forked from adaniefei/AccessMath_Pose
-
Notifications
You must be signed in to change notification settings - Fork 1
/
spk_summ_05_temporal_segmentation.py
80 lines (60 loc) · 3.04 KB
/
spk_summ_05_temporal_segmentation.py
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
import os
import sys
from AM_CommonTools.configuration.configuration import Configuration
from AccessMath.util.misc_helper import MiscHelper
from AccessMath.data.meta_data_DB import MetaDataDB
from AccessMath.speaker.actions.video_segmenter import VideoSegmenter
from AccessMath.speaker.util.result_reader import ResultReader
def main():
# usage check
if len(sys.argv) < 2:
print("Usage:")
print("")
print("\tpython {0:s} config [gt_labels]".format(sys.argv[0]))
print("")
print("Where")
print("\tconfig:\tPath to AccessMath configuration file")
print("\tgt_labels:\tuse ground truth action labels (Default= False)")
return
# read the configuration file ....
config = Configuration.from_file(sys.argv[1])
try:
database = MetaDataDB.from_file(config.get_str("VIDEO_DATABASE_PATH"))
except:
print("Invalid AccessMath Database file")
return
output_dir = config.get_str("OUTPUT_PATH")
video_metadata_dir = output_dir + "/" + config.get_str("SPEAKER_ACTION_VIDEO_META_DATA_DIR")
action_class_probabilities_dir = output_dir + "/" + config.get("SPEAKER_ACTION_CLASSIFICATION_PROBABILITIES_DIR")
output_bboxes_dir = output_dir + "/" + config.get("SPEAKER_ACTION_CLASSIFICATION_BBOXES_DIR")
temporal_segments_dir = output_dir + "/" + config.get("SPEAKER_ACTION_TEMPORAL_SEGMENTS_DIR")
os.makedirs(temporal_segments_dir, exist_ok=True)
dataset_name = config.get("SPEAKER_TESTING_SET_NAME")
testing_set = database.datasets[dataset_name]
valid_actions = config.get("SPEAKER_VALID_ACTIONS")
if len(sys.argv) >= 3:
use_ground_truth = int(sys.argv[2]) > 0
else:
use_ground_truth = False
for current_lecture in testing_set:
info_filename = video_metadata_dir + "/" + database.name + "_" + current_lecture.title + ".pickle"
proba_filename = action_class_probabilities_dir + "/" + database.name + "_" + current_lecture.title + ".csv"
video_info = MiscHelper.dump_load(info_filename)
segmenter = VideoSegmenter.FromConfig(config, video_info["width"], video_info["height"])
# read label data ....
prob_info = ResultReader.read_actions_probabilities_file(proba_filename, valid_actions)
segments, gt_actions, pred_actions, prob_actions = prob_info
# read bbox data ...
bbox_filename = output_bboxes_dir + "/" + database.name + "_" + current_lecture.title + ".csv"
frame_idxs, frame_actions, body_bboxes, rh_bboxes = ResultReader.read_bbox_file(bbox_filename, use_ground_truth)
# (splits_frames, video_keyframes)
video_data = segmenter.get_keyframes(pred_actions, segments, frame_idxs, body_bboxes, rh_bboxes)
print("")
print("video key_frames")
print(video_data[0])
print(video_data[1])
print("")
output_filename = temporal_segments_dir + "/" + database.name + "_" + current_lecture.title + ".pickle"
MiscHelper.dump_save(video_data, output_filename)
if __name__ == "__main__":
main()