토마스의 기술 정리함

간단히 CUI 환경에서 텍스트를 편집하고자 할 때 많이 사용하는 Nano 편집기에 대해 정리해보고자 한다.

1. 기본 명령어

• 명령어
  • nano 혹은 nano [file-name]
    • 새로운 file 생성
  • 저장
    • Ctrl+o
  • 잘라내기
    • Ctrl+k
  • 복사하기
    • Ctrl+6
  •  붙여넣기
    • Ctrl+u

  
  

  
  

2. 기본 단축키

• 단축키
  • F1
    • 도움말 표시
  • F2
    • Nano 종료
  • F3
    • 현재 편집중인 파일 저장
  • F4
    • 문단의 행의 끝을 나란이 맞춘다.
  • F5
    • 현재 file에 다른 file 내용을 추가한다.
  • F6
    • text 검색
  • F9
    • 현재의 line 혹은 선택된 text 삭제 (그 후 저장)
  • F10
    • 붙여넣기
  • F11
    • 현재의 cursor 위치 표시하기
  • F12
    • 단어 체크 시작

자료참조

• HowtoForge
  • https://www.howtoforge.com/linux-nano-command/
• 스터디 메이크
  • https://studymake.tistory.com/247

오렌지파이 제로를 이용하기 위해서는 이더넷을 연결해야만 편하게 쓸 수 있는데, 사실 와이파이가 있는 요즘 세상에 이더넷 연결하기란 여간 귀찮은 일이 아니다. 따라서 와이파이 설정을 하여 인터넷을 사용해보도록 한다.

1. interfaces 변경

• cd 명령어를 통해 /etc/network로 들어간다.
  • cd /etc/network
• nano를 사용하여 interfaces 내용을 다음과 같이 변경한다.

2. wpa_supplicant.conf 변경

• cd 명령어를 통해 /etc/wpa_supplicant로 들어간다
  • cd /etc/wpa_supplicant

  
  

  
  

• sudo nano명령어를 사용하여 wpa_supplicant.conf 내용을 다음과 같이 작성한다.

3. 이후 재부팅을 하고나면 다음과 같은 내용이 뜰 것이다.

4. 성공! 이제 즐겁게 사용하시길-

3D 오브젝트의 Material 색을 변화시키고 싶을 때 아래 코드를 사용한다.

using System.Collections;
using System.Collections.Generic;
using UnityEngine;
public class TestRenderer : MonoBehaviour {

public Renderer m_Renderer;

float m_R = 0.4f;
float m_G = 0.5f;
float m_B = 0.3f;
float m_Alpha = 1f;

    void Start(){
        m_Renderer.material.color = new Color(m_R, m_G, m_B, m_Alpha);
    }

    void Update(){

        }
}

영상처리 프로젝트를 진행하기 시작하면서 단순 CPU만 사용하는 프로그래밍에 한계를 체감하기 시작했다.

기본적으로 1280x720 픽셀데이터를 다루는데만 한번 모든 픽셀을 훓는데만 921,600번의 연산이 필요하고 여기에 갖가지 영상처리 알고리즘이 들어가면 기하급수적인 연산이 걸린다. 각 연산은 단순 사칙연산인 경우가 많으나 연산이 너무 많으니 한번 프로그램을 돌리면 짧게는 몇분, 길게는 몇십분씩 연산이 돌아가니 점점 머리가 미쳐가기 시작했다.

이 많은 연산을 해결하기 위해 우리의 위대한 선조들은 이미 그래픽카드를 개발했고 이 그래픽카드의 GPU속 100코어가 넘는 코어들을 이용해 수많은 계산을 병렬처리로 연산하면서 심하게 오래걸리는 연산들을 단 몇초, 몇 밀리초 안에 해결하도록 솔루션을 내 놓았다.

최근 다양한 GPU Acceleration을 찾아보던 중 익숙한 C#으로 다룰 수 있는 OpenCL 라이브러리를 사용해 GPU Acceleration을 사용해보기로 했다.

먼저 라이브러리를 받아야 하므로 아래 링크에서 가입하거나 로그인해서 라이브러리를 받자.

https://www.codeproject.com/KB/dotnet/1116907/OpenCLLib.zip

비주얼 스튜디오에 DLL 파일을 참조할 수 있는 사람들은 패스하시고 못하시는 분은 아래글을 따라 참조한다.

