idx_all),hash_vals.shape
for idx_foreign, row in idx_info:
# hash_vals_curr=hash_vals[row];
for hash_table, hash_val in enumerate(hash_vals[row]):
# pass;
# print type(idx_foreign),type(hash_table),type(int(hash_val))
mani_hash.insert(idx=idx_foreign,
hash_table=hash_table,
hash_val=int(hash_val),
commit=False)
if idx_deep_features_path % 10 == 0:
mani_hash.session.commit()
# print time.time()-t;
mani_hash.closeSession()
mani.closeSession()
def getScoreForIdx(table_idx,
path_to_db,
class_idx_pascal=None,
npz_path=None,
n_jobs=1,
total_counts=None):
mani = Tube_Manipulator(path_to_db)
mani.openSession()
mani_hash = TubeHash_Manipulator(path_to_db)
mani_hash.openSession()
toSelect = (Tube.class_idx_pascal, Tube.video_id, Tube.img_path)
criterion = (Tube.idx == table_idx, )
[(class_idx_gt, video_idx, frame_path)] = mani.select(toSelect, criterion)
if class_idx_pascal is not None:
class_idx = class_idx_pascal
else:
class_idx = class_idx_gt
toSelect = (TubeHash.hash_table, TubeHash.hash_val)
criterion = (TubeHash.idx == table_idx, )
hash_table_info = mani_hash.select(toSelect, criterion)
print len(hash_table_info)
mani_hash.closeSession()
mani.closeSession()
args = []
for hash_table_no in range(len(hash_table_info)):
hash_table = hash_table_info[hash_table_no][0]
hash_val = hash_table_info[hash_table_no][1]
if npz_path is not None:
args.append((npz_path, hash_table, hash_val, class_idx, video_idx,
class_idx_gt, total_counts))
else:
args.append((path_to_db, hash_table, hash_val, class_idx,
video_idx, class_idx_gt))
if n_jobs > 1:
p = multiprocessing.Pool(min(multiprocessing.cpu_count(), n_jobs))
if npz_path is not None:
scores = p.map(getScoreForHashValFromNpz, args)
else:
scores = p.map(getScoreForHashVal, args)
else:
scores = []
for arg in args:
if npz_path is not None:
scores.append(getScoreForHashValFromNpz(arg))
else:
scores.append(getScoreForHashVal(arg))
return scores, class_idx_gt, frame_path
def getInfoForFeatureExtractionForVideo(path_to_db,video_info,numberOfFrames):
info_for_extraction=[];
mani=Tube_Manipulator(path_to_db);
mani.openSession();
for pascal_id in video_info:
video_ids=video_info[pascal_id];
for video_id in video_ids:
info=mani.select((Tube.img_path,Tube.class_id_pascal,Tube.deep_features_path,Tube.deep_features_idx),(Tube.video_id==video_id,Tube.class_id_pascal==pascal_id),distinct=True,limit=numberOfFrames);
info_for_extraction=info_for_extraction+info;
mani.closeSession();
return info_for_extraction
def getTubePathsForShot(path_to_db,class_id_pascal,video_id,shot_id,frame_to_choose='middle'):
mani=Tube_Manipulator(path_to_db);
mani.openSession();
frame_ids=mani.select((Tube.frame_id,),(Tube.class_id_pascal==class_id_pascal,Tube.shot_id==shot_id,Tube.video_id==video_id),distinct=True);
frame_ids=[frame_id[0] for frame_id in frame_ids];
frame_ids.sort();
if frame_to_choose=='middle':
middle_idx=len(frame_ids)/2;
frame_id=frame_ids[middle_idx];
else:
frame_id=0;
paths=mani.select((Tube.img_path,),(Tube.class_id_pascal==class_id_pascal,Tube.shot_id==shot_id,Tube.video_id==video_id,Tube.frame_id==frame_id),distinct=True);
paths=[path[0] for path in paths];
mani.closeSession();
return paths;
def main():
return
path_to_db='sqlite://///disk2/novemberExperiments/experiments_youtube/patches_nn_hash.db';
mani=Tube_Manipulator(path_to_db);
mani_hash=TubeHash_Manipulator(path_to_db);
mani.openSession();
deep_features_path_all=mani.select((Tube.deep_features_path,),distinct=True);
deep_features_path_all=[x[0] for x in deep_features_path_all];
print len(deep_features_path_all);
# print deep_features_path_all[:10]
mani_hash.openSession();
for idx_deep_features_path,deep_features_path in enumerate(deep_features_path_all[11:]):
t=time.time();
hash_file=deep_features_path[:-4]+'_hash.npy';
print hash_file
idx_info=mani.select((Tube.idx,Tube.deep_features_idx),(Tube.deep_features_path==deep_features_path,));
