def getScoreForHashVal((path_to_db, hash_table, hash_val, class_idx, video_idx,
class_idx_gt)):
mani = Tube_Manipulator(path_to_db)
mani.openSession()
toSelect = (Tube.class_idx_pascal, Tube.video_id)
criterion = (TubeHash.hash_table == hash_table,
TubeHash.hash_val == hash_val)
vals = mani.selectMix(toSelect=toSelect, criterion=criterion)
vals = np.array(vals)
if not hasattr(class_idx, '__iter__'):
class_idx = [class_idx]
total_count_total = vals.shape[0]
video_count_total = sum(
np.logical_and(vals[:, 0] == class_idx_gt, vals[:, 1] == video_idx))
scores = []
for class_idx_curr in class_idx:
class_count = sum(vals[:, 0] == class_idx_curr)
if class_idx_curr == class_idx_gt:
video_count = video_count_total
else:
video_count = 0
class_count = class_count - video_count
total_count = total_count_total - video_count_total
score = class_count / float(total_count)
scores.append(score)
mani.closeSession()
return scores
def getScoreForHashVal((path_to_db,hash_table,hash_val,class_idx,video_idx,class_idx_gt)):
mani=Tube_Manipulator(path_to_db);
mani.openSession();
toSelect=(Tube.class_idx_pascal,Tube.video_id)
criterion=(TubeHash.hash_table==hash_table,TubeHash.hash_val==hash_val);
vals=mani.selectMix(toSelect=toSelect,criterion=criterion);
vals=np.array(vals);
if not hasattr(class_idx,'__iter__'):
class_idx=[class_idx];
total_count_total=vals.shape[0];
video_count_total=sum(np.logical_and(vals[:,0]==class_idx_gt,vals[:,1]==video_idx));
scores=[];
for class_idx_curr in class_idx:
class_count=sum(vals[:,0]==class_idx_curr);
if class_idx_curr==class_idx_gt:
video_count=video_count_total;
else:
video_count=0;
class_count=class_count-video_count;
total_count=total_count_total-video_count_total;
score=class_count/float(total_count);
scores.append(score);
mani.closeSession();
return scores;
def script_saveHashAnalysisImages(params):
path_to_db = params.path_to_db
class_labels_map = params.class_labels_map
percents = params.percents
out_file_class_pre = params.out_file_class_pre
out_file_hash_simple = params.out_file_hash_simple
out_file_hash_byClass = params.out_file_hash_byClass
hashtable = params.hashtable
inc = params.inc
dtype = params.dtype
# in_file = params.in_file;
if not os.path.exists(out_file_class_pre + '.npz'):
mani = Tube_Manipulator(path_to_db)
mani.openSession()
ids = mani.selectMix((Tube.class_idx_pascal, TubeHash.hash_val),
(TubeHash.hash_table == hashtable, ))
mani.closeSession()
ids = np.array(ids, dtype=dtype)
np.savez(out_file_class_pre, ids)
ids = np.load(out_file_class_pre + '.npz')['arr_0']
# ids=np.load(in_file)['arr_0'];
counts_all, class_ids_breakdown = getClassIdsCount(ids[:, 0], ids[:, 1])
ranks = getDiscriminativeScore(counts_all)
sort_idx = np.argsort(ranks)
counts_all = [counts_all[idx] for idx in sort_idx]
class_ids_breakdown = [class_ids_breakdown[idx] for idx in sort_idx]
im_simple = getHashAnalysisIm(counts_all,
class_ids_breakdown,
inc=inc,
colorByClass=False)
im_byClass = getHashAnalysisIm(counts_all,
class_ids_breakdown,
inc=inc,
colorByClass=True)
visualize.saveMatAsImage(im_simple, out_file_hash_simple)
visualize.saveMatAsImage(im_byClass, out_file_hash_byClass)
counts_all_ravel = np.array([c for counts in counts_all for c in counts])
class_ids_breakdown_ravel = np.array(
[c for class_ids in class_ids_breakdown for c in class_ids])
