print "Shape : {} " .format(out.shape)
print out
return out
#np.save( npyFilePath , out )
if __name__=='__main__':
# Read the database, version and taxonomy from JSON file
with open(ANNOTATION_FILE, "r") as fobj:
data = json.load(fobj)
database = data["database"]
taxonomy = data["taxonomy"]
version = data["version"]
non_existing_videos = utils.crosscheck_videos(VIDEOPATH, ANNOTATION_FILE)
print "No of non-existing videos: %d" % len(non_existing_videos)
train_vids_all = []
[train_vids_all.append(x) for x in database if database[x]['subset']=='training']
# Find list of available training videos
train_existing_vids = list(set(train_vids_all) - set(non_existing_videos))
val_vids_all = []
[val_vids_all.append(x) for x in database if database[x]['subset']==SUBSET]
# Find list of available training videos
val_existing_vids = list(set(val_vids_all) - set(non_existing_videos))
###########################################################################
# Get categories information from the database (Train+Validation sets)
break
if i == 5:
break
if __name__ == '__main__':
# Read the database, version and taxonomy from JSON file
with open("data/activity_net.v1-3.min.json", "r") as fobj:
data = json.load(fobj)
database = data["database"]
taxonomy = data["taxonomy"]
version = data["version"]
non_existing_videos = utils.crosscheck_videos(VIDEOPATH, JSONFILE)
print "No of non-existing videos: %d" % len(non_existing_videos)
train_vids_all = []
[
train_vids_all.append(x) for x in database
if database[x]['subset'] == SUBSET
]
# Find list of available training videos
train_existing_vids = list(set(train_vids_all) - set(non_existing_videos))
###########################################################################
# Get categories information from the database (Train+Validation sets)
category = []