# idx_all,deep_features_idx_all=zip(*idx_info);
hash_vals=np.load(hash_file);
# print len(idx_all),hash_vals.shape
for idx_foreign,row in idx_info:
# hash_vals_curr=hash_vals[row];
for hash_table,hash_val in enumerate(hash_vals[row]):
# pass;
# print type(idx_foreign),type(hash_table),type(int(hash_val))
mani_hash.insert(idx=idx_foreign,hash_table=hash_table,hash_val=int(hash_val),commit=False);
if idx_deep_features_path%10==0:
mani_hash.session.commit();
# print time.time()-t;
mani_hash.closeSession();
mani.closeSession();
def saveTotalClassBreakdowns(path_to_db,out_file):
mani=Tube_Manipulator(path_to_db);
mani.openSession();
toSelect=(Tube.class_idx_pascal,Tube.video_id,Tube.shot_id,Tube.tube_id);
vals=mani.select(toSelect,distinct=True);
mani.closeSession();
vals=np.array(vals);
class_idx_db=vals[:,0];
ids_db=vals[:,1:];
column_names=['video','shot','tube'];
counts=getClassCountsByIdType(class_idx_db,ids_db,column_names)
pickle.dump(counts,open(out_file,'wb'));
def saveTotalClassBreakdowns(path_to_db, out_file):
mani = Tube_Manipulator(path_to_db)
mani.openSession()
toSelect = (Tube.class_idx_pascal, Tube.video_id, Tube.shot_id,
Tube.tube_id)
vals = mani.select(toSelect, distinct=True)
mani.closeSession()
vals = np.array(vals)
class_idx_db = vals[:, 0]
ids_db = vals[:, 1:]
column_names = ['video', 'shot', 'tube']
counts = getClassCountsByIdType(class_idx_db, ids_db, column_names)
pickle.dump(counts, open(out_file, 'wb'))
def getNVideosByPascalIds(path_to_db,pascal_ids,numberofVideos):
dict_out={};
mani=Tube_Manipulator(path_to_db);
mani.openSession();
for pascal_id in pascal_ids:
total_ids=mani.select((Tube.video_id,),(Tube.class_id_pascal==pascal_id,),distinct=True,limit=numberofVideos);
total_ids=[total_id[0] for total_id in total_ids];
# random.shuffle(total_ids);
# selected_ids=total_ids[:numberofVideos];
dict_out[pascal_id]=total_ids;
mani.closeSession();
return dict_out
def getScoreForIdx(table_idx,path_to_db,class_idx_pascal=None,npz_path=None,n_jobs=1,total_counts=None):
mani=Tube_Manipulator(path_to_db);
mani.openSession();
mani_hash=TubeHash_Manipulator(path_to_db);
mani_hash.openSession();
toSelect = (Tube.class_idx_pascal,Tube.video_id,Tube.img_path)
criterion = (Tube.idx==table_idx,);
[(class_idx_gt,video_idx,frame_path)] = mani.select(toSelect,criterion)
if class_idx_pascal is not None:
class_idx=class_idx_pascal;
else:
class_idx=class_idx_gt
toSelect=(TubeHash.hash_table,TubeHash.hash_val);
criterion=(TubeHash.idx==table_idx,)
hash_table_info=mani_hash.select(toSelect,criterion);
print len(hash_table_info);
mani_hash.closeSession();
mani.closeSession();
args=[];
for hash_table_no in range(len(hash_table_info)):
hash_table=hash_table_info[hash_table_no][0];
hash_val=hash_table_info[hash_table_no][1];
if npz_path is not None:
args.append((npz_path,hash_table,hash_val,class_idx,video_idx,class_idx_gt,total_counts));
else:
args.append((path_to_db,hash_table,hash_val,class_idx,video_idx,class_idx_gt));
if n_jobs>1:
p = multiprocessing.Pool(min(multiprocessing.cpu_count(),n_jobs))
if npz_path is not None:
scores=p.map(getScoreForHashValFromNpz,args)
else:
scores=p.map(getScoreForHashVal,args)
else:
scores=[];
for arg in args:
if npz_path is not None:
scores.append(getScoreForHashValFromNpz(arg));
else:
scores.append(getScoreForHashVal(arg));
return scores,class_idx_gt,frame_path
def script_saveBigFeatureMats(params):
out_file_featureMats_pre = params.out_file_featureMats_pre
out_file_meta_pre = params.out_file_meta_pre
path_to_db = params.path_to_db
out_file_paths = params.out_file_paths
num_batches = params.num_batches
if not os.path.exists(out_file_paths):
mani = Tube_Manipulator(path_to_db)
mani.openSession()
paths_to_features = mani.select((Tube.deep_features_path, ),
distinct=True)
paths_to_features = [path_curr[0] for path_curr in paths_to_features]