class_id_pascal, class_idx_pascal = zip(*class_labels_map)
for class_id_idx, class_id in enumerate(class_idx_pascal):
frequency = counts_all_ravel[class_ids_breakdown_ravel == class_id]
out_file = out_file_class_pre + '_' + class_id_pascal[
class_id_idx] + '.png'
title = class_id_pascal[class_id_idx] + ' ' + str(class_id)
cum_freq, idx_perc = getCumulativeInfo(frequency, percents)
savePerClassCumulativeGraph(cum_freq / float(cum_freq[-1]), idx_perc,
percents, out_file, title)
def script_saveHashAnalysisImages(params):
path_to_db = params.path_to_db;
class_labels_map = params.class_labels_map;
percents = params.percents;
out_file_class_pre = params.out_file_class_pre;
out_file_hash_simple = params.out_file_hash_simple;
out_file_hash_byClass = params.out_file_hash_byClass;
hashtable = params.hashtable;
inc = params.inc;
dtype = params.dtype;
# in_file = params.in_file;
if not os.path.exists(out_file_class_pre+'.npz'):
mani=Tube_Manipulator(path_to_db);
mani.openSession();
ids=mani.selectMix((Tube.class_idx_pascal,TubeHash.hash_val),(TubeHash.hash_table==hashtable,));
mani.closeSession();
ids=np.array(ids,dtype=dtype);
np.savez(out_file_class_pre,ids);
ids=np.load(out_file_class_pre+'.npz')['arr_0'];
# ids=np.load(in_file)['arr_0'];
counts_all,class_ids_breakdown = getClassIdsCount(ids[:,0],ids[:,1]);
ranks = getDiscriminativeScore(counts_all);
sort_idx=np.argsort(ranks);
counts_all=[counts_all[idx] for idx in sort_idx];
class_ids_breakdown=[class_ids_breakdown[idx] for idx in sort_idx];
im_simple = getHashAnalysisIm(counts_all,class_ids_breakdown,inc=inc,colorByClass=False);
im_byClass = getHashAnalysisIm(counts_all,class_ids_breakdown,inc=inc,colorByClass=True);
visualize.saveMatAsImage(im_simple,out_file_hash_simple)
visualize.saveMatAsImage(im_byClass,out_file_hash_byClass)
counts_all_ravel=np.array([c for counts in counts_all for c in counts]);
class_ids_breakdown_ravel=np.array([c for class_ids in class_ids_breakdown for c in class_ids]);
class_id_pascal,class_idx_pascal = zip(*class_labels_map);
for class_id_idx,class_id in enumerate(class_idx_pascal):
frequency = counts_all_ravel[class_ids_breakdown_ravel==class_id]
out_file=out_file_class_pre+'_'+class_id_pascal[class_id_idx]+'.png'
title=class_id_pascal[class_id_idx]+' '+str(class_id)
cum_freq,idx_perc=getCumulativeInfo(frequency,percents)
savePerClassCumulativeGraph(cum_freq/float(cum_freq[-1]),idx_perc,percents,out_file,title)
def getHashBinClassBreakdowns((hash_table,hash_val,path_to_db,out_file,idx)):
print idx
mani=Tube_Manipulator(path_to_db)
mani.openSession();
toSelect=(Tube.class_idx_pascal,Tube.video_id,Tube.shot_id,Tube.tube_id);
criterion=(TubeHash.hash_table==hash_table,TubeHash.hash_val==hash_val);
vals=mani.selectMix(toSelect,criterion=criterion,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)
# for k in counts.keys():
# for k2 in counts[k].keys():
# print k,k2,counts[k][k2];
# return counts
pickle.dump(counts,open(out_file,'wb'));
def getHashBinClassBreakdowns((hash_table, hash_val, path_to_db, out_file,
idx)):
print idx
mani = Tube_Manipulator(path_to_db)
mani.openSession()
toSelect = (Tube.class_idx_pascal, Tube.video_id, Tube.shot_id,
Tube.tube_id)
criterion = (TubeHash.hash_table == hash_table,
TubeHash.hash_val == hash_val)
vals = mani.selectMix(toSelect, criterion=criterion, 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)
# for k in counts.keys():
# for k2 in counts[k].keys():
# print k,k2,counts[k][k2];
# return counts
pickle.dump(counts, open(out_file, 'wb'))
def script_saveNpzScorePerShot_normalized(params):
path_to_db = params['path_to_db']
file_binCounts = params['file_binCounts']
class_idx = params['class_idx']
video_id = params['video_id']
shot_id = params['shot_id']
out_file_scores = params['out_file_scores']
num_hash_tables = params['num_hash_tables']
total_counts = params['total_class_counts']
class_idx_assume = params.get('class_idx_assume',None);
if class_idx_assume is None:
class_idx_assume=class_idx;
print params['idx']
mani=Tube_Manipulator(path_to_db);
mani.openSession();
toSelect=(Tube.tube_id,Tube.deep_features_idx,TubeHash.hash_table,TubeHash.hash_val);
criterion=(Tube.video_id==video_id,Tube.class_idx_pascal==class_idx,Tube.shot_id==shot_id);
vals=mani.selectMix(toSelect,criterion);
total_frames = getShotFrameCount(mani,class_idx,video_id,shot_id);
mani.closeSession();
hash_count_keys,hash_counts=pickle.load(open(file_binCounts,'rb'));
hash_info=[tuple(r) for r in vals[:,2:]];
hash_counts = dict(Counter(hash_info))
# total_counts=np.sum(hash_counts,axis=0);
scores_all={};
vals=np.array(vals)
tube_ids_uni=np.unique(vals[:,0]);
for tube_id in tube_ids_uni:
vals_rel=vals[vals[:,0]==tube_id,1:];
deep_features_idx_uni = np.unique(vals_rel[:,0]);
scores_tube=np.empty((len(deep_features_idx_uni),num_hash_tables));
scores_tube[:]=np.nan;
for deep_features_idx in deep_features_idx_uni:
hash_info=vals_rel[vals_rel[:,0]==deep_features_idx,1:];
assert len(hash_info)==num_hash_tables
scores=[];
for hash_info_curr in hash_info:
idx_curr=hash_count_keys.index(tuple(hash_info_curr));
counts_curr=hash_counts[idx_curr,:];
deno=counts_curr/total_counts.astype(dtype=float);
numo=deno[class_idx_assume];
deno=sum(deno);
score_curr=numo/float(deno);
scores.append(score_curr);
scores_tube[deep_features_idx,:]=scores;
scores_all[tube_id]=scores_tube;
for tube_id in scores_all:
tube_scores=scores_all[tube_id];
assert np.sum(np.isnan(tube_scores))==0;
pickle.dump(scores_all,open(out_file_scores,'wb'));
def script_saveNpzScorePerShot(params):
path_to_db = params['path_to_db']
total_class_counts = params['total_class_counts']
class_idx = params['class_idx']
video_id = params['video_id']
shot_id = params['shot_id']
out_file_scores = params['out_file_scores']
path_to_hash = params['path_to_hash']
num_hash_tables = params['num_hash_tables']
class_idx_assume = params.get('class_idx_assume',None);
if class_idx_assume is None:
class_idx_assume=class_idx;
print params['idx']
# print 'getting vals and frame count from db',
# t=time.time();
mani=Tube_Manipulator(path_to_db);
mani.openSession();
toSelect=(Tube.deep_features_path,Tube.tube_id,Tube.deep_features_idx,TubeHash.hash_table,TubeHash.hash_val);
criterion=(Tube.video_id==video_id,Tube.class_idx_pascal==class_idx,Tube.shot_id==shot_id);
vals=mani.selectMix(toSelect,criterion);
total_frames = getShotFrameCount(mani,class_idx,video_id,shot_id);
mani.closeSession();
# print time.time()-t
# print 'getting hash_counts',
# t=time.time();
hash_info=[(tuple_curr[3],tuple_curr[4]) for tuple_curr in vals];
