import argparse

import sys

import numpy as np

caffe_root = '/home/zhujiagang/temporal-segment-networks/lib/caffeaction/'

sys.path.insert(0, caffe_root + 'python')

from pyActionRecog.utils.video_funcs import default_aggregation_func

from pyActionRecog.utils.metrics import mean_class_accuracy

score_files = [['/home/zjg/zjg/tsncaffe/score/ucf101_split_1_tsn_rgb_score.npz', '/home/zjg/zjg/tsncaffe/score/ucf101_split_1_tsn_flow_score.npz'],

['/home/zjg/zjg/tsncaffe/score/ucf101_split_2_tsn_rgb_score.npz', '/home/zjg/zjg/tsncaffe/score/ucf101_split_2_tsn_flow_score.npz'],

['/home/zjg/zjg/tsncaffe/score/ucf101_split_3_tsn_rgb_score.npz', '/home/zjg/zjg/tsncaffe/score/ucf101_split_3_tsn_flow_score.npz']]

score_files = [['/home/zjg/zjg/tsncaffe/score/hmdb51_split_1_tsn_rgb_score.npz', '/home/zjg/zjg/tsncaffe/score/hmdb51_split_1_tsn_flow_score.npz'],

['/home/zjg/zjg/tsncaffe/score/hmdb51_split_2_tsn_rgb_score.npz', '/home/zjg/zjg/tsncaffe/score/hmdb51_split_2_tsn_flow_score.npz'],

['/home/zjg/zjg/tsncaffe/score/hmdb51_split_3_tsn_rgb_score.npz', '/home/zjg/zjg/tsncaffe/score/hmdb51_split_3_tsn_flow_score.npz']]

ii=3

accuracy = [[] for row in range(ii)]

for iii in xrange(ii):

print 'ii', iii

score_npz_files = [np.load(x) for x in score_files[iii]]

score_list = [x['scores'][:, 0] for x in score_npz_files]

label_list = [x['labels'] for x in score_npz_files]

for ii in xrange(11):

print ii

score_weights = [ii,10-ii]

# label verification

# score_aggregation

agg_score_list = []

for score_vec in score_list:

agg_score_vec = [default_aggregation_func(x, normalization=False, crop_agg=getattr(np, 'mean')) for x in score_vec]

#print len(agg_score_vec)

agg_score_list.append(np.array(agg_score_vec))

final_scores = np.zeros_like(agg_score_list[0])

for i, agg_score in enumerate(agg_score_list):

final_scores += agg_score * score_weights[i]

# accuracy

acc = mean_class_accuracy(final_scores, label_list[0])

#print acc

print 'Final accuracy {:02f}%'.format(acc * 100)

accuracy[iii].append(np.array(acc))

#print accuracy

print '\nFinal mAP:'

print np.mean(accuracy, axis = 0)