jangsoopark

#!/bin/bash

function build_ffmpeg
{
echo "Configuration"

./configure \
	--prefix=$prefix \
	--enable-neon \
	--enable-hwaccels \

jangsoopark

cmake_minimum_required(VERSION 3.5)
project(live555 C CXX)

include_directories(
    BasicUsageEnvironment/include
    groupsock/include
    liveMedia/include
    UsageEnvironment/include
)

jangsoopark

#include <iostream>

#include "pipeline/manager.h"
#include "video/analyze.h"

#include "probes.h"


int main(int argc, char** argv)

jangsoopark

import scipy.ndimage as ndimage
import cv2
import numpy as np


def generate_dct(n):
    m = np.zeros((n, n))
    for i in range(n):
        for j in range(n):
            m[i, j] = np.sqrt(2 / n) * np.cos((np.pi / n) * (i + 0.5) * (j + 0.5))

jangsoopark

2.5	2.4
0.5	0.7
2.2	2.9
1.9	2.2
3.1	3.0
2.3	2.7
2	1.6
1	1.1
1.5	1.6
1.1	0.9

jangsoopark

from PIL import Image

import glob
import os


original_label_dir = '{YOLO DATASET PATH}'

image_dir = '{PNG IMAGE DIR}'
label_dir = '{KITTI TYPE LABEL PATH}'

jangsoopark

jangsoopark

import neuralnet_pytorch as nnt
import torch as T
from torch_scatter import scatter_add


def pointcloud2voxel_fast(pc: T.Tensor, voxel_size: int, grid_size=1., filter_outlier=True):
    b, n, _ = pc.shape
    half_size = grid_size / 2.
    valid = (pc >= -half_size) & (pc <= half_size)
    valid = T.all(valid, 2)

jangsoopark

// 소스출처 : http://www.kma.go.kr/weather/forecast/digital_forecast.jsp  내부에 있음
// 기상청에서 이걸 왜 공식적으로 공개하지 않을까?
//
// (사용 예)
// var rs = dfs_xy_conv("toLL","60","127");
// console.log(rs.lat, rs.lng);
//

<script language="javascript">
    //<!--

jangsoopark

// https://github.com/AlexeyAB/darknet/wiki/How-to-evaluate-accuracy-and-speed-of-YOLOv4
// g++ -I/usr/local/include/opencv4/ main.cpp -lopencv_core -lopencv_imgproc -lopencv_dnn -lopencv_imgcodecs -O3 -std=c++17 -lstdc++fs

#include <iostream>
#include <queue>
#include <iterator>
#include <sstream>
#include <fstream>
#include <iomanip>
#include <chrono>