File size: 3,731 Bytes
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import numpy as np
import pandas as pd
import json
import os
from joblib import Parallel, delayed
import yaml
# with open("./config/anet.yaml", 'r', encoding='utf-8') as f:
# tmp = f.read()
# config = yaml.load(tmp, Loader=yaml.FullLoader)
# vid_path = config['dataset']['test']['visual_feature_path']
# nms_thresh = config['testing']['nms_thresh']
def load_json(file):
with open(file) as json_file:
data = json.load(json_file)
return data
def get_infer_dict(vid_anno,subset='test'):
# df = pd.read_csv(vid_info)
database = load_json(vid_anno)
video_dict = {}
for i in range(len(database)):
video_name = video_name = os.path.splitext(os.path.basename(database[i]['file']))[0]
# if os.path.exists(os.path.join(vid_path+"/test",video_name+".npy")):
video_info = database[i]
video_new_info = {}
video_new_info['duration_frame'] = video_info['video_frames']
video_new_info['duration_second'] = video_info['duration']
video_new_info["feature_frame"] = video_info['video_frames']
video_subset = video_info["split"]
video_anno = video_info['fake_periods']
video_new_info['fake_periods'] = video_info['fake_periods']
if len(video_anno) > 0:
video_label = 'Fake'
if video_subset == subset:
video_dict[video_name] = video_new_info
return video_dict
def Soft_NMS(df, nms_threshold=1e-5, num_prop=100):
df = df.sort_values(by="score", ascending=False)
tstart = list(df.xmin.values[:])
tend = list(df.xmax.values[:])
tscore = list(df.score.values[:])
tlabel = list(df.label.values[:])
rstart = []
rend = []
rscore = []
rlabel = []
while len(tscore) > 1 and len(rscore) < num_prop and max(tscore)>0:
max_index = tscore.index(max(tscore))
for idx in range(0, len(tscore)):
if idx != max_index:
tmp_iou = IOU(tstart[max_index], tend[max_index], tstart[idx], tend[idx])
if tmp_iou > 0:
tscore[idx] = tscore[idx] * (np.exp(-np.square(tmp_iou)*10) / nms_threshold)
rstart.append(tstart[max_index])
rend.append(tend[max_index])
rscore.append(tscore[max_index])
rlabel.append(tlabel[max_index])
tstart.pop(max_index)
tend.pop(max_index)
tscore.pop(max_index)
tlabel.pop(max_index)
newDf = pd.DataFrame()
newDf['score'] = rscore
newDf['xmin'] = rstart
newDf['xmax'] = rend
newDf['label'] = rlabel
return newDf
def IOU(s1, e1, s2, e2):
if (s2 > e1) or (s1 > e2):
return 0
Aor = max(e1, e2) - min(s1, s2)
Aand = min(e1, e2) - min(s1, s2)
return float(Aand) / (Aor - Aand + (e2-s2))
def multithread_detection(video_name, video_cls, pred_prop):
old_df = pred_prop[pred_prop.video_name == video_name]
# print(df)
df = pd.DataFrame()
df['score'] = old_df.score.values[:]
df['label'] = old_df.label.values[:]
df['xmin'] = old_df.xmin.values[:]
df['xmax'] = old_df.xmax.values[:]
best_score = np.max(video_cls)
# if len(df) > 1:
# df = Soft_NMS(df, nms_thresh)
df = df.sort_values(by="score", ascending=False)
proposal_list = []
for j in range(min(100, len(df))):
tmp_proposal = {}
tmp_proposal["label"] = 'Fake'
tmp_proposal["score"] = float(df.score.values[j])*best_score
tmp_proposal["segment"] = [max(0, df.xmin.values[j]),
min(1, df.xmax.values[j])]
proposal_list.append(tmp_proposal)
return {video_name: proposal_list}
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