1. '솔루션 탐색기'에서 '참조'를 오른쪽 클릭 후 '참조 추가'를 누른다.

2. 새로이 뜨는 '참조 관리자'창 우측 하단의 '찾아보기(B)'를 클릭 후 넣고자 하는 라이브러리.dll의 경로를 찾아 '추가'한다.(여기서는 OpenCLlib.dll과 Cloo.dll이다.)

'참조 관리자' 창 내에 추가된 라이브러리의 좌측 체크박스를 체크한다.

이후 '확인' 버튼을 클릭한다.

이렇게 추가한 후 코드 상단에

using OpenCL;

을 추가하여 사용한다.

코드 예제 (소수를 찾는 예제.)

using System;
using System.Drawing;

using Accord.Imaging.Filters;
using Accord.DataSets;

					
public class Program
{
    public static void Main()
    {
        int[] Primes = Enumerable.Range(2, 1000000).ToArray(); //2부터 1,000,000까지의 배열 생성
        EasyCL cl = new EasyCL(); // EasyCL 초기화
        blur.Accelerator = AcceleratorDevice.GPU; // GPU Acceleration Device 설정
 cl.LoadKernel(IsPrime); // IsPrime을 커널로 로드
 cl.Invoke("GetIfPrime", 0, Primes.Length, Primes); // GetIfPrime을 인코딩하고 0에서 Prime 길이만큼 변수 셋팅 후 Prime 대입
    }


    static string IsPrime
    {
         get
{
return @" //인코딩 될 부분
kernel void GetIfPrime(global int* message) // 소수점 찾는 메서드
{
     int index = get_global_id(0);

     int upperl=(int)sqrt((float)message[index]);
     for(int i=2;i<=upperl;i++)
     {
           if(message[index]%i==0)
           {
                 //printf("" %d / %d\n"",index,i );
                 message[index]=0;
                 return;
            }
      }
      //printf("" % d"",index);
 }";
}
    }
}

Accord.NET framework은 171가지의 이미지 필터링을 제공하고 있으며 그 목록은 다음과 같다. 

