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preprocessing.py
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preprocessing.py
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import os
import sys
import re
import math
import json
import glob
import copy
import pysrt
import numpy as np
from tqdm import tqdm
from utils import read_json_lines, load_json, save_json
def merge_list_dicts(list_dicts):
z = list_dicts[0].copy() # start with x's keys and values
for i in range(1, len(list_dicts)):
z.update(list_dicts[i]) # modifies z with y's keys and values & returns None
return z
def get_vidname2cnt_per_show(base_path):
""" get jpg file count for each sub dirs in the base_path
the resulting file is a python dict with {subdir_name: count}
"""
subdirs = [name for name in os.listdir(base_path) if os.path.isdir(os.path.join(base_path, name))]
vidname2cnt = {}
for ele in tqdm(subdirs):
cur_subdir_path = os.path.join(base_path, ele)
cur_files = glob.glob(os.path.join(cur_subdir_path, "*jpg"))
vidname2cnt[ele] = len(cur_files)
return vidname2cnt
def get_vidname2cnt_all(frame_root_path, vidname2cnt_cache_path):
if os.path.exists(vidname2cnt_cache_path):
print("Found frame cnt cache, loading ...")
return load_json(vidname2cnt_cache_path)
show_names = ["bbt", "friends", "grey", "met", "castle", "house"]
vidname2cnt_list = []
for sn in show_names:
print("Count frames in %s" % sn)
cur_base_path = os.path.join(frame_root_path, "%s_frames" % sn)
vidname2cnt_list.append(get_vidname2cnt_per_show(cur_base_path))
vidname2cnt = merge_list_dicts(vidname2cnt_list)
save_json(vidname2cnt, vidname2cnt_cache_path)
return
def clean_str(string):
""" Tokenization/string cleaning for strings.
Taken from https://github.com/yoonkim/CNN_sentence/blob/master/process_data.py
"""
string = re.sub(r"[^A-Za-z0-9(),!?:.\'`]", " ", string) # <> are added after the cleaning process
string = re.sub(r"\'s", " \'s", string)
string = re.sub(r"\'ve", " \'ve", string) # split as two words
string = re.sub(r"n\'t", " n\'t", string)
string = re.sub(r"\'re", " \'re", string)
string = re.sub(r"\'d", " \'d", string)
string = re.sub(r"\'ll", " \'ll", string)
string = re.sub(r"\'m", " \'m", string)
string = re.sub(r":", " : ", string)
string = re.sub(r",", " , ", string)
string = re.sub(r"!", " ! ", string)
string = re.sub(r"\.\.\.", " . ", string)
string = re.sub(r"\.", " . ", string)
string = re.sub(r"\(", " ", string)
string = re.sub(r"\)", " ", string)
string = re.sub(r"\?", " ? ", string)
string = re.sub(r"\s{2,}", " ", string)
return string.strip()
def load_srt(srt_dir, srt_cache_path):
"""
return: A python dict, the keys are the video names, the entries are lists,
each contains all the text from a .srt file
sub_times are the start time of the sentences.
"""
if os.path.exists(srt_cache_path):
print("Found srt data cache, loading ...")
return load_json(srt_cache_path)
print("Loading srt files from %s ..." % srt_dir)
srt_paths = glob.glob(os.path.join(srt_dir, "*.srt"))
name2sub_text = {}
name2sub_time = {}
for i in tqdm(range(len(srt_paths))):
subs = pysrt.open(srt_paths[i], encoding="iso-8859-1")
if len(subs) == 0:
subs = pysrt.open(srt_paths[i])
text_list = []
sub_time_list = []
for j in range(len(subs)):
cur_sub = subs[j]
cur_str = cur_sub.text
cur_str = "(<UNKNAME>:)" + cur_str if cur_str[0] != "(" else cur_str
cur_str = cur_str.replace("\n", " ")
text_list.append(cur_str)
sub_time_list.append(
60 * cur_sub.start.minutes + cur_sub.start.seconds + 0.001 * cur_sub.start.milliseconds)
key_str = os.path.splitext(os.path.basename(srt_paths[i]))[0]
name2sub_text[key_str] = text_list
name2sub_time[key_str] = sub_time_list
srt_data = {"sub_text": name2sub_text, "sub_time": name2sub_time}
save_json(srt_data, srt_cache_path)
return srt_data
def convert_ts(ts):
""" 26.2-34.4 --> [26.2, 34.4] ,
also replace any NaN value with [10, 30], a simple replacement, will fix later"""
new_ts = [float(ele) for ele in ts.split("-")]
is_nan = False
if math.isnan(new_ts[0]) or math.isnan(new_ts[1]):
new_ts = [10, 30] #
is_nan = True
return new_ts, is_nan
def interval2frame(interval, num_frame, fps=3):
""" downsample to 300 frame max,
:param interval: e.g. [26.2, 34.4]
:param num_frame: number of frame for this clip
:param fps: number of frames used per second
:return:
"""
# 0.0356 of the video has more than 300 frames, for those, downsample to 300.
max_num_frame = 300.
if num_frame > max_num_frame:
frame_start_end = [(max_num_frame / num_frame) * fps * ele for ele in interval]
else:
frame_start_end = [fps * ele for ele in interval]
frame_start_end = np.asarray([frame_start_end[0] - fps, frame_start_end[1] + fps])
frame_start_end = np.floor(np.clip(frame_start_end, 0, 300))
if frame_start_end[0] == frame_start_end[1]:
frame_start_end[0] = max(0, frame_start_end[0] - 3)
frame_start_end = [int(x) for x in frame_start_end]
return frame_start_end
def tokenize_qa(data_dicts):
"""tokenize the text in QAs"""
tokenized_data_dicts = []
text_keys = ["q", "a0", "a1", "a2", "a3", "a4"]
all_keys = data_dicts[0].keys()
print("Tokenize QA ...")
