-
Notifications
You must be signed in to change notification settings - Fork 0
/
predict_stat.py
199 lines (138 loc) · 5.2 KB
/
predict_stat.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
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
import argparse
import json
import os
import math
import torch
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim
from tqdm import tqdm
from sklearn.metrics import balanced_accuracy_score , accuracy_score
import warnings
from sklearn.exceptions import DataConversionWarning
import statistics
import random
from collections import Counter
from PIL import Image, ImageDraw
from PIL import ImageFont
from lib.utils.utils import loadarg, AverageMeter , get_labels , remap_acc_to_emotions, multiclass_accuracy
import pickle
def save_pickle(file_save, mydict):
f = open(file_save, "wb")
pickle.dump(mydict, f, protocol=pickle.HIGHEST_PROTOCOL)
f.close()
def load_pickle(file_pickle):
f = open(file_pickle, "rb")
mydict = pickle.load(f)
f.close()
return mydict
def new_dir(path):
if os.path.exists(path):
print("exist: ", path)
return 1
else:
os.mkdir(path)
return -1
def load_predict(file_predict, *args ):
print("load_predict", args)
yV = []
for keyV in args[0]:
print("keyV", keyV)
file_save = f"{file_predict}_{keyV}"
yV.append(load_pickle(file_save))
return yV
def save_predict(file_predict, **kwargs):
for keyV in kwargs:
file_save = f"{file_predict}_{keyV}"
save_pickle(file_save, kwargs[keyV])
def assemble( y_V, y_t, y_id):
profile_V, stat_V = {}, Counter()
for i, id in enumerate(y_id):
p_V, t = y_V[i], y_t[i]
#print("p_V, t", p_V, t)
if id not in profile_V: profile_V[id] = []
profile_V[id].append([p_V,t])
stat_V[p_V] +=1
print("stat_V", stat_V)
return profile_V
def make_cell_overleaf( v):
return "\makecell {" + v + "}\n"
def make_cell_overleaf_youtube_link( id, title):
link = "\makecell{ \href{https://www.youtube.com/watch?v=" + id +"}{" + title + "}}"
return link
def main():
global args_model, args_data
parser = argparse.ArgumentParser()
parser.add_argument('--info', type=str) # profile_P: for prediction
parser.add_argument('--predicted', type=str) # profile_P: for prediction
args_in = parser.parse_args()
""" creates folder for output results"""
if args_in.predicted:
path_save_predicted = f'{args_in.predicted}'
else:
print("No data for prediction is provided, you need to specify --predicted <path_to_predicted>")
print("<path_to_predicted> file with { id_video: [[label,time_0], [label,time_1], .. , [label,time_end]]")
print("where id_video: video file id (without .mp4) for each video form --data <dataset_info.json>")
print("label -> [0,1,2,..,7] predicted emotion id for time_K ")
exit()
info = loadarg(f'{args_in.info}')
"""load prediction"""
#yV = ["y_pred", "y_t", "y_id"]
#[y_V, y_t, y_id] = load_predict(file_predictV2, yV)
"""assemble prediction for each video"""
#profile_P = assemble(y_V, y_t, y_id) # profile_V[id].append([p_V,t])
profile_P = loadarg(f'{args_in.predicted}')
EIDS = {0: "Anger",
1: "Contempt",
2: "Disgust",
3: "Fear",
4: "Happiness",
5: "Neutral",
6: "Sadness",
7: "Surprise"
}
print("profile_P.keys():", profile_P.keys())
stat_V , stat_VID = Counter(), {}
for id in profile_P:
stat_VID[id] = Counter()
for [p_V,t] in profile_P[id]:
stat_VID[id][p_V] += 1
stat_V[p_V] += 1
stat_VID_R, tableROWS = {} , []
for id in profile_P:
stat_VID_R[id] = Counter()
#print("stat_VID: ", id, info[id]["title"], stat_VID[id])
all = 0
for eid in stat_VID[id]:
all += stat_VID[id][eid]
for eid in stat_VID[id]:
stat_VID_R[id][eid] = stat_VID[id][eid] / all
#print("stat_VID_R: ", id, stat_VID_R[id])
#\href{http://www.overleaf.com}{Something Linky}
title = info[id]["title"]
title_link_cell = make_cell_overleaf_youtube_link(id, title)
count = 0
top_emotion_str1, top_emotion_str2, top_emotion_str3 = "", "", ""
for eid, rate in sorted(stat_VID_R[id].items(), key=lambda item: item[1], reverse=True):
emotion = EIDS[eid]
rate = str(round(stat_VID_R[id][eid], 2))
rate4 = str(round(stat_VID_R[id][4], 2))
rate6 = str(round(stat_VID_R[id][6], 2))
count +=1
if count == 1: top_emotion_str1 = emotion + ": " + rate
if count == 2: top_emotion_str2 = emotion + ": " + rate
if count == 3: top_emotion_str3 = emotion + ": " + rate
top_emotion_str1 = make_cell_overleaf(top_emotion_str1)
top_emotion_str2 = make_cell_overleaf(top_emotion_str2)
top_emotion_str3 = make_cell_overleaf(top_emotion_str3)
table_row = title_link_cell + "&\n"
table_row += top_emotion_str1 + "&\n"
table_row += top_emotion_str2 + "\n"
#table_row += top_emotion_str3 + " &\n"
table_row += " \\\\"
tableROWS.append([table_row , rate6])
for row in sorted(tableROWS, key=lambda x: x[1], reverse=True):
print(f"{row[0]} \n")
if __name__ == '__main__':
main()
#