File size: 2,520 Bytes
fb5d697 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 | #####################
#Annotate in naive way
#####################
import json
import pickle
import numpy as np
# Error : somewhere [0,0,0] produced - SOLVED
av_train_annotation_path = '/scratch/junhyeok/ego4d_dataset/v2/annotations/av_val.json'
with open(av_train_annotation_path, "r") as f:
av_train_annotations = json.load(f)
av_train_processed = {}
for video in av_train_annotations['videos'] :
for clip in video['clips'] :
uid = clip['clip_uid']
# Make embedding template
end_frame = clip['clip_end_frame']
#feature_num = int(np.ceil(end_frame/6))
feature_num = int(np.round(end_frame/6))
if feature_num != 1500 :
print('uid : {} , feature_length : {}'.format(uid , feature_num))
anno = np.zeros((feature_num , 3))
# Suppose no action in frame for default
anno[:,0]=1
i=0
if clip['transcriptions'] == [] :
continue
else:
av_train_processed[uid] = {}
for transcript in clip['transcriptions']:
# To make annotation by clip frame, need to subtract video_start_frame from transcription's start_frame
if i==0 :
initial_frame = clip['video_start_frame']
i+=1
#Save frame range
start_frame = transcript['video_start_frame'] - initial_frame
end_frame = transcript['video_end_frame'] - initial_frame
#Save action encoding
if int(transcript['person_id']) == 0 :
encode_num = 1
if int(transcript['person_id']) >= 1 :
encode_num = 2
if int(transcript['person_id']) == -1 :
encode_num = 0
#Save encoding by feature
if end_frame//6 - start_frame//6 == 0 :
anno[(start_frame//6)-1 , 0] = 0
anno[(start_frame//6)-1 , encode_num]=1
else :
anno[(start_frame//6)-1 : (end_frame//6) , 0] = 0
anno[(start_frame//6)-1 : (end_frame//6) , encode_num]=1
#Save annotation to uid list
av_train_processed[uid]['anno'] = anno
av_train_processed[uid]['feature_length'] = feature_num
print('Processing DONE!')
with open('val_perfeature_sample.pickle' , 'wb') as f :
pickle.dump(av_train_processed , f)
print('SAVED') |