Emad Ehsan emadehsan

Why Learn Java as First Programming Language

Here I've tried to make my case on why should you start your Software Engineering Journey with Java.

Java is All Rounder

Java is Mobile

You can develop anything for Android the most widely used Operating System.

Java is Web

Install Screen

$ sudo apt install screen

Enter a new Screen Session

$ screen

Detach from current screen session

Radek has provided good example code that we would use.

Include these libraries in the <head>

<!-- VideoJs CSS -->
<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/video.js/7.2.4/alt/video-js-cdn.min.css" />

<!-- Necessary libs. Video.js 7, Contrib Quality Levels, Jquery -->
<script src="https://cdnjs.cloudflare.com/ajax/libs/video.js/7.2.4/video.min.js"></script>

In the Tests tab of your request, type this code

let cookie = postman.getResponseHeader("Set-Cookie");
postman.setEnvironmentVariable("cookie", cookie);

This will set an environment vairable. In all the requests that need cookie, add to the Request Header

Key: Cookie


	Start with:
	Create a file `index.html` and paste the following code
	```
	<!doctype html>
	<html>

	<head>
	<title>Chat Room</title>
	</head>

	# this is a comment
	s = 'Quick brown fox jumps over lazy white dog'

	scount = 0

	for c in s :
	if c == 'a' or c == 'o' or c == 'u' or c == 'i' or c == 'e':
	# "count" in the following line is being used for the first time. It is one level deep i.e. inside the loop. So its value will not be available outside the loop.
	# count += 1

	async function takePhoto(quality) {
	// create html elements
	const div = document.createElement('div');
	const video = document.createElement('video');
	video.style.display = 'block';

	// request the stream. This will ask for Permission to access
	// a connected Camera/Webcam
	const stream = await navigator.mediaDevices.getUserMedia({video: true});

	import os
	import sys
	import random
	import math
	import numpy as np
	import skimage.io
	import matplotlib
	import matplotlib.pyplot as plt

	# Root directory of the project

	# Create model object in inference mode.
	model = modellib.MaskRCNN(mode="inference", model_dir=MODEL_DIR, config=config)

	# Load weights trained on MS-COCO
	model.load_weights(COCO_MODEL_PATH, by_name=True)

	# Load a random image from the images folder
	file_names = next(os.walk(IMAGE_DIR))[2]
	image = skimage.io.imread(os.path.join(IMAGE_DIR, random.choice(file_names)))

	# Run detection
	results = model.detect([image], verbose=1)

	# Visualize results
	r = results[0]
	visualize.display_instances(image, r['rois'], r['masks'], r['class_ids'],