Datasets:
File size: 3,746 Bytes
d79be43 | 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 74 75 76 77 78 79 80 81 82 | import os
import argparse
import numpy as np
import pandas
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument('--dataset_path', type=str,
default='/home/alexa/Shijia/Struggle-aware-deep-models/EPIC-Struggle-Dataset/')
return parser.parse_args()
if __name__ == '__main__':
args = get_args()
for epic_task in ['EPIC_Pipes', 'EPIC_Tent', 'EPIC_Tower']:
print('Dataset ' + epic_task)
for split in [1, 2, 3, 4]:
print('test split {}'.format(split))
# test video list directories
vid_split_dir = os.path.join(args.dataset_path, 'splits', epic_task, 'test_{}.txt'.format(split))
# annotation directories for different tasks
if epic_task == 'EPIC_Tent':
annotation_dir = os.path.join(args.dataset_path, 'annotation', 'UoB_str_tent.csv')
num_voters = 20
elif epic_task == 'EPIC_Pipes':
annotation_dir = os.path.join(args.dataset_path, 'annotation', 'UoB_str_pipe.csv')
num_voters = 20
elif epic_task == 'EPIC_Tower':
annotation_dir = os.path.join(args.dataset_path, 'annotation', 'UoB_str_tower.csv')
num_voters = 15
with open(vid_split_dir) as file:
vid_list = [line.rstrip() for line in file]
for num_classes in [2, 4]:
print('Number of classes: {}'.format(num_classes))
acc_per_vid = 0
for vid in vid_list:
if not os.path.exists(os.path.join(args.dataset_path, 'extracted_frames', epic_task, vid)):
continue
df = pandas.read_csv(annotation_dir)
row_index = df.index[df['VideoID'] == vid].tolist()[0]
vid_label = df.loc[row_index, 'GA'] # Using Golden Annotation 1, 2, 3, and 4
if num_classes == 4:
# manipulate golden annotations
ga_label = np.array([vid_label - 1])
# manipulate voters annotations
voters_labels = []
for i in range(num_voters):
voter_individual = df.loc[row_index, 'Vote{}'.format(i+1)]
voters_labels.append(voter_individual-1)
elif num_classes == 2:
# manipulate golden annotations
if vid_label == 1 or vid_label == 2:
# non-struggle
ga_label = np.array([0])
elif vid_label == 3 or vid_label == 4:
# struggle
ga_label = np.array([1])
# manipulate voters annotations
voters_labels = []
for i in range(num_voters):
voter_individual = df.loc[row_index, 'Vote{}'.format(i+1)]
if voter_individual == 1 or voter_individual == 2:
# non-struggle
voters_labels.append(0)
elif voter_individual == 3 or voter_individual == 4:
# struggle
voters_labels.append(1)
voters_labels_arr = np.array(voters_labels)
acc_per_vid += sum(voters_labels_arr == ga_label) / len(voters_labels)
acc_total = acc_per_vid / len(vid_list) * 100
print('Human Baseline Accuracy: {:.2f}%'.format(acc_total))
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