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 = []