kndt84

#!/usr/bin/env python3

import sys
import gi
import argparse
import pathlib

parser = argparse.ArgumentParser(description='Create a RTSP stream from a video file')
parser.add_argument('-f', '--file')
parser.add_argument('-p', '--port', default='554')

kndt84

#/bin/bash
PROG="VLC_STARTER"
IP=""
ID=""
PASS=""
export DISPLAY=:0
if [ "$1" = "" ]; then
    echo "Usage: $0 [RGB|THERMO] boot"
    exit 1
fi

kndt84

FROM ubuntu:18.04
USER root

ENV INSTALLDIR /opt/intel/openvino
ARG DOWNLOAD_LINK=http://registrationcenter-download.intel.com/akdlm/irc_nas/16345/l_openvino_toolkit_p_2020.1.023.tgz

WORKDIR /

RUN useradd -ms /bin/bash openvino && \
    chown openvino -R /home/openvino

kndt84

#!/bin/sh

for var in video0_2018-10-18 video0_2018-10-19 video0_2018-10-20 video0_2018-10-21 video0_2018-10-22
do
docker run --runtime=nvidia --rm -v /mnt:/data sensevideo /opt/algorithm/build/cli --input /data/src/${var}.mp4 --output /data/dst/${var}.mp4 --log /data/dst/${var}.txt &
done

kndt84

#!/bin/bash

EXT=$1
OUTFILENAME=$2
TXTFILE=filelist.txt

ls -d ./*.${EXT} | sed 's/^/file /' > $TXTFILE
ffmpeg -f concat -safe 0 -i $TXTFILE -c copy ../$2 > /dev/null 2>&1 </dev/null

kndt84

#!/usr/local/bin/python3
import glob
import numpy as np
import cv2
import sys
import os
import shutil

label='Ossicle'

kndt84

# Install CUDA Toolkit 9.1 including NVIDIA Driver
wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/cuda-repo-ubuntu1604_9.1.85-1_amd64.deb
sudo dpkg -i cuda-repo-ubuntu1604_9.1.85-1_amd64.deb
sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/7fa2af80.pub
sudo apt-get update
sudo apt-get install cuda


# Install Docker CE 
sudo apt-get install apt-transport-https ca-certificates curl software-properties-common

kndt84

name: "MOBILENET_V2"
layer {
  name: "train-data"
  type: "Data"
  top: "data"
  top: "label"
  transform_param {
    mirror: true
    crop_size: 224
  }

kndt84

name: "MOBILENET"
layer {
  name: "train-data"
  type: "Data"
  top: "data"
  top: "label"
  transform_param {
    mirror: true
    crop_size: 224
  }

kndt84

import os
import glob
from PIL import Image, ImageOps

path_list = glob.glob('*.png')

for path in path_list:

    filename, ext = os.path.splitext( os.path.basename(path) )
    im = Image.open(path)