Class	Description
	AdaptiveSmoothing	Adaptive Smoothing - noise removal with edges preserving.
	Add	Add fillter - add pixel values of two images.
	AdditiveNoise	Additive noise filter.
	ApplyMask	Apply mask to the specified image.
	BackwardQuadrilateralTransformation	Performs backward quadrilateral transformation into an area in destination image.
	BaseFilter	Base class for filters, which produce new image of the same size as a result of image processing.
	BaseFilter2	Base class for filters, which operate with two images of the same size and format and produce new image as a result.
	BaseInPlaceFilter	Base class for filters, which may be applied directly to the source image.
	BaseInPlaceFilter2	Base class for filters, which operate with two images of the same size and format and may be applied directly to the source image.
	BaseInPlacePartialFilter	Base class for filters, which may be applied directly to the source image or its part.
	BaseResizeFilter	Base class for image resizing filters.
	BaseRotateFilter	Base class for image rotation filters.
	BaseTransformationFilter	Base class for filters, which may produce new image of different size as a result of image processing.
	BaseUsingCopyPartialFilter	Base class for filters, which require source image backup to make them applicable to source image (or its part) directly.
	BayerDithering	Ordered dithering using Bayer matrix.
	BayerFilter	Generic Bayer fileter image processing routine.
	BayerFilterOptimized	Optimized Bayer fileter image processing routine.
	BilateralSmoothing	Bilateral filter implementation - edge preserving smoothing and noise reduction that uses chromatic and spatial factors.
	BinaryDilation3x3	Binary dilation operator from Mathematical Morphology with 3x3 structuring element.
	BinaryErosion3x3	Binary erosion operator from Mathematical Morphology with 3x3 structuring element.
	BinaryWatershed	Watershed filter.
	Blend	Linear Gradient Blending filter.
	BlobsFiltering	Blobs filtering by size.
	Blur	Blur filter.
	BottomHat	Bottop-hat operator from Mathematical Morphology.
	BradleyLocalThresholding	Adaptive thresholding using the internal image.
	BrightnessCorrection	Brightness adjusting in RGB color space.
	BurkesDithering	Dithering using Burkes error diffusion.
	CannyEdgeDetector	Canny edge detector.
	CanvasCrop	Fill areas outiside of specified region.
	CanvasFill	Fill areas iniside of the specified region.
	CanvasMove	Move canvas to the specified point.
	ChannelFiltering	Channels filters.
	Closing	Closing operator from Mathematical Morphology.
	ColorFiltering	Color filtering.
	ColorRemapping	Color remapping.
	CombineChannel	Combine channel filter.
	CompassConvolution	Compass convolution filter.
	Concatenate	Concatenation filter.
	ConnectedComponentsLabeling	Connected components labeling.
	ConservativeSmoothing	Conservative smoothing.
	ContrastCorrection	Contrast adjusting in RGB color space.
	ContrastStretch	Contrast stretching filter.
	Convolution	Convolution filter.
	CornersMarker	Filter to mark (highlight) corners of objects.
	Crop	Crop an image.
	Difference	Difference filter - get the difference between overlay and source images.
	DifferenceEdgeDetector	Difference edge detector.
	DifferenceOfGaussians	Difference of Gaussians filter.
	Dilation	dilation operator from Mathematical Morphology.
	Dilation3x3	dilation operator from Mathematical Morphology with 3x3 structuring element.
	DistanceTransform	Distance transform filter.
	Divide	Divide filter - divide pixel values of two images.
	Edges	Simple edge detector.
	Erosion	Erosion operator from Mathematical Morphology.
	Erosion3x3	Erosion operator from Mathematical Morphology with 3x3 structuring element.
	ErrorDiffusionDithering	Base class for error diffusion dithering.
	ErrorDiffusionToAdjacentNeighbors	Base class for error diffusion dithering, where error is diffused to adjacent neighbor pixels.
	EuclideanColorFiltering	Euclidean color filtering.
	Exponential	Exponential filter.
	ExtractBiggestBlob	Extract the biggest blob from image.
	ExtractChannel	Extract RGB channel from image.
	ExtractNormalizedRGBChannel	Extract normalized RGB channel from color image.
	FastBoxBlur	Fast Box Blur filter.
	FastGuidedFilter	Fast Guided Filter (non-commercial).
	FastVariance	Fast Variance filter.
	FeaturesMarker	Filter to mark (highlight) feature points in a image.
	FillHoles	Fill holes in objects in binary image.
	FilterIterator	Filter iterator.
	FiltersSequence	Filters' collection to apply to an image in sequence.
	FlatFieldCorrection	Flat field correction filter.
	FloydSteinbergDithering	Dithering using Floyd-Steinberg error diffusion.
	GaborFilter	Gabor filter.
	GammaCorrection	Gamma correction filter.
	GaussianBlur	Gaussian blur filter.
	GaussianSharpen	Gaussian sharpen filter.
	Grayscale	Base class for image grayscaling.
	Grayscale.CommonAlgorithms	Set of predefined common grayscaling algorithms, which have already initialized grayscaling coefficients.
	GrayscaleBT709	Obsolete. Grayscale image using BT709 algorithm.
	GrayscaleRMY	Obsolete. Grayscale image using R-Y algorithm.