mani.closeSession()
random.shuffle(paths_to_features)
pickle.dump(paths_to_features, open(out_file_paths, 'wb'))
paths_to_features = pickle.load(open(out_file_paths, 'rb'))
paths_to_features.sort()
batch_size = len(paths_to_features) / num_batches
idxRange = util.getIdxRange(len(paths_to_features), batch_size)
print len(idxRange), idxRange[-1]
# start_idx=0;
for start_idx in range(len(idxRange) - 1):
out_file_curr = out_file_featureMats_pre + '_' + str(
start_idx) + '.npz'
out_file_meta_curr = out_file_meta_pre + '_' + str(start_idx) + '.p'
print start_idx, idxRange[start_idx], idxRange[
start_idx + 1], out_file_curr, out_file_meta_curr,
paths_to_features_curr = paths_to_features[
idxRange[start_idx]:idxRange[start_idx + 1]]
t = time.time()
train, shape_record = getGiantFeaturesMatGPU(paths_to_features_curr)
train = np.array(train)
np.savez(out_file_curr, train)
pickle.dump([paths_to_features_curr, shape_record],
open(out_file_meta_curr, 'wb'))
print time.time() - t
break
def getHashInfoForImg(path_to_db,img_path):
mani=Tube_Manipulator(path_to_db);
mani.openSession();
#get patch id
patch_id=mani.select((Tube.idx,),(Tube.img_path==img_path,));
assert len(patch_id)==1;
patch_id=patch_id[0][0];
mani.closeSession();
#get hash vals
mani_hash=TubeHash_Manipulator(path_to_db);
mani_hash.openSession();
toSelect=(TubeHash.hash_table,TubeHash.hash_val)
criterion=(TubeHash.idx==patch_id,);
hash_info_patch=mani_hash.select(toSelect,criterion);
mani_hash.closeSession();
return hash_info_patch
def getHashInfoForImg(path_to_db, img_path):
mani = Tube_Manipulator(path_to_db)
mani.openSession()
#get patch id
patch_id = mani.select((Tube.idx, ), (Tube.img_path == img_path, ))
assert len(patch_id) == 1
patch_id = patch_id[0][0]
mani.closeSession()
#get hash vals
mani_hash = TubeHash_Manipulator(path_to_db)
mani_hash.openSession()
toSelect = (TubeHash.hash_table, TubeHash.hash_val)
criterion = (TubeHash.idx == patch_id, )
hash_info_patch = mani_hash.select(toSelect, criterion)
mani_hash.closeSession()
return hash_info_patch
def script_saveBigFeatureMats(params):
out_file_featureMats_pre=params.out_file_featureMats_pre
out_file_meta_pre=params.out_file_meta_pre
path_to_db=params.path_to_db
out_file_paths=params.out_file_paths
num_batches=params.num_batches
if not os.path.exists(out_file_paths):
mani=Tube_Manipulator(path_to_db);
mani.openSession()
paths_to_features=mani.select((Tube.deep_features_path,),distinct=True);
paths_to_features=[path_curr[0] for path_curr in paths_to_features];
mani.closeSession();
random.shuffle(paths_to_features);
pickle.dump(paths_to_features,open(out_file_paths,'wb'));
paths_to_features=pickle.load(open(out_file_paths,'rb'));
paths_to_features.sort();
batch_size=len(paths_to_features)/num_batches;
idxRange=util.getIdxRange(len(paths_to_features),batch_size);
print len(idxRange),idxRange[-1];
# start_idx=0;
for start_idx in range(len(idxRange)-1):
out_file_curr=out_file_featureMats_pre+'_'+str(start_idx)+'.npz';
out_file_meta_curr=out_file_meta_pre+'_'+str(start_idx)+'.p';
print start_idx,idxRange[start_idx],idxRange[start_idx+1],out_file_curr,out_file_meta_curr,
paths_to_features_curr=paths_to_features[idxRange[start_idx]:idxRange[start_idx+1]]
t=time.time();
train,shape_record=getGiantFeaturesMatGPU(paths_to_features_curr);
train=np.array(train);
np.savez(out_file_curr,train);
pickle.dump([paths_to_features_curr,shape_record],open(out_file_meta_curr,'wb'))
print time.time()-t
break
def getInfoForExtractionForTube(path_to_db,pascal_id,video_id,shot_id,tube_id):
mani=Tube_Manipulator(path_to_db);
mani.openSession();
info=mani.select((Tube.img_path,Tube.class_id_pascal,Tube.deep_features_path,Tube.deep_features_idx),(Tube.video_id==video_id,Tube.class_id_pascal==pascal_id,Tube.tube_id==tube_id,Tube.shot_id==shot_id),distinct=True);
mani.closeSession();
return info
