81 lines
3.4 KiB
Python
81 lines
3.4 KiB
Python
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import argparse
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import numpy as np
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import pandas as pd
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import os.path as osp
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def str2sec(string):
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hh, mm, ss = string.split(':')
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return float(hh) * 3600 + float(mm) * 60 + float(ss)
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def main():
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parser = argparse.ArgumentParser(description='Generate targets')
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parser.add_argument('--dataset', type=str,
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choices=['EK55', 'EK100'], default='EK55')
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parser.add_argument('--data-path', type=str,
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default='/scratch/cluster/yzhao/Datasets/EPIC-KITCHENS/')
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parser.add_argument('--targets-path', type=str,
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default='/scratch/cluster/yzhao/Datasets/'
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'EPIC-KITCHENS/features/target_perframe/')
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parser.add_argument('--fps', type=int, default=30)
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parser.add_argument('--tick', type=float, default=7.5)
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parser.add_argument('--anno-path', type=str,
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default='external/rulstm/RULSTM/data/ek55/')
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parser.add_argument('--class-type', type=str, default='action',
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choices=['verb', 'noun', 'action'])
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args = parser.parse_args()
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# initialize the targets
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targets = {}
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if args.class_type == 'action':
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num_classes = 2513 + 1
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elif args.class_type == 'verb':
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actions = pd.read_csv(osp.join(args.anno_path, 'actions.csv'))
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a_to_v = {a[1]['id'] + 1: a[1]['verb'] + 1
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for a in actions.iterrows()}
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num_classes = max(a_to_v.values()) + 1
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elif args.class_type == 'noun':
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actions = pd.read_csv(osp.join(args.anno_path, 'actions.csv'))
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a_to_n = {a[1]['id'] + 1: a[1]['noun'] + 1
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for a in actions.iterrows()}
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num_classes = max(a_to_n.values()) + 1
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for subset in ['training', 'validation']:
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vids = [line.strip() for line in open('{}/{}_videos.csv'.format(args.anno_path, subset))]
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anno_per_video = {}
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for line in open('{}/{}.csv'.format(args.anno_path, subset)):
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idx, vid, f_start, f_end, c_verb, c_noun, c_verbnoun = line.strip().split(',')
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vid = vid.strip()
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if vid not in anno_per_video:
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anno_per_video[vid] = [(int(f_start), int(f_end), int(c_verb), int(c_noun), int(c_verbnoun))]
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else:
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anno_per_video[vid].append((int(f_start), int(f_end), int(c_verb), int(c_noun), int(c_verbnoun)))
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for vid in vids:
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feature = np.load('{}/features/rgb_kinetics_resnet50/{}.npy'.format(args.data_path, vid))
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feature_len = feature.shape[0]
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targets[vid] = np.zeros((feature_len,
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num_classes))
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targets[vid][:, 0] = 1
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for anno in anno_per_video[vid]:
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print(vid, anno)
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ss = int(np.floor(anno[0] / args.tick))
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ee = min(int(np.ceil(anno[1] / args.tick)), feature_len)
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if args.class_type == 'action':
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ci = anno[4]
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elif args.class_type == 'verb':
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ci = anno[2]
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elif args.class_type == 'noun':
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ci = anno[3]
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for si in range(ss, ee):
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targets[vid][si, ci + 1] = 1
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targets[vid][si, 0] = 0
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print('Dumping npy files..')
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for vid in targets:
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np.save(args.targets_path + '{}.npy'.format(vid), targets[vid])
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if __name__ == '__main__':
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main()
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