	GrayscaleToRGB	Convert grayscale image to RGB.
	GrayscaleY	Obsolete. Grayscale image using Y algorithm.
	GrayWorld	Gray World filter for color normalization.
	HighBoost	High boost filter.
	HistogramEqualization	Histogram equalization filter.
	HitAndMiss	Hit-And-Miss operator from Mathematical Morphology.
	HomogenityEdgeDetector	Homogenity edge detector.
	HorizontalRunLengthSmoothing	Horizontal run length smoothing algorithm.
	HSLFiltering	Color filtering in HSL color space.
	HSLLinear	Luminance and saturation linear correction.
	HueModifier	Hue modifier.
	ImageWarp	Image warp effect filter.
	Intersect	Intersect filter - get MIN of pixels in two images.
	Invert	Invert image.
	IterativeThreshold	Iterative threshold search and binarization.
	JarvisJudiceNinkeDithering	Dithering using Jarvis, Judice and Ninke error diffusion.
	Jitter	Jitter filter.
	KirschEdgeDetector	Kirsch's Edge Detector
	Kuwahara	Kuwahara filter.
	LevelsLinear	Linear correction of RGB channels.
	LevelsLinear16bpp	Linear correction of RGB channels for images, which have 16 bpp planes (16 bit gray images or 48/64 bit colour images).
	LineMarker	Filter to mark (highlight) lines in a image.
	Logarithm	Log filter.
	MaskedFilter	Apply filter according to the specified mask.
	Mean	Mean filter.
	Median	Median filter.
	Merge	Merge filter - get MAX of pixels in two images.
	Mirror	Mirroring filter.
	Morph	Morph filter.
	MoveTowards	Move towards filter.
	Multiply	Multiply filter - multiply pixel values of two images.
	NiblackThreshold	Niblack Threshold.
	OilPainting	Oil painting filter.
	Opening	Opening operator from Mathematical Morphology.
	OrderedDithering	Binarization with thresholds matrix.
	OtsuThreshold	Otsu thresholding.
	PairsMarker	Filter to mark (highlight) pairs of points in a image.
	Pixellate	Pixellate filter.
	PointedColorFloodFill	Flood filling with specified color starting from specified point.
	PointedMeanFloodFill	Flood filling with mean color starting from specified point.
	PointsMarker	Filter to mark (highlight) points in a image.
	QuadrilateralTransformation	Performs quadrilateral transformation of an area in a given source image.
	QuadrilateralTransformationBilinear	Obsolete. Performs quadrilateral transformation using bilinear algorithm for interpolation.
	QuadrilateralTransformationNearestNeighbor	Obsolete. Performs quadrilateral transformation using nearest neighbor algorithm for interpolation.
	RectanglesMarker	Filter to mark (highlight) rectangles in a image.
	Rectification	Rectification filter for projective transformation.
	ReplaceChannel	Replace RGB channel of color imgae.
	ResizeBicubic	Resize image using bicubic interpolation algorithm.
	ResizeBilinear	Resize image using bilinear interpolation algorithm.
	ResizeNearestNeighbor	Resize image using nearest neighbor algorithm.
	RGChromacity	RG Chromaticity.
	RobinsonEdgeDetector	Robinson's Edge Detector
	RotateBicubic	Rotate image using bicubic interpolation.
	RotateBilinear	Rotate image using bilinear interpolation.
	RotateChannels	Rotate RGB channels.
	RotateNearestNeighbor	Rotate image using nearest neighbor algorithm.
	SaltAndPepperNoise	Salt and pepper noise.
	SaturationCorrection	Saturation adjusting in HSL color space.
	SauvolaThreshold	Sauvola Threshold.
	Sepia	Sepia filter - old brown photo.
	Sharpen	Sharpen filter
	Shrink	Shrink an image by removing specified color from its boundaries.
	SierraDithering	Dithering using Sierra error diffusion.
	SimplePosterization	Simple posterization of an image.
	SimpleQuadrilateralTransformation	Performs quadrilateral transformation of an area in the source image.
	SimpleSkeletonization	Simple skeletonization filter.
	SISThreshold	Threshold using Simple Image Statistics (SIS).
	SobelEdgeDetector	Sobel edge detector.
	StereoAnaglyph	Stereo anaglyph filter.
	StuckiDithering	Dithering using Stucki error diffusion.
	Subtract	Subtract filter - subtract pixel values of two images.
	TexturedFilter	Textured filter - filter an image using texture.
	TexturedMerge	Merge two images using factors from texture.
	Texturer	Texturer filter.
	Threshold	Threshold binarization.
	ThresholdedDifference	Calculate difference between two images and threshold it.
	ThresholdedEuclideanDifference	Calculate Euclidean difference between two images and threshold it.
	ThresholdWithCarry	Threshold binarization with error carry.
	TopHat	Top-hat operator from Mathematical Morphology.
	TransformFromPolar	Transform polar image into rectangle.
	TransformToPolar	Transform rectangle image into circle (to polar coordinates).
	Variance	Variance filter.
	VerticalRunLengthSmoothing	Vertical run length smoothing algorithm.
	WaterWave	Simple water wave effect filter.
	WaveletTransform	Wavelet transform filter.
	WhitePatch	White Patch filter for color normalization.
	WolfJolionThreshold	Wolf Jolion Threshold.
	YCbCrExtractChannel	Extract YCbCr channel from image.
	YCbCrFiltering	Color filtering in YCbCr color space.
	YCbCrLinear	Linear correction of YCbCr channels.
	YCbCrReplaceChannel	Replace channel of YCbCr color space.
	ZhangSuenSkeletonization	Zhang-Suen skeletonization filter.

이 필터들은 .NET Standard 2.0 과 .NET Core 2.0 에 완벽하게 호환 제공되고 있다.

이 필터들을 사용하기 위해서는 단순히

using Accord.Imaging.Filters;

을 네임스페이스 위에 삽입하여 사용하면 된다.

예제코드(가우시안 블러 사용 예제)

using System;
using System.Drawing;

using Accord.Imaging.Filters;
using Accord.DataSets;

					
public class Program
{
    public static void Main()
    {
        TestImages t = new TestImages();
        Bitmap baboon = t.GetImage("baboon.bmp");
		
        // We can create a new Gaussian Blur:
        GaussianBlur blur = new GaussianBlur();
        
        // Now we can either apply it to the image, creating a
        // new resulting image to hold the output of the filter:
        Bitmap result = blur.Apply(baboon);
        
        // Or we can apply the operation in place,
        // overwriting the original image:
        blur.ApplyInPlace(baboon);
    }
}

Accord.NET은 C#으로만 작성된 .NET framework로써 선형 대수학, 수치 최적화, 통계학, 머신러닝, 인공 신경망, 신호 및 영상처리 등을 처리할 수 있으며  Gnu Lesser Public License 을 따른다.

설치는 다음과 같다.

Visual Studio의 프로젝트를 시작한 후 NuGet Package를 열고 아래 인스톨 명령을 실행한다.

    PM> Install-Package Accord.MachineLearning
    PM> Install-Package Accord.Controls

설치가 완료되면 이제 Accord.NET을 쓸 준비가 된 것이다.

예제코드

using Accord.Controls;
using Accord.MachineLearning.VectorMachines.Learning;
using Accord.Math.Optimization.Losses;
using Accord.Statistics;
using Accord.Statistics.Kernels;
using System;

namespace GettingStarted
{
    class Program
    {
        [MTAThread]
        static void Main(string[] args)
        {
            double[][] inputs =
            {
                /* 1.*/ new double[] { 0, 0 },
                /* 2.*/ new double[] { 1, 0 }, 
                /* 3.*/ new double[] { 0, 1 }, 
                /* 4.*/ new double[] { 1, 1 },
            };

            int[] outputs =
            { 
                /* 1. 0 xor 0 = 0: */ 0,
                /* 2. 1 xor 0 = 1: */ 1,
                /* 3. 0 xor 1 = 1: */ 1,
                /* 4. 1 xor 1 = 0: */ 0,
            };

            // Create the learning algorithm with the chosen kernel
            var smo = new SequentialMinimalOptimization<Gaussian>()
            {
                Complexity = 100 // Create a hard-margin SVM 
            };

            // Use the algorithm to learn the svm
            var svm = smo.Learn(inputs, outputs);

            // Compute the machine's answers for the given inputs
            bool[] prediction = svm.Decide(inputs);

            // Compute the classification error between the expected 
            // values and the values actually predicted by the machine:
            double error = new AccuracyLoss(outputs).Loss(prediction);

            Console.WriteLine("Error: " + error);

            // Show results on screen 
            ScatterplotBox.Show("Training data", inputs, outputs);
            ScatterplotBox.Show("SVM results", inputs, prediction.ToZeroOne());

            Console.ReadKey();
        }
    }
}

유니티의 여러가지 시간 측정 방법중 한가지인 스탑워치 사용하기에 대한 방법이다.

using System.Diagnostics;
using debug = UnityEngine.Debug;
public class TimerScript : MonoBehaviour {

Stopwatch sw = new Stopwatch();
float time;

    void Start(){
        sw.Start();
    }

    void Update(){
        time = sw.ElapsedMilliseconds;
        if(time>5000){
            sw.Stop();
            debug.Log("Time: "+time);
        }
    }