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File Magic Numbers

Magic numbers are the first bits of a file which uniquely identify the type of file. This makes programming easier because complicated file structures need not be searched in order to identify the file type.

For example, a jpeg file starts with ffd8 ffe0 0010 4a46 4946 0001 0101 0047 ......JFIF.....G ffd8 shows that it's a JPEG file, and ffe0 identify a JFIF type structure. There is an ascii encoding of "JFIF" which comes after a length code, but that is not necessary in order to identify the file. The first 4 bytes do that uniquely.

This gives an ongoing list of file-type magic numbers.

Image Files

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using System;
using System.Net;
using System.Net.Sockets;
 
namespace BrunoGarcia.Net
{
    public class TcpForwarderSlim
    {
        private readonly Socket _mainSocket = new Socket(AddressFamily.InterNetwork, SocketType.Stream, ProtocolType.Tcp);
 

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The Mosquitto Publish CLI
Linux
$> mosquitto_pub --cert certificate.pem.crt --key private.pem.key --cafile AmazonCA1.pem -h XXXXXXXXYYYYY-ats.iot.us-west-2.amazonaws.com -p 8883 -t 'test/thing' -m 'Hello from Mosquitto'

Windows
mosquitto_pub --cert certificate.pem.crt --key private.pem.key --cafile AmazonRootCA1.pem -h af20rc7y2njn8-ats.iot.us-east-2.amazonaws.com -p 8883 -t "vender/svm/check" -m "Hello from Mosquitto"

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private void Log(string message)
{
    if (TestTextBox.InvokeRequired)
    {
        Invoke(new Action<string>(Log), message);
        return;
    }

    TestTextBox.AppendText($"{DateTime.Now:yyyy/MM/dd HH:mm:ss.fff}|{message}|{Environment.NewLine}");
}

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# "x" represent file name.
# "*" represent password.
openssl pkcs12 -export -out xxxxxxxx.pfx -inkey xxxxxxxx-private.pem.key -in xxxxxxxx-certificate.pem.crt -passout pass:********

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# numpy
# pip install numpy
import numpy as np

arr = np.load('test.npy')

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# skimage
# pip install scikit-image
import skimage.io as skio

image = skio.imread('test.jpg')

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# pillow
# pip install pillow
from PIL import Image

image = Image.open('test.jpg')

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# opencv
# pip install opencv
import cv2


# Read Image. (BGR format)
image = cv2.imread('test.jpg')


# Convert color mode.

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# Matrix.
import scipy.io as sio

data = sio.loadmat('matrix.mat')