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post_processing.py
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post_processing.py
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import argparse
import json
import multiprocessing as mp
import os
import threading
import numpy as np
import pandas as pd
import tqdm
parser = argparse.ArgumentParser()
parser.add_argument('input_dir', type=str)
parser.add_argument('output_file', type=str)
parser.add_argument('top_number', type=int, nargs='?', default=100)
parser.add_argument('-t', '--thread', type=int, nargs='?', default=8)
parser.add_argument('-m', '--mode', type=str, nargs='?', default='validation')
args = parser.parse_args()
video_info_file = 'data/video_info_19993.json'
top_number = args.top_number
thread_num = args.thread
def load_json(file):
with open(file) as json_file:
data = json.load(json_file)
return data
def getDatasetDict():
"""Load dataset file
"""
json_data = load_json(video_info_file)
database = json_data
train_dict = {}
val_dict = {}
test_dict = {}
video_lists = list(json_data.keys())
for video_name in video_lists[:]:
video_info = database[video_name]
video_new_info = {}
video_new_info["duration_second"] = video_info["duration"]
video_subset = video_info['subset']
video_new_info["annotations"] = video_info["annotations"]
if video_subset == "training":
train_dict[video_name] = video_new_info
elif video_subset == "validation":
val_dict[video_name] = video_new_info
elif video_subset == "testing":
test_dict[video_name] = video_new_info
return train_dict, val_dict, test_dict
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) - max(s1, s2)
return float(Aand) / Aor
def NMS(df, nms_threshold):
df = df.sort(columns="score", ascending=False)
tstart = list(df.xmin.values[:])
tend = list(df.xmax.values[:])
tscore = list(df.score.values[:])
rstart = []
rend = []
rscore = []
while len(tstart) > 1 and len(rscore) < top_number:
idx = 1
while idx < len(tstart):
if IOU(tstart[0], tend[0], tstart[idx], tend[idx]) > nms_threshold:
tstart.pop(idx)
tend.pop(idx)
tscore.pop(idx)
else:
idx += 1
rstart.append(tstart[0])
rend.append(tend[0])
rscore.append(tscore[0])
tstart.pop(0)
tend.pop(0)
tscore.pop(0)
newDf = pd.DataFrame()
newDf['score'] = rscore
newDf['xmin'] = rstart
newDf['xmax'] = rend
return newDf
def softNMS(df):
tstart = list(df.xmin.values[:])
tend = list(df.xmax.values[:])
tscore = list(df.score.values[:])
rstart = []
rend = []
rscore = []
while len(tscore) > 1 and len(rscore) < top_number:
max_index = tscore.index(max(tscore))
tmp_start = tstart[max_index]
tmp_end = tend[max_index]
tmp_score = tscore[max_index]
rstart.append(tmp_start)
rend.append(tmp_end)
rscore.append(tmp_score)
tstart.pop(max_index)
tend.pop(max_index)
tscore.pop(max_index)
tstart = np.array(tstart)
tend = np.array(tend)
tscore = np.array(tscore)
tt1 = np.maximum(tmp_start, tstart)
tt2 = np.minimum(tmp_end, tend)
intersection = tt2 - tt1
duration = tend - tstart
tmp_width = tmp_end - tmp_start
iou = intersection / (tmp_width + duration - intersection).astype(np.float)
idxs = np.where(iou > 0.65 + 0.25 * tmp_width)[0]
tscore[idxs] = tscore[idxs] * np.exp(-np.square(iou[idxs]) / 0.75)
tstart = list(tstart)
tend = list(tend)
tscore = list(tscore)
newDf = pd.DataFrame()
newDf['score'] = rscore
newDf['xmin'] = rstart
newDf['xmax'] = rend
return newDf
def min_max(x):
x = (x - min(x)) / (max(x) - min(x))
return x
def sub_processor(lock, pid, video_list):
text = 'processor %d' % pid
with lock:
progress = tqdm.tqdm(
total=len(video_list),
position=pid,
desc=text
)
for i in range(len(video_list)):
video_name = video_list[i]
df = pd.read_csv(os.path.join(result_dir, video_name + ".csv"))
df['score'] = df.iou.values[:] * df.start.values[:] * df.end.values[:]
if len(df) > 1:
df = softNMS(df)
df = df.sort_values(by="score", ascending=False)
video_info = video_dict[video_name]
video_duration = video_info["duration_second"]
proposal_list = []
for j in range(min(top_number, len(df))):
tmp_proposal = {}
tmp_proposal["score"] = df.score.values[j]
tmp_proposal["segment"] = [max(0, df.xmin.values[j]) * video_duration,
min(1, df.xmax.values[j]) * video_duration]
proposal_list.append(tmp_proposal)
result_dict[video_name[2:]] = proposal_list
with lock:
progress.update(1)
with lock:
progress.close()
train_dict, val_dict, test_dict = getDatasetDict()
mode = args.mode
if mode == 'validation':
video_dict = val_dict
else:
video_dict = test_dict
result_dir = args.input_dir
video_list = list(video_dict.keys())
""" Post processing using multiprocessing
"""
global result_dict
result_dict = mp.Manager().dict()
processes = []
lock = threading.Lock()
total_video_num = len(video_list)
per_thread_video_num = total_video_num // thread_num
for i in range(thread_num):
if i == thread_num - 1:
sub_video_list = video_list[i * per_thread_video_num:]
else:
sub_video_list = video_list[i * per_thread_video_num: (i + 1) * per_thread_video_num]
p = mp.Process(target=sub_processor, args=(lock, i, sub_video_list))
p.start()
processes.append(p)
for p in processes:
p.join()
result_dict = dict(result_dict)
output_dict = {"version": "VERSION 1.3", "results": result_dict, "external_data": {}}
with open(args.output_file, 'w') as outfile:
json.dump(output_dict, outfile)