for ele in tqdm(data_dicts):
tmp_dict = {}
for k in all_keys:
if k in text_keys:
tmp_dict[k] = clean_str(ele[k])
else:
tmp_dict[k] = ele[k]
tokenized_data_dicts.append(tmp_dict)
return tokenized_data_dicts
def tokenize_srt(srt_data):
"""tokenize the text in srt"""
tokenized_srt_data = {"sub_text": {}, "sub_time": srt_data["sub_time"]}
print("Tokenize subtitle ...")
for k in tqdm(srt_data["sub_text"].keys()):
tokenized_srt_data["sub_text"][k] = [clean_str(s) for s in srt_data["sub_text"][k]]
return tokenized_srt_data
def add_srt(raw_data_dicts, srt_data, eos_token="<eos>"):
""" add entries 'sub_time', 'sub_text' """
data_dicts = copy.deepcopy(raw_data_dicts)
eos_token = " %s " % eos_token # add space around
print("Adding subtitle ...")
for i in tqdm(range(len(data_dicts))):
vid_name = data_dicts[i]["vid_name"]
data_dicts[i]["sub_text"] = eos_token.join(srt_data["sub_text"][vid_name])
data_dicts[i]["sub_time"] = srt_data["sub_time"][vid_name]
return data_dicts
def find_nearest(array, value):
"""closet value in an array to a given value"""
idx = (np.abs(array-value)).argmin()
return idx # array[idx]
def get_located_sub_text(ts, sub_text_list, sub_time, eos_token="<eos>"):
"""return the located subtitle text according to the timestep annotation
:param ts: (list) e.g. [26.2, 34.4]
:param sub_text_list: (list) each element is a subtitle sentence
:param sub_time: (list) each element is a float number indicates the start time of a subtitle sentence
"""
located_indices = []
for idx in range(len(sub_time)):
if ts[0] < sub_time[idx] < ts[1]:
located_indices.append(idx)
# deal with 0-length: use three sub sentences most close to START
if len(located_indices) == 0:
closest_1 = find_nearest(np.asarray(sub_time), ts[0])
located_indices.extend([closest_1 - 1, closest_1, closest_1 + 1])
# rm the indices larger than length of sub_text_list or negative
located_indices = [located_indices[i] for i in range(len(located_indices))
if located_indices[i] <= len(sub_text_list) - 1 and
located_indices[i] >= 0 ]
# add the one before the first located ts, no need to do it for the last one
if 0 not in located_indices:
located_indices = [located_indices[0] - 1] + located_indices
eos_token = " %s " % eos_token
located_sub_text = eos_token.join([sub_text_list[idx] for idx in located_indices])
return located_sub_text
def add_located(raw_data_dicts, srt_data, frame_cnt):
""" add entries 'located_frame', 'located_sub_text' """
data_dicts = copy.deepcopy(raw_data_dicts)
nan_cnt = 0
for i in tqdm(range(len(data_dicts))):
vid_name = data_dicts[i]["vid_name"]
sub_text_list = srt_data["sub_text"][vid_name]
sub_time = srt_data["sub_time"][vid_name]
ts, is_nan = convert_ts(data_dicts[i]["ts"])
nan_cnt += is_nan
data_dicts[i]["ts"] = ts
data_dicts[i]["located_frame"] = interval2frame(ts, frame_cnt[vid_name])
data_dicts[i]["located_sub_text"] = get_located_sub_text(ts, sub_text_list, sub_time)
print("There are %d NaN values in ts, which are replaced by [10, 30], will be fixed later" % nan_cnt)
return data_dicts
def process_qa(qa_path, processed_srt, frame_base_path, frame_cnt_cache_path, save_path):
qa_data = read_json_lines(qa_path)
qa_data = tokenize_qa(qa_data)
qa_srt_data = add_srt(qa_data, processed_srt, eos_token="<eos>")
frame_cnt_dict = get_vidname2cnt_all(frame_base_path, frame_cnt_cache_path)
qa_srt_located_data = add_located(qa_srt_data, processed_srt, frame_cnt_dict)
save_json(qa_srt_located_data, save_path)
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--data_dir", type=str, default="./data", help="data dir path")
parser.add_argument("--frm_dir", type=str,
help="video frame dir path, the program will use provided cache if it exists. "
"Only used to get number of extracted frames for each video.")
args = parser.parse_args()
data_dir = args.data_dir
sub_dir = os.path.join(data_dir, "tvqa_subtitles")
raw_qa_files = glob.glob(os.path.join(data_dir, "tvqa_qa_release", "*jsonl"))
sub_cache_path = os.path.join(data_dir, "srt_data_cache.json")
frm_cnt_cache_path = os.path.join(data_dir, "frm_cnt_cache.json")
srt_data = load_srt(sub_dir, sub_cache_path)
srt_data = tokenize_srt(srt_data)
for i, qa_file in enumerate(raw_qa_files):
print("-"*60)
print("Processing %s" % qa_file)
processed_qa_path = os.path.join(data_dir, os.path.split(qa_file)[1].replace(".jsonl", "_processed.json"))
process_qa(qa_file, srt_data, args.frm_dir, frm_cnt_cache_path, processed_qa_path)