hash_counts = dict(Counter(hash_info))
# print time.time()-t
# print 'getting hash_bin_scores',
# t=time.time();
if class_idx_assume ==class_idx:
total_class_count=total_class_counts[class_idx_assume]-total_frames;
else:
total_class_count=total_class_counts[class_idx_assume];
hash_bin_scores={};
for idx,k in enumerate(hash_counts.keys()):
in_file=str(k[0])+'_'+str(k[1])+'_counts.p'
class_id_counts=pickle.load(open(os.path.join(path_to_hash,in_file),'rb'));
if class_idx_assume ==class_idx:
hash_bin_count=class_id_counts.get(class_idx_assume,0)-hash_counts[k];
else:
hash_bin_count=class_id_counts.get(class_idx_assume,0)
hash_bin_scores[k]=hash_bin_count/float(total_class_count);
# print time.time()-t;
# print 'getting tube_scores_all',
# t=time.time();
vals_org=np.array(vals);
deep_features_paths=vals_org[:,0];
vals=np.array(vals_org[:,1:],dtype=int);
# Tube.tube_id,Tube.deep_features_idx,TubeHash.hash_table,TubeHash.hash_val
tube_ids=np.unique(vals[:,0])
# deep_features_idx=np.unique(vals[:,1]);
tube_scores_all={};
for tube_id in tube_ids:
# print tube_id,len(deep_features_idx),len(np.unique(vals[vals[:,0]==tube_id,1]));
deep_features_idx=np.unique(vals[vals[:,0]==tube_id,1])
tube_scores_all[tube_id]=getTubeScoresMat(tube_id,vals,hash_bin_scores,deep_features_idx,num_hash_tables);
# print time.time()-t;
# for tube_id in tube_scores_all:
# tube_scores=tube_scores_all[tube_id];
# print tube_id,np.sum(np.isnan(tube_scores)),tube_scores.shape
# # print tube_scores[0,:]
# assert np.sum(np.isnan(tube_scores))==0;
# out_file_temp='/disk2/temp/temp.p';
# np.savez_compressed(out_file_scores,tube_scores_all.values(),tube_scores_all.keys())
pickle.dump(tube_scores_all,open(out_file_scores,'wb'));
def script_saveNpzScorePerShot(params):
path_to_db = params['path_to_db']
total_class_counts = params['total_class_counts']
class_idx = params['class_idx']
video_id = params['video_id']
shot_id = params['shot_id']
out_file_scores = params['out_file_scores']
path_to_hash = params['path_to_hash']
num_hash_tables = params['num_hash_tables']
class_idx_assume = params.get('class_idx_assume', None)
if class_idx_assume is None:
class_idx_assume = class_idx
print params['idx']
# print 'getting vals and frame count from db',
# t=time.time();
mani = Tube_Manipulator(path_to_db)
mani.openSession()
toSelect = (Tube.deep_features_path, Tube.tube_id, Tube.deep_features_idx,
TubeHash.hash_table, TubeHash.hash_val)
criterion = (Tube.video_id == video_id, Tube.class_idx_pascal == class_idx,
Tube.shot_id == shot_id)
vals = mani.selectMix(toSelect, criterion)
total_frames = getShotFrameCount(mani, class_idx, video_id, shot_id)
mani.closeSession()
# print time.time()-t
# print 'getting hash_counts',
# t=time.time();
hash_info = [(tuple_curr[3], tuple_curr[4]) for tuple_curr in vals]
hash_counts = dict(Counter(hash_info))
# print time.time()-t
# print 'getting hash_bin_scores',
# t=time.time();
if class_idx_assume == class_idx:
total_class_count = total_class_counts[class_idx_assume] - total_frames
else:
total_class_count = total_class_counts[class_idx_assume]
hash_bin_scores = {}
for idx, k in enumerate(hash_counts.keys()):
in_file = str(k[0]) + '_' + str(k[1]) + '_counts.p'
class_id_counts = pickle.load(
open(os.path.join(path_to_hash, in_file), 'rb'))
if class_idx_assume == class_idx:
hash_bin_count = class_id_counts.get(class_idx_assume,
0) - hash_counts[k]
else:
hash_bin_count = class_id_counts.get(class_idx_assume, 0)
hash_bin_scores[k] = hash_bin_count / float(total_class_count)
# print time.time()-t;
# print 'getting tube_scores_all',
# t=time.time();
vals_org = np.array(vals)
deep_features_paths = vals_org[:, 0]
vals = np.array(vals_org[:, 1:], dtype=int)
# Tube.tube_id,Tube.deep_features_idx,TubeHash.hash_table,TubeHash.hash_val
tube_ids = np.unique(vals[:, 0])
# deep_features_idx=np.unique(vals[:,1]);
tube_scores_all = {}
for tube_id in tube_ids:
# print tube_id,len(deep_features_idx),len(np.unique(vals[vals[:,0]==tube_id,1]));
deep_features_idx = np.unique(vals[vals[:, 0] == tube_id, 1])
tube_scores_all[tube_id] = getTubeScoresMat(tube_id, vals,
hash_bin_scores,
deep_features_idx,
num_hash_tables)
# print time.time()-t;
# for tube_id in tube_scores_all:
# tube_scores=tube_scores_all[tube_id];
# print tube_id,np.sum(np.isnan(tube_scores)),tube_scores.shape
# # print tube_scores[0,:]
# assert np.sum(np.isnan(tube_scores))==0;
# out_file_temp='/disk2/temp/temp.p';
# np.savez_compressed(out_file_scores,tube_scores_all.values(),tube_scores_all.keys())
pickle.dump(tube_scores_all, open(out_file_scores, 'wb'))
def script_saveNpzScorePerShot_normalized(params):
path_to_db = params['path_to_db']
file_binCounts = params['file_binCounts']
class_idx = params['class_idx']
video_id = params['video_id']
shot_id = params['shot_id']
out_file_scores = params['out_file_scores']
num_hash_tables = params['num_hash_tables']
total_counts = params['total_class_counts']
class_idx_assume = params.get('class_idx_assume', None)
if class_idx_assume is None:
class_idx_assume = class_idx
print params['idx']
mani = Tube_Manipulator(path_to_db)
mani.openSession()
toSelect = (Tube.tube_id, Tube.deep_features_idx, TubeHash.hash_table,
TubeHash.hash_val)
criterion = (Tube.video_id == video_id, Tube.class_idx_pascal == class_idx,
Tube.shot_id == shot_id)
vals = mani.selectMix(toSelect, criterion)
total_frames = getShotFrameCount(mani, class_idx, video_id, shot_id)
mani.closeSession()
hash_count_keys, hash_counts = pickle.load(open(file_binCounts, 'rb'))
hash_info = [tuple(r) for r in vals[:, 2:]]
hash_counts = dict(Counter(hash_info))
# total_counts=np.sum(hash_counts,axis=0);
scores_all = {}
vals = np.array(vals)
tube_ids_uni = np.unique(vals[:, 0])
for tube_id in tube_ids_uni:
vals_rel = vals[vals[:, 0] == tube_id, 1:]
deep_features_idx_uni = np.unique(vals_rel[:, 0])
scores_tube = np.empty((len(deep_features_idx_uni), num_hash_tables))
scores_tube[:] = np.nan
for deep_features_idx in deep_features_idx_uni:
hash_info = vals_rel[vals_rel[:, 0] == deep_features_idx, 1:]
assert len(hash_info) == num_hash_tables
scores = []
for hash_info_curr in hash_info:
idx_curr = hash_count_keys.index(tuple(hash_info_curr))
counts_curr = hash_counts[idx_curr, :]
deno = counts_curr / total_counts.astype(dtype=float)
numo = deno[class_idx_assume]
deno = sum(deno)
score_curr = numo / float(deno)
scores.append(score_curr)
scores_tube[deep_features_idx, :] = scores
scores_all[tube_id] = scores_tube
for tube_id in scores_all:
tube_scores = scores_all[tube_id]
assert np.sum(np.isnan(tube_scores)) == 0
pickle.dump(scores_all, open(out_file_scores, 'wb'))
