anno_preprocess/open_pickle.py

import pickle
import json
import numpy as np
myfile = 'train_offset_5_test.pickle'

#THUMOS : {video_validation_xx: {anno: , feature_length: } , video_validation_xxx : ,..}
#anno shape : (feature_length, 22)

objects = []
with (open(myfile, "rb")) as openfile:
    pickle = pickle.load(openfile)
print(pickle["fc2b2014-9dc4-4a5d-8a1d-25a6911bff7c"]['anno'][100:200])
#print(pickle['604bf883-ce55-4c59-bcc1-3ceea1128211'])

# outlier = {}
# for uid in pickle :
#     all = 0
#     both = 0
#     one = 0
#     last = np.array([1,0,0])
#     annos = pickle[uid]['anno']
#     for anno in annos :
#         all+=1
#         if np.all(anno == np.array([0,1,1])) :
#             both +=1
#         elif np.all(last == np.array([0,0,1])) and np.all(anno == np.array([0,1,0])) :
#             one +=1
#         last = anno
#     outlier[uid] = {}
#     outlier[uid]['011'] = both
#     outlier[uid]['001and010'] = one

# with open('outlier_train.json', 'w') as f :
#     json.dump(outlier, f, indent = 4)



# with open('pickle_to_txt.txt', 'w') as f:
#     json.dump(objects, f, indent = '\t')

# class NumpyEncoder(json.JSONEncoder):
#     """ Special json encoder for numpy types """
#     def default(self, obj):
#         if isinstance(obj, np.integer):
#             return int(obj)
#         elif isinstance(obj, np.floating):
#             return float(obj)
#         elif isinstance(obj, np.ndarray):
#             return obj.tolist()
#         return json.JSONEncoder.default(self, obj)

# dumped = json.dumps(pickle, cls=NumpyEncoder)

# with open('pickle_to_json.json', 'w') as f:
#     json.dump(dumped, f)