-
Notifications
You must be signed in to change notification settings - Fork 4
/
format_output.py
116 lines (94 loc) · 5.34 KB
/
format_output.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
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
import os,sys
import numpy as np
import csv
import time, datetime
def shot_labels(finetune_class, svm_class, imagenet, scene, person):
finetune_dict = {}
svm_dict = {}
imagenet_dict = {}
scene_dict = {}
person_dict = {'True':0, 'False':0}
for idx, svm_label in enumerate(svm_class):
if svm_label in svm_dict:
svm_dict[svm_label] += 1
else:
svm_dict[svm_label] = 1
if finetune_class[idx] in finetune_dict:
finetune_dict[finetune_class[idx]] += 1
else:
finetune_dict[finetune_class[idx]] = 1
if imagenet[idx] in imagenet_dict:
imagenet_dict[imagenet[idx]] += 1
else:
imagenet_dict[imagenet[idx]] = 1
if scene[idx] in scene_dict:
scene_dict[scene[idx]] += 1
else:
scene_dict[scene[idx]] = 1
if int(person[idx]) > 0:
person_dict['True'] += 1
else:
person_dict['False'] += 1
finetune_label = max(finetune_dict.iterkeys(), key = (lambda key: finetune_dict[key]))
svm_label = max(svm_dict.iterkeys(), key = (lambda key: svm_dict[key]))
imagenet_label = max(imagenet_dict.iterkeys(), key = (lambda key: imagenet_dict[key]))
scene_label = max(scene_dict.iterkeys(), key = (lambda key: scene_dict[key]))
person_label = max(person_dict.iterkeys(), key = (lambda key: person_dict[key]))
return finetune_label, svm_label, imagenet_label, scene_label, person_label
def output_labels(exec_time, filename, name, timestamps, image_files, shot_boundaries, classifier_label_list, finetune_output, finetune_labels, googlenet_cat, googlenet_labels, scene_type_list, places_labels, scene_attributes_list, person_count, obj_loc_set):
with open(filename + '.sht', 'w+') as out_file:
credit_line = "SHT_01|" + exec_time + "|Source_Program=ShotClass-01.py|Source_Person=Shruti Gullapuram\n"
out_file.write(credit_line)
date = name.split('_')[0]
date = ''.join(map(str, date.split('-')))
hour = name.split('_')[1]
dh = date + hour
video_time = time.mktime(datetime.datetime.strptime(dh, "%Y%m%d%H%M").timetuple())
count = 0
for idx, boundary in enumerate(shot_boundaries):
finetune_class = []
svm_class = []
imagenet = []
scene = []
person = []
if idx == 0:
current_shot_timestamp = 0.000
else:
current_shot_timestamp = shot_boundaries[idx-1]
current_shot_timestamp = float('{0:.3f}'.format(current_shot_timestamp))
new_cur_shot_time = video_time + current_shot_timestamp
cur_shot_time_struct = datetime.datetime.fromtimestamp(new_cur_shot_time)
cur_shot_timestamp_string = cur_shot_time_struct.strftime("%Y%m%d%H%M%S.%f")[:-3]
boundary = float('{0:.3f}'.format(boundary))
new_boundary_time = video_time + boundary
boundary_time_struct = datetime.datetime.fromtimestamp(new_boundary_time)
boundary_timestamp_string = boundary_time_struct.strftime("%Y%m%d%H%M%S.%f")[:-3]
frame_labels = []
while timestamps[count] <= boundary and count+1 <= len(timestamps):
frame_time = float('{0:.3f}'.format(timestamps[count]))
new_frame_time = video_time + frame_time
frame_time_struct = datetime.datetime.fromtimestamp(new_frame_time)
frame_timestamp_string = frame_time_struct.strftime("%Y%m%d%H%M%S.%f")[:-3]
finetune_line = frame_timestamp_string + '|' + frame_timestamp_string + '|SHT_01|FINETUNED_CLASS|' + finetune_output[count] + '|' + finetune_labels[count] + '\n'
svm_line = frame_timestamp_string + '|' + frame_timestamp_string + '|SHT_01|SVM_CLASS|' + classifier_label_list[count] + '\n'
obj_line = frame_timestamp_string + '|' + frame_timestamp_string + '|SHT_01|OBJ_CLASS|' + googlenet_cat[count] + '|' + googlenet_labels[count] + '\n'
scene_line = frame_timestamp_string + '|' + frame_timestamp_string + '|SHT_01|SCENE_LOCATION|' + scene_type_list[count] + '|' + places_labels[count] + '\n'
attr_line = frame_timestamp_string + '|' + frame_timestamp_string + '|SHT_01|SCENE_ATTRIBUTES|' + scene_attributes_list[count] + '\n'
yolo_line = frame_timestamp_string + '|' + frame_timestamp_string + '|SHT_01|YOLO/PERSONS|Count=' + person_count[count] + '|' + obj_loc_set[count] + '\n\n'
frame_labels.append(finetune_line + svm_line + obj_line + scene_line + attr_line + yolo_line)
finetune_class.append(finetune_output[count])
svm_class.append(classifier_label_list[count])
imagenet.append(googlenet_cat[count])
scene.append(scene_type_list[count])
person.append(person_count[count])
count += 1
[ft_shot_type, svm_shot_type, obj_type, scenetype, yolo_person] = shot_labels(finetune_class, svm_class, imagenet, scene, person)
# if yolo_person == 'not':
# boundary_label = cur_shot_timestamp_string + '|' + boundary_timestamp_string + '|SHT_01|SHOT_DETECTED >>|Finetuned_Shot_Class=' + ft_shot_type + '|SVM_Shot_Class=' + svm_shot_type + '|Obj_Class=' + obj_type + '|Scene_Type=' + scenetype + '\n'
# else:
# boundary_label = cur_shot_timestamp_string + '|' + boundary_timestamp_string + '|SHT_01|SHOT_DETECTED >>|' + yolo_person + '|Finetuned_Shot_Class=' + ft_shot_type + '|SVM_Shot_Class=' + svm_shot_type + '|Obj_Class=' + obj_type + '|Scene_Type=' + scenetype + '\n'
boundary_label = cur_shot_timestamp_string + '|' + boundary_timestamp_string + '|SHT_01|SHOT_DETECTED|YOLO/PERSONS=' + yolo_person + '|FINETUNED_SHOT_CLASS=' + ft_shot_type + '|SVM_SHOT_CLASS=' + svm_shot_type + '|OBJ_SHOT_CLASS=' + obj_type + '|SCENE_TYPE=' + scenetype + '\n'
with open(filename + '.sht', 'aw') as file:
file.write(boundary_label)
for frame_label in frame_labels:
file.write(frame_label)