kiyoon

#!/bin/bash

if [ $# -lt 2 ] 
then
    echo "usage: $0 [input_dir] [output_dir]"
    echo "chooses 20 video files randomly from the input directory and run Faster R-CNN"
    exit 1
fi

input_dir="$1"

kiyoon

c:v=libx264 preset=slow profile:v=high crf=%quality coder=1 pix_fmt=yuv420p movflags=+faststart g=12 bf=2 c:a=aac ab=%audiobitrate+'k' profile:a=aac_low f=mp4

kiyoon

ffmpeg -hwaccel cuvid -c:v h264_cuvid -i <input.MP4> -c:v h264_nvenc -rc:v vbr_hq -cq:v 19 -b:v 10000k -maxrate:v 20000k -profile:v high -color_range pc -colorspace bt709 -color_trc bt709 -color_primaries bt709 -c:a copy <output.mp4>

kiyoon

#docker-compose.yml : Make container as described here.
#Author : Hyeonsu Lyu, hslyu@unist.ac.kr, +82 10-5117-9780
#         Kiyoon Kim (kiyoon.kim@ed.ac.uk)
#First version, Jan. 30, 2019

version: '2'
services:
    aislab-docker:
        container_name: base_env 
        image: kiyoon/docker-for-ML:cuda10.1-cudnn7

kiyoon

#!/bin/bash

if [ $# -lt 2 ] 
then
    echo "usage: $0 [input_dir] [output_dir] [output_resolution=224x224]"
    echo "Crops the video into 4 corners and 1 centre, and flips the video horizontally to do the same, resulting in 10 augmentation per video."
    exit 1
fi

input_dir="$1"

kiyoon

import argparse

from rich_argparse import ArgumentDefaultsRichHelpFormatter


def get_parser():
    parser = argparse.ArgumentParser(
        description="Example of argparse usage, with default values printed in help.",
        formatter_class=ArgumentDefaultsRichHelpFormatter,
    )

kiyoon

from nvidia.dali.pipeline import Pipeline
from nvidia.dali.plugin import pytorch
import nvidia.dali.ops as ops
import nvidia.dali.types as types

import argparse

parser = argparse.ArgumentParser()
parser.add_argument('--file_list', type=str, default='file_list.txt',
                    help='DALI file_list for VideoReader')

kiyoon

git clone https://git.videolan.org/git/ffmpeg/nv-codec-headers.git
cd nv-codec-headers
vi Makefile   # change the first line to PREFIX = ${CONDA_PREFIX}
make install
cd ..

git clone https://git.ffmpeg.org/ffmpeg.git
cd ffmpeg
git checkout n4.2.2
conda install nasm

kiyoon

#!/usr/bin/env python3


# For command line usage, include the token and chat ids in advance in the script.
# If you want to use only the functions, there's no need to include this.
telegram_token = ""
# Get chat id by opening the URL: https://api.telegram.org/bot{token}/getUpdates
telegram_chat_ids = [""]

kiyoon

#!/usr/bin/env python3
# pip install coloredlogs verboselogs

import os
import coloredlogs, logging, verboselogs

#logger = logging.getLogger(__name__)
logger = verboselogs.VerboseLogger(__name__)    # add logger.success

log_path = 'train.log'