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#!/usr/bin/env python3
import argparse
from pathlib import Path
from datetime import datetime, timedelta
import pandas as pd
import csv
argparser = argparse.ArgumentParser(
description="Combines results of OCR analysis with log files"
)
argparser.add_argument(
"vp_dir", help="Directory containing analysis_results.csv and VPCODE.csv"
)
args = argparser.parse_args()
vp_path = Path(args.vp_dir)
video_path = next(vp_path.glob("*.mkv"))
date_format = "%Y-%m-%d %H-%M-%S"
video_date = datetime.strptime(video_path.stem, date_format)
print(video_date)
# video_delta = timedelta(hours=video_date.hour, minutes=video_date.minute, seconds=video_date.second)
def add_video_time_to_start(x, video_date):
start = timedelta(seconds=int(x))
return (start + video_date).time().isoformat()
analysis = pd.read_csv(vp_path / "analysis_results.csv")
analysis["Starttime"] = analysis["start_time"].apply(add_video_time_to_start, args=(video_date,))
logs = pd.read_csv(vp_path / f"{vp_path.name}.csv")
def get_log_url(start_time):
start_time = datetime.strptime(start_time, "%H:%M:%S")
for _, row in logs.iterrows():
log_start = datetime.strptime(row[0], "%H:%M:%S")
log_end = datetime.strptime(row[1], "%H:%M:%S")
if start_time >= log_start and start_time <= log_end:
return row[3]
return 0
analysis["log_url"] = analysis.apply(
lambda row: get_log_url(row.Starttime), axis=1
)
analysis.to_csv(vp_path / "merged.csv", quoting=csv.QUOTE_NONNUMERIC)
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