tasks.json과 launch.json 은 .vscode 디렉토리 하에 복사
libavcodec/h264dec.c
- h264_decode_frame ( ... ) 함수가 H264 Decoding Entrypoint로 보임
tasks.json과 launch.json 은 .vscode 디렉토리 하에 복사
libavcodec/h264dec.c
#include <stdbool.h> | |
#include <stdlib.h> | |
#include <string.h> | |
#include <stdio.h> | |
#include <math.h> | |
typedef struct Deque | |
{ | |
int* buffer; |
#include <stdbool.h> | |
#include <stdlib.h> | |
#include <string.h> | |
#include <stdio.h> | |
#include <math.h> | |
typedef struct Queue | |
{ | |
int* buffer; |
# -*- coding: utf-8 -*- | |
""" | |
SCAN: A Structural Clustering Algorithm for Networks | |
As described in http://ualr.edu/nxyuruk/publications/kdd07.pdf | |
""" | |
from collections import deque | |
import numpy as np | |
from scipy.sparse import csr_matrix |
" ------------------------------------------------------------- | |
set enc=utf-8 | |
set fenc=utf-8 | |
set termencoding=utf-8 | |
set nocompatible | |
set autoindent | |
set smartindent |
import numpy as np | |
import cv2 | |
import sys | |
cap = cv2.VideoCapture(0) | |
face_cascade = cv2.CascadeClassifier('<PATH_TO_CASCADES_FOLDER>/haarcascade_frontalface_default.xml') | |
while(True): | |
# Capture frame-by-frame |
// 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> |
// 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"> | |
//<!-- |
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) |