])
run = 0
n_runs += 1
# if gap in subs or street times, restart run
if tdelta_vid.seconds != 1 or (abs(tdelta_street.seconds - 1) >=
frame_skip_tolerance):
run = 0
# start new run
if run == 0:
run_start_t_street = t_street
run_start_t_vid = t_vid
# update prev time
prev_t_street = t_street
prev_t_vid = t_vid
run += 1
logger.info(f"Found {n_runs} runs in {fn}")
logger.info(f"Found {len(runs)} total runs in {len(all_sub_files)} files")
logger.info(f"Converting to DataFrame and adding day/hour/dayofweek")
# dataframe of segments with no major gaps
runs_df = pd.DataFrame(runs,
columns=[
"video", "camera", "start_t", "stop_t",
"start_t_secs", "stop_t_secs", "start_t_street",
"stop_t_street"
])
runs_df["day"] = pd.to_datetime(runs_df["start_t_street"]).dt.day
runs_df["hour"] = pd.to_datetime(runs_df["start_t_street"]).dt.hour
runs_df["dayofweek"] = pd.to_datetime(runs_df["start_t_street"]).dt.dayofweek
runs_df.to_csv(os.path.join(vid_run_path), index=False)
from src.modules.utils.setup import setup, IndentLogger
logger = IndentLogger(logging.getLogger(''), {})
# =========== Config File Loading ================
from src.modules.utils.config_loader import get_config
conf, confp = get_config()
# ======== Load Configuration Parameters =========
path = confp.dirs.subtitles
out_dir = confp.dirs.output
vid_run_path = confp.paths.video_runs
# ================================================
setup("video_sampling")
outpath = os.path.join(out_dir, "video_sample.csv")
logger.info("Loading Data")
vids = pd.read_csv(vid_run_path)
#vids = pd.read_csv(vid_run_path)
logger.info("Adding Columns")
all_cams = vids["camera"].unique()
logger.info(f"Found {len(all_cams)} cameras")
n_missing = 0
sample = []
# for each camera:
for camera